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		96.06 259	97.62 184	91.38 338	98.65 266	98.57 76	98.85 72	96.95 302	96.86 210	99.90 5	99.16 98	99.18 12	98.40 349	89.23 325	99.77 105	77.18 354

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	99.62 157	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	98.76 177	96.38 175	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	98.20 274	99.37 36	99.70 15	99.65 25	92.65 265	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	99.77 105	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	94.13 255	99.30 216	98.91 241

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	99.76 199	98.44 76	99.77 105	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	95.33 355	96.56 175	99.74 116	99.31 180

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	95.13 315	99.63 52	85.84 337	98.35 115	98.21 273	98.23 120	99.54 35	99.46 52	95.02 218	99.68 240	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	99.67 246	96.71 163	99.77 105	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	97.66 180	99.03 106	99.79 24	97.56 153	99.19 39	92.47 346	99.62 16	99.52 39	99.66 22	89.61 280	99.96 8	99.25 34	99.81 89	99.56 75

no-one		97.98 168	98.10 150	97.61 243	99.55 73	93.82 280	96.70 253	98.94 218	96.18 235	99.52 39	99.41 61	95.90 196	99.81 142	96.72 160	99.99 11	99.20 203

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	95.92 206	99.69 138	99.04 224

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	96.16 197	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	99.75 205	97.17 133	99.66 153	99.63 44

N_pmnet		97.63 190	97.17 207	98.99 113	99.27 134	97.86 131	95.98 285	93.41 338	95.25 263	99.47 49	98.90 151	95.63 202	99.85 88	96.91 146	99.73 119	99.27 188

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	99.73 219	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	96.12 182	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	99.74 214	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	95.54 258	99.42 57	99.19 90	97.27 113	99.63 262	97.89 100	99.97 23	99.20 203

semantic-postprocess				96.87 272	99.27 134	91.16 321		99.25 153	99.10 65	99.41 58	99.35 68	92.91 261	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	99.76 199	97.70 112	99.79 97	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	99.76 199	98.78 59	99.68 143	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	96.02 185	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	96.00 187	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		86.71 355		99.37 64	99.52 45	89.93 278	99.92 34	98.99 51	99.72 124	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 93	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	99.70 131	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	99.77 194	97.79 105	99.74 116	99.04 224

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	96.37 176	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	96.57 283	99.24 138	90.28 322	95.52 311	99.21 160	98.86 85	99.33 72	99.33 72	93.11 257	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	98.39 286	98.97 51	95.03 322	99.18 174	96.88 209	99.33 72	98.78 173	98.16 57	99.28 330	96.74 158	99.62 157	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	99.72 228	95.98 204	99.76 114	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	95.84 198	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	99.62 264	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	98.84 236	97.55 163	99.31 79	97.71 267	94.61 233	99.88 63	96.14 199	99.19 233	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	99.36 206	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	95.98 191	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	99.00 216		99.27 82	99.21 87	96.99 134	99.50 301	96.55 176	99.50 196	99.26 191

Patchmatch-RL test		97.26 215	97.02 212	97.99 227	99.52 81	95.53 234	96.13 282	99.71 12	97.47 169	99.27 82	99.16 98	84.30 310	99.62 264	97.89 100	99.77 105	98.81 251

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	96.34 177	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	98.94 218	96.96 205	99.24 90	98.89 156	97.83 76	99.81 142	96.88 149	99.49 197	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	96.45 183	99.62 157	99.50 104

TSAR-MVS + GP.		98.18 154	97.98 159	98.77 141	98.71 249	97.88 129	96.32 273	98.66 257	96.33 230	99.23 93	98.51 215	97.48 100	99.40 314	97.16 134	99.46 198	99.02 227

SMA-MVS		98.47 124	98.11 148	99.53 32	99.16 167	99.27 16	98.05 140	99.30 138	94.34 284	99.22 94	99.10 109	97.72 82	99.79 174	96.45 183	99.68 143	99.53 91

ppachtmachnet_test		97.50 197	97.74 175	96.78 276	98.70 253	91.23 320	94.55 331	99.05 200	96.36 229	99.21 95	98.79 172	96.39 173	99.78 184	96.74 158	99.82 82	99.34 170

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	98.87 230	99.20 50	99.19 97	98.99 134	97.30 110	99.85 88	98.77 62	99.79 97	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 97	97.65 271	98.96 19	99.49 302	96.50 180	98.99 256	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 97	99.28 75	94.14 242	99.82 129	97.97 99	99.80 93	99.29 186

EI-MVSNet-Vis-set		98.68 90	98.70 74	98.63 157	99.09 178	96.40 201	97.23 219	98.86 234	99.20 50	99.18 100	98.97 139	97.29 112	99.85 88	98.72 64	99.78 101	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	99.17 101	98.85 162	97.45 101	99.86 77	98.48 75	99.70 131	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	99.17 101	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	98.81 242	96.66 220	99.17 101	99.21 87	94.81 227	99.77 194	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	99.12 104	98.73 179	96.77 151	99.86 77	98.63 67	99.80 93	99.46 129

VDD-MVS		98.56 109	98.39 119	99.07 97	99.13 173	98.07 110	98.59 85	97.01 299	99.59 19	99.11 105	99.27 77	94.82 225	99.79 174	98.34 81	99.63 156	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	99.11 105	98.61 203	99.33 8	99.30 327	96.23 191	98.38 285	99.28 187

Regformer-3		98.61 104	98.61 89	98.63 157	99.02 197	96.53 194	97.17 227	98.84 236	99.13 60	99.10 107	98.85 162	97.24 118	99.79 174	98.41 79	99.70 131	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	99.10 107	99.06 118	98.71 27	99.83 117	95.58 224	99.78 101	99.62 45

LGP-MVS_train				99.47 48	99.57 62	98.97 51		99.48 74	96.60 223	99.10 107	99.06 118	98.71 27	99.83 117	95.58 224	99.78 101	99.62 45

EI-MVSNet		98.40 132	98.51 97	98.04 224	99.10 175	94.73 250	97.20 223	98.87 230	98.97 78	99.06 110	99.02 129	96.00 187	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	99.06 110	98.59 205	96.71 156	99.93 26	98.57 70	99.77 105	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	99.06 110	98.76 177	96.76 153	99.93 26	98.57 70	99.77 105	99.50 104

MVSTER		96.86 237	96.55 240	97.79 232	97.91 309	94.21 267	97.56 198	98.87 230	97.49 168	99.06 110	99.05 123	80.72 322	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	99.06 110	98.88 157	97.99 69	99.28 330	94.38 250	99.58 170	99.18 209

test_part2					99.36 121	99.10 43				99.05 115

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	99.05 115	98.71 181	97.56 91	99.86 77	93.00 281	99.57 174	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	99.05 115	98.98 137	99.35 7	99.32 324	95.72 217	99.68 143	99.18 209

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	99.04 118	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	99.04 118	98.96 142	98.84 21	99.79 174	97.43 125	99.65 154	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	99.03 120	98.98 137	97.89 74	99.85 88	96.54 177	99.42 201	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	99.01 121	98.64 195	97.37 107	99.84 103	97.75 111	99.57 174	99.52 97

HyFIR lowres test		97.19 221	96.60 237	98.96 115	99.62 54	97.28 165	95.17 319	99.50 65	94.21 287	99.01 121	98.32 232	86.61 291	99.99 2	97.10 141	99.84 73	99.60 52

CVMVSNet		96.25 257	97.21 205	93.38 335	99.10 175	80.56 355	97.20 223	98.19 276	96.94 206	99.00 123	99.02 129	89.50 282	99.80 154	96.36 188	99.59 164	99.78 15

Regformer-1		98.55 113	98.44 112	98.87 127	98.85 229	97.29 163	96.91 241	98.99 217	98.97 78	98.99 124	98.64 195	97.26 116	99.81 142	97.79 105	99.57 174	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	94.45 280	98.99 124	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	98.98 126	98.99 134	97.54 94	99.84 103	95.88 207	99.74 116	99.23 197

IS-MVSNet		98.19 153	97.90 167	99.08 96	99.57 62	97.97 120	99.31 20	98.32 270	99.01 74	98.98 126	99.03 128	91.59 272	99.79 174	95.49 226	99.80 93	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	94.87 271	98.97 128	98.99 134	98.01 66	99.88 63	97.29 130	99.70 131	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	97.29 294	99.67 8	98.97 128	99.50 46	90.45 277	99.80 154	97.88 102	99.20 229	99.48 117

USDC		97.41 207	97.40 195	97.44 253	98.94 209	93.67 284	95.17 319	99.53 59	94.03 289	98.97 128	99.10 109	95.29 212	99.34 321	95.84 213	99.73 119	99.30 183

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	98.95 131	99.55 41	94.14 242	99.86 77	97.77 107	99.69 138	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	98.95 131	99.55 41	94.14 242	99.86 77	97.77 107	99.69 138	99.41 145

FMVSNet3		97.50 197	97.24 204	98.29 207	98.08 302	95.83 226	97.86 165	98.91 226	97.89 139	98.95 131	98.95 143	87.06 289	99.81 142	97.77 107	99.69 138	99.23 197

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	98.95 131	98.91 148	98.34 46	99.79 174	95.63 221	99.91 54	98.86 246

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	98.94 135	98.86 160	98.75 25	99.82 129	97.53 119	99.71 128	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	96.16 239	98.93 136	98.82 168	96.00 187	99.83 117	97.32 129	99.73 119	99.36 164

YYNet1		97.60 191	97.67 177	97.39 256	99.04 192	93.04 292	95.27 316	98.38 269	97.25 191	98.92 137	98.95 143	95.48 209	99.73 219	96.99 143	98.74 266	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	98.90 138	98.90 151	98.00 67	99.88 63	96.15 198	99.72 124	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	98.90 138	99.26 79	96.12 182	99.52 295	95.72 217	99.71 128	99.32 176

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	98.89 140	98.90 151	96.98 135	99.92 34	97.16 134	99.70 131	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	98.89 140	98.90 151	96.98 135	99.92 34	97.16 134	99.70 131	99.56 75

WR-MVS		98.40 132	98.19 138	99.03 106	99.00 200	97.65 149	96.85 245	98.94 218	98.57 103	98.89 140	98.50 218	95.60 203	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	98.88 143	99.06 118	97.65 85	99.57 282	94.45 244	99.61 161	99.37 158

TestCases				99.16 85	99.50 86	98.55 77		99.58 36	96.80 212	98.88 143	99.06 118	97.65 85	99.57 282	94.45 244	99.61 161	99.37 158

MDA-MVSNet_test_wron		97.60 191	97.66 180	97.41 255	99.04 192	93.09 289	95.27 316	98.42 267	97.26 190	98.88 143	98.95 143	95.43 210	99.73 219	97.02 142	98.72 267	99.41 145

VNet		98.42 129	98.30 131	98.79 136	98.79 242	97.29 163	98.23 120	98.66 257	99.31 41	98.85 146	98.80 170	94.80 228	99.78 184	98.13 90	99.13 243	99.31 180

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	98.85 146	98.04 253	97.66 84	99.84 103	96.72 160	99.81 89	99.13 217

CHOSEN 1792x2688		97.49 199	97.14 210	98.54 177	99.68 43	96.09 216	96.50 263	99.62 28	91.58 317	98.84 148	98.97 139	92.36 267	99.88 63	96.76 157	99.95 30	99.67 31

test1235678		97.06 228	96.84 222	97.73 236	98.55 275	94.46 263	94.80 326	99.36 111	96.85 211	98.83 149	98.26 235	92.72 264	99.82 129	92.49 294	99.70 131	98.91 241

mvs_anonymous		97.83 181	98.16 142	96.87 272	98.18 299	91.89 302	97.31 214	98.90 227	97.37 180	98.83 149	99.46 52	96.28 178	99.79 174	98.90 53	98.16 294	98.95 235

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	98.83 149	99.11 107	94.31 239	99.85 88	96.60 169	98.72 267	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	98.82 152	98.83 165	96.83 146	99.84 103	97.50 121	99.81 89	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	98.82 152	99.01 131	97.71 83	99.87 72	96.29 190	99.69 138	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	95.76 249	98.81 154	98.82 168	98.36 45	99.82 129	94.75 235	99.77 105	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	98.78 155	99.12 106	98.02 65	99.84 103	97.13 138	99.67 149	99.59 58

LFMVS		97.20 220	96.72 227	98.64 155	98.72 247	96.95 181	98.93 66	94.14 336	99.74 5	98.78 155	99.01 131	84.45 307	99.73 219	97.44 124	99.27 221	99.25 193

Patchmtry		97.35 208	96.97 214	98.50 184	97.31 331	96.47 197	98.18 124	98.92 224	98.95 82	98.78 155	99.37 65	85.44 302	99.85 88	95.96 205	99.83 79	99.17 213

UnsupCasMVSNet_eth		97.89 171	97.60 185	98.75 145	99.31 130	97.17 172	97.62 190	99.35 117	98.72 94	98.76 158	98.68 186	92.57 266	99.74 214	97.76 110	95.60 339	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	98.75 159	98.92 147	98.18 56	99.65 259	96.68 165	99.56 181	99.37 158

DeepC-MVS_fast	96.85 6	98.30 140	98.15 144	98.75 145	98.61 268	97.23 166	97.76 174	99.09 193	97.31 186	98.75 159	98.66 190	97.56 91	99.64 261	96.10 200	99.55 183	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	97.67 177	99.42 51	99.11 174	98.93 55	97.76 174	99.28 142	94.97 268	98.72 161	98.77 175	97.04 129	99.85 88	93.79 265	99.54 184	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	98.69 162	98.88 157	98.00 67	99.89 56	95.87 210	99.59 164	99.58 65

DI_MVS_plusplus_test		97.57 195	97.40 195	98.07 221	99.06 185	95.71 230	96.58 261	96.96 300	96.71 217	98.69 162	98.13 242	93.81 249	99.68 240	97.45 123	99.19 233	98.80 254

test_normal		97.58 193	97.41 194	98.10 216	99.03 195	95.72 229	96.21 278	97.05 298	96.71 217	98.65 164	98.12 246	93.87 246	99.69 235	97.68 116	99.35 208	98.88 244

OpenMVS_ROB	95.38 14	95.84 263	95.18 272	97.81 231	98.41 285	97.15 174	97.37 210	98.62 260	83.86 347	98.65 164	98.37 226	94.29 240	99.68 240	88.41 327	98.62 276	96.60 332

MS-PatchMatch		97.68 186	97.75 174	97.45 252	98.23 296	93.78 281	97.29 215	98.84 236	96.10 241	98.64 166	98.65 192	96.04 184	99.36 319	96.84 152	99.14 240	99.20 203

1111		93.99 306	93.72 302	94.80 318	99.33 128	85.20 341	95.97 286	99.39 100	97.88 140	98.64 166	98.56 210	57.79 361	99.80 154	96.02 201	99.87 68	99.40 150

.test1245		79.71 329	84.30 330	65.96 343	99.33 128	85.20 341	95.97 286	99.39 100	97.88 140	98.64 166	98.56 210	57.79 361	99.80 154	96.02 201	15.07 355	12.86 356

Test4		97.43 205	97.18 206	98.18 214	99.05 190	96.02 217	96.62 259	99.09 193	96.25 234	98.63 169	97.70 268	90.49 276	99.68 240	97.50 121	99.30 216	98.83 248

pmmvs5		97.64 189	97.49 189	98.08 220	99.14 172	95.12 245	96.70 253	99.05 200	93.77 291	98.62 170	98.83 165	93.23 254	99.75 205	98.33 83	99.76 114	99.36 164

ab-mvs		98.41 130	98.36 123	98.59 166	99.19 160	97.23 166	99.32 17	98.81 242	97.66 151	98.62 170	99.40 64	96.82 147	99.80 154	95.88 207	99.51 191	98.75 260

pmmvs4		97.58 193	97.28 203	98.51 183	98.84 232	96.93 182	95.40 315	98.52 263	93.60 293	98.61 172	98.65 192	95.10 217	99.60 271	96.97 144	99.79 97	98.99 230

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	98.61 172	98.38 225	98.62 30	99.87 72	96.47 181	99.67 149	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	98.60 174	99.62 28	98.22 52	99.51 300	97.70 112	99.73 119	97.89 292
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CDS-MVSNet		97.69 185	97.35 201	98.69 151	98.73 246	97.02 179	96.92 240	98.75 250	95.89 247	98.59 175	98.67 188	92.08 271	99.74 214	96.72 160	99.81 89	99.32 176
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	98.07 278	99.03 72	98.59 175	99.13 105	92.16 269	99.90 47	96.87 150	99.68 143	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	98.58 177	98.50 218	97.97 71	99.85 88	96.57 172	99.59 164	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	96.30 233	98.58 177	98.50 218	97.97 71	99.85 88	95.68 220	99.59 164	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	98.56 179	98.54 213	97.75 81	99.88 63	96.57 172	99.59 164	99.58 65

new_pmnet		96.99 233	96.76 225	97.67 238	98.72 247	94.89 248	95.95 293	98.20 274	92.62 304	98.55 180	98.54 213	94.88 223	99.52 295	93.96 259	99.44 200	98.59 270

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	98.55 180	99.33 72	97.95 73	99.90 47	97.16 134	99.67 149	99.44 135

OMC-MVS		97.88 173	97.49 189	99.04 105	98.89 223	98.63 69	96.94 237	99.25 153	95.02 266	98.53 182	98.51 215	97.27 113	99.47 306	93.50 274	99.51 191	99.01 228

jason		97.45 204	97.35 201	97.76 234	99.24 138	93.93 274	95.86 297	98.42 267	94.24 286	98.50 183	98.13 242	94.82 225	99.91 43	97.22 132	99.73 119	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	98.46 184	98.95 143	95.93 193	99.60 271	96.51 179	98.98 258	99.31 180
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	98.45 185	98.71 181	97.50 96	99.82 129	98.21 87	99.59 164	98.93 238
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	98.44 186	98.51 215	97.83 76	99.88 63	96.46 182	99.58 170	99.58 65

BH-untuned		96.83 238	96.75 226	97.08 262	98.74 245	93.33 288	96.71 252	98.26 272	96.72 215	98.44 186	97.37 289	95.20 214	99.47 306	91.89 298	97.43 318	98.44 276

LS3D		98.63 98	98.38 121	99.36 57	97.25 332	99.38 6	99.12 48	99.32 129	99.21 47	98.44 186	98.88 157	97.31 109	99.80 154	96.58 170	99.34 210	98.92 239

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	98.41 189	97.14 296	98.47 39	99.92 34	98.02 95	99.05 248	96.92 322

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	98.41 189	97.14 296	98.47 39	99.92 34	98.02 95	99.05 248	96.92 322

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	98.41 189	97.14 296	98.47 39	99.92 34	98.02 95	99.05 248	96.92 322

Patchmatch-test		96.55 249	96.34 246	97.17 261	98.35 288	93.06 290	98.40 113	97.79 283	97.33 183	98.41 189	98.67 188	83.68 314	99.69 235	95.16 228	99.31 215	98.77 257

MSDG		97.71 184	97.52 188	98.28 208	98.91 218	96.82 184	94.42 332	99.37 107	97.65 152	98.37 193	98.29 234	97.40 105	99.33 323	94.09 256	99.22 226	98.68 268

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	98.30 194	98.40 224	97.86 75	99.89 56	96.53 178	99.72 124	99.56 75

UnsupCasMVSNet_bld		97.30 212	96.92 216	98.45 190	99.28 133	96.78 189	96.20 280	99.27 147	95.42 261	98.28 195	98.30 233	93.16 256	99.71 229	94.99 231	97.37 319	98.87 245

ITE_SJBPF				98.87 127	99.22 144	98.48 84		99.35 117	97.50 166	98.28 195	98.60 204	97.64 88	99.35 320	93.86 263	99.27 221	98.79 255

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	98.16 197	98.13 242	93.81 249	99.97 3	99.26 32	99.57 174	99.43 140

lupinMVS		97.06 228	96.86 220	97.65 240	98.88 224	93.89 278	95.48 312	97.97 280	93.53 294	98.16 197	97.58 274	93.81 249	99.91 43	96.77 156	99.57 174	99.17 213

Vis-MVSNet (Re-imp)		97.46 203	97.16 208	98.34 202	99.55 73	96.10 214	98.94 64	98.44 266	98.32 114	98.16 197	98.62 201	88.76 285	99.73 219	93.88 262	99.79 97	99.18 209

TAPA-MVS	96.21 11	96.63 246	95.95 253	98.65 154	98.93 211	98.09 105	96.93 238	99.28 142	83.58 348	98.13 200	97.78 264	96.13 181	99.40 314	93.52 272	99.29 219	98.45 275
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	98.10 201	98.24 237	98.25 48	99.34 321	96.69 164	99.65 154	99.12 218

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	98.09 202	98.68 186	97.62 89	99.89 56	96.22 192	99.62 157	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	98.07 203	99.28 75	97.11 127	99.84 103	96.84 152	99.32 213	99.47 125

PHI-MVS		98.29 143	97.95 161	99.34 65	98.44 283	99.16 29	98.12 130	99.38 103	96.01 245	98.06 204	98.43 222	97.80 80	99.67 246	95.69 219	99.58 170	99.20 203

CLD-MVS		97.49 199	97.16 208	98.48 186	99.07 182	97.03 177	94.71 328	99.21 160	94.46 278	98.06 204	97.16 294	97.57 90	99.48 305	94.46 243	99.78 101	98.95 235
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	98.48 279	94.73 250	98.18 124	99.02 209	97.69 150	98.04 206	99.11 107	97.22 122	99.56 285	98.57 70	98.90 262	98.71 262

FMVSNet5		96.01 260	95.20 271	98.41 193	97.53 322	96.10 214	98.74 75	99.50 65	97.22 199	98.03 207	99.04 125	69.80 353	99.88 63	97.27 131	99.71 128	99.25 193

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	97.99 208	98.25 236	98.15 59	99.38 318	96.87 150	99.57 174	99.42 143

MCST-MVS		98.00 165	97.63 183	99.10 93	99.24 138	98.17 100	96.89 243	98.73 253	95.66 250	97.92 209	97.70 268	97.17 123	99.66 254	96.18 196	99.23 225	99.47 125

MG-MVS		96.77 242	96.61 236	97.26 259	98.31 291	93.06 290	95.93 294	98.12 277	96.45 227	97.92 209	98.73 179	93.77 252	99.39 316	91.19 314	99.04 251	99.33 175

LP		96.60 248	96.57 239	96.68 278	97.64 318	91.70 304	98.11 131	97.74 285	97.29 189	97.91 211	99.24 82	88.35 286	99.85 88	97.11 140	95.76 338	98.49 273

tfpn1000		94.81 286	94.25 289	96.47 286	99.01 199	93.47 287	98.56 87	92.30 349	96.17 236	97.90 212	96.29 310	76.70 347	99.77 194	93.02 280	98.29 286	96.16 336

MSLP-MVS++		98.02 162	98.14 146	97.64 242	98.58 271	95.19 242	97.48 205	99.23 159	97.47 169	97.90 212	98.62 201	97.04 129	98.81 347	97.55 117	99.41 202	98.94 237

BH-RMVSNet		96.83 238	96.58 238	97.58 246	98.47 280	94.05 270	96.67 255	97.36 292	96.70 219	97.87 214	97.98 256	95.14 216	99.44 311	90.47 321	98.58 278	99.25 193

MIMVSNet		96.62 247	96.25 250	97.71 237	99.04 192	94.66 253	99.16 42	96.92 304	97.23 196	97.87 214	99.10 109	86.11 295	99.65 259	91.65 301	99.21 228	98.82 250

PNet_i23d		91.80 325	92.35 317	90.14 340	98.65 266	73.10 359	89.22 352	99.02 209	95.23 265	97.87 214	97.82 263	78.45 340	98.89 345	88.73 326	86.14 353	98.42 278

view600		94.87 279	94.41 281	96.26 289	99.22 144	91.37 310	98.49 97	94.45 325	98.75 89	97.85 217	95.98 314	80.38 324	99.75 205	86.06 335	98.49 280	97.66 305

view800		94.87 279	94.41 281	96.26 289	99.22 144	91.37 310	98.49 97	94.45 325	98.75 89	97.85 217	95.98 314	80.38 324	99.75 205	86.06 335	98.49 280	97.66 305

conf0.05thres1000		94.87 279	94.41 281	96.26 289	99.22 144	91.37 310	98.49 97	94.45 325	98.75 89	97.85 217	95.98 314	80.38 324	99.75 205	86.06 335	98.49 280	97.66 305

tfpn		94.87 279	94.41 281	96.26 289	99.22 144	91.37 310	98.49 97	94.45 325	98.75 89	97.85 217	95.98 314	80.38 324	99.75 205	86.06 335	98.49 280	97.66 305

LF4IMVS		97.90 170	97.69 176	98.52 179	99.17 165	97.66 148	97.19 226	99.47 80	96.31 232	97.85 217	98.20 241	96.71 156	99.52 295	94.62 239	99.72 124	98.38 280

CPTT-MVS		97.84 180	97.36 199	99.27 74	99.31 130	98.46 85	98.29 116	99.27 147	94.90 270	97.83 222	98.37 226	94.90 220	99.84 103	93.85 264	99.54 184	99.51 99

CMPMVS	75.91 23	96.29 255	95.44 264	98.84 131	96.25 347	98.69 67	97.02 233	99.12 189	88.90 336	97.83 222	98.86 160	89.51 281	98.90 344	91.92 297	99.51 191	98.92 239
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
E-PMN		94.17 301	94.37 286	93.58 332	96.86 338	85.71 339	90.11 350	97.07 297	98.17 124	97.82 224	97.19 292	84.62 306	98.94 342	89.77 323	97.68 315	96.09 340

conf0.01		94.82 284	94.07 290	97.06 264	99.21 150	94.53 257	98.47 103	92.69 340	95.61 251	97.81 225	95.54 321	77.71 341	99.80 154	91.49 306	98.11 297	96.86 325

conf0.002		94.82 284	94.07 290	97.06 264	99.21 150	94.53 257	98.47 103	92.69 340	95.61 251	97.81 225	95.54 321	77.71 341	99.80 154	91.49 306	98.11 297	96.86 325

thresconf0.02		94.70 288	94.07 290	96.58 279	99.21 150	94.53 257	98.47 103	92.69 340	95.61 251	97.81 225	95.54 321	77.71 341	99.80 154	91.49 306	98.11 297	95.42 344

tfpn_n400		94.70 288	94.07 290	96.58 279	99.21 150	94.53 257	98.47 103	92.69 340	95.61 251	97.81 225	95.54 321	77.71 341	99.80 154	91.49 306	98.11 297	95.42 344

tfpnconf		94.70 288	94.07 290	96.58 279	99.21 150	94.53 257	98.47 103	92.69 340	95.61 251	97.81 225	95.54 321	77.71 341	99.80 154	91.49 306	98.11 297	95.42 344

tfpnview11		94.70 288	94.07 290	96.58 279	99.21 150	94.53 257	98.47 103	92.69 340	95.61 251	97.81 225	95.54 321	77.71 341	99.80 154	91.49 306	98.11 297	95.42 344

CDPH-MVS		97.26 215	96.66 234	99.07 97	99.00 200	98.15 101	96.03 284	99.01 212	91.21 323	97.79 231	97.85 261	96.89 143	99.69 235	92.75 289	99.38 205	99.39 151

HQP_MVS		97.99 167	97.67 177	98.93 119	99.19 160	97.65 149	97.77 172	99.27 147	98.20 121	97.79 231	97.98 256	94.90 220	99.70 231	94.42 246	99.51 191	99.45 133

plane_prior3						97.78 140			97.41 177	97.79 231

MDTV_nov1_ep13_2view						74.92 357	97.69 180		90.06 332	97.75 234		85.78 298		93.52 272		98.69 265

pmmvs3		95.03 276	94.40 285	96.93 268	97.70 316	92.53 295	95.08 321	97.71 287	88.57 337	97.71 235	98.08 251	79.39 335	99.82 129	96.19 194	99.11 246	98.43 277

DP-MVS Recon		97.33 210	96.92 216	98.57 169	99.09 178	97.99 115	96.79 246	99.35 117	93.18 297	97.71 235	98.07 252	95.00 219	99.31 325	93.97 258	99.13 243	98.42 278

QAPM		97.31 211	96.81 223	98.82 133	98.80 241	97.49 156	99.06 53	99.19 170	90.22 329	97.69 237	99.16 98	96.91 138	99.90 47	90.89 318	99.41 202	99.07 221

Patchmatch-test1		96.44 254	96.72 227	95.60 310	98.24 294	88.35 328	95.85 299	96.88 306	96.11 240	97.67 238	98.57 207	93.10 258	99.69 235	94.79 234	99.22 226	98.77 257

Effi-MVS+-dtu		98.26 146	97.90 167	99.35 62	98.02 304	99.49 3	98.02 149	99.16 183	98.29 118	97.64 239	97.99 255	96.44 171	99.95 13	96.66 166	98.93 261	98.60 269

CNVR-MVS		98.17 156	97.87 170	99.07 97	98.67 260	98.24 95	97.01 234	98.93 221	97.25 191	97.62 240	98.34 229	97.27 113	99.57 282	96.42 186	99.33 211	99.39 151

PVSNet_BlendedMVS		97.55 196	97.53 187	97.60 244	98.92 215	93.77 282	96.64 257	99.43 93	94.49 276	97.62 240	99.18 92	96.82 147	99.67 246	94.73 236	99.93 39	99.36 164

PVSNet_Blended		96.88 236	96.68 231	97.47 251	98.92 215	93.77 282	94.71 328	99.43 93	90.98 324	97.62 240	97.36 290	96.82 147	99.67 246	94.73 236	99.56 181	98.98 231

alignmvs		97.35 208	96.88 219	98.78 139	98.54 276	98.09 105	97.71 178	97.69 288	99.20 50	97.59 243	95.90 318	88.12 288	99.55 288	98.18 89	98.96 259	98.70 264

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	97.58 244	98.66 190	97.40 105	99.88 63	94.72 238	99.60 163	99.54 86

DSMNet-mixed		97.42 206	97.60 185	96.87 272	99.15 171	91.46 307	98.54 90	99.12 189	92.87 301	97.58 244	99.63 27	96.21 179	99.90 47	95.74 216	99.54 184	99.27 188

test0.0.03 1		94.51 293	93.69 303	96.99 267	96.05 348	93.61 285	94.97 323	93.49 337	96.17 236	97.57 246	94.88 340	82.30 319	99.01 341	93.60 270	94.17 348	98.37 282

PCF-MVS	92.86 18	94.36 295	93.00 313	98.42 192	98.70 253	97.56 153	93.16 343	99.11 191	79.59 351	97.55 247	97.43 285	92.19 268	99.73 219	79.85 351	99.45 199	97.97 291
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	97.54 248	98.63 199	97.50 96	99.83 117	96.79 154	99.53 188	99.56 75

X-MVStestdata		94.32 297	92.59 314	99.53 32	99.46 103	99.21 22	98.65 78	99.34 121	98.62 97	97.54 248	45.85 354	97.50 96	99.83 117	96.79 154	99.53 188	99.56 75

旧先验2							95.76 301		88.56 338	97.52 250			99.66 254	94.48 242

PMVS	91.26 20	97.86 175	97.94 163	97.65 240	99.71 34	97.94 125	98.52 91	98.68 256	98.99 75	97.52 250	99.35 68	97.41 104	98.18 350	91.59 304	99.67 149	96.82 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-MVSNAJ		97.08 227	97.39 197	96.16 297	98.56 273	92.46 296	95.24 318	98.85 235	97.25 191	97.49 252	95.99 313	98.07 61	99.90 47	96.37 187	98.67 273	96.12 339

xiu_mvs_v2_base		97.16 223	97.49 189	96.17 295	98.54 276	92.46 296	95.45 313	98.84 236	97.25 191	97.48 253	96.49 305	98.31 47	99.90 47	96.34 189	98.68 272	96.15 338

canonicalmvs		98.34 136	98.26 133	98.58 167	98.46 281	97.82 136	98.96 63	99.46 82	99.19 54	97.46 254	95.46 328	98.59 32	99.46 308	98.08 92	98.71 270	98.46 274

testdata				98.09 217	98.93 211	95.40 239		98.80 244	90.08 331	97.45 255	98.37 226	95.26 213	99.70 231	93.58 271	98.95 260	99.17 213

thres600view7		94.45 294	93.83 299	96.29 287	99.06 185	91.53 306	97.99 152	94.24 332	98.34 110	97.44 256	95.01 334	79.84 329	99.67 246	84.33 341	98.23 288	97.66 305

EMVS		93.83 309	94.02 296	93.23 336	96.83 340	84.96 343	89.77 351	96.32 315	97.92 130	97.43 257	96.36 309	86.17 293	98.93 343	87.68 330	97.73 314	95.81 341

tfpn_ndepth		94.12 303	93.51 307	95.94 302	98.86 226	93.60 286	98.16 127	91.90 351	94.66 275	97.41 258	95.24 331	76.24 348	99.73 219	91.21 312	97.88 312	94.50 349

tfpn111		94.33 296	93.78 300	95.96 301	99.06 185	91.35 314	98.03 142	94.24 332	98.33 111	97.40 259	94.98 336	79.84 329	99.68 240	83.94 342	98.22 290	96.86 325

conf200view11		94.24 299	93.67 304	95.94 302	99.06 185	91.35 314	98.03 142	94.24 332	98.33 111	97.40 259	94.98 336	79.84 329	99.62 264	83.05 344	98.08 305	96.86 325

thres100view900		94.19 300	93.67 304	95.75 307	99.06 185	91.35 314	98.03 142	94.24 332	98.33 111	97.40 259	94.98 336	79.84 329	99.62 264	83.05 344	98.08 305	96.29 333

Fast-Effi-MVS+-dtu		98.27 144	98.09 151	98.81 134	98.43 284	98.11 104	97.61 192	99.50 65	98.64 95	97.39 262	97.52 278	98.12 60	99.95 13	96.90 148	98.71 270	98.38 280

API-MVS		97.04 231	96.91 218	97.42 254	97.88 311	98.23 99	98.18 124	98.50 264	97.57 160	97.39 262	96.75 301	96.77 151	99.15 336	90.16 322	99.02 252	94.88 348

testus		95.52 268	95.32 267	96.13 299	97.91 309	89.49 325	93.62 340	99.61 30	92.41 306	97.38 264	95.42 330	94.72 232	99.63 262	88.06 329	98.72 267	99.26 191

PatchMatch-RL		97.24 218	96.78 224	98.61 162	99.03 195	97.83 133	96.36 271	99.06 196	93.49 296	97.36 265	97.78 264	95.75 199	99.49 302	93.44 275	98.77 265	98.52 272

test12356		94.85 283	95.12 273	94.03 328	98.25 292	83.12 350	93.85 338	99.33 126	94.17 288	97.28 266	97.20 291	85.83 297	99.75 205	90.85 319	99.33 211	99.22 201

sss		97.21 219	96.93 215	98.06 222	98.83 234	95.22 241	96.75 250	98.48 265	94.49 276	97.27 267	97.90 260	92.77 263	99.80 154	96.57 172	99.32 213	99.16 216

MVS_0304		98.02 162	97.88 169	98.46 188	98.22 297	96.39 202	96.50 263	99.49 71	98.03 126	97.24 268	98.33 231	94.80 228	99.90 47	98.31 84	99.95 30	99.08 219

WTY-MVS		96.67 244	96.27 248	97.87 229	98.81 239	94.61 255	96.77 248	97.92 282	94.94 269	97.12 269	97.74 266	91.11 274	99.82 129	93.89 261	98.15 295	99.18 209

tfpn200view9		94.03 305	93.44 308	95.78 306	98.93 211	91.44 308	97.60 193	94.29 330	97.94 128	97.10 270	94.31 345	79.67 333	99.62 264	83.05 344	98.08 305	96.29 333

thres400		94.14 302	93.44 308	96.24 293	98.93 211	91.44 308	97.60 193	94.29 330	97.94 128	97.10 270	94.31 345	79.67 333	99.62 264	83.05 344	98.08 305	97.66 305

PatchmatchNet		95.58 266	95.67 259	95.30 314	97.34 330	87.32 332	97.65 185	96.65 310	95.30 262	97.07 272	98.69 184	84.77 304	99.75 205	94.97 232	98.64 274	98.83 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNLPA		97.17 222	96.71 229	98.55 174	98.56 273	98.05 112	96.33 272	98.93 221	96.91 208	97.06 273	97.39 287	94.38 238	99.45 310	91.66 300	99.18 235	98.14 286

NCCC		97.86 175	97.47 193	99.05 103	98.61 268	98.07 110	96.98 235	98.90 227	97.63 153	97.04 274	97.93 259	95.99 190	99.66 254	95.31 227	98.82 264	99.43 140

TR-MVS		95.55 267	95.12 273	96.86 275	97.54 321	93.94 273	96.49 265	96.53 313	94.36 283	97.03 275	96.61 303	94.26 241	99.16 335	86.91 332	96.31 334	97.47 317

MDTV_nov1_ep13			95.22 270		97.06 335	83.20 349	97.74 176	96.16 316	94.37 282	96.99 276	98.83 165	83.95 312	99.53 291	93.90 260	97.95 310

CANet		97.87 174	97.76 173	98.19 213	97.75 312	95.51 235	96.76 249	99.05 200	97.74 147	96.93 277	98.21 240	95.59 204	99.89 56	97.86 104	99.93 39	99.19 208

EPMVS		93.72 310	93.27 310	95.09 316	96.04 349	87.76 330	98.13 128	85.01 356	94.69 274	96.92 278	98.64 195	78.47 339	99.31 325	95.04 229	96.46 333	98.20 284

AdaColmap		97.14 224	96.71 229	98.46 188	98.34 289	97.80 139	96.95 236	98.93 221	95.58 257	96.92 278	97.66 270	95.87 197	99.53 291	90.97 315	99.14 240	98.04 289

CR-MVSNet		96.28 256	95.95 253	97.28 257	97.71 314	94.22 265	98.11 131	98.92 224	92.31 308	96.91 280	99.37 65	85.44 302	99.81 142	97.39 127	97.36 321	97.81 298

RPMNet		96.82 240	96.66 234	97.28 257	97.71 314	94.22 265	98.11 131	96.90 305	99.37 36	96.91 280	99.34 70	86.72 290	99.81 142	97.53 119	97.36 321	97.81 298

HPM-MVS++		98.10 158	97.64 182	99.48 45	99.09 178	99.13 38	97.52 202	98.75 250	97.46 174	96.90 282	97.83 262	96.01 186	99.84 103	95.82 214	99.35 208	99.46 129

PatchT		96.65 245	96.35 245	97.54 248	97.40 328	95.32 240	97.98 153	96.64 311	99.33 40	96.89 283	99.42 59	84.32 309	99.81 142	97.69 114	97.49 316	97.48 316

1112_ss		97.29 214	96.86 220	98.58 167	99.34 127	96.32 204	96.75 250	99.58 36	93.14 298	96.89 283	97.48 281	92.11 270	99.86 77	96.91 146	99.54 184	99.57 70

test222					98.92 215	96.93 182	95.54 309	98.78 246	85.72 345	96.86 285	98.11 247	94.43 236			99.10 247	99.23 197

thres200		93.72 310	93.14 311	95.46 312	98.66 265	91.29 318	96.61 260	94.63 324	97.39 179	96.83 286	93.71 348	79.88 328	99.56 285	82.40 348	98.13 296	95.54 343

UGNet		98.53 118	98.45 110	98.79 136	97.94 307	96.96 180	99.08 49	98.54 262	99.10 65	96.82 287	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		96.99 233	96.55 240	98.31 205	99.35 125	95.47 237	95.84 300	99.53 59	91.51 319	96.80 288	98.48 221	91.36 273	99.83 117	96.58 170	99.53 188	99.62 45

新几何1				98.91 122	98.94 209	97.76 141		98.76 247	87.58 341	96.75 289	98.10 248	94.80 228	99.78 184	92.73 290	99.00 255	99.20 203

Effi-MVS+		98.02 162	97.82 172	98.62 159	98.53 278	97.19 170	97.33 212	99.68 16	97.30 187	96.68 290	97.46 283	98.56 36	99.80 154	96.63 168	98.20 291	98.86 246

GA-MVS		95.86 262	95.32 267	97.49 250	98.60 270	94.15 269	93.83 339	97.93 281	95.49 259	96.68 290	97.42 286	83.21 315	99.30 327	96.22 192	98.55 279	99.01 228

F-COLMAP		97.30 212	96.68 231	99.14 88	99.19 160	98.39 89	97.27 216	99.30 138	92.93 299	96.62 292	98.00 254	95.73 200	99.68 240	92.62 291	98.46 284	99.35 169

PAPM_NR		96.82 240	96.32 247	98.30 206	99.07 182	96.69 192	97.48 205	98.76 247	95.81 248	96.61 293	96.47 307	94.12 245	99.17 334	90.82 320	97.78 313	99.06 222

1121		96.73 243	96.00 251	98.91 122	98.95 208	97.76 141	98.07 136	98.73 253	87.65 340	96.54 294	98.13 242	94.52 235	99.73 219	92.38 295	99.02 252	99.24 196

test12				98.93 119	98.58 271	97.83 133		98.66 257		96.53 295		95.51 207	99.69 235		99.13 243	99.27 188

BH-w/o		95.13 274	94.89 278	95.86 304	98.20 298	91.31 317	95.65 306	97.37 291	93.64 292	96.52 296	95.70 319	93.04 259	99.02 339	88.10 328	95.82 337	97.24 320

ADS-MVSNet2		95.43 271	94.98 276	96.76 277	98.14 300	91.74 303	97.92 158	97.76 284	90.23 327	96.51 297	98.91 148	85.61 299	99.85 88	92.88 284	96.90 327	98.69 265

ADS-MVSNet		95.24 273	94.93 277	96.18 294	98.14 300	90.10 323	97.92 158	97.32 293	90.23 327	96.51 297	98.91 148	85.61 299	99.74 214	92.88 284	96.90 327	98.69 265

114514_t		96.50 252	95.77 255	98.69 151	99.48 97	97.43 160	97.84 167	99.55 54	81.42 350	96.51 297	98.58 206	95.53 205	99.67 246	93.41 276	99.58 170	98.98 231

PVSNet	93.40 17	95.67 265	95.70 257	95.57 311	98.83 234	88.57 326	92.50 345	97.72 286	92.69 303	96.49 300	96.44 308	93.72 253	99.43 312	93.61 269	99.28 220	98.71 262

mvs-test1		97.83 181	97.48 192	98.89 125	98.02 304	99.20 24	97.20 223	99.16 183	98.29 118	96.46 301	97.17 293	96.44 171	99.92 34	96.66 166	97.90 311	97.54 315

tpmrst		95.07 275	95.46 263	93.91 329	97.11 334	84.36 347	97.62 190	96.96 300	94.98 267	96.35 302	98.80 170	85.46 301	99.59 275	95.60 222	96.23 335	97.79 301

OpenMVS	96.65 7	97.09 226	96.68 231	98.32 203	98.32 290	97.16 173	98.86 71	99.37 107	89.48 333	96.29 303	99.15 102	96.56 164	99.90 47	92.90 283	99.20 229	97.89 292

diffmvs		97.49 199	97.36 199	97.91 228	98.38 287	95.70 231	97.95 156	99.31 131	94.87 271	96.14 304	98.78 173	94.84 224	99.43 312	97.69 114	98.26 287	98.59 270

Fast-Effi-MVS+		97.67 187	97.38 198	98.57 169	98.71 249	97.43 160	97.23 219	99.45 85	94.82 273	96.13 305	96.51 304	98.52 38	99.91 43	96.19 194	98.83 263	98.37 282

test_prior3		97.48 202	97.00 213	98.95 116	98.69 255	97.95 123	95.74 303	99.03 205	96.48 225	96.11 306	97.63 272	95.92 194	99.59 275	94.16 251	99.20 229	99.30 183

test_prior2							95.74 303		96.48 225	96.11 306	97.63 272	95.92 194		94.16 251	99.20 229

dp		93.47 312	93.59 306	93.13 337	96.64 341	81.62 354	97.66 183	96.42 314	92.80 302	96.11 306	98.64 195	78.55 338	99.59 275	93.31 277	92.18 352	98.16 285

原ACMM1				98.35 201	98.90 219	96.25 210		98.83 241	92.48 305	96.07 309	98.10 248	95.39 211	99.71 229	92.61 292	98.99 256	99.08 219

PMMVS		96.51 250	95.98 252	98.09 217	97.53 322	95.84 225	94.92 324	98.84 236	91.58 317	96.05 310	95.58 320	95.68 201	99.66 254	95.59 223	98.09 304	98.76 259

tpm		94.67 292	94.34 287	95.66 308	97.68 317	88.42 327	97.88 162	94.90 322	94.46 278	96.03 311	98.56 210	78.66 336	99.79 174	95.88 207	95.01 342	98.78 256

TEST9					98.71 249	98.08 108	95.96 290	99.03 205	91.40 320	95.85 312	97.53 276	96.52 166	99.76 199

train_agg		97.10 225	96.45 243	99.07 97	98.71 249	98.08 108	95.96 290	99.03 205	91.64 314	95.85 312	97.53 276	96.47 169	99.76 199	93.67 267	99.16 236	99.36 164

test_8					98.67 260	98.01 114	95.91 296	99.02 209	91.64 314	95.79 314	97.50 279	96.47 169	99.76 199

agg_prior3		96.95 235	96.27 248	99.00 112	98.68 257	97.91 126	95.96 290	99.01 212	90.74 326	95.60 315	97.45 284	96.14 180	99.74 214	93.67 267	99.16 236	99.36 164

agg_prior1		97.06 228	96.40 244	99.03 106	98.68 257	97.99 115	95.76 301	99.01 212	91.73 313	95.59 316	97.50 279	96.49 168	99.77 194	93.71 266	99.14 240	99.34 170

agg_prior					98.68 257	97.99 115		99.01 212		95.59 316			99.77 194

PLC	94.65 16	96.51 250	95.73 256	98.85 130	98.75 244	97.91 126	96.42 269	99.06 196	90.94 325	95.59 316	97.38 288	94.41 237	99.59 275	90.93 316	98.04 309	99.05 223
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HQP4-MVS										95.56 319			99.54 289			99.32 176

HQP-NCC					98.67 260		96.29 274		96.05 242	95.55 320

ACMP_Plane					98.67 260		96.29 274		96.05 242	95.55 320

HQP-MVS		97.00 232	96.49 242	98.55 174	98.67 260	96.79 185	96.29 274	99.04 203	96.05 242	95.55 320	96.84 299	93.84 247	99.54 289	92.82 286	99.26 223	99.32 176

MAR-MVS		96.47 253	95.70 257	98.79 136	97.92 308	99.12 40	98.28 117	98.60 261	92.16 311	95.54 323	96.17 311	94.77 231	99.52 295	89.62 324	98.23 288	97.72 304
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		95.02 277	95.25 269	94.33 323	96.39 346	85.87 336	98.08 134	96.83 307	95.46 260	95.51 324	98.69 184	85.91 296	99.53 291	94.16 251	96.23 335	97.58 313

test2356		91.64 326	90.19 329	96.00 300	94.30 354	89.58 324	90.84 348	96.68 309	91.76 312	95.48 325	93.69 349	67.05 356	99.52 295	84.83 340	97.08 326	98.91 241

MVS-HIRNet		94.32 297	95.62 260	90.42 339	98.46 281	75.36 356	96.29 274	89.13 354	95.25 263	95.38 326	99.75 7	92.88 262	99.19 333	94.07 257	99.39 204	96.72 331

PAPR		95.29 272	94.47 280	97.75 235	97.50 326	95.14 244	94.89 325	98.71 255	91.39 321	95.35 327	95.48 327	94.57 234	99.14 337	84.95 339	97.37 319	98.97 234

HY-MVS	95.94 13	95.90 261	95.35 266	97.55 247	97.95 306	94.79 249	98.81 74	96.94 303	92.28 309	95.17 328	98.57 207	89.90 279	99.75 205	91.20 313	97.33 323	98.10 287

CANet_DTU		97.26 215	97.06 211	97.84 230	97.57 319	94.65 254	96.19 281	98.79 245	97.23 196	95.14 329	98.24 237	93.22 255	99.84 103	97.34 128	99.84 73	99.04 224

cascas		94.79 287	94.33 288	96.15 298	96.02 350	92.36 299	92.34 347	99.26 152	85.34 346	95.08 330	94.96 339	92.96 260	98.53 348	94.41 249	98.59 277	97.56 314

CostFormer		93.97 307	93.78 300	94.51 322	97.53 322	85.83 338	97.98 153	95.96 317	89.29 335	94.99 331	98.63 199	78.63 337	99.62 264	94.54 241	96.50 332	98.09 288

CHOSEN 280x420		95.51 270	95.47 262	95.65 309	98.25 292	88.27 329	93.25 342	98.88 229	93.53 294	94.65 332	97.15 295	86.17 293	99.93 26	97.41 126	99.93 39	98.73 261

JIA-IIPM		95.52 268	95.03 275	97.00 266	96.85 339	94.03 271	96.93 238	95.82 318	99.20 50	94.63 333	99.71 14	83.09 316	99.60 271	94.42 246	94.64 343	97.36 318

MVS		93.19 315	92.09 319	96.50 285	96.91 337	94.03 271	98.07 136	98.06 279	68.01 352	94.56 334	96.48 306	95.96 192	99.30 327	83.84 343	96.89 329	96.17 335

PatchFormer-LS_test		94.08 304	93.91 297	94.59 321	96.93 336	86.86 334	97.55 200	96.57 312	94.27 285	94.38 335	93.64 350	80.96 321	99.59 275	96.44 185	94.48 346	97.31 319

1314		95.74 264	95.60 261	96.17 295	97.53 322	92.75 293	98.07 136	98.31 271	91.22 322	94.25 336	96.68 302	95.53 205	99.03 338	91.64 302	97.18 324	96.74 330

tpm cat1		93.29 314	93.13 312	93.75 330	97.39 329	84.74 344	97.39 209	97.65 289	83.39 349	94.16 337	98.41 223	82.86 318	99.39 316	91.56 305	95.35 341	97.14 321

test-LLR		93.90 308	93.85 298	94.04 326	96.53 342	84.62 345	94.05 335	92.39 347	96.17 236	94.12 338	95.07 332	82.30 319	99.67 246	95.87 210	98.18 292	97.82 296

test-mter		92.33 321	91.76 323	94.04 326	96.53 342	84.62 345	94.05 335	92.39 347	94.00 290	94.12 338	95.07 332	65.63 360	99.67 246	95.87 210	98.18 292	97.82 296

tpm2		93.09 316	92.58 315	94.62 320	97.56 320	86.53 335	97.66 183	95.79 319	86.15 344	94.07 340	98.23 239	75.95 349	99.53 291	90.91 317	96.86 330	97.81 298

TESTMET0.1,1		92.19 323	91.77 322	93.46 333	96.48 344	82.80 352	94.05 335	91.52 352	94.45 280	94.00 341	94.88 340	66.65 357	99.56 285	95.78 215	98.11 297	98.02 290

PVSNet_0	89.98 21	91.15 327	90.30 327	93.70 331	97.72 313	84.34 348	90.24 349	97.42 290	90.20 330	93.79 342	93.09 351	90.90 275	98.89 345	86.57 333	72.76 354	97.87 294

FPMVS		93.44 313	92.23 318	97.08 262	99.25 137	97.86 131	95.61 307	97.16 296	92.90 300	93.76 343	98.65 192	75.94 350	95.66 353	79.30 352	97.49 316	97.73 303

EPNet		96.14 258	95.44 264	98.25 209	90.76 357	95.50 236	97.92 158	94.65 323	98.97 78	92.98 344	98.85 162	89.12 284	99.87 72	95.99 203	99.68 143	99.39 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DWT-MVSNet_test		92.75 318	92.05 320	94.85 317	96.48 344	87.21 333	97.83 168	94.99 321	92.22 310	92.72 345	94.11 347	70.75 352	99.46 308	95.01 230	94.33 347	97.87 294

IB-MVS	91.63 19	92.24 322	90.90 325	96.27 288	97.22 333	91.24 319	94.36 333	93.33 339	92.37 307	92.24 346	94.58 344	66.20 358	99.89 56	93.16 279	94.63 344	97.66 305
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		92.37 320	91.20 324	95.85 305	95.80 351	92.38 298	99.31 20	81.84 358	99.75 4	91.83 347	99.74 8	68.29 354	99.02 339	87.15 331	97.12 325	96.16 336

DeepMVS_CX				93.44 334	98.24 294	94.21 267		94.34 329	64.28 353	91.34 348	94.87 342	89.45 283	92.77 356	77.54 353	93.14 349	93.35 351

tpmp4_e23		92.91 317	92.45 316	94.29 324	97.41 327	85.62 340	97.95 156	96.77 308	87.55 342	91.33 349	98.57 207	74.21 351	99.59 275	91.62 303	96.64 331	97.65 312

testpf		89.08 328	90.27 328	85.50 341	94.03 355	82.85 351	96.87 244	91.09 353	91.61 316	90.96 350	94.86 343	66.15 359	95.83 352	94.58 240	92.27 351	77.82 353

PAPM		91.88 324	90.34 326	96.51 284	98.06 303	92.56 294	92.44 346	97.17 295	86.35 343	90.38 351	96.01 312	86.61 291	99.21 332	70.65 354	95.43 340	97.75 302

EPNet_dtu		94.93 278	94.78 279	95.38 313	93.58 356	87.68 331	96.78 247	95.69 320	97.35 182	89.14 352	98.09 250	88.15 287	99.49 302	94.95 233	99.30 216	98.98 231
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				94.76 319	94.54 353	92.13 301	99.31 20	80.47 359		88.73 353	91.01 353	67.59 355	98.16 351	82.30 349	94.53 345	93.98 350

tmp_tt		78.77 330	78.73 331	78.90 342	58.45 358	74.76 358	94.20 334	78.26 360	39.16 354	86.71 354	92.82 352	80.50 323	75.19 357	86.16 334	92.29 350	86.74 352

MVE	83.40 22	92.50 319	91.92 321	94.25 325	98.83 234	91.64 305	92.71 344	83.52 357	95.92 246	86.46 355	95.46 328	95.20 214	95.40 354	80.51 350	98.64 274	95.73 342
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		17.12 333	20.53 334	6.87 346	12.05 359	4.20 361	93.62 340	6.73 361	4.62 356	10.41 356	24.33 355	8.28 364	3.56 359	9.69 356	15.07 355	12.86 356

test123		17.04 334	20.11 335	7.82 345	10.25 360	4.91 360	94.80 326	4.47 362	4.93 355	10.00 357	24.28 356	9.69 363	3.64 358	10.14 355	12.43 357	14.92 355

cdsmvs_eth3d_5k		24.66 332	32.88 333	0.00 347	0.00 361	0.00 362	0.00 353	99.10 192	0.00 357	0.00 358	97.58 274	99.21 11	0.00 360	0.00 357	0.00 358	0.00 358

pcd_1.5k_mvsjas		8.17 335	10.90 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	98.07 61	0.00 360	0.00 357	0.00 358	0.00 358

pcd1.5k->3k		41.59 331	44.35 332	33.30 344	99.87 12	0.00 362	0.00 353	99.58 36	0.00 357	0.00 358	0.00 359	99.70 2	0.00 360	0.00 357	99.99 11	99.91 2

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

ab-mvs-re		8.12 336	10.83 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	97.48 281	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																98.81 251

test_part3							97.25 217		96.66 220		98.71 181		99.86 77	93.00 281

test_part1								99.28 142				97.56 91			99.57 174	99.53 91

sam_mvs1												84.74 305				98.81 251

sam_mvs												84.29 311

MTGPA								99.20 164

test_post1							97.59 195				20.48 358	83.07 317	99.66 254	94.16 251

test_post											21.25 357	83.86 313	99.70 231

patchmatchnet-post											98.77 175	84.37 308	99.85 88

MTMP								91.91 350

gm-plane-assit					94.83 352	81.97 353			88.07 339		94.99 335		99.60 271	91.76 299

test9_res														93.28 278	99.15 239	99.38 157

agg_prior2														92.50 293	99.16 236	99.37 158

test_prior4						97.97 120	95.86 297

test_prior				98.95 116	98.69 255	97.95 123		99.03 205					99.59 275			99.30 183

新几何2							95.93 294

旧先验1					98.82 237	97.45 159		98.76 247			98.34 229	95.50 208			99.01 254	99.23 197

无先验							95.74 303	98.74 252	89.38 334				99.73 219	92.38 295		99.22 201

原ACMM2							95.53 310

testdata2													99.79 174	92.80 288

segment_acmp												97.02 132

testdata1							95.44 314		96.32 231

plane_prior7					99.19 160	97.87 130

plane_prior6					98.99 202	97.70 147						94.90 220

plane_prior5								99.27 147					99.70 231	94.42 246	99.51 191	99.45 133

plane_prior4											97.98 256

plane_prior2							97.77 172		98.20 121

plane_prior1					99.05 190

plane_prior						97.65 149	97.07 232		96.72 215						99.36 206

n2								0.00 363

nn								0.00 363

door-mid								99.57 43

test11								98.87 230

door								99.41 97

HQP5-MVS						96.79 185

BP-MVS														92.82 286

HQP3-MVS								99.04 203							99.26 223

HQP2-MVS												93.84 247

NP-MVS					98.84 232	97.39 162					96.84 299

ACMMP++_ref															99.77 105

ACMMP++															99.68 143

Test By Simon												96.52 166

High-resolution multi-view benchmark