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		96.06 260	97.62 185	91.38 339	98.65 267	98.57 76	98.85 72	96.95 303	96.86 210	99.90 5	99.16 98	99.18 12	98.40 350	89.23 326	99.77 105	77.18 355

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	99.81 143	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	99.83 118	99.06 47	99.62 158	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	98.76 178	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	98.20 275	99.37 36	99.70 15	99.65 25	92.65 266	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	99.82 130	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	94.13 256	99.30 217	98.91 242

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	99.82 130	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	99.76 200	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	95.33 356	96.56 176	99.74 116	99.31 181

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	99.83 118	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	99.83 118	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	99.83 118	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	99.82 130	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	99.81 143	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	95.13 316	99.63 52	85.84 338	98.35 115	98.21 274	98.23 120	99.54 35	99.46 52	95.02 219	99.68 241	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	99.80 155	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	99.67 247	96.71 164	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	99.81 143	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	97.66 181	99.03 106	99.79 24	97.56 153	99.19 39	92.47 347	99.62 16	99.52 39	99.66 22	89.61 281	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	98.94 219	96.18 236	99.52 39	99.41 61	95.90 197	99.81 143	96.72 161	99.99 11	99.20 204

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	95.92 207	99.69 138	99.04 225

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	96.16 198	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	99.81 143	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	99.83 118	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	99.75 206	97.17 134	99.66 154	99.63 44

N_pmnet		97.63 190	97.17 208	98.99 113	99.27 134	97.86 131	95.98 285	93.41 339	95.25 264	99.47 49	98.90 151	95.63 203	99.85 88	96.91 147	99.73 119	99.27 189

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	99.73 220	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	99.81 143	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	99.74 215	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	99.80 155	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	95.54 259	99.42 57	99.19 90	97.27 113	99.63 263	97.89 100	99.97 23	99.20 204

semantic-postprocess				96.87 272	99.27 134	91.16 321		99.25 153	99.10 65	99.41 58	99.35 68	92.91 262	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	99.76 200	97.70 112	99.79 97	99.39 152

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	99.76 200	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		86.71 356		99.37 64	99.52 45	89.93 279	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	99.78 185	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	99.78 185	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	99.78 185	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	99.77 195	97.79 105	99.74 116	99.04 225

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	99.82 130	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	93.11 258	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	98.39 287	98.97 51	95.03 322	99.18 174	96.88 209	99.33 72	98.78 174	98.16 57	99.28 331	96.74 159	99.62 158	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	99.72 229	95.98 205	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	95.84 199	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	99.62 265	98.37 80	99.85 71	99.39 152

HSP-MVS		98.34 136	97.94 163	99.54 25	99.57 62	99.25 19	98.57 86	98.84 237	97.55 163	99.31 79	97.71 268	94.61 234	99.88 63	96.14 200	99.19 234	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	99.82 130	98.09 91	99.36 207	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	99.79 175	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	99.50 302	96.55 177	99.50 197	99.26 192

Patchmatch-RL test		97.26 216	97.02 213	97.99 227	99.52 81	95.53 234	96.13 282	99.71 12	97.47 169	99.27 82	99.16 98	84.30 311	99.62 265	97.89 100	99.77 105	98.81 252

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	99.80 155	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	99.78 185	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	99.78 185	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	99.78 185	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	99.78 185	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	98.94 219	96.96 205	99.24 90	98.89 156	97.83 76	99.81 143	96.88 150	99.49 198	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	99.82 130	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	96.45 184	99.62 158	99.50 104

TSAR-MVS + GP.		98.18 154	97.98 159	98.77 141	98.71 249	97.88 129	96.32 273	98.66 258	96.33 231	99.23 93	98.51 216	97.48 100	99.40 315	97.16 135	99.46 199	99.02 228

SMA-MVS		98.47 124	98.11 148	99.53 32	99.16 167	99.27 16	98.05 140	99.30 138	94.34 285	99.22 94	99.10 109	97.72 82	99.79 175	96.45 184	99.68 143	99.53 91

ppachtmachnet_test		97.50 197	97.74 175	96.78 276	98.70 253	91.23 320	94.55 332	99.05 200	96.36 230	99.21 95	98.79 173	96.39 173	99.78 185	96.74 159	99.82 82	99.34 171

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	99.83 118	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	98.87 231	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	97.65 272	98.96 19	99.49 303	96.50 181	98.99 257	99.34 171

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	94.14 243	99.82 130	97.97 99	99.80 93	99.29 187

EI-MVSNet-Vis-set		98.68 90	98.70 74	98.63 157	99.09 178	96.40 201	97.23 219	98.86 235	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	99.80 155	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	98.81 243	96.66 220	99.17 101	99.21 87	94.81 228	99.77 195	96.96 146	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	98.73 180	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	97.01 300	99.59 19	99.11 105	99.27 77	94.82 226	99.79 175	98.34 81	99.63 157	99.34 171

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	98.61 204	99.33 8	99.30 328	96.23 192	98.38 286	99.28 188

Regformer-3		98.61 104	98.61 89	98.63 157	99.02 197	96.53 194	97.17 227	98.84 237	99.13 60	99.10 107	98.85 162	97.24 118	99.79 175	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	99.83 118	95.58 225	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	99.83 118	95.58 225	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	98.87 231	98.97 78	99.06 110	99.02 129	96.00 187	99.80 155	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	98.59 206	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	98.76 178	96.76 153	99.93 26	98.57 70	99.77 105	99.50 104

MVSTER		96.86 238	96.55 241	97.79 232	97.91 310	94.21 267	97.56 198	98.87 231	97.49 168	99.06 110	99.05 123	80.72 323	99.80 155	98.44 76	99.82 82	99.37 159

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	99.28 331	94.38 251	99.58 171	99.18 210

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	98.71 182	97.56 91	99.86 77	93.00 282	99.57 175	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	99.32 325	95.72 218	99.68 143	99.18 210

our_test_3		97.39 208	97.73 176	96.34 287	98.70 253	89.78 324	94.61 330	98.97 218	96.50 225	99.04 118	98.85 162	95.98 191	99.84 103	97.26 132	99.67 149	99.41 145

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	99.79 175	97.43 125	99.65 155	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	99.03 121	98.98 137	97.89 74	99.85 88	96.54 178	99.42 202	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	99.01 122	98.64 196	97.37 107	99.84 103	97.75 111	99.57 175	99.52 97

HyFIR lowres test		97.19 222	96.60 238	98.96 115	99.62 54	97.28 165	95.17 319	99.50 65	94.21 288	99.01 122	98.32 233	86.61 292	99.99 2	97.10 142	99.84 73	99.60 52

CVMVSNet		96.25 258	97.21 206	93.38 336	99.10 175	80.56 356	97.20 223	98.19 277	96.94 206	99.00 124	99.02 129	89.50 283	99.80 155	96.36 189	99.59 165	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	98.99 125	98.64 196	97.26 116	99.81 143	97.79 105	99.57 175	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	94.45 281	98.99 125	99.27 77	96.87 144	99.94 20	97.13 139	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	98.98 127	98.99 134	97.54 94	99.84 103	95.88 208	99.74 116	99.23 198

IS-MVSNet		98.19 153	97.90 167	99.08 96	99.57 62	97.97 120	99.31 20	98.32 271	99.01 74	98.98 127	99.03 128	91.59 273	99.79 175	95.49 227	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	94.87 272	98.97 129	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	97.29 295	99.67 8	98.97 129	99.50 46	90.45 278	99.80 155	97.88 102	99.20 230	99.48 117

USDC		97.41 207	97.40 196	97.44 253	98.94 209	93.67 284	95.17 319	99.53 59	94.03 290	98.97 129	99.10 109	95.29 213	99.34 322	95.84 214	99.73 119	99.30 184

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	98.95 132	99.55 41	94.14 243	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	98.95 132	99.55 41	94.14 243	99.86 77	97.77 107	99.69 138	99.41 145

FMVSNet3		97.50 197	97.24 205	98.29 207	98.08 303	95.83 226	97.86 165	98.91 227	97.89 139	98.95 132	98.95 143	87.06 290	99.81 143	97.77 107	99.69 138	99.23 198

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	98.95 132	98.91 148	98.34 46	99.79 175	95.63 222	99.91 54	98.86 247

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	98.94 136	98.86 160	98.75 25	99.82 130	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	96.16 240	98.93 137	98.82 169	96.00 187	99.83 118	97.32 129	99.73 119	99.36 165

YYNet1		97.60 191	97.67 178	97.39 256	99.04 192	93.04 292	95.27 316	98.38 270	97.25 191	98.92 138	98.95 143	95.48 210	99.73 220	96.99 144	98.74 267	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	98.90 139	98.90 151	98.00 67	99.88 63	96.15 199	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	98.90 139	99.26 79	96.12 182	99.52 296	95.72 218	99.71 128	99.32 177

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	98.89 141	98.90 151	96.98 135	99.92 34	97.16 135	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	98.89 141	98.90 151	96.98 135	99.92 34	97.16 135	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	98.94 219	98.57 103	98.89 141	98.50 219	95.60 204	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	98.88 144	99.06 118	97.65 85	99.57 283	94.45 245	99.61 162	99.37 159

TestCases				99.16 85	99.50 86	98.55 77		99.58 36	96.80 212	98.88 144	99.06 118	97.65 85	99.57 283	94.45 245	99.61 162	99.37 159

MDA-MVSNet_test_wron		97.60 191	97.66 181	97.41 255	99.04 192	93.09 289	95.27 316	98.42 268	97.26 190	98.88 144	98.95 143	95.43 211	99.73 220	97.02 143	98.72 268	99.41 145

VNet		98.42 129	98.30 131	98.79 136	98.79 242	97.29 163	98.23 120	98.66 258	99.31 41	98.85 147	98.80 171	94.80 229	99.78 185	98.13 90	99.13 244	99.31 181

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	98.85 147	98.04 254	97.66 84	99.84 103	96.72 161	99.81 89	99.13 218

CHOSEN 1792x2688		97.49 199	97.14 211	98.54 177	99.68 43	96.09 216	96.50 263	99.62 28	91.58 318	98.84 149	98.97 139	92.36 268	99.88 63	96.76 158	99.95 30	99.67 31

test1235678		97.06 229	96.84 223	97.73 236	98.55 276	94.46 263	94.80 326	99.36 111	96.85 211	98.83 150	98.26 236	92.72 265	99.82 130	92.49 295	99.70 131	98.91 242

mvs_anonymous		97.83 181	98.16 142	96.87 272	98.18 300	91.89 302	97.31 214	98.90 228	97.37 180	98.83 150	99.46 52	96.28 178	99.79 175	98.90 53	98.16 295	98.95 236

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	98.83 150	99.11 107	94.31 240	99.85 88	96.60 170	98.72 268	99.37 159

PMMVS2		98.07 161	98.08 154	98.04 224	99.41 115	94.59 256	94.59 331	99.40 98	97.50 166	98.82 153	98.83 166	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	98.82 153	99.01 131	97.71 83	99.87 72	96.29 191	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	95.76 250	98.81 155	98.82 169	98.36 45	99.82 130	94.75 236	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	98.78 156	99.12 106	98.02 65	99.84 103	97.13 139	99.67 149	99.59 58

LFMVS		97.20 221	96.72 228	98.64 155	98.72 247	96.95 181	98.93 66	94.14 337	99.74 5	98.78 156	99.01 131	84.45 308	99.73 220	97.44 124	99.27 222	99.25 194

Patchmtry		97.35 209	96.97 215	98.50 184	97.31 332	96.47 197	98.18 124	98.92 225	98.95 82	98.78 156	99.37 65	85.44 303	99.85 88	95.96 206	99.83 79	99.17 214

UnsupCasMVSNet_eth		97.89 171	97.60 186	98.75 145	99.31 130	97.17 172	97.62 190	99.35 117	98.72 94	98.76 159	98.68 187	92.57 267	99.74 215	97.76 110	95.60 340	99.34 171

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	98.75 160	98.92 147	98.18 56	99.65 260	96.68 166	99.56 182	99.37 159

DeepC-MVS_fast	96.85 6	98.30 140	98.15 144	98.75 145	98.61 269	97.23 166	97.76 174	99.09 193	97.31 186	98.75 160	98.66 191	97.56 91	99.64 262	96.10 201	99.55 184	99.39 152
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS		98.10 158	97.67 178	99.42 51	99.11 174	98.93 55	97.76 174	99.28 142	94.97 269	98.72 162	98.77 176	97.04 129	99.85 88	93.79 266	99.54 185	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	98.69 163	98.88 157	98.00 67	99.89 56	95.87 211	99.59 165	99.58 65

DI_MVS_plusplus_test		97.57 195	97.40 196	98.07 221	99.06 185	95.71 230	96.58 261	96.96 301	96.71 217	98.69 163	98.13 243	93.81 250	99.68 241	97.45 123	99.19 234	98.80 255

test_normal		97.58 193	97.41 195	98.10 216	99.03 195	95.72 229	96.21 278	97.05 299	96.71 217	98.65 165	98.12 247	93.87 247	99.69 236	97.68 116	99.35 209	98.88 245

OpenMVS_ROB	95.38 14	95.84 264	95.18 273	97.81 231	98.41 286	97.15 174	97.37 210	98.62 261	83.86 348	98.65 165	98.37 227	94.29 241	99.68 241	88.41 328	98.62 277	96.60 333

MS-PatchMatch		97.68 186	97.75 174	97.45 252	98.23 297	93.78 281	97.29 215	98.84 237	96.10 242	98.64 167	98.65 193	96.04 184	99.36 320	96.84 153	99.14 241	99.20 204

1111		93.99 307	93.72 303	94.80 319	99.33 128	85.20 342	95.97 286	99.39 100	97.88 140	98.64 167	98.56 211	57.79 362	99.80 155	96.02 202	99.87 68	99.40 151

.test1245		79.71 330	84.30 331	65.96 344	99.33 128	85.20 342	95.97 286	99.39 100	97.88 140	98.64 167	98.56 211	57.79 362	99.80 155	96.02 202	15.07 356	12.86 357

Test4		97.43 205	97.18 207	98.18 214	99.05 190	96.02 217	96.62 259	99.09 193	96.25 235	98.63 170	97.70 269	90.49 277	99.68 241	97.50 121	99.30 217	98.83 249

pmmvs5		97.64 189	97.49 190	98.08 220	99.14 172	95.12 245	96.70 253	99.05 200	93.77 292	98.62 171	98.83 166	93.23 255	99.75 206	98.33 83	99.76 114	99.36 165

ab-mvs		98.41 130	98.36 123	98.59 166	99.19 160	97.23 166	99.32 17	98.81 243	97.66 151	98.62 171	99.40 64	96.82 147	99.80 155	95.88 208	99.51 192	98.75 261

pmmvs4		97.58 193	97.28 204	98.51 183	98.84 232	96.93 182	95.40 315	98.52 264	93.60 294	98.61 173	98.65 193	95.10 218	99.60 272	96.97 145	99.79 97	98.99 231

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	98.61 173	98.38 226	98.62 30	99.87 72	96.47 182	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	98.60 175	99.62 28	98.22 52	99.51 301	97.70 112	99.73 119	97.89 293
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CDS-MVSNet		97.69 185	97.35 202	98.69 151	98.73 246	97.02 179	96.92 240	98.75 251	95.89 248	98.59 176	98.67 189	92.08 272	99.74 215	96.72 161	99.81 89	99.32 177
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	98.07 279	99.03 72	98.59 176	99.13 105	92.16 270	99.90 47	96.87 151	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	98.58 178	98.50 219	97.97 71	99.85 88	96.57 173	99.59 165	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	96.30 234	98.58 178	98.50 219	97.97 71	99.85 88	95.68 221	99.59 165	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	98.56 180	98.54 214	97.75 81	99.88 63	96.57 173	99.59 165	99.58 65

new_pmnet		96.99 234	96.76 226	97.67 238	98.72 247	94.89 248	95.95 293	98.20 275	92.62 305	98.55 181	98.54 214	94.88 224	99.52 296	93.96 260	99.44 201	98.59 271

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	98.55 181	99.33 72	97.95 73	99.90 47	97.16 135	99.67 149	99.44 135

OMC-MVS		97.88 173	97.49 190	99.04 105	98.89 223	98.63 69	96.94 237	99.25 153	95.02 267	98.53 183	98.51 216	97.27 113	99.47 307	93.50 275	99.51 192	99.01 229

jason		97.45 204	97.35 202	97.76 234	99.24 138	93.93 274	95.86 297	98.42 268	94.24 287	98.50 184	98.13 243	94.82 226	99.91 43	97.22 133	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	96.41 229	98.46 185	98.95 143	95.93 194	99.60 272	96.51 180	98.98 259	99.31 181
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	98.45 186	98.71 182	97.50 96	99.82 130	98.21 87	99.59 165	98.93 239
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	98.44 187	98.51 216	97.83 76	99.88 63	96.46 183	99.58 171	99.58 65

BH-untuned		96.83 239	96.75 227	97.08 262	98.74 245	93.33 288	96.71 252	98.26 273	96.72 215	98.44 187	97.37 290	95.20 215	99.47 307	91.89 299	97.43 319	98.44 277

LS3D		98.63 98	98.38 121	99.36 57	97.25 333	99.38 6	99.12 48	99.32 129	99.21 47	98.44 187	98.88 157	97.31 109	99.80 155	96.58 171	99.34 211	98.92 240

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	98.41 190	97.14 297	98.47 39	99.92 34	98.02 95	99.05 249	96.92 323

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	98.41 190	97.14 297	98.47 39	99.92 34	98.02 95	99.05 249	96.92 323

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	98.41 190	97.14 297	98.47 39	99.92 34	98.02 95	99.05 249	96.92 323

Patchmatch-test		96.55 250	96.34 247	97.17 261	98.35 289	93.06 290	98.40 113	97.79 284	97.33 183	98.41 190	98.67 189	83.68 315	99.69 236	95.16 229	99.31 216	98.77 258

MSDG		97.71 184	97.52 189	98.28 208	98.91 218	96.82 184	94.42 333	99.37 107	97.65 152	98.37 194	98.29 235	97.40 105	99.33 324	94.09 257	99.22 227	98.68 269

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	98.30 195	98.40 225	97.86 75	99.89 56	96.53 179	99.72 124	99.56 75

UnsupCasMVSNet_bld		97.30 213	96.92 217	98.45 190	99.28 133	96.78 189	96.20 280	99.27 147	95.42 262	98.28 196	98.30 234	93.16 257	99.71 230	94.99 232	97.37 320	98.87 246

ITE_SJBPF				98.87 127	99.22 144	98.48 84		99.35 117	97.50 166	98.28 196	98.60 205	97.64 88	99.35 321	93.86 264	99.27 222	98.79 256

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	98.16 198	98.13 243	93.81 250	99.97 3	99.26 32	99.57 175	99.43 140

lupinMVS		97.06 229	96.86 221	97.65 240	98.88 224	93.89 278	95.48 312	97.97 281	93.53 295	98.16 198	97.58 275	93.81 250	99.91 43	96.77 157	99.57 175	99.17 214

Vis-MVSNet (Re-imp)		97.46 203	97.16 209	98.34 202	99.55 73	96.10 214	98.94 64	98.44 267	98.32 114	98.16 198	98.62 202	88.76 286	99.73 220	93.88 263	99.79 97	99.18 210

TAPA-MVS	96.21 11	96.63 247	95.95 254	98.65 154	98.93 211	98.09 105	96.93 238	99.28 142	83.58 349	98.13 201	97.78 265	96.13 181	99.40 315	93.52 273	99.29 220	98.45 276
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	98.10 202	98.24 238	98.25 48	99.34 322	96.69 165	99.65 155	99.12 219

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	98.09 203	98.68 187	97.62 89	99.89 56	96.22 193	99.62 158	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	98.07 204	99.28 75	97.11 127	99.84 103	96.84 153	99.32 214	99.47 125

PHI-MVS		98.29 143	97.95 161	99.34 65	98.44 284	99.16 29	98.12 130	99.38 103	96.01 246	98.06 205	98.43 223	97.80 80	99.67 247	95.69 220	99.58 171	99.20 204

CLD-MVS		97.49 199	97.16 209	98.48 186	99.07 182	97.03 177	94.71 328	99.21 160	94.46 279	98.06 205	97.16 295	97.57 90	99.48 306	94.46 244	99.78 101	98.95 236
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	98.48 280	94.73 250	98.18 124	99.02 209	97.69 150	98.04 207	99.11 107	97.22 122	99.56 286	98.57 70	98.90 263	98.71 263

FMVSNet5		96.01 261	95.20 272	98.41 193	97.53 323	96.10 214	98.74 75	99.50 65	97.22 199	98.03 208	99.04 125	69.80 354	99.88 63	97.27 131	99.71 128	99.25 194

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	97.99 209	98.25 237	98.15 59	99.38 319	96.87 151	99.57 175	99.42 143

MCST-MVS		98.00 165	97.63 184	99.10 93	99.24 138	98.17 100	96.89 243	98.73 254	95.66 251	97.92 210	97.70 269	97.17 123	99.66 255	96.18 197	99.23 226	99.47 125

MG-MVS		96.77 243	96.61 237	97.26 259	98.31 292	93.06 290	95.93 294	98.12 278	96.45 228	97.92 210	98.73 180	93.77 253	99.39 317	91.19 315	99.04 252	99.33 176

LP		96.60 249	96.57 240	96.68 278	97.64 319	91.70 304	98.11 131	97.74 286	97.29 189	97.91 212	99.24 82	88.35 287	99.85 88	97.11 141	95.76 339	98.49 274

tfpn1000		94.81 287	94.25 290	96.47 286	99.01 199	93.47 287	98.56 87	92.30 350	96.17 237	97.90 213	96.29 311	76.70 348	99.77 195	93.02 281	98.29 287	96.16 337

MSLP-MVS++		98.02 162	98.14 146	97.64 242	98.58 272	95.19 242	97.48 205	99.23 159	97.47 169	97.90 213	98.62 202	97.04 129	98.81 348	97.55 117	99.41 203	98.94 238

BH-RMVSNet		96.83 239	96.58 239	97.58 246	98.47 281	94.05 270	96.67 255	97.36 293	96.70 219	97.87 215	97.98 257	95.14 217	99.44 312	90.47 322	98.58 279	99.25 194

MIMVSNet		96.62 248	96.25 251	97.71 237	99.04 192	94.66 253	99.16 42	96.92 305	97.23 196	97.87 215	99.10 109	86.11 296	99.65 260	91.65 302	99.21 229	98.82 251

PNet_i23d		91.80 326	92.35 318	90.14 341	98.65 267	73.10 360	89.22 353	99.02 209	95.23 266	97.87 215	97.82 264	78.45 341	98.89 346	88.73 327	86.14 354	98.42 279

view600		94.87 280	94.41 282	96.26 290	99.22 144	91.37 310	98.49 97	94.45 326	98.75 89	97.85 218	95.98 315	80.38 325	99.75 206	86.06 336	98.49 281	97.66 306

view800		94.87 280	94.41 282	96.26 290	99.22 144	91.37 310	98.49 97	94.45 326	98.75 89	97.85 218	95.98 315	80.38 325	99.75 206	86.06 336	98.49 281	97.66 306

conf0.05thres1000		94.87 280	94.41 282	96.26 290	99.22 144	91.37 310	98.49 97	94.45 326	98.75 89	97.85 218	95.98 315	80.38 325	99.75 206	86.06 336	98.49 281	97.66 306

tfpn		94.87 280	94.41 282	96.26 290	99.22 144	91.37 310	98.49 97	94.45 326	98.75 89	97.85 218	95.98 315	80.38 325	99.75 206	86.06 336	98.49 281	97.66 306

LF4IMVS		97.90 170	97.69 177	98.52 179	99.17 165	97.66 148	97.19 226	99.47 80	96.31 233	97.85 218	98.20 242	96.71 156	99.52 296	94.62 240	99.72 124	98.38 281

CPTT-MVS		97.84 180	97.36 200	99.27 74	99.31 130	98.46 85	98.29 116	99.27 147	94.90 271	97.83 223	98.37 227	94.90 221	99.84 103	93.85 265	99.54 185	99.51 99

CMPMVS	75.91 23	96.29 256	95.44 265	98.84 131	96.25 348	98.69 67	97.02 233	99.12 189	88.90 337	97.83 223	98.86 160	89.51 282	98.90 345	91.92 298	99.51 192	98.92 240
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
E-PMN		94.17 302	94.37 287	93.58 333	96.86 339	85.71 340	90.11 351	97.07 298	98.17 124	97.82 225	97.19 293	84.62 307	98.94 343	89.77 324	97.68 316	96.09 341

conf0.01		94.82 285	94.07 291	97.06 264	99.21 150	94.53 257	98.47 103	92.69 341	95.61 252	97.81 226	95.54 322	77.71 342	99.80 155	91.49 307	98.11 298	96.86 326

conf0.002		94.82 285	94.07 291	97.06 264	99.21 150	94.53 257	98.47 103	92.69 341	95.61 252	97.81 226	95.54 322	77.71 342	99.80 155	91.49 307	98.11 298	96.86 326

thresconf0.02		94.70 289	94.07 291	96.58 279	99.21 150	94.53 257	98.47 103	92.69 341	95.61 252	97.81 226	95.54 322	77.71 342	99.80 155	91.49 307	98.11 298	95.42 345

tfpn_n400		94.70 289	94.07 291	96.58 279	99.21 150	94.53 257	98.47 103	92.69 341	95.61 252	97.81 226	95.54 322	77.71 342	99.80 155	91.49 307	98.11 298	95.42 345

tfpnconf		94.70 289	94.07 291	96.58 279	99.21 150	94.53 257	98.47 103	92.69 341	95.61 252	97.81 226	95.54 322	77.71 342	99.80 155	91.49 307	98.11 298	95.42 345

tfpnview11		94.70 289	94.07 291	96.58 279	99.21 150	94.53 257	98.47 103	92.69 341	95.61 252	97.81 226	95.54 322	77.71 342	99.80 155	91.49 307	98.11 298	95.42 345

CDPH-MVS		97.26 216	96.66 235	99.07 97	99.00 200	98.15 101	96.03 284	99.01 212	91.21 324	97.79 232	97.85 262	96.89 143	99.69 236	92.75 290	99.38 206	99.39 152

HQP_MVS		97.99 167	97.67 178	98.93 119	99.19 160	97.65 149	97.77 172	99.27 147	98.20 121	97.79 232	97.98 257	94.90 221	99.70 232	94.42 247	99.51 192	99.45 133

plane_prior3						97.78 140			97.41 177	97.79 232

MDTV_nov1_ep13_2view						74.92 358	97.69 180		90.06 333	97.75 235		85.78 299		93.52 273		98.69 266

pmmvs3		95.03 277	94.40 286	96.93 268	97.70 317	92.53 295	95.08 321	97.71 288	88.57 338	97.71 236	98.08 252	79.39 336	99.82 130	96.19 195	99.11 247	98.43 278

DP-MVS Recon		97.33 211	96.92 217	98.57 169	99.09 178	97.99 115	96.79 246	99.35 117	93.18 298	97.71 236	98.07 253	95.00 220	99.31 326	93.97 259	99.13 244	98.42 279

QAPM		97.31 212	96.81 224	98.82 133	98.80 241	97.49 156	99.06 53	99.19 170	90.22 330	97.69 238	99.16 98	96.91 138	99.90 47	90.89 319	99.41 203	99.07 222

Patchmatch-test1		96.44 255	96.72 228	95.60 311	98.24 295	88.35 329	95.85 299	96.88 307	96.11 241	97.67 239	98.57 208	93.10 259	99.69 236	94.79 235	99.22 227	98.77 258

Effi-MVS+-dtu		98.26 146	97.90 167	99.35 62	98.02 305	99.49 3	98.02 149	99.16 183	98.29 118	97.64 240	97.99 256	96.44 171	99.95 13	96.66 167	98.93 262	98.60 270

CNVR-MVS		98.17 156	97.87 170	99.07 97	98.67 261	98.24 95	97.01 234	98.93 222	97.25 191	97.62 241	98.34 230	97.27 113	99.57 283	96.42 187	99.33 212	99.39 152

PVSNet_BlendedMVS		97.55 196	97.53 188	97.60 244	98.92 215	93.77 282	96.64 257	99.43 93	94.49 277	97.62 241	99.18 92	96.82 147	99.67 247	94.73 237	99.93 39	99.36 165

PVSNet_Blended		96.88 237	96.68 232	97.47 251	98.92 215	93.77 282	94.71 328	99.43 93	90.98 325	97.62 241	97.36 291	96.82 147	99.67 247	94.73 237	99.56 182	98.98 232

alignmvs		97.35 209	96.88 220	98.78 139	98.54 277	98.09 105	97.71 178	97.69 289	99.20 50	97.59 244	95.90 319	88.12 289	99.55 289	98.18 89	98.96 260	98.70 265

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	97.58 245	98.66 191	97.40 105	99.88 63	94.72 239	99.60 164	99.54 86
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DSMNet-mixed		97.42 206	97.60 186	96.87 272	99.15 171	91.46 307	98.54 90	99.12 189	92.87 302	97.58 245	99.63 27	96.21 179	99.90 47	95.74 217	99.54 185	99.27 189

test0.0.03 1		94.51 294	93.69 304	96.99 267	96.05 349	93.61 285	94.97 323	93.49 338	96.17 237	97.57 247	94.88 341	82.30 320	99.01 342	93.60 271	94.17 349	98.37 283

PCF-MVS	92.86 18	94.36 296	93.00 314	98.42 192	98.70 253	97.56 153	93.16 344	99.11 191	79.59 352	97.55 248	97.43 286	92.19 269	99.73 220	79.85 352	99.45 200	97.97 292
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	97.54 249	98.63 200	97.50 96	99.83 118	96.79 155	99.53 189	99.56 75

X-MVStestdata		94.32 298	92.59 315	99.53 32	99.46 103	99.21 22	98.65 78	99.34 121	98.62 97	97.54 249	45.85 355	97.50 96	99.83 118	96.79 155	99.53 189	99.56 75

旧先验2							95.76 301		88.56 339	97.52 251			99.66 255	94.48 243

PMVS	91.26 20	97.86 175	97.94 163	97.65 240	99.71 34	97.94 125	98.52 91	98.68 257	98.99 75	97.52 251	99.35 68	97.41 104	98.18 351	91.59 305	99.67 149	96.82 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-MVSNAJ		97.08 228	97.39 198	96.16 298	98.56 274	92.46 296	95.24 318	98.85 236	97.25 191	97.49 253	95.99 314	98.07 61	99.90 47	96.37 188	98.67 274	96.12 340

xiu_mvs_v2_base		97.16 224	97.49 190	96.17 296	98.54 277	92.46 296	95.45 313	98.84 237	97.25 191	97.48 254	96.49 306	98.31 47	99.90 47	96.34 190	98.68 273	96.15 339

canonicalmvs		98.34 136	98.26 133	98.58 167	98.46 282	97.82 136	98.96 63	99.46 82	99.19 54	97.46 255	95.46 329	98.59 32	99.46 309	98.08 92	98.71 271	98.46 275

testdata				98.09 217	98.93 211	95.40 239		98.80 245	90.08 332	97.45 256	98.37 227	95.26 214	99.70 232	93.58 272	98.95 261	99.17 214

thres600view7		94.45 295	93.83 300	96.29 288	99.06 185	91.53 306	97.99 152	94.24 333	98.34 110	97.44 257	95.01 335	79.84 330	99.67 247	84.33 342	98.23 289	97.66 306

EMVS		93.83 310	94.02 297	93.23 337	96.83 341	84.96 344	89.77 352	96.32 316	97.92 130	97.43 258	96.36 310	86.17 294	98.93 344	87.68 331	97.73 315	95.81 342

tfpn_ndepth		94.12 304	93.51 308	95.94 303	98.86 226	93.60 286	98.16 127	91.90 352	94.66 276	97.41 259	95.24 332	76.24 349	99.73 220	91.21 313	97.88 313	94.50 350

tfpn111		94.33 297	93.78 301	95.96 302	99.06 185	91.35 314	98.03 142	94.24 333	98.33 111	97.40 260	94.98 337	79.84 330	99.68 241	83.94 343	98.22 291	96.86 326

conf200view11		94.24 300	93.67 305	95.94 303	99.06 185	91.35 314	98.03 142	94.24 333	98.33 111	97.40 260	94.98 337	79.84 330	99.62 265	83.05 345	98.08 306	96.86 326

thres100view900		94.19 301	93.67 305	95.75 308	99.06 185	91.35 314	98.03 142	94.24 333	98.33 111	97.40 260	94.98 337	79.84 330	99.62 265	83.05 345	98.08 306	96.29 334

Fast-Effi-MVS+-dtu		98.27 144	98.09 151	98.81 134	98.43 285	98.11 104	97.61 192	99.50 65	98.64 95	97.39 263	97.52 279	98.12 60	99.95 13	96.90 149	98.71 271	98.38 281

API-MVS		97.04 232	96.91 219	97.42 254	97.88 312	98.23 99	98.18 124	98.50 265	97.57 160	97.39 263	96.75 302	96.77 151	99.15 337	90.16 323	99.02 253	94.88 349

testus		95.52 269	95.32 268	96.13 300	97.91 310	89.49 326	93.62 341	99.61 30	92.41 307	97.38 265	95.42 331	94.72 233	99.63 263	88.06 330	98.72 268	99.26 192

PatchMatch-RL		97.24 219	96.78 225	98.61 162	99.03 195	97.83 133	96.36 271	99.06 196	93.49 297	97.36 266	97.78 265	95.75 200	99.49 303	93.44 276	98.77 266	98.52 273

test12356		94.85 284	95.12 274	94.03 329	98.25 293	83.12 351	93.85 339	99.33 126	94.17 289	97.28 267	97.20 292	85.83 298	99.75 206	90.85 320	99.33 212	99.22 202

sss		97.21 220	96.93 216	98.06 222	98.83 234	95.22 241	96.75 250	98.48 266	94.49 277	97.27 268	97.90 261	92.77 264	99.80 155	96.57 173	99.32 214	99.16 217

MVS_0304		98.02 162	97.88 169	98.46 188	98.22 298	96.39 202	96.50 263	99.49 71	98.03 126	97.24 269	98.33 232	94.80 229	99.90 47	98.31 84	99.95 30	99.08 220

WTY-MVS		96.67 245	96.27 249	97.87 229	98.81 239	94.61 255	96.77 248	97.92 283	94.94 270	97.12 270	97.74 267	91.11 275	99.82 130	93.89 262	98.15 296	99.18 210

tfpn200view9		94.03 306	93.44 309	95.78 307	98.93 211	91.44 308	97.60 193	94.29 331	97.94 128	97.10 271	94.31 346	79.67 334	99.62 265	83.05 345	98.08 306	96.29 334

thres400		94.14 303	93.44 309	96.24 294	98.93 211	91.44 308	97.60 193	94.29 331	97.94 128	97.10 271	94.31 346	79.67 334	99.62 265	83.05 345	98.08 306	97.66 306

PatchmatchNet		95.58 267	95.67 260	95.30 315	97.34 331	87.32 333	97.65 185	96.65 311	95.30 263	97.07 273	98.69 185	84.77 305	99.75 206	94.97 233	98.64 275	98.83 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNLPA		97.17 223	96.71 230	98.55 174	98.56 274	98.05 112	96.33 272	98.93 222	96.91 208	97.06 274	97.39 288	94.38 239	99.45 311	91.66 301	99.18 236	98.14 287

NCCC		97.86 175	97.47 194	99.05 103	98.61 269	98.07 110	96.98 235	98.90 228	97.63 153	97.04 275	97.93 260	95.99 190	99.66 255	95.31 228	98.82 265	99.43 140

TR-MVS		95.55 268	95.12 274	96.86 275	97.54 322	93.94 273	96.49 265	96.53 314	94.36 284	97.03 276	96.61 304	94.26 242	99.16 336	86.91 333	96.31 335	97.47 318

MDTV_nov1_ep13			95.22 271		97.06 336	83.20 350	97.74 176	96.16 317	94.37 283	96.99 277	98.83 166	83.95 313	99.53 292	93.90 261	97.95 311

CANet		97.87 174	97.76 173	98.19 213	97.75 313	95.51 235	96.76 249	99.05 200	97.74 147	96.93 278	98.21 241	95.59 205	99.89 56	97.86 104	99.93 39	99.19 209

EPMVS		93.72 311	93.27 311	95.09 317	96.04 350	87.76 331	98.13 128	85.01 357	94.69 275	96.92 279	98.64 196	78.47 340	99.31 326	95.04 230	96.46 334	98.20 285

AdaColmap		97.14 225	96.71 230	98.46 188	98.34 290	97.80 139	96.95 236	98.93 222	95.58 258	96.92 279	97.66 271	95.87 198	99.53 292	90.97 316	99.14 241	98.04 290

CR-MVSNet		96.28 257	95.95 254	97.28 257	97.71 315	94.22 265	98.11 131	98.92 225	92.31 309	96.91 281	99.37 65	85.44 303	99.81 143	97.39 127	97.36 322	97.81 299

RPMNet		96.82 241	96.66 235	97.28 257	97.71 315	94.22 265	98.11 131	96.90 306	99.37 36	96.91 281	99.34 70	86.72 291	99.81 143	97.53 119	97.36 322	97.81 299

HPM-MVS++		98.10 158	97.64 183	99.48 45	99.09 178	99.13 38	97.52 202	98.75 251	97.46 174	96.90 283	97.83 263	96.01 186	99.84 103	95.82 215	99.35 209	99.46 129

PatchT		96.65 246	96.35 246	97.54 248	97.40 329	95.32 240	97.98 153	96.64 312	99.33 40	96.89 284	99.42 59	84.32 310	99.81 143	97.69 114	97.49 317	97.48 317

1112_ss		97.29 215	96.86 221	98.58 167	99.34 127	96.32 204	96.75 250	99.58 36	93.14 299	96.89 284	97.48 282	92.11 271	99.86 77	96.91 147	99.54 185	99.57 70

test222					98.92 215	96.93 182	95.54 309	98.78 247	85.72 346	96.86 286	98.11 248	94.43 237			99.10 248	99.23 198

thres200		93.72 311	93.14 312	95.46 313	98.66 266	91.29 318	96.61 260	94.63 325	97.39 179	96.83 287	93.71 349	79.88 329	99.56 286	82.40 349	98.13 297	95.54 344

UGNet		98.53 118	98.45 110	98.79 136	97.94 308	96.96 180	99.08 49	98.54 263	99.10 65	96.82 288	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		96.99 234	96.55 241	98.31 205	99.35 125	95.47 237	95.84 300	99.53 59	91.51 320	96.80 289	98.48 222	91.36 274	99.83 118	96.58 171	99.53 189	99.62 45

新几何1				98.91 122	98.94 209	97.76 141		98.76 248	87.58 342	96.75 290	98.10 249	94.80 229	99.78 185	92.73 291	99.00 256	99.20 204

Effi-MVS+		98.02 162	97.82 172	98.62 159	98.53 279	97.19 170	97.33 212	99.68 16	97.30 187	96.68 291	97.46 284	98.56 36	99.80 155	96.63 169	98.20 292	98.86 247

GA-MVS		95.86 263	95.32 268	97.49 250	98.60 271	94.15 269	93.83 340	97.93 282	95.49 260	96.68 291	97.42 287	83.21 316	99.30 328	96.22 193	98.55 280	99.01 229

F-COLMAP		97.30 213	96.68 232	99.14 88	99.19 160	98.39 89	97.27 216	99.30 138	92.93 300	96.62 293	98.00 255	95.73 201	99.68 241	92.62 292	98.46 285	99.35 170

PAPM_NR		96.82 241	96.32 248	98.30 206	99.07 182	96.69 192	97.48 205	98.76 248	95.81 249	96.61 294	96.47 308	94.12 246	99.17 335	90.82 321	97.78 314	99.06 223

1121		96.73 244	96.00 252	98.91 122	98.95 208	97.76 141	98.07 136	98.73 254	87.65 341	96.54 295	98.13 243	94.52 236	99.73 220	92.38 296	99.02 253	99.24 197

test12				98.93 119	98.58 272	97.83 133		98.66 258		96.53 296		95.51 208	99.69 236		99.13 244	99.27 189

BH-w/o		95.13 275	94.89 279	95.86 305	98.20 299	91.31 317	95.65 306	97.37 292	93.64 293	96.52 297	95.70 320	93.04 260	99.02 340	88.10 329	95.82 338	97.24 321

ADS-MVSNet2		95.43 272	94.98 277	96.76 277	98.14 301	91.74 303	97.92 158	97.76 285	90.23 328	96.51 298	98.91 148	85.61 300	99.85 88	92.88 285	96.90 328	98.69 266

ADS-MVSNet		95.24 274	94.93 278	96.18 295	98.14 301	90.10 323	97.92 158	97.32 294	90.23 328	96.51 298	98.91 148	85.61 300	99.74 215	92.88 285	96.90 328	98.69 266

114514_t		96.50 253	95.77 256	98.69 151	99.48 97	97.43 160	97.84 167	99.55 54	81.42 351	96.51 298	98.58 207	95.53 206	99.67 247	93.41 277	99.58 171	98.98 232

PVSNet	93.40 17	95.67 266	95.70 258	95.57 312	98.83 234	88.57 327	92.50 346	97.72 287	92.69 304	96.49 301	96.44 309	93.72 254	99.43 313	93.61 270	99.28 221	98.71 263

mvs-test1		97.83 181	97.48 193	98.89 125	98.02 305	99.20 24	97.20 223	99.16 183	98.29 118	96.46 302	97.17 294	96.44 171	99.92 34	96.66 167	97.90 312	97.54 316

tpmrst		95.07 276	95.46 264	93.91 330	97.11 335	84.36 348	97.62 190	96.96 301	94.98 268	96.35 303	98.80 171	85.46 302	99.59 276	95.60 223	96.23 336	97.79 302

OpenMVS	96.65 7	97.09 227	96.68 232	98.32 203	98.32 291	97.16 173	98.86 71	99.37 107	89.48 334	96.29 304	99.15 102	96.56 164	99.90 47	92.90 284	99.20 230	97.89 293

diffmvs		97.49 199	97.36 200	97.91 228	98.38 288	95.70 231	97.95 156	99.31 131	94.87 272	96.14 305	98.78 174	94.84 225	99.43 313	97.69 114	98.26 288	98.59 271

Fast-Effi-MVS+		97.67 187	97.38 199	98.57 169	98.71 249	97.43 160	97.23 219	99.45 85	94.82 274	96.13 306	96.51 305	98.52 38	99.91 43	96.19 195	98.83 264	98.37 283

test_prior3		97.48 202	97.00 214	98.95 116	98.69 256	97.95 123	95.74 303	99.03 205	96.48 226	96.11 307	97.63 273	95.92 195	99.59 276	94.16 252	99.20 230	99.30 184

test_prior2							95.74 303		96.48 226	96.11 307	97.63 273	95.92 195		94.16 252	99.20 230

dp		93.47 313	93.59 307	93.13 338	96.64 342	81.62 355	97.66 183	96.42 315	92.80 303	96.11 307	98.64 196	78.55 339	99.59 276	93.31 278	92.18 353	98.16 286

原ACMM1				98.35 201	98.90 219	96.25 210		98.83 242	92.48 306	96.07 310	98.10 249	95.39 212	99.71 230	92.61 293	98.99 257	99.08 220

PMMVS		96.51 251	95.98 253	98.09 217	97.53 323	95.84 225	94.92 324	98.84 237	91.58 318	96.05 311	95.58 321	95.68 202	99.66 255	95.59 224	98.09 305	98.76 260

tpm		94.67 293	94.34 288	95.66 309	97.68 318	88.42 328	97.88 162	94.90 323	94.46 279	96.03 312	98.56 211	78.66 337	99.79 175	95.88 208	95.01 343	98.78 257

TEST9					98.71 249	98.08 108	95.96 290	99.03 205	91.40 321	95.85 313	97.53 277	96.52 166	99.76 200

train_agg		97.10 226	96.45 244	99.07 97	98.71 249	98.08 108	95.96 290	99.03 205	91.64 315	95.85 313	97.53 277	96.47 169	99.76 200	93.67 268	99.16 237	99.36 165

test_8					98.67 261	98.01 114	95.91 296	99.02 209	91.64 315	95.79 315	97.50 280	96.47 169	99.76 200

agg_prior3		96.95 236	96.27 249	99.00 112	98.68 258	97.91 126	95.96 290	99.01 212	90.74 327	95.60 316	97.45 285	96.14 180	99.74 215	93.67 268	99.16 237	99.36 165

agg_prior1		97.06 229	96.40 245	99.03 106	98.68 258	97.99 115	95.76 301	99.01 212	91.73 314	95.59 317	97.50 280	96.49 168	99.77 195	93.71 267	99.14 241	99.34 171

agg_prior					98.68 258	97.99 115		99.01 212		95.59 317			99.77 195

PLC	94.65 16	96.51 251	95.73 257	98.85 130	98.75 244	97.91 126	96.42 269	99.06 196	90.94 326	95.59 317	97.38 289	94.41 238	99.59 276	90.93 317	98.04 310	99.05 224
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HQP4-MVS										95.56 320			99.54 290			99.32 177

HQP-NCC					98.67 261		96.29 274		96.05 243	95.55 321

ACMP_Plane					98.67 261		96.29 274		96.05 243	95.55 321

HQP-MVS		97.00 233	96.49 243	98.55 174	98.67 261	96.79 185	96.29 274	99.04 203	96.05 243	95.55 321	96.84 300	93.84 248	99.54 290	92.82 287	99.26 224	99.32 177

MAR-MVS		96.47 254	95.70 258	98.79 136	97.92 309	99.12 40	98.28 117	98.60 262	92.16 312	95.54 324	96.17 312	94.77 232	99.52 296	89.62 325	98.23 289	97.72 305
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		95.02 278	95.25 270	94.33 324	96.39 347	85.87 337	98.08 134	96.83 308	95.46 261	95.51 325	98.69 185	85.91 297	99.53 292	94.16 252	96.23 336	97.58 314

test2356		91.64 327	90.19 330	96.00 301	94.30 355	89.58 325	90.84 349	96.68 310	91.76 313	95.48 326	93.69 350	67.05 357	99.52 296	84.83 341	97.08 327	98.91 242

MVS-HIRNet		94.32 298	95.62 261	90.42 340	98.46 282	75.36 357	96.29 274	89.13 355	95.25 264	95.38 327	99.75 7	92.88 263	99.19 334	94.07 258	99.39 205	96.72 332

PAPR		95.29 273	94.47 281	97.75 235	97.50 327	95.14 244	94.89 325	98.71 256	91.39 322	95.35 328	95.48 328	94.57 235	99.14 338	84.95 340	97.37 320	98.97 235

HY-MVS	95.94 13	95.90 262	95.35 267	97.55 247	97.95 307	94.79 249	98.81 74	96.94 304	92.28 310	95.17 329	98.57 208	89.90 280	99.75 206	91.20 314	97.33 324	98.10 288

CANet_DTU		97.26 216	97.06 212	97.84 230	97.57 320	94.65 254	96.19 281	98.79 246	97.23 196	95.14 330	98.24 238	93.22 256	99.84 103	97.34 128	99.84 73	99.04 225

cascas		94.79 288	94.33 289	96.15 299	96.02 351	92.36 299	92.34 348	99.26 152	85.34 347	95.08 331	94.96 340	92.96 261	98.53 349	94.41 250	98.59 278	97.56 315

CostFormer		93.97 308	93.78 301	94.51 323	97.53 323	85.83 339	97.98 153	95.96 318	89.29 336	94.99 332	98.63 200	78.63 338	99.62 265	94.54 242	96.50 333	98.09 289

CHOSEN 280x420		95.51 271	95.47 263	95.65 310	98.25 293	88.27 330	93.25 343	98.88 230	93.53 295	94.65 333	97.15 296	86.17 294	99.93 26	97.41 126	99.93 39	98.73 262

JIA-IIPM		95.52 269	95.03 276	97.00 266	96.85 340	94.03 271	96.93 238	95.82 319	99.20 50	94.63 334	99.71 14	83.09 317	99.60 272	94.42 247	94.64 344	97.36 319

MVS		93.19 316	92.09 320	96.50 285	96.91 338	94.03 271	98.07 136	98.06 280	68.01 353	94.56 335	96.48 307	95.96 193	99.30 328	83.84 344	96.89 330	96.17 336

PatchFormer-LS_test		94.08 305	93.91 298	94.59 322	96.93 337	86.86 335	97.55 200	96.57 313	94.27 286	94.38 336	93.64 351	80.96 322	99.59 276	96.44 186	94.48 347	97.31 320

1314		95.74 265	95.60 262	96.17 296	97.53 323	92.75 293	98.07 136	98.31 272	91.22 323	94.25 337	96.68 303	95.53 206	99.03 339	91.64 303	97.18 325	96.74 331

tpm cat1		93.29 315	93.13 313	93.75 331	97.39 330	84.74 345	97.39 209	97.65 290	83.39 350	94.16 338	98.41 224	82.86 319	99.39 317	91.56 306	95.35 342	97.14 322

test-LLR		93.90 309	93.85 299	94.04 327	96.53 343	84.62 346	94.05 336	92.39 348	96.17 237	94.12 339	95.07 333	82.30 320	99.67 247	95.87 211	98.18 293	97.82 297

test-mter		92.33 322	91.76 324	94.04 327	96.53 343	84.62 346	94.05 336	92.39 348	94.00 291	94.12 339	95.07 333	65.63 361	99.67 247	95.87 211	98.18 293	97.82 297

tpm2		93.09 317	92.58 316	94.62 321	97.56 321	86.53 336	97.66 183	95.79 320	86.15 345	94.07 341	98.23 240	75.95 350	99.53 292	90.91 318	96.86 331	97.81 299

TESTMET0.1,1		92.19 324	91.77 323	93.46 334	96.48 345	82.80 353	94.05 336	91.52 353	94.45 281	94.00 342	94.88 341	66.65 358	99.56 286	95.78 216	98.11 298	98.02 291

PVSNet_0	89.98 21	91.15 328	90.30 328	93.70 332	97.72 314	84.34 349	90.24 350	97.42 291	90.20 331	93.79 343	93.09 352	90.90 276	98.89 346	86.57 334	72.76 355	97.87 295

FPMVS		93.44 314	92.23 319	97.08 262	99.25 137	97.86 131	95.61 307	97.16 297	92.90 301	93.76 344	98.65 193	75.94 351	95.66 354	79.30 353	97.49 317	97.73 304

EPNet		96.14 259	95.44 265	98.25 209	90.76 358	95.50 236	97.92 158	94.65 324	98.97 78	92.98 345	98.85 162	89.12 285	99.87 72	95.99 204	99.68 143	99.39 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DWT-MVSNet_test		92.75 319	92.05 321	94.85 318	96.48 345	87.21 334	97.83 168	94.99 322	92.22 311	92.72 346	94.11 348	70.75 353	99.46 309	95.01 231	94.33 348	97.87 295

IB-MVS	91.63 19	92.24 323	90.90 326	96.27 289	97.22 334	91.24 319	94.36 334	93.33 340	92.37 308	92.24 347	94.58 345	66.20 359	99.89 56	93.16 280	94.63 345	97.66 306
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		92.37 321	91.20 325	95.85 306	95.80 352	92.38 298	99.31 20	81.84 359	99.75 4	91.83 348	99.74 8	68.29 355	99.02 340	87.15 332	97.12 326	96.16 337

DeepMVS_CX				93.44 335	98.24 295	94.21 267		94.34 330	64.28 354	91.34 349	94.87 343	89.45 284	92.77 357	77.54 354	93.14 350	93.35 352

tpmp4_e23		92.91 318	92.45 317	94.29 325	97.41 328	85.62 341	97.95 156	96.77 309	87.55 343	91.33 350	98.57 208	74.21 352	99.59 276	91.62 304	96.64 332	97.65 313

testpf		89.08 329	90.27 329	85.50 342	94.03 356	82.85 352	96.87 244	91.09 354	91.61 317	90.96 351	94.86 344	66.15 360	95.83 353	94.58 241	92.27 352	77.82 354

PAPM		91.88 325	90.34 327	96.51 284	98.06 304	92.56 294	92.44 347	97.17 296	86.35 344	90.38 352	96.01 313	86.61 292	99.21 333	70.65 355	95.43 341	97.75 303

EPNet_dtu		94.93 279	94.78 280	95.38 314	93.58 357	87.68 332	96.78 247	95.69 321	97.35 182	89.14 353	98.09 251	88.15 288	99.49 303	94.95 234	99.30 217	98.98 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				94.76 320	94.54 354	92.13 301	99.31 20	80.47 360		88.73 354	91.01 354	67.59 356	98.16 352	82.30 350	94.53 346	93.98 351

tmp_tt		78.77 331	78.73 332	78.90 343	58.45 359	74.76 359	94.20 335	78.26 361	39.16 355	86.71 355	92.82 353	80.50 324	75.19 358	86.16 335	92.29 351	86.74 353

MVE	83.40 22	92.50 320	91.92 322	94.25 326	98.83 234	91.64 305	92.71 345	83.52 358	95.92 247	86.46 356	95.46 329	95.20 215	95.40 355	80.51 351	98.64 275	95.73 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		17.12 334	20.53 335	6.87 347	12.05 360	4.20 362	93.62 341	6.73 362	4.62 357	10.41 357	24.33 356	8.28 365	3.56 360	9.69 357	15.07 356	12.86 357

test123		17.04 335	20.11 336	7.82 346	10.25 361	4.91 361	94.80 326	4.47 363	4.93 356	10.00 358	24.28 357	9.69 364	3.64 359	10.14 356	12.43 358	14.92 356

cdsmvs_eth3d_5k		24.66 333	32.88 334	0.00 348	0.00 362	0.00 363	0.00 354	99.10 192	0.00 358	0.00 359	97.58 275	99.21 11	0.00 361	0.00 358	0.00 359	0.00 359

pcd_1.5k_mvsjas		8.17 336	10.90 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	98.07 61	0.00 361	0.00 358	0.00 359	0.00 359

pcd1.5k->3k		41.59 332	44.35 333	33.30 345	99.87 12	0.00 363	0.00 354	99.58 36	0.00 358	0.00 359	0.00 360	99.70 2	0.00 361	0.00 358	99.99 11	99.91 2

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

ab-mvs-re		8.12 337	10.83 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	97.48 282	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																98.81 252

test_part3							97.25 217		96.66 220		98.71 182		99.86 77	93.00 282

test_part1								99.28 142				97.56 91			99.57 175	99.53 91

sam_mvs1												84.74 306				98.81 252

sam_mvs												84.29 312

MTGPA								99.20 164

test_post1							97.59 195				20.48 359	83.07 318	99.66 255	94.16 252

test_post											21.25 358	83.86 314	99.70 232

patchmatchnet-post											98.77 176	84.37 309	99.85 88

MTMP								91.91 351

gm-plane-assit					94.83 353	81.97 354			88.07 340		94.99 336		99.60 272	91.76 300

test9_res														93.28 279	99.15 240	99.38 158

agg_prior2														92.50 294	99.16 237	99.37 159

test_prior4						97.97 120	95.86 297

test_prior				98.95 116	98.69 256	97.95 123		99.03 205					99.59 276			99.30 184

新几何2							95.93 294

旧先验1					98.82 237	97.45 159		98.76 248			98.34 230	95.50 209			99.01 255	99.23 198

无先验							95.74 303	98.74 253	89.38 335				99.73 220	92.38 296		99.22 202

原ACMM2							95.53 310

testdata2													99.79 175	92.80 289

segment_acmp												97.02 132

testdata1							95.44 314		96.32 232

plane_prior7					99.19 160	97.87 130

plane_prior6					98.99 202	97.70 147						94.90 221

plane_prior5								99.27 147					99.70 232	94.42 247	99.51 192	99.45 133

plane_prior4											97.98 257

plane_prior2							97.77 172		98.20 121

plane_prior1					99.05 190

plane_prior						97.65 149	97.07 232		96.72 215						99.36 207

n2								0.00 364

nn								0.00 364

door-mid								99.57 43

test11								98.87 231

door								99.41 97

HQP5-MVS						96.79 185

BP-MVS														92.82 287

HQP3-MVS								99.04 203							99.26 224

HQP2-MVS												93.84 248

NP-MVS					98.84 232	97.39 162					96.84 300

ACMMP++_ref															99.77 105

ACMMP++															99.68 143

Test By Simon												96.52 166

High-resolution multi-view benchmark