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 8	99.67 4	99.28 71	99.89 6	98.09 108	99.14 44	99.93 1	99.82 2	99.93 2	99.81 3	99.17 13	99.94 20	99.31 31	100.00 1	99.82 10

PS-MVSNAJss		99.46 13	99.49 11	99.35 62	99.90 4	98.15 104	99.20 35	99.65 19	99.48 25	99.92 3	99.71 13	98.07 60	99.96 8	99.53 21	100.00 1	99.93 1

mvs_tets		99.63 4	99.67 4	99.49 44	99.88 7	98.61 72	99.34 15	99.71 11	99.27 45	99.90 4	99.74 7	99.68 3	99.97 3	99.55 20	99.99 11	99.88 5

wuyk23d		96.06 268	97.62 191	91.38 350	98.65 275	98.57 76	98.85 73	96.95 311	96.86 222	99.90 4	99.16 101	99.18 11	98.40 360	89.23 336	99.77 105	77.18 365

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

jajsoiax		99.58 7	99.61 6	99.48 45	99.87 11	98.61 72	99.28 29	99.66 18	99.09 69	99.89 7	99.68 18	99.53 4	99.97 3	99.50 22	99.99 11	99.87 6

pmmvs6		99.67 2	99.70 3	99.60 11	99.90 4	99.27 15	99.53 8	99.76 6	99.64 10	99.84 8	99.83 2	99.50 5	99.87 74	99.36 29	99.92 48	99.64 41

Anonymous20231211		99.27 28	99.27 32	99.26 76	99.29 134	98.18 102	99.49 9	99.51 63	99.70 6	99.80 9	99.68 18	96.84 146	99.83 119	99.21 39	99.91 53	99.77 16

OurMVSNet-221017-0		99.37 23	99.31 27	99.53 32	99.91 3	98.98 50	99.63 5	99.58 35	99.44 30	99.78 10	99.76 5	96.39 176	99.92 33	99.44 26	99.92 48	99.68 30

LTVRE_ROB	98.40 1	99.67 2	99.71 2	99.56 18	99.85 17	99.11 43	99.90 1	99.78 4	99.63 12	99.78 10	99.67 21	99.48 6	99.81 145	99.30 32	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 14	99.47 14	99.36 57	99.80 21	98.58 75	99.27 31	99.57 42	99.39 33	99.75 12	99.62 28	99.17 13	99.83 119	99.06 50	99.62 159	99.66 33

NR-MVSNet		98.95 56	98.82 57	99.36 57	99.16 172	98.72 66	99.22 34	99.20 167	99.10 66	99.72 13	98.76 184	96.38 178	99.86 79	98.00 102	99.82 82	99.50 104

MIMVSNet1		99.38 22	99.32 26	99.55 20	99.86 15	99.19 25	99.41 12	99.59 33	99.59 19	99.71 14	99.57 39	97.12 124	99.90 47	99.21 39	99.87 68	99.54 87

test_djsdf		99.52 10	99.51 10	99.53 32	99.86 15	98.74 61	99.39 13	99.56 48	99.11 62	99.70 15	99.73 9	99.00 16	99.97 3	99.26 33	99.98 19	99.89 3

SixPastTwentyTwo		98.75 76	98.62 87	99.16 86	99.83 18	97.96 125	99.28 29	98.20 281	99.37 36	99.70 15	99.65 25	92.65 270	99.93 25	99.04 51	99.84 73	99.60 53

new-patchmatchnet		98.35 140	98.74 67	97.18 270	99.24 141	92.23 311	96.42 279	99.48 74	98.30 119	99.69 17	99.53 44	97.44 101	99.82 132	98.84 60	99.77 105	99.49 111

v748		99.44 14	99.48 12	99.33 67	99.88 7	98.43 87	99.42 11	99.53 58	99.63 12	99.69 17	99.60 34	97.99 68	99.91 43	99.60 14	99.96 28	99.66 33

LCM-MVSNet-Re		98.64 97	98.48 104	99.11 93	98.85 236	98.51 82	98.49 102	99.83 3	98.37 112	99.69 17	99.46 52	98.21 53	99.92 33	94.13 268	99.30 221	98.91 247

v7n		99.53 9	99.57 9	99.41 53	99.88 7	98.54 80	99.45 10	99.61 29	99.66 9	99.68 20	99.66 22	98.44 41	99.95 13	99.73 8	99.96 28	99.75 22

v52		99.59 5	99.60 7	99.55 20	99.87 11	99.00 48	99.59 6	99.56 48	99.56 22	99.68 20	99.72 10	98.57 33	99.93 25	99.85 1	99.99 11	99.72 24

V4		99.59 5	99.60 7	99.55 20	99.87 11	99.00 48	99.59 6	99.56 48	99.56 22	99.68 20	99.72 10	98.57 33	99.93 25	99.85 1	99.99 11	99.72 24

anonymousdsp		99.51 11	99.47 14	99.62 5	99.88 7	99.08 47	99.34 15	99.69 14	98.93 84	99.65 23	99.72 10	98.93 19	99.95 13	99.11 47	100.00 1	99.82 10

pm-mvs1		99.44 14	99.48 12	99.33 67	99.80 21	98.63 69	99.29 25	99.63 24	99.30 42	99.65 23	99.60 34	99.16 15	99.82 132	99.07 49	99.83 79	99.56 76

ACMH	96.65 7	99.25 30	99.24 35	99.26 76	99.72 32	98.38 90	99.07 52	99.55 53	98.30 119	99.65 23	99.45 56	99.22 9	99.76 207	98.44 80	99.77 105	99.64 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SD-MVS		98.40 135	98.68 80	97.54 258	98.96 212	97.99 118	97.88 170	99.36 113	98.20 127	99.63 26	99.04 130	98.76 23	95.33 366	96.56 183	99.74 116	99.31 182

v13		99.24 31	99.39 17	98.77 144	99.63 51	96.79 189	99.24 33	99.65 19	99.39 33	99.62 27	99.70 15	97.50 95	99.84 104	99.78 5	100.00 1	99.67 31

v12		99.21 32	99.37 19	98.74 152	99.60 54	96.72 194	99.19 39	99.65 19	99.35 39	99.62 27	99.69 16	97.43 102	99.83 119	99.76 6	100.00 1	99.66 33

V9		99.18 34	99.34 23	98.70 153	99.58 56	96.63 197	99.14 44	99.64 23	99.30 42	99.61 29	99.68 18	97.33 107	99.83 119	99.75 7	100.00 1	99.65 38

v11		99.12 40	99.31 27	98.53 182	99.59 55	96.11 219	99.08 49	99.65 19	99.15 57	99.60 30	99.69 16	97.26 115	99.83 119	99.81 3	100.00 1	99.66 33

V14		99.14 37	99.30 30	98.66 156	99.56 68	96.53 199	99.08 49	99.63 24	99.24 47	99.60 30	99.66 22	97.23 119	99.82 132	99.73 8	100.00 1	99.65 38

v15		99.11 41	99.27 32	98.62 162	99.52 80	96.43 203	99.01 55	99.63 24	99.18 56	99.59 32	99.64 26	97.13 123	99.81 145	99.71 10	100.00 1	99.64 41

PEN-MVS		99.41 19	99.34 23	99.62 5	99.73 27	99.14 36	99.29 25	99.54 57	99.62 16	99.56 33	99.42 59	98.16 56	99.96 8	98.78 62	99.93 38	99.77 16

DTE-MVSNet		99.43 17	99.35 21	99.66 3	99.71 33	99.30 11	99.31 20	99.51 63	99.64 10	99.56 33	99.46 52	98.23 49	99.97 3	98.78 62	99.93 38	99.72 24

Anonymous20240529		98.93 57	98.87 53	99.12 91	99.19 163	98.22 101	99.01 55	98.99 221	99.25 46	99.54 35	99.37 65	97.04 128	99.80 157	97.89 104	99.52 191	99.35 170

EU-MVSNet		97.66 195	98.50 100	95.13 327	99.63 51	85.84 349	98.35 120	98.21 280	98.23 124	99.54 35	99.46 52	95.02 224	99.68 248	98.24 89	99.87 68	99.87 6

DeepC-MVS	97.60 4	98.97 54	98.93 51	99.10 95	99.35 126	97.98 122	98.01 156	99.46 82	97.56 171	99.54 35	99.50 46	98.97 17	99.84 104	98.06 97	99.92 48	99.49 111
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TDRefinement		99.42 18	99.38 18	99.55 20	99.76 25	99.33 10	99.68 4	99.71 11	99.38 35	99.53 38	99.61 30	98.64 28	99.80 157	98.24 89	99.84 73	99.52 96

ACMH+	96.62 9	99.08 42	99.00 49	99.33 67	99.71 33	98.83 57	98.60 87	99.58 35	99.11 62	99.53 38	99.18 94	98.81 22	99.67 254	96.71 171	99.77 105	99.50 104

v8		99.01 47	99.16 41	98.57 173	99.47 99	96.31 210	98.90 68	99.47 80	99.03 73	99.52 40	99.57 39	96.93 138	99.81 145	99.60 14	99.98 19	99.60 53

VPA-MVSNet		99.30 27	99.30 30	99.28 71	99.49 92	98.36 92	99.00 60	99.45 85	99.63 12	99.52 40	99.44 57	98.25 47	99.88 65	99.09 48	99.84 73	99.62 46

K. test v3		98.00 171	97.66 187	99.03 109	99.79 23	97.56 156	99.19 39	92.47 356	99.62 16	99.52 40	99.66 22	89.61 286	99.96 8	99.25 35	99.81 89	99.56 76

no-one		97.98 174	98.10 156	97.61 253	99.55 72	93.82 289	96.70 263	98.94 225	96.18 247	99.52 40	99.41 61	95.90 201	99.81 145	96.72 168	99.99 11	99.20 205

tfpnnormal		98.90 61	98.90 52	98.91 125	99.67 43	97.82 139	99.00 60	99.44 88	99.45 29	99.51 44	99.24 83	98.20 54	99.86 79	95.92 215	99.69 138	99.04 228

testmv		98.51 123	98.47 106	98.61 165	99.24 141	96.53 199	96.66 266	99.73 9	98.56 109	99.50 45	99.23 87	97.24 117	99.87 74	96.16 205	99.93 38	99.44 135

WR-MVS_H		99.33 26	99.22 36	99.65 4	99.71 33	99.24 18	99.32 17	99.55 53	99.46 28	99.50 45	99.34 71	97.30 109	99.93 25	98.90 55	99.93 38	99.77 16

v17		99.07 43	99.22 36	98.61 165	99.50 86	96.42 204	99.01 55	99.60 31	99.15 57	99.48 47	99.61 30	97.05 127	99.81 145	99.64 12	99.98 19	99.61 50

v10		98.97 54	99.11 44	98.55 178	99.44 109	96.21 216	98.90 68	99.55 53	98.73 96	99.48 47	99.60 34	96.63 161	99.83 119	99.70 11	99.99 11	99.61 50

DP-MVS		98.93 57	98.81 59	99.28 71	99.21 153	98.45 86	98.46 114	99.33 128	99.63 12	99.48 47	99.15 105	97.23 119	99.75 213	97.17 140	99.66 154	99.63 45

N_pmnet		97.63 197	97.17 214	98.99 116	99.27 136	97.86 134	95.98 295	93.41 348	95.25 275	99.47 50	98.90 157	95.63 208	99.85 88	96.91 153	99.73 119	99.27 190

nrg030		99.40 20	99.35 21	99.54 25	99.58 56	99.13 39	98.98 63	99.48 74	99.68 7	99.46 51	99.26 80	98.62 29	99.73 227	99.17 45	99.92 48	99.76 20

v16		99.07 43	99.22 36	98.61 165	99.50 86	96.42 204	99.01 55	99.60 31	99.15 57	99.46 51	99.61 30	97.04 128	99.81 145	99.64 12	99.97 23	99.61 50

PS-CasMVS		99.40 20	99.33 25	99.62 5	99.71 33	99.10 44	99.29 25	99.53 58	99.53 24	99.46 51	99.41 61	98.23 49	99.95 13	98.89 57	99.95 30	99.81 12

v1240		98.55 116	98.62 87	98.32 208	99.22 147	95.58 237	97.51 213	99.45 85	97.16 212	99.45 54	99.24 83	96.12 186	99.85 88	99.60 14	99.88 64	99.55 84

ESAPD		98.59 110	98.26 137	99.57 15	99.27 136	99.15 34	97.01 243	99.39 100	97.67 159	99.44 55	98.99 139	97.53 93	99.89 57	95.40 236	99.68 143	99.66 33

testing_2		98.93 57	98.99 50	98.76 146	99.57 61	97.03 181	97.85 175	99.13 190	98.46 111	99.44 55	99.44 57	98.22 51	99.74 222	98.85 58	99.94 33	99.51 99

v18		99.02 46	99.17 39	98.57 173	99.45 106	96.31 210	98.94 65	99.58 35	99.06 71	99.43 57	99.58 38	96.91 139	99.80 157	99.60 14	99.97 23	99.59 59

FMVSNet1		99.17 35	99.17 39	99.17 83	99.55 72	98.24 96	99.20 35	99.44 88	99.21 48	99.43 57	99.55 41	97.82 78	99.86 79	98.42 82	99.89 63	99.41 145

pmmvs-eth3d		98.47 128	98.34 129	98.86 132	99.30 133	97.76 144	97.16 238	99.28 142	95.54 270	99.42 59	99.19 92	97.27 112	99.63 271	97.89 104	99.97 23	99.20 205

semantic-postprocess				96.87 283	99.27 136	91.16 332		99.25 153	99.10 66	99.41 60	99.35 69	92.91 266	99.96 8	98.65 69	99.94 33	99.49 111

test20.03		98.78 73	98.77 63	98.78 142	99.46 103	97.20 172	97.78 179	99.24 159	99.04 72	99.41 60	98.90 157	97.65 84	99.76 207	97.70 118	99.79 97	99.39 152

FC-MVSNet-test		99.27 28	99.25 34	99.34 65	99.77 24	98.37 91	99.30 24	99.57 42	99.61 18	99.40 62	99.50 46	97.12 124	99.85 88	99.02 52	99.94 33	99.80 13

EG-PatchMatch MVS		98.99 49	99.01 48	98.94 121	99.50 86	97.47 160	98.04 147	99.59 33	98.15 131	99.40 62	99.36 68	98.58 32	99.76 207	98.78 62	99.68 143	99.59 59

v1921920		98.54 119	98.60 92	98.38 203	99.20 162	95.76 234	97.56 207	99.36 113	97.23 207	99.38 64	99.17 100	96.02 189	99.84 104	99.57 18	99.90 57	99.54 87

IterMVS-LS		98.55 116	98.70 75	98.09 223	99.48 97	94.73 258	97.22 231	99.39 100	98.97 79	99.38 64	99.31 75	96.00 191	99.93 25	98.58 71	99.97 23	99.60 53
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
lessismore_v0				98.97 117	99.73 27	97.53 158		86.71 368		99.37 66	99.52 45	89.93 284	99.92 33	98.99 53	99.72 124	99.44 135

XXY-MVS		99.14 37	99.15 43	99.10 95	99.76 25	97.74 147	98.85 73	99.62 27	98.48 110	99.37 66	99.49 49	98.75 24	99.86 79	98.20 92	99.80 93	99.71 27

v1		98.63 99	98.70 75	98.41 197	99.39 117	95.96 226	97.64 195	99.20 167	97.92 138	99.36 68	99.07 122	96.63 161	99.78 190	99.25 35	99.90 57	99.50 104

TranMVSNet+NR-MVSNet		99.17 35	99.07 47	99.46 50	99.37 120	98.87 56	98.39 119	99.42 96	99.42 31	99.36 68	99.06 123	98.38 43	99.95 13	98.34 85	99.90 57	99.57 71

v1141		98.63 99	98.70 75	98.41 197	99.39 117	95.96 226	97.64 195	99.21 163	97.92 138	99.35 70	99.08 117	96.61 165	99.78 190	99.25 35	99.90 57	99.50 104

divwei89l23v2f112		98.63 99	98.70 75	98.41 197	99.39 117	95.96 226	97.64 195	99.21 163	97.92 138	99.35 70	99.08 117	96.61 165	99.78 190	99.25 35	99.90 57	99.50 104

APDe-MVS		98.99 49	98.79 60	99.60 11	99.21 153	99.15 34	98.87 70	99.48 74	97.57 169	99.35 70	99.24 83	97.83 75	99.89 57	97.88 107	99.70 131	99.75 22

PM-MVS		98.82 67	98.72 71	99.12 91	99.64 49	98.54 80	97.98 159	99.68 15	97.62 163	99.34 73	99.18 94	97.54 91	99.77 201	97.79 110	99.74 116	99.04 228

v1192		98.60 107	98.66 83	98.41 197	99.27 136	95.88 230	97.52 211	99.36 113	97.41 188	99.33 74	99.20 90	96.37 179	99.82 132	99.57 18	99.92 48	99.55 84

CP-MVSNet		99.21 32	99.09 45	99.56 18	99.65 46	98.96 54	99.13 46	99.34 123	99.42 31	99.33 74	99.26 80	97.01 133	99.94 20	98.74 66	99.93 38	99.79 14

IterMVS		97.73 190	98.11 155	96.57 294	99.24 141	90.28 333	95.52 321	99.21 163	98.86 88	99.33 74	99.33 73	93.11 262	99.94 20	98.49 77	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	98.32 143	98.01 165	99.23 80	98.39 297	98.97 51	95.03 332	99.18 177	96.88 221	99.33 74	98.78 181	98.16 56	99.28 341	96.74 166	99.62 159	99.44 135

COLMAP_ROB	96.50 10	98.99 49	98.85 55	99.41 53	99.58 56	99.10 44	98.74 76	99.56 48	99.09 69	99.33 74	99.19 92	98.40 42	99.72 236	95.98 213	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		98.54 119	98.57 94	98.45 194	99.21 153	95.98 224	97.63 198	99.36 113	97.15 214	99.32 79	99.18 94	95.84 203	99.84 104	99.50 22	99.91 53	99.54 87

v148		98.45 130	98.60 92	98.00 232	99.44 109	94.98 254	97.44 217	99.06 200	98.30 119	99.32 79	98.97 145	96.65 160	99.62 274	98.37 84	99.85 71	99.39 152

HSP-MVS		98.34 141	97.94 170	99.54 25	99.57 61	99.25 17	98.57 90	98.84 243	97.55 172	99.31 81	97.71 277	94.61 238	99.88 65	96.14 207	99.19 238	99.48 117

VPNet		98.87 63	98.83 56	99.01 113	99.70 39	97.62 155	98.43 116	99.35 119	99.47 27	99.28 82	99.05 128	96.72 157	99.82 132	98.09 95	99.36 209	99.59 59

v2v482		98.56 112	98.62 87	98.37 204	99.42 114	95.81 233	97.58 205	99.16 186	97.90 146	99.28 82	99.01 136	95.98 195	99.79 179	99.33 30	99.90 57	99.51 99

ambc				98.24 216	98.82 244	95.97 225	98.62 84	99.00 220		99.27 84	99.21 88	96.99 134	99.50 313	96.55 184	99.50 199	99.26 193

Patchmatch-RL test		97.26 222	97.02 219	97.99 233	99.52 80	95.53 239	96.13 292	99.71 11	97.47 178	99.27 84	99.16 101	84.30 318	99.62 274	97.89 104	99.77 105	98.81 260

v1144		98.60 107	98.66 83	98.41 197	99.36 121	95.90 229	97.58 205	99.34 123	97.51 174	99.27 84	99.15 105	96.34 180	99.80 157	99.47 24	99.93 38	99.51 99

v6		98.70 83	98.76 64	98.52 183	99.47 99	96.30 212	98.03 148	99.18 177	97.92 138	99.27 84	99.08 117	96.91 139	99.78 190	99.19 42	99.82 82	99.48 117

Vis-MVSNet		99.34 25	99.36 20	99.27 74	99.73 27	98.26 94	99.17 41	99.78 4	99.11 62	99.27 84	99.48 50	98.82 21	99.95 13	98.94 54	99.93 38	99.59 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1neww		98.70 83	98.76 64	98.52 183	99.47 99	96.30 212	98.03 148	99.18 177	97.92 138	99.26 89	99.08 117	96.91 139	99.78 190	99.19 42	99.82 82	99.47 125

v7new		98.70 83	98.76 64	98.52 183	99.47 99	96.30 212	98.03 148	99.18 177	97.92 138	99.26 89	99.08 117	96.91 139	99.78 190	99.19 42	99.82 82	99.47 125

V42		98.78 73	98.78 61	98.76 146	99.44 109	97.04 180	98.27 123	99.19 173	97.87 150	99.25 91	99.16 101	96.84 146	99.78 190	99.21 39	99.84 73	99.46 129

TSAR-MVS + MP.		98.63 99	98.49 103	99.06 105	99.64 49	97.90 131	98.51 100	98.94 225	96.96 217	99.24 92	98.89 162	97.83 75	99.81 145	96.88 157	99.49 200	99.48 117

FIs		99.14 37	99.09 45	99.29 70	99.70 39	98.28 93	99.13 46	99.52 62	99.48 25	99.24 92	99.41 61	96.79 152	99.82 132	98.69 68	99.88 64	99.76 20

abl_6		98.99 49	98.78 61	99.61 8	99.45 106	99.46 3	98.60 87	99.50 65	98.59 103	99.24 92	99.04 130	98.54 36	99.89 57	96.45 191	99.62 159	99.50 104

TSAR-MVS + GP.		98.18 159	97.98 166	98.77 144	98.71 257	97.88 132	96.32 283	98.66 264	96.33 242	99.23 95	98.51 221	97.48 99	99.40 325	97.16 141	99.46 201	99.02 232

ppachtmachnet_test		97.50 204	97.74 181	96.78 287	98.70 261	91.23 331	94.55 342	99.05 204	96.36 241	99.21 96	98.79 180	96.39 176	99.78 190	96.74 166	99.82 82	99.34 172

Baseline_NR-MVSNet		98.98 53	98.86 54	99.36 57	99.82 19	98.55 77	97.47 216	99.57 42	99.37 36	99.21 96	99.61 30	96.76 155	99.83 119	98.06 97	99.83 79	99.71 27

EI-MVSNet-UG-set		98.69 88	98.71 72	98.62 162	99.10 180	96.37 208	97.23 228	98.87 237	99.20 51	99.19 98	98.99 139	97.30 109	99.85 88	98.77 65	99.79 97	99.65 38

testgi		98.32 143	98.39 122	98.13 221	99.57 61	95.54 238	97.78 179	99.49 71	97.37 191	99.19 98	97.65 281	98.96 18	99.49 314	96.50 188	98.99 263	99.34 172

FMVSNet2		98.49 125	98.40 120	98.75 148	98.90 225	97.14 179	98.61 86	99.13 190	98.59 103	99.19 98	99.28 76	94.14 247	99.82 132	97.97 103	99.80 93	99.29 188

EI-MVSNet-Vis-set		98.68 91	98.70 75	98.63 160	99.09 183	96.40 206	97.23 228	98.86 241	99.20 51	99.18 101	98.97 145	97.29 111	99.85 88	98.72 67	99.78 101	99.64 41

Regformer-4		98.73 79	98.68 80	98.89 128	99.02 202	97.22 171	97.17 236	99.06 200	99.21 48	99.17 102	98.85 169	97.45 100	99.86 79	98.48 78	99.70 131	99.60 53

v7		98.67 93	98.73 68	98.50 188	99.43 113	96.21 216	98.00 157	99.31 133	97.58 167	99.17 102	99.18 94	96.63 161	99.80 157	99.42 28	99.88 64	99.48 117

TAMVS		98.24 154	98.05 162	98.80 138	99.07 187	97.18 174	97.88 170	98.81 249	96.66 232	99.17 102	99.21 88	94.81 232	99.77 201	96.96 152	99.88 64	99.44 135

UniMVSNet (Re)		98.87 63	98.71 72	99.35 62	99.24 141	98.73 64	97.73 186	99.38 104	98.93 84	99.12 105	98.73 186	96.77 153	99.86 79	98.63 70	99.80 93	99.46 129

Anonymous202405211		97.90 176	97.50 196	99.08 98	98.90 225	98.25 95	98.53 95	96.16 325	98.87 86	99.11 106	98.86 166	90.40 283	99.78 190	97.36 133	99.31 219	99.19 211

VDD-MVS		98.56 112	98.39 122	99.07 100	99.13 178	98.07 113	98.59 89	97.01 308	99.59 19	99.11 106	99.27 78	94.82 230	99.79 179	98.34 85	99.63 158	99.34 172

XVG-OURS-SEG-HR		98.49 125	98.28 136	99.14 89	99.49 92	98.83 57	96.54 272	99.48 74	97.32 196	99.11 106	98.61 209	99.33 8	99.30 338	96.23 199	98.38 295	99.28 189

Regformer-3		98.61 105	98.61 90	98.63 160	99.02 202	96.53 199	97.17 236	98.84 243	99.13 61	99.10 109	98.85 169	97.24 117	99.79 179	98.41 83	99.70 131	99.57 71

LPG-MVS_test		98.71 81	98.46 109	99.47 48	99.57 61	98.97 51	98.23 126	99.48 74	96.60 234	99.10 109	99.06 123	98.71 26	99.83 119	95.58 233	99.78 101	99.62 46

LGP-MVS_train				99.47 48	99.57 61	98.97 51		99.48 74	96.60 234	99.10 109	99.06 123	98.71 26	99.83 119	95.58 233	99.78 101	99.62 46

EI-MVSNet		98.40 135	98.51 98	98.04 230	99.10 180	94.73 258	97.20 232	98.87 237	98.97 79	99.06 112	99.02 134	96.00 191	99.80 157	98.58 71	99.82 82	99.60 53

UniMVSNet_NR-MVSNet		98.86 65	98.68 80	99.40 55	99.17 170	98.74 61	97.68 190	99.40 98	99.14 60	99.06 112	98.59 211	96.71 158	99.93 25	98.57 73	99.77 105	99.53 92

DU-MVS		98.82 67	98.63 86	99.39 56	99.16 172	98.74 61	97.54 210	99.25 153	98.84 89	99.06 112	98.76 184	96.76 155	99.93 25	98.57 73	99.77 105	99.50 104

MVSTER		96.86 244	96.55 247	97.79 240	97.91 322	94.21 276	97.56 207	98.87 237	97.49 177	99.06 112	99.05 128	80.72 330	99.80 157	98.44 80	99.82 82	99.37 159

TinyColmap		97.89 178	97.98 166	97.60 254	98.86 233	94.35 273	96.21 288	99.44 88	97.45 185	99.06 112	98.88 163	97.99 68	99.28 341	94.38 263	99.58 172	99.18 213

test_part2					99.36 121	99.10 44				99.05 117

v1.0		41.09 344	54.78 344	0.00 359	99.36 121	0.00 374	0.00 365	99.28 142	96.66 232	99.05 117	98.71 188	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

XVG-OURS		98.53 121	98.34 129	99.11 93	99.50 86	98.82 59	95.97 296	99.50 65	97.30 198	99.05 117	98.98 143	99.35 7	99.32 335	95.72 226	99.68 143	99.18 213

our_test_3		97.39 214	97.73 182	96.34 298	98.70 261	89.78 335	94.61 340	98.97 224	96.50 236	99.04 120	98.85 169	95.98 195	99.84 104	97.26 138	99.67 149	99.41 145

UA-Net		99.47 12	99.40 16	99.70 2	99.49 92	99.29 12	99.80 3	99.72 10	99.82 2	99.04 120	99.81 3	98.05 63	99.96 8	98.85 58	99.99 11	99.86 8

ACMM	96.08 12	98.91 60	98.73 68	99.48 45	99.55 72	99.14 36	98.07 142	99.37 108	97.62 163	99.04 120	98.96 148	98.84 20	99.79 179	97.43 130	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 66	98.61 90	99.53 32	99.19 163	99.27 15	98.49 102	99.33 128	98.64 99	99.03 123	98.98 143	97.89 73	99.85 88	96.54 185	99.42 204	99.46 129

Regformer-2		98.60 107	98.46 109	99.02 112	98.85 236	97.71 149	96.91 251	99.09 197	98.98 78	99.01 124	98.64 201	97.37 106	99.84 104	97.75 117	99.57 176	99.52 96

HyFIR lowres test		97.19 228	96.60 244	98.96 118	99.62 53	97.28 168	95.17 329	99.50 65	94.21 297	99.01 124	98.32 240	86.61 297	99.99 2	97.10 148	99.84 73	99.60 53

CVMVSNet		96.25 266	97.21 212	93.38 347	99.10 180	80.56 367	97.20 232	98.19 283	96.94 218	99.00 126	99.02 134	89.50 288	99.80 157	96.36 196	99.59 166	99.78 15

Regformer-1		98.55 116	98.44 114	98.87 130	98.85 236	97.29 166	96.91 251	98.99 221	98.97 79	98.99 127	98.64 201	97.26 115	99.81 145	97.79 110	99.57 176	99.51 99

PVSNet_Blended_VisFu		98.17 161	98.15 150	98.22 217	99.73 27	95.15 251	97.36 220	99.68 15	94.45 291	98.99 127	99.27 78	96.87 145	99.94 20	97.13 145	99.91 53	99.57 71

SMA-MVS		98.40 135	98.03 164	99.51 40	99.16 172	99.21 20	98.05 146	99.22 162	94.16 299	98.98 129	99.10 114	97.52 94	99.79 179	96.45 191	99.64 157	99.53 92

XVG-ACMP-BASELINE		98.56 112	98.34 129	99.22 81	99.54 76	98.59 74	97.71 187	99.46 82	97.25 202	98.98 129	98.99 139	97.54 91	99.84 104	95.88 216	99.74 116	99.23 199

IS-MVSNet		98.19 158	97.90 174	99.08 98	99.57 61	97.97 123	99.31 20	98.32 277	99.01 75	98.98 129	99.03 133	91.59 277	99.79 179	95.49 235	99.80 93	99.48 117

MP-MVS-pluss		98.57 111	98.23 139	99.60 11	99.69 41	99.35 8	97.16 238	99.38 104	94.87 283	98.97 132	98.99 139	98.01 65	99.88 65	97.29 136	99.70 131	99.58 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDDNet		98.21 156	97.95 168	99.01 113	99.58 56	97.74 147	99.01 55	97.29 303	99.67 8	98.97 132	99.50 46	90.45 282	99.80 157	97.88 107	99.20 234	99.48 117

USDC		97.41 213	97.40 203	97.44 263	98.94 215	93.67 294	95.17 329	99.53 58	94.03 301	98.97 132	99.10 114	95.29 218	99.34 332	95.84 222	99.73 119	99.30 185

GBi-Net		98.65 95	98.47 106	99.17 83	98.90 225	98.24 96	99.20 35	99.44 88	98.59 103	98.95 135	99.55 41	94.14 247	99.86 79	97.77 112	99.69 138	99.41 145

test1		98.65 95	98.47 106	99.17 83	98.90 225	98.24 96	99.20 35	99.44 88	98.59 103	98.95 135	99.55 41	94.14 247	99.86 79	97.77 112	99.69 138	99.41 145

FMVSNet3		97.50 204	97.24 211	98.29 212	98.08 315	95.83 232	97.86 173	98.91 233	97.89 147	98.95 135	98.95 149	87.06 295	99.81 145	97.77 112	99.69 138	99.23 199

test_0402		98.76 75	98.71 72	98.93 122	99.56 68	98.14 106	98.45 115	99.34 123	99.28 44	98.95 135	98.91 154	98.34 45	99.79 179	95.63 230	99.91 53	98.86 254

HPM-MVS_fast		99.01 47	98.82 57	99.57 15	99.71 33	99.35 8	99.00 60	99.50 65	97.33 194	98.94 139	98.86 166	98.75 24	99.82 132	97.53 124	99.71 128	99.56 76

Anonymous20231206		98.21 156	98.21 140	98.20 218	99.51 83	95.43 243	98.13 134	99.32 131	96.16 251	98.93 140	98.82 176	96.00 191	99.83 119	97.32 135	99.73 119	99.36 165

YYNet1		97.60 198	97.67 184	97.39 266	99.04 197	93.04 302	95.27 326	98.38 276	97.25 202	98.92 141	98.95 149	95.48 215	99.73 227	96.99 150	98.74 275	99.41 145

SteuartSystems-ACMMP		98.79 70	98.54 95	99.54 25	99.73 27	99.16 29	98.23 126	99.31 133	97.92 138	98.90 142	98.90 157	98.00 66	99.88 65	96.15 206	99.72 124	99.58 66
Skip Steuart: Steuart Systems R&D Blog.
RPSCF		98.62 104	98.36 126	99.42 51	99.65 46	99.42 4	98.55 93	99.57 42	97.72 157	98.90 142	99.26 80	96.12 186	99.52 307	95.72 226	99.71 128	99.32 178

zzz-MVS		98.79 70	98.52 97	99.61 8	99.67 43	99.36 6	97.33 221	99.20 167	98.83 90	98.89 144	98.90 157	96.98 135	99.92 33	97.16 141	99.70 131	99.56 76

MTAPA		98.88 62	98.64 85	99.61 8	99.67 43	99.36 6	98.43 116	99.20 167	98.83 90	98.89 144	98.90 157	96.98 135	99.92 33	97.16 141	99.70 131	99.56 76

WR-MVS		98.40 135	98.19 143	99.03 109	99.00 205	97.65 152	96.85 255	98.94 225	98.57 107	98.89 144	98.50 224	95.60 209	99.85 88	97.54 123	99.85 71	99.59 59

AllTest		98.44 131	98.20 141	99.16 86	99.50 86	98.55 77	98.25 125	99.58 35	96.80 224	98.88 147	99.06 123	97.65 84	99.57 292	94.45 257	99.61 163	99.37 159

TestCases				99.16 86	99.50 86	98.55 77		99.58 35	96.80 224	98.88 147	99.06 123	97.65 84	99.57 292	94.45 257	99.61 163	99.37 159

MDA-MVSNet_test_wron		97.60 198	97.66 187	97.41 265	99.04 197	93.09 299	95.27 326	98.42 274	97.26 201	98.88 147	98.95 149	95.43 216	99.73 227	97.02 149	98.72 276	99.41 145

VNet		98.42 132	98.30 134	98.79 139	98.79 249	97.29 166	98.23 126	98.66 264	99.31 41	98.85 150	98.80 178	94.80 233	99.78 190	98.13 94	99.13 249	99.31 182

CSCG		98.68 91	98.50 100	99.20 82	99.45 106	98.63 69	98.56 91	99.57 42	97.87 150	98.85 150	98.04 261	97.66 83	99.84 104	96.72 168	99.81 89	99.13 221

CHOSEN 1792x2688		97.49 206	97.14 217	98.54 181	99.68 42	96.09 222	96.50 273	99.62 27	91.58 329	98.84 152	98.97 145	92.36 272	99.88 65	96.76 165	99.95 30	99.67 31

test1235678		97.06 235	96.84 229	97.73 245	98.55 285	94.46 271	94.80 336	99.36 113	96.85 223	98.83 153	98.26 243	92.72 269	99.82 132	92.49 305	99.70 131	98.91 247

mvs_anonymous		97.83 188	98.16 148	96.87 283	98.18 312	91.89 313	97.31 223	98.90 234	97.37 191	98.83 153	99.46 52	96.28 182	99.79 179	98.90 55	98.16 305	98.95 240

MDA-MVSNet-bldmvs		97.94 175	97.91 173	98.06 228	99.44 109	94.96 255	96.63 268	99.15 189	98.35 113	98.83 153	99.11 112	94.31 244	99.85 88	96.60 177	98.72 276	99.37 159

PMMVS2		98.07 167	98.08 160	98.04 230	99.41 115	94.59 264	94.59 341	99.40 98	97.50 175	98.82 156	98.83 173	96.83 148	99.84 104	97.50 126	99.81 89	99.71 27

ACMMP		98.75 76	98.50 100	99.52 37	99.56 68	99.16 29	98.87 70	99.37 108	97.16 212	98.82 156	99.01 136	97.71 81	99.87 74	96.29 198	99.69 138	99.54 87
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	98.41 133	98.09 157	99.36 57	99.51 83	98.79 60	97.68 190	99.38 104	95.76 261	98.81 158	98.82 176	98.36 44	99.82 132	94.75 247	99.77 105	99.48 117
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMMP_Plus		98.75 76	98.48 104	99.57 15	99.58 56	99.29 12	97.82 178	99.25 153	96.94 218	98.78 159	99.12 110	98.02 64	99.84 104	97.13 145	99.67 149	99.59 59

LFMVS		97.20 227	96.72 234	98.64 158	98.72 255	96.95 185	98.93 67	94.14 346	99.74 5	98.78 159	99.01 136	84.45 315	99.73 227	97.44 129	99.27 226	99.25 195

Patchmtry		97.35 215	96.97 221	98.50 188	97.31 344	96.47 202	98.18 130	98.92 231	98.95 83	98.78 159	99.37 65	85.44 310	99.85 88	95.96 214	99.83 79	99.17 217

UnsupCasMVSNet_eth		97.89 178	97.60 192	98.75 148	99.31 131	97.17 175	97.62 199	99.35 119	98.72 98	98.76 162	98.68 192	92.57 271	99.74 222	97.76 116	95.60 350	99.34 172

OPM-MVS		98.56 112	98.32 133	99.25 78	99.41 115	98.73 64	97.13 240	99.18 177	97.10 215	98.75 163	98.92 153	98.18 55	99.65 268	96.68 173	99.56 181	99.37 159

DeepC-MVS_fast	96.85 6	98.30 145	98.15 150	98.75 148	98.61 277	97.23 169	97.76 183	99.09 197	97.31 197	98.75 163	98.66 196	97.56 90	99.64 270	96.10 208	99.55 183	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		98.10 163	97.67 184	99.42 51	99.11 179	98.93 55	97.76 183	99.28 142	94.97 280	98.72 165	98.77 182	97.04 128	99.85 88	93.79 278	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 94	98.37 125	99.55 20	99.53 78	99.18 26	98.23 126	99.49 71	97.01 216	98.69 166	98.88 163	98.00 66	99.89 57	95.87 219	99.59 166	99.58 66

DI_MVS_plusplus_test		97.57 202	97.40 203	98.07 227	99.06 190	95.71 236	96.58 271	96.96 309	96.71 229	98.69 166	98.13 250	93.81 254	99.68 248	97.45 128	99.19 238	98.80 263

GST-MVS		98.61 105	98.30 134	99.52 37	99.51 83	99.20 23	98.26 124	99.25 153	97.44 186	98.67 168	98.39 231	97.68 82	99.85 88	96.00 211	99.51 192	99.52 96

tttt0517		95.64 275	94.98 285	97.64 251	99.36 121	93.81 290	98.72 78	90.47 365	98.08 132	98.67 168	98.34 236	73.88 361	99.92 33	97.77 112	99.51 192	99.20 205

test_normal		97.58 200	97.41 202	98.10 222	99.03 200	95.72 235	96.21 288	97.05 307	96.71 229	98.65 170	98.12 254	93.87 251	99.69 243	97.68 121	99.35 211	98.88 252

OpenMVS_ROB	95.38 14	95.84 272	95.18 281	97.81 239	98.41 296	97.15 177	97.37 219	98.62 267	83.86 359	98.65 170	98.37 233	94.29 245	99.68 248	88.41 338	98.62 285	96.60 343

MS-PatchMatch		97.68 193	97.75 180	97.45 262	98.23 309	93.78 291	97.29 224	98.84 243	96.10 253	98.64 172	98.65 198	96.04 188	99.36 330	96.84 160	99.14 246	99.20 205

1111		93.99 318	93.72 313	94.80 330	99.33 129	85.20 353	95.97 296	99.39 100	97.88 148	98.64 172	98.56 216	57.79 372	99.80 157	96.02 209	99.87 68	99.40 151

.test1245		79.71 341	84.30 342	65.96 355	99.33 129	85.20 353	95.97 296	99.39 100	97.88 148	98.64 172	98.56 216	57.79 372	99.80 157	96.02 209	15.07 366	12.86 367

Test4		97.43 211	97.18 213	98.18 220	99.05 195	96.02 223	96.62 269	99.09 197	96.25 246	98.63 175	97.70 278	90.49 281	99.68 248	97.50 126	99.30 221	98.83 257

pmmvs5		97.64 196	97.49 197	98.08 226	99.14 177	95.12 253	96.70 263	99.05 204	93.77 303	98.62 176	98.83 173	93.23 259	99.75 213	98.33 87	99.76 114	99.36 165

ab-mvs		98.41 133	98.36 126	98.59 170	99.19 163	97.23 169	99.32 17	98.81 249	97.66 160	98.62 176	99.40 64	96.82 149	99.80 157	95.88 216	99.51 192	98.75 270

pmmvs4		97.58 200	97.28 210	98.51 187	98.84 239	96.93 186	95.40 325	98.52 270	93.60 305	98.61 178	98.65 198	95.10 223	99.60 281	96.97 151	99.79 97	98.99 235

HPM-MVS		98.79 70	98.53 96	99.59 14	99.65 46	99.29 12	99.16 42	99.43 93	96.74 226	98.61 178	98.38 232	98.62 29	99.87 74	96.47 189	99.67 149	99.59 59
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	98.64 158	99.94 2	98.51 82	99.32 17	99.75 7	99.58 21	98.60 180	99.62 28	98.22 51	99.51 312	97.70 118	99.73 119	97.89 303
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CDS-MVSNet		97.69 192	97.35 208	98.69 154	98.73 254	97.02 183	96.92 250	98.75 257	95.89 259	98.59 181	98.67 194	92.08 276	99.74 222	96.72 168	99.81 89	99.32 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		98.30 145	98.04 163	99.07 100	99.56 68	97.83 136	99.29 25	98.07 285	99.03 73	98.59 181	99.13 109	92.16 274	99.90 47	96.87 158	99.68 143	99.49 111

HFP-MVS		98.71 81	98.44 114	99.51 40	99.49 92	99.16 29	98.52 96	99.31 133	97.47 178	98.58 183	98.50 224	97.97 70	99.85 88	96.57 180	99.59 166	99.53 92

#test#		98.50 124	98.16 148	99.51 40	99.49 92	99.16 29	98.03 148	99.31 133	96.30 245	98.58 183	98.50 224	97.97 70	99.85 88	95.68 229	99.59 166	99.53 92

ACMMPR		98.70 83	98.42 118	99.54 25	99.52 80	99.14 36	98.52 96	99.31 133	97.47 178	98.56 185	98.54 219	97.75 80	99.88 65	96.57 180	99.59 166	99.58 66

new_pmnet		96.99 240	96.76 232	97.67 247	98.72 255	94.89 256	95.95 303	98.20 281	92.62 316	98.55 186	98.54 219	94.88 229	99.52 307	93.96 272	99.44 203	98.59 281

3Dnovator	98.27 2	98.81 69	98.73 68	99.05 106	98.76 250	97.81 141	99.25 32	99.30 139	98.57 107	98.55 186	99.33 73	97.95 72	99.90 47	97.16 141	99.67 149	99.44 135

OMC-MVS		97.88 180	97.49 197	99.04 108	98.89 230	98.63 69	96.94 247	99.25 153	95.02 278	98.53 188	98.51 221	97.27 112	99.47 318	93.50 287	99.51 192	99.01 233

jason		97.45 210	97.35 208	97.76 242	99.24 141	93.93 283	95.86 307	98.42 274	94.24 296	98.50 189	98.13 250	94.82 230	99.91 43	97.22 139	99.73 119	99.43 140
jason: jason.
MVP-Stereo		98.08 165	97.92 172	98.57 173	98.96 212	96.79 189	97.90 169	99.18 177	96.41 240	98.46 190	98.95 149	95.93 198	99.60 281	96.51 187	98.98 265	99.31 182
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DELS-MVS		98.27 149	98.20 141	98.48 190	98.86 233	96.70 195	95.60 318	99.20 167	97.73 156	98.45 191	98.71 188	97.50 95	99.82 132	98.21 91	99.59 166	98.93 244
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
region2R		98.69 88	98.40 120	99.54 25	99.53 78	99.17 27	98.52 96	99.31 133	97.46 183	98.44 192	98.51 221	97.83 75	99.88 65	96.46 190	99.58 172	99.58 66

BH-untuned		96.83 245	96.75 233	97.08 272	98.74 253	93.33 298	96.71 262	98.26 279	96.72 227	98.44 192	97.37 300	95.20 220	99.47 318	91.89 309	97.43 329	98.44 286

LS3D		98.63 99	98.38 124	99.36 57	97.25 345	99.38 5	99.12 48	99.32 131	99.21 48	98.44 192	98.88 163	97.31 108	99.80 157	96.58 178	99.34 214	98.92 245

xiu_mvs_v1_base_debu		97.86 182	98.17 144	96.92 280	98.98 209	93.91 284	96.45 276	99.17 183	97.85 152	98.41 195	97.14 307	98.47 38	99.92 33	98.02 99	99.05 255	96.92 333

xiu_mvs_v1_base		97.86 182	98.17 144	96.92 280	98.98 209	93.91 284	96.45 276	99.17 183	97.85 152	98.41 195	97.14 307	98.47 38	99.92 33	98.02 99	99.05 255	96.92 333

xiu_mvs_v1_base_debi		97.86 182	98.17 144	96.92 280	98.98 209	93.91 284	96.45 276	99.17 183	97.85 152	98.41 195	97.14 307	98.47 38	99.92 33	98.02 99	99.05 255	96.92 333

Patchmatch-test		96.55 258	96.34 253	97.17 271	98.35 299	93.06 300	98.40 118	97.79 290	97.33 194	98.41 195	98.67 194	83.68 322	99.69 243	95.16 238	99.31 219	98.77 267

MSDG		97.71 191	97.52 195	98.28 213	98.91 224	96.82 188	94.42 343	99.37 108	97.65 161	98.37 199	98.29 242	97.40 104	99.33 334	94.09 269	99.22 231	98.68 278

CP-MVS		98.70 83	98.42 118	99.52 37	99.36 121	99.12 41	98.72 78	99.36 113	97.54 173	98.30 200	98.40 230	97.86 74	99.89 57	96.53 186	99.72 124	99.56 76

UnsupCasMVSNet_bld		97.30 219	96.92 223	98.45 194	99.28 135	96.78 193	96.20 290	99.27 147	95.42 273	98.28 201	98.30 241	93.16 261	99.71 237	94.99 241	97.37 330	98.87 253

ITE_SJBPF				98.87 130	99.22 147	98.48 84		99.35 119	97.50 175	98.28 201	98.60 210	97.64 87	99.35 331	93.86 276	99.27 226	98.79 265

thisisatest0530		95.27 283	94.45 291	97.74 244	99.19 163	94.37 272	97.86 173	90.20 366	97.17 211	98.22 203	97.65 281	73.53 362	99.90 47	96.90 155	99.35 211	98.95 240

0601test		96.69 251	96.29 255	97.90 234	98.28 303	95.24 246	97.29 224	97.36 299	98.21 125	98.17 204	97.86 269	86.27 299	99.55 298	94.87 244	98.32 296	98.89 250

Anonymous20240521		96.69 251	96.29 255	97.90 234	98.28 303	95.24 246	97.29 224	97.36 299	98.21 125	98.17 204	97.86 269	86.27 299	99.55 298	94.87 244	98.32 296	98.89 250

MVSFormer		98.26 151	98.43 116	97.77 241	98.88 231	93.89 287	99.39 13	99.56 48	99.11 62	98.16 206	98.13 250	93.81 254	99.97 3	99.26 33	99.57 176	99.43 140

lupinMVS		97.06 235	96.86 227	97.65 249	98.88 231	93.89 287	95.48 322	97.97 287	93.53 306	98.16 206	97.58 285	93.81 254	99.91 43	96.77 164	99.57 176	99.17 217

Vis-MVSNet (Re-imp)		97.46 209	97.16 215	98.34 207	99.55 72	96.10 220	98.94 65	98.44 273	98.32 118	98.16 206	98.62 207	88.76 291	99.73 227	93.88 275	99.79 97	99.18 213

TAPA-MVS	96.21 11	96.63 255	95.95 262	98.65 157	98.93 217	98.09 108	96.93 248	99.28 142	83.58 360	98.13 209	97.78 274	96.13 185	99.40 325	93.52 285	99.29 224	98.45 285
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_111021_LR		98.30 145	98.12 154	98.83 135	99.16 172	98.03 116	96.09 293	99.30 139	97.58 167	98.10 210	98.24 245	98.25 47	99.34 332	96.69 172	99.65 155	99.12 222

mPP-MVS		98.64 97	98.34 129	99.54 25	99.54 76	99.17 27	98.63 83	99.24 159	97.47 178	98.09 211	98.68 192	97.62 88	99.89 57	96.22 200	99.62 159	99.57 71

3Dnovator+	97.89 3	98.69 88	98.51 98	99.24 79	98.81 246	98.40 88	99.02 54	99.19 173	98.99 76	98.07 212	99.28 76	97.11 126	99.84 104	96.84 160	99.32 217	99.47 125

PHI-MVS		98.29 148	97.95 168	99.34 65	98.44 294	99.16 29	98.12 136	99.38 104	96.01 257	98.06 213	98.43 228	97.80 79	99.67 254	95.69 228	99.58 172	99.20 205

CLD-MVS		97.49 206	97.16 215	98.48 190	99.07 187	97.03 181	94.71 338	99.21 163	94.46 289	98.06 213	97.16 305	97.57 89	99.48 317	94.46 256	99.78 101	98.95 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_Test		98.18 159	98.36 126	97.67 247	98.48 290	94.73 258	98.18 130	99.02 213	97.69 158	98.04 215	99.11 112	97.22 121	99.56 295	98.57 73	98.90 271	98.71 272

FMVSNet5		96.01 269	95.20 280	98.41 197	97.53 335	96.10 220	98.74 76	99.50 65	97.22 210	98.03 216	99.04 130	69.80 364	99.88 65	97.27 137	99.71 128	99.25 195

MVS_111021_HR		98.25 153	98.08 160	98.75 148	99.09 183	97.46 161	95.97 296	99.27 147	97.60 166	97.99 217	98.25 244	98.15 58	99.38 329	96.87 158	99.57 176	99.42 143

MCST-MVS		98.00 171	97.63 190	99.10 95	99.24 141	98.17 103	96.89 253	98.73 260	95.66 262	97.92 218	97.70 278	97.17 122	99.66 262	96.18 204	99.23 230	99.47 125

MG-MVS		96.77 249	96.61 243	97.26 269	98.31 302	93.06 300	95.93 304	98.12 284	96.45 239	97.92 218	98.73 186	93.77 257	99.39 327	91.19 325	99.04 258	99.33 177

LP		96.60 257	96.57 246	96.68 289	97.64 331	91.70 315	98.11 137	97.74 292	97.29 200	97.91 220	99.24 83	88.35 292	99.85 88	97.11 147	95.76 349	98.49 283

tfpn1000		94.81 297	94.25 300	96.47 297	99.01 204	93.47 297	98.56 91	92.30 359	96.17 248	97.90 221	96.29 321	76.70 355	99.77 201	93.02 293	98.29 298	96.16 347

MSLP-MVS++		98.02 168	98.14 152	97.64 251	98.58 281	95.19 250	97.48 214	99.23 161	97.47 178	97.90 221	98.62 207	97.04 128	98.81 358	97.55 122	99.41 205	98.94 243

casdiffmvs1		98.49 125	98.45 111	98.61 165	98.99 207	97.15 177	98.70 80	99.25 153	97.42 187	97.87 223	99.20 90	96.29 181	99.66 262	99.44 26	98.91 270	99.03 231

BH-RMVSNet		96.83 245	96.58 245	97.58 256	98.47 291	94.05 279	96.67 265	97.36 299	96.70 231	97.87 223	97.98 264	95.14 222	99.44 323	90.47 332	98.58 287	99.25 195

MIMVSNet		96.62 256	96.25 259	97.71 246	99.04 197	94.66 261	99.16 42	96.92 313	97.23 207	97.87 223	99.10 114	86.11 303	99.65 268	91.65 312	99.21 233	98.82 259

PNet_i23d		91.80 337	92.35 329	90.14 352	98.65 275	73.10 371	89.22 364	99.02 213	95.23 277	97.87 223	97.82 273	78.45 348	98.89 356	88.73 337	86.14 364	98.42 288

view600		94.87 290	94.41 292	96.26 301	99.22 147	91.37 321	98.49 102	94.45 335	98.75 92	97.85 227	95.98 325	80.38 332	99.75 213	86.06 346	98.49 289	97.66 316

view800		94.87 290	94.41 292	96.26 301	99.22 147	91.37 321	98.49 102	94.45 335	98.75 92	97.85 227	95.98 325	80.38 332	99.75 213	86.06 346	98.49 289	97.66 316

conf0.05thres1000		94.87 290	94.41 292	96.26 301	99.22 147	91.37 321	98.49 102	94.45 335	98.75 92	97.85 227	95.98 325	80.38 332	99.75 213	86.06 346	98.49 289	97.66 316

tfpn		94.87 290	94.41 292	96.26 301	99.22 147	91.37 321	98.49 102	94.45 335	98.75 92	97.85 227	95.98 325	80.38 332	99.75 213	86.06 346	98.49 289	97.66 316

LF4IMVS		97.90 176	97.69 183	98.52 183	99.17 170	97.66 151	97.19 235	99.47 80	96.31 244	97.85 227	98.20 249	96.71 158	99.52 307	94.62 251	99.72 124	98.38 290

CPTT-MVS		97.84 187	97.36 207	99.27 74	99.31 131	98.46 85	98.29 121	99.27 147	94.90 282	97.83 232	98.37 233	94.90 226	99.84 104	93.85 277	99.54 184	99.51 99

CMPMVS	75.91 23	96.29 264	95.44 273	98.84 134	96.25 360	98.69 67	97.02 242	99.12 192	88.90 348	97.83 232	98.86 166	89.51 287	98.90 355	91.92 308	99.51 192	98.92 245
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
E-PMN		94.17 312	94.37 297	93.58 344	96.86 351	85.71 351	90.11 362	97.07 306	98.17 130	97.82 234	97.19 303	84.62 314	98.94 353	89.77 334	97.68 326	96.09 351

conf0.01		94.82 295	94.07 301	97.06 274	99.21 153	94.53 265	98.47 108	92.69 350	95.61 263	97.81 235	95.54 333	77.71 349	99.80 157	91.49 317	98.11 308	96.86 336

conf0.002		94.82 295	94.07 301	97.06 274	99.21 153	94.53 265	98.47 108	92.69 350	95.61 263	97.81 235	95.54 333	77.71 349	99.80 157	91.49 317	98.11 308	96.86 336

thresconf0.02		94.70 299	94.07 301	96.58 290	99.21 153	94.53 265	98.47 108	92.69 350	95.61 263	97.81 235	95.54 333	77.71 349	99.80 157	91.49 317	98.11 308	95.42 355

tfpn_n400		94.70 299	94.07 301	96.58 290	99.21 153	94.53 265	98.47 108	92.69 350	95.61 263	97.81 235	95.54 333	77.71 349	99.80 157	91.49 317	98.11 308	95.42 355

tfpnconf		94.70 299	94.07 301	96.58 290	99.21 153	94.53 265	98.47 108	92.69 350	95.61 263	97.81 235	95.54 333	77.71 349	99.80 157	91.49 317	98.11 308	95.42 355

tfpnview11		94.70 299	94.07 301	96.58 290	99.21 153	94.53 265	98.47 108	92.69 350	95.61 263	97.81 235	95.54 333	77.71 349	99.80 157	91.49 317	98.11 308	95.42 355

CDPH-MVS		97.26 222	96.66 241	99.07 100	99.00 205	98.15 104	96.03 294	99.01 216	91.21 335	97.79 241	97.85 271	96.89 144	99.69 243	92.75 300	99.38 208	99.39 152

HQP_MVS		97.99 173	97.67 184	98.93 122	99.19 163	97.65 152	97.77 181	99.27 147	98.20 127	97.79 241	97.98 264	94.90 226	99.70 239	94.42 259	99.51 192	99.45 133

plane_prior3						97.78 143			97.41 188	97.79 241

MDTV_nov1_ep13_2view						74.92 369	97.69 189		90.06 344	97.75 244		85.78 306		93.52 285		98.69 275

pmmvs3		95.03 287	94.40 296	96.93 279	97.70 329	92.53 306	95.08 331	97.71 294	88.57 349	97.71 245	98.08 259	79.39 343	99.82 132	96.19 202	99.11 253	98.43 287

DP-MVS Recon		97.33 217	96.92 223	98.57 173	99.09 183	97.99 118	96.79 256	99.35 119	93.18 309	97.71 245	98.07 260	95.00 225	99.31 336	93.97 271	99.13 249	98.42 288

QAPM		97.31 218	96.81 230	98.82 136	98.80 248	97.49 159	99.06 53	99.19 173	90.22 341	97.69 247	99.16 101	96.91 139	99.90 47	90.89 329	99.41 205	99.07 225

Patchmatch-test1		96.44 263	96.72 234	95.60 322	98.24 307	88.35 340	95.85 309	96.88 315	96.11 252	97.67 248	98.57 213	93.10 263	99.69 243	94.79 246	99.22 231	98.77 267

Effi-MVS+-dtu		98.26 151	97.90 174	99.35 62	98.02 317	99.49 2	98.02 155	99.16 186	98.29 122	97.64 249	97.99 263	96.44 174	99.95 13	96.66 174	98.93 269	98.60 280

CNVR-MVS		98.17 161	97.87 177	99.07 100	98.67 269	98.24 96	97.01 243	98.93 228	97.25 202	97.62 250	98.34 236	97.27 112	99.57 292	96.42 194	99.33 215	99.39 152

PVSNet_BlendedMVS		97.55 203	97.53 194	97.60 254	98.92 221	93.77 292	96.64 267	99.43 93	94.49 287	97.62 250	99.18 94	96.82 149	99.67 254	94.73 248	99.93 38	99.36 165

PVSNet_Blended		96.88 243	96.68 238	97.47 261	98.92 221	93.77 292	94.71 338	99.43 93	90.98 336	97.62 250	97.36 301	96.82 149	99.67 254	94.73 248	99.56 181	98.98 236

alignmvs		97.35 215	96.88 226	98.78 142	98.54 286	98.09 108	97.71 187	97.69 295	99.20 51	97.59 253	95.90 329	88.12 294	99.55 298	98.18 93	98.96 266	98.70 274

MP-MVS		98.46 129	98.09 157	99.54 25	99.57 61	99.22 19	98.50 101	99.19 173	97.61 165	97.58 254	98.66 196	97.40 104	99.88 65	94.72 250	99.60 165	99.54 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DSMNet-mixed		97.42 212	97.60 192	96.87 283	99.15 176	91.46 318	98.54 94	99.12 192	92.87 313	97.58 254	99.63 27	96.21 183	99.90 47	95.74 225	99.54 184	99.27 190

test0.0.03 1		94.51 304	93.69 314	96.99 277	96.05 361	93.61 295	94.97 333	93.49 347	96.17 248	97.57 256	94.88 352	82.30 327	99.01 352	93.60 283	94.17 359	98.37 292

PCF-MVS	92.86 18	94.36 306	93.00 325	98.42 196	98.70 261	97.56 156	93.16 355	99.11 195	79.59 363	97.55 257	97.43 296	92.19 273	99.73 227	79.85 362	99.45 202	97.97 302
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XVS		98.72 80	98.45 111	99.53 32	99.46 103	99.21 20	98.65 81	99.34 123	98.62 101	97.54 258	98.63 205	97.50 95	99.83 119	96.79 162	99.53 188	99.56 76

X-MVStestdata		94.32 308	92.59 326	99.53 32	99.46 103	99.21 20	98.65 81	99.34 123	98.62 101	97.54 258	45.85 366	97.50 95	99.83 119	96.79 162	99.53 188	99.56 76

旧先验2							95.76 311		88.56 350	97.52 260			99.66 262	94.48 255

PMVS	91.26 20	97.86 182	97.94 170	97.65 249	99.71 33	97.94 128	98.52 96	98.68 263	98.99 76	97.52 260	99.35 69	97.41 103	98.18 361	91.59 315	99.67 149	96.82 340
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-MVSNAJ		97.08 234	97.39 205	96.16 309	98.56 283	92.46 307	95.24 328	98.85 242	97.25 202	97.49 262	95.99 324	98.07 60	99.90 47	96.37 195	98.67 282	96.12 350

xiu_mvs_v2_base		97.16 230	97.49 197	96.17 307	98.54 286	92.46 307	95.45 323	98.84 243	97.25 202	97.48 263	96.49 316	98.31 46	99.90 47	96.34 197	98.68 281	96.15 349

canonicalmvs		98.34 141	98.26 137	98.58 171	98.46 292	97.82 139	98.96 64	99.46 82	99.19 55	97.46 264	95.46 340	98.59 31	99.46 320	98.08 96	98.71 279	98.46 284

testdata				98.09 223	98.93 217	95.40 244		98.80 251	90.08 343	97.45 265	98.37 233	95.26 219	99.70 239	93.58 284	98.95 267	99.17 217

thres600view7		94.45 305	93.83 310	96.29 299	99.06 190	91.53 317	97.99 158	94.24 342	98.34 114	97.44 266	95.01 346	79.84 337	99.67 254	84.33 352	98.23 299	97.66 316

EMVS		93.83 321	94.02 307	93.23 348	96.83 353	84.96 355	89.77 363	96.32 324	97.92 138	97.43 267	96.36 320	86.17 301	98.93 354	87.68 341	97.73 325	95.81 352

tfpn_ndepth		94.12 314	93.51 318	95.94 314	98.86 233	93.60 296	98.16 133	91.90 361	94.66 286	97.41 268	95.24 343	76.24 356	99.73 227	91.21 323	97.88 323	94.50 360

tfpn111		94.33 307	93.78 311	95.96 313	99.06 190	91.35 325	98.03 148	94.24 342	98.33 115	97.40 269	94.98 348	79.84 337	99.68 248	83.94 353	98.22 301	96.86 336

conf200view11		94.24 310	93.67 315	95.94 314	99.06 190	91.35 325	98.03 148	94.24 342	98.33 115	97.40 269	94.98 348	79.84 337	99.62 274	83.05 355	98.08 316	96.86 336

thres100view900		94.19 311	93.67 315	95.75 319	99.06 190	91.35 325	98.03 148	94.24 342	98.33 115	97.40 269	94.98 348	79.84 337	99.62 274	83.05 355	98.08 316	96.29 344

Fast-Effi-MVS+-dtu		98.27 149	98.09 157	98.81 137	98.43 295	98.11 107	97.61 201	99.50 65	98.64 99	97.39 272	97.52 289	98.12 59	99.95 13	96.90 155	98.71 279	98.38 290

API-MVS		97.04 238	96.91 225	97.42 264	97.88 324	98.23 100	98.18 130	98.50 271	97.57 169	97.39 272	96.75 312	96.77 153	99.15 347	90.16 333	99.02 259	94.88 359

testus		95.52 278	95.32 276	96.13 311	97.91 322	89.49 337	93.62 352	99.61 29	92.41 318	97.38 274	95.42 342	94.72 237	99.63 271	88.06 340	98.72 276	99.26 193

PatchMatch-RL		97.24 225	96.78 231	98.61 165	99.03 200	97.83 136	96.36 281	99.06 200	93.49 308	97.36 275	97.78 274	95.75 205	99.49 314	93.44 288	98.77 274	98.52 282

test12356		94.85 294	95.12 282	94.03 340	98.25 305	83.12 362	93.85 349	99.33 128	94.17 298	97.28 276	97.20 302	85.83 305	99.75 213	90.85 330	99.33 215	99.22 203

sss		97.21 226	96.93 222	98.06 228	98.83 241	95.22 248	96.75 260	98.48 272	94.49 287	97.27 277	97.90 268	92.77 268	99.80 157	96.57 180	99.32 217	99.16 220

MVS_0304		98.02 168	97.88 176	98.46 192	98.22 310	96.39 207	96.50 273	99.49 71	98.03 134	97.24 278	98.33 239	94.80 233	99.90 47	98.31 88	99.95 30	99.08 223

diffmvs1		98.39 139	98.43 116	98.27 214	98.53 288	96.18 218	97.91 168	99.37 108	98.73 96	97.22 279	99.15 105	96.97 137	99.77 201	98.80 61	99.18 240	98.86 254

WTY-MVS		96.67 253	96.27 257	97.87 237	98.81 246	94.61 263	96.77 258	97.92 289	94.94 281	97.12 280	97.74 276	91.11 279	99.82 132	93.89 274	98.15 306	99.18 213

tfpn200view9		94.03 317	93.44 319	95.78 318	98.93 217	91.44 319	97.60 202	94.29 340	97.94 136	97.10 281	94.31 357	79.67 341	99.62 274	83.05 355	98.08 316	96.29 344

thres400		94.14 313	93.44 319	96.24 305	98.93 217	91.44 319	97.60 202	94.29 340	97.94 136	97.10 281	94.31 357	79.67 341	99.62 274	83.05 355	98.08 316	97.66 316

PatchmatchNet		95.58 276	95.67 268	95.30 326	97.34 343	87.32 344	97.65 194	96.65 319	95.30 274	97.07 283	98.69 190	84.77 312	99.75 213	94.97 242	98.64 283	98.83 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNLPA		97.17 229	96.71 236	98.55 178	98.56 283	98.05 115	96.33 282	98.93 228	96.91 220	97.06 284	97.39 298	94.38 243	99.45 322	91.66 311	99.18 240	98.14 297

NCCC		97.86 182	97.47 201	99.05 106	98.61 277	98.07 113	96.98 245	98.90 234	97.63 162	97.04 285	97.93 267	95.99 194	99.66 262	95.31 237	98.82 273	99.43 140

TR-MVS		95.55 277	95.12 282	96.86 286	97.54 334	93.94 282	96.49 275	96.53 322	94.36 294	97.03 286	96.61 314	94.26 246	99.16 346	86.91 343	96.31 345	97.47 328

MDTV_nov1_ep13			95.22 279		97.06 348	83.20 361	97.74 185	96.16 325	94.37 293	96.99 287	98.83 173	83.95 320	99.53 303	93.90 273	97.95 321

CANet		97.87 181	97.76 179	98.19 219	97.75 325	95.51 240	96.76 259	99.05 204	97.74 155	96.93 288	98.21 248	95.59 210	99.89 57	97.86 109	99.93 38	99.19 211

EPMVS		93.72 322	93.27 321	95.09 328	96.04 362	87.76 342	98.13 134	85.01 369	94.69 285	96.92 289	98.64 201	78.47 347	99.31 336	95.04 239	96.46 344	98.20 294

AdaColmap		97.14 231	96.71 236	98.46 192	98.34 300	97.80 142	96.95 246	98.93 228	95.58 269	96.92 289	97.66 280	95.87 202	99.53 303	90.97 326	99.14 246	98.04 300

thisisatest0515		94.12 314	93.16 322	96.97 278	98.60 279	92.90 303	93.77 351	90.61 364	94.10 300	96.91 291	95.87 330	74.99 359	99.80 157	94.52 254	99.12 252	98.20 294

CR-MVSNet		96.28 265	95.95 262	97.28 267	97.71 327	94.22 274	98.11 137	98.92 231	92.31 320	96.91 291	99.37 65	85.44 310	99.81 145	97.39 132	97.36 332	97.81 309

RPMNet		96.82 247	96.66 241	97.28 267	97.71 327	94.22 274	98.11 137	96.90 314	99.37 36	96.91 291	99.34 71	86.72 296	99.81 145	97.53 124	97.36 332	97.81 309

HPM-MVS++		98.10 163	97.64 189	99.48 45	99.09 183	99.13 39	97.52 211	98.75 257	97.46 183	96.90 294	97.83 272	96.01 190	99.84 104	95.82 223	99.35 211	99.46 129

PatchT		96.65 254	96.35 252	97.54 258	97.40 341	95.32 245	97.98 159	96.64 320	99.33 40	96.89 295	99.42 59	84.32 317	99.81 145	97.69 120	97.49 327	97.48 327

1112_ss		97.29 221	96.86 227	98.58 171	99.34 128	96.32 209	96.75 260	99.58 35	93.14 310	96.89 295	97.48 292	92.11 275	99.86 79	96.91 153	99.54 184	99.57 71

test222					98.92 221	96.93 186	95.54 319	98.78 253	85.72 357	96.86 297	98.11 255	94.43 241			99.10 254	99.23 199

thres200		93.72 322	93.14 323	95.46 324	98.66 274	91.29 329	96.61 270	94.63 334	97.39 190	96.83 298	93.71 360	79.88 336	99.56 295	82.40 359	98.13 307	95.54 354

UGNet		98.53 121	98.45 111	98.79 139	97.94 320	96.96 184	99.08 49	98.54 269	99.10 66	96.82 299	99.47 51	96.55 168	99.84 104	98.56 76	99.94 33	99.55 84
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		96.99 240	96.55 247	98.31 210	99.35 126	95.47 242	95.84 310	99.53 58	91.51 331	96.80 300	98.48 227	91.36 278	99.83 119	96.58 178	99.53 188	99.62 46

新几何1				98.91 125	98.94 215	97.76 144		98.76 254	87.58 353	96.75 301	98.10 256	94.80 233	99.78 190	92.73 301	99.00 262	99.20 205

Effi-MVS+		98.02 168	97.82 178	98.62 162	98.53 288	97.19 173	97.33 221	99.68 15	97.30 198	96.68 302	97.46 294	98.56 35	99.80 157	96.63 176	98.20 302	98.86 254

GA-MVS		95.86 271	95.32 276	97.49 260	98.60 279	94.15 278	93.83 350	97.93 288	95.49 271	96.68 302	97.42 297	83.21 323	99.30 338	96.22 200	98.55 288	99.01 233

F-COLMAP		97.30 219	96.68 238	99.14 89	99.19 163	98.39 89	97.27 227	99.30 139	92.93 311	96.62 304	98.00 262	95.73 206	99.68 248	92.62 302	98.46 293	99.35 170

PAPM_NR		96.82 247	96.32 254	98.30 211	99.07 187	96.69 196	97.48 214	98.76 254	95.81 260	96.61 305	96.47 318	94.12 250	99.17 345	90.82 331	97.78 324	99.06 226

1121		96.73 250	96.00 260	98.91 125	98.95 214	97.76 144	98.07 142	98.73 260	87.65 352	96.54 306	98.13 250	94.52 240	99.73 227	92.38 306	99.02 259	99.24 198

test12				98.93 122	98.58 281	97.83 136		98.66 264		96.53 307		95.51 213	99.69 243		99.13 249	99.27 190

BH-w/o		95.13 285	94.89 288	95.86 316	98.20 311	91.31 328	95.65 316	97.37 298	93.64 304	96.52 308	95.70 331	93.04 264	99.02 350	88.10 339	95.82 348	97.24 331

ADS-MVSNet2		95.43 281	94.98 285	96.76 288	98.14 313	91.74 314	97.92 165	97.76 291	90.23 339	96.51 309	98.91 154	85.61 307	99.85 88	92.88 295	96.90 338	98.69 275

ADS-MVSNet		95.24 284	94.93 287	96.18 306	98.14 313	90.10 334	97.92 165	97.32 302	90.23 339	96.51 309	98.91 154	85.61 307	99.74 222	92.88 295	96.90 338	98.69 275

114514_t		96.50 261	95.77 264	98.69 154	99.48 97	97.43 163	97.84 176	99.55 53	81.42 362	96.51 309	98.58 212	95.53 211	99.67 254	93.41 289	99.58 172	98.98 236

PVSNet	93.40 17	95.67 274	95.70 266	95.57 323	98.83 241	88.57 338	92.50 357	97.72 293	92.69 315	96.49 312	96.44 319	93.72 258	99.43 324	93.61 282	99.28 225	98.71 272

mvs-test1		97.83 188	97.48 200	98.89 128	98.02 317	99.20 23	97.20 232	99.16 186	98.29 122	96.46 313	97.17 304	96.44 174	99.92 33	96.66 174	97.90 322	97.54 326

casdiffmvs		98.22 155	98.17 144	98.35 205	98.75 251	96.62 198	98.62 84	99.12 192	98.04 133	96.46 313	99.12 110	95.81 204	99.63 271	99.17 45	98.45 294	98.80 263

tpmrst		95.07 286	95.46 272	93.91 341	97.11 347	84.36 359	97.62 199	96.96 309	94.98 279	96.35 315	98.80 178	85.46 309	99.59 285	95.60 231	96.23 346	97.79 312

OpenMVS	96.65 7	97.09 233	96.68 238	98.32 208	98.32 301	97.16 176	98.86 72	99.37 108	89.48 345	96.29 316	99.15 105	96.56 167	99.90 47	92.90 294	99.20 234	97.89 303

Fast-Effi-MVS+		97.67 194	97.38 206	98.57 173	98.71 257	97.43 163	97.23 228	99.45 85	94.82 284	96.13 317	96.51 315	98.52 37	99.91 43	96.19 202	98.83 272	98.37 292

test_prior3		97.48 208	97.00 220	98.95 119	98.69 264	97.95 126	95.74 313	99.03 209	96.48 237	96.11 318	97.63 283	95.92 199	99.59 285	94.16 264	99.20 234	99.30 185

test_prior2							95.74 313		96.48 237	96.11 318	97.63 283	95.92 199		94.16 264	99.20 234

dp		93.47 324	93.59 317	93.13 349	96.64 354	81.62 366	97.66 192	96.42 323	92.80 314	96.11 318	98.64 201	78.55 346	99.59 285	93.31 290	92.18 363	98.16 296

原ACMM1				98.35 205	98.90 225	96.25 215		98.83 248	92.48 317	96.07 321	98.10 256	95.39 217	99.71 237	92.61 303	98.99 263	99.08 223

PMMVS		96.51 259	95.98 261	98.09 223	97.53 335	95.84 231	94.92 334	98.84 243	91.58 329	96.05 322	95.58 332	95.68 207	99.66 262	95.59 232	98.09 315	98.76 269

tpm		94.67 303	94.34 298	95.66 320	97.68 330	88.42 339	97.88 170	94.90 332	94.46 289	96.03 323	98.56 216	78.66 344	99.79 179	95.88 216	95.01 353	98.78 266

diffmvs		98.08 165	98.14 152	97.88 236	98.37 298	95.22 248	97.93 163	98.99 221	98.87 86	95.93 324	99.18 94	96.63 161	99.79 179	98.45 79	98.95 267	98.64 279

TEST9					98.71 257	98.08 111	95.96 300	99.03 209	91.40 332	95.85 325	97.53 287	96.52 169	99.76 207

train_agg		97.10 232	96.45 250	99.07 100	98.71 257	98.08 111	95.96 300	99.03 209	91.64 326	95.85 325	97.53 287	96.47 172	99.76 207	93.67 280	99.16 242	99.36 165

test_8					98.67 269	98.01 117	95.91 306	99.02 213	91.64 326	95.79 327	97.50 290	96.47 172	99.76 207

agg_prior3		96.95 242	96.27 257	99.00 115	98.68 266	97.91 129	95.96 300	99.01 216	90.74 338	95.60 328	97.45 295	96.14 184	99.74 222	93.67 280	99.16 242	99.36 165

agg_prior1		97.06 235	96.40 251	99.03 109	98.68 266	97.99 118	95.76 311	99.01 216	91.73 325	95.59 329	97.50 290	96.49 171	99.77 201	93.71 279	99.14 246	99.34 172

agg_prior					98.68 266	97.99 118		99.01 216		95.59 329			99.77 201

PLC	94.65 16	96.51 259	95.73 265	98.85 133	98.75 251	97.91 129	96.42 279	99.06 200	90.94 337	95.59 329	97.38 299	94.41 242	99.59 285	90.93 327	98.04 320	99.05 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HQP4-MVS										95.56 332			99.54 301			99.32 178

HQP-NCC					98.67 269		96.29 284		96.05 254	95.55 333

ACMP_Plane					98.67 269		96.29 284		96.05 254	95.55 333

HQP-MVS		97.00 239	96.49 249	98.55 178	98.67 269	96.79 189	96.29 284	99.04 207	96.05 254	95.55 333	96.84 310	93.84 252	99.54 301	92.82 297	99.26 228	99.32 178

MAR-MVS		96.47 262	95.70 266	98.79 139	97.92 321	99.12 41	98.28 122	98.60 268	92.16 323	95.54 336	96.17 322	94.77 236	99.52 307	89.62 335	98.23 299	97.72 315
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		95.02 288	95.25 278	94.33 335	96.39 359	85.87 348	98.08 140	96.83 316	95.46 272	95.51 337	98.69 190	85.91 304	99.53 303	94.16 264	96.23 346	97.58 324

test2356		91.64 338	90.19 341	96.00 312	94.30 367	89.58 336	90.84 360	96.68 318	91.76 324	95.48 338	93.69 361	67.05 367	99.52 307	84.83 351	97.08 337	98.91 247

MVS-HIRNet		94.32 308	95.62 269	90.42 351	98.46 292	75.36 368	96.29 284	89.13 367	95.25 275	95.38 339	99.75 6	92.88 267	99.19 344	94.07 270	99.39 207	96.72 342

PAPR		95.29 282	94.47 290	97.75 243	97.50 339	95.14 252	94.89 335	98.71 262	91.39 333	95.35 340	95.48 339	94.57 239	99.14 348	84.95 350	97.37 330	98.97 239

HY-MVS	95.94 13	95.90 270	95.35 275	97.55 257	97.95 319	94.79 257	98.81 75	96.94 312	92.28 321	95.17 341	98.57 213	89.90 285	99.75 213	91.20 324	97.33 334	98.10 298

CANet_DTU		97.26 222	97.06 218	97.84 238	97.57 332	94.65 262	96.19 291	98.79 252	97.23 207	95.14 342	98.24 245	93.22 260	99.84 104	97.34 134	99.84 73	99.04 228

cascas		94.79 298	94.33 299	96.15 310	96.02 363	92.36 310	92.34 359	99.26 152	85.34 358	95.08 343	94.96 351	92.96 265	98.53 359	94.41 262	98.59 286	97.56 325

CostFormer		93.97 319	93.78 311	94.51 334	97.53 335	85.83 350	97.98 159	95.96 327	89.29 347	94.99 344	98.63 205	78.63 345	99.62 274	94.54 253	96.50 343	98.09 299

CHOSEN 280x420		95.51 280	95.47 271	95.65 321	98.25 305	88.27 341	93.25 354	98.88 236	93.53 306	94.65 345	97.15 306	86.17 301	99.93 25	97.41 131	99.93 38	98.73 271

JIA-IIPM		95.52 278	95.03 284	97.00 276	96.85 352	94.03 280	96.93 248	95.82 328	99.20 51	94.63 346	99.71 13	83.09 324	99.60 281	94.42 259	94.64 354	97.36 329

MVS		93.19 327	92.09 331	96.50 296	96.91 350	94.03 280	98.07 142	98.06 286	68.01 364	94.56 347	96.48 317	95.96 197	99.30 338	83.84 354	96.89 340	96.17 346

PatchFormer-LS_test		94.08 316	93.91 308	94.59 333	96.93 349	86.86 346	97.55 209	96.57 321	94.27 295	94.38 348	93.64 362	80.96 329	99.59 285	96.44 193	94.48 357	97.31 330

1314		95.74 273	95.60 270	96.17 307	97.53 335	92.75 304	98.07 142	98.31 278	91.22 334	94.25 349	96.68 313	95.53 211	99.03 349	91.64 313	97.18 335	96.74 341

tpm cat1		93.29 326	93.13 324	93.75 342	97.39 342	84.74 356	97.39 218	97.65 296	83.39 361	94.16 350	98.41 229	82.86 326	99.39 327	91.56 316	95.35 352	97.14 332

test-LLR		93.90 320	93.85 309	94.04 338	96.53 355	84.62 357	94.05 346	92.39 357	96.17 248	94.12 351	95.07 344	82.30 327	99.67 254	95.87 219	98.18 303	97.82 307

test-mter		92.33 333	91.76 335	94.04 338	96.53 355	84.62 357	94.05 346	92.39 357	94.00 302	94.12 351	95.07 344	65.63 371	99.67 254	95.87 219	98.18 303	97.82 307

tpm2		93.09 328	92.58 327	94.62 332	97.56 333	86.53 347	97.66 192	95.79 329	86.15 356	94.07 353	98.23 247	75.95 357	99.53 303	90.91 328	96.86 341	97.81 309

TESTMET0.1,1		92.19 335	91.77 334	93.46 345	96.48 357	82.80 364	94.05 346	91.52 362	94.45 291	94.00 354	94.88 352	66.65 368	99.56 295	95.78 224	98.11 308	98.02 301

PVSNet_0	89.98 21	91.15 339	90.30 339	93.70 343	97.72 326	84.34 360	90.24 361	97.42 297	90.20 342	93.79 355	93.09 363	90.90 280	98.89 356	86.57 344	72.76 365	97.87 305

FPMVS		93.44 325	92.23 330	97.08 272	99.25 140	97.86 134	95.61 317	97.16 305	92.90 312	93.76 356	98.65 198	75.94 358	95.66 364	79.30 363	97.49 327	97.73 314

EPNet		96.14 267	95.44 273	98.25 215	90.76 370	95.50 241	97.92 165	94.65 333	98.97 79	92.98 357	98.85 169	89.12 290	99.87 74	95.99 212	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		92.75 330	92.05 332	94.85 329	96.48 357	87.21 345	97.83 177	94.99 331	92.22 322	92.72 358	94.11 359	70.75 363	99.46 320	95.01 240	94.33 358	97.87 305

IB-MVS	91.63 19	92.24 334	90.90 337	96.27 300	97.22 346	91.24 330	94.36 344	93.33 349	92.37 319	92.24 359	94.58 356	66.20 369	99.89 57	93.16 292	94.63 355	97.66 316
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		92.37 332	91.20 336	95.85 317	95.80 364	92.38 309	99.31 20	81.84 371	99.75 4	91.83 360	99.74 7	68.29 365	99.02 350	87.15 342	97.12 336	96.16 347

DeepMVS_CX				93.44 346	98.24 307	94.21 276		94.34 339	64.28 365	91.34 361	94.87 354	89.45 289	92.77 367	77.54 364	93.14 360	93.35 362

tpmp4_e23		92.91 329	92.45 328	94.29 336	97.41 340	85.62 352	97.95 162	96.77 317	87.55 354	91.33 362	98.57 213	74.21 360	99.59 285	91.62 314	96.64 342	97.65 323

testpf		89.08 340	90.27 340	85.50 353	94.03 368	82.85 363	96.87 254	91.09 363	91.61 328	90.96 363	94.86 355	66.15 370	95.83 363	94.58 252	92.27 362	77.82 364

PAPM		91.88 336	90.34 338	96.51 295	98.06 316	92.56 305	92.44 358	97.17 304	86.35 355	90.38 364	96.01 323	86.61 297	99.21 343	70.65 365	95.43 351	97.75 313

EPNet_dtu		94.93 289	94.78 289	95.38 325	93.58 369	87.68 343	96.78 257	95.69 330	97.35 193	89.14 365	98.09 258	88.15 293	99.49 314	94.95 243	99.30 221	98.98 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				94.76 331	94.54 366	92.13 312	99.31 20	80.47 372		88.73 366	91.01 365	67.59 366	98.16 362	82.30 360	94.53 356	93.98 361

tmp_tt		78.77 342	78.73 343	78.90 354	58.45 371	74.76 370	94.20 345	78.26 373	39.16 366	86.71 367	92.82 364	80.50 331	75.19 368	86.16 345	92.29 361	86.74 363

MVE	83.40 22	92.50 331	91.92 333	94.25 337	98.83 241	91.64 316	92.71 356	83.52 370	95.92 258	86.46 368	95.46 340	95.20 220	95.40 365	80.51 361	98.64 283	95.73 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		17.12 346	20.53 347	6.87 358	12.05 372	4.20 373	93.62 352	6.73 374	4.62 368	10.41 369	24.33 367	8.28 375	3.56 370	9.69 367	15.07 366	12.86 367

test123		17.04 347	20.11 348	7.82 357	10.25 373	4.91 372	94.80 336	4.47 375	4.93 367	10.00 370	24.28 368	9.69 374	3.64 369	10.14 366	12.43 368	14.92 366

cdsmvs_eth3d_5k		24.66 345	32.88 346	0.00 359	0.00 374	0.00 374	0.00 365	99.10 196	0.00 369	0.00 371	97.58 285	99.21 10	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		8.17 348	10.90 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	98.07 60	0.00 371	0.00 368	0.00 369	0.00 369

pcd1.5k->3k		41.59 343	44.35 345	33.30 356	99.87 11	0.00 374	0.00 365	99.58 35	0.00 369	0.00 371	0.00 371	99.70 2	0.00 371	0.00 368	99.99 11	99.91 2

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		8.12 349	10.83 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	97.48 292	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																98.81 260

test_part1				0.00 359		0.00 374	0.00 365	99.28 142				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												84.74 313				98.81 260

sam_mvs												84.29 319

MTGPA								99.20 167

test_post1							97.59 204				20.48 370	83.07 325	99.66 262	94.16 264

test_post											21.25 369	83.86 321	99.70 239

patchmatchnet-post											98.77 182	84.37 316	99.85 88

MTMP							97.93 163	91.91 360

gm-plane-assit					94.83 365	81.97 365			88.07 351		94.99 347		99.60 281	91.76 310

test9_res														93.28 291	99.15 245	99.38 158

agg_prior2														92.50 304	99.16 242	99.37 159

test_prior4						97.97 123	95.86 307

test_prior				98.95 119	98.69 264	97.95 126		99.03 209					99.59 285			99.30 185

新几何2							95.93 304

旧先验1					98.82 244	97.45 162		98.76 254			98.34 236	95.50 214			99.01 261	99.23 199

无先验							95.74 313	98.74 259	89.38 346				99.73 227	92.38 306		99.22 203

原ACMM2							95.53 320

testdata2													99.79 179	92.80 299

segment_acmp												97.02 132

testdata1							95.44 324		96.32 243

plane_prior7					99.19 163	97.87 133

plane_prior6					98.99 207	97.70 150						94.90 226

plane_prior5								99.27 147					99.70 239	94.42 259	99.51 192	99.45 133

plane_prior4											97.98 264

plane_prior2							97.77 181		98.20 127

plane_prior1					99.05 195

plane_prior						97.65 152	97.07 241		96.72 227						99.36 209

n2								0.00 376

nn								0.00 376

door-mid								99.57 42

test11								98.87 237

door								99.41 97

HQP5-MVS						96.79 189

BP-MVS														92.82 297

HQP3-MVS								99.04 207							99.26 228

HQP2-MVS												93.84 252

NP-MVS					98.84 239	97.39 165					96.84 310

ACMMP++_ref															99.77 105

ACMMP++															99.68 143

Test By Simon												96.52 169

High-resolution multi-view benchmark