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.

pcd_1.5k_mvsjas		16.61 335	22.14 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	100.00 1	99.28 39	0.00 360	0.00 357	0.00 358	0.00 358

pcd1.5k->3k		49.97 331	55.52 332	33.31 344	99.95 13	0.00 362	0.00 353	99.81 56	0.00 357	0.00 358	100.00 1	99.96 1	0.00 360	0.00 357	100.00 1	99.92 3

sosnet-low-res		8.33 336	11.11 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	100.00 1	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		8.33 336	11.11 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	100.00 1	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		8.33 336	11.11 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	100.00 1	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		8.33 336	11.11 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	100.00 1	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		8.33 336	11.11 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	100.00 1	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Anonymous20231211		99.83 11	99.81 10	99.89 6	99.97 4	99.95 2	99.88 4	99.93 6	99.87 13	99.94 20	99.98 8	99.55 21	99.95 41	99.21 79	99.98 36	99.78 31

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

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 3	99.65 54	100.00 1	99.90 6	100.00 1	99.97 10	99.61 17	99.97 16	99.75 31	100.00 1	99.84 15

MVS-HIRNet		97.86 268	98.22 241	96.76 327	99.28 278	91.53 349	98.38 271	92.60 356	99.13 150	99.31 216	99.96 11	97.18 239	99.68 311	98.34 156	99.83 144	99.07 274

pmmvs6		99.86 6	99.86 6	99.83 24	99.94 15	99.90 4	99.83 8	99.91 11	99.85 19	99.94 20	99.95 12	99.73 10	99.90 109	99.65 35	99.97 47	99.69 56

gg-mvs-nofinetune		95.87 323	95.17 326	97.97 300	98.19 346	96.95 300	99.69 38	89.23 359	99.89 10	96.24 349	99.94 13	81.19 353	99.51 343	93.99 330	98.20 333	97.44 340

anonymousdsp		99.80 13	99.77 14	99.90 4	99.96 5	99.88 8	99.73 21	99.85 29	99.70 49	99.92 31	99.93 14	99.45 23	99.97 16	99.36 61	100.00 1	99.85 14

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 5	99.79 33	99.72 25	99.88 18	99.92 5	99.98 3	99.93 14	99.94 2	99.98 7	99.77 30	100.00 1	99.92 3

OurMVSNet-221017-0		99.75 19	99.71 25	99.84 21	99.96 5	99.83 22	99.83 8	99.85 29	99.80 31	99.93 26	99.93 14	98.54 141	99.93 66	99.59 39	99.98 36	99.76 37

PS-MVSNAJss		99.84 9	99.82 9	99.89 6	99.96 5	99.77 37	99.68 41	99.85 29	99.95 3	99.98 3	99.92 17	99.28 39	99.98 7	99.75 31	100.00 1	99.94 2

test_djsdf		99.84 9	99.81 10	99.91 2	99.94 15	99.84 18	99.77 13	99.80 60	99.73 42	99.97 6	99.92 17	99.77 9	99.98 7	99.43 53	100.00 1	99.90 5

TDRefinement		99.72 25	99.70 28	99.77 39	99.90 25	99.85 13	99.86 7	99.92 7	99.69 53	99.78 82	99.92 17	99.37 30	99.88 139	98.93 121	99.95 66	99.60 124

UA-Net		99.78 15	99.76 18	99.86 18	99.72 130	99.71 52	99.91 3	99.95 5	99.96 2	99.71 107	99.91 20	99.15 53	99.97 16	99.50 48	100.00 1	99.90 5

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

v13		99.76 17	99.77 14	99.73 63	99.86 35	99.55 96	99.77 13	99.86 22	99.79 33	99.96 8	99.91 20	98.90 84	99.87 159	99.91 5	100.00 1	99.78 31

jajsoiax		99.89 3	99.89 3	99.89 6	99.96 5	99.78 35	99.70 29	99.86 22	99.89 10	99.98 3	99.90 23	99.94 2	99.98 7	99.75 31	100.00 1	99.90 5

v12		99.75 19	99.77 14	99.72 68	99.85 39	99.53 99	99.75 17	99.86 22	99.78 34	99.96 8	99.90 23	98.88 87	99.86 179	99.91 5	100.00 1	99.77 34

v11		99.75 19	99.76 18	99.71 72	99.85 39	99.49 102	99.73 21	99.84 37	99.75 39	99.95 16	99.90 23	98.93 80	99.86 179	99.92 3	100.00 1	99.77 34

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

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

V9		99.74 23	99.75 20	99.71 72	99.84 42	99.50 100	99.74 19	99.86 22	99.76 38	99.96 8	99.90 23	98.83 90	99.85 195	99.91 5	100.00 1	99.77 34

SixPastTwentyTwo		99.42 84	99.30 101	99.76 42	99.92 19	99.67 68	99.70 29	99.14 281	99.65 65	99.89 38	99.90 23	96.20 263	99.94 55	99.42 57	99.92 89	99.67 69

DeepC-MVS	98.90 4	99.62 43	99.61 41	99.67 85	99.72 130	99.44 117	99.24 140	99.71 104	99.27 123	99.93 26	99.90 23	99.70 12	99.93 66	98.99 108	99.99 20	99.64 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
V14		99.73 24	99.74 21	99.69 79	99.83 46	99.48 105	99.72 25	99.85 29	99.74 40	99.96 8	99.89 31	98.79 98	99.85 195	99.91 5	100.00 1	99.76 37

TransMVSNet (Re)		99.78 15	99.77 14	99.81 27	99.91 21	99.85 13	99.75 17	99.86 22	99.70 49	99.91 33	99.89 31	99.60 19	99.87 159	99.59 39	99.74 191	99.71 49

MIMVSNet1		99.66 36	99.62 38	99.80 29	99.94 15	99.87 9	99.69 38	99.77 73	99.78 34	99.93 26	99.89 31	97.94 191	99.92 83	99.65 35	99.98 36	99.62 113

v15		99.72 25	99.73 24	99.68 82	99.82 53	99.44 117	99.70 29	99.85 29	99.72 45	99.95 16	99.88 34	98.76 105	99.84 211	99.90 9	100.00 1	99.75 40

Baseline_NR-MVSNet		99.49 68	99.37 87	99.82 25	99.91 21	99.84 18	98.83 224	99.86 22	99.68 56	99.65 127	99.88 34	97.67 211	99.87 159	99.03 105	99.86 127	99.76 37

K. test v3		98.87 202	98.60 209	99.69 79	99.93 18	99.46 110	99.74 19	94.97 354	99.78 34	99.88 46	99.88 34	93.66 286	99.97 16	99.61 38	99.95 66	99.64 95

new-patchmatchnet		99.35 102	99.57 48	98.71 275	99.82 53	96.62 304	98.55 251	99.75 84	99.50 90	99.88 46	99.87 37	99.31 35	99.88 139	99.43 53	100.00 1	99.62 113

v748		99.76 17	99.74 21	99.84 21	99.95 13	99.83 22	99.82 10	99.80 60	99.82 27	99.95 16	99.87 37	98.72 112	99.93 66	99.72 34	99.98 36	99.75 40

pm-mvs1		99.79 14	99.79 13	99.78 37	99.91 21	99.83 22	99.76 16	99.87 20	99.73 42	99.89 38	99.87 37	99.63 15	99.87 159	99.54 44	99.92 89	99.63 99

v17		99.70 28	99.71 25	99.67 85	99.81 61	99.44 117	99.70 29	99.83 40	99.69 53	99.94 20	99.87 37	98.70 113	99.84 211	99.88 14	99.99 20	99.73 43

v16		99.70 28	99.71 25	99.67 85	99.81 61	99.43 123	99.70 29	99.83 40	99.70 49	99.94 20	99.87 37	98.69 115	99.84 211	99.88 14	99.99 20	99.73 43

v10		99.69 32	99.69 29	99.66 93	99.81 61	99.39 135	99.66 49	99.75 84	99.60 80	99.92 31	99.87 37	98.75 108	99.86 179	99.90 9	99.99 20	99.73 43

JIA-IIPM		98.06 264	97.92 261	98.50 282	98.59 338	97.02 299	98.80 229	98.51 308	99.88 12	97.89 328	99.87 37	91.89 300	99.90 109	98.16 174	97.68 345	98.59 300

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 12	99.91 21	99.90 4	99.96 1	99.92 7	99.90 6	99.97 6	99.87 37	99.81 7	99.95 41	99.54 44	99.99 20	99.80 25
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
v18		99.68 33	99.69 29	99.65 97	99.79 82	99.40 132	99.68 41	99.83 40	99.66 62	99.93 26	99.85 45	98.65 124	99.84 211	99.87 18	99.99 20	99.71 49

v8		99.68 33	99.69 29	99.65 97	99.80 69	99.40 132	99.66 49	99.76 79	99.64 67	99.93 26	99.85 45	98.66 122	99.84 211	99.88 14	99.99 20	99.71 49

EU-MVSNet		99.39 93	99.62 38	98.72 274	99.88 28	96.44 306	99.56 70	99.85 29	99.90 6	99.90 35	99.85 45	98.09 180	99.83 227	99.58 41	99.95 66	99.90 5

DSMNet-mixed		99.48 70	99.65 34	98.95 249	99.71 133	97.27 295	99.50 74	99.82 48	99.59 82	99.41 190	99.85 45	99.62 16	100.00 1	99.53 46	99.89 107	99.59 135

ACMH	98.42 6	99.59 45	99.54 53	99.72 68	99.86 35	99.62 83	99.56 70	99.79 68	98.77 188	99.80 74	99.85 45	99.64 14	99.85 195	98.70 137	99.89 107	99.70 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XXY-MVS		99.71 27	99.67 32	99.81 27	99.89 27	99.72 51	99.59 65	99.82 48	99.39 111	99.82 65	99.84 50	99.38 28	99.91 92	99.38 58	99.93 86	99.80 25

FC-MVSNet-test		99.70 28	99.65 34	99.86 18	99.88 28	99.86 12	99.72 25	99.78 70	99.90 6	99.82 65	99.83 51	98.45 154	99.87 159	99.51 47	99.97 47	99.86 12

lessismore_v0				99.64 104	99.86 35	99.38 141		90.66 357		99.89 38	99.83 51	94.56 280	99.97 16	99.56 43	99.92 89	99.57 143

no-one		99.28 117	99.23 117	99.45 172	99.87 32	99.08 201	98.95 208	99.52 201	98.88 174	99.77 87	99.83 51	97.78 203	99.90 109	98.46 149	99.99 20	99.38 213

GBi-Net		99.42 84	99.31 96	99.73 63	99.49 217	99.77 37	99.68 41	99.70 107	99.44 101	99.62 139	99.83 51	97.21 235	99.90 109	98.96 115	99.90 101	99.53 157

test1		99.42 84	99.31 96	99.73 63	99.49 217	99.77 37	99.68 41	99.70 107	99.44 101	99.62 139	99.83 51	97.21 235	99.90 109	98.96 115	99.90 101	99.53 157

FMVSNet1		99.66 36	99.63 37	99.73 63	99.78 88	99.77 37	99.68 41	99.70 107	99.67 58	99.82 65	99.83 51	98.98 74	99.90 109	99.24 78	99.97 47	99.53 157

TAMVS		99.49 68	99.45 73	99.63 108	99.48 223	99.42 127	99.45 79	99.57 176	99.66 62	99.78 82	99.83 51	97.85 198	99.86 179	99.44 52	99.96 59	99.61 118

SD-MVS		99.01 178	99.30 101	98.15 296	99.50 212	99.40 132	98.94 211	99.61 148	99.22 137	99.75 91	99.82 58	99.54 22	95.51 357	97.48 215	99.87 120	99.54 154

ab-mvs		99.33 110	99.28 108	99.47 165	99.57 184	99.39 135	99.78 12	99.43 226	98.87 175	99.57 150	99.82 58	98.06 183	99.87 159	98.69 138	99.73 197	99.15 251

PMVS	92.94 21	98.82 207	98.81 195	98.85 259	99.84 42	97.99 276	99.20 150	99.47 215	99.71 47	99.42 184	99.82 58	98.09 180	99.47 345	93.88 331	99.85 130	99.07 274
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
VPA-MVSNet		99.66 36	99.62 38	99.79 34	99.68 149	99.75 44	99.62 56	99.69 113	99.85 19	99.80 74	99.81 61	98.81 91	99.91 92	99.47 50	99.88 113	99.70 53

UGNet		99.38 95	99.34 92	99.49 160	98.90 313	98.90 220	99.70 29	99.35 247	99.86 16	98.57 297	99.81 61	98.50 150	99.93 66	99.38 58	99.98 36	99.66 79
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
ambc				99.20 229	99.35 255	98.53 241	99.17 159	99.46 218		99.67 117	99.80 63	98.46 153	99.70 297	97.92 186	99.70 205	99.38 213

VDDNet		98.97 184	98.82 194	99.42 179	99.71 133	98.81 229	99.62 56	98.68 302	99.81 28	99.38 202	99.80 63	94.25 282	99.85 195	98.79 129	99.32 270	99.59 135

mvs_anonymous		99.28 117	99.39 82	98.94 250	99.19 290	97.81 283	99.02 194	99.55 182	99.78 34	99.85 57	99.80 63	98.24 171	99.86 179	99.57 42	99.50 244	99.15 251

QAPM		98.40 244	97.99 255	99.65 97	99.39 247	99.47 106	99.67 46	99.52 201	91.70 345	98.78 280	99.80 63	98.55 139	99.95 41	94.71 321	99.75 184	99.53 157

wuykxyi23d		99.65 41	99.64 36	99.69 79	99.92 19	99.20 186	98.89 213	99.99 2	98.73 196	99.95 16	99.80 63	99.84 4	99.99 4	99.64 37	99.98 36	99.89 9

3Dnovator	99.15 2	99.43 81	99.36 90	99.65 97	99.39 247	99.42 127	99.70 29	99.56 179	99.23 134	99.35 206	99.80 63	99.17 51	99.95 41	98.21 166	99.84 134	99.59 135

CMPMVS	77.52 23	98.50 232	98.19 246	99.41 186	98.33 344	99.56 93	99.01 196	99.59 166	95.44 324	99.57 150	99.80 63	95.64 270	99.46 348	96.47 269	99.92 89	99.21 241
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FIs		99.65 41	99.58 45	99.84 21	99.84 42	99.85 13	99.66 49	99.75 84	99.86 16	99.74 99	99.79 70	98.27 169	99.85 195	99.37 60	99.93 86	99.83 18

testing_2		99.58 46	99.56 51	99.62 116	99.81 61	99.44 117	99.14 172	99.43 226	99.69 53	99.82 65	99.79 70	99.14 54	99.79 262	99.31 70	99.95 66	99.63 99

testmv		99.53 65	99.51 66	99.59 127	99.73 120	99.31 157	98.48 260	99.92 7	99.57 84	99.87 51	99.79 70	99.12 58	99.91 92	99.16 91	99.99 20	99.55 147

LCM-MVSNet-Re		99.28 117	99.15 123	99.67 85	99.33 269	99.76 41	99.34 106	99.97 3	98.93 169	99.91 33	99.79 70	98.68 117	99.93 66	96.80 251	99.56 230	99.30 231

CHOSEN 1792x2688		99.39 93	99.30 101	99.65 97	99.88 28	99.25 173	98.78 233	99.88 18	98.66 200	99.96 8	99.79 70	97.45 222	99.93 66	99.34 63	99.99 20	99.78 31

LP		98.34 251	98.44 221	98.05 298	98.88 320	95.31 329	99.28 130	98.74 299	99.12 151	98.98 255	99.79 70	93.40 288	99.93 66	98.38 152	99.41 260	98.90 286

CR-MVSNet		98.35 249	98.20 243	98.83 263	99.05 307	98.12 268	99.30 121	99.67 120	97.39 284	99.16 238	99.79 70	91.87 301	99.91 92	98.78 132	98.77 304	98.44 308

Patchmtry		98.78 212	98.54 217	99.49 160	98.89 317	99.19 188	99.32 111	99.67 120	99.65 65	99.72 103	99.79 70	91.87 301	99.95 41	98.00 183	99.97 47	99.33 225

wuyk23d		97.58 275	99.13 127	92.93 341	99.69 142	99.49 102	99.52 72	99.77 73	97.97 252	99.96 8	99.79 70	99.84 4	99.94 55	95.85 292	99.82 153	79.36 354

DTE-MVSNet		99.68 33	99.61 41	99.88 12	99.80 69	99.87 9	99.67 46	99.71 104	99.72 45	99.84 60	99.78 79	98.67 120	99.97 16	99.30 71	99.95 66	99.80 25

EG-PatchMatch MVS		99.57 47	99.56 51	99.62 116	99.77 98	99.33 154	99.26 134	99.76 79	99.32 119	99.80 74	99.78 79	99.29 37	99.87 159	99.15 92	99.91 99	99.66 79

RPSCF		99.18 147	99.02 161	99.64 104	99.83 46	99.85 13	99.44 81	99.82 48	98.33 235	99.50 172	99.78 79	97.90 193	99.65 328	96.78 252	99.83 144	99.44 197

3Dnovator+	98.92 3	99.35 102	99.24 115	99.67 85	99.35 255	99.47 106	99.62 56	99.50 206	99.44 101	99.12 243	99.78 79	98.77 104	99.94 55	97.87 189	99.72 202	99.62 113

Gipuma		99.57 47	99.59 43	99.49 160	99.98 3	99.71 52	99.72 25	99.84 37	99.81 28	99.94 20	99.78 79	98.91 83	99.71 296	98.41 151	99.95 66	99.05 276
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
COLMAP_ROB	98.06 12	99.45 79	99.37 87	99.70 78	99.83 46	99.70 59	99.38 92	99.78 70	99.53 87	99.67 117	99.78 79	99.19 49	99.86 179	97.32 223	99.87 120	99.55 147
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
USDC		98.96 187	98.93 176	99.05 243	99.54 198	97.99 276	97.07 340	99.80 60	98.21 241	99.75 91	99.77 85	98.43 155	99.64 330	97.90 187	99.88 113	99.51 168

EPP-MVSNet		99.17 150	99.00 166	99.66 93	99.80 69	99.43 123	99.70 29	99.24 272	99.48 92	99.56 157	99.77 85	94.89 276	99.93 66	98.72 136	99.89 107	99.63 99

OpenMVS	98.12 10	98.23 256	97.89 265	99.26 219	99.19 290	99.26 169	99.65 54	99.69 113	91.33 346	98.14 319	99.77 85	98.28 168	99.96 33	95.41 311	99.55 236	98.58 302

PatchT		98.45 238	98.32 236	98.83 263	98.94 311	98.29 259	99.24 140	98.82 295	99.84 23	99.08 246	99.76 88	91.37 304	99.94 55	98.82 128	99.00 292	98.26 315

MIMVSNet		98.43 239	98.20 243	99.11 235	99.53 201	98.38 250	99.58 67	98.61 304	98.96 165	99.33 212	99.76 88	90.92 308	99.81 254	97.38 221	99.76 181	99.15 251

DP-MVS		99.48 70	99.39 82	99.74 55	99.57 184	99.62 83	99.29 129	99.61 148	99.87 13	99.74 99	99.76 88	98.69 115	99.87 159	98.20 167	99.80 167	99.75 40

ACMH+	98.40 8	99.50 66	99.43 78	99.71 72	99.86 35	99.76 41	99.32 111	99.77 73	99.53 87	99.77 87	99.76 88	99.26 45	99.78 270	97.77 195	99.88 113	99.60 124

v1240		99.56 50	99.58 45	99.51 156	99.80 69	99.00 206	99.00 198	99.65 133	99.15 147	99.90 35	99.75 92	99.09 61	99.88 139	99.90 9	99.96 59	99.67 69

Vis-MVSNet		99.75 19	99.74 21	99.79 34	99.88 28	99.66 71	99.69 38	99.92 7	99.67 58	99.77 87	99.75 92	99.61 17	99.98 7	99.35 62	99.98 36	99.72 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
RPMNet		98.53 230	98.44 221	98.83 263	99.05 307	98.12 268	99.30 121	98.78 297	99.86 16	99.16 238	99.74 94	92.53 297	99.91 92	98.75 133	98.77 304	98.44 308

FMVSNet2		99.35 102	99.28 108	99.55 146	99.49 217	99.35 151	99.45 79	99.57 176	99.44 101	99.70 109	99.74 94	97.21 235	99.87 159	99.03 105	99.94 78	99.44 197

IterMVS		98.97 184	99.16 121	98.42 284	99.74 117	95.64 324	98.06 300	99.83 40	99.83 26	99.85 57	99.74 94	96.10 266	99.99 4	99.27 77	100.00 1	99.63 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS_ROB	97.31 17	97.36 280	96.84 290	98.89 258	99.29 276	99.45 115	98.87 217	99.48 211	86.54 351	99.44 178	99.74 94	97.34 229	99.86 179	91.61 335	99.28 274	97.37 342

semantic-postprocess				98.51 279	99.75 111	95.90 317		99.84 37	99.84 23	99.89 38	99.73 98	95.96 268	99.99 4	99.33 65	100.00 1	99.63 99

ACMMP_Plus		99.28 117	99.11 133	99.79 34	99.75 111	99.81 28	98.95 208	99.53 191	98.27 239	99.53 166	99.73 98	98.75 108	99.87 159	97.70 199	99.83 144	99.68 62

v1144		99.54 59	99.53 61	99.59 127	99.79 82	99.28 163	99.10 181	99.61 148	99.20 138	99.84 60	99.73 98	98.67 120	99.84 211	99.86 19	99.98 36	99.64 95

PM-MVS		99.36 100	99.29 106	99.58 131	99.83 46	99.66 71	98.95 208	99.86 22	98.85 177	99.81 71	99.73 98	98.40 159	99.92 83	98.36 154	99.83 144	99.17 249

PEN-MVS		99.66 36	99.59 43	99.89 6	99.83 46	99.87 9	99.66 49	99.73 92	99.70 49	99.84 60	99.73 98	98.56 135	99.96 33	99.29 74	99.94 78	99.83 18

PVSNet_Blended_VisFu		99.40 90	99.38 84	99.44 174	99.90 25	98.66 236	98.94 211	99.91 11	97.97 252	99.79 79	99.73 98	99.05 69	99.97 16	99.15 92	99.99 20	99.68 62

Patchmatch-RL test		98.60 223	98.36 232	99.33 203	99.77 98	99.07 203	98.27 278	99.87 20	98.91 172	99.74 99	99.72 104	90.57 315	99.79 262	98.55 145	99.85 130	99.11 260

v144192		99.55 54	99.54 53	99.58 131	99.78 88	99.20 186	99.11 180	99.62 144	99.18 140	99.89 38	99.72 104	98.66 122	99.87 159	99.88 14	99.97 47	99.66 79

v1192		99.57 47	99.57 48	99.57 137	99.77 98	99.22 180	99.04 191	99.60 162	99.18 140	99.87 51	99.72 104	99.08 64	99.85 195	99.89 13	99.98 36	99.66 79

AllTest		99.21 140	99.07 147	99.63 108	99.78 88	99.64 77	99.12 179	99.83 40	98.63 203	99.63 132	99.72 104	98.68 117	99.75 284	96.38 270	99.83 144	99.51 168

TestCases				99.63 108	99.78 88	99.64 77		99.83 40	98.63 203	99.63 132	99.72 104	98.68 117	99.75 284	96.38 270	99.83 144	99.51 168

v7		99.56 50	99.54 53	99.61 119	99.80 69	99.39 135	99.30 121	99.59 166	99.14 149	99.82 65	99.72 104	98.75 108	99.84 211	99.83 20	99.94 78	99.61 118

ACMM	98.09 11	99.46 77	99.38 84	99.72 68	99.80 69	99.69 63	99.13 177	99.65 133	98.99 163	99.64 129	99.72 104	99.39 24	99.86 179	98.23 164	99.81 162	99.60 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		99.56 50	99.57 48	99.55 146	99.75 111	99.11 195	99.05 189	99.61 148	99.15 147	99.88 46	99.71 111	99.08 64	99.87 159	99.90 9	99.97 47	99.66 79

APDe-MVS		99.48 70	99.36 90	99.85 20	99.55 197	99.81 28	99.50 74	99.69 113	98.99 163	99.75 91	99.71 111	98.79 98	99.93 66	98.46 149	99.85 130	99.80 25

PS-CasMVS		99.66 36	99.58 45	99.89 6	99.80 69	99.85 13	99.66 49	99.73 92	99.62 71	99.84 60	99.71 111	98.62 129	99.96 33	99.30 71	99.96 59	99.86 12

XVG-ACMP-BASELINE		99.23 128	99.10 140	99.63 108	99.82 53	99.58 90	98.83 224	99.72 101	98.36 225	99.60 146	99.71 111	98.92 81	99.91 92	97.08 238	99.84 134	99.40 208

PVSNet_BlendedMVS		99.03 172	99.01 164	99.09 237	99.54 198	97.99 276	98.58 246	99.82 48	97.62 271	99.34 210	99.71 111	98.52 147	99.77 276	97.98 184	99.97 47	99.52 165

IS-MVSNet		99.03 172	98.85 188	99.55 146	99.80 69	99.25 173	99.73 21	99.15 280	99.37 113	99.61 144	99.71 111	94.73 278	99.81 254	97.70 199	99.88 113	99.58 139

LS3D		99.24 127	99.11 133	99.61 119	98.38 343	99.79 33	99.57 68	99.68 116	99.61 75	99.15 240	99.71 111	98.70 113	99.91 92	97.54 212	99.68 208	99.13 258

TSAR-MVS + MP.		99.34 107	99.24 115	99.63 108	99.82 53	99.37 144	99.26 134	99.35 247	98.77 188	99.57 150	99.70 118	99.27 42	99.88 139	97.71 198	99.75 184	99.65 89

V42		99.56 50	99.54 53	99.63 108	99.79 82	99.46 110	99.39 86	99.59 166	99.24 132	99.86 56	99.70 118	98.55 139	99.82 235	99.79 26	99.95 66	99.60 124

MDA-MVSNet-bldmvs		99.06 166	99.05 154	99.07 241	99.80 69	97.83 282	98.89 213	99.72 101	99.29 120	99.63 132	99.70 118	96.47 256	99.89 124	98.17 173	99.82 153	99.50 174

CDS-MVSNet		99.22 137	99.13 127	99.50 158	99.35 255	99.11 195	98.96 207	99.54 186	99.46 99	99.61 144	99.70 118	96.31 260	99.83 227	99.34 63	99.88 113	99.55 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DeepPCF-MVS	98.42 6	99.18 147	99.02 161	99.67 85	99.22 285	99.75 44	97.25 338	99.47 215	98.72 197	99.66 121	99.70 118	99.29 37	99.63 332	98.07 179	99.81 162	99.62 113

TinyColmap		98.97 184	98.93 176	99.07 241	99.46 232	98.19 264	97.75 322	99.75 84	98.79 185	99.54 163	99.70 118	98.97 76	99.62 333	96.63 261	99.83 144	99.41 207

tfpnnormal		99.43 81	99.38 84	99.60 125	99.87 32	99.75 44	99.59 65	99.78 70	99.71 47	99.90 35	99.69 124	98.85 89	99.90 109	97.25 229	99.78 175	99.15 251

tmp_tt		95.75 325	95.42 322	96.76 327	89.90 358	94.42 334	98.86 218	97.87 325	78.01 352	99.30 220	99.69 124	97.70 206	95.89 356	99.29 74	98.14 337	99.95 1

v1141		99.54 59	99.52 63	99.57 137	99.78 88	99.27 167	99.15 167	99.61 148	99.26 127	99.89 38	99.69 124	98.56 135	99.82 235	99.82 23	99.97 47	99.63 99

v1neww		99.55 54	99.54 53	99.61 119	99.80 69	99.39 135	99.32 111	99.61 148	99.18 140	99.87 51	99.69 124	98.64 127	99.82 235	99.79 26	99.94 78	99.60 124

v7new		99.55 54	99.54 53	99.61 119	99.80 69	99.39 135	99.32 111	99.61 148	99.18 140	99.87 51	99.69 124	98.64 127	99.82 235	99.79 26	99.94 78	99.60 124

divwei89l23v2f112		99.54 59	99.52 63	99.57 137	99.78 88	99.27 167	99.15 167	99.61 148	99.26 127	99.89 38	99.69 124	98.56 135	99.82 235	99.82 23	99.96 59	99.63 99

VDD-MVS		99.20 142	99.11 133	99.44 174	99.43 239	98.98 208	99.50 74	98.32 316	99.80 31	99.56 157	99.69 124	96.99 245	99.85 195	98.99 108	99.73 197	99.50 174

WR-MVS_H		99.61 44	99.53 61	99.87 16	99.80 69	99.83 22	99.67 46	99.75 84	99.58 83	99.85 57	99.69 124	98.18 178	99.94 55	99.28 76	99.95 66	99.83 18

LPG-MVS_test		99.22 137	99.05 154	99.74 55	99.82 53	99.63 81	99.16 165	99.73 92	97.56 275	99.64 129	99.69 124	99.37 30	99.89 124	96.66 259	99.87 120	99.69 56

LGP-MVS_train				99.74 55	99.82 53	99.63 81		99.73 92	97.56 275	99.64 129	99.69 124	99.37 30	99.89 124	96.66 259	99.87 120	99.69 56

FMVSNet5		97.80 269	97.25 280	99.42 179	98.83 323	98.97 210	99.38 92	99.80 60	98.87 175	99.25 224	99.69 124	80.60 357	99.91 92	98.96 115	99.90 101	99.38 213

ACMMP		99.25 124	99.08 143	99.74 55	99.79 82	99.68 66	99.50 74	99.65 133	98.07 246	99.52 168	99.69 124	98.57 134	99.92 83	97.18 235	99.79 170	99.63 99
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
MVP-Stereo		99.16 152	99.08 143	99.43 177	99.48 223	99.07 203	99.08 186	99.55 182	98.63 203	99.31 216	99.68 136	98.19 177	99.78 270	98.18 171	99.58 229	99.45 192
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
nrg030		99.70 28	99.66 33	99.82 25	99.76 103	99.84 18	99.61 60	99.70 107	99.93 4	99.78 82	99.68 136	99.10 59	99.78 270	99.45 51	99.96 59	99.83 18

v6		99.55 54	99.54 53	99.61 119	99.80 69	99.39 135	99.32 111	99.60 162	99.18 140	99.87 51	99.68 136	98.65 124	99.82 235	99.79 26	99.95 66	99.61 118

v1		99.54 59	99.52 63	99.58 131	99.77 98	99.28 163	99.15 167	99.61 148	99.26 127	99.88 46	99.68 136	98.56 135	99.82 235	99.82 23	99.97 47	99.63 99

XVG-OURS		99.21 140	99.06 149	99.65 97	99.82 53	99.62 83	97.87 319	99.74 89	98.36 225	99.66 121	99.68 136	99.71 11	99.90 109	96.84 249	99.88 113	99.43 203

N_pmnet		98.73 217	98.53 218	99.35 200	99.72 130	98.67 235	98.34 274	94.65 355	98.35 230	99.79 79	99.68 136	98.03 184	99.93 66	98.28 162	99.92 89	99.44 197

EI-MVSNet		99.38 95	99.44 75	99.21 227	99.58 175	98.09 272	99.26 134	99.46 218	99.62 71	99.75 91	99.67 142	98.54 141	99.85 195	99.15 92	99.92 89	99.68 62

CVMVSNet		98.61 222	98.88 184	97.80 309	99.58 175	93.60 337	99.26 134	99.64 138	99.66 62	99.72 103	99.67 142	93.26 289	99.93 66	99.30 71	99.81 162	99.87 10

MVS_Test		99.28 117	99.31 96	99.19 230	99.35 255	98.79 231	99.36 98	99.49 210	99.17 145	99.21 232	99.67 142	98.78 101	99.66 321	99.09 101	99.66 217	99.10 264

SteuartSystems-ACMMP		99.30 114	99.14 124	99.76 42	99.87 32	99.66 71	99.18 152	99.60 162	98.55 209	99.57 150	99.67 142	99.03 71	99.94 55	97.01 241	99.80 167	99.69 56
Skip Steuart: Steuart Systems R&D Blog.
pmmvs-eth3d		99.48 70	99.47 69	99.51 156	99.77 98	99.41 131	98.81 228	99.66 124	99.42 108	99.75 91	99.66 146	99.20 48	99.76 278	98.98 110	99.99 20	99.36 220

EI-MVSNet-UG-set		99.48 70	99.50 67	99.42 179	99.57 184	98.65 238	99.24 140	99.46 218	99.68 56	99.80 74	99.66 146	98.99 73	99.89 124	99.19 83	99.90 101	99.72 46

YYNet1		98.95 190	98.99 169	98.84 261	99.64 159	97.14 298	98.22 282	99.32 252	98.92 171	99.59 147	99.66 146	97.40 224	99.83 227	98.27 163	99.90 101	99.55 147

MDA-MVSNet_test_wron		98.95 190	98.99 169	98.85 259	99.64 159	97.16 297	98.23 281	99.33 250	98.93 169	99.56 157	99.66 146	97.39 226	99.83 227	98.29 161	99.88 113	99.55 147

MVSTER		98.47 236	98.22 241	99.24 224	99.06 306	98.35 252	99.08 186	99.46 218	99.27 123	99.75 91	99.66 146	88.61 325	99.85 195	99.14 98	99.92 89	99.52 165

EI-MVSNet-Vis-set		99.47 76	99.49 68	99.42 179	99.57 184	98.66 236	99.24 140	99.46 218	99.67 58	99.79 79	99.65 151	98.97 76	99.89 124	99.15 92	99.89 107	99.71 49

SMA-MVS		99.23 128	99.06 149	99.74 55	99.46 232	99.76 41	99.13 177	99.58 174	97.62 271	99.68 113	99.64 152	99.02 72	99.83 227	97.61 208	99.82 153	99.63 99

APD-MVS_3200maxsize		99.31 113	99.16 121	99.74 55	99.53 201	99.75 44	99.27 133	99.61 148	99.19 139	99.57 150	99.64 152	98.76 105	99.90 109	97.29 225	99.62 222	99.56 144

ADS-MVSNet2		97.78 270	97.66 276	98.12 297	99.14 294	95.36 327	99.22 146	98.75 298	96.97 295	98.25 311	99.64 152	90.90 309	99.94 55	96.51 266	99.56 230	99.08 270

ADS-MVSNet		97.72 272	97.67 275	97.86 307	99.14 294	94.65 333	99.22 146	98.86 292	96.97 295	98.25 311	99.64 152	90.90 309	99.84 211	96.51 266	99.56 230	99.08 270

CP-MVSNet		99.54 59	99.43 78	99.87 16	99.76 103	99.82 27	99.57 68	99.61 148	99.54 85	99.80 74	99.64 152	97.79 202	99.95 41	99.21 79	99.94 78	99.84 15

FMVSNet3		98.80 210	98.63 208	99.32 207	99.13 296	98.72 232	99.10 181	99.48 211	99.23 134	99.62 139	99.64 152	92.57 295	99.86 179	98.96 115	99.90 101	99.39 210

IterMVS-LS		99.41 87	99.47 69	99.25 222	99.81 61	98.09 272	98.85 221	99.76 79	99.62 71	99.83 64	99.64 152	98.54 141	99.97 16	99.15 92	99.99 20	99.68 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	98.47 5	99.23 128	99.12 130	99.56 143	99.28 278	99.22 180	98.99 201	99.40 235	99.08 157	99.58 148	99.64 152	98.90 84	99.83 227	97.44 217	99.75 184	99.63 99
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPM-MVS		99.26 123	99.13 127	99.63 108	99.70 140	99.61 87	98.58 246	99.48 211	98.50 213	99.52 168	99.63 160	99.14 54	99.76 278	97.89 188	99.77 179	99.51 168

zzz-MVS		99.30 114	99.14 124	99.80 29	99.81 61	99.81 28	98.73 238	99.53 191	99.27 123	99.42 184	99.63 160	98.21 174	99.95 41	97.83 192	99.79 170	99.65 89

MTAPA		99.35 102	99.20 120	99.80 29	99.81 61	99.81 28	99.33 108	99.53 191	99.27 123	99.42 184	99.63 160	98.21 174	99.95 41	97.83 192	99.79 170	99.65 89

abl_6		99.36 100	99.23 117	99.75 51	99.71 133	99.74 49	99.33 108	99.76 79	99.07 159	99.65 127	99.63 160	99.09 61	99.92 83	97.13 237	99.76 181	99.58 139

APD-MVS		98.87 202	98.59 210	99.71 72	99.50 212	99.62 83	99.01 196	99.57 176	96.80 300	99.54 163	99.63 160	98.29 167	99.91 92	95.24 314	99.71 203	99.61 118
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MG-MVS		98.52 231	98.39 228	98.94 250	99.15 293	97.39 294	98.18 284	99.21 275	98.89 173	99.23 228	99.63 160	97.37 228	99.74 288	94.22 327	99.61 226	99.69 56

FPMVS		96.32 314	95.50 321	98.79 266	99.60 169	98.17 266	98.46 265	98.80 296	97.16 290	96.28 347	99.63 160	82.19 352	99.09 351	88.45 345	98.89 296	99.10 264

ppachtmachnet_test		98.89 200	99.12 130	98.20 294	99.66 154	95.24 330	97.63 324	99.68 116	99.08 157	99.78 82	99.62 167	98.65 124	99.88 139	98.02 180	99.96 59	99.48 181

pmmvs5		99.19 145	99.11 133	99.42 179	99.76 103	98.88 223	98.55 251	99.73 92	98.82 181	99.72 103	99.62 167	96.56 252	99.82 235	99.32 68	99.95 66	99.56 144

patchmatchnet-post											99.62 167	90.58 314	99.94 55

v2v482		99.50 66	99.47 69	99.58 131	99.78 88	99.25 173	99.14 172	99.58 174	99.25 130	99.81 71	99.62 167	98.24 171	99.84 211	99.83 20	99.97 47	99.64 95

test20.03		99.55 54	99.54 53	99.58 131	99.79 82	99.37 144	99.02 194	99.89 15	99.60 80	99.82 65	99.62 167	98.81 91	99.89 124	99.43 53	99.86 127	99.47 186

TSAR-MVS + GP.		99.12 158	99.04 159	99.38 192	99.34 265	99.16 190	98.15 287	99.29 260	98.18 243	99.63 132	99.62 167	99.18 50	99.68 311	98.20 167	99.74 191	99.30 231

EPNet		98.13 260	97.77 271	99.18 233	94.57 357	97.99 276	99.24 140	97.96 321	99.74 40	97.29 342	99.62 167	93.13 290	99.97 16	98.59 142	99.83 144	99.58 139
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		98.90 197	98.72 201	99.44 174	99.39 247	99.42 127	98.58 246	99.64 138	97.31 287	99.44 178	99.62 167	98.59 133	99.69 303	96.17 277	99.79 170	99.22 238

v148		99.40 90	99.41 80	99.39 190	99.76 103	98.94 213	99.09 185	99.59 166	99.17 145	99.81 71	99.61 175	98.41 157	99.69 303	99.32 68	99.94 78	99.53 157

DELS-MVS		99.34 107	99.30 101	99.48 163	99.51 207	99.36 147	98.12 291	99.53 191	99.36 115	99.41 190	99.61 175	99.22 47	99.87 159	99.21 79	99.68 208	99.20 242
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
MDTV_nov1_ep13			97.73 272		98.70 336	90.83 352	99.15 167	98.02 319	98.51 212	98.82 274	99.61 175	90.98 307	99.66 321	96.89 247	98.92 293

Regformer-3		99.41 87	99.41 80	99.40 187	99.52 203	98.70 233	99.17 159	99.44 223	99.62 71	99.75 91	99.60 178	98.90 84	99.85 195	98.89 123	99.84 134	99.65 89

Regformer-4		99.45 79	99.44 75	99.50 158	99.52 203	98.94 213	99.17 159	99.53 191	99.64 67	99.76 90	99.60 178	98.96 79	99.90 109	98.91 122	99.84 134	99.67 69

PGM-MVS		99.20 142	99.01 164	99.77 39	99.75 111	99.71 52	99.16 165	99.72 101	97.99 250	99.42 184	99.60 178	98.81 91	99.93 66	96.91 245	99.74 191	99.66 79

HyFIR lowres test		98.91 195	98.64 207	99.73 63	99.85 39	99.47 106	98.07 299	99.83 40	98.64 202	99.89 38	99.60 178	92.57 295	100.00 1	99.33 65	99.97 47	99.72 46

CSCG		99.37 97	99.29 106	99.60 125	99.71 133	99.46 110	99.43 82	99.85 29	98.79 185	99.41 190	99.60 178	98.92 81	99.92 83	98.02 180	99.92 89	99.43 203

ACMP	97.51 14	99.05 169	98.84 190	99.67 85	99.78 88	99.55 96	98.88 215	99.66 124	97.11 294	99.47 175	99.60 178	99.07 66	99.89 124	96.18 276	99.85 130	99.58 139
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
dp		96.86 298	97.07 282	96.24 337	98.68 337	90.30 356	99.19 151	98.38 315	97.35 286	98.23 313	99.59 184	87.23 330	99.82 235	96.27 274	98.73 310	98.59 300

EPMVS		96.53 308	96.32 306	97.17 325	98.18 347	92.97 340	99.39 86	89.95 358	98.21 241	98.61 293	99.59 184	86.69 339	99.72 291	96.99 242	99.23 282	98.81 293

MP-MVS-pluss		99.14 155	98.92 179	99.80 29	99.83 46	99.83 22	98.61 242	99.63 141	96.84 298	99.44 178	99.58 186	98.81 91	99.91 92	97.70 199	99.82 153	99.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MS-PatchMatch		99.00 181	98.97 173	99.09 237	99.11 301	98.19 264	98.76 234	99.33 250	98.49 214	99.44 178	99.58 186	98.21 174	99.69 303	98.20 167	99.62 222	99.39 210

LFMVS		98.46 237	98.19 246	99.26 219	99.24 283	98.52 242	99.62 56	96.94 339	99.87 13	99.31 216	99.58 186	91.04 306	99.81 254	98.68 140	99.42 258	99.45 192

VPNet		99.46 77	99.37 87	99.71 72	99.82 53	99.59 88	99.48 78	99.70 107	99.81 28	99.69 111	99.58 186	97.66 215	99.86 179	99.17 88	99.44 251	99.67 69

diffmvs		98.94 193	98.87 185	99.13 234	99.37 252	98.90 220	99.25 138	99.64 138	97.55 277	99.04 251	99.58 186	97.23 234	99.64 330	98.73 135	99.44 251	98.86 289

PMMVS2		99.48 70	99.45 73	99.57 137	99.76 103	98.99 207	98.09 295	99.90 14	98.95 166	99.78 82	99.58 186	99.57 20	99.93 66	99.48 49	99.95 66	99.79 30

PatchmatchNet		97.65 273	97.80 268	97.18 324	98.82 326	92.49 341	99.17 159	98.39 314	98.12 244	98.79 278	99.58 186	90.71 313	99.89 124	97.23 230	99.41 260	99.16 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_part3							98.74 235		97.71 266		99.57 193		99.90 109	94.47 323

ESAPD		98.87 202	98.58 212	99.74 55	99.62 166	99.67 68	98.74 235	99.53 191	97.71 266	99.55 160	99.57 193	98.40 159	99.90 109	94.47 323	99.68 208	99.66 79

Patchmatch-test		98.10 262	97.98 257	98.48 283	99.27 280	96.48 305	99.40 85	99.07 284	98.81 182	99.23 228	99.57 193	90.11 319	99.87 159	96.69 257	99.64 220	99.09 267

VNet		99.18 147	99.06 149	99.56 143	99.24 283	99.36 147	99.33 108	99.31 256	99.67 58	99.47 175	99.57 193	96.48 255	99.84 211	99.15 92	99.30 272	99.47 186

PNet_i23d		97.02 293	97.87 266	94.49 340	99.69 142	84.81 359	95.18 352	99.85 29	97.83 262	99.32 214	99.57 193	95.53 273	99.47 345	96.09 278	97.74 344	99.18 247

MSLP-MVS++		99.05 169	99.09 142	98.91 253	99.21 286	98.36 251	98.82 227	99.47 215	98.85 177	98.90 269	99.56 198	98.78 101	99.09 351	98.57 143	99.68 208	99.26 235

TranMVSNet+NR-MVSNet		99.54 59	99.47 69	99.76 42	99.58 175	99.64 77	99.30 121	99.63 141	99.61 75	99.71 107	99.56 198	98.76 105	99.96 33	99.14 98	99.92 89	99.68 62

114514_t		98.49 234	98.11 249	99.64 104	99.73 120	99.58 90	99.24 140	99.76 79	89.94 348	99.42 184	99.56 198	97.76 204	99.86 179	97.74 197	99.82 153	99.47 186

Vis-MVSNet (Re-imp)		98.77 213	98.58 212	99.34 201	99.78 88	98.88 223	99.61 60	99.56 179	99.11 152	99.24 227	99.56 198	93.00 293	99.78 270	97.43 218	99.89 107	99.35 222

test_0402		99.22 137	99.14 124	99.45 172	99.79 82	99.43 123	99.28 130	99.68 116	99.54 85	99.40 194	99.56 198	99.07 66	99.82 235	96.01 284	99.96 59	99.11 260

tpmvs		97.39 278	97.69 273	96.52 333	98.41 342	91.76 346	99.30 121	98.94 291	97.74 264	97.85 331	99.55 203	92.40 298	99.73 290	96.25 275	98.73 310	98.06 324

MSDG		99.08 164	98.98 172	99.37 196	99.60 169	99.13 193	97.54 328	99.74 89	98.84 180	99.53 166	99.55 203	99.10 59	99.79 262	97.07 239	99.86 127	99.18 247

tpmrst		97.73 271	98.07 251	96.73 329	98.71 335	92.00 343	99.10 181	98.86 292	98.52 211	98.92 266	99.54 205	91.90 299	99.82 235	98.02 180	99.03 290	98.37 310

new_pmnet		98.88 201	98.89 183	98.84 261	99.70 140	97.62 289	98.15 287	99.50 206	97.98 251	99.62 139	99.54 205	98.15 179	99.94 55	97.55 211	99.84 134	98.95 282

Anonymous20231206		99.35 102	99.31 96	99.47 165	99.74 117	99.06 205	99.28 130	99.74 89	99.23 134	99.72 103	99.53 207	97.63 217	99.88 139	99.11 100	99.84 134	99.48 181

ITE_SJBPF				99.38 192	99.63 161	99.44 117		99.73 92	98.56 208	99.33 212	99.53 207	98.88 87	99.68 311	96.01 284	99.65 219	99.02 279

CHOSEN 280x420		98.41 242	98.41 226	98.40 286	99.34 265	95.89 318	96.94 341	99.44 223	98.80 184	99.25 224	99.52 209	93.51 287	99.98 7	98.94 120	99.98 36	99.32 229

Patchmatch-test1		98.13 260	98.40 227	97.31 323	99.20 289	92.99 339	98.17 286	98.49 310	98.24 240	99.10 245	99.52 209	96.01 267	99.83 227	97.22 231	99.62 222	99.12 259

Regformer-1		99.32 112	99.27 110	99.47 165	99.41 243	98.95 212	98.99 201	99.48 211	99.48 92	99.66 121	99.52 209	98.78 101	99.87 159	98.36 154	99.74 191	99.60 124

Regformer-2		99.34 107	99.27 110	99.53 151	99.41 243	99.10 198	98.99 201	99.53 191	99.47 96	99.66 121	99.52 209	98.80 95	99.89 124	98.31 159	99.74 191	99.60 124

CANet_DTU		98.91 195	98.85 188	99.09 237	98.79 328	98.13 267	98.18 284	99.31 256	99.48 92	98.86 272	99.51 213	96.56 252	99.95 41	99.05 104	99.95 66	99.19 244

pmmvs3		98.08 263	97.80 268	98.91 253	99.41 243	97.69 287	97.87 319	99.66 124	95.87 316	99.50 172	99.51 213	90.35 317	99.97 16	98.55 145	99.47 248	99.08 270

HY-MVS	98.23 9	98.21 258	97.95 259	98.99 247	99.03 309	98.24 260	99.61 60	98.72 300	96.81 299	98.73 283	99.51 213	94.06 283	99.86 179	96.91 245	98.20 333	98.86 289

mPP-MVS		99.19 145	99.00 166	99.76 42	99.76 103	99.68 66	99.38 92	99.54 186	98.34 234	99.01 253	99.50 216	98.53 145	99.93 66	97.18 235	99.78 175	99.66 79

HPM-MVS_fast		99.43 81	99.30 101	99.80 29	99.83 46	99.81 28	99.52 72	99.70 107	98.35 230	99.51 170	99.50 216	99.31 35	99.88 139	98.18 171	99.84 134	99.69 56

MP-MVS		99.06 166	98.83 193	99.76 42	99.76 103	99.71 52	99.32 111	99.50 206	98.35 230	98.97 256	99.48 218	98.37 162	99.92 83	95.95 290	99.75 184	99.63 99

test1235678		98.93 194	98.84 190	99.19 230	99.46 232	98.55 240	97.53 330	99.77 73	98.76 191	99.69 111	99.48 218	96.69 249	99.90 109	98.30 160	99.91 99	99.11 260

MVS_111021_LR		99.13 156	99.03 160	99.42 179	99.58 175	99.32 156	97.91 318	99.73 92	98.68 199	99.31 216	99.48 218	99.09 61	99.66 321	97.70 199	99.77 179	99.29 234

XVS		99.27 122	99.11 133	99.75 51	99.71 133	99.71 52	99.37 96	99.61 148	99.29 120	98.76 281	99.47 221	98.47 151	99.88 139	97.62 206	99.73 197	99.67 69

EPNet_dtu		97.62 274	97.79 270	97.11 326	96.67 356	92.31 342	98.51 257	98.04 318	99.24 132	95.77 351	99.47 221	93.78 285	99.66 321	98.98 110	99.62 222	99.37 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		99.12 158	99.02 161	99.40 187	99.50 212	99.11 195	97.92 316	99.71 104	98.76 191	99.08 246	99.47 221	99.17 51	99.54 341	97.85 191	99.76 181	99.54 154

tpm cat1		96.78 303	96.98 286	96.16 338	98.85 322	90.59 355	99.08 186	99.32 252	92.37 343	97.73 338	99.46 224	91.15 305	99.69 303	96.07 280	98.80 301	98.21 319

PHI-MVS		99.11 161	98.95 175	99.59 127	99.13 296	99.59 88	99.17 159	99.65 133	97.88 256	99.25 224	99.46 224	98.97 76	99.80 259	97.26 228	99.82 153	99.37 217

pmmvs4		99.13 156	99.06 149	99.36 199	99.57 184	99.10 198	98.01 303	99.25 269	98.78 187	99.58 148	99.44 226	98.24 171	99.76 278	98.74 134	99.93 86	99.22 238

1111		97.29 281	96.71 300	99.04 244	99.65 157	97.72 284	98.35 272	99.80 60	99.40 109	99.66 121	99.43 227	75.10 361	99.87 159	98.98 110	99.98 36	99.52 165

.test1245		85.84 330	89.27 331	75.54 343	99.65 157	97.72 284	98.35 272	99.80 60	99.40 109	99.66 121	99.43 227	75.10 361	99.87 159	98.98 110	33.07 355	29.03 356

XVG-OURS-SEG-HR		99.16 152	98.99 169	99.66 93	99.84 42	99.64 77	98.25 280	99.73 92	98.39 222	99.63 132	99.43 227	99.70 12	99.90 109	97.34 222	98.64 313	99.44 197

CNVR-MVS		98.99 183	98.80 197	99.56 143	99.25 281	99.43 123	98.54 254	99.27 264	98.58 207	98.80 277	99.43 227	98.53 145	99.70 297	97.22 231	99.59 228	99.54 154

LF4IMVS		99.01 178	98.92 179	99.27 214	99.71 133	99.28 163	98.59 245	99.77 73	98.32 236	99.39 195	99.41 231	98.62 129	99.84 211	96.62 262	99.84 134	98.69 297

testdata				99.42 179	99.51 207	98.93 217		99.30 259	96.20 311	98.87 271	99.40 232	98.33 166	99.89 124	96.29 273	99.28 274	99.44 197

Test_1112_low_res		98.95 190	98.73 200	99.63 108	99.68 149	99.15 192	98.09 295	99.80 60	97.14 291	99.46 177	99.40 232	96.11 265	99.89 124	99.01 107	99.84 134	99.84 15

PCF-MVS	96.03 18	96.73 304	95.86 316	99.33 203	99.44 237	99.16 190	96.87 342	99.44 223	86.58 350	98.95 262	99.40 232	94.38 281	99.88 139	87.93 347	99.80 167	98.95 282
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
旧先验1					99.49 217	99.29 161		99.26 266			99.39 235	97.67 211			99.36 266	99.46 190

ACMMPR		99.23 128	99.06 149	99.76 42	99.74 117	99.69 63	99.31 118	99.59 166	98.36 225	99.35 206	99.38 236	98.61 131	99.93 66	97.43 218	99.75 184	99.67 69

HFP-MVS		99.25 124	99.08 143	99.76 42	99.73 120	99.70 59	99.31 118	99.59 166	98.36 225	99.36 204	99.37 237	98.80 95	99.91 92	97.43 218	99.75 184	99.68 62

#test#		99.12 158	98.90 182	99.76 42	99.73 120	99.70 59	99.10 181	99.59 166	97.60 273	99.36 204	99.37 237	98.80 95	99.91 92	96.84 249	99.75 184	99.68 62

test12356		98.43 239	98.39 228	98.55 278	99.46 232	96.36 307	97.32 337	99.81 56	97.60 273	99.62 139	99.37 237	94.57 279	99.89 124	97.80 194	99.92 89	99.40 208

CPTT-MVS		98.74 215	98.44 221	99.64 104	99.61 168	99.38 141	99.18 152	99.55 182	96.49 308	99.27 221	99.37 237	97.11 241	99.92 83	95.74 297	99.67 214	99.62 113

DP-MVS Recon		98.50 232	98.23 240	99.31 209	99.49 217	99.46 110	98.56 250	99.63 141	94.86 333	98.85 273	99.37 237	97.81 200	99.59 338	96.08 279	99.44 251	98.88 287

region2R		99.23 128	99.05 154	99.77 39	99.76 103	99.70 59	99.31 118	99.59 166	98.41 220	99.32 214	99.36 242	98.73 111	99.93 66	97.29 225	99.74 191	99.67 69

DU-MVS		99.33 110	99.21 119	99.71 72	99.43 239	99.56 93	98.83 224	99.53 191	99.38 112	99.67 117	99.36 242	97.67 211	99.95 41	99.17 88	99.81 162	99.63 99

UniMVSNet (Re)		99.37 97	99.26 112	99.68 82	99.51 207	99.58 90	98.98 205	99.60 162	99.43 106	99.70 109	99.36 242	97.70 206	99.88 139	99.20 82	99.87 120	99.59 135

NR-MVSNet		99.40 90	99.31 96	99.68 82	99.43 239	99.55 96	99.73 21	99.50 206	99.46 99	99.88 46	99.36 242	97.54 218	99.87 159	98.97 114	99.87 120	99.63 99

UnsupCasMVSNet_eth		98.83 205	98.57 214	99.59 127	99.68 149	99.45 115	98.99 201	99.67 120	99.48 92	99.55 160	99.36 242	94.92 275	99.86 179	98.95 119	96.57 349	99.45 192

UnsupCasMVSNet_bld		98.55 229	98.27 238	99.40 187	99.56 195	99.37 144	97.97 311	99.68 116	97.49 280	99.08 246	99.35 247	95.41 274	99.82 235	97.70 199	98.19 335	99.01 280

sss		98.90 197	98.77 198	99.27 214	99.48 223	98.44 244	98.72 239	99.32 252	97.94 254	99.37 203	99.35 247	96.31 260	99.91 92	98.85 125	99.63 221	99.47 186

CostFormer		96.71 305	96.79 293	96.46 334	98.90 313	90.71 353	99.41 83	98.68 302	94.69 336	98.14 319	99.34 249	86.32 346	99.80 259	97.60 209	98.07 338	98.88 287

原ACMM1				99.37 196	99.47 228	98.87 225		99.27 264	96.74 301	98.26 310	99.32 250	97.93 192	99.82 235	95.96 289	99.38 263	99.43 203

tpm		97.15 290	96.95 287	97.75 311	98.91 312	94.24 335	99.32 111	97.96 321	97.71 266	98.29 308	99.32 250	86.72 338	99.92 83	98.10 178	96.24 351	99.09 267

test222					99.51 207	99.08 201	97.83 321	99.29 260	95.21 328	98.68 289	99.31 252	97.28 231			99.38 263	99.43 203

tpmp4_e23		96.11 318	96.06 311	96.27 335	98.90 313	90.70 354	99.34 106	99.03 288	93.72 340	96.56 346	99.31 252	83.63 350	99.75 284	96.06 281	98.02 340	98.35 311

BH-RMVSNet		98.41 242	98.14 248	99.21 227	99.21 286	98.47 243	98.60 244	98.26 317	98.35 230	98.93 264	99.31 252	97.20 238	99.66 321	94.32 325	99.10 286	99.51 168

MVS_0304		99.17 150	99.10 140	99.38 192	99.08 304	98.86 226	98.46 265	99.73 92	99.53 87	99.35 206	99.30 255	97.11 241	99.96 33	99.33 65	99.99 20	99.33 225

MVSFormer		99.41 87	99.44 75	99.31 209	99.57 184	98.40 247	99.77 13	99.80 60	99.73 42	99.63 132	99.30 255	98.02 186	99.98 7	99.43 53	99.69 206	99.55 147

jason		99.16 152	99.11 133	99.32 207	99.75 111	98.44 244	98.26 279	99.39 238	98.70 198	99.74 99	99.30 255	98.54 141	99.97 16	98.48 148	99.82 153	99.55 147
jason: jason.
1121		98.56 227	98.24 239	99.52 153	99.49 217	99.24 176	99.30 121	99.22 274	95.77 319	98.52 299	99.29 258	97.39 226	99.85 195	95.79 295	99.34 267	99.46 190

新几何1				99.52 153	99.50 212	99.22 180		99.26 266	95.66 323	98.60 294	99.28 259	97.67 211	99.89 124	95.95 290	99.32 270	99.45 192

UniMVSNet_NR-MVSNet		99.37 97	99.25 114	99.72 68	99.47 228	99.56 93	98.97 206	99.61 148	99.43 106	99.67 117	99.28 259	97.85 198	99.95 41	99.17 88	99.81 162	99.65 89

CP-MVS		99.23 128	99.05 154	99.75 51	99.66 154	99.66 71	99.38 92	99.62 144	98.38 223	99.06 250	99.27 261	98.79 98	99.94 55	97.51 214	99.82 153	99.66 79

AdaColmap		98.60 223	98.35 233	99.38 192	99.12 298	99.22 180	98.67 241	99.42 228	97.84 261	98.81 275	99.27 261	97.32 230	99.81 254	95.14 315	99.53 241	99.10 264

NCCC		98.82 207	98.57 214	99.58 131	99.21 286	99.31 157	98.61 242	99.25 269	98.65 201	98.43 305	99.26 263	97.86 197	99.81 254	96.55 264	99.27 277	99.61 118

TAPA-MVS	97.92 13	98.03 265	97.55 277	99.46 168	99.47 228	99.44 117	98.50 258	99.62 144	86.79 349	99.07 249	99.26 263	98.26 170	99.62 333	97.28 227	99.73 197	99.31 230
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MCST-MVS		99.02 174	98.81 195	99.65 97	99.58 175	99.49 102	98.58 246	99.07 284	98.40 221	99.04 251	99.25 265	98.51 149	99.80 259	97.31 224	99.51 243	99.65 89

HQP_MVS		98.90 197	98.68 204	99.55 146	99.58 175	99.24 176	98.80 229	99.54 186	98.94 167	99.14 241	99.25 265	97.24 232	99.82 235	95.84 293	99.78 175	99.60 124

plane_prior4											99.25 265

HPM-MVS		99.25 124	99.07 147	99.78 37	99.81 61	99.75 44	99.61 60	99.67 120	97.72 265	99.35 206	99.25 265	99.23 46	99.92 83	97.21 233	99.82 153	99.67 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PatchMatch-RL		98.68 219	98.47 219	99.30 211	99.44 237	99.28 163	98.14 289	99.54 186	97.12 293	99.11 244	99.25 265	97.80 201	99.70 297	96.51 266	99.30 272	98.93 284

Effi-MVS+-dtu		99.07 165	98.92 179	99.52 153	98.89 317	99.78 35	99.15 167	99.66 124	99.34 116	98.92 266	99.24 270	97.69 208	99.98 7	98.11 176	99.28 274	98.81 293

test_normal		98.82 207	98.67 205	99.27 214	99.56 195	98.83 228	98.22 282	98.01 320	99.03 161	99.49 174	99.24 270	96.21 262	99.76 278	98.69 138	99.56 230	99.22 238

WTY-MVS		98.59 225	98.37 231	99.26 219	99.43 239	98.40 247	98.74 235	99.13 283	98.10 245	99.21 232	99.24 270	94.82 277	99.90 109	97.86 190	98.77 304	99.49 180

DI_MVS_plusplus_test		98.80 210	98.65 206	99.27 214	99.57 184	98.90 220	98.44 267	97.95 323	99.02 162	99.51 170	99.23 273	96.18 264	99.76 278	98.52 147	99.42 258	99.14 255

CANet		99.11 161	99.05 154	99.28 212	98.83 323	98.56 239	98.71 240	99.41 229	99.25 130	99.23 228	99.22 274	97.66 215	99.94 55	99.19 83	99.97 47	99.33 225

tpm2		96.35 313	96.22 307	96.73 329	98.88 320	91.75 347	99.21 149	98.51 308	93.27 342	97.89 328	99.21 275	84.83 349	99.70 297	96.04 282	98.18 336	98.75 296

WR-MVS		99.11 161	98.93 176	99.66 93	99.30 275	99.42 127	98.42 269	99.37 244	99.04 160	99.57 150	99.20 276	96.89 247	99.86 179	98.66 141	99.87 120	99.70 53

F-COLMAP		98.74 215	98.45 220	99.62 116	99.57 184	99.47 106	98.84 222	99.65 133	96.31 310	98.93 264	99.19 277	97.68 210	99.87 159	96.52 265	99.37 265	99.53 157

1112_ss		99.05 169	98.84 190	99.67 85	99.66 154	99.29 161	98.52 256	99.82 48	97.65 270	99.43 182	99.16 278	96.42 258	99.91 92	99.07 103	99.84 134	99.80 25

ab-mvs-re		8.26 341	11.02 342	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	99.16 278	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

cdsmvs_eth3d_5k		24.88 334	33.17 334	0.00 347	0.00 361	0.00 362	0.00 353	99.62 144	0.00 357	0.00 358	99.13 280	99.82 6	0.00 360	0.00 357	0.00 358	0.00 358

lupinMVS		98.96 187	98.87 185	99.24 224	99.57 184	98.40 247	98.12 291	99.18 277	98.28 238	99.63 132	99.13 280	98.02 186	99.97 16	98.22 165	99.69 206	99.35 222

PVSNet	97.47 15	98.42 241	98.44 221	98.35 288	99.46 232	96.26 308	96.70 345	99.34 249	97.68 269	99.00 254	99.13 280	97.40 224	99.72 291	97.59 210	99.68 208	99.08 270

CLD-MVS		98.76 214	98.57 214	99.33 203	99.57 184	98.97 210	97.53 330	99.55 182	96.41 309	99.27 221	99.13 280	99.07 66	99.78 270	96.73 256	99.89 107	99.23 237
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
1314		98.00 266	97.90 264	98.27 293	98.90 313	97.45 293	99.30 121	99.06 286	94.98 330	97.21 343	99.12 284	98.43 155	99.67 316	95.58 306	98.56 323	97.71 337

E-PMN		97.14 292	97.43 278	96.27 335	98.79 328	91.62 348	95.54 349	99.01 289	99.44 101	98.88 270	99.12 284	92.78 294	99.68 311	94.30 326	99.03 290	97.50 339

CDPH-MVS		98.56 227	98.20 243	99.61 119	99.50 212	99.46 110	98.32 276	99.41 229	95.22 327	99.21 232	99.10 286	98.34 164	99.82 235	95.09 317	99.66 217	99.56 144

conf0.01		97.19 288	96.74 294	98.51 279	99.73 120	98.35 252	99.35 99	95.78 345	96.54 302	99.39 195	99.08 287	86.57 340	99.88 139	95.69 298	98.57 316	97.30 343

conf0.002		97.19 288	96.74 294	98.51 279	99.73 120	98.35 252	99.35 99	95.78 345	96.54 302	99.39 195	99.08 287	86.57 340	99.88 139	95.69 298	98.57 316	97.30 343

thresconf0.02		97.25 283	96.74 294	98.75 269	99.73 120	98.35 252	99.35 99	95.78 345	96.54 302	99.39 195	99.08 287	86.57 340	99.88 139	95.69 298	98.57 316	98.02 326

tfpn_n400		97.25 283	96.74 294	98.75 269	99.73 120	98.35 252	99.35 99	95.78 345	96.54 302	99.39 195	99.08 287	86.57 340	99.88 139	95.69 298	98.57 316	98.02 326

tfpnconf		97.25 283	96.74 294	98.75 269	99.73 120	98.35 252	99.35 99	95.78 345	96.54 302	99.39 195	99.08 287	86.57 340	99.88 139	95.69 298	98.57 316	98.02 326

tfpnview11		97.25 283	96.74 294	98.75 269	99.73 120	98.35 252	99.35 99	95.78 345	96.54 302	99.39 195	99.08 287	86.57 340	99.88 139	95.69 298	98.57 316	98.02 326

MVS		95.72 326	94.63 329	98.99 247	98.56 339	97.98 281	99.30 121	98.86 292	72.71 354	97.30 341	99.08 287	98.34 164	99.74 288	89.21 343	98.33 330	99.26 235

tfpn1000		97.28 282	96.83 291	98.64 276	99.67 153	97.68 288	99.41 83	95.47 352	97.14 291	99.43 182	99.07 294	85.87 347	99.88 139	96.78 252	98.67 312	98.34 312

HPM-MVS++		98.96 187	98.70 202	99.74 55	99.52 203	99.71 52	98.86 218	99.19 276	98.47 216	98.59 295	99.06 295	98.08 182	99.91 92	96.94 244	99.60 227	99.60 124

Fast-Effi-MVS+-dtu		99.20 142	99.12 130	99.43 177	99.25 281	99.69 63	99.05 189	99.82 48	99.50 90	98.97 256	99.05 296	98.98 74	99.98 7	98.20 167	99.24 280	98.62 298

test_prior3		98.62 221	98.34 234	99.46 168	99.35 255	99.22 180	97.95 312	99.39 238	97.87 257	98.05 321	99.05 296	97.90 193	99.69 303	95.99 286	99.49 246	99.48 181

test_prior2							97.95 312		97.87 257	98.05 321	99.05 296	97.90 193		95.99 286	99.49 246

testgi		99.29 116	99.26 112	99.37 196	99.75 111	98.81 229	98.84 222	99.89 15	98.38 223	99.75 91	99.04 299	99.36 33	99.86 179	99.08 102	99.25 278	99.45 192

HSP-MVS		99.01 178	98.76 199	99.76 42	99.78 88	99.73 50	99.35 99	99.31 256	98.54 210	99.54 163	98.99 300	96.81 248	99.93 66	96.97 243	99.53 241	99.61 118

TEST9					99.35 255	99.35 151	98.11 293	99.41 229	94.83 335	97.92 326	98.99 300	98.02 186	99.85 195

train_agg		98.35 249	97.95 259	99.57 137	99.35 255	99.35 151	98.11 293	99.41 229	94.90 331	97.92 326	98.99 300	98.02 186	99.85 195	95.38 312	99.44 251	99.50 174

agg_prior1		98.33 252	97.92 261	99.57 137	99.35 255	99.36 147	97.99 307	99.39 238	94.85 334	97.76 336	98.98 303	98.03 184	99.85 195	95.49 307	99.44 251	99.51 168

PVSNet_Blended		98.70 218	98.59 210	99.02 246	99.54 198	97.99 276	97.58 327	99.82 48	95.70 321	99.34 210	98.98 303	98.52 147	99.77 276	97.98 184	99.83 144	99.30 231

CNLPA		98.57 226	98.34 234	99.28 212	99.18 292	99.10 198	98.34 274	99.41 229	98.48 215	98.52 299	98.98 303	97.05 243	99.78 270	95.59 305	99.50 244	98.96 281

test_8					99.34 265	99.31 157	98.08 298	99.40 235	94.90 331	97.87 330	98.97 306	98.02 186	99.84 211

GA-MVS		97.99 267	97.68 274	98.93 252	99.52 203	98.04 275	97.19 339	99.05 287	98.32 236	98.81 275	98.97 306	89.89 322	99.41 349	98.33 157	99.05 288	99.34 224

agg_prior3		98.24 254	97.81 267	99.53 151	99.34 265	99.26 169	98.09 295	99.39 238	94.21 339	97.77 335	98.96 308	97.74 205	99.84 211	95.38 312	99.44 251	99.50 174

PLC	97.35 16	98.36 246	97.99 255	99.48 163	99.32 270	99.24 176	98.50 258	99.51 203	95.19 329	98.58 296	98.96 308	96.95 246	99.83 227	95.63 304	99.25 278	99.37 217
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Test4		98.65 220	98.44 221	99.27 214	99.57 184	98.86 226	98.43 268	99.41 229	98.85 177	99.57 150	98.95 310	93.05 291	99.75 284	98.57 143	99.56 230	99.19 244

xiu_mvs_v1_base_debu		99.23 128	99.34 92	98.91 253	99.59 172	98.23 261	98.47 261	99.66 124	99.61 75	99.68 113	98.94 311	99.39 24	99.97 16	99.18 85	99.55 236	98.51 305

mvs-test1		98.83 205	98.70 202	99.22 226	98.89 317	99.65 75	98.88 215	99.66 124	99.34 116	98.29 308	98.94 311	97.69 208	99.96 33	98.11 176	98.54 324	98.04 325

Effi-MVS+		99.06 166	98.97 173	99.34 201	99.31 271	98.98 208	98.31 277	99.91 11	98.81 182	98.79 278	98.94 311	99.14 54	99.84 211	98.79 129	98.74 308	99.20 242

xiu_mvs_v1_base		99.23 128	99.34 92	98.91 253	99.59 172	98.23 261	98.47 261	99.66 124	99.61 75	99.68 113	98.94 311	99.39 24	99.97 16	99.18 85	99.55 236	98.51 305

xiu_mvs_v1_base_debi		99.23 128	99.34 92	98.91 253	99.59 172	98.23 261	98.47 261	99.66 124	99.61 75	99.68 113	98.94 311	99.39 24	99.97 16	99.18 85	99.55 236	98.51 305

EMVS		96.96 295	97.28 279	95.99 339	98.76 332	91.03 351	95.26 351	98.61 304	99.34 116	98.92 266	98.88 316	93.79 284	99.66 321	92.87 332	99.05 288	97.30 343

NP-MVS					99.40 246	99.13 193					98.83 317

HQP-MVS		98.36 246	98.02 254	99.39 190	99.31 271	98.94 213	97.98 308	99.37 244	97.45 281	98.15 315	98.83 317	96.67 250	99.70 297	94.73 319	99.67 214	99.53 157

MAR-MVS		98.24 254	97.92 261	99.19 230	98.78 330	99.65 75	99.17 159	99.14 281	95.36 325	98.04 323	98.81 319	97.47 221	99.72 291	95.47 309	99.06 287	98.21 319
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
tfpn_ndepth		96.93 297	96.43 305	98.42 284	99.60 169	97.72 284	99.22 146	95.16 353	95.91 315	99.26 223	98.79 320	85.56 348	99.87 159	96.03 283	98.35 329	97.68 338

API-MVS		98.38 245	98.39 228	98.35 288	98.83 323	99.26 169	99.14 172	99.18 277	98.59 206	98.66 290	98.78 321	98.61 131	99.57 340	94.14 328	99.56 230	96.21 350

testpf		94.48 329	95.31 323	91.99 342	97.22 354	89.64 357	98.86 218	96.52 340	94.36 338	96.09 350	98.76 322	82.21 351	98.73 353	97.05 240	96.74 348	87.60 353

BH-untuned		98.22 257	98.09 250	98.58 277	99.38 250	97.24 296	98.55 251	98.98 290	97.81 263	99.20 237	98.76 322	97.01 244	99.65 328	94.83 318	98.33 330	98.86 289

Fast-Effi-MVS+		99.02 174	98.87 185	99.46 168	99.38 250	99.50 100	99.04 191	99.79 68	97.17 289	98.62 292	98.74 324	99.34 34	99.95 41	98.32 158	99.41 260	98.92 285

MVE	92.54 22	96.66 306	96.11 310	98.31 291	99.68 149	97.55 291	97.94 314	95.60 351	99.37 113	90.68 355	98.70 325	96.56 252	98.61 355	86.94 353	99.55 236	98.77 295
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM		95.61 327	94.71 328	98.31 291	99.12 298	96.63 303	96.66 346	98.46 311	90.77 347	96.25 348	98.68 326	93.01 292	99.69 303	81.60 354	97.86 343	98.62 298

test-LLR		97.15 290	96.95 287	97.74 312	98.18 347	95.02 331	97.38 333	96.10 341	98.00 248	97.81 332	98.58 327	90.04 320	99.91 92	97.69 204	98.78 302	98.31 313

test-mter		96.23 317	95.73 319	97.74 312	98.18 347	95.02 331	97.38 333	96.10 341	97.90 255	97.81 332	98.58 327	79.12 359	99.91 92	97.69 204	98.78 302	98.31 313

PAPM_NR		98.36 246	98.04 253	99.33 203	99.48 223	98.93 217	98.79 232	99.28 263	97.54 278	98.56 298	98.57 329	97.12 240	99.69 303	94.09 329	98.90 295	99.38 213

TESTMET0.1,1		96.24 316	95.84 317	97.41 320	98.24 345	93.84 336	97.38 333	95.84 344	98.43 217	97.81 332	98.56 330	79.77 358	99.89 124	97.77 195	98.77 304	98.52 304

xiu_mvs_v2_base		99.02 174	99.11 133	98.77 267	99.37 252	98.09 272	98.13 290	99.51 203	99.47 96	99.42 184	98.54 331	99.38 28	99.97 16	98.83 126	99.33 269	98.24 317

TR-MVS		97.44 277	97.15 281	98.32 290	98.53 340	97.46 292	98.47 261	97.91 324	96.85 297	98.21 314	98.51 332	96.42 258	99.51 343	92.16 334	97.29 346	97.98 330

PS-MVSNAJ		99.00 181	99.08 143	98.76 268	99.37 252	98.10 271	98.00 305	99.51 203	99.47 96	99.41 190	98.50 333	99.28 39	99.97 16	98.83 126	99.34 267	98.20 321

testus		98.15 259	98.06 252	98.40 286	99.11 301	95.95 313	96.77 343	99.89 15	95.83 317	99.23 228	98.47 334	97.50 220	99.84 211	96.58 263	99.20 283	99.39 210

gm-plane-assit					97.59 351	89.02 358			93.47 341		98.30 335		99.84 211	96.38 270

DeepMVS_CX				97.98 299	99.69 142	96.95 300		99.26 266	75.51 353	95.74 352	98.28 336	96.47 256	99.62 333	91.23 337	97.89 342	97.38 341

PAPR		97.56 276	97.07 282	99.04 244	98.80 327	98.11 270	97.63 324	99.25 269	94.56 337	98.02 324	98.25 337	97.43 223	99.68 311	90.90 338	98.74 308	99.33 225

PMMVS		98.49 234	98.29 237	99.11 235	98.96 310	98.42 246	97.54 328	99.32 252	97.53 279	98.47 304	98.15 338	97.88 196	99.82 235	97.46 216	99.24 280	99.09 267

test0.0.03 1		97.37 279	96.91 289	98.74 273	97.72 350	97.57 290	97.60 326	97.36 338	98.00 248	99.21 232	98.02 339	90.04 320	99.79 262	98.37 153	95.89 352	98.86 289

BH-w/o		97.20 287	97.01 285	97.76 310	99.08 304	95.69 323	98.03 302	98.52 307	95.76 320	97.96 325	98.02 339	95.62 271	99.47 345	92.82 333	97.25 347	98.12 323

alignmvs		98.28 253	97.96 258	99.25 222	99.12 298	98.93 217	99.03 193	98.42 313	99.64 67	98.72 284	97.85 341	90.86 311	99.62 333	98.88 124	99.13 284	99.19 244

test2356		95.99 322	95.26 325	98.18 295	96.93 355	95.53 326	95.31 350	98.71 301	95.67 322	98.48 303	97.83 342	80.72 355	99.88 139	95.47 309	98.21 332	99.11 260

view600		96.86 298	96.52 301	97.88 303	99.69 142	95.87 319	99.39 86	97.68 327	99.11 152	98.96 258	97.82 343	87.40 326	99.79 262	89.78 339	98.83 297	97.98 330

view800		96.86 298	96.52 301	97.88 303	99.69 142	95.87 319	99.39 86	97.68 327	99.11 152	98.96 258	97.82 343	87.40 326	99.79 262	89.78 339	98.83 297	97.98 330

conf0.05thres1000		96.86 298	96.52 301	97.88 303	99.69 142	95.87 319	99.39 86	97.68 327	99.11 152	98.96 258	97.82 343	87.40 326	99.79 262	89.78 339	98.83 297	97.98 330

tfpn		96.86 298	96.52 301	97.88 303	99.69 142	95.87 319	99.39 86	97.68 327	99.11 152	98.96 258	97.82 343	87.40 326	99.79 262	89.78 339	98.83 297	97.98 330

PVSNet_0	95.53 19	95.85 324	95.31 323	97.47 318	98.78 330	93.48 338	95.72 348	99.40 235	96.18 312	97.37 340	97.73 347	95.73 269	99.58 339	95.49 307	81.40 354	99.36 220

canonicalmvs		99.02 174	99.00 166	99.09 237	99.10 303	98.70 233	99.61 60	99.66 124	99.63 70	98.64 291	97.65 348	99.04 70	99.54 341	98.79 129	98.92 293	99.04 277

DWT-MVSNet_test		96.03 321	95.80 318	96.71 331	98.50 341	91.93 344	99.25 138	97.87 325	95.99 314	96.81 345	97.61 349	81.02 354	99.66 321	97.20 234	97.98 341	98.54 303

cascas		96.99 294	96.82 292	97.48 317	97.57 353	95.64 324	96.43 347	99.56 179	91.75 344	97.13 344	97.61 349	95.58 272	98.63 354	96.68 258	99.11 285	98.18 322

IB-MVS	95.41 20	95.30 328	94.46 330	97.84 308	98.76 332	95.33 328	97.33 336	96.07 343	96.02 313	95.37 353	97.41 351	76.17 360	99.96 33	97.54 212	95.44 353	98.22 318
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
thres600view7		96.60 307	96.16 308	97.93 301	99.63 161	96.09 312	99.18 152	97.57 331	98.77 188	98.72 284	97.32 352	87.04 331	99.72 291	88.57 344	98.62 314	97.98 330

tfpn111		96.50 309	96.12 309	97.65 314	99.63 161	95.93 314	99.18 152	97.57 331	98.75 193	98.70 286	97.31 353	87.04 331	99.72 291	88.27 346	98.61 315	97.30 343

conf200view11		96.43 310	96.03 312	97.63 315	99.63 161	95.93 314	99.18 152	97.57 331	98.75 193	98.70 286	97.31 353	87.04 331	99.67 316	87.62 348	98.51 325	97.30 343

thres100view900		96.39 312	96.03 312	97.47 318	99.63 161	95.93 314	99.18 152	97.57 331	98.75 193	98.70 286	97.31 353	87.04 331	99.67 316	87.62 348	98.51 325	96.81 348

GG-mvs-BLEND				97.36 321	97.59 351	96.87 302	99.70 29	88.49 360		94.64 354	97.26 356	80.66 356	99.12 350	91.50 336	96.50 350	96.08 352

PatchFormer-LS_test		96.95 296	97.07 282	96.62 332	98.76 332	91.85 345	99.18 152	98.45 312	97.29 288	97.73 338	97.22 357	88.77 324	99.76 278	98.13 175	98.04 339	98.25 316

tfpn200view9		96.30 315	95.89 314	97.53 316	99.58 175	96.11 310	99.00 198	97.54 336	98.43 217	98.52 299	96.98 358	86.85 335	99.67 316	87.62 348	98.51 325	96.81 348

thres400		96.40 311	95.89 314	97.92 302	99.58 175	96.11 310	99.00 198	97.54 336	98.43 217	98.52 299	96.98 358	86.85 335	99.67 316	87.62 348	98.51 325	97.98 330

thres200		96.09 319	95.68 320	97.33 322	99.48 223	96.22 309	98.53 255	97.57 331	98.06 247	98.37 307	96.73 360	86.84 337	99.61 337	86.99 352	98.57 316	96.16 351

X-MVStestdata		96.09 319	94.87 327	99.75 51	99.71 133	99.71 52	99.37 96	99.61 148	99.29 120	98.76 281	61.30 361	98.47 151	99.88 139	97.62 206	99.73 197	99.67 69

test_post											52.41 362	90.25 318	99.86 179

test_post1							99.14 172				51.63 363	89.54 323	99.82 235	96.86 248

testmvs		28.94 333	33.33 333	15.79 346	26.03 359	9.81 361	96.77 343	15.67 361	11.55 356	23.87 357	50.74 364	19.03 364	8.53 359	23.21 356	33.07 355	29.03 356

test123		29.31 332	33.05 335	18.08 345	25.93 360	12.24 360	97.53 330	10.93 362	11.78 355	24.21 356	50.08 365	21.04 363	8.60 358	23.51 355	32.43 357	33.39 355

GSMVS																99.14 255

test_part2					99.62 166	99.67 68				99.55 160

test_part1								99.53 191				98.40 159			99.68 208	99.66 79

sam_mvs1												90.81 312				99.14 255

sam_mvs												90.52 316

MTGPA								99.53 191

MTMP								98.59 306

test9_res														95.10 316	99.44 251	99.50 174

agg_prior2														94.58 322	99.46 250	99.50 174

agg_prior					99.35 255	99.36 147		99.39 238		97.76 336			99.85 195

test_prior4						99.19 188	98.00 305

test_prior				99.46 168	99.35 255	99.22 180		99.39 238					99.69 303			99.48 181

旧先验2							97.94 314		95.33 326	98.94 263			99.88 139	96.75 254

新几何2							98.04 301

无先验							98.01 303	99.23 273	95.83 317				99.85 195	95.79 295		99.44 197

原ACMM2							97.92 316

testdata2													99.89 124	95.99 286

segment_acmp												98.37 162

testdata1							97.72 323		97.86 260

test12				99.54 150	99.29 276	99.33 154		99.16 279		98.43 305		97.54 218	99.82 235		99.47 248	99.48 181

plane_prior7					99.58 175	99.38 141

plane_prior6					99.47 228	99.26 169						97.24 232

plane_prior5								99.54 186					99.82 235	95.84 293	99.78 175	99.60 124

plane_prior3						99.31 157			98.36 225	99.14 241

plane_prior2							98.80 229		98.94 167

plane_prior1					99.51 207

plane_prior						99.24 176	98.42 269		97.87 257						99.71 203

n2								0.00 363

nn								0.00 363

door-mid								99.83 40

test11								99.29 260

door								99.77 73

HQP5-MVS						98.94 213

HQP-NCC					99.31 271		97.98 308		97.45 281	98.15 315

ACMP_Plane					99.31 271		97.98 308		97.45 281	98.15 315

BP-MVS														94.73 319

HQP4-MVS										98.15 315			99.70 297			99.53 157

HQP3-MVS								99.37 244							99.67 214

HQP2-MVS												96.67 250

MDTV_nov1_ep13_2view						91.44 350	99.14 172		97.37 285	99.21 232		91.78 303		96.75 254		99.03 278

ACMMP++_ref															99.94 78

ACMMP++															99.79 170

Test By Simon												98.41 157

High-resolution multi-view benchmark