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		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 4

UA-Net		98.88 6	98.76 15	99.22 2	99.11 77	97.89 10	99.47 3	99.32 7	99.08 9	97.87 141	99.67 2	96.47 72	99.92 4	97.88 37	99.98 3	99.85 4

pmmvs6		99.07 3	99.24 3	98.56 44	99.81 2	96.38 56	98.87 9	99.30 8	99.01 15	99.63 9	99.66 3	99.27 2	99.68 103	97.75 44	99.89 32	99.62 32

OurMVSNet-221017-0		98.61 18	98.61 26	98.63 40	99.77 3	96.35 57	99.17 5	99.05 38	98.05 43	99.61 11	99.52 4	93.72 166	99.88 18	98.72 20	99.88 33	99.65 25

ANet_high		98.31 32	98.94 7	96.41 181	99.33 47	89.64 218	97.92 56	99.56 4	99.27 5	99.66 8	99.50 5	97.67 24	99.83 29	97.55 52	99.98 3	99.77 9

mvs_tets		98.90 4	98.94 7	98.75 29	99.69 7	96.48 54	98.54 21	99.22 9	96.23 114	99.71 4	99.48 6	98.77 6	99.93 2	98.89 10	99.95 13	99.84 6

gg-mvs-nofinetune		88.28 322	86.96 326	92.23 317	92.84 355	84.44 317	98.19 41	74.60 363	99.08 9	87.01 354	99.47 7	56.93 359	98.23 335	78.91 341	95.61 328	94.01 343

PS-MVSNAJss		98.53 23	98.63 22	98.21 69	99.68 8	94.82 105	98.10 45	99.21 10	96.91 90	99.75 3	99.45 8	95.82 89	99.92 4	98.80 13	99.96 11	99.89 1

test_djsdf		98.73 12	98.74 18	98.69 36	99.63 12	96.30 59	98.67 13	99.02 51	96.50 103	99.32 21	99.44 9	97.43 29	99.92 4	98.73 17	99.95 13	99.86 3

Anonymous20231211		98.55 21	98.76 15	97.94 83	98.79 104	94.37 120	98.84 10	99.15 18	99.37 2	99.67 6	99.43 10	95.61 100	99.72 69	98.12 30	99.86 38	99.73 15

v52		98.85 7	99.01 4	98.37 55	99.61 14	95.53 83	99.01 6	99.04 45	98.48 27	99.31 22	99.41 11	96.82 56	99.87 20	99.44 2	99.95 13	99.70 19

V4		98.85 7	99.01 4	98.37 55	99.61 14	95.53 83	99.01 6	99.04 45	98.48 27	99.31 22	99.41 11	96.81 57	99.87 20	99.44 2	99.95 13	99.70 19

wuykxyi23d		98.68 16	98.53 27	99.13 3	99.44 34	97.97 7	96.85 120	99.02 51	95.81 132	99.88 2	99.38 13	98.14 13	99.69 97	98.32 29	99.95 13	99.73 15

anonymousdsp		98.72 15	98.63 22	98.99 10	99.62 13	97.29 34	98.65 16	99.19 13	95.62 137	99.35 20	99.37 14	97.38 31	99.90 13	98.59 23	99.91 26	99.77 9

jajsoiax		98.77 10	98.79 14	98.74 31	99.66 9	96.48 54	98.45 26	99.12 22	95.83 131	99.67 6	99.37 14	98.25 10	99.92 4	98.77 14	99.94 19	99.82 7

K. test v3		96.44 157	96.28 159	96.95 149	99.41 40	91.53 191	97.65 73	90.31 340	98.89 18	98.93 45	99.36 16	84.57 284	99.92 4	97.81 40	99.56 98	99.39 96

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 34	99.71 6	96.99 39	99.69 2	99.57 3	99.02 14	99.62 10	99.36 16	98.53 7	99.52 165	98.58 24	99.95 13	99.66 24
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
v13		98.02 45	98.52 28	96.51 174	99.02 88	90.14 209	98.07 47	99.09 29	98.10 42	99.13 34	99.35 18	94.84 123	99.74 58	99.12 5	99.98 3	99.65 25

SixPastTwentyTwo		97.49 92	97.57 82	97.26 135	99.56 18	92.33 170	98.28 32	96.97 263	98.30 35	99.45 14	99.35 18	88.43 258	99.89 16	98.01 34	99.76 51	99.54 46

v12		97.97 48	98.47 29	96.46 178	98.98 92	90.01 213	97.97 52	99.08 30	98.00 45	99.11 36	99.34 20	94.70 126	99.73 63	99.07 6	99.98 3	99.64 28

Gipuma		98.07 42	98.31 38	97.36 129	99.76 4	96.28 60	98.51 22	99.10 25	98.76 21	96.79 189	99.34 20	96.61 63	98.82 295	96.38 86	99.50 114	96.98 294
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v11		97.82 69	98.36 35	96.17 199	98.93 94	89.16 236	97.79 63	99.08 30	97.64 63	99.19 31	99.32 22	94.28 145	99.72 69	99.07 6	99.97 8	99.63 30

V9		97.90 60	98.40 33	96.40 182	98.93 94	89.86 215	97.86 59	99.07 34	97.88 49	99.05 38	99.30 23	94.53 137	99.72 69	99.01 8	99.98 3	99.63 30

JIA-IIPM		91.79 288	90.69 299	95.11 243	93.80 346	90.98 197	94.16 260	91.78 326	96.38 107	90.30 341	99.30 23	72.02 337	98.90 285	88.28 280	90.17 346	95.45 331

V14		97.83 66	98.33 37	96.35 183	98.88 100	89.72 216	97.75 67	99.05 38	97.74 53	99.01 40	99.27 25	94.35 142	99.71 80	98.95 9	99.97 8	99.62 32

TransMVSNet (Re)		98.38 29	98.67 19	97.51 111	99.51 26	93.39 156	98.20 40	98.87 83	98.23 37	99.48 12	99.27 25	98.47 8	99.55 157	96.52 81	99.53 107	99.60 35

v15		97.77 72	98.26 41	96.30 188	98.81 102	89.59 223	97.62 76	99.04 45	97.59 65	98.97 44	99.24 27	94.19 150	99.70 88	98.88 11	99.97 8	99.61 34

Baseline_NR-MVSNet		97.72 75	97.79 61	97.50 114	99.56 18	93.29 157	95.44 191	98.86 85	98.20 39	98.37 79	99.24 27	94.69 127	99.55 157	95.98 100	99.79 48	99.65 25

v7n		98.73 12	98.99 6	97.95 82	99.64 11	94.20 128	98.67 13	99.14 20	99.08 9	99.42 16	99.23 29	96.53 66	99.91 12	99.27 4	99.93 21	99.73 15

pm-mvs1		98.47 25	98.67 19	97.86 87	99.52 24	94.58 113	98.28 32	99.00 62	97.57 66	99.27 26	99.22 30	98.32 9	99.50 177	97.09 71	99.75 55	99.50 52

TDRefinement		98.90 4	98.86 10	99.02 8	99.54 22	98.06 6	99.34 4	99.44 6	98.85 19	99.00 42	99.20 31	97.42 30	99.59 145	97.21 64	99.76 51	99.40 92

GBi-Net		96.99 115	96.80 133	97.56 106	97.96 213	93.67 145	98.23 35	98.66 132	95.59 139	97.99 119	99.19 32	89.51 249	99.73 63	94.60 154	99.44 133	99.30 111

test1		96.99 115	96.80 133	97.56 106	97.96 213	93.67 145	98.23 35	98.66 132	95.59 139	97.99 119	99.19 32	89.51 249	99.73 63	94.60 154	99.44 133	99.30 111

FMVSNet1		97.95 52	98.08 47	97.56 106	99.14 75	93.67 145	98.23 35	98.66 132	97.41 81	99.00 42	99.19 32	95.47 105	99.73 63	95.83 104	99.76 51	99.30 111

v17		97.70 77	98.17 43	96.28 191	98.77 107	89.59 223	97.62 76	99.01 60	97.54 68	98.72 56	99.18 35	94.06 154	99.68 103	98.74 16	99.92 23	99.58 37

v16		97.69 78	98.16 44	96.29 190	98.75 108	89.60 221	97.62 76	99.01 60	97.53 70	98.69 58	99.18 35	94.05 155	99.68 103	98.73 17	99.88 33	99.58 37

VDDNet		96.98 118	96.84 130	97.41 126	99.40 41	93.26 158	97.94 54	95.31 292	99.26 6	98.39 78	99.18 35	87.85 265	99.62 128	95.13 137	99.09 192	99.35 105

DSMNet-mixed		92.19 277	91.83 273	93.25 298	96.18 309	83.68 323	96.27 144	93.68 306	76.97 353	92.54 323	99.18 35	89.20 254	98.55 318	83.88 323	98.60 238	97.51 279

Anonymous20240521		98.58 19	98.65 21	98.36 58	99.52 24	95.60 78	98.96 8	98.95 73	98.36 31	99.25 27	99.17 39	95.28 113	99.80 37	98.46 25	99.88 33	99.68 23

v10		97.55 88	97.97 53	96.31 187	98.60 132	89.64 218	97.44 88	99.02 51	96.60 99	98.72 56	99.16 40	93.48 170	99.72 69	98.76 15	99.92 23	99.58 37

v748		98.58 19	98.89 9	97.67 101	99.61 14	93.53 152	98.59 17	98.90 77	98.97 17	99.43 15	99.15 41	96.53 66	99.85 23	98.88 11	99.91 26	99.64 28

MIMVSNet1		98.51 24	98.45 32	98.67 37	99.72 5	96.71 45	98.76 11	98.89 79	98.49 26	99.38 18	99.14 42	95.44 107	99.84 27	96.47 84	99.80 47	99.47 66

v18		97.60 85	98.06 49	96.23 192	98.68 124	89.46 227	97.48 87	98.98 68	97.33 84	98.60 62	99.13 43	93.86 158	99.67 109	98.62 21	99.87 36	99.56 42

Vis-MVSNet		98.27 33	98.34 36	98.07 74	99.33 47	95.21 95	98.04 49	99.46 5	97.32 85	97.82 145	99.11 44	96.75 59	99.86 22	97.84 39	99.36 155	99.15 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v8		97.60 85	98.06 49	96.23 192	98.71 116	89.44 228	97.43 89	98.82 101	97.29 86	98.74 54	99.10 45	93.86 158	99.68 103	98.61 22	99.94 19	99.56 42

MVS-HIRNet		88.40 321	90.20 307	82.99 345	97.01 287	60.04 363	93.11 299	85.61 358	84.45 325	88.72 348	99.09 46	84.72 283	98.23 335	82.52 328	96.59 315	90.69 355

ACMH	93.61 9	98.44 26	98.76 15	97.51 111	99.43 37	93.54 151	98.23 35	99.05 38	97.40 82	99.37 19	99.08 47	98.79 5	99.47 184	97.74 45	99.71 64	99.50 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DTE-MVSNet		98.79 9	98.86 10	98.59 41	99.55 20	96.12 64	98.48 25	99.10 25	99.36 3	99.29 25	99.06 48	97.27 36	99.93 2	97.71 46	99.91 26	99.70 19

PEN-MVS		98.75 11	98.85 12	98.44 50	99.58 17	95.67 76	98.45 26	99.15 18	99.33 4	99.30 24	99.00 49	97.27 36	99.92 4	97.64 47	99.92 23	99.75 13

DeepC-MVS	95.41 4	97.82 69	97.70 67	98.16 70	98.78 106	95.72 73	96.23 149	99.02 51	93.92 202	98.62 59	98.99 50	97.69 22	99.62 128	96.18 90	99.87 36	99.15 135
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VPA-MVSNet		98.27 33	98.46 30	97.70 97	99.06 82	93.80 141	97.76 66	99.00 62	98.40 30	99.07 37	98.98 51	96.89 49	99.75 53	97.19 67	99.79 48	99.55 45

lessismore_v0				97.05 144	99.36 45	92.12 178		84.07 360		98.77 53	98.98 51	85.36 278	99.74 58	97.34 61	99.37 152	99.30 111

testing_2		97.43 95	97.71 66	96.60 166	98.91 97	90.85 199	96.01 159	98.54 146	94.78 174	98.78 50	98.96 53	96.35 76	99.54 159	97.25 62	99.82 43	99.40 92

PS-CasMVS		98.73 12	98.85 12	98.39 54	99.55 20	95.47 85	98.49 23	99.13 21	99.22 7	99.22 30	98.96 53	97.35 32	99.92 4	97.79 42	99.93 21	99.79 8

EU-MVSNet		94.25 233	94.47 220	93.60 289	98.14 196	82.60 325	97.24 96	92.72 319	85.08 319	98.48 71	98.94 55	82.59 288	98.76 301	97.47 58	99.53 107	99.44 81

LCM-MVSNet-Re		97.33 104	97.33 94	97.32 131	98.13 199	93.79 142	96.99 112	99.65 2	96.74 97	99.47 13	98.93 56	96.91 48	99.84 27	90.11 253	99.06 198	98.32 232

XXY-MVS		97.54 89	97.70 67	97.07 143	99.46 32	92.21 174	97.22 97	99.00 62	94.93 170	98.58 64	98.92 57	97.31 34	99.41 210	94.44 157	99.43 140	99.59 36

mvs_anonymous		95.36 198	96.07 166	93.21 299	96.29 304	81.56 328	94.60 242	97.66 233	93.30 218	96.95 184	98.91 58	93.03 184	99.38 224	96.60 78	97.30 303	98.69 201

UGNet		96.81 137	96.56 144	97.58 105	96.64 296	93.84 140	97.75 67	97.12 258	96.47 106	93.62 296	98.88 59	93.22 179	99.53 161	95.61 114	99.69 68	99.36 104
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
Anonymous20240529		97.96 49	98.04 51	97.71 95	98.69 122	94.28 125	97.86 59	98.31 183	98.79 20	99.23 29	98.86 60	95.76 96	99.61 134	95.49 118	99.36 155	99.23 124

FC-MVSNet-test		98.16 37	98.37 34	97.56 106	99.49 30	93.10 160	98.35 29	99.21 10	98.43 29	98.89 46	98.83 61	94.30 144	99.81 32	97.87 38	99.91 26	99.77 9

new-patchmatchnet		95.67 181	96.58 142	92.94 307	97.48 261	80.21 334	92.96 300	98.19 196	94.83 172	98.82 48	98.79 62	93.31 177	99.51 175	95.83 104	99.04 199	99.12 143

WR-MVS_H		98.65 17	98.62 24	98.75 29	99.51 26	96.61 50	98.55 20	99.17 14	99.05 12	99.17 33	98.79 62	95.47 105	99.89 16	97.95 35	99.91 26	99.75 13

ab-mvs		96.59 150	96.59 141	96.60 166	98.64 125	92.21 174	98.35 29	97.67 231	94.45 183	96.99 177	98.79 62	94.96 121	99.49 179	90.39 250	99.07 195	98.08 250

EG-PatchMatch MVS		97.69 78	97.79 61	97.40 127	99.06 82	93.52 153	95.96 166	98.97 70	94.55 182	98.82 48	98.76 65	97.31 34	99.29 241	97.20 66	99.44 133	99.38 98

no-one		94.84 215	94.76 208	95.09 245	98.29 163	87.49 276	91.82 321	97.49 243	88.21 287	97.84 144	98.75 66	91.51 223	99.27 244	88.96 270	99.99 2	98.52 213

nrg030		98.54 22	98.62 24	98.32 61	99.22 56	95.66 77	97.90 57	99.08 30	98.31 34	99.02 39	98.74 67	97.68 23	99.61 134	97.77 43	99.85 40	99.70 19

VDD-MVS		97.37 100	97.25 99	97.74 94	98.69 122	94.50 116	97.04 110	95.61 289	98.59 24	98.51 68	98.72 68	92.54 199	99.58 147	96.02 97	99.49 121	99.12 143

PatchT		93.75 248	93.57 244	94.29 273	95.05 330	87.32 281	96.05 156	92.98 314	97.54 68	94.25 272	98.72 68	75.79 317	99.24 248	95.92 102	95.81 322	96.32 318

RPSCF		97.87 63	97.51 86	98.95 14	99.15 67	98.43 3	97.56 82	99.06 36	96.19 115	98.48 71	98.70 70	94.72 125	99.24 248	94.37 162	99.33 166	99.17 131

APDe-MVS		98.14 38	98.03 52	98.47 49	98.72 113	96.04 66	98.07 47	99.10 25	95.96 124	98.59 63	98.69 71	96.94 47	99.81 32	96.64 77	99.58 92	99.57 41

IterMVS-LS		96.92 125	97.29 96	95.79 222	98.51 146	88.13 259	95.10 217	98.66 132	96.99 87	98.46 74	98.68 72	92.55 197	99.74 58	96.91 75	99.79 48	99.50 52
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpnnormal		97.72 75	97.97 53	96.94 150	99.26 51	92.23 173	97.83 62	98.45 154	98.25 36	99.13 34	98.66 73	96.65 61	99.69 97	93.92 180	99.62 80	98.91 175

FIs		97.93 56	98.07 48	97.48 118	99.38 43	92.95 162	98.03 51	99.11 23	98.04 44	98.62 59	98.66 73	93.75 165	99.78 40	97.23 63	99.84 41	99.73 15

CP-MVSNet		98.42 27	98.46 30	98.30 64	99.46 32	95.22 93	98.27 34	98.84 89	99.05 12	99.01 40	98.65 75	95.37 108	99.90 13	97.57 51	99.91 26	99.77 9

FMVSNet2		96.72 143	96.67 139	96.87 155	97.96 213	91.88 185	97.15 98	98.06 210	95.59 139	98.50 70	98.62 76	89.51 249	99.65 116	94.99 142	99.60 88	99.07 153

PMVS	89.60 17	96.71 145	96.97 123	95.95 215	99.51 26	97.81 13	97.42 90	97.49 243	97.93 47	95.95 226	98.58 77	96.88 51	96.91 349	89.59 260	99.36 155	93.12 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CR-MVSNet		93.29 259	92.79 257	94.78 255	95.44 325	88.15 257	96.18 151	97.20 253	84.94 321	94.10 277	98.57 78	77.67 304	99.39 219	95.17 132	95.81 322	96.81 302

Patchmtry		95.03 211	94.59 215	96.33 185	94.83 332	90.82 201	96.38 139	97.20 253	96.59 100	97.49 153	98.57 78	77.67 304	99.38 224	92.95 200	99.62 80	98.80 191

ambc				96.56 173	98.23 180	91.68 190	97.88 58	98.13 202		98.42 77	98.56 80	94.22 149	99.04 269	94.05 175	99.35 159	98.95 165

3Dnovator	96.53 2	97.61 84	97.64 74	97.50 114	97.74 242	93.65 149	98.49 23	98.88 81	96.86 94	97.11 171	98.55 81	95.82 89	99.73 63	95.94 101	99.42 143	99.13 138

semantic-postprocess				94.85 253	97.68 247	85.53 296		97.63 239	96.99 87	98.36 81	98.54 82	87.44 267	99.75 53	97.07 72	99.08 193	99.27 121

COLMAP_ROB	94.48 6	98.25 35	98.11 46	98.64 39	99.21 59	97.35 32	97.96 53	99.16 15	98.34 33	98.78 50	98.52 83	97.32 33	99.45 193	94.08 171	99.67 74	99.13 138
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	93.58 10	98.23 36	98.31 38	97.98 81	99.39 42	95.22 93	97.55 83	99.20 12	98.21 38	99.25 27	98.51 84	98.21 11	99.40 213	94.79 148	99.72 60	99.32 107

RPMNet		94.22 234	94.03 236	94.78 255	95.44 325	88.15 257	96.18 151	93.73 303	97.43 73	94.10 277	98.49 85	79.40 297	99.39 219	95.69 107	95.81 322	96.81 302

IterMVS		95.42 195	95.83 175	94.20 274	97.52 259	83.78 322	92.41 312	97.47 248	95.49 143	98.06 114	98.49 85	87.94 261	99.58 147	96.02 97	99.02 200	99.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		97.87 63	97.89 57	97.81 90	98.62 130	94.82 105	97.13 101	98.79 103	98.98 16	98.74 54	98.49 85	95.80 95	99.49 179	95.04 141	99.44 133	99.11 146

TranMVSNet+NR-MVSNet		98.33 30	98.30 40	98.43 51	99.07 81	95.87 69	96.73 126	99.05 38	98.67 22	98.84 47	98.45 88	97.58 26	99.88 18	96.45 85	99.86 38	99.54 46

3Dnovator+	96.13 3	97.73 74	97.59 80	98.15 71	98.11 200	95.60 78	98.04 49	98.70 124	98.13 40	96.93 185	98.45 88	95.30 112	99.62 128	95.64 112	98.96 204	99.24 123

VPNet		97.26 108	97.49 88	96.59 169	99.47 31	90.58 205	96.27 144	98.53 147	97.77 51	98.46 74	98.41 90	94.59 133	99.68 103	94.61 153	99.29 172	99.52 49

test_0402		97.84 65	97.97 53	97.47 119	99.19 62	94.07 131	96.71 127	98.73 114	98.66 23	98.56 65	98.41 90	96.84 54	99.69 97	94.82 145	99.81 44	98.64 203

v1240		96.74 140	97.02 122	95.91 218	98.18 189	88.52 251	95.39 199	98.88 81	93.15 226	98.46 74	98.40 92	92.80 189	99.71 80	98.45 26	99.49 121	99.49 60

v7		96.93 123	97.17 109	96.23 192	98.59 134	89.64 218	95.96 166	98.66 132	94.41 186	97.87 141	98.38 93	93.47 171	99.64 119	97.93 36	99.24 177	99.43 85

ACMMP_Plus		97.89 61	97.63 76	98.67 37	99.35 46	96.84 42	96.36 140	98.79 103	95.07 166	97.88 136	98.35 94	97.24 39	99.72 69	96.05 94	99.58 92	99.45 73

v1192		96.83 135	97.06 120	96.15 200	98.28 166	89.29 233	95.36 201	98.77 107	93.73 211	98.11 105	98.34 95	93.02 186	99.67 109	98.35 27	99.58 92	99.50 52

casdiffmvs		96.43 159	96.38 154	96.60 166	97.51 260	91.95 184	97.08 109	98.41 163	93.69 213	93.95 285	98.34 95	93.03 184	99.45 193	98.09 32	97.30 303	98.39 223

SMA-MVS		97.46 94	97.09 117	98.58 42	98.73 110	96.67 47	96.74 124	98.73 114	91.61 255	98.48 71	98.33 97	96.53 66	99.66 114	95.15 135	99.54 104	99.40 92

pmmvs-eth3d		96.49 154	96.18 161	97.42 125	98.25 177	94.29 122	94.77 238	98.07 209	89.81 273	97.97 123	98.33 97	93.11 180	99.08 265	95.46 120	99.84 41	98.89 179

PM-MVS		97.36 103	97.10 115	98.14 72	98.91 97	96.77 44	96.20 150	98.63 139	93.82 209	98.54 66	98.33 97	93.98 156	99.05 268	95.99 99	99.45 132	98.61 207

MP-MVS-pluss		97.69 78	97.36 92	98.70 35	99.50 29	96.84 42	95.38 200	98.99 65	92.45 243	98.11 105	98.31 100	97.25 38	99.77 48	96.60 78	99.62 80	99.48 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1144		96.84 132	97.08 118	96.13 204	98.42 156	89.28 234	95.41 198	98.67 130	94.21 195	97.97 123	98.31 100	93.06 181	99.65 116	98.06 33	99.62 80	99.45 73

LFMVS		95.32 200	94.88 203	96.62 165	98.03 204	91.47 193	97.65 73	90.72 335	99.11 8	97.89 134	98.31 100	79.20 298	99.48 182	93.91 181	99.12 190	98.93 171

V42		97.04 113	97.16 110	96.68 164	98.59 134	91.05 196	96.33 142	98.36 169	94.60 178	97.99 119	98.30 103	93.32 176	99.62 128	97.40 60	99.53 107	99.38 98

v144192		96.69 146	96.90 128	96.03 210	98.25 177	88.92 242	95.49 189	98.77 107	93.05 228	98.09 109	98.29 104	92.51 201	99.70 88	98.11 31	99.56 98	99.47 66

MVS_Test		96.27 162	96.79 135	94.73 257	96.94 291	86.63 290	96.18 151	98.33 174	94.94 168	96.07 223	98.28 105	95.25 114	99.26 246	97.21 64	97.90 268	98.30 235

FMVSNet5		93.39 257	92.35 263	96.50 175	95.83 318	90.81 203	97.31 91	98.27 184	92.74 238	96.27 215	98.28 105	62.23 356	99.67 109	90.86 232	99.36 155	99.03 157

abl_6		98.42 27	98.19 42	99.09 4	99.16 64	98.10 5	97.73 71	99.11 23	97.76 52	98.62 59	98.27 107	97.88 20	99.80 37	95.67 108	99.50 114	99.38 98

v1921920		96.72 143	96.96 125	95.99 211	98.21 182	88.79 248	95.42 196	98.79 103	93.22 220	98.19 98	98.26 108	92.68 192	99.70 88	98.34 28	99.55 102	99.49 60

diffmvs		96.10 169	96.43 152	95.12 242	96.52 300	87.85 269	95.95 169	97.91 214	96.52 102	93.02 312	98.25 109	94.28 145	99.28 242	97.11 70	98.26 252	98.24 241

v2v482		96.78 139	97.06 120	95.95 215	98.57 137	88.77 249	95.36 201	98.26 186	95.18 157	97.85 143	98.23 110	92.58 196	99.63 122	97.80 41	99.69 68	99.45 73

LPG-MVS_test		97.94 54	97.67 70	98.74 31	99.15 67	97.02 37	97.09 107	99.02 51	95.15 159	98.34 83	98.23 110	97.91 18	99.70 88	94.41 159	99.73 57	99.50 52

LGP-MVS_train				98.74 31	99.15 67	97.02 37		99.02 51	95.15 159	98.34 83	98.23 110	97.91 18	99.70 88	94.41 159	99.73 57	99.50 52

HPM-MVS_fast		98.32 31	98.13 45	98.88 22	99.54 22	97.48 27	98.35 29	99.03 50	95.88 127	97.88 136	98.22 113	98.15 12	99.74 58	96.50 83	99.62 80	99.42 87

MIMVSNet		93.42 256	92.86 255	95.10 244	98.17 191	88.19 256	98.13 44	93.69 304	92.07 246	95.04 249	98.21 114	80.95 293	99.03 272	81.42 334	98.06 259	98.07 252

v1141		96.86 129	97.14 112	96.04 207	98.55 139	89.06 239	95.44 191	98.33 174	95.14 161	97.93 129	98.19 115	93.36 174	99.62 128	97.61 48	99.69 68	99.44 81

divwei89l23v2f112		96.86 129	97.14 112	96.04 207	98.54 142	89.06 239	95.44 191	98.33 174	95.14 161	97.93 129	98.19 115	93.36 174	99.61 134	97.61 48	99.68 72	99.44 81

EI-MVSNet		96.63 149	96.93 126	95.74 223	97.26 278	88.13 259	95.29 208	97.65 235	96.99 87	97.94 126	98.19 115	92.55 197	99.58 147	96.91 75	99.56 98	99.50 52

CVMVSNet		92.33 275	92.79 257	90.95 327	97.26 278	75.84 349	95.29 208	92.33 322	81.86 332	96.27 215	98.19 115	81.44 290	98.46 322	94.23 169	98.29 247	98.55 212

v1		96.86 129	97.14 112	96.04 207	98.55 139	89.06 239	95.44 191	98.33 174	95.14 161	97.94 126	98.18 119	93.39 173	99.61 134	97.61 48	99.69 68	99.44 81

PVSNet_Blended_VisFu		95.95 174	95.80 176	96.42 180	99.28 50	90.62 204	95.31 206	99.08 30	88.40 284	96.97 183	98.17 120	92.11 209	99.78 40	93.64 187	99.21 179	98.86 186

v1neww		96.97 119	97.24 101	96.15 200	98.70 118	89.44 228	95.97 162	98.33 174	95.25 151	97.88 136	98.15 121	93.83 161	99.61 134	97.50 55	99.50 114	99.41 89

v7new		96.97 119	97.24 101	96.15 200	98.70 118	89.44 228	95.97 162	98.33 174	95.25 151	97.88 136	98.15 121	93.83 161	99.61 134	97.50 55	99.50 114	99.41 89

v6		96.97 119	97.24 101	96.15 200	98.71 116	89.44 228	95.97 162	98.33 174	95.25 151	97.89 134	98.15 121	93.86 158	99.61 134	97.51 54	99.50 114	99.42 87

EI-MVSNet-UG-set		97.32 105	97.40 90	97.09 142	97.34 274	92.01 182	95.33 204	97.65 235	97.74 53	98.30 90	98.14 124	95.04 119	99.69 97	97.55 52	99.52 111	99.58 37

APD-MVS_3200maxsize		98.13 40	97.90 56	98.79 27	98.79 104	97.31 33	97.55 83	98.92 75	97.72 57	98.25 93	98.13 125	97.10 42	99.75 53	95.44 121	99.24 177	99.32 107

QAPM		95.88 177	95.57 182	96.80 156	97.90 218	91.84 187	98.18 42	98.73 114	88.41 283	96.42 203	98.13 125	94.73 124	99.75 53	88.72 273	98.94 208	98.81 190

ACMM	93.33 11	98.05 43	97.79 61	98.85 23	99.15 67	97.55 23	96.68 128	98.83 97	95.21 154	98.36 81	98.13 125	98.13 15	99.62 128	96.04 95	99.54 104	99.39 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EI-MVSNet-Vis-set		97.32 105	97.39 91	97.11 140	97.36 270	92.08 180	95.34 203	97.65 235	97.74 53	98.29 91	98.11 128	95.05 117	99.68 103	97.50 55	99.50 114	99.56 42

wuyk23d		93.25 260	95.20 191	87.40 341	96.07 313	95.38 86	97.04 110	94.97 293	95.33 148	99.70 5	98.11 128	98.14 13	91.94 357	77.76 346	99.68 72	74.89 357

ESAPD		97.64 81	97.35 93	98.50 46	98.85 101	96.18 61	95.21 214	98.99 65	95.84 130	98.78 50	98.08 130	96.84 54	99.81 32	93.98 178	99.57 95	99.52 49

SD-MVS		97.37 100	97.70 67	96.35 183	98.14 196	95.13 96	96.54 130	98.92 75	95.94 125	99.19 31	98.08 130	97.74 21	95.06 355	95.24 128	99.54 104	98.87 185

OPM-MVS		97.54 89	97.25 99	98.41 52	99.11 77	96.61 50	95.24 212	98.46 153	94.58 181	98.10 108	98.07 132	97.09 43	99.39 219	95.16 133	99.44 133	99.21 126

AllTest		97.20 111	96.92 127	98.06 75	99.08 79	96.16 62	97.14 100	99.16 15	94.35 190	97.78 146	98.07 132	95.84 86	99.12 258	91.41 220	99.42 143	98.91 175

TestCases				98.06 75	99.08 79	96.16 62		99.16 15	94.35 190	97.78 146	98.07 132	95.84 86	99.12 258	91.41 220	99.42 143	98.91 175

TSAR-MVS + MP.		97.42 96	97.23 105	98.00 80	99.38 43	95.00 99	97.63 75	98.20 192	93.00 229	98.16 100	98.06 135	95.89 84	99.72 69	95.67 108	99.10 191	99.28 118

EPP-MVSNet		96.84 132	96.58 142	97.65 102	99.18 63	93.78 143	98.68 12	96.34 274	97.91 48	97.30 163	98.06 135	88.46 257	99.85 23	93.85 182	99.40 150	99.32 107

ACMMP		98.05 43	97.75 65	98.93 18	99.23 55	97.60 19	98.09 46	98.96 71	95.75 134	97.91 131	98.06 135	96.89 49	99.76 49	95.32 125	99.57 95	99.43 85
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
Anonymous202405211		96.34 161	95.98 170	97.43 124	98.25 177	93.85 139	96.74 124	94.41 299	97.72 57	98.37 79	98.03 138	87.15 270	99.53 161	94.06 172	99.07 195	98.92 174

XVG-ACMP-BASELINE		97.58 87	97.28 98	98.49 47	99.16 64	96.90 41	96.39 135	98.98 68	95.05 167	98.06 114	98.02 139	95.86 85	99.56 153	94.37 162	99.64 78	99.00 159

PVSNet_BlendedMVS		95.02 212	94.93 201	95.27 238	97.79 236	87.40 279	94.14 262	98.68 127	88.94 279	94.51 267	98.01 140	93.04 182	99.30 238	89.77 258	99.49 121	99.11 146

OpenMVS	94.22 8	95.48 190	95.20 191	96.32 186	97.16 283	91.96 183	97.74 69	98.84 89	87.26 295	94.36 271	98.01 140	93.95 157	99.67 109	90.70 241	98.75 224	97.35 288

MVSTER		94.21 237	93.93 239	95.05 247	95.83 318	86.46 291	95.18 215	97.65 235	92.41 244	97.94 126	98.00 142	72.39 335	99.58 147	96.36 87	99.56 98	99.12 143

IS-MVSNet		96.93 123	96.68 138	97.70 97	99.25 54	94.00 134	98.57 18	96.74 271	98.36 31	98.14 103	97.98 143	88.23 259	99.71 80	93.10 197	99.72 60	99.38 98

zzz-MVS		98.01 47	97.66 71	99.06 5	99.44 34	97.90 8	95.66 182	98.73 114	97.69 60	97.90 132	97.96 144	95.81 93	99.82 30	96.13 91	99.61 85	99.45 73

MTAPA		98.14 38	97.84 59	99.06 5	99.44 34	97.90 8	97.25 94	98.73 114	97.69 60	97.90 132	97.96 144	95.81 93	99.82 30	96.13 91	99.61 85	99.45 73

v148		96.58 151	96.97 123	95.42 234	98.63 129	87.57 274	95.09 219	97.90 216	95.91 126	98.24 94	97.96 144	93.42 172	99.39 219	96.04 95	99.52 111	99.29 117

MDA-MVSNet-bldmvs		95.69 179	95.67 179	95.74 223	98.48 150	88.76 250	92.84 301	97.25 251	96.00 122	97.59 148	97.95 147	91.38 226	99.46 189	93.16 196	96.35 318	98.99 162

PGM-MVS		97.88 62	97.52 85	98.96 13	99.20 60	97.62 18	97.09 107	99.06 36	95.45 144	97.55 149	97.94 148	97.11 41	99.78 40	94.77 150	99.46 128	99.48 63

LS3D		97.77 72	97.50 87	98.57 43	96.24 305	97.58 21	98.45 26	98.85 86	98.58 25	97.51 151	97.94 148	95.74 97	99.63 122	95.19 130	98.97 203	98.51 214

USDC		94.56 227	94.57 218	94.55 266	97.78 240	86.43 292	92.75 304	98.65 138	85.96 308	96.91 186	97.93 150	90.82 232	98.74 302	90.71 240	99.59 90	98.47 216

test20.03		96.58 151	96.61 140	96.48 177	98.49 148	91.72 189	95.68 181	97.69 230	96.81 95	98.27 92	97.92 151	94.18 151	98.71 305	90.78 236	99.66 76	99.00 159

FMVSNet3		95.26 204	94.94 199	96.22 196	96.53 299	90.06 210	95.99 160	97.66 233	94.11 199	97.99 119	97.91 152	80.22 296	99.63 122	94.60 154	99.44 133	98.96 164

Regformer-3		97.25 109	97.29 96	97.11 140	97.35 271	92.32 171	95.26 210	97.62 240	97.67 62	98.17 99	97.89 153	95.05 117	99.56 153	97.16 68	99.42 143	99.46 68

Regformer-4		97.53 91	97.47 89	97.71 95	97.35 271	93.91 136	95.26 210	98.14 201	97.97 46	98.34 83	97.89 153	95.49 103	99.71 80	97.41 59	99.42 143	99.51 51

SteuartSystems-ACMMP		98.02 45	97.76 64	98.79 27	99.43 37	97.21 36	97.15 98	98.90 77	96.58 101	98.08 111	97.87 155	97.02 46	99.76 49	95.25 127	99.59 90	99.40 92
Skip Steuart: Steuart Systems R&D Blog.
DU-MVS		97.79 71	97.60 79	98.36 58	98.73 110	95.78 71	95.65 184	98.87 83	97.57 66	98.31 88	97.83 156	94.69 127	99.85 23	97.02 73	99.71 64	99.46 68

NR-MVSNet		97.96 49	97.86 58	98.26 66	98.73 110	95.54 81	98.14 43	98.73 114	97.79 50	99.42 16	97.83 156	94.40 141	99.78 40	95.91 103	99.76 51	99.46 68

CHOSEN 1792x2688		94.10 241	93.41 246	96.18 198	99.16 64	90.04 211	92.15 315	98.68 127	79.90 342	96.22 218	97.83 156	87.92 264	99.42 199	89.18 266	99.65 77	99.08 151

TAMVS		95.49 188	94.94 199	97.16 137	98.31 161	93.41 155	95.07 222	96.82 268	91.09 261	97.51 151	97.82 159	89.96 243	99.42 199	88.42 278	99.44 133	98.64 203

UniMVSNet (Re)		97.83 66	97.65 72	98.35 60	98.80 103	95.86 70	95.92 171	99.04 45	97.51 71	98.22 95	97.81 160	94.68 129	99.78 40	97.14 69	99.75 55	99.41 89

VNet		96.84 132	96.83 131	96.88 154	98.06 202	92.02 181	96.35 141	97.57 242	97.70 59	97.88 136	97.80 161	92.40 204	99.54 159	94.73 152	98.96 204	99.08 151

YYNet1		94.73 218	94.84 205	94.41 269	97.47 265	85.09 304	90.29 337	95.85 284	92.52 240	97.53 150	97.76 162	91.97 213	99.18 253	93.31 191	96.86 308	98.95 165

MDA-MVSNet_test_wron		94.73 218	94.83 207	94.42 268	97.48 261	85.15 302	90.28 338	95.87 282	92.52 240	97.48 156	97.76 162	91.92 217	99.17 256	93.32 190	96.80 311	98.94 167

TinyColmap		96.00 173	96.34 157	94.96 248	97.90 218	87.91 267	94.13 263	98.49 151	94.41 186	98.16 100	97.76 162	96.29 78	98.68 310	90.52 245	99.42 143	98.30 235

Patchmatch-RL test		94.66 222	94.49 219	95.19 240	98.54 142	88.91 243	92.57 308	98.74 113	91.46 258	98.32 86	97.75 165	77.31 309	98.81 297	96.06 93	99.61 85	97.85 266

MP-MVS		97.64 81	97.18 108	99.00 9	99.32 49	97.77 14	97.49 86	98.73 114	96.27 111	95.59 239	97.75 165	96.30 77	99.78 40	93.70 186	99.48 124	99.45 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMP	92.54 13	97.47 93	97.10 115	98.55 45	99.04 85	96.70 46	96.24 148	98.89 79	93.71 212	97.97 123	97.75 165	97.44 28	99.63 122	93.22 194	99.70 67	99.32 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVP-Stereo		95.69 179	95.28 189	96.92 151	98.15 195	93.03 161	95.64 186	98.20 192	90.39 267	96.63 194	97.73 168	91.63 221	99.10 263	91.84 213	97.31 302	98.63 205
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
testmv		95.51 186	95.33 188	96.05 206	98.23 180	89.51 226	93.50 288	98.63 139	94.25 193	98.22 95	97.73 168	92.51 201	99.47 184	85.22 314	99.72 60	99.17 131

mPP-MVS		97.91 59	97.53 84	99.04 7	99.22 56	97.87 11	97.74 69	98.78 106	96.04 120	97.10 172	97.73 168	96.53 66	99.78 40	95.16 133	99.50 114	99.46 68

XVG-OURS		97.12 112	96.74 136	98.26 66	98.99 90	97.45 29	93.82 276	99.05 38	95.19 156	98.32 86	97.70 171	95.22 115	98.41 324	94.27 167	98.13 257	98.93 171

UniMVSNet_NR-MVSNet		97.83 66	97.65 72	98.37 55	98.72 113	95.78 71	95.66 182	99.02 51	98.11 41	98.31 88	97.69 172	94.65 131	99.85 23	97.02 73	99.71 64	99.48 63

XVS		97.96 49	97.63 76	98.94 15	99.15 67	97.66 16	97.77 64	98.83 97	97.42 74	96.32 211	97.64 173	96.49 70	99.72 69	95.66 110	99.37 152	99.45 73

ACMMPR		97.95 52	97.62 78	98.94 15	99.20 60	97.56 22	97.59 80	98.83 97	96.05 118	97.46 158	97.63 174	96.77 58	99.76 49	95.61 114	99.46 128	99.49 60

Anonymous20231206		95.27 203	95.06 197	95.88 219	98.72 113	89.37 232	95.70 178	97.85 219	88.00 291	96.98 178	97.62 175	91.95 214	99.34 231	89.21 265	99.53 107	98.94 167

region2R		97.92 57	97.59 80	98.92 19	99.22 56	97.55 23	97.60 79	98.84 89	96.00 122	97.22 165	97.62 175	96.87 52	99.76 49	95.48 119	99.43 140	99.46 68

ESAPD.		40.70 338	54.26 338	0.00 353	99.03 86	0.00 368	0.00 359	98.84 89	94.84 171	98.08 111	97.60 177	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

ppachtmachnet_test		94.49 229	94.84 205	93.46 293	96.16 310	82.10 327	90.59 334	97.48 245	90.53 266	97.01 176	97.59 178	91.01 229	99.36 228	93.97 179	99.18 183	98.94 167

APD-MVS		97.00 114	96.53 148	98.41 52	98.55 139	96.31 58	96.32 143	98.77 107	92.96 235	97.44 159	97.58 179	95.84 86	99.74 58	91.96 208	99.35 159	99.19 128
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		97.94 54	97.64 74	98.83 24	99.15 67	97.50 25	97.59 80	98.84 89	96.05 118	97.49 153	97.54 180	97.07 44	99.70 88	95.61 114	99.46 128	99.30 111

#test#		97.62 83	97.22 106	98.83 24	99.15 67	97.50 25	96.81 122	98.84 89	94.25 193	97.49 153	97.54 180	97.07 44	99.70 88	94.37 162	99.46 128	99.30 111

UnsupCasMVSNet_eth		95.91 175	95.73 178	96.44 179	98.48 150	91.52 192	95.31 206	98.45 154	95.76 133	97.48 156	97.54 180	89.53 248	98.69 307	94.43 158	94.61 334	99.13 138

XVG-OURS-SEG-HR		97.38 99	97.07 119	98.30 64	99.01 89	97.41 31	94.66 240	99.02 51	95.20 155	98.15 102	97.52 183	98.83 4	98.43 323	94.87 143	96.41 317	99.07 153

MG-MVS		94.08 243	94.00 237	94.32 271	97.09 285	85.89 293	93.19 298	95.96 280	92.52 240	94.93 252	97.51 184	89.54 246	98.77 300	87.52 296	97.71 282	98.31 233

Regformer-1		97.27 107	97.16 110	97.61 104	97.21 280	93.86 138	94.85 234	98.04 212	97.62 64	98.03 117	97.50 185	95.34 109	99.63 122	96.52 81	99.31 168	99.35 105

Regformer-2		97.41 97	97.24 101	97.93 84	97.21 280	94.72 108	94.85 234	98.27 184	97.74 53	98.11 105	97.50 185	95.58 101	99.69 97	96.57 80	99.31 168	99.37 103

HPM-MVS		98.11 41	97.83 60	98.92 19	99.42 39	97.46 28	98.57 18	99.05 38	95.43 146	97.41 160	97.50 185	97.98 16	99.79 39	95.58 117	99.57 95	99.50 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		97.92 57	97.56 83	98.99 10	98.99 90	97.82 12	97.93 55	98.96 71	96.11 117	96.89 187	97.45 188	96.85 53	99.78 40	95.19 130	99.63 79	99.38 98

N_pmnet		95.18 205	94.23 228	98.06 75	97.85 220	96.55 52	92.49 310	91.63 327	89.34 275	98.09 109	97.41 189	90.33 237	99.06 267	91.58 219	99.31 168	98.56 210

tpm		91.08 299	90.85 296	91.75 320	95.33 328	78.09 340	95.03 227	91.27 330	88.75 280	93.53 300	97.40 190	71.24 338	99.30 238	91.25 225	93.87 336	97.87 265

MDTV_nov1_ep13			91.28 280		94.31 339	73.51 353	94.80 236	93.16 313	86.75 303	93.45 304	97.40 190	76.37 313	98.55 318	88.85 271	96.43 316

DeepPCF-MVS	94.58 5	96.90 127	96.43 152	98.31 63	97.48 261	97.23 35	92.56 309	98.60 142	92.84 237	98.54 66	97.40 190	96.64 62	98.78 299	94.40 161	99.41 149	98.93 171

MSLP-MVS++		96.42 160	96.71 137	95.57 229	97.82 226	90.56 207	95.71 177	98.84 89	94.72 176	96.71 192	97.39 193	94.91 122	98.10 339	95.28 126	99.02 200	98.05 256

EPNet		93.72 249	92.62 261	97.03 147	87.61 364	92.25 172	96.27 144	91.28 329	96.74 97	87.65 352	97.39 193	85.00 281	99.64 119	92.14 207	99.48 124	99.20 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		93.66 251	94.07 234	92.45 314	97.57 255	80.67 333	86.46 350	96.00 278	93.99 200	97.10 172	97.38 195	89.90 244	97.82 342	88.76 272	99.47 126	98.86 186

DeepC-MVS_fast	94.34 7	96.74 140	96.51 150	97.44 123	97.69 246	94.15 129	96.02 158	98.43 158	93.17 225	97.30 163	97.38 195	95.48 104	99.28 242	93.74 185	99.34 161	98.88 183
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS		96.90 127	96.81 132	97.16 137	98.56 138	92.20 176	94.33 248	98.12 203	97.34 83	98.20 97	97.33 197	92.81 188	99.75 53	94.79 148	99.81 44	99.54 46

ITE_SJBPF				97.85 88	98.64 125	96.66 48		98.51 150	95.63 136	97.22 165	97.30 198	95.52 102	98.55 318	90.97 229	98.90 210	98.34 231

Vis-MVSNet (Re-imp)		95.11 207	94.85 204	95.87 220	99.12 76	89.17 235	97.54 85	94.92 294	96.50 103	96.58 195	97.27 199	83.64 285	99.48 182	88.42 278	99.67 74	98.97 163

pmmvs4		94.82 217	94.19 231	96.70 162	97.42 268	92.75 165	92.09 318	96.76 269	86.80 302	95.73 236	97.22 200	89.28 252	98.89 287	93.28 192	99.14 185	98.46 218

OMC-MVS		96.48 155	96.00 168	97.91 85	98.30 162	96.01 68	94.86 233	98.60 142	91.88 253	97.18 167	97.21 201	96.11 80	99.04 269	90.49 248	99.34 161	98.69 201

LP		93.12 261	92.78 259	94.14 275	94.50 337	85.48 297	95.73 176	95.68 287	92.97 234	95.05 248	97.17 202	81.93 289	99.40 213	93.06 198	88.96 349	97.55 277

pmmvs5		94.63 224	94.34 226	95.50 232	97.63 253	88.34 255	94.02 266	97.13 257	87.15 298	95.22 245	97.15 203	87.50 266	99.27 244	93.99 177	99.26 176	98.88 183

our_test_3		94.20 239	94.58 216	93.07 302	96.16 310	81.20 330	90.42 336	96.84 266	90.72 265	97.14 169	97.13 204	90.47 235	99.11 261	94.04 176	98.25 253	98.91 175

CPTT-MVS		96.69 146	96.08 165	98.49 47	98.89 99	96.64 49	97.25 94	98.77 107	92.89 236	96.01 225	97.13 204	92.23 206	99.67 109	92.24 206	99.34 161	99.17 131

MS-PatchMatch		94.83 216	94.91 202	94.57 265	96.81 295	87.10 285	94.23 254	97.34 249	88.74 281	97.14 169	97.11 206	91.94 215	98.23 335	92.99 199	97.92 266	98.37 225

FPMVS		89.92 311	88.63 318	93.82 285	98.37 158	96.94 40	91.58 323	93.34 311	88.00 291	90.32 340	97.10 207	70.87 340	91.13 358	71.91 354	96.16 321	93.39 347

DELS-MVS		96.17 167	96.23 160	95.99 211	97.55 258	90.04 211	92.38 313	98.52 148	94.13 198	96.55 200	97.06 208	94.99 120	99.58 147	95.62 113	99.28 173	98.37 225
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
CNVR-MVS		96.92 125	96.55 145	98.03 79	98.00 210	95.54 81	94.87 232	98.17 197	94.60 178	96.38 205	97.05 209	95.67 98	99.36 228	95.12 138	99.08 193	99.19 128

旧先验1					97.80 231	93.87 137		97.75 225			97.04 210	93.57 169			98.68 231	98.72 199

testdata				95.70 226	98.16 193	90.58 205		97.72 228	80.38 340	95.62 238	97.02 211	92.06 212	98.98 278	89.06 269	98.52 240	97.54 278

PatchmatchNet		91.98 281	91.87 272	92.30 316	94.60 335	79.71 335	95.12 216	93.59 309	89.52 274	93.61 297	97.02 211	77.94 302	99.18 253	90.84 233	94.57 335	98.01 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		93.60 253	93.25 249	94.63 260	96.14 312	87.47 277	96.04 157	94.50 298	93.57 215	96.47 201	96.97 213	76.50 312	98.61 313	90.67 242	98.41 246	97.81 269

CostFormer		89.75 312	89.25 311	91.26 324	94.69 334	78.00 343	95.32 205	91.98 324	81.50 335	90.55 337	96.96 214	71.06 339	98.89 287	88.59 276	92.63 341	96.87 299

test_normal		95.51 186	95.46 185	95.68 227	97.97 212	89.12 238	93.73 279	95.86 283	91.98 249	97.17 168	96.94 215	91.55 222	99.42 199	95.21 129	98.73 228	98.51 214

DI_MVS_plusplus_test		95.46 192	95.43 186	95.55 230	98.05 203	88.84 246	94.18 258	95.75 285	91.92 252	97.32 162	96.94 215	91.44 224	99.39 219	94.81 146	98.48 243	98.43 220

114514_t		93.96 245	93.22 250	96.19 197	99.06 82	90.97 198	95.99 160	98.94 74	73.88 356	93.43 305	96.93 217	92.38 205	99.37 227	89.09 267	99.28 173	98.25 240

MVS_0304		96.22 164	95.94 174	97.04 145	97.07 286	92.54 166	94.19 257	99.04 45	95.17 158	93.74 291	96.92 218	91.77 220	99.73 63	95.76 106	99.81 44	98.85 188

Test_1112_low_res		93.53 255	92.86 255	95.54 231	98.60 132	88.86 245	92.75 304	98.69 125	82.66 331	92.65 320	96.92 218	84.75 282	99.56 153	90.94 230	97.76 271	98.19 246

tpmrst		90.31 305	90.61 301	89.41 334	94.06 344	72.37 356	95.06 224	93.69 304	88.01 290	92.32 325	96.86 220	77.45 306	98.82 295	91.04 226	87.01 352	97.04 293

PHI-MVS		96.96 122	96.53 148	98.25 68	97.48 261	96.50 53	96.76 123	98.85 86	93.52 216	96.19 220	96.85 221	95.94 83	99.42 199	93.79 184	99.43 140	98.83 189

patchmatchnet-post											96.84 222	77.36 308	99.42 199

ADS-MVSNet2		91.47 296	90.51 302	94.36 270	95.51 323	85.63 294	95.05 225	95.70 286	83.46 328	92.69 318	96.84 222	79.15 299	99.41 210	85.66 310	90.52 344	98.04 257

ADS-MVSNet		90.95 302	90.26 305	93.04 303	95.51 323	82.37 326	95.05 225	93.41 310	83.46 328	92.69 318	96.84 222	79.15 299	98.70 306	85.66 310	90.52 344	98.04 257

HY-MVS	91.43 15	92.58 266	91.81 274	94.90 251	96.49 301	88.87 244	97.31 91	94.62 296	85.92 309	90.50 339	96.84 222	85.05 280	99.40 213	83.77 325	95.78 325	96.43 317

tpmp4_e23		88.46 320	87.54 323	91.22 325	94.56 336	78.08 341	95.63 187	93.17 312	79.08 346	85.85 355	96.80 226	65.86 355	98.85 294	84.10 321	92.85 339	96.72 306

UnsupCasMVSNet_bld		94.72 220	94.26 227	96.08 205	98.62 130	90.54 208	93.38 292	98.05 211	90.30 268	97.02 175	96.80 226	89.54 246	99.16 257	88.44 277	96.18 320	98.56 210

HQP_MVS		96.66 148	96.33 158	97.68 100	98.70 118	94.29 122	96.50 132	98.75 111	96.36 108	96.16 221	96.77 228	91.91 218	99.46 189	92.59 202	99.20 180	99.28 118

plane_prior4											96.77 228

MVS_111021_HR		96.73 142	96.54 147	97.27 133	98.35 160	93.66 148	93.42 290	98.36 169	94.74 175	96.58 195	96.76 230	96.54 65	98.99 276	94.87 143	99.27 175	99.15 135

CANet		95.86 178	95.65 180	96.49 176	96.41 303	90.82 201	94.36 247	98.41 163	94.94 168	92.62 322	96.73 231	92.68 192	99.71 80	95.12 138	99.60 88	98.94 167

1121		94.26 232	93.26 248	97.27 133	98.26 176	94.73 107	95.86 172	97.71 229	77.96 350	94.53 266	96.71 232	91.93 216	99.40 213	87.71 284	98.64 234	97.69 272

TSAR-MVS + GP.		96.47 156	96.12 162	97.49 117	97.74 242	95.23 90	94.15 261	96.90 265	93.26 219	98.04 116	96.70 233	94.41 140	98.89 287	94.77 150	99.14 185	98.37 225

test222					98.17 191	93.24 159	92.74 306	97.61 241	75.17 354	94.65 257	96.69 234	90.96 231			98.66 232	97.66 273

Patchmatch-test1		93.38 258	93.59 243	92.73 310	96.24 305	81.40 329	93.24 296	94.00 302	91.58 257	94.57 264	96.67 235	87.94 261	99.03 272	90.42 249	97.66 287	97.77 270

新几何1				97.25 136	98.29 163	94.70 110		97.73 227	77.98 349	94.83 254	96.67 235	92.08 211	99.45 193	88.17 282	98.65 233	97.61 275

MVS_111021_LR		96.82 136	96.55 145	97.62 103	98.27 168	95.34 88	93.81 277	98.33 174	94.59 180	96.56 197	96.63 237	96.61 63	98.73 303	94.80 147	99.34 161	98.78 194

CDPH-MVS		95.45 194	94.65 211	97.84 89	98.28 166	94.96 101	93.73 279	98.33 174	85.03 320	95.44 240	96.60 238	95.31 111	99.44 197	90.01 255	99.13 187	99.11 146

CMPMVS	73.10 23	92.74 265	91.39 277	96.77 158	93.57 349	94.67 111	94.21 256	97.67 231	80.36 341	93.61 297	96.60 238	82.85 287	97.35 346	84.86 317	98.78 221	98.29 237
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CDS-MVSNet		94.88 214	94.12 233	97.14 139	97.64 252	93.57 150	93.96 271	97.06 260	90.05 271	96.30 214	96.55 240	86.10 274	99.47 184	90.10 254	99.31 168	98.40 221
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LF4IMVS		96.07 170	95.63 181	97.36 129	98.19 186	95.55 80	95.44 191	98.82 101	92.29 245	95.70 237	96.55 240	92.63 195	98.69 307	91.75 217	99.33 166	97.85 266

HPM-MVS++		96.99 115	96.38 154	98.81 26	98.64 125	97.59 20	95.97 162	98.20 192	95.51 142	95.06 247	96.53 242	94.10 153	99.70 88	94.29 166	99.15 184	99.13 138

EPMVS		89.26 315	88.55 319	91.39 322	92.36 357	79.11 337	95.65 184	79.86 361	88.60 282	93.12 311	96.53 242	70.73 341	98.10 339	90.75 237	89.32 348	96.98 294

HyFIR lowres test		93.72 249	92.65 260	96.91 153	98.93 94	91.81 188	91.23 329	98.52 148	82.69 330	96.46 202	96.52 244	80.38 295	99.90 13	90.36 251	98.79 220	99.03 157

BH-RMVSNet		94.56 227	94.44 224	94.91 249	97.57 255	87.44 278	93.78 278	96.26 275	93.69 213	96.41 204	96.50 245	92.10 210	99.00 275	85.96 306	97.71 282	98.31 233

HSP-MVS		97.37 100	96.85 129	98.92 19	99.26 51	97.70 15	97.66 72	98.23 188	95.65 135	98.51 68	96.46 246	92.15 207	99.81 32	95.14 136	98.58 239	99.26 122

1111		88.78 317	89.39 310	86.96 342	98.53 144	62.84 361	91.49 324	97.48 245	94.45 183	96.56 197	96.45 247	43.83 366	98.87 291	86.33 304	99.40 150	99.18 130

.test1245		73.49 335	79.27 336	56.15 348	98.53 144	62.84 361	91.49 324	97.48 245	94.45 183	96.56 197	96.45 247	43.83 366	98.87 291	86.33 304	8.32 361	6.75 361

原ACMM1				96.58 170	98.16 193	92.12 178		98.15 200	85.90 310	93.49 301	96.43 249	92.47 203	99.38 224	87.66 287	98.62 235	98.23 242

tpm2		88.47 319	87.69 322	90.79 328	94.98 331	77.34 345	95.09 219	91.83 325	77.51 352	89.40 345	96.41 250	67.83 353	98.73 303	83.58 327	92.60 342	96.29 319

OpenMVS_ROB	91.80 14	93.64 252	93.05 251	95.42 234	97.31 277	91.21 195	95.08 221	96.68 273	81.56 334	96.88 188	96.41 250	90.44 236	99.25 247	85.39 313	97.67 286	95.80 325

F-COLMAP		95.30 201	94.38 225	98.05 78	98.64 125	96.04 66	95.61 188	98.66 132	89.00 278	93.22 310	96.40 252	92.90 187	99.35 230	87.45 297	97.53 293	98.77 195

NCCC		96.52 153	95.99 169	98.10 73	97.81 227	95.68 75	95.00 228	98.20 192	95.39 147	95.40 242	96.36 253	93.81 163	99.45 193	93.55 189	98.42 245	99.17 131

new_pmnet		92.34 274	91.69 275	94.32 271	96.23 307	89.16 236	92.27 314	92.88 316	84.39 326	95.29 243	96.35 254	85.66 276	96.74 352	84.53 319	97.56 291	97.05 292

Test4		95.39 196	95.24 190	95.82 221	98.07 201	89.60 221	94.40 246	98.49 151	91.39 259	97.40 161	96.32 255	87.32 269	99.41 210	95.09 140	98.71 230	98.44 219

tpmvs		90.79 304	90.87 295	90.57 330	92.75 356	76.30 347	95.79 175	93.64 307	91.04 262	91.91 328	96.26 256	77.19 310	98.86 293	89.38 263	89.85 347	96.56 311

test_prior3		95.91 175	95.39 187	97.46 120	97.79 236	94.26 126	93.33 294	98.42 161	94.21 195	94.02 281	96.25 257	93.64 167	99.34 231	91.90 209	98.96 204	98.79 192

test_prior2							93.33 294		94.21 195	94.02 281	96.25 257	93.64 167		91.90 209	98.96 204

testgi		96.07 170	96.50 151	94.80 254	99.26 51	87.69 273	95.96 166	98.58 145	95.08 165	98.02 118	96.25 257	97.92 17	97.60 345	88.68 275	98.74 225	99.11 146

DP-MVS Recon		95.55 185	95.13 193	96.80 156	98.51 146	93.99 135	94.60 242	98.69 125	90.20 269	95.78 233	96.21 260	92.73 191	98.98 278	90.58 244	98.86 217	97.42 281

MVSFormer		96.14 168	96.36 156	95.49 233	97.68 247	87.81 271	98.67 13	99.02 51	96.50 103	94.48 269	96.15 261	86.90 271	99.92 4	98.73 17	99.13 187	98.74 197

jason		94.39 231	94.04 235	95.41 236	98.29 163	87.85 269	92.74 306	96.75 270	85.38 318	95.29 243	96.15 261	88.21 260	99.65 116	94.24 168	99.34 161	98.74 197
jason: jason.
0601test		94.40 230	94.00 237	95.59 228	96.95 289	89.52 225	94.75 239	95.55 291	96.18 116	96.79 189	96.14 263	81.09 292	99.18 253	90.75 237	97.77 270	98.07 252

dp		88.08 323	88.05 321	88.16 340	92.85 354	68.81 358	94.17 259	92.88 316	85.47 314	91.38 332	96.14 263	68.87 350	98.81 297	86.88 301	83.80 356	96.87 299

MCST-MVS		96.24 163	95.80 176	97.56 106	98.75 108	94.13 130	94.66 240	98.17 197	90.17 270	96.21 219	96.10 265	95.14 116	99.43 198	94.13 170	98.85 219	99.13 138

TEST9					97.84 224	95.23 90	93.62 283	98.39 165	86.81 301	93.78 288	95.99 266	94.68 129	99.52 165

train_agg		95.46 192	94.66 210	97.88 86	97.84 224	95.23 90	93.62 283	98.39 165	87.04 299	93.78 288	95.99 266	94.58 134	99.52 165	91.76 215	98.90 210	98.89 179

MSDG		95.33 199	95.13 193	95.94 217	97.40 269	91.85 186	91.02 330	98.37 168	95.30 149	96.31 213	95.99 266	94.51 138	98.38 328	89.59 260	97.65 288	97.60 276

agg_prior1		95.39 196	94.60 214	97.75 93	97.80 231	94.96 101	93.39 291	98.36 169	87.20 297	93.49 301	95.97 269	94.65 131	99.53 161	91.69 218	98.86 217	98.77 195

test_8					97.81 227	95.07 98	93.54 286	98.38 167	87.04 299	93.71 292	95.96 270	94.58 134	99.52 165

CSCG		97.40 98	97.30 95	97.69 99	98.95 93	94.83 104	97.28 93	98.99 65	96.35 110	98.13 104	95.95 271	95.99 82	99.66 114	94.36 165	99.73 57	98.59 208

TAPA-MVS	93.32 12	94.93 213	94.23 228	97.04 145	98.18 189	94.51 114	95.22 213	98.73 114	81.22 337	96.25 217	95.95 271	93.80 164	98.98 278	89.89 256	98.87 215	97.62 274
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
agg_prior3		95.30 201	94.46 223	97.80 91	97.80 231	95.00 99	93.63 282	98.34 173	86.33 305	93.40 308	95.84 273	94.15 152	99.50 177	91.76 215	98.90 210	98.89 179

sss		94.22 234	93.72 241	95.74 223	97.71 245	89.95 214	93.84 275	96.98 262	88.38 286	93.75 290	95.74 274	87.94 261	98.89 287	91.02 227	98.10 258	98.37 225

CNLPA		95.04 210	94.47 220	96.75 159	97.81 227	95.25 89	94.12 264	97.89 217	94.41 186	94.57 264	95.69 275	90.30 240	98.35 331	86.72 303	98.76 223	96.64 308

PCF-MVS	89.43 18	92.12 279	90.64 300	96.57 172	97.80 231	93.48 154	89.88 343	98.45 154	74.46 355	96.04 224	95.68 276	90.71 233	99.31 236	73.73 350	99.01 202	96.91 298
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-untuned		94.69 221	94.75 209	94.52 267	97.95 217	87.53 275	94.07 265	97.01 261	93.99 200	97.10 172	95.65 277	92.65 194	98.95 283	87.60 294	96.74 312	97.09 290

CANet_DTU		94.65 223	94.21 230	95.96 213	95.90 316	89.68 217	93.92 272	97.83 222	93.19 221	90.12 342	95.64 278	88.52 256	99.57 152	93.27 193	99.47 126	98.62 206

PatchMatch-RL		94.61 225	93.81 240	97.02 148	98.19 186	95.72 73	93.66 281	97.23 252	88.17 288	94.94 251	95.62 279	91.43 225	98.57 315	87.36 298	97.68 285	96.76 304

tpm cat1		88.01 324	87.33 324	90.05 333	94.48 338	76.28 348	94.47 245	94.35 301	73.84 357	89.26 346	95.61 280	73.64 324	98.30 333	84.13 320	86.20 353	95.57 330

Effi-MVS+-dtu		96.81 137	96.09 164	98.99 10	96.90 293	98.69 2	96.42 134	98.09 205	95.86 128	95.15 246	95.54 281	94.26 147	99.81 32	94.06 172	98.51 242	98.47 216

AdaColmap		95.11 207	94.62 213	96.58 170	97.33 275	94.45 117	94.92 230	98.08 207	93.15 226	93.98 284	95.53 282	94.34 143	99.10 263	85.69 309	98.61 236	96.20 320

WTY-MVS		93.55 254	93.00 253	95.19 240	97.81 227	87.86 268	93.89 273	96.00 278	89.02 277	94.07 279	95.44 283	86.27 273	99.33 234	87.69 286	96.82 309	98.39 223

test1235678		92.95 262	92.40 262	94.61 261	96.95 289	86.87 287	90.75 332	97.75 225	91.00 263	96.33 207	95.38 284	85.21 279	98.92 284	79.00 340	99.20 180	98.03 259

PLC	91.02 16	94.05 244	92.90 254	97.51 111	98.00 210	95.12 97	94.25 252	98.25 187	86.17 306	91.48 331	95.25 285	91.01 229	99.19 252	85.02 316	96.69 313	98.22 243
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		90.00 308	88.90 317	93.32 295	94.20 343	85.34 298	91.25 328	92.56 321	78.59 347	93.82 287	95.17 286	67.36 354	98.69 307	89.08 268	98.03 260	95.92 321

NP-MVS					98.14 196	93.72 144					95.08 287

HQP-MVS		95.17 206	94.58 216	96.92 151	97.85 220	92.47 168	94.26 249	98.43 158	93.18 222	92.86 315	95.08 287	90.33 237	99.23 250	90.51 246	98.74 225	99.05 156

cdsmvs_eth3d_5k		24.22 339	32.30 340	0.00 353	0.00 368	0.00 368	0.00 359	98.10 204	0.00 363	0.00 365	95.06 289	97.54 27	0.00 365	0.00 362	0.00 363	0.00 363

lupinMVS		93.77 247	93.28 247	95.24 239	97.68 247	87.81 271	92.12 316	96.05 277	84.52 323	94.48 269	95.06 289	86.90 271	99.63 122	93.62 188	99.13 187	98.27 238

1112_ss		94.12 240	93.42 245	96.23 192	98.59 134	90.85 199	94.24 253	98.85 86	85.49 313	92.97 313	94.94 291	86.01 275	99.64 119	91.78 214	97.92 266	98.20 245

ab-mvs-re		7.91 343	10.55 344	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	94.94 291	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

Fast-Effi-MVS+-dtu		96.44 157	96.12 162	97.39 128	97.18 282	94.39 118	95.46 190	98.73 114	96.03 121	94.72 255	94.92 293	96.28 79	99.69 97	93.81 183	97.98 261	98.09 249

EPNet_dtu		91.39 297	90.75 298	93.31 296	90.48 362	82.61 324	94.80 236	92.88 316	93.39 217	81.74 360	94.90 294	81.36 291	99.11 261	88.28 280	98.87 215	98.21 244
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		96.19 166	96.01 167	96.71 161	97.43 267	92.19 177	96.12 154	99.10 25	95.45 144	93.33 309	94.71 295	97.23 40	99.56 153	93.21 195	97.54 292	98.37 225

GA-MVS		92.83 264	92.15 266	94.87 252	96.97 288	87.27 282	90.03 339	96.12 276	91.83 254	94.05 280	94.57 296	76.01 316	98.97 282	92.46 204	97.34 301	98.36 230

xiu_mvs_v1_base_debu		95.62 182	95.96 171	94.60 262	98.01 207	88.42 252	93.99 268	98.21 189	92.98 230	95.91 227	94.53 297	96.39 73	99.72 69	95.43 122	98.19 254	95.64 327

xiu_mvs_v1_base		95.62 182	95.96 171	94.60 262	98.01 207	88.42 252	93.99 268	98.21 189	92.98 230	95.91 227	94.53 297	96.39 73	99.72 69	95.43 122	98.19 254	95.64 327

xiu_mvs_v1_base_debi		95.62 182	95.96 171	94.60 262	98.01 207	88.42 252	93.99 268	98.21 189	92.98 230	95.91 227	94.53 297	96.39 73	99.72 69	95.43 122	98.19 254	95.64 327

view600		92.56 267	92.11 267	93.91 280	98.45 152	84.76 309	97.10 103	90.23 341	97.42 74	96.98 178	94.48 300	73.62 325	99.60 141	82.49 329	98.28 248	97.36 282

view800		92.56 267	92.11 267	93.91 280	98.45 152	84.76 309	97.10 103	90.23 341	97.42 74	96.98 178	94.48 300	73.62 325	99.60 141	82.49 329	98.28 248	97.36 282

conf0.05thres1000		92.56 267	92.11 267	93.91 280	98.45 152	84.76 309	97.10 103	90.23 341	97.42 74	96.98 178	94.48 300	73.62 325	99.60 141	82.49 329	98.28 248	97.36 282

tfpn		92.56 267	92.11 267	93.91 280	98.45 152	84.76 309	97.10 103	90.23 341	97.42 74	96.98 178	94.48 300	73.62 325	99.60 141	82.49 329	98.28 248	97.36 282

PVSNet_Blended		93.96 245	93.65 242	94.91 249	97.79 236	87.40 279	91.43 326	98.68 127	84.50 324	94.51 267	94.48 300	93.04 182	99.30 238	89.77 258	98.61 236	98.02 261

PAPM_NR		94.61 225	94.17 232	95.96 213	98.36 159	91.23 194	95.93 170	97.95 213	92.98 230	93.42 306	94.43 305	90.53 234	98.38 328	87.60 294	96.29 319	98.27 238

API-MVS		95.09 209	95.01 198	95.31 237	96.61 297	94.02 133	96.83 121	97.18 255	95.60 138	95.79 232	94.33 306	94.54 136	98.37 330	85.70 308	98.52 240	93.52 345

mvs-test1		96.20 165	95.50 184	98.32 61	96.90 293	98.16 4	95.07 222	98.09 205	95.86 128	93.63 295	94.32 307	94.26 147	99.71 80	94.06 172	97.27 305	97.07 291

alignmvs		96.01 172	95.52 183	97.50 114	97.77 241	94.71 109	96.07 155	96.84 266	97.48 72	96.78 191	94.28 308	85.50 277	99.40 213	96.22 89	98.73 228	98.40 221

CLD-MVS		95.47 191	95.07 195	96.69 163	98.27 168	92.53 167	91.36 327	98.67 130	91.22 260	95.78 233	94.12 309	95.65 99	98.98 278	90.81 234	99.72 60	98.57 209
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TR-MVS		92.54 271	92.20 265	93.57 290	96.49 301	86.66 289	93.51 287	94.73 295	89.96 272	94.95 250	93.87 310	90.24 242	98.61 313	81.18 335	94.88 331	95.45 331

canonicalmvs		97.23 110	97.21 107	97.30 132	97.65 251	94.39 118	97.84 61	99.05 38	97.42 74	96.68 193	93.85 311	97.63 25	99.33 234	96.29 88	98.47 244	98.18 248

xiu_mvs_v2_base		94.22 234	94.63 212	92.99 306	97.32 276	84.84 307	92.12 316	97.84 220	91.96 250	94.17 274	93.43 312	96.07 81	99.71 80	91.27 223	97.48 295	94.42 337

test12356		87.98 325	88.41 320	86.69 343	95.84 317	63.49 360	87.15 349	97.32 250	87.21 296	91.78 330	93.36 313	70.66 342	98.39 326	74.70 349	97.64 289	98.19 246

CHOSEN 280x420		89.98 309	89.19 315	92.37 315	95.60 322	81.13 331	86.22 351	97.09 259	81.44 336	87.44 353	93.15 314	73.99 320	99.47 184	88.69 274	99.07 195	96.52 312

thres600view7		92.03 280	91.43 276	93.82 285	98.19 186	84.61 313	96.27 144	90.39 336	96.81 95	96.37 206	93.11 315	73.44 331	99.49 179	80.32 336	97.95 262	97.36 282

E-PMN		89.52 314	89.78 309	88.73 336	93.14 351	77.61 344	83.26 355	92.02 323	94.82 173	93.71 292	93.11 315	75.31 318	96.81 350	85.81 307	96.81 310	91.77 352

tfpn111		91.92 282	91.39 277	93.49 292	98.21 182	84.50 314	96.39 135	90.39 336	96.87 91	96.33 207	93.08 317	73.44 331	99.51 175	79.87 337	97.94 265	96.46 313

conf200view11		91.81 287	91.26 282	93.46 293	98.21 182	84.50 314	96.39 135	90.39 336	96.87 91	96.33 207	93.08 317	73.44 331	99.42 199	78.85 342	97.74 272	96.46 313

thres100view900		91.76 289	91.26 282	93.26 297	98.21 182	84.50 314	96.39 135	90.39 336	96.87 91	96.33 207	93.08 317	73.44 331	99.42 199	78.85 342	97.74 272	95.85 323

1314		92.38 273	92.30 264	92.64 312	95.42 327	85.15 302	95.86 172	96.97 263	85.40 317	90.62 335	93.06 320	91.12 228	97.80 343	86.74 302	95.49 330	94.97 335

PAPM		87.64 328	85.84 331	93.04 303	96.54 298	84.99 305	88.42 347	95.57 290	79.52 343	83.82 357	93.05 321	80.57 294	98.41 324	62.29 358	92.79 340	95.71 326

Fast-Effi-MVS+		95.49 188	95.07 195	96.75 159	97.67 250	92.82 163	94.22 255	98.60 142	91.61 255	93.42 306	92.90 322	96.73 60	99.70 88	92.60 201	97.89 269	97.74 271

PNet_i23d		83.82 333	83.39 333	85.10 344	96.07 313	65.16 359	81.87 357	94.37 300	90.87 264	93.92 286	92.89 323	52.80 364	96.44 354	77.52 348	70.22 358	93.70 344

tfpn1000		91.88 286	91.20 284	93.89 284	97.96 213	87.13 284	97.13 101	88.16 356	94.41 186	94.87 253	92.77 324	68.34 351	99.47 184	89.24 264	97.95 262	95.06 333

MVS		90.02 307	89.20 314	92.47 313	94.71 333	86.90 286	95.86 172	96.74 271	64.72 358	90.62 335	92.77 324	92.54 199	98.39 326	79.30 339	95.56 329	92.12 350

BH-w/o		92.14 278	91.94 271	92.73 310	97.13 284	85.30 299	92.46 311	95.64 288	89.33 276	94.21 273	92.74 326	89.60 245	98.24 334	81.68 333	94.66 333	94.66 336

PAPR		92.22 276	91.27 281	95.07 246	95.73 321	88.81 247	91.97 319	97.87 218	85.80 311	90.91 333	92.73 327	91.16 227	98.33 332	79.48 338	95.76 326	98.08 250

MAR-MVS		94.21 237	93.03 252	97.76 92	96.94 291	97.44 30	96.97 119	97.15 256	87.89 293	92.00 327	92.73 327	92.14 208	99.12 258	83.92 322	97.51 294	96.73 305
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PS-MVSNAJ		94.10 241	94.47 220	93.00 305	97.35 271	84.88 306	91.86 320	97.84 220	91.96 250	94.17 274	92.50 329	95.82 89	99.71 80	91.27 223	97.48 295	94.40 338

PMMVS		92.39 272	91.08 285	96.30 188	93.12 352	92.81 164	90.58 335	95.96 280	79.17 345	91.85 329	92.27 330	90.29 241	98.66 312	89.85 257	96.68 314	97.43 280

PVSNet	86.72 19	91.10 298	90.97 294	91.49 321	97.56 257	78.04 342	87.17 348	94.60 297	84.65 322	92.34 324	92.20 331	87.37 268	98.47 321	85.17 315	97.69 284	97.96 263

tfpn200view9		91.55 295	91.00 286	93.21 299	98.02 205	84.35 318	95.70 178	90.79 333	96.26 112	95.90 230	92.13 332	73.62 325	99.42 199	78.85 342	97.74 272	95.85 323

thres400		91.68 294	91.00 286	93.71 287	98.02 205	84.35 318	95.70 178	90.79 333	96.26 112	95.90 230	92.13 332	73.62 325	99.42 199	78.85 342	97.74 272	97.36 282

MVE	73.61 22	86.48 330	85.92 330	88.18 339	96.23 307	85.28 300	81.78 358	75.79 362	86.01 307	82.53 359	91.88 334	92.74 190	87.47 360	71.42 355	94.86 332	91.78 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		89.06 316	89.22 312	88.61 337	93.00 353	77.34 345	82.91 356	90.92 332	94.64 177	92.63 321	91.81 335	76.30 314	97.02 348	83.83 324	96.90 307	91.48 353

conf0.01		91.90 283	90.98 288	94.67 258	98.27 168	88.03 261	96.98 113	88.58 349	93.90 203	94.64 258	91.45 336	69.62 344	99.52 165	87.62 288	97.74 272	96.46 313

conf0.002		91.90 283	90.98 288	94.67 258	98.27 168	88.03 261	96.98 113	88.58 349	93.90 203	94.64 258	91.45 336	69.62 344	99.52 165	87.62 288	97.74 272	96.46 313

thresconf0.02		91.72 290	90.98 288	93.97 276	98.27 168	88.03 261	96.98 113	88.58 349	93.90 203	94.64 258	91.45 336	69.62 344	99.52 165	87.62 288	97.74 272	94.35 339

tfpn_n400		91.72 290	90.98 288	93.97 276	98.27 168	88.03 261	96.98 113	88.58 349	93.90 203	94.64 258	91.45 336	69.62 344	99.52 165	87.62 288	97.74 272	94.35 339

tfpnconf		91.72 290	90.98 288	93.97 276	98.27 168	88.03 261	96.98 113	88.58 349	93.90 203	94.64 258	91.45 336	69.62 344	99.52 165	87.62 288	97.74 272	94.35 339

tfpnview11		91.72 290	90.98 288	93.97 276	98.27 168	88.03 261	96.98 113	88.58 349	93.90 203	94.64 258	91.45 336	69.62 344	99.52 165	87.62 288	97.74 272	94.35 339

cascas		91.89 285	91.35 279	93.51 291	94.27 340	85.60 295	88.86 346	98.61 141	79.32 344	92.16 326	91.44 342	89.22 253	98.12 338	90.80 235	97.47 297	96.82 301

IB-MVS	85.98 20	88.63 318	86.95 327	93.68 288	95.12 329	84.82 308	90.85 331	90.17 345	87.55 294	88.48 349	91.34 343	58.01 358	99.59 145	87.24 299	93.80 337	96.63 310
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
thres200		91.00 300	90.42 304	92.77 309	97.47 265	83.98 321	94.01 267	91.18 331	95.12 164	95.44 240	91.21 344	73.93 321	99.31 236	77.76 346	97.63 290	95.01 334

test0.0.03 1		90.11 306	89.21 313	92.83 308	93.89 345	86.87 287	91.74 322	88.74 348	92.02 247	94.71 256	91.14 345	73.92 322	94.48 356	83.75 326	92.94 338	97.16 289

test-LLR		89.97 310	89.90 308	90.16 331	94.24 341	74.98 350	89.89 340	89.06 346	92.02 247	89.97 343	90.77 346	73.92 322	98.57 315	91.88 211	97.36 299	96.92 296

test-mter		87.92 326	87.17 325	90.16 331	94.24 341	74.98 350	89.89 340	89.06 346	86.44 304	89.97 343	90.77 346	54.96 362	98.57 315	91.88 211	97.36 299	96.92 296

testus		90.90 303	90.51 302	92.06 318	96.07 313	79.45 336	88.99 344	98.44 157	85.46 315	94.15 276	90.77 346	89.12 255	98.01 341	73.66 351	97.95 262	98.71 200

tfpn_ndepth		90.98 301	90.24 306	93.20 301	97.72 244	87.18 283	96.52 131	88.20 355	92.63 239	93.69 294	90.70 349	68.22 352	99.42 199	86.98 300	97.47 297	93.00 349

testpf		82.70 334	84.35 332	77.74 346	88.97 363	73.23 354	93.85 274	84.33 359	88.10 289	85.06 356	90.42 350	52.62 365	91.05 359	91.00 228	84.82 355	68.93 358

TESTMET0.1,1		87.20 329	86.57 329	89.07 335	93.62 347	72.84 355	89.89 340	87.01 357	85.46 315	89.12 347	90.20 351	56.00 361	97.72 344	90.91 231	96.92 306	96.64 308

gm-plane-assit					91.79 358	71.40 357			81.67 333		90.11 352		98.99 276	84.86 317

DWT-MVSNet_test		87.92 326	86.77 328	91.39 322	93.18 350	78.62 338	95.10 217	91.42 328	85.58 312	88.00 350	88.73 353	60.60 357	98.90 285	90.60 243	87.70 351	96.65 307

PatchFormer-LS_test		89.62 313	89.12 316	91.11 326	93.62 347	78.42 339	94.57 244	93.62 308	88.39 285	90.54 338	88.40 354	72.33 336	99.03 272	92.41 205	88.20 350	95.89 322

DeepMVS_CX				77.17 347	90.94 361	85.28 300		74.08 365	52.51 359	80.87 361	88.03 355	75.25 319	70.63 361	59.23 359	84.94 354	75.62 356

test2356		85.45 331	83.26 334	92.01 319	91.12 359	80.76 332	85.16 352	92.90 315	83.90 327	90.63 334	87.71 356	53.10 363	97.24 347	69.20 356	95.65 327	98.03 259

PVSNet_0	81.89 21	84.49 332	83.21 335	88.34 338	95.76 320	74.97 352	83.49 354	92.70 320	78.47 348	87.94 351	86.90 357	83.38 286	96.63 353	73.44 352	66.86 359	93.40 346

GG-mvs-BLEND				90.60 329	91.00 360	84.21 320	98.23 35	72.63 366		82.76 358	84.11 358	56.14 360	96.79 351	72.20 353	92.09 343	90.78 354

tmp_tt		57.23 336	62.50 337	41.44 349	34.77 365	49.21 365	83.93 353	60.22 367	15.31 360	71.11 362	79.37 359	70.09 343	44.86 362	64.76 357	82.93 357	30.25 359

X-MVStestdata		92.86 263	90.83 297	98.94 15	99.15 67	97.66 16	97.77 64	98.83 97	97.42 74	96.32 211	36.50 360	96.49 70	99.72 69	95.66 110	99.37 152	99.45 73

testmvs		12.33 341	15.23 342	3.64 352	5.77 367	2.23 367	88.99 344	3.62 368	2.30 362	5.29 363	13.09 361	4.52 369	1.95 363	5.16 361	8.32 361	6.75 361

test123		12.59 340	15.49 341	3.87 351	6.07 366	2.55 366	90.75 332	2.59 369	2.52 361	5.20 364	13.02 362	4.96 368	1.85 364	5.20 360	9.09 360	7.23 360

test_post											10.87 363	76.83 311	99.07 266

test_post1							94.98 229				10.37 364	76.21 315	99.04 269	89.47 262

pcd_1.5k_mvsjas		7.98 342	10.65 343	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	95.82 89	0.00 365	0.00 362	0.00 363	0.00 363

pcd1.5k->3k		41.47 337	44.19 339	33.29 350	99.65 10	0.00 368	0.00 359	99.07 34	0.00 363	0.00 365	0.00 365	99.04 3	0.00 365	0.00 362	99.96 11	99.87 2

sosnet-low-res		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sosnet		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uncertanet		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

Regformer		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uanet		0.00 344	0.00 345	0.00 353	0.00 368	0.00 368	0.00 359	0.00 370	0.00 363	0.00 365	0.00 365	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

GSMVS																98.06 254

test_part2					99.03 86	96.07 65				98.08 111

test_part1				0.00 353		0.00 368	0.00 359	98.84 89				0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sam_mvs1												77.80 303				98.06 254

sam_mvs												77.38 307

MTGPA								98.73 114

MTMP							96.55 129	74.60 363

test9_res														91.29 222	98.89 214	99.00 159

agg_prior2														90.34 252	98.90 210	99.10 150

agg_prior					97.80 231	94.96 101		98.36 169		93.49 301			99.53 161

test_prior4						95.38 86	93.61 285

test_prior				97.46 120	97.79 236	94.26 126		98.42 161					99.34 231			98.79 192

旧先验2							93.35 293		77.95 351	95.77 235			98.67 311	90.74 239

新几何2							93.43 289

无先验							93.20 297	97.91 214	80.78 338				99.40 213	87.71 284		97.94 264

原ACMM2							92.82 302

testdata2													99.46 189	87.84 283

segment_acmp												95.34 109

testdata1							92.77 303		93.78 210

test12				97.46 120	97.61 254	94.07 131		97.78 224		93.57 299		93.31 177	99.42 199		98.78 221	98.89 179

plane_prior7					98.70 118	94.67 111

plane_prior6					98.38 157	94.37 120						91.91 218

plane_prior5								98.75 111					99.46 189	92.59 202	99.20 180	99.28 118

plane_prior3						94.51 114			95.29 150	96.16 221

plane_prior2							96.50 132		96.36 108

plane_prior1					98.49 148

plane_prior						94.29 122	95.42 196		94.31 192						98.93 209

n2								0.00 370

nn								0.00 370

door-mid								98.17 197

test11								98.08 207

door								97.81 223

HQP5-MVS						92.47 168

HQP-NCC					97.85 220		94.26 249		93.18 222	92.86 315

ACMP_Plane					97.85 220		94.26 249		93.18 222	92.86 315

BP-MVS														90.51 246

HQP4-MVS										92.87 314			99.23 250			99.06 155

HQP3-MVS								98.43 158							98.74 225

HQP2-MVS												90.33 237

MDTV_nov1_ep13_2view						57.28 364	94.89 231		80.59 339	94.02 281		78.66 301		85.50 312		97.82 268

ACMMP++_ref															99.52 111

ACMMP++															99.55 102

Test By Simon												94.51 138

High-resolution multi-view benchmark