High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 1	100.00 1	99.85 8

test_part1		99.79 2	99.79 2	99.78 2	99.85 13	99.46 3	99.79 4	99.81 4	99.98 1	99.97 2	99.87 2	99.27 9	99.97 3	99.60 4	99.99 5	99.91 2

pmmvs6		99.67 4	99.70 4	99.60 14	99.90 4	99.27 21	99.53 8	99.76 7	99.64 12	99.84 9	99.83 3	99.50 5	99.87 79	99.36 15	99.92 34	99.64 39

UA-Net		99.47 12	99.40 15	99.70 3	99.49 84	99.29 18	99.80 3	99.72 9	99.82 4	99.04 110	99.81 4	98.05 68	99.96 9	98.85 39	99.99 5	99.86 7

ANet_high		99.57 8	99.67 6	99.28 79	99.89 6	98.09 124	99.14 41	99.93 1	99.82 4	99.93 3	99.81 4	99.17 13	99.94 23	99.31 17	100.00 1	99.82 10

UniMVSNet_ETH3D		99.69 3	99.69 5	99.69 4	99.84 15	99.34 15	99.69 5	99.58 27	99.90 3	99.86 8	99.78 6	99.58 3	99.95 15	99.00 32	99.95 16	99.78 15

OurMVSNet-221017-0		99.37 22	99.31 23	99.53 37	99.91 3	98.98 61	99.63 7	99.58 27	99.44 29	99.78 11	99.76 7	96.39 176	99.92 33	99.44 14	99.92 34	99.68 31

MVS-HIRNet		94.32 297	95.62 268	90.42 337	98.46 276	75.36 358	96.29 270	89.13 354	95.25 264	95.38 327	99.75 8	92.88 266	99.19 333	94.07 273	99.39 208	96.72 336

gg-mvs-nofinetune		92.37 318	91.20 323	95.85 306	95.80 354	92.38 311	99.31 19	81.84 358	99.75 6	91.83 349	99.74 9	68.29 356	99.02 339	87.15 338	97.12 328	96.16 341

mvs_tets		99.63 6	99.67 6	99.49 49	99.88 7	98.61 87	99.34 14	99.71 10	99.27 41	99.90 5	99.74 9	99.68 2	99.97 3	99.55 9	99.99 5	99.88 4

test_djsdf		99.52 10	99.51 10	99.53 37	99.86 11	98.74 76	99.39 12	99.56 41	99.11 54	99.70 16	99.73 11	99.00 16	99.97 3	99.26 19	99.98 10	99.89 3

anonymousdsp		99.51 11	99.47 13	99.62 7	99.88 7	99.08 60	99.34 14	99.69 13	98.93 75	99.65 23	99.72 12	98.93 19	99.95 15	99.11 26	100.00 1	99.82 10

PS-MVSNAJss		99.46 13	99.49 11	99.35 69	99.90 4	98.15 120	99.20 33	99.65 18	99.48 24	99.92 4	99.71 13	98.07 65	99.96 9	99.53 10	100.00 1	99.93 1

JIA-IIPM		95.52 280	95.03 287	97.00 277	96.85 339	94.03 278	96.93 234	95.82 328	99.20 46	94.63 334	99.71 13	83.09 327	99.60 269	94.42 260	94.64 345	97.36 328

Anonymous20231211		99.27 26	99.27 25	99.26 84	99.29 121	98.18 118	99.49 9	99.51 54	99.70 8	99.80 10	99.68 15	96.84 149	99.83 131	99.21 22	99.91 39	99.77 17

jajsoiax		99.58 7	99.61 8	99.48 51	99.87 10	98.61 87	99.28 28	99.66 17	99.09 61	99.89 7	99.68 15	99.53 4	99.97 3	99.50 11	99.99 5	99.87 5

LTVRE_ROB	98.40 1	99.67 4	99.71 3	99.56 25	99.85 13	99.11 56	99.90 1	99.78 5	99.63 14	99.78 11	99.67 17	99.48 6	99.81 154	99.30 18	99.97 12	99.77 17
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
v7n		99.53 9	99.57 9	99.41 60	99.88 7	98.54 95	99.45 10	99.61 22	99.66 11	99.68 20	99.66 18	98.44 39	99.95 15	99.73 2	99.96 15	99.75 23

K. test v3		98.00 160	97.66 179	99.03 120	99.79 20	97.56 176	99.19 37	92.47 345	99.62 17	99.52 34	99.66 18	89.61 288	99.96 9	99.25 21	99.81 68	99.56 70

SixPastTwentyTwo		98.75 71	98.62 72	99.16 95	99.83 16	97.96 146	99.28 28	98.20 285	99.37 34	99.70 16	99.65 20	92.65 270	99.93 27	99.04 30	99.84 55	99.60 48

DSMNet-mixed		97.42 204	97.60 185	96.87 285	99.15 156	91.46 320	98.54 82	99.12 193	92.87 304	97.58 247	99.63 21	96.21 183	99.90 46	95.74 224	99.54 178	99.27 190

TransMVSNet (Re)		99.44 14	99.47 13	99.36 64	99.80 18	98.58 90	99.27 30	99.57 34	99.39 32	99.75 13	99.62 22	99.17 13	99.83 131	99.06 29	99.62 150	99.66 34

Gipuma		99.03 35	99.16 30	98.64 168	99.94 2	98.51 97	99.32 16	99.75 8	99.58 22	98.60 176	99.62 22	98.22 55	99.51 298	97.70 101	99.73 105	97.89 305
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Baseline_NR-MVSNet		98.98 42	98.86 45	99.36 64	99.82 17	98.55 92	97.47 198	99.57 34	99.37 34	99.21 83	99.61 24	96.76 158	99.83 131	98.06 79	99.83 61	99.71 26

TDRefinement		99.42 17	99.38 16	99.55 27	99.76 22	99.33 16	99.68 6	99.71 10	99.38 33	99.53 32	99.61 24	98.64 28	99.80 163	98.24 70	99.84 55	99.52 92

pm-mvs1		99.44 14	99.48 12	99.33 74	99.80 18	98.63 84	99.29 24	99.63 19	99.30 39	99.65 23	99.60 26	99.16 15	99.82 141	99.07 28	99.83 61	99.56 70

v10		98.97 43	99.11 33	98.55 185	99.44 100	96.21 227	98.90 58	99.55 44	98.73 83	99.48 39	99.60 26	96.63 165	99.83 131	99.70 3	99.99 5	99.61 47

v8		99.01 36	99.16 30	98.57 180	99.47 94	96.31 225	98.90 58	99.47 72	99.03 64	99.52 34	99.57 28	96.93 145	99.81 154	99.60 4	99.98 10	99.60 48

MIMVSNet1		99.38 21	99.32 22	99.55 27	99.86 11	99.19 35	99.41 11	99.59 25	99.59 20	99.71 15	99.57 28	97.12 134	99.90 46	99.21 22	99.87 51	99.54 82

GBi-Net		98.65 88	98.47 95	99.17 92	98.90 206	98.24 111	99.20 33	99.44 80	98.59 91	98.95 125	99.55 30	94.14 245	99.86 86	97.77 95	99.69 125	99.41 138

test1		98.65 88	98.47 95	99.17 92	98.90 206	98.24 111	99.20 33	99.44 80	98.59 91	98.95 125	99.55 30	94.14 245	99.86 86	97.77 95	99.69 125	99.41 138

FMVSNet1		99.17 30	99.17 29	99.17 92	99.55 65	98.24 111	99.20 33	99.44 80	99.21 43	99.43 47	99.55 30	97.82 84	99.86 86	98.42 64	99.89 47	99.41 138

new-patchmatchnet		98.35 130	98.74 55	97.18 271	99.24 128	92.23 314	96.42 264	99.48 66	98.30 105	99.69 18	99.53 33	97.44 117	99.82 141	98.84 41	99.77 89	99.49 104

lessismore_v0				98.97 127	99.73 24	97.53 178		86.71 355		99.37 56	99.52 34	89.93 286	99.92 33	98.99 33	99.72 111	99.44 129

FC-MVSNet-test		99.27 26	99.25 26	99.34 72	99.77 21	98.37 105	99.30 23	99.57 34	99.61 19	99.40 52	99.50 35	97.12 134	99.85 99	99.02 31	99.94 20	99.80 13

VDDNet		98.21 145	97.95 158	99.01 124	99.58 50	97.74 167	99.01 49	97.29 308	99.67 10	98.97 122	99.50 35	90.45 283	99.80 163	97.88 90	99.20 238	99.48 110

DeepC-MVS	97.60 4	98.97 43	98.93 42	99.10 104	99.35 114	97.98 141	98.01 141	99.46 74	97.56 159	99.54 29	99.50 35	98.97 17	99.84 116	98.06 79	99.92 34	99.49 104
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XXY-MVS		99.14 32	99.15 32	99.10 104	99.76 22	97.74 167	98.85 63	99.62 20	98.48 97	99.37 56	99.49 38	98.75 24	99.86 86	98.20 73	99.80 76	99.71 26

Vis-MVSNet		99.34 23	99.36 17	99.27 82	99.73 24	98.26 109	99.17 38	99.78 5	99.11 54	99.27 72	99.48 39	98.82 21	99.95 15	98.94 34	99.93 25	99.59 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		98.53 111	98.45 99	98.79 152	97.94 305	96.96 207	99.08 45	98.54 271	99.10 58	96.82 287	99.47 40	96.55 168	99.84 116	98.56 58	99.94 20	99.55 78
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
EU-MVSNet		97.66 188	98.50 88	95.13 317	99.63 48	85.84 344	98.35 105	98.21 284	98.23 113	99.54 29	99.46 41	95.02 223	99.68 240	98.24 70	99.87 51	99.87 5

LCM-MVSNet-Re		98.64 90	98.48 93	99.11 102	98.85 217	98.51 97	98.49 90	99.83 3	98.37 100	99.69 18	99.46 41	98.21 57	99.92 33	94.13 271	99.30 224	98.91 252

mvs_anonymous		97.83 179	98.16 139	96.87 285	98.18 293	91.89 316	97.31 208	98.90 232	97.37 180	98.83 148	99.46 41	96.28 182	99.79 176	98.90 36	98.16 304	98.95 243

DTE-MVSNet		99.43 16	99.35 18	99.66 5	99.71 30	99.30 17	99.31 19	99.51 54	99.64 12	99.56 27	99.46 41	98.23 52	99.97 3	98.78 43	99.93 25	99.72 25

ACMH	96.65 7	99.25 28	99.24 27	99.26 84	99.72 29	98.38 104	99.07 46	99.55 44	98.30 105	99.65 23	99.45 45	99.22 10	99.76 200	98.44 62	99.77 89	99.64 39
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testing_2		98.93 48	98.99 41	98.76 158	99.57 54	97.03 204	97.85 157	99.13 190	98.46 98	99.44 45	99.44 46	98.22 55	99.74 211	98.85 39	99.94 20	99.51 95

VPA-MVSNet		99.30 25	99.30 24	99.28 79	99.49 84	98.36 106	99.00 51	99.45 77	99.63 14	99.52 34	99.44 46	98.25 50	99.88 63	99.09 27	99.84 55	99.62 43

PEN-MVS		99.41 18	99.34 20	99.62 7	99.73 24	99.14 49	99.29 24	99.54 48	99.62 17	99.56 27	99.42 48	98.16 61	99.96 9	98.78 43	99.93 25	99.77 17

PatchT		96.65 251	96.35 251	97.54 256	97.40 328	95.32 248	97.98 144	96.64 320	99.33 37	96.89 283	99.42 48	84.32 320	99.81 154	97.69 103	97.49 318	97.48 326

FIs		99.14 32	99.09 34	99.29 77	99.70 36	98.28 108	99.13 42	99.52 53	99.48 24	99.24 79	99.41 50	96.79 155	99.82 141	98.69 50	99.88 48	99.76 21

PS-CasMVS		99.40 19	99.33 21	99.62 7	99.71 30	99.10 57	99.29 24	99.53 50	99.53 23	99.46 42	99.41 50	98.23 52	99.95 15	98.89 38	99.95 16	99.81 12

ab-mvs		98.41 123	98.36 114	98.59 176	99.19 141	97.23 191	99.32 16	98.81 250	97.66 149	98.62 172	99.40 52	96.82 152	99.80 163	95.88 215	99.51 188	98.75 271

Anonymous20240529		98.93 48	98.87 44	99.12 100	99.19 141	98.22 116	99.01 49	98.99 221	99.25 42	99.54 29	99.37 53	97.04 137	99.80 163	97.89 87	99.52 185	99.35 166

CR-MVSNet		96.28 263	95.95 260	97.28 268	97.71 315	94.22 271	98.11 124	98.92 229	92.31 310	96.91 279	99.37 53	85.44 313	99.81 154	97.39 113	97.36 324	97.81 311

Patchmtry		97.35 208	96.97 219	98.50 194	97.31 332	96.47 220	98.18 117	98.92 229	98.95 74	98.78 155	99.37 53	85.44 313	99.85 99	95.96 213	99.83 61	99.17 214

EG-PatchMatch MVS		98.99 38	99.01 38	98.94 131	99.50 77	97.47 180	98.04 135	99.59 25	98.15 122	99.40 52	99.36 56	98.58 32	99.76 200	98.78 43	99.68 130	99.59 54

IterMVS-SCA-FT		97.85 176	98.18 135	96.87 285	99.27 123	91.16 329	95.53 302	99.25 154	99.10 58	99.41 49	99.35 57	93.10 261	99.96 9	98.65 51	99.94 20	99.49 104

PMVS	91.26 20	97.86 171	97.94 160	97.65 245	99.71 30	97.94 149	98.52 84	98.68 265	98.99 67	97.52 253	99.35 57	97.41 118	98.18 350	91.59 319	99.67 136	96.82 334
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WR-MVS_H		99.33 24	99.22 28	99.65 6	99.71 30	99.24 24	99.32 16	99.55 44	99.46 27	99.50 38	99.34 59	97.30 123	99.93 27	98.90 36	99.93 25	99.77 17

RPMNet		97.02 235	96.93 220	97.30 267	97.71 315	94.22 271	98.11 124	99.30 136	99.37 34	96.91 279	99.34 59	86.72 300	99.87 79	97.53 107	97.36 324	97.81 311

IterMVS		97.73 183	98.11 145	96.57 292	99.24 128	90.28 330	95.52 304	99.21 163	98.86 78	99.33 62	99.33 61	93.11 260	99.94 23	98.49 59	99.94 20	99.48 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator	98.27 2	98.81 61	98.73 56	99.05 117	98.76 231	97.81 161	99.25 31	99.30 136	98.57 95	98.55 183	99.33 61	97.95 77	99.90 46	97.16 123	99.67 136	99.44 129

IterMVS-LS		98.55 106	98.70 63	98.09 221	99.48 92	94.73 261	97.22 216	99.39 94	98.97 70	99.38 54	99.31 63	96.00 190	99.93 27	98.58 53	99.97 12	99.60 48
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		98.49 115	98.40 107	98.75 161	98.90 206	97.14 202	98.61 74	99.13 190	98.59 91	99.19 85	99.28 64	94.14 245	99.82 141	97.97 85	99.80 76	99.29 187

3Dnovator+	97.89 3	98.69 81	98.51 86	99.24 87	98.81 227	98.40 102	99.02 48	99.19 170	98.99 67	98.07 216	99.28 64	97.11 136	99.84 116	96.84 152	99.32 219	99.47 118

VDD-MVS		98.56 102	98.39 110	99.07 110	99.13 159	98.07 130	98.59 77	97.01 312	99.59 20	99.11 94	99.27 66	94.82 229	99.79 176	98.34 67	99.63 147	99.34 168

PVSNet_Blended_VisFu		98.17 150	98.15 141	98.22 216	99.73 24	95.15 253	97.36 204	99.68 14	94.45 280	98.99 117	99.27 66	96.87 148	99.94 23	97.13 127	99.91 39	99.57 65

nrg030		99.40 19	99.35 18	99.54 30	99.58 50	99.13 52	98.98 54	99.48 66	99.68 9	99.46 42	99.26 68	98.62 29	99.73 216	99.17 25	99.92 34	99.76 21

CP-MVSNet		99.21 29	99.09 34	99.56 25	99.65 43	98.96 65	99.13 42	99.34 115	99.42 30	99.33 62	99.26 68	97.01 141	99.94 23	98.74 47	99.93 25	99.79 14

RPSCF		98.62 94	98.36 114	99.42 57	99.65 43	99.42 5	98.55 81	99.57 34	97.72 146	98.90 134	99.26 68	96.12 185	99.52 294	95.72 225	99.71 115	99.32 176

tfpnnormal		98.90 53	98.90 43	98.91 135	99.67 40	97.82 159	99.00 51	99.44 80	99.45 28	99.51 37	99.24 71	98.20 58	99.86 86	95.92 214	99.69 125	99.04 229

v1240		98.55 106	98.62 72	98.32 208	99.22 133	95.58 239	97.51 194	99.45 77	97.16 203	99.45 44	99.24 71	96.12 185	99.85 99	99.60 4	99.88 48	99.55 78

APDe-MVS		98.99 38	98.79 51	99.60 14	99.21 135	99.15 46	98.87 60	99.48 66	97.57 157	99.35 59	99.24 71	97.83 81	99.89 55	97.88 90	99.70 119	99.75 23

ambc				98.24 215	98.82 225	95.97 232	98.62 73	99.00 220		99.27 72	99.21 74	96.99 142	99.50 299	96.55 180	99.50 195	99.26 193

TAMVS		98.24 143	98.05 151	98.80 150	99.07 171	97.18 198	97.88 152	98.81 250	96.66 225	99.17 90	99.21 74	94.81 231	99.77 193	96.96 139	99.88 48	99.44 129

v1192		98.60 97	98.66 68	98.41 201	99.27 123	95.88 234	97.52 192	99.36 104	97.41 176	99.33 62	99.20 76	96.37 179	99.82 141	99.57 7	99.92 34	99.55 78

pmmvs-eth3d		98.47 117	98.34 117	98.86 142	99.30 120	97.76 164	97.16 223	99.28 145	95.54 257	99.42 48	99.19 77	97.27 126	99.63 260	97.89 87	99.97 12	99.20 203

COLMAP_ROB	96.50 10	98.99 38	98.85 46	99.41 60	99.58 50	99.10 57	98.74 66	99.56 41	99.09 61	99.33 62	99.19 77	98.40 41	99.72 224	95.98 212	99.76 98	99.42 136
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v144192		98.54 109	98.57 80	98.45 198	99.21 135	95.98 231	97.63 179	99.36 104	97.15 205	99.32 67	99.18 79	95.84 201	99.84 116	99.50 11	99.91 39	99.54 82

PM-MVS		98.82 59	98.72 58	99.12 100	99.64 46	98.54 95	97.98 144	99.68 14	97.62 152	99.34 61	99.18 79	97.54 104	99.77 193	97.79 93	99.74 102	99.04 229

PVSNet_BlendedMVS		97.55 195	97.53 188	97.60 249	98.92 202	93.77 291	96.64 252	99.43 85	94.49 276	97.62 243	99.18 79	96.82 152	99.67 243	94.73 249	99.93 25	99.36 162

ACMH+	96.62 9	99.08 34	99.00 39	99.33 74	99.71 30	98.83 70	98.60 75	99.58 27	99.11 54	99.53 32	99.18 79	98.81 22	99.67 243	96.71 165	99.77 89	99.50 100

v1921920		98.54 109	98.60 77	98.38 204	99.20 138	95.76 238	97.56 188	99.36 104	97.23 198	99.38 54	99.17 83	96.02 188	99.84 116	99.57 7	99.90 43	99.54 82

casdiffmvs		98.95 46	99.00 39	98.81 148	99.38 107	97.33 186	97.82 160	99.57 34	99.17 51	99.35 59	99.17 83	98.35 46	99.69 231	98.46 61	99.73 105	99.41 138

Patchmatch-RL test		97.26 215	97.02 216	97.99 230	99.52 72	95.53 241	96.13 277	99.71 10	97.47 166	99.27 72	99.16 85	84.30 321	99.62 262	97.89 87	99.77 89	98.81 262

V42		98.78 66	98.78 52	98.76 158	99.44 100	97.04 203	98.27 108	99.19 170	97.87 137	99.25 78	99.16 85	96.84 149	99.78 187	99.21 22	99.84 55	99.46 120

QAPM		97.31 211	96.81 230	98.82 146	98.80 229	97.49 179	99.06 47	99.19 170	90.22 330	97.69 239	99.16 85	96.91 146	99.90 46	90.89 327	99.41 205	99.07 223

wuyk23d		96.06 267	97.62 183	91.38 336	98.65 259	98.57 91	98.85 63	96.95 314	96.86 217	99.90 5	99.16 85	99.18 12	98.40 349	89.23 333	99.77 89	77.18 352

v1144		98.60 97	98.66 68	98.41 201	99.36 110	95.90 233	97.58 186	99.34 115	97.51 162	99.27 72	99.15 89	96.34 181	99.80 163	99.47 13	99.93 25	99.51 95

DP-MVS		98.93 48	98.81 50	99.28 79	99.21 135	98.45 101	98.46 95	99.33 120	99.63 14	99.48 39	99.15 89	97.23 131	99.75 207	97.17 122	99.66 141	99.63 42

OpenMVS	96.65 7	97.09 228	96.68 237	98.32 208	98.32 284	97.16 200	98.86 62	99.37 100	89.48 334	96.29 305	99.15 89	96.56 167	99.90 46	92.90 298	99.20 238	97.89 305

EPP-MVSNet		98.30 134	98.04 152	99.07 110	99.56 62	97.83 156	99.29 24	98.07 289	99.03 64	98.59 177	99.13 92	92.16 274	99.90 46	96.87 149	99.68 130	99.49 104

ACMMP_NAP		98.75 71	98.48 93	99.57 19	99.58 50	99.29 18	97.82 160	99.25 154	96.94 213	98.78 155	99.12 93	98.02 69	99.84 116	97.13 127	99.67 136	99.59 54

RRT_MVS		97.07 230	96.57 245	98.58 177	95.89 353	96.33 223	97.36 204	98.77 255	97.85 139	99.08 100	99.12 93	82.30 331	99.96 9	98.82 42	99.90 43	99.45 124

MVS_Test		98.18 148	98.36 114	97.67 243	98.48 274	94.73 261	98.18 117	99.02 214	97.69 147	98.04 220	99.11 95	97.22 132	99.56 282	98.57 55	98.90 277	98.71 273

MDA-MVSNet-bldmvs		97.94 164	97.91 162	98.06 225	99.44 100	94.96 257	96.63 253	99.15 189	98.35 101	98.83 148	99.11 95	94.31 242	99.85 99	96.60 171	98.72 283	99.37 156

SMA-MVS		98.40 125	98.03 153	99.51 46	99.16 152	99.21 27	98.05 133	99.22 162	94.16 287	98.98 119	99.10 97	97.52 108	99.79 176	96.45 187	99.64 144	99.53 88
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MIMVSNet		96.62 253	96.25 257	97.71 242	99.04 178	94.66 264	99.16 39	96.92 316	97.23 198	97.87 227	99.10 97	86.11 307	99.65 256	91.65 317	99.21 237	98.82 260

USDC		97.41 205	97.40 196	97.44 262	98.94 196	93.67 293	95.17 312	99.53 50	94.03 290	98.97 122	99.10 97	95.29 217	99.34 320	95.84 221	99.73 105	99.30 183

test0726					99.50 77	99.21 27	98.17 120	99.35 109	97.97 129	99.26 76	99.06 100	97.61 99

AllTest		98.44 120	98.20 132	99.16 95	99.50 77	98.55 92	98.25 110	99.58 27	96.80 218	98.88 141	99.06 100	97.65 94	99.57 279	94.45 258	99.61 156	99.37 156

TestCases				99.16 95	99.50 77	98.55 92		99.58 27	96.80 218	98.88 141	99.06 100	97.65 94	99.57 279	94.45 258	99.61 156	99.37 156

TranMVSNet+NR-MVSNet		99.17 30	99.07 36	99.46 56	99.37 109	98.87 67	98.39 101	99.42 88	99.42 30	99.36 58	99.06 100	98.38 42	99.95 15	98.34 67	99.90 43	99.57 65

LPG-MVS_test		98.71 76	98.46 97	99.47 54	99.57 54	98.97 62	98.23 111	99.48 66	96.60 226	99.10 97	99.06 100	98.71 26	99.83 131	95.58 234	99.78 85	99.62 43

LGP-MVS_train				99.47 54	99.57 54	98.97 62		99.48 66	96.60 226	99.10 97	99.06 100	98.71 26	99.83 131	95.58 234	99.78 85	99.62 43

baseline		98.96 45	99.02 37	98.76 158	99.38 107	97.26 190	98.49 90	99.50 56	98.86 78	99.19 85	99.06 100	98.23 52	99.69 231	98.71 49	99.76 98	99.33 174

VPNet		98.87 55	98.83 47	99.01 124	99.70 36	97.62 175	98.43 98	99.35 109	99.47 26	99.28 70	99.05 107	96.72 161	99.82 141	98.09 77	99.36 213	99.59 54

RRT_test8_iter05		95.24 285	95.13 285	95.57 312	97.32 331	87.02 341	97.99 142	99.41 89	98.06 125	99.12 92	99.05 107	66.85 358	99.85 99	98.93 35	99.47 199	99.84 9

MVSTER		96.86 242	96.55 246	97.79 237	97.91 307	94.21 273	97.56 188	98.87 237	97.49 165	99.06 103	99.05 107	80.72 335	99.80 163	98.44 62	99.82 64	99.37 156

SD-MVS		98.40 125	98.68 65	97.54 256	98.96 193	97.99 137	97.88 152	99.36 104	98.20 117	99.63 26	99.04 110	98.76 23	95.33 354	96.56 177	99.74 102	99.31 180
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
abl_6		98.99 38	98.78 52	99.61 10	99.45 98	99.46 3	98.60 75	99.50 56	98.59 91	99.24 79	99.04 110	98.54 34	99.89 55	96.45 187	99.62 150	99.50 100

FMVSNet5		96.01 268	95.20 283	98.41 201	97.53 323	96.10 228	98.74 66	99.50 56	97.22 201	98.03 221	99.04 110	69.80 355	99.88 63	97.27 118	99.71 115	99.25 194

IS-MVSNet		98.19 147	97.90 163	99.08 107	99.57 54	97.97 142	99.31 19	98.32 280	99.01 66	98.98 119	99.03 113	91.59 278	99.79 176	95.49 236	99.80 76	99.48 110

SED-MVS		98.91 51	98.72 58	99.49 49	99.49 84	99.17 37	98.10 126	99.31 127	98.03 126	99.66 21	99.02 114	98.36 43	99.88 63	96.91 141	99.62 150	99.41 138

test_241102_TWO								99.30 136	98.03 126	99.26 76	99.02 114	97.51 109	99.88 63	96.91 141	99.60 158	99.66 34

DVP-MVS		98.77 68	98.52 84	99.52 42	99.50 77	99.21 27	98.02 138	98.84 244	97.97 129	99.08 100	99.02 114	97.61 99	99.88 63	96.99 135	99.63 147	99.48 110

test_0728_THIRD									98.17 120	99.08 100	99.02 114	97.89 78	99.88 63	97.07 130	99.71 115	99.70 29

EI-MVSNet		98.40 125	98.51 86	98.04 227	99.10 164	94.73 261	97.20 217	98.87 237	98.97 70	99.06 103	99.02 114	96.00 190	99.80 163	98.58 53	99.82 64	99.60 48

CVMVSNet		96.25 264	97.21 208	93.38 333	99.10 164	80.56 357	97.20 217	98.19 287	96.94 213	99.00 116	99.02 114	89.50 290	99.80 163	96.36 193	99.59 160	99.78 15

LFMVS		97.20 221	96.72 234	98.64 168	98.72 237	96.95 208	98.93 57	94.14 340	99.74 7	98.78 155	99.01 120	84.45 318	99.73 216	97.44 110	99.27 228	99.25 194

v2v482		98.56 102	98.62 72	98.37 205	99.42 104	95.81 237	97.58 186	99.16 183	97.90 135	99.28 70	99.01 120	95.98 194	99.79 176	99.33 16	99.90 43	99.51 95

ACMMP		98.75 71	98.50 88	99.52 42	99.56 62	99.16 41	98.87 60	99.37 100	97.16 203	98.82 152	99.01 120	97.71 90	99.87 79	96.29 198	99.69 125	99.54 82
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
DPE-MVS		98.59 100	98.26 126	99.57 19	99.27 123	99.15 46	97.01 228	99.39 94	97.67 148	99.44 45	98.99 123	97.53 106	99.89 55	95.40 238	99.68 130	99.66 34

MP-MVS-pluss		98.57 101	98.23 130	99.60 14	99.69 38	99.35 12	97.16 223	99.38 96	94.87 271	98.97 122	98.99 123	98.01 70	99.88 63	97.29 117	99.70 119	99.58 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EI-MVSNet-UG-set		98.69 81	98.71 60	98.62 172	99.10 164	96.37 222	97.23 213	98.87 237	99.20 46	99.19 85	98.99 123	97.30 123	99.85 99	98.77 46	99.79 81	99.65 38

XVG-ACMP-BASELINE		98.56 102	98.34 117	99.22 89	99.54 68	98.59 89	97.71 171	99.46 74	97.25 192	98.98 119	98.99 123	97.54 104	99.84 116	95.88 215	99.74 102	99.23 198

APD-MVS_3200maxsize		98.84 58	98.61 75	99.53 37	99.19 141	99.27 21	98.49 90	99.33 120	98.64 85	99.03 113	98.98 127	97.89 78	99.85 99	96.54 181	99.42 204	99.46 120

XVG-OURS		98.53 111	98.34 117	99.11 102	99.50 77	98.82 72	95.97 281	99.50 56	97.30 187	99.05 108	98.98 127	99.35 7	99.32 323	95.72 225	99.68 130	99.18 210

v148		98.45 119	98.60 77	98.00 229	99.44 100	94.98 256	97.44 200	99.06 201	98.30 105	99.32 67	98.97 129	96.65 164	99.62 262	98.37 66	99.85 53	99.39 147

EI-MVSNet-Vis-set		98.68 84	98.70 63	98.63 170	99.09 167	96.40 221	97.23 213	98.86 242	99.20 46	99.18 89	98.97 129	97.29 125	99.85 99	98.72 48	99.78 85	99.64 39

CHOSEN 1792x2688		97.49 199	97.14 213	98.54 188	99.68 39	96.09 230	96.50 259	99.62 20	91.58 318	98.84 147	98.97 129	92.36 272	99.88 63	96.76 158	99.95 16	99.67 33

SR-MVS-dyc-post		98.81 61	98.55 81	99.57 19	99.20 138	99.38 6	98.48 93	99.30 136	98.64 85	98.95 125	98.96 132	97.49 113	99.86 86	96.56 177	99.39 208	99.45 124

RE-MVS-def			98.58 79		99.20 138	99.38 6	98.48 93	99.30 136	98.64 85	98.95 125	98.96 132	97.75 88		96.56 177	99.39 208	99.45 124

D2MVS		97.84 177	97.84 167	97.83 235	99.14 157	94.74 260	96.94 232	98.88 235	95.84 251	98.89 137	98.96 132	94.40 240	99.69 231	97.55 104	99.95 16	99.05 225

ACMM	96.08 12	98.91 51	98.73 56	99.48 51	99.55 65	99.14 49	98.07 129	99.37 100	97.62 152	99.04 110	98.96 132	98.84 20	99.79 176	97.43 111	99.65 142	99.49 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		98.08 154	97.92 161	98.57 180	98.96 193	96.79 212	97.90 151	99.18 174	96.41 232	98.46 189	98.95 136	95.93 197	99.60 269	96.51 183	98.98 273	99.31 180
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
YYNet1		97.60 192	97.67 176	97.39 265	99.04 178	93.04 301	95.27 309	98.38 279	97.25 192	98.92 133	98.95 136	95.48 214	99.73 216	96.99 135	98.74 281	99.41 138

MDA-MVSNet_test_wron		97.60 192	97.66 179	97.41 264	99.04 178	93.09 297	95.27 309	98.42 277	97.26 191	98.88 141	98.95 136	95.43 215	99.73 216	97.02 132	98.72 283	99.41 138

FMVSNet3		97.50 197	97.24 207	98.29 212	98.08 299	95.83 236	97.86 155	98.91 231	97.89 136	98.95 125	98.95 136	87.06 298	99.81 154	97.77 95	99.69 125	99.23 198

OPM-MVS		98.56 102	98.32 121	99.25 86	99.41 105	98.73 79	97.13 225	99.18 174	97.10 206	98.75 160	98.92 140	98.18 59	99.65 256	96.68 167	99.56 175	99.37 156

ADS-MVSNet2		95.43 282	94.98 288	96.76 291	98.14 295	91.74 317	97.92 148	97.76 296	90.23 328	96.51 298	98.91 141	85.61 310	99.85 99	92.88 299	96.90 330	98.69 276

ADS-MVSNet		95.24 285	94.93 290	96.18 300	98.14 295	90.10 331	97.92 148	97.32 307	90.23 328	96.51 298	98.91 141	85.61 310	99.74 211	92.88 299	96.90 330	98.69 276

test_0402		98.76 69	98.71 60	98.93 132	99.56 62	98.14 122	98.45 97	99.34 115	99.28 40	98.95 125	98.91 141	98.34 47	99.79 176	95.63 231	99.91 39	98.86 257

test_241102_ONE					99.49 84	99.17 37		99.31 127	97.98 128	99.66 21	98.90 144	98.36 43	99.48 303

xxxxxxxxxxxxxcwj		98.44 120	98.24 128	99.06 115	99.11 160	97.97 142	96.53 256	99.54 48	98.24 111	98.83 148	98.90 144	97.80 85	99.82 141	95.68 228	99.52 185	99.38 153

SF-MVS		98.53 111	98.27 125	99.32 76	99.31 117	98.75 75	98.19 116	99.41 89	96.77 220	98.83 148	98.90 144	97.80 85	99.82 141	95.68 228	99.52 185	99.38 153

zzz-MVS		98.79 63	98.52 84	99.61 10	99.67 40	99.36 10	97.33 206	99.20 165	98.83 81	98.89 137	98.90 144	96.98 143	99.92 33	97.16 123	99.70 119	99.56 70

MTAPA		98.88 54	98.64 70	99.61 10	99.67 40	99.36 10	98.43 98	99.20 165	98.83 81	98.89 137	98.90 144	96.98 143	99.92 33	97.16 123	99.70 119	99.56 70

test20.03		98.78 66	98.77 54	98.78 155	99.46 95	97.20 196	97.78 162	99.24 159	99.04 63	99.41 49	98.90 144	97.65 94	99.76 200	97.70 101	99.79 81	99.39 147

SteuartSystems-ACMMP		98.79 63	98.54 82	99.54 30	99.73 24	99.16 41	98.23 111	99.31 127	97.92 133	98.90 134	98.90 144	98.00 71	99.88 63	96.15 206	99.72 111	99.58 60
Skip Steuart: Steuart Systems R&D Blog.
N_pmnet		97.63 191	97.17 209	98.99 126	99.27 123	97.86 154	95.98 280	93.41 342	95.25 264	99.47 41	98.90 144	95.63 206	99.85 99	96.91 141	99.73 105	99.27 190

TSAR-MVS + MP.		98.63 92	98.49 91	99.06 115	99.64 46	97.90 151	98.51 88	98.94 224	96.96 212	99.24 79	98.89 152	97.83 81	99.81 154	96.88 148	99.49 196	99.48 110
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test1172		98.76 69	98.49 91	99.57 19	99.18 148	99.37 9	98.39 101	99.31 127	98.43 99	98.90 134	98.88 153	97.49 113	99.86 86	96.43 189	99.37 212	99.48 110

PGM-MVS		98.66 87	98.37 113	99.55 27	99.53 70	99.18 36	98.23 111	99.49 64	97.01 211	98.69 164	98.88 153	98.00 71	99.89 55	95.87 218	99.59 160	99.58 60

TinyColmap		97.89 167	97.98 156	97.60 249	98.86 215	94.35 270	96.21 274	99.44 80	97.45 173	99.06 103	98.88 153	97.99 74	99.28 329	94.38 264	99.58 166	99.18 210

LS3D		98.63 92	98.38 112	99.36 64	97.25 333	99.38 6	99.12 44	99.32 122	99.21 43	98.44 191	98.88 153	97.31 122	99.80 163	96.58 172	99.34 217	98.92 249

Anonymous202405211		97.90 165	97.50 190	99.08 107	98.90 206	98.25 110	98.53 83	96.16 324	98.87 77	99.11 94	98.86 157	90.40 284	99.78 187	97.36 114	99.31 221	99.19 208

HPM-MVS_fast		99.01 36	98.82 48	99.57 19	99.71 30	99.35 12	99.00 51	99.50 56	97.33 183	98.94 131	98.86 157	98.75 24	99.82 141	97.53 107	99.71 115	99.56 70

CMPMVS	75.91 23	96.29 262	95.44 275	98.84 144	96.25 349	98.69 82	97.02 227	99.12 193	88.90 337	97.83 230	98.86 157	89.51 289	98.90 344	91.92 313	99.51 188	98.92 249
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SR-MVS		98.71 76	98.43 103	99.57 19	99.18 148	99.35 12	98.36 104	99.29 143	98.29 108	98.88 141	98.85 160	97.53 106	99.87 79	96.14 207	99.31 221	99.48 110

our_test_3		97.39 206	97.73 174	96.34 296	98.70 244	89.78 332	94.61 329	98.97 223	96.50 228	99.04 110	98.85 160	95.98 194	99.84 116	97.26 119	99.67 136	99.41 138

MVS_0304		97.64 189	97.35 201	98.52 189	97.87 309	96.69 217	98.59 77	98.05 291	97.44 174	93.74 344	98.85 160	93.69 255	99.88 63	98.11 76	99.81 68	98.98 238

Regformer-3		98.61 95	98.61 75	98.63 170	99.02 183	96.53 219	97.17 221	98.84 244	99.13 53	99.10 97	98.85 160	97.24 130	99.79 176	98.41 65	99.70 119	99.57 65

Regformer-4		98.73 74	98.68 65	98.89 138	99.02 183	97.22 193	97.17 221	99.06 201	99.21 43	99.17 90	98.85 160	97.45 116	99.86 86	98.48 60	99.70 119	99.60 48

EPNet		96.14 266	95.44 275	98.25 214	90.76 358	95.50 243	97.92 148	94.65 333	98.97 70	92.98 345	98.85 160	89.12 292	99.87 79	95.99 211	99.68 130	99.39 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		97.64 189	97.49 191	98.08 224	99.14 157	95.12 255	96.70 250	99.05 205	93.77 293	98.62 172	98.83 166	93.23 257	99.75 207	98.33 69	99.76 98	99.36 162

PMMVS2		98.07 155	98.08 149	98.04 227	99.41 105	94.59 267	94.59 330	99.40 92	97.50 163	98.82 152	98.83 166	96.83 151	99.84 116	97.50 109	99.81 68	99.71 26

MDTV_nov1_ep13			95.22 282		97.06 336	83.20 353	97.74 169	96.16 324	94.37 282	96.99 275	98.83 166	83.95 323	99.53 290	93.90 277	97.95 313

Anonymous20231206		98.21 145	98.21 131	98.20 217	99.51 74	95.43 246	98.13 121	99.32 122	96.16 240	98.93 132	98.82 169	96.00 190	99.83 131	97.32 116	99.73 105	99.36 162

ACMP	95.32 15	98.41 123	98.09 146	99.36 64	99.51 74	98.79 74	97.68 174	99.38 96	95.76 254	98.81 154	98.82 169	98.36 43	99.82 141	94.75 248	99.77 89	99.48 110
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VNet		98.42 122	98.30 122	98.79 152	98.79 230	97.29 187	98.23 111	98.66 266	99.31 38	98.85 145	98.80 171	94.80 232	99.78 187	98.13 75	99.13 253	99.31 180

tpmrst		95.07 288	95.46 273	93.91 327	97.11 335	84.36 351	97.62 180	96.96 313	94.98 267	96.35 304	98.80 171	85.46 312	99.59 273	95.60 232	96.23 338	97.79 314

ppachtmachnet_test		97.50 197	97.74 172	96.78 290	98.70 244	91.23 328	94.55 331	99.05 205	96.36 233	99.21 83	98.79 173	96.39 176	99.78 187	96.74 160	99.82 64	99.34 168

miper_lstm_enhance		97.18 223	97.16 210	97.25 270	98.16 294	92.85 303	95.15 314	99.31 127	97.25 192	98.74 162	98.78 174	90.07 285	99.78 187	97.19 121	99.80 76	99.11 221

DeepPCF-MVS	96.93 5	98.32 132	98.01 154	99.23 88	98.39 281	98.97 62	95.03 316	99.18 174	96.88 216	99.33 62	98.78 174	98.16 61	99.28 329	96.74 160	99.62 150	99.44 129

patchmatchnet-post											98.77 176	84.37 319	99.85 99

APD-MVS		98.10 152	97.67 176	99.42 57	99.11 160	98.93 66	97.76 167	99.28 145	94.97 268	98.72 163	98.77 176	97.04 137	99.85 99	93.79 282	99.54 178	99.49 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DU-MVS		98.82 59	98.63 71	99.39 63	99.16 152	98.74 76	97.54 190	99.25 154	98.84 80	99.06 103	98.76 178	96.76 158	99.93 27	98.57 55	99.77 89	99.50 100

NR-MVSNet		98.95 46	98.82 48	99.36 64	99.16 152	98.72 81	99.22 32	99.20 165	99.10 58	99.72 14	98.76 178	96.38 178	99.86 86	98.00 84	99.82 64	99.50 100

eth_miper_zixun_eth		97.23 219	97.25 205	97.17 272	98.00 303	92.77 305	94.71 323	99.18 174	97.27 190	98.56 181	98.74 180	91.89 277	99.69 231	97.06 131	99.81 68	99.05 225

UniMVSNet (Re)		98.87 55	98.71 60	99.35 69	99.24 128	98.73 79	97.73 170	99.38 96	98.93 75	99.12 92	98.73 181	96.77 156	99.86 86	98.63 52	99.80 76	99.46 120

MG-MVS		96.77 246	96.61 242	97.26 269	98.31 285	93.06 298	95.93 286	98.12 288	96.45 231	97.92 223	98.73 181	93.77 253	99.39 315	91.19 323	99.04 263	99.33 174

cl_fuxian		97.36 207	97.37 199	97.31 266	98.09 298	93.25 296	95.01 317	99.16 183	97.05 208	98.77 158	98.72 183	92.88 266	99.64 258	96.93 140	99.76 98	99.05 225

cl-mvsnet_		97.02 235	96.83 229	97.58 251	97.82 311	94.04 277	94.66 326	99.16 183	97.04 209	98.63 170	98.71 184	88.68 295	99.69 231	97.00 133	99.81 68	99.00 236

cl-mvsnet1		97.02 235	96.84 228	97.58 251	97.82 311	94.03 278	94.66 326	99.16 183	97.04 209	98.63 170	98.71 184	88.69 294	99.69 231	97.00 133	99.81 68	99.01 233

DELS-MVS		98.27 138	98.20 132	98.48 195	98.86 215	96.70 216	95.60 300	99.20 165	97.73 145	98.45 190	98.71 184	97.50 110	99.82 141	98.21 72	99.59 160	98.93 248
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
9.14			97.78 169		99.07 171		97.53 191	99.32 122	95.53 259	98.54 185	98.70 187	97.58 101	99.76 200	94.32 265	99.46 200

tpmvs		95.02 290	95.25 281	94.33 323	96.39 348	85.87 343	98.08 128	96.83 318	95.46 261	95.51 326	98.69 188	85.91 308	99.53 290	94.16 266	96.23 338	97.58 323

PatchmatchNet		95.58 278	95.67 267	95.30 316	97.34 330	87.32 339	97.65 178	96.65 319	95.30 263	97.07 271	98.69 188	84.77 315	99.75 207	94.97 244	98.64 289	98.83 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mPP-MVS		98.64 90	98.34 117	99.54 30	99.54 68	99.17 37	98.63 72	99.24 159	97.47 166	98.09 215	98.68 190	97.62 98	99.89 55	96.22 201	99.62 150	99.57 65

UnsupCasMVSNet_eth		97.89 167	97.60 185	98.75 161	99.31 117	97.17 199	97.62 180	99.35 109	98.72 84	98.76 159	98.68 190	92.57 271	99.74 211	97.76 99	95.60 341	99.34 168

SCA		96.41 260	96.66 240	95.67 309	98.24 289	88.35 335	95.85 291	96.88 317	96.11 241	97.67 240	98.67 192	93.10 261	99.85 99	94.16 266	99.22 235	98.81 262

Patchmatch-test		96.55 254	96.34 252	97.17 272	98.35 282	93.06 298	98.40 100	97.79 295	97.33 183	98.41 195	98.67 192	83.68 325	99.69 231	95.16 240	99.31 221	98.77 269

CDS-MVSNet		97.69 185	97.35 201	98.69 165	98.73 236	97.02 206	96.92 236	98.75 259	95.89 250	98.59 177	98.67 192	92.08 276	99.74 211	96.72 163	99.81 68	99.32 176
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MP-MVS		98.46 118	98.09 146	99.54 30	99.57 54	99.22 26	98.50 89	99.19 170	97.61 154	97.58 247	98.66 195	97.40 119	99.88 63	94.72 251	99.60 158	99.54 82
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	96.85 6	98.30 134	98.15 141	98.75 161	98.61 260	97.23 191	97.76 167	99.09 197	97.31 186	98.75 160	98.66 195	97.56 103	99.64 258	96.10 209	99.55 177	99.39 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MS-PatchMatch		97.68 186	97.75 171	97.45 261	98.23 291	93.78 290	97.29 209	98.84 244	96.10 242	98.64 169	98.65 197	96.04 187	99.36 318	96.84 152	99.14 250	99.20 203

pmmvs4		97.58 194	97.28 204	98.51 192	98.84 220	96.93 209	95.40 308	98.52 273	93.60 295	98.61 174	98.65 197	95.10 222	99.60 269	96.97 138	99.79 81	98.99 237

FPMVS		93.44 312	92.23 317	97.08 275	99.25 127	97.86 154	95.61 299	97.16 310	92.90 303	93.76 343	98.65 197	75.94 350	95.66 352	79.30 352	97.49 318	97.73 316

Regformer-1		98.55 106	98.44 101	98.87 140	98.85 217	97.29 187	96.91 237	98.99 221	98.97 70	98.99 117	98.64 200	97.26 129	99.81 154	97.79 93	99.57 170	99.51 95

Regformer-2		98.60 97	98.46 97	99.02 123	98.85 217	97.71 169	96.91 237	99.09 197	98.98 69	99.01 114	98.64 200	97.37 121	99.84 116	97.75 100	99.57 170	99.52 92

dp		93.47 311	93.59 305	93.13 335	96.64 342	81.62 356	97.66 176	96.42 322	92.80 305	96.11 307	98.64 200	78.55 347	99.59 273	93.31 294	92.18 352	98.16 298

EPMVS		93.72 309	93.27 308	95.09 318	96.04 351	87.76 337	98.13 121	85.01 356	94.69 274	96.92 277	98.64 200	78.47 348	99.31 324	95.04 241	96.46 336	98.20 296

XVS		98.72 75	98.45 99	99.53 37	99.46 95	99.21 27	98.65 70	99.34 115	98.62 89	97.54 251	98.63 204	97.50 110	99.83 131	96.79 154	99.53 182	99.56 70

CostFormer		93.97 305	93.78 302	94.51 322	97.53 323	85.83 345	97.98 144	95.96 327	89.29 336	94.99 332	98.63 204	78.63 345	99.62 262	94.54 254	96.50 335	98.09 301

ETH3D-3000-0.1		98.03 156	97.62 183	99.29 77	99.11 160	98.80 73	97.47 198	99.32 122	95.54 257	98.43 194	98.62 206	96.61 166	99.77 193	93.95 276	99.49 196	99.30 183

MSLP-MVS++		98.02 158	98.14 143	97.64 247	98.58 265	95.19 252	97.48 196	99.23 161	97.47 166	97.90 225	98.62 206	97.04 137	98.81 346	97.55 104	99.41 205	98.94 247

Vis-MVSNet (Re-imp)		97.46 202	97.16 210	98.34 207	99.55 65	96.10 228	98.94 56	98.44 276	98.32 104	98.16 208	98.62 206	88.76 293	99.73 216	93.88 279	99.79 81	99.18 210

XVG-OURS-SEG-HR		98.49 115	98.28 124	99.14 98	99.49 84	98.83 70	96.54 255	99.48 66	97.32 185	99.11 94	98.61 209	99.33 8	99.30 326	96.23 200	98.38 296	99.28 188

ITE_SJBPF				98.87 140	99.22 133	98.48 99		99.35 109	97.50 163	98.28 203	98.60 210	97.64 97	99.35 319	93.86 280	99.27 228	98.79 267

UniMVSNet_NR-MVSNet		98.86 57	98.68 65	99.40 62	99.17 150	98.74 76	97.68 174	99.40 92	99.14 52	99.06 103	98.59 211	96.71 162	99.93 27	98.57 55	99.77 89	99.53 88

114514_t		96.50 257	95.77 262	98.69 165	99.48 92	97.43 183	97.84 158	99.55 44	81.42 351	96.51 298	98.58 212	95.53 209	99.67 243	93.41 292	99.58 166	98.98 238

HY-MVS	95.94 13	95.90 271	95.35 279	97.55 255	97.95 304	94.79 259	98.81 65	96.94 315	92.28 311	95.17 329	98.57 213	89.90 287	99.75 207	91.20 322	97.33 326	98.10 300

tpm		94.67 293	94.34 297	95.66 310	97.68 319	88.42 334	97.88 152	94.90 332	94.46 278	96.03 312	98.56 214	78.66 344	99.79 176	95.88 215	95.01 344	98.78 268

ACMMPR		98.70 79	98.42 105	99.54 30	99.52 72	99.14 49	98.52 84	99.31 127	97.47 166	98.56 181	98.54 215	97.75 88	99.88 63	96.57 174	99.59 160	99.58 60

new_pmnet		96.99 239	96.76 232	97.67 243	98.72 237	94.89 258	95.95 285	98.20 285	92.62 307	98.55 183	98.54 215	94.88 228	99.52 294	93.96 275	99.44 203	98.59 282

OPU-MVS				98.82 146	98.59 264	98.30 107	98.10 126				98.52 217	98.18 59	98.75 347	94.62 252	99.48 198	99.41 138

region2R		98.69 81	98.40 107	99.54 30	99.53 70	99.17 37	98.52 84	99.31 127	97.46 171	98.44 191	98.51 218	97.83 81	99.88 63	96.46 186	99.58 166	99.58 60

TSAR-MVS + GP.		98.18 148	97.98 156	98.77 157	98.71 240	97.88 152	96.32 269	98.66 266	96.33 234	99.23 82	98.51 218	97.48 115	99.40 313	97.16 123	99.46 200	99.02 232

OMC-MVS		97.88 169	97.49 191	99.04 119	98.89 211	98.63 84	96.94 232	99.25 154	95.02 266	98.53 186	98.51 218	97.27 126	99.47 305	93.50 290	99.51 188	99.01 233

testtj		97.79 182	97.25 205	99.42 57	99.03 181	98.85 68	97.78 162	99.18 174	95.83 252	98.12 212	98.50 221	95.50 212	99.86 86	92.23 312	99.07 259	99.54 82

HFP-MVS		98.71 76	98.44 101	99.51 46	99.49 84	99.16 41	98.52 84	99.31 127	97.47 166	98.58 179	98.50 221	97.97 75	99.85 99	96.57 174	99.59 160	99.53 88

#test#		98.50 114	98.16 139	99.51 46	99.49 84	99.16 41	98.03 136	99.31 127	96.30 237	98.58 179	98.50 221	97.97 75	99.85 99	95.68 228	99.59 160	99.53 88

diffmvs		98.22 144	98.24 128	98.17 219	99.00 186	95.44 245	96.38 266	99.58 27	97.79 143	98.53 186	98.50 221	96.76 158	99.74 211	97.95 86	99.64 144	99.34 168

WR-MVS		98.40 125	98.19 134	99.03 120	99.00 186	97.65 172	96.85 240	98.94 224	98.57 95	98.89 137	98.50 221	95.60 207	99.85 99	97.54 106	99.85 53	99.59 54

Test_1112_low_res		96.99 239	96.55 246	98.31 210	99.35 114	95.47 244	95.84 292	99.53 50	91.51 320	96.80 288	98.48 226	91.36 279	99.83 131	96.58 172	99.53 182	99.62 43

miper_ehance_all_eth		97.06 231	97.03 215	97.16 274	97.83 310	93.06 298	94.66 326	99.09 197	95.99 247	98.69 164	98.45 227	92.73 269	99.61 268	96.79 154	99.03 264	98.82 260

PHI-MVS		98.29 137	97.95 158	99.34 72	98.44 278	99.16 41	98.12 123	99.38 96	96.01 246	98.06 217	98.43 228	97.80 85	99.67 243	95.69 227	99.58 166	99.20 203

tpm cat1		93.29 313	93.13 312	93.75 328	97.39 329	84.74 348	97.39 201	97.65 300	83.39 350	94.16 337	98.41 229	82.86 329	99.39 315	91.56 320	95.35 343	97.14 330

ETH3D cwj APD-0.16		97.55 195	97.00 217	99.19 91	98.51 272	98.64 83	96.85 240	99.13 190	94.19 286	97.65 241	98.40 230	95.78 202	99.81 154	93.37 293	99.16 246	99.12 219

CP-MVS		98.70 79	98.42 105	99.52 42	99.36 110	99.12 54	98.72 68	99.36 104	97.54 161	98.30 201	98.40 230	97.86 80	99.89 55	96.53 182	99.72 111	99.56 70

ZNCC-MVS		98.68 84	98.40 107	99.54 30	99.57 54	99.21 27	98.46 95	99.29 143	97.28 189	98.11 213	98.39 232	98.00 71	99.87 79	96.86 151	99.64 144	99.55 78

GST-MVS		98.61 95	98.30 122	99.52 42	99.51 74	99.20 33	98.26 109	99.25 154	97.44 174	98.67 166	98.39 232	97.68 91	99.85 99	96.00 210	99.51 188	99.52 92

HPM-MVS		98.79 63	98.53 83	99.59 18	99.65 43	99.29 18	99.16 39	99.43 85	96.74 221	98.61 174	98.38 234	98.62 29	99.87 79	96.47 185	99.67 136	99.59 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				98.09 221	98.93 198	95.40 247		98.80 252	90.08 332	97.45 259	98.37 235	95.26 218	99.70 227	93.58 287	98.95 275	99.17 214

CPTT-MVS		97.84 177	97.36 200	99.27 82	99.31 117	98.46 100	98.29 106	99.27 148	94.90 270	97.83 230	98.37 235	94.90 225	99.84 116	93.85 281	99.54 178	99.51 95

OpenMVS_ROB	95.38 14	95.84 273	95.18 284	97.81 236	98.41 280	97.15 201	97.37 203	98.62 269	83.86 348	98.65 168	98.37 235	94.29 243	99.68 240	88.41 335	98.62 291	96.60 337

tttt0517		95.64 277	94.98 288	97.64 247	99.36 110	93.81 289	98.72 68	90.47 351	98.08 124	98.67 166	98.34 238	73.88 352	99.92 33	97.77 95	99.51 188	99.20 203

旧先验1					98.82 225	97.45 182		98.76 256			98.34 238	95.50 212			99.01 269	99.23 198

CNVR-MVS		98.17 150	97.87 165	99.07 110	98.67 253	98.24 111	97.01 228	98.93 226	97.25 192	97.62 243	98.34 238	97.27 126	99.57 279	96.42 190	99.33 218	99.39 147

HyFIR lowres test		97.19 222	96.60 243	98.96 128	99.62 49	97.28 189	95.17 312	99.50 56	94.21 285	99.01 114	98.32 241	86.61 301	99.99 2	97.10 129	99.84 55	99.60 48

UnsupCasMVSNet_bld		97.30 212	96.92 222	98.45 198	99.28 122	96.78 215	96.20 275	99.27 148	95.42 262	98.28 203	98.30 242	93.16 259	99.71 225	94.99 243	97.37 322	98.87 256

MSDG		97.71 184	97.52 189	98.28 213	98.91 205	96.82 211	94.42 333	99.37 100	97.65 150	98.37 200	98.29 243	97.40 119	99.33 322	94.09 272	99.22 235	98.68 279

MVS_111021_HR		98.25 142	98.08 149	98.75 161	99.09 167	97.46 181	95.97 281	99.27 148	97.60 155	97.99 222	98.25 244	98.15 63	99.38 317	96.87 149	99.57 170	99.42 136

CANet_DTU		97.26 215	97.06 214	97.84 234	97.57 320	94.65 265	96.19 276	98.79 253	97.23 198	95.14 330	98.24 245	93.22 258	99.84 116	97.34 115	99.84 55	99.04 229

MVS_111021_LR		98.30 134	98.12 144	98.83 145	99.16 152	98.03 135	96.09 278	99.30 136	97.58 156	98.10 214	98.24 245	98.25 50	99.34 320	96.69 166	99.65 142	99.12 219

tpm2		93.09 315	92.58 316	94.62 321	97.56 321	86.53 342	97.66 176	95.79 329	86.15 345	94.07 340	98.23 247	75.95 349	99.53 290	90.91 326	96.86 333	97.81 311

CANet		97.87 170	97.76 170	98.19 218	97.75 313	95.51 242	96.76 246	99.05 205	97.74 144	96.93 276	98.21 248	95.59 208	99.89 55	97.86 92	99.93 25	99.19 208

LF4IMVS		97.90 165	97.69 175	98.52 189	99.17 150	97.66 171	97.19 220	99.47 72	96.31 236	97.85 229	98.20 249	96.71 162	99.52 294	94.62 252	99.72 111	98.38 291

cl-mvsnet2		95.79 274	95.39 278	96.98 279	96.77 341	92.79 304	94.40 334	98.53 272	94.59 275	97.89 226	98.17 250	82.82 330	99.24 331	96.37 191	99.03 264	98.92 249

1121		96.73 247	96.00 258	98.91 135	98.95 195	97.76 164	98.07 129	98.73 262	87.65 342	96.54 295	98.13 251	94.52 237	99.73 216	92.38 310	99.02 267	99.24 197

MVSFormer		98.26 140	98.43 103	97.77 238	98.88 212	93.89 287	99.39 12	99.56 41	99.11 54	98.16 208	98.13 251	93.81 251	99.97 3	99.26 19	99.57 170	99.43 133

jason		97.45 203	97.35 201	97.76 239	99.24 128	93.93 283	95.86 289	98.42 277	94.24 284	98.50 188	98.13 251	94.82 229	99.91 43	97.22 120	99.73 105	99.43 133
jason: jason.
ZD-MVS					99.01 185	98.84 69		99.07 200	94.10 288	98.05 219	98.12 254	96.36 180	99.86 86	92.70 306	99.19 242

test222					98.92 202	96.93 209	95.54 301	98.78 254	85.72 346	96.86 285	98.11 255	94.43 238			99.10 258	99.23 198

新几何1				98.91 135	98.94 196	97.76 164		98.76 256	87.58 343	96.75 289	98.10 256	94.80 232	99.78 187	92.73 305	99.00 270	99.20 203

原ACMM1				98.35 206	98.90 206	96.25 226		98.83 249	92.48 308	96.07 310	98.10 256	95.39 216	99.71 225	92.61 308	98.99 271	99.08 222

EPNet_dtu		94.93 291	94.78 292	95.38 315	93.58 357	87.68 338	96.78 244	95.69 330	97.35 182	89.14 353	98.09 258	88.15 296	99.49 300	94.95 245	99.30 224	98.98 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		95.03 289	94.40 295	96.93 281	97.70 317	92.53 308	95.08 315	97.71 298	88.57 339	97.71 237	98.08 259	79.39 342	99.82 141	96.19 203	99.11 257	98.43 289

DP-MVS Recon		97.33 210	96.92 222	98.57 180	99.09 167	97.99 137	96.79 243	99.35 109	93.18 299	97.71 237	98.07 260	95.00 224	99.31 324	93.97 274	99.13 253	98.42 290

CSCG		98.68 84	98.50 88	99.20 90	99.45 98	98.63 84	98.56 80	99.57 34	97.87 137	98.85 145	98.04 261	97.66 93	99.84 116	96.72 163	99.81 68	99.13 218

F-COLMAP		97.30 212	96.68 237	99.14 98	99.19 141	98.39 103	97.27 212	99.30 136	92.93 302	96.62 293	98.00 262	95.73 204	99.68 240	92.62 307	98.46 295	99.35 166

Effi-MVS+-dtu		98.26 140	97.90 163	99.35 69	98.02 301	99.49 2	98.02 138	99.16 183	98.29 108	97.64 242	97.99 263	96.44 174	99.95 15	96.66 168	98.93 276	98.60 280

HQP_MVS		97.99 163	97.67 176	98.93 132	99.19 141	97.65 172	97.77 165	99.27 148	98.20 117	97.79 233	97.98 264	94.90 225	99.70 227	94.42 260	99.51 188	99.45 124

plane_prior4											97.98 264

BH-RMVSNet		96.83 243	96.58 244	97.58 251	98.47 275	94.05 276	96.67 251	97.36 304	96.70 224	97.87 227	97.98 264	95.14 221	99.44 310	90.47 329	98.58 293	99.25 194

AUN-MVS		96.24 265	95.45 274	98.60 175	98.70 244	97.22 193	97.38 202	97.65 300	95.95 248	95.53 325	97.96 267	82.11 334	99.79 176	96.31 195	97.44 320	98.80 266

NCCC		97.86 171	97.47 195	99.05 117	98.61 260	98.07 130	96.98 230	98.90 232	97.63 151	97.04 273	97.93 268	95.99 193	99.66 251	95.31 239	98.82 279	99.43 133

sss		97.21 220	96.93 220	98.06 225	98.83 222	95.22 251	96.75 247	98.48 275	94.49 276	97.27 266	97.90 269	92.77 268	99.80 163	96.57 174	99.32 219	99.16 217

test_yl		96.69 248	96.29 254	97.90 231	98.28 286	95.24 249	97.29 209	97.36 304	98.21 114	98.17 206	97.86 270	86.27 303	99.55 285	94.87 246	98.32 297	98.89 253

DCV-MVSNet		96.69 248	96.29 254	97.90 231	98.28 286	95.24 249	97.29 209	97.36 304	98.21 114	98.17 206	97.86 270	86.27 303	99.55 285	94.87 246	98.32 297	98.89 253

CDPH-MVS		97.26 215	96.66 240	99.07 110	99.00 186	98.15 120	96.03 279	99.01 217	91.21 324	97.79 233	97.85 272	96.89 147	99.69 231	92.75 304	99.38 211	99.39 147

HPM-MVS++		98.10 152	97.64 181	99.48 51	99.09 167	99.13 52	97.52 192	98.75 259	97.46 171	96.90 282	97.83 273	96.01 189	99.84 116	95.82 222	99.35 215	99.46 120

ETH3 D test6400		96.46 259	95.59 270	99.08 107	98.88 212	98.21 117	96.53 256	99.18 174	88.87 338	97.08 270	97.79 274	93.64 256	99.77 193	88.92 334	99.40 207	99.28 188

PatchMatch-RL		97.24 218	96.78 231	98.61 174	99.03 181	97.83 156	96.36 267	99.06 201	93.49 298	97.36 265	97.78 275	95.75 203	99.49 300	93.44 291	98.77 280	98.52 283

TAPA-MVS	96.21 11	96.63 252	95.95 260	98.65 167	98.93 198	98.09 124	96.93 234	99.28 145	83.58 349	98.13 211	97.78 275	96.13 184	99.40 313	93.52 288	99.29 226	98.45 287
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		95.96 270	95.44 275	97.52 258	98.51 272	93.99 281	98.39 101	96.09 326	98.21 114	98.40 199	97.76 277	86.88 299	99.63 260	95.42 237	89.27 353	98.95 243

WTY-MVS		96.67 250	96.27 256	97.87 233	98.81 227	94.61 266	96.77 245	97.92 294	94.94 269	97.12 267	97.74 278	91.11 280	99.82 141	93.89 278	98.15 305	99.18 210

MSP-MVS		98.40 125	98.00 155	99.61 10	99.57 54	99.25 23	98.57 79	99.35 109	97.55 160	99.31 69	97.71 279	94.61 235	99.88 63	96.14 207	99.19 242	99.70 29

MCST-MVS		98.00 160	97.63 182	99.10 104	99.24 128	98.17 119	96.89 239	98.73 262	95.66 255	97.92 223	97.70 280	97.17 133	99.66 251	96.18 205	99.23 234	99.47 118

AdaColmap		97.14 226	96.71 235	98.46 197	98.34 283	97.80 162	96.95 231	98.93 226	95.58 256	96.92 277	97.66 281	95.87 200	99.53 290	90.97 324	99.14 250	98.04 302

thisisatest0530		95.27 284	94.45 294	97.74 241	99.19 141	94.37 269	97.86 155	90.20 352	97.17 202	98.22 205	97.65 282	73.53 353	99.90 46	96.90 146	99.35 215	98.95 243

testgi		98.32 132	98.39 110	98.13 220	99.57 54	95.54 240	97.78 162	99.49 64	97.37 180	99.19 85	97.65 282	98.96 18	99.49 300	96.50 184	98.99 271	99.34 168

test_prior3		97.48 201	97.00 217	98.95 129	98.69 248	97.95 147	95.74 295	99.03 210	96.48 229	96.11 307	97.63 284	95.92 198	99.59 273	94.16 266	99.20 238	99.30 183

test_prior2							95.74 295		96.48 229	96.11 307	97.63 284	95.92 198		94.16 266	99.20 238

cdsmvs_eth3d_5k		24.66 325	32.88 328	0.00 341	0.00 362	0.00 363	0.00 353	99.10 196	0.00 358	0.00 359	97.58 286	99.21 11	0.00 359	0.00 357	0.00 357	0.00 355

lupinMVS		97.06 231	96.86 226	97.65 245	98.88 212	93.89 287	95.48 305	97.97 292	93.53 296	98.16 208	97.58 286	93.81 251	99.91 43	96.77 157	99.57 170	99.17 214

TEST9					98.71 240	98.08 128	95.96 283	99.03 210	91.40 321	95.85 314	97.53 288	96.52 169	99.76 200

train_agg		97.10 227	96.45 249	99.07 110	98.71 240	98.08 128	95.96 283	99.03 210	91.64 316	95.85 314	97.53 288	96.47 172	99.76 200	93.67 284	99.16 246	99.36 162

Fast-Effi-MVS+-dtu		98.27 138	98.09 146	98.81 148	98.43 279	98.11 123	97.61 182	99.50 56	98.64 85	97.39 263	97.52 290	98.12 64	99.95 15	96.90 146	98.71 285	98.38 291

test_8					98.67 253	98.01 136	95.91 288	99.02 214	91.64 316	95.79 316	97.50 291	96.47 172	99.76 200

agg_prior1		97.06 231	96.40 250	99.03 120	98.68 251	97.99 137	95.76 293	99.01 217	91.73 315	95.59 317	97.50 291	96.49 171	99.77 193	93.71 283	99.14 250	99.34 168

1112_ss		97.29 214	96.86 226	98.58 177	99.34 116	96.32 224	96.75 247	99.58 27	93.14 300	96.89 283	97.48 293	92.11 275	99.86 86	96.91 141	99.54 178	99.57 65

ab-mvs-re		8.12 329	10.83 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	97.48 293	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Effi-MVS+		98.02 158	97.82 168	98.62 172	98.53 271	97.19 197	97.33 206	99.68 14	97.30 187	96.68 290	97.46 295	98.56 33	99.80 163	96.63 170	98.20 301	98.86 257

PCF-MVS	92.86 18	94.36 296	93.00 313	98.42 200	98.70 244	97.56 176	93.16 345	99.11 195	79.59 352	97.55 250	97.43 296	92.19 273	99.73 216	79.85 351	99.45 202	97.97 304
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GA-MVS		95.86 272	95.32 280	97.49 259	98.60 262	94.15 275	93.83 340	97.93 293	95.49 260	96.68 290	97.42 297	83.21 326	99.30 326	96.22 201	98.55 294	99.01 233

CNLPA		97.17 224	96.71 235	98.55 185	98.56 267	98.05 133	96.33 268	98.93 226	96.91 215	97.06 272	97.39 298	94.38 241	99.45 309	91.66 316	99.18 244	98.14 299

PLC	94.65 16	96.51 255	95.73 264	98.85 143	98.75 234	97.91 150	96.42 264	99.06 201	90.94 327	95.59 317	97.38 299	94.41 239	99.59 273	90.93 325	98.04 312	99.05 225
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 243	96.75 233	97.08 275	98.74 235	93.33 295	96.71 249	98.26 282	96.72 222	98.44 191	97.37 300	95.20 219	99.47 305	91.89 314	97.43 321	98.44 288

PVSNet_Blended		96.88 241	96.68 237	97.47 260	98.92 202	93.77 291	94.71 323	99.43 85	90.98 326	97.62 243	97.36 301	96.82 152	99.67 243	94.73 249	99.56 175	98.98 238

miper_enhance_ethall		96.01 268	95.74 263	96.81 289	96.41 347	92.27 313	93.69 342	98.89 234	91.14 325	98.30 201	97.35 302	90.58 282	99.58 278	96.31 195	99.03 264	98.60 280

DPM-MVS		96.32 261	95.59 270	98.51 192	98.76 231	97.21 195	94.54 332	98.26 282	91.94 314	96.37 303	97.25 303	93.06 263	99.43 311	91.42 321	98.74 281	98.89 253

E-PMN		94.17 301	94.37 296	93.58 330	96.86 338	85.71 346	90.11 351	97.07 311	98.17 120	97.82 232	97.19 304	84.62 317	98.94 342	89.77 331	97.68 317	96.09 344

mvs-test1		97.83 179	97.48 194	98.89 138	98.02 301	99.20 33	97.20 217	99.16 183	98.29 108	96.46 302	97.17 305	96.44 174	99.92 33	96.66 168	97.90 314	97.54 325

CLD-MVS		97.49 199	97.16 210	98.48 195	99.07 171	97.03 204	94.71 323	99.21 163	94.46 278	98.06 217	97.16 306	97.57 102	99.48 303	94.46 257	99.78 85	98.95 243
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 280x420		95.51 281	95.47 272	95.65 311	98.25 288	88.27 336	93.25 344	98.88 235	93.53 296	94.65 333	97.15 307	86.17 305	99.93 27	97.41 112	99.93 25	98.73 272

xiu_mvs_v1_base_debu		97.86 171	98.17 136	96.92 282	98.98 190	93.91 284	96.45 261	99.17 180	97.85 139	98.41 195	97.14 308	98.47 36	99.92 33	98.02 81	99.05 260	96.92 331

xiu_mvs_v1_base		97.86 171	98.17 136	96.92 282	98.98 190	93.91 284	96.45 261	99.17 180	97.85 139	98.41 195	97.14 308	98.47 36	99.92 33	98.02 81	99.05 260	96.92 331

xiu_mvs_v1_base_debi		97.86 171	98.17 136	96.92 282	98.98 190	93.91 284	96.45 261	99.17 180	97.85 139	98.41 195	97.14 308	98.47 36	99.92 33	98.02 81	99.05 260	96.92 331

NP-MVS					98.84 220	97.39 185					96.84 311

HQP-MVS		97.00 238	96.49 248	98.55 185	98.67 253	96.79 212	96.29 270	99.04 208	96.05 243	95.55 321	96.84 311	93.84 249	99.54 288	92.82 301	99.26 231	99.32 176

API-MVS		97.04 234	96.91 224	97.42 263	97.88 308	98.23 115	98.18 117	98.50 274	97.57 157	97.39 263	96.75 313	96.77 156	99.15 336	90.16 330	99.02 267	94.88 348

1314		95.74 275	95.60 269	96.17 301	97.53 323	92.75 306	98.07 129	98.31 281	91.22 323	94.25 336	96.68 314	95.53 209	99.03 338	91.64 318	97.18 327	96.74 335

TR-MVS		95.55 279	95.12 286	96.86 288	97.54 322	93.94 282	96.49 260	96.53 321	94.36 283	97.03 274	96.61 315	94.26 244	99.16 335	86.91 339	96.31 337	97.47 327

Fast-Effi-MVS+		97.67 187	97.38 198	98.57 180	98.71 240	97.43 183	97.23 213	99.45 77	94.82 272	96.13 306	96.51 316	98.52 35	99.91 43	96.19 203	98.83 278	98.37 293

xiu_mvs_v2_base		97.16 225	97.49 191	96.17 301	98.54 269	92.46 309	95.45 306	98.84 244	97.25 192	97.48 257	96.49 317	98.31 48	99.90 46	96.34 194	98.68 287	96.15 342

MVS		93.19 314	92.09 318	96.50 294	96.91 337	94.03 278	98.07 129	98.06 290	68.01 353	94.56 335	96.48 318	95.96 196	99.30 326	83.84 344	96.89 332	96.17 340

PAPM_NR		96.82 245	96.32 253	98.30 211	99.07 171	96.69 217	97.48 196	98.76 256	95.81 253	96.61 294	96.47 319	94.12 248	99.17 334	90.82 328	97.78 315	99.06 224

PVSNet	93.40 17	95.67 276	95.70 265	95.57 312	98.83 222	88.57 333	92.50 347	97.72 297	92.69 306	96.49 301	96.44 320	93.72 254	99.43 311	93.61 285	99.28 227	98.71 273

EMVS		93.83 307	94.02 299	93.23 334	96.83 340	84.96 347	89.77 352	96.32 323	97.92 133	97.43 261	96.36 321	86.17 305	98.93 343	87.68 337	97.73 316	95.81 345

MAR-MVS		96.47 258	95.70 265	98.79 152	97.92 306	99.12 54	98.28 107	98.60 270	92.16 313	95.54 324	96.17 322	94.77 234	99.52 294	89.62 332	98.23 299	97.72 317
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
PAPM		91.88 322	90.34 325	96.51 293	98.06 300	92.56 307	92.44 348	97.17 309	86.35 344	90.38 351	96.01 323	86.61 301	99.21 332	70.65 354	95.43 342	97.75 315

PS-MVSNAJ		97.08 229	97.39 197	96.16 303	98.56 267	92.46 309	95.24 311	98.85 243	97.25 192	97.49 256	95.99 324	98.07 65	99.90 46	96.37 191	98.67 288	96.12 343

baseline2		93.73 308	92.83 314	96.42 295	97.70 317	91.28 326	96.84 242	89.77 353	93.96 292	92.44 347	95.93 325	79.14 343	99.77 193	92.94 297	96.76 334	98.21 295

alignmvs		97.35 208	96.88 225	98.78 155	98.54 269	98.09 124	97.71 171	97.69 299	99.20 46	97.59 246	95.90 326	88.12 297	99.55 285	98.18 74	98.96 274	98.70 275

ET-MVSNet_ETH3D		94.30 299	93.21 309	97.58 251	98.14 295	94.47 268	94.78 322	93.24 344	94.72 273	89.56 352	95.87 327	78.57 346	99.81 154	96.91 141	97.11 329	98.46 285

thisisatest0515		94.12 303	93.16 310	96.97 280	98.60 262	92.90 302	93.77 341	90.61 350	94.10 288	96.91 279	95.87 327	74.99 351	99.80 163	94.52 255	99.12 256	98.20 296

BH-w/o		95.13 287	94.89 291	95.86 305	98.20 292	91.31 324	95.65 298	97.37 303	93.64 294	96.52 297	95.70 329	93.04 264	99.02 339	88.10 336	95.82 340	97.24 329

PMMVS		96.51 255	95.98 259	98.09 221	97.53 323	95.84 235	94.92 319	98.84 244	91.58 318	96.05 311	95.58 330	95.68 205	99.66 251	95.59 233	98.09 308	98.76 270

EIA-MVS		98.00 160	97.74 172	98.80 150	98.72 237	98.09 124	98.05 133	99.60 24	97.39 178	96.63 292	95.55 331	97.68 91	99.80 163	96.73 162	99.27 228	98.52 283

ETV-MVS		98.03 156	97.86 166	98.56 184	98.69 248	98.07 130	97.51 194	99.50 56	98.10 123	97.50 255	95.51 332	98.41 40	99.88 63	96.27 199	99.24 233	97.71 318

PAPR		95.29 283	94.47 293	97.75 240	97.50 327	95.14 254	94.89 320	98.71 264	91.39 322	95.35 328	95.48 333	94.57 236	99.14 337	84.95 342	97.37 322	98.97 242

canonicalmvs		98.34 131	98.26 126	98.58 177	98.46 276	97.82 159	98.96 55	99.46 74	99.19 50	97.46 258	95.46 334	98.59 31	99.46 307	98.08 78	98.71 285	98.46 285

MVE	83.40 22	92.50 317	91.92 320	94.25 324	98.83 222	91.64 318	92.71 346	83.52 357	95.92 249	86.46 356	95.46 334	95.20 219	95.40 353	80.51 350	98.64 289	95.73 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-LLR		93.90 306	93.85 300	94.04 325	96.53 343	84.62 349	94.05 337	92.39 346	96.17 238	94.12 338	95.07 336	82.30 331	99.67 243	95.87 218	98.18 302	97.82 309

test-mter		92.33 319	91.76 322	94.04 325	96.53 343	84.62 349	94.05 337	92.39 346	94.00 291	94.12 338	95.07 336	65.63 361	99.67 243	95.87 218	98.18 302	97.82 309

thres600view7		94.45 295	93.83 301	96.29 297	99.06 175	91.53 319	97.99 142	94.24 338	98.34 102	97.44 260	95.01 338	79.84 338	99.67 243	84.33 343	98.23 299	97.66 320

gm-plane-assit					94.83 355	81.97 355			88.07 341		94.99 339		99.60 269	91.76 315

thres100view900		94.19 300	93.67 304	95.75 308	99.06 175	91.35 323	98.03 136	94.24 338	98.33 103	97.40 262	94.98 340	79.84 338	99.62 262	83.05 345	98.08 309	96.29 338

cascas		94.79 292	94.33 298	96.15 304	96.02 352	92.36 312	92.34 349	99.26 153	85.34 347	95.08 331	94.96 341	92.96 265	98.53 348	94.41 263	98.59 292	97.56 324

TESTMET0.1,1		92.19 321	91.77 321	93.46 331	96.48 345	82.80 354	94.05 337	91.52 349	94.45 280	94.00 341	94.88 342	66.65 359	99.56 282	95.78 223	98.11 307	98.02 303

test0.0.03 1		94.51 294	93.69 303	96.99 278	96.05 350	93.61 294	94.97 318	93.49 341	96.17 238	97.57 249	94.88 342	82.30 331	99.01 341	93.60 286	94.17 349	98.37 293

CS-MVS		97.82 181	97.59 187	98.52 189	98.76 231	98.04 134	98.20 115	99.61 22	97.10 206	96.02 313	94.87 344	98.27 49	99.84 116	96.31 195	99.17 245	97.69 319

DeepMVS_CX				93.44 332	98.24 289	94.21 273		94.34 335	64.28 354	91.34 350	94.87 344	89.45 291	92.77 355	77.54 353	93.14 350	93.35 350

IB-MVS	91.63 19	92.24 320	90.90 324	96.27 298	97.22 334	91.24 327	94.36 335	93.33 343	92.37 309	92.24 348	94.58 346	66.20 360	99.89 55	93.16 296	94.63 346	97.66 320
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
tfpn200view9		94.03 304	93.44 306	95.78 307	98.93 198	91.44 321	97.60 183	94.29 336	97.94 131	97.10 268	94.31 347	79.67 340	99.62 262	83.05 345	98.08 309	96.29 338

thres400		94.14 302	93.44 306	96.24 299	98.93 198	91.44 321	97.60 183	94.29 336	97.94 131	97.10 268	94.31 347	79.67 340	99.62 262	83.05 345	98.08 309	97.66 320

DWT-MVSNet_test		92.75 316	92.05 319	94.85 319	96.48 345	87.21 340	97.83 159	94.99 331	92.22 312	92.72 346	94.11 349	70.75 354	99.46 307	95.01 242	94.33 348	97.87 307

thres200		93.72 309	93.14 311	95.46 314	98.66 258	91.29 325	96.61 254	94.63 334	97.39 178	96.83 286	93.71 350	79.88 337	99.56 282	82.40 348	98.13 306	95.54 347

PVSNet_0	89.98 21	91.15 323	90.30 326	93.70 329	97.72 314	84.34 352	90.24 350	97.42 302	90.20 331	93.79 342	93.09 351	90.90 281	98.89 345	86.57 340	72.76 354	97.87 307

tmp_tt		78.77 324	78.73 327	78.90 338	58.45 359	74.76 360	94.20 336	78.26 360	39.16 355	86.71 355	92.82 352	80.50 336	75.19 356	86.16 341	92.29 351	86.74 351

GG-mvs-BLEND				94.76 320	94.54 356	92.13 315	99.31 19	80.47 359		88.73 354	91.01 353	67.59 357	98.16 351	82.30 349	94.53 347	93.98 349

X-MVStestdata		94.32 297	92.59 315	99.53 37	99.46 95	99.21 27	98.65 70	99.34 115	98.62 89	97.54 251	45.85 354	97.50 110	99.83 131	96.79 154	99.53 182	99.56 70

testmvs		17.12 326	20.53 329	6.87 340	12.05 360	4.20 362	93.62 343	6.73 361	4.62 357	10.41 357	24.33 355	8.28 363	3.56 358	9.69 356	15.07 355	12.86 354

test123		17.04 327	20.11 330	7.82 339	10.25 361	4.91 361	94.80 321	4.47 362	4.93 356	10.00 358	24.28 356	9.69 362	3.64 357	10.14 355	12.43 356	14.92 353

test_post											21.25 357	83.86 324	99.70 227

test_post1							97.59 185				20.48 358	83.07 328	99.66 251	94.16 266

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		8.17 328	10.90 331	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	98.07 65	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

IU-MVS					99.49 84	99.15 46		98.87 237	92.97 301	99.41 49				96.76 158	99.62 150	99.66 34

save fliter					99.11 160	97.97 142	96.53 256	99.02 214	98.24 111

test_0728_SECOND				99.60 14	99.50 77	99.23 25	98.02 138	99.32 122					99.88 63	96.99 135	99.63 147	99.68 31

GSMVS																98.81 262

test_part2					99.36 110	99.10 57				99.05 108

sam_mvs1												84.74 316				98.81 262

sam_mvs												84.29 322

MTGPA								99.20 165

MTMP							97.93 147	91.91 348

test9_res														93.28 295	99.15 249	99.38 153

agg_prior2														92.50 309	99.16 246	99.37 156

agg_prior					98.68 251	97.99 137		99.01 217		95.59 317			99.77 193

test_prior4						97.97 142	95.86 289

test_prior				98.95 129	98.69 248	97.95 147		99.03 210					99.59 273			99.30 183

旧先验2							95.76 293		88.56 340	97.52 253			99.66 251	94.48 256

新几何2							95.93 286

无先验							95.74 295	98.74 261	89.38 335				99.73 216	92.38 310		99.22 202

原ACMM2							95.53 302

testdata2													99.79 176	92.80 303

segment_acmp												97.02 140

testdata1							95.44 307		96.32 235

test12				98.93 132	98.58 265	97.83 156		98.66 266		96.53 296		95.51 211	99.69 231		99.13 253	99.27 190

plane_prior7					99.19 141	97.87 153

plane_prior6					98.99 189	97.70 170						94.90 225

plane_prior5								99.27 148					99.70 227	94.42 260	99.51 188	99.45 124

plane_prior3						97.78 163			97.41 176	97.79 233

plane_prior2							97.77 165		98.20 117

plane_prior1					99.05 177

plane_prior						97.65 172	97.07 226		96.72 222						99.36 213

n2								0.00 363

nn								0.00 363

door-mid								99.57 34

test11								98.87 237

door								99.41 89

HQP5-MVS						96.79 212

HQP-NCC					98.67 253		96.29 270		96.05 243	95.55 321

ACMP_Plane					98.67 253		96.29 270		96.05 243	95.55 321

BP-MVS														92.82 301

HQP4-MVS										95.56 320			99.54 288			99.32 176

HQP3-MVS								99.04 208							99.26 231

HQP2-MVS												93.84 249

MDTV_nov1_ep13_2view						74.92 359	97.69 173		90.06 333	97.75 236		85.78 309		93.52 288		98.69 276

ACMMP++_ref															99.77 89

ACMMP++															99.68 130

Test By Simon												96.52 169

High-resolution multi-view benchmark