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

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

v10		98.97 43	99.11 33	98.55 184	99.44 100	96.21 226	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

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

v1240		98.55 106	98.62 72	98.32 207	99.22 133	95.58 238	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

v8		99.01 36	99.16 30	98.57 179	99.47 94	96.31 224	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

v1921920		98.54 109	98.60 77	98.38 203	99.20 138	95.76 237	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

v1192		98.60 97	98.66 68	98.41 200	99.27 123	95.88 233	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

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

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

v144192		98.54 109	98.57 80	98.45 197	99.21 135	95.98 230	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

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

v1144		98.60 97	98.66 68	98.41 200	99.36 110	95.90 232	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

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

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

v2v482		98.56 102	98.62 72	98.37 204	99.42 104	95.81 236	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

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

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
MVSFormer		98.26 140	98.43 103	97.77 237	98.88 212	93.89 286	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

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

K. test v3		98.00 160	97.66 179	99.03 120	99.79 20	97.56 176	99.19 37	92.47 344	99.62 17	99.52 34	99.66 18	89.61 288	99.96 9	99.25 21	99.81 68	99.56 70

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

V42		98.78 66	98.78 52	98.76 158	99.44 100	97.04 202	98.27 108	99.19 170	97.87 137	99.25 78	99.16 85	96.84 149	99.78 186	99.21 22	99.84 55	99.46 120

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

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	99.73 215	99.17 25	99.92 34	99.76 21

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

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

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

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

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

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

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

lessismore_v0				98.97 127	99.73 24	97.53 178		86.71 354		99.37 56	99.52 34	89.93 286	99.92 33	98.99 33	99.72 111	99.44 129

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
RRT_test8_iter05		95.24 284	95.13 284	95.57 311	97.32 330	87.02 340	97.99 142	99.41 89	98.06 125	99.12 92	99.05 107	66.85 357	99.85 99	98.93 35	99.47 199	99.84 9

mvs_anonymous		97.83 179	98.16 139	96.87 284	98.18 292	91.89 315	97.31 207	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

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

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

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

testing_2		98.93 48	98.99 41	98.76 158	99.57 54	97.03 203	97.85 157	99.13 190	98.46 98	99.44 45	99.44 46	98.22 55	99.74 210	98.85 39	99.94 20	99.51 95

new-patchmatchnet		98.35 130	98.74 55	97.18 270	99.24 128	92.23 313	96.42 263	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

RRT_MVS		97.07 230	96.57 245	98.58 176	95.89 352	96.33 222	97.36 203	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

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

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

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	99.76 199	98.78 43	99.68 130	99.59 54

EI-MVSNet-UG-set		98.69 81	98.71 60	98.62 172	99.10 164	96.37 221	97.23 212	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

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

EI-MVSNet-Vis-set		98.68 84	98.70 63	98.63 170	99.09 167	96.40 220	97.23 212	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

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	99.69 230	98.71 49	99.76 98	99.33 174

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

IterMVS-SCA-FT		97.85 176	98.18 135	96.87 284	99.27 123	91.16 328	95.53 301	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

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

EI-MVSNet		98.40 125	98.51 86	98.04 226	99.10 164	94.73 260	97.20 216	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

IterMVS-LS		98.55 106	98.70 63	98.09 220	99.48 92	94.73 260	97.22 215	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.
MVS_Test		98.18 148	98.36 114	97.67 242	98.48 273	94.73 260	98.18 117	99.02 214	97.69 147	98.04 220	99.11 95	97.22 132	99.56 281	98.57 55	98.90 277	98.71 272

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

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

UGNet		98.53 111	98.45 99	98.79 152	97.94 304	96.96 206	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
IterMVS		97.73 183	98.11 145	96.57 291	99.24 128	90.28 329	95.52 303	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.
Regformer-4		98.73 74	98.68 65	98.89 138	99.02 183	97.22 193	97.17 220	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

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	99.69 230	98.46 61	99.73 105	99.41 138

MVSTER		96.86 242	96.55 246	97.79 236	97.91 306	94.21 272	97.56 188	98.87 237	97.49 165	99.06 103	99.05 107	80.72 334	99.80 163	98.44 62	99.82 64	99.37 156

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	99.76 199	98.44 62	99.77 89	99.64 39
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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

Regformer-3		98.61 95	98.61 75	98.63 170	99.02 183	96.53 218	97.17 220	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

v148		98.45 119	98.60 77	98.00 228	99.44 100	94.98 255	97.44 200	99.06 201	98.30 105	99.32 67	98.97 129	96.65 164	99.62 261	98.37 66	99.85 53	99.39 147

VDD-MVS		98.56 102	98.39 110	99.07 110	99.13 159	98.07 130	98.59 77	97.01 311	99.59 20	99.11 94	99.27 66	94.82 229	99.79 176	98.34 67	99.63 147	99.34 168

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

pmmvs5		97.64 189	97.49 191	98.08 223	99.14 157	95.12 254	96.70 249	99.05 205	93.77 292	98.62 172	98.83 166	93.23 257	99.75 206	98.33 69	99.76 98	99.36 162

EU-MVSNet		97.66 188	98.50 88	95.13 316	99.63 48	85.84 343	98.35 105	98.21 284	98.23 113	99.54 29	99.46 41	95.02 223	99.68 239	98.24 70	99.87 51	99.87 5

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

DELS-MVS		98.27 138	98.20 132	98.48 194	98.86 215	96.70 215	95.60 299	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
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

alignmvs		97.35 208	96.88 225	98.78 155	98.54 268	98.09 124	97.71 171	97.69 299	99.20 46	97.59 246	95.90 325	88.12 297	99.55 284	98.18 74	98.96 274	98.70 274

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	99.78 186	98.13 75	99.13 253	99.31 180

MVS_0304		97.64 189	97.35 201	98.52 188	97.87 308	96.69 216	98.59 77	98.05 291	97.44 174	93.74 343	98.85 160	93.69 255	99.88 63	98.11 76	99.81 68	98.98 238

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

canonicalmvs		98.34 131	98.26 126	98.58 176	98.46 275	97.82 159	98.96 55	99.46 74	99.19 50	97.46 258	95.46 333	98.59 31	99.46 306	98.08 78	98.71 285	98.46 284

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

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
xiu_mvs_v1_base_debu		97.86 171	98.17 136	96.92 281	98.98 190	93.91 283	96.45 260	99.17 180	97.85 139	98.41 195	97.14 307	98.47 36	99.92 33	98.02 81	99.05 260	96.92 330

xiu_mvs_v1_base		97.86 171	98.17 136	96.92 281	98.98 190	93.91 283	96.45 260	99.17 180	97.85 139	98.41 195	97.14 307	98.47 36	99.92 33	98.02 81	99.05 260	96.92 330

xiu_mvs_v1_base_debi		97.86 171	98.17 136	96.92 281	98.98 190	93.91 283	96.45 260	99.17 180	97.85 139	98.41 195	97.14 307	98.47 36	99.92 33	98.02 81	99.05 260	96.92 330

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

FMVSNet2		98.49 115	98.40 107	98.75 161	98.90 206	97.14 201	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

diffmvs		98.22 144	98.24 128	98.17 218	99.00 186	95.44 244	96.38 265	99.58 27	97.79 143	98.53 186	98.50 221	96.76 158	99.74 210	97.95 86	99.64 144	99.34 168

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

pmmvs-eth3d		98.47 117	98.34 117	98.86 142	99.30 120	97.76 164	97.16 222	99.28 145	95.54 256	99.42 48	99.19 77	97.27 126	99.63 259	97.89 87	99.97 12	99.20 203

Patchmatch-RL test		97.26 215	97.02 216	97.99 229	99.52 72	95.53 240	96.13 276	99.71 10	97.47 166	99.27 72	99.16 85	84.30 321	99.62 261	97.89 87	99.77 89	98.81 262

VDDNet		98.21 145	97.95 158	99.01 124	99.58 50	97.74 167	99.01 49	97.29 307	99.67 10	98.97 122	99.50 35	90.45 283	99.80 163	97.88 90	99.20 238	99.48 110

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

CANet		97.87 170	97.76 170	98.19 217	97.75 312	95.51 241	96.76 245	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

Regformer-1		98.55 106	98.44 101	98.87 140	98.85 217	97.29 187	96.91 236	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

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	99.77 192	97.79 93	99.74 102	99.04 229

tttt0517		95.64 276	94.98 287	97.64 246	99.36 110	93.81 288	98.72 68	90.47 350	98.08 124	98.67 166	98.34 238	73.88 351	99.92 33	97.77 95	99.51 188	99.20 203

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

FMVSNet3		97.50 197	97.24 207	98.29 211	98.08 298	95.83 235	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

UnsupCasMVSNet_eth		97.89 167	97.60 185	98.75 161	99.31 117	97.17 198	97.62 180	99.35 109	98.72 84	98.76 159	98.68 190	92.57 271	99.74 210	97.76 99	95.60 340	99.34 168

Regformer-2		98.60 97	98.46 97	99.02 123	98.85 217	97.71 169	96.91 236	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

test20.03		98.78 66	98.77 54	98.78 155	99.46 95	97.20 195	97.78 162	99.24 159	99.04 63	99.41 49	98.90 144	97.65 94	99.76 199	97.70 101	99.79 81	99.39 147

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	99.51 297	97.70 101	99.73 105	97.89 304
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PatchT		96.65 251	96.35 251	97.54 255	97.40 327	95.32 247	97.98 144	96.64 319	99.33 37	96.89 283	99.42 48	84.32 320	99.81 154	97.69 103	97.49 318	97.48 325

D2MVS		97.84 177	97.84 167	97.83 234	99.14 157	94.74 259	96.94 231	98.88 235	95.84 250	98.89 137	98.96 132	94.40 240	99.69 230	97.55 104	99.95 16	99.05 225

MSLP-MVS++		98.02 158	98.14 143	97.64 246	98.58 264	95.19 251	97.48 196	99.23 161	97.47 166	97.90 225	98.62 206	97.04 137	98.81 345	97.55 104	99.41 205	98.94 247

WR-MVS		98.40 125	98.19 134	99.03 120	99.00 186	97.65 172	96.85 239	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

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

RPMNet		97.02 235	96.93 220	97.30 266	97.71 314	94.22 270	98.11 124	99.30 136	99.37 34	96.91 279	99.34 59	86.72 300	99.87 79	97.53 107	97.36 323	97.81 310

PMMVS2		98.07 155	98.08 149	98.04 226	99.41 105	94.59 266	94.59 329	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

LFMVS		97.20 221	96.72 234	98.64 168	98.72 237	96.95 207	98.93 57	94.14 339	99.74 7	98.78 155	99.01 120	84.45 318	99.73 215	97.44 110	99.27 228	99.25 194

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
CHOSEN 280x420		95.51 280	95.47 272	95.65 310	98.25 287	88.27 335	93.25 343	98.88 235	93.53 295	94.65 332	97.15 306	86.17 305	99.93 27	97.41 112	99.93 25	98.73 271

CR-MVSNet		96.28 263	95.95 260	97.28 267	97.71 314	94.22 270	98.11 124	98.92 229	92.31 309	96.91 279	99.37 53	85.44 313	99.81 154	97.39 113	97.36 323	97.81 310

Anonymous202405211		97.90 165	97.50 190	99.08 107	98.90 206	98.25 110	98.53 83	96.16 323	98.87 77	99.11 94	98.86 157	90.40 284	99.78 186	97.36 114	99.31 221	99.19 208

CANet_DTU		97.26 215	97.06 214	97.84 233	97.57 319	94.65 264	96.19 275	98.79 253	97.23 198	95.14 329	98.24 245	93.22 258	99.84 116	97.34 115	99.84 55	99.04 229

Anonymous20231206		98.21 145	98.21 131	98.20 216	99.51 74	95.43 245	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

MP-MVS-pluss		98.57 101	98.23 130	99.60 14	99.69 38	99.35 12	97.16 222	99.38 96	94.87 270	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
FMVSNet5		96.01 267	95.20 282	98.41 200	97.53 322	96.10 227	98.74 66	99.50 56	97.22 201	98.03 221	99.04 110	69.80 354	99.88 63	97.27 118	99.71 115	99.25 194

our_test_3		97.39 206	97.73 174	96.34 295	98.70 244	89.78 331	94.61 328	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

jason		97.45 203	97.35 201	97.76 238	99.24 128	93.93 282	95.86 288	98.42 277	94.24 283	98.50 188	98.13 251	94.82 229	99.91 43	97.22 120	99.73 105	99.43 133
jason: jason.
miper_lstm_enhance		97.18 223	97.16 210	97.25 269	98.16 293	92.85 302	95.15 313	99.31 127	97.25 192	98.74 162	98.78 174	90.07 285	99.78 186	97.19 121	99.80 76	99.11 221

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	99.75 206	97.17 122	99.66 141	99.63 42

zzz-MVS		98.79 63	98.52 84	99.61 10	99.67 40	99.36 10	97.33 205	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

TSAR-MVS + GP.		98.18 148	97.98 156	98.77 157	98.71 240	97.88 152	96.32 268	98.66 266	96.33 234	99.23 82	98.51 218	97.48 115	99.40 312	97.16 123	99.46 200	99.02 232

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

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

PVSNet_Blended_VisFu		98.17 150	98.15 141	98.22 215	99.73 24	95.15 252	97.36 203	99.68 14	94.45 279	98.99 117	99.27 66	96.87 148	99.94 23	97.13 127	99.91 39	99.57 65

HyFIR lowres test		97.19 222	96.60 243	98.96 128	99.62 49	97.28 189	95.17 311	99.50 56	94.21 284	99.01 114	98.32 241	86.61 301	99.99 2	97.10 129	99.84 55	99.60 48

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

eth_miper_zixun_eth		97.23 219	97.25 205	97.17 271	98.00 302	92.77 304	94.71 322	99.18 174	97.27 190	98.56 181	98.74 180	91.89 277	99.69 230	97.06 131	99.81 68	99.05 225

MDA-MVSNet_test_wron		97.60 192	97.66 179	97.41 263	99.04 178	93.09 296	95.27 308	98.42 277	97.26 191	98.88 141	98.95 136	95.43 215	99.73 215	97.02 132	98.72 283	99.41 138

cl-mvsnet_		97.02 235	96.83 229	97.58 250	97.82 310	94.04 276	94.66 325	99.16 183	97.04 209	98.63 170	98.71 184	88.68 295	99.69 230	97.00 133	99.81 68	99.00 236

cl-mvsnet1		97.02 235	96.84 228	97.58 250	97.82 310	94.03 277	94.66 325	99.16 183	97.04 209	98.63 170	98.71 184	88.69 294	99.69 230	97.00 133	99.81 68	99.01 233

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_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

YYNet1		97.60 192	97.67 176	97.39 264	99.04 178	93.04 300	95.27 308	98.38 279	97.25 192	98.92 133	98.95 136	95.48 214	99.73 215	96.99 135	98.74 281	99.41 138

pmmvs4		97.58 194	97.28 204	98.51 191	98.84 220	96.93 208	95.40 307	98.52 273	93.60 294	98.61 174	98.65 197	95.10 222	99.60 268	96.97 138	99.79 81	98.99 237

TAMVS		98.24 143	98.05 151	98.80 150	99.07 171	97.18 197	97.88 152	98.81 250	96.66 225	99.17 90	99.21 74	94.81 231	99.77 192	96.96 139	99.88 48	99.44 129

cl_fuxian		97.36 207	97.37 199	97.31 265	98.09 297	93.25 295	95.01 316	99.16 183	97.05 208	98.77 158	98.72 183	92.88 266	99.64 257	96.93 140	99.76 98	99.05 225

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

ET-MVSNet_ETH3D		94.30 298	93.21 308	97.58 250	98.14 294	94.47 267	94.78 321	93.24 343	94.72 272	89.56 351	95.87 326	78.57 345	99.81 154	96.91 141	97.11 328	98.46 284

N_pmnet		97.63 191	97.17 209	98.99 126	99.27 123	97.86 154	95.98 279	93.41 341	95.25 263	99.47 41	98.90 144	95.63 206	99.85 99	96.91 141	99.73 105	99.27 190

1112_ss		97.29 214	96.86 226	98.58 176	99.34 116	96.32 223	96.75 246	99.58 27	93.14 299	96.89 283	97.48 292	92.11 275	99.86 86	96.91 141	99.54 178	99.57 65

thisisatest0530		95.27 283	94.45 293	97.74 240	99.19 141	94.37 268	97.86 155	90.20 351	97.17 202	98.22 205	97.65 281	73.53 352	99.90 46	96.90 146	99.35 215	98.95 243

Fast-Effi-MVS+-dtu		98.27 138	98.09 146	98.81 148	98.43 278	98.11 123	97.61 182	99.50 56	98.64 85	97.39 263	97.52 289	98.12 64	99.95 15	96.90 146	98.71 285	98.38 290

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
MVS_111021_HR		98.25 142	98.08 149	98.75 161	99.09 167	97.46 181	95.97 280	99.27 148	97.60 155	97.99 222	98.25 244	98.15 63	99.38 316	96.87 149	99.57 170	99.42 136

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

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

MS-PatchMatch		97.68 186	97.75 171	97.45 260	98.23 290	93.78 289	97.29 208	98.84 244	96.10 242	98.64 169	98.65 197	96.04 187	99.36 317	96.84 152	99.14 250	99.20 203

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

miper_ehance_all_eth		97.06 231	97.03 215	97.16 273	97.83 309	93.06 297	94.66 325	99.09 197	95.99 247	98.69 164	98.45 227	92.73 269	99.61 267	96.79 154	99.03 264	98.82 260

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

X-MVStestdata		94.32 296	92.59 314	99.53 37	99.46 95	99.21 27	98.65 70	99.34 115	98.62 89	97.54 251	45.85 353	97.50 110	99.83 131	96.79 154	99.53 182	99.56 70

lupinMVS		97.06 231	96.86 226	97.65 244	98.88 212	93.89 286	95.48 304	97.97 292	93.53 295	98.16 208	97.58 285	93.81 251	99.91 43	96.77 157	99.57 170	99.17 214

IU-MVS					99.49 84	99.15 46		98.87 237	92.97 300	99.41 49				96.76 158	99.62 150	99.66 34

CHOSEN 1792x2688		97.49 199	97.14 213	98.54 187	99.68 39	96.09 229	96.50 258	99.62 20	91.58 317	98.84 147	98.97 129	92.36 272	99.88 63	96.76 158	99.95 16	99.67 33

ppachtmachnet_test		97.50 197	97.74 172	96.78 289	98.70 244	91.23 327	94.55 330	99.05 205	96.36 233	99.21 83	98.79 173	96.39 176	99.78 186	96.74 160	99.82 64	99.34 168

DeepPCF-MVS	96.93 5	98.32 132	98.01 154	99.23 88	98.39 280	98.97 62	95.03 315	99.18 174	96.88 216	99.33 62	98.78 174	98.16 61	99.28 328	96.74 160	99.62 150	99.44 129

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	95.55 330	97.68 91	99.80 163	96.73 162	99.27 228	98.52 282

CDS-MVSNet		97.69 185	97.35 201	98.69 165	98.73 236	97.02 205	96.92 235	98.75 259	95.89 249	98.59 177	98.67 192	92.08 276	99.74 210	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
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

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	99.67 242	96.71 165	99.77 89	99.50 100

MVS_111021_LR		98.30 134	98.12 144	98.83 145	99.16 152	98.03 135	96.09 277	99.30 136	97.58 156	98.10 214	98.24 245	98.25 50	99.34 319	96.69 166	99.65 142	99.12 219

OPM-MVS		98.56 102	98.32 121	99.25 86	99.41 105	98.73 79	97.13 224	99.18 174	97.10 206	98.75 160	98.92 140	98.18 59	99.65 255	96.68 167	99.56 175	99.37 156

Effi-MVS+-dtu		98.26 140	97.90 163	99.35 69	98.02 300	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	98.60 279

mvs-test1		97.83 179	97.48 194	98.89 138	98.02 300	99.20 33	97.20 216	99.16 183	98.29 108	96.46 302	97.17 304	96.44 174	99.92 33	96.66 168	97.90 314	97.54 324

Effi-MVS+		98.02 158	97.82 168	98.62 172	98.53 270	97.19 196	97.33 205	99.68 14	97.30 187	96.68 290	97.46 294	98.56 33	99.80 163	96.63 170	98.20 301	98.86 257

MDA-MVSNet-bldmvs		97.94 164	97.91 162	98.06 224	99.44 100	94.96 256	96.63 252	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

Test_1112_low_res		96.99 239	96.55 246	98.31 209	99.35 114	95.47 243	95.84 291	99.53 50	91.51 319	96.80 288	98.48 226	91.36 279	99.83 131	96.58 172	99.53 182	99.62 43

LS3D		98.63 92	98.38 112	99.36 64	97.25 332	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

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

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

sss		97.21 220	96.93 220	98.06 224	98.83 222	95.22 250	96.75 246	98.48 275	94.49 275	97.27 266	97.90 268	92.77 268	99.80 163	96.57 174	99.32 219	99.16 217

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

SD-MVS		98.40 125	98.68 65	97.54 255	98.96 193	97.99 137	97.88 152	99.36 104	98.20 117	99.63 26	99.04 110	98.76 23	95.33 353	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
ambc				98.24 214	98.82 225	95.97 231	98.62 73	99.00 220		99.27 72	99.21 74	96.99 142	99.50 298	96.55 180	99.50 195	99.26 193

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

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

MVP-Stereo		98.08 154	97.92 161	98.57 179	98.96 193	96.79 211	97.90 151	99.18 174	96.41 232	98.46 189	98.95 136	95.93 197	99.60 268	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.
testgi		98.32 132	98.39 110	98.13 219	99.57 54	95.54 239	97.78 162	99.49 64	97.37 180	99.19 85	97.65 281	98.96 18	99.49 299	96.50 184	98.99 271	99.34 168

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
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

SMA-MVS		98.40 125	98.03 153	99.51 46	99.16 152	99.21 27	98.05 133	99.22 162	94.16 286	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
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

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

CNVR-MVS		98.17 150	97.87 165	99.07 110	98.67 252	98.24 111	97.01 227	98.93 226	97.25 192	97.62 243	98.34 238	97.27 126	99.57 278	96.42 190	99.33 218	99.39 147

cl-mvsnet2		95.79 273	95.39 277	96.98 278	96.77 340	92.79 303	94.40 333	98.53 272	94.59 274	97.89 226	98.17 250	82.82 330	99.24 330	96.37 191	99.03 264	98.92 249

PS-MVSNAJ		97.08 229	97.39 197	96.16 302	98.56 266	92.46 308	95.24 310	98.85 243	97.25 192	97.49 256	95.99 323	98.07 65	99.90 46	96.37 191	98.67 288	96.12 342

CVMVSNet		96.25 264	97.21 208	93.38 332	99.10 164	80.56 356	97.20 216	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

xiu_mvs_v2_base		97.16 225	97.49 191	96.17 300	98.54 268	92.46 308	95.45 305	98.84 244	97.25 192	97.48 257	96.49 316	98.31 48	99.90 46	96.34 194	98.68 287	96.15 341

miper_enhance_ethall		96.01 267	95.74 263	96.81 288	96.41 346	92.27 312	93.69 341	98.89 234	91.14 324	98.30 201	97.35 301	90.58 282	99.58 277	96.31 195	99.03 264	98.60 279

CS-MVS		97.82 181	97.59 187	98.52 188	98.76 231	98.04 134	98.20 115	99.61 22	97.10 206	96.02 313	94.87 343	98.27 49	99.84 116	96.31 195	99.17 245	97.69 318

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	96.29 197	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
ETV-MVS		98.03 156	97.86 166	98.56 183	98.69 247	98.07 130	97.51 194	99.50 56	98.10 123	97.50 255	95.51 331	98.41 40	99.88 63	96.27 198	99.24 233	97.71 317

XVG-OURS-SEG-HR		98.49 115	98.28 124	99.14 98	99.49 84	98.83 70	96.54 254	99.48 66	97.32 185	99.11 94	98.61 209	99.33 8	99.30 325	96.23 199	98.38 296	99.28 188

GA-MVS		95.86 271	95.32 279	97.49 258	98.60 261	94.15 274	93.83 339	97.93 293	95.49 259	96.68 290	97.42 296	83.21 326	99.30 325	96.22 200	98.55 294	99.01 233

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	96.22 200	99.62 150	99.57 65

Fast-Effi-MVS+		97.67 187	97.38 198	98.57 179	98.71 240	97.43 183	97.23 212	99.45 77	94.82 271	96.13 306	96.51 315	98.52 35	99.91 43	96.19 202	98.83 278	98.37 292

pmmvs3		95.03 288	94.40 294	96.93 280	97.70 316	92.53 307	95.08 314	97.71 298	88.57 338	97.71 237	98.08 259	79.39 341	99.82 141	96.19 202	99.11 257	98.43 288

MCST-MVS		98.00 160	97.63 182	99.10 104	99.24 128	98.17 119	96.89 238	98.73 262	95.66 254	97.92 223	97.70 279	97.17 133	99.66 250	96.18 204	99.23 234	99.47 118

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	96.15 205	99.72 111	99.58 60
Skip Steuart: Steuart Systems R&D Blog.
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	96.14 206	99.31 221	99.48 110

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	97.71 278	94.61 235	99.88 63	96.14 206	99.19 242	99.70 29

DeepC-MVS_fast	96.85 6	98.30 134	98.15 141	98.75 161	98.61 259	97.23 191	97.76 167	99.09 197	97.31 186	98.75 160	98.66 195	97.56 103	99.64 257	96.10 208	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
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	96.00 209	99.51 188	99.52 92

EPNet		96.14 265	95.44 274	98.25 213	90.76 357	95.50 242	97.92 148	94.65 332	98.97 70	92.98 344	98.85 160	89.12 292	99.87 79	95.99 210	99.68 130	99.39 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
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	99.72 223	95.98 211	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
Patchmtry		97.35 208	96.97 219	98.50 193	97.31 331	96.47 219	98.18 117	98.92 229	98.95 74	98.78 155	99.37 53	85.44 313	99.85 99	95.96 212	99.83 61	99.17 214

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	95.92 213	99.69 125	99.04 229

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	95.88 214	99.74 102	99.23 198

tpm		94.67 292	94.34 296	95.66 309	97.68 318	88.42 333	97.88 152	94.90 331	94.46 277	96.03 312	98.56 214	78.66 343	99.79 176	95.88 214	95.01 343	98.78 267

ab-mvs		98.41 123	98.36 114	98.59 175	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	95.88 214	99.51 188	98.75 270

test-LLR		93.90 305	93.85 299	94.04 324	96.53 342	84.62 348	94.05 336	92.39 345	96.17 238	94.12 337	95.07 335	82.30 331	99.67 242	95.87 217	98.18 302	97.82 308

test-mter		92.33 318	91.76 321	94.04 324	96.53 342	84.62 348	94.05 336	92.39 345	94.00 290	94.12 337	95.07 335	65.63 360	99.67 242	95.87 217	98.18 302	97.82 308

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	95.87 217	99.59 160	99.58 60

USDC		97.41 205	97.40 196	97.44 261	98.94 196	93.67 292	95.17 311	99.53 50	94.03 289	98.97 122	99.10 97	95.29 217	99.34 319	95.84 220	99.73 105	99.30 183

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	97.83 272	96.01 189	99.84 116	95.82 221	99.35 215	99.46 120

TESTMET0.1,1		92.19 320	91.77 320	93.46 330	96.48 344	82.80 353	94.05 336	91.52 348	94.45 279	94.00 340	94.88 341	66.65 358	99.56 281	95.78 222	98.11 307	98.02 302

DSMNet-mixed		97.42 204	97.60 185	96.87 284	99.15 156	91.46 319	98.54 82	99.12 193	92.87 303	97.58 247	99.63 21	96.21 183	99.90 46	95.74 223	99.54 178	99.27 190

XVG-OURS		98.53 111	98.34 117	99.11 102	99.50 77	98.82 72	95.97 280	99.50 56	97.30 187	99.05 108	98.98 127	99.35 7	99.32 322	95.72 224	99.68 130	99.18 210

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	99.52 293	95.72 224	99.71 115	99.32 176

PHI-MVS		98.29 137	97.95 158	99.34 72	98.44 277	99.16 41	98.12 123	99.38 96	96.01 246	98.06 217	98.43 228	97.80 85	99.67 242	95.69 226	99.58 166	99.20 203

xxxxxxxxxxxxxcwj		98.44 120	98.24 128	99.06 115	99.11 160	97.97 142	96.53 255	99.54 48	98.24 111	98.83 148	98.90 144	97.80 85	99.82 141	95.68 227	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	95.68 227	99.52 185	99.38 153

#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	95.68 227	99.59 160	99.53 88

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	95.63 230	99.91 39	98.86 257

tpmrst		95.07 287	95.46 273	93.91 326	97.11 334	84.36 350	97.62 180	96.96 312	94.98 266	96.35 304	98.80 171	85.46 312	99.59 272	95.60 231	96.23 337	97.79 313

PMMVS		96.51 255	95.98 259	98.09 220	97.53 322	95.84 234	94.92 318	98.84 244	91.58 317	96.05 311	95.58 329	95.68 205	99.66 250	95.59 232	98.09 308	98.76 269

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	95.58 233	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	95.58 233	99.78 85	99.62 43

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	95.49 235	99.80 76	99.48 110

baseline1		95.96 269	95.44 274	97.52 257	98.51 271	93.99 280	98.39 101	96.09 325	98.21 114	98.40 199	97.76 276	86.88 299	99.63 259	95.42 236	89.27 352	98.95 243

DPE-MVS		98.59 100	98.26 126	99.57 19	99.27 123	99.15 46	97.01 227	99.39 94	97.67 148	99.44 45	98.99 123	97.53 106	99.89 55	95.40 237	99.68 130	99.66 34

NCCC		97.86 171	97.47 195	99.05 117	98.61 259	98.07 130	96.98 229	98.90 232	97.63 151	97.04 273	97.93 267	95.99 193	99.66 250	95.31 238	98.82 279	99.43 133

Patchmatch-test		96.55 254	96.34 252	97.17 271	98.35 281	93.06 297	98.40 100	97.79 295	97.33 183	98.41 195	98.67 192	83.68 325	99.69 230	95.16 239	99.31 221	98.77 268

EPMVS		93.72 308	93.27 307	95.09 317	96.04 350	87.76 336	98.13 121	85.01 355	94.69 273	96.92 277	98.64 200	78.47 347	99.31 323	95.04 240	96.46 335	98.20 295

DWT-MVSNet_test		92.75 315	92.05 318	94.85 318	96.48 344	87.21 339	97.83 159	94.99 330	92.22 311	92.72 345	94.11 348	70.75 353	99.46 306	95.01 241	94.33 347	97.87 306

UnsupCasMVSNet_bld		97.30 212	96.92 222	98.45 197	99.28 122	96.78 214	96.20 274	99.27 148	95.42 261	98.28 203	98.30 242	93.16 259	99.71 224	94.99 242	97.37 321	98.87 256

PatchmatchNet		95.58 277	95.67 267	95.30 315	97.34 329	87.32 338	97.65 178	96.65 318	95.30 262	97.07 271	98.69 188	84.77 315	99.75 206	94.97 243	98.64 289	98.83 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		94.93 290	94.78 291	95.38 314	93.58 356	87.68 337	96.78 243	95.69 329	97.35 182	89.14 352	98.09 258	88.15 296	99.49 299	94.95 244	99.30 224	98.98 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_yl		96.69 248	96.29 254	97.90 230	98.28 285	95.24 248	97.29 208	97.36 303	98.21 114	98.17 206	97.86 269	86.27 303	99.55 284	94.87 245	98.32 297	98.89 253

DCV-MVSNet		96.69 248	96.29 254	97.90 230	98.28 285	95.24 248	97.29 208	97.36 303	98.21 114	98.17 206	97.86 269	86.27 303	99.55 284	94.87 245	98.32 297	98.89 253

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	95.76 253	98.81 154	98.82 169	98.36 43	99.82 141	94.75 247	99.77 89	99.48 110
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		97.55 195	97.53 188	97.60 248	98.92 202	93.77 290	96.64 251	99.43 85	94.49 275	97.62 243	99.18 79	96.82 152	99.67 242	94.73 248	99.93 25	99.36 162

PVSNet_Blended		96.88 241	96.68 237	97.47 259	98.92 202	93.77 290	94.71 322	99.43 85	90.98 325	97.62 243	97.36 300	96.82 152	99.67 242	94.73 248	99.56 175	98.98 238

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	94.72 250	99.60 158	99.54 82
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
OPU-MVS				98.82 146	98.59 263	98.30 107	98.10 126				98.52 217	98.18 59	98.75 346	94.62 251	99.48 198	99.41 138

LF4IMVS		97.90 165	97.69 175	98.52 188	99.17 150	97.66 171	97.19 219	99.47 72	96.31 236	97.85 229	98.20 249	96.71 162	99.52 293	94.62 251	99.72 111	98.38 290

CostFormer		93.97 304	93.78 301	94.51 321	97.53 322	85.83 344	97.98 144	95.96 326	89.29 335	94.99 331	98.63 204	78.63 344	99.62 261	94.54 253	96.50 334	98.09 300

thisisatest0515		94.12 302	93.16 309	96.97 279	98.60 261	92.90 301	93.77 340	90.61 349	94.10 287	96.91 279	95.87 326	74.99 350	99.80 163	94.52 254	99.12 256	98.20 295

旧先验2							95.76 292		88.56 339	97.52 253			99.66 250	94.48 255

CLD-MVS		97.49 199	97.16 210	98.48 194	99.07 171	97.03 203	94.71 322	99.21 163	94.46 277	98.06 217	97.16 305	97.57 102	99.48 302	94.46 256	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
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	99.57 278	94.45 257	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	99.57 278	94.45 257	99.61 156	99.37 156

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	99.70 226	94.42 259	99.51 188	99.45 124

plane_prior5								99.27 148					99.70 226	94.42 259	99.51 188	99.45 124

JIA-IIPM		95.52 279	95.03 286	97.00 276	96.85 338	94.03 277	96.93 233	95.82 327	99.20 46	94.63 333	99.71 13	83.09 327	99.60 268	94.42 259	94.64 344	97.36 327

cascas		94.79 291	94.33 297	96.15 303	96.02 351	92.36 311	92.34 348	99.26 153	85.34 346	95.08 330	94.96 340	92.96 265	98.53 347	94.41 262	98.59 292	97.56 323

TinyColmap		97.89 167	97.98 156	97.60 248	98.86 215	94.35 269	96.21 273	99.44 80	97.45 173	99.06 103	98.88 153	97.99 74	99.28 328	94.38 263	99.58 166	99.18 210

9.14			97.78 169		99.07 171		97.53 191	99.32 122	95.53 258	98.54 185	98.70 187	97.58 101	99.76 199	94.32 264	99.46 200

test_post1							97.59 185				20.48 357	83.07 328	99.66 250	94.16 265

SCA		96.41 260	96.66 240	95.67 308	98.24 288	88.35 334	95.85 290	96.88 316	96.11 241	97.67 240	98.67 192	93.10 261	99.85 99	94.16 265	99.22 235	98.81 262

test_prior3		97.48 201	97.00 217	98.95 129	98.69 247	97.95 147	95.74 294	99.03 210	96.48 229	96.11 307	97.63 283	95.92 198	99.59 272	94.16 265	99.20 238	99.30 183

test_prior2							95.74 294		96.48 229	96.11 307	97.63 283	95.92 198		94.16 265	99.20 238

tpmvs		95.02 289	95.25 280	94.33 322	96.39 347	85.87 342	98.08 128	96.83 317	95.46 260	95.51 325	98.69 188	85.91 308	99.53 289	94.16 265	96.23 337	97.58 322

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	94.13 270	99.30 224	98.91 252

MSDG		97.71 184	97.52 189	98.28 212	98.91 205	96.82 210	94.42 332	99.37 100	97.65 150	98.37 200	98.29 243	97.40 119	99.33 321	94.09 271	99.22 235	98.68 278

MVS-HIRNet		94.32 296	95.62 268	90.42 336	98.46 275	75.36 357	96.29 269	89.13 353	95.25 263	95.38 326	99.75 8	92.88 266	99.19 332	94.07 272	99.39 208	96.72 335

DP-MVS Recon		97.33 210	96.92 222	98.57 179	99.09 167	97.99 137	96.79 242	99.35 109	93.18 298	97.71 237	98.07 260	95.00 224	99.31 323	93.97 273	99.13 253	98.42 289

new_pmnet		96.99 239	96.76 232	97.67 242	98.72 237	94.89 257	95.95 284	98.20 285	92.62 306	98.55 183	98.54 215	94.88 228	99.52 293	93.96 274	99.44 203	98.59 281

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	95.54 256	98.43 194	98.62 206	96.61 166	99.77 192	93.95 275	99.49 196	99.30 183

MDTV_nov1_ep13			95.22 281		97.06 335	83.20 352	97.74 169	96.16 323	94.37 281	96.99 275	98.83 166	83.95 323	99.53 289	93.90 276	97.95 313

WTY-MVS		96.67 250	96.27 256	97.87 232	98.81 227	94.61 265	96.77 244	97.92 294	94.94 268	97.12 267	97.74 277	91.11 280	99.82 141	93.89 277	98.15 305	99.18 210

Vis-MVSNet (Re-imp)		97.46 202	97.16 210	98.34 206	99.55 65	96.10 227	98.94 56	98.44 276	98.32 104	98.16 208	98.62 206	88.76 293	99.73 215	93.88 278	99.79 81	99.18 210

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	99.35 318	93.86 279	99.27 228	98.79 266

CPTT-MVS		97.84 177	97.36 200	99.27 82	99.31 117	98.46 100	98.29 106	99.27 148	94.90 269	97.83 230	98.37 235	94.90 225	99.84 116	93.85 280	99.54 178	99.51 95

APD-MVS		98.10 152	97.67 176	99.42 57	99.11 160	98.93 66	97.76 167	99.28 145	94.97 267	98.72 163	98.77 176	97.04 137	99.85 99	93.79 281	99.54 178	99.49 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
agg_prior1		97.06 231	96.40 250	99.03 120	98.68 250	97.99 137	95.76 292	99.01 217	91.73 314	95.59 317	97.50 290	96.49 171	99.77 192	93.71 282	99.14 250	99.34 168

train_agg		97.10 227	96.45 249	99.07 110	98.71 240	98.08 128	95.96 282	99.03 210	91.64 315	95.85 314	97.53 287	96.47 172	99.76 199	93.67 283	99.16 246	99.36 162

PVSNet	93.40 17	95.67 275	95.70 265	95.57 311	98.83 222	88.57 332	92.50 346	97.72 297	92.69 305	96.49 301	96.44 319	93.72 254	99.43 310	93.61 284	99.28 227	98.71 272

test0.0.03 1		94.51 293	93.69 302	96.99 277	96.05 349	93.61 293	94.97 317	93.49 340	96.17 238	97.57 249	94.88 341	82.30 331	99.01 340	93.60 285	94.17 348	98.37 292

testdata				98.09 220	98.93 198	95.40 246		98.80 252	90.08 331	97.45 259	98.37 235	95.26 218	99.70 226	93.58 286	98.95 275	99.17 214

MDTV_nov1_ep13_2view						74.92 358	97.69 173		90.06 332	97.75 236		85.78 309		93.52 287		98.69 275

TAPA-MVS	96.21 11	96.63 252	95.95 260	98.65 167	98.93 198	98.09 124	96.93 233	99.28 145	83.58 348	98.13 211	97.78 274	96.13 184	99.40 312	93.52 287	99.29 226	98.45 286
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		97.88 169	97.49 191	99.04 119	98.89 211	98.63 84	96.94 231	99.25 154	95.02 265	98.53 186	98.51 218	97.27 126	99.47 304	93.50 289	99.51 188	99.01 233

PatchMatch-RL		97.24 218	96.78 231	98.61 174	99.03 181	97.83 156	96.36 266	99.06 201	93.49 297	97.36 265	97.78 274	95.75 203	99.49 299	93.44 290	98.77 280	98.52 282

114514_t		96.50 257	95.77 262	98.69 165	99.48 92	97.43 183	97.84 158	99.55 44	81.42 350	96.51 298	98.58 212	95.53 209	99.67 242	93.41 291	99.58 166	98.98 238

ETH3D cwj APD-0.16		97.55 195	97.00 217	99.19 91	98.51 271	98.64 83	96.85 239	99.13 190	94.19 285	97.65 241	98.40 230	95.78 202	99.81 154	93.37 292	99.16 246	99.12 219

dp		93.47 310	93.59 304	93.13 334	96.64 341	81.62 355	97.66 176	96.42 321	92.80 304	96.11 307	98.64 200	78.55 346	99.59 272	93.31 293	92.18 351	98.16 297

test9_res														93.28 294	99.15 249	99.38 153

IB-MVS	91.63 19	92.24 319	90.90 323	96.27 297	97.22 333	91.24 326	94.36 334	93.33 342	92.37 308	92.24 347	94.58 345	66.20 359	99.89 55	93.16 295	94.63 345	97.66 319
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
baseline2		93.73 307	92.83 313	96.42 294	97.70 316	91.28 325	96.84 241	89.77 352	93.96 291	92.44 346	95.93 324	79.14 342	99.77 192	92.94 296	96.76 333	98.21 294

OpenMVS	96.65 7	97.09 228	96.68 237	98.32 207	98.32 283	97.16 199	98.86 62	99.37 100	89.48 333	96.29 305	99.15 89	96.56 167	99.90 46	92.90 297	99.20 238	97.89 304

ADS-MVSNet2		95.43 281	94.98 287	96.76 290	98.14 294	91.74 316	97.92 148	97.76 296	90.23 327	96.51 298	98.91 141	85.61 310	99.85 99	92.88 298	96.90 329	98.69 275

ADS-MVSNet		95.24 284	94.93 289	96.18 299	98.14 294	90.10 330	97.92 148	97.32 306	90.23 327	96.51 298	98.91 141	85.61 310	99.74 210	92.88 298	96.90 329	98.69 275

BP-MVS														92.82 300

HQP-MVS		97.00 238	96.49 248	98.55 184	98.67 252	96.79 211	96.29 269	99.04 208	96.05 243	95.55 321	96.84 310	93.84 249	99.54 287	92.82 300	99.26 231	99.32 176

testdata2													99.79 176	92.80 302

CDPH-MVS		97.26 215	96.66 240	99.07 110	99.00 186	98.15 120	96.03 278	99.01 217	91.21 323	97.79 233	97.85 271	96.89 147	99.69 230	92.75 303	99.38 211	99.39 147

新几何1				98.91 135	98.94 196	97.76 164		98.76 256	87.58 342	96.75 289	98.10 256	94.80 232	99.78 186	92.73 304	99.00 270	99.20 203

ZD-MVS					99.01 185	98.84 69		99.07 200	94.10 287	98.05 219	98.12 254	96.36 180	99.86 86	92.70 305	99.19 242

F-COLMAP		97.30 212	96.68 237	99.14 98	99.19 141	98.39 103	97.27 211	99.30 136	92.93 301	96.62 293	98.00 262	95.73 204	99.68 239	92.62 306	98.46 295	99.35 166

原ACMM1				98.35 205	98.90 206	96.25 225		98.83 249	92.48 307	96.07 310	98.10 256	95.39 216	99.71 224	92.61 307	98.99 271	99.08 222

agg_prior2														92.50 308	99.16 246	99.37 156

无先验							95.74 294	98.74 261	89.38 334				99.73 215	92.38 309		99.22 202

1121		96.73 247	96.00 258	98.91 135	98.95 195	97.76 164	98.07 129	98.73 262	87.65 341	96.54 295	98.13 251	94.52 237	99.73 215	92.38 309	99.02 267	99.24 197

testtj		97.79 182	97.25 205	99.42 57	99.03 181	98.85 68	97.78 162	99.18 174	95.83 251	98.12 212	98.50 221	95.50 212	99.86 86	92.23 311	99.07 259	99.54 82

CMPMVS	75.91 23	96.29 262	95.44 274	98.84 144	96.25 348	98.69 82	97.02 226	99.12 193	88.90 336	97.83 230	98.86 157	89.51 289	98.90 343	91.92 312	99.51 188	98.92 249
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
BH-untuned		96.83 243	96.75 233	97.08 274	98.74 235	93.33 294	96.71 248	98.26 282	96.72 222	98.44 191	97.37 299	95.20 219	99.47 304	91.89 313	97.43 320	98.44 287

gm-plane-assit					94.83 354	81.97 354			88.07 340		94.99 338		99.60 268	91.76 314

CNLPA		97.17 224	96.71 235	98.55 184	98.56 266	98.05 133	96.33 267	98.93 226	96.91 215	97.06 272	97.39 297	94.38 241	99.45 308	91.66 315	99.18 244	98.14 298

MIMVSNet		96.62 253	96.25 257	97.71 241	99.04 178	94.66 263	99.16 39	96.92 315	97.23 198	97.87 227	99.10 97	86.11 307	99.65 255	91.65 316	99.21 237	98.82 260

1314		95.74 274	95.60 269	96.17 300	97.53 322	92.75 305	98.07 129	98.31 281	91.22 322	94.25 335	96.68 313	95.53 209	99.03 337	91.64 317	97.18 326	96.74 334

PMVS	91.26 20	97.86 171	97.94 160	97.65 244	99.71 30	97.94 149	98.52 84	98.68 265	98.99 67	97.52 253	99.35 57	97.41 118	98.18 349	91.59 318	99.67 136	96.82 333
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tpm cat1		93.29 312	93.13 311	93.75 327	97.39 328	84.74 347	97.39 201	97.65 300	83.39 349	94.16 336	98.41 229	82.86 329	99.39 314	91.56 319	95.35 342	97.14 329

DPM-MVS		96.32 261	95.59 270	98.51 191	98.76 231	97.21 194	94.54 331	98.26 282	91.94 313	96.37 303	97.25 302	93.06 263	99.43 310	91.42 320	98.74 281	98.89 253

HY-MVS	95.94 13	95.90 270	95.35 278	97.55 254	97.95 303	94.79 258	98.81 65	96.94 314	92.28 310	95.17 328	98.57 213	89.90 287	99.75 206	91.20 321	97.33 325	98.10 299

MG-MVS		96.77 246	96.61 242	97.26 268	98.31 284	93.06 297	95.93 285	98.12 288	96.45 231	97.92 223	98.73 181	93.77 253	99.39 314	91.19 322	99.04 263	99.33 174

AdaColmap		97.14 226	96.71 235	98.46 196	98.34 282	97.80 162	96.95 230	98.93 226	95.58 255	96.92 277	97.66 280	95.87 200	99.53 289	90.97 323	99.14 250	98.04 301

PLC	94.65 16	96.51 255	95.73 264	98.85 143	98.75 234	97.91 150	96.42 263	99.06 201	90.94 326	95.59 317	97.38 298	94.41 239	99.59 272	90.93 324	98.04 312	99.05 225
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm2		93.09 314	92.58 315	94.62 320	97.56 320	86.53 341	97.66 176	95.79 328	86.15 344	94.07 339	98.23 247	75.95 348	99.53 289	90.91 325	96.86 332	97.81 310

QAPM		97.31 211	96.81 230	98.82 146	98.80 229	97.49 179	99.06 47	99.19 170	90.22 329	97.69 239	99.16 85	96.91 146	99.90 46	90.89 326	99.41 205	99.07 223

PAPM_NR		96.82 245	96.32 253	98.30 210	99.07 171	96.69 216	97.48 196	98.76 256	95.81 252	96.61 294	96.47 318	94.12 248	99.17 333	90.82 327	97.78 315	99.06 224

BH-RMVSNet		96.83 243	96.58 244	97.58 250	98.47 274	94.05 275	96.67 250	97.36 303	96.70 224	97.87 227	97.98 264	95.14 221	99.44 309	90.47 328	98.58 293	99.25 194

API-MVS		97.04 234	96.91 224	97.42 262	97.88 307	98.23 115	98.18 117	98.50 274	97.57 157	97.39 263	96.75 312	96.77 156	99.15 335	90.16 329	99.02 267	94.88 347

E-PMN		94.17 300	94.37 295	93.58 329	96.86 337	85.71 345	90.11 350	97.07 310	98.17 120	97.82 232	97.19 303	84.62 317	98.94 341	89.77 330	97.68 317	96.09 343

MAR-MVS		96.47 258	95.70 265	98.79 152	97.92 305	99.12 54	98.28 107	98.60 270	92.16 312	95.54 324	96.17 321	94.77 234	99.52 293	89.62 331	98.23 299	97.72 316
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
wuyk23d		96.06 266	97.62 183	91.38 335	98.65 258	98.57 91	98.85 63	96.95 313	96.86 217	99.90 5	99.16 85	99.18 12	98.40 348	89.23 332	99.77 89	77.18 351

ETH3 D test6400		96.46 259	95.59 270	99.08 107	98.88 212	98.21 117	96.53 255	99.18 174	88.87 337	97.08 270	97.79 273	93.64 256	99.77 192	88.92 333	99.40 207	99.28 188

OpenMVS_ROB	95.38 14	95.84 272	95.18 283	97.81 235	98.41 279	97.15 200	97.37 202	98.62 269	83.86 347	98.65 168	98.37 235	94.29 243	99.68 239	88.41 334	98.62 291	96.60 336

BH-w/o		95.13 286	94.89 290	95.86 304	98.20 291	91.31 323	95.65 297	97.37 302	93.64 293	96.52 297	95.70 328	93.04 264	99.02 338	88.10 335	95.82 339	97.24 328

EMVS		93.83 306	94.02 298	93.23 333	96.83 339	84.96 346	89.77 351	96.32 322	97.92 133	97.43 261	96.36 320	86.17 305	98.93 342	87.68 336	97.73 316	95.81 344

gg-mvs-nofinetune		92.37 317	91.20 322	95.85 305	95.80 353	92.38 310	99.31 19	81.84 357	99.75 6	91.83 348	99.74 9	68.29 355	99.02 338	87.15 337	97.12 327	96.16 340

TR-MVS		95.55 278	95.12 285	96.86 287	97.54 321	93.94 281	96.49 259	96.53 320	94.36 282	97.03 274	96.61 314	94.26 244	99.16 334	86.91 338	96.31 336	97.47 326

PVSNet_0	89.98 21	91.15 322	90.30 325	93.70 328	97.72 313	84.34 351	90.24 349	97.42 301	90.20 330	93.79 341	93.09 350	90.90 281	98.89 344	86.57 339	72.76 353	97.87 306

tmp_tt		78.77 323	78.73 326	78.90 337	58.45 358	74.76 359	94.20 335	78.26 359	39.16 354	86.71 354	92.82 351	80.50 335	75.19 355	86.16 340	92.29 350	86.74 350

PAPR		95.29 282	94.47 292	97.75 239	97.50 326	95.14 253	94.89 319	98.71 264	91.39 321	95.35 327	95.48 332	94.57 236	99.14 336	84.95 341	97.37 321	98.97 242

thres600view7		94.45 294	93.83 300	96.29 296	99.06 175	91.53 318	97.99 142	94.24 337	98.34 102	97.44 260	95.01 337	79.84 337	99.67 242	84.33 342	98.23 299	97.66 319

MVS		93.19 313	92.09 317	96.50 293	96.91 336	94.03 277	98.07 129	98.06 290	68.01 352	94.56 334	96.48 317	95.96 196	99.30 325	83.84 343	96.89 331	96.17 339

thres100view900		94.19 299	93.67 303	95.75 307	99.06 175	91.35 322	98.03 136	94.24 337	98.33 103	97.40 262	94.98 339	79.84 337	99.62 261	83.05 344	98.08 309	96.29 337

tfpn200view9		94.03 303	93.44 305	95.78 306	98.93 198	91.44 320	97.60 183	94.29 335	97.94 131	97.10 268	94.31 346	79.67 339	99.62 261	83.05 344	98.08 309	96.29 337

thres400		94.14 301	93.44 305	96.24 298	98.93 198	91.44 320	97.60 183	94.29 335	97.94 131	97.10 268	94.31 346	79.67 339	99.62 261	83.05 344	98.08 309	97.66 319

thres200		93.72 308	93.14 310	95.46 313	98.66 257	91.29 324	96.61 253	94.63 333	97.39 178	96.83 286	93.71 349	79.88 336	99.56 281	82.40 347	98.13 306	95.54 346

GG-mvs-BLEND				94.76 319	94.54 355	92.13 314	99.31 19	80.47 358		88.73 353	91.01 352	67.59 356	98.16 350	82.30 348	94.53 346	93.98 348

MVE	83.40 22	92.50 316	91.92 319	94.25 323	98.83 222	91.64 317	92.71 345	83.52 356	95.92 248	86.46 355	95.46 333	95.20 219	95.40 352	80.51 349	98.64 289	95.73 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PCF-MVS	92.86 18	94.36 295	93.00 312	98.42 199	98.70 244	97.56 176	93.16 344	99.11 195	79.59 351	97.55 250	97.43 295	92.19 273	99.73 215	79.85 350	99.45 202	97.97 303
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FPMVS		93.44 311	92.23 316	97.08 274	99.25 127	97.86 154	95.61 298	97.16 309	92.90 302	93.76 342	98.65 197	75.94 349	95.66 351	79.30 351	97.49 318	97.73 315

DeepMVS_CX				93.44 331	98.24 288	94.21 272		94.34 334	64.28 353	91.34 349	94.87 343	89.45 291	92.77 354	77.54 352	93.14 349	93.35 349

PAPM		91.88 321	90.34 324	96.51 292	98.06 299	92.56 306	92.44 347	97.17 308	86.35 343	90.38 350	96.01 322	86.61 301	99.21 331	70.65 353	95.43 341	97.75 314

test123		17.04 326	20.11 329	7.82 338	10.25 360	4.91 360	94.80 320	4.47 361	4.93 355	10.00 357	24.28 355	9.69 361	3.64 356	10.14 354	12.43 355	14.92 352

testmvs		17.12 325	20.53 328	6.87 339	12.05 359	4.20 361	93.62 342	6.73 360	4.62 356	10.41 356	24.33 354	8.28 362	3.56 357	9.69 355	15.07 354	12.86 353

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		24.66 324	32.88 327	0.00 340	0.00 361	0.00 362	0.00 352	99.10 196	0.00 357	0.00 358	97.58 285	99.21 11	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		8.17 327	10.90 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	98.07 65	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		8.12 328	10.83 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	97.48 292	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

test_241102_ONE					99.49 84	99.17 37		99.31 127	97.98 128	99.66 21	98.90 144	98.36 43	99.48 302

save fliter					99.11 160	97.97 142	96.53 255	99.02 214	98.24 111

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

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

test_post											21.25 356	83.86 324	99.70 226

patchmatchnet-post											98.77 176	84.37 319	99.85 99

MTMP							97.93 147	91.91 347

TEST9					98.71 240	98.08 128	95.96 282	99.03 210	91.40 320	95.85 314	97.53 287	96.52 169	99.76 199

test_8					98.67 252	98.01 136	95.91 287	99.02 214	91.64 315	95.79 316	97.50 290	96.47 172	99.76 199

agg_prior					98.68 250	97.99 137		99.01 217		95.59 317			99.77 192

test_prior4						97.97 142	95.86 288

test_prior				98.95 129	98.69 247	97.95 147		99.03 210					99.59 272			99.30 183

新几何2							95.93 285

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

原ACMM2							95.53 301

test222					98.92 202	96.93 208	95.54 300	98.78 254	85.72 345	96.86 285	98.11 255	94.43 238			99.10 258	99.23 198

segment_acmp												97.02 140

testdata1							95.44 306		96.32 235

test12				98.93 132	98.58 264	97.83 156		98.66 266		96.53 296		95.51 211	99.69 230		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_prior4											97.98 264

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	97.07 225		96.72 222						99.36 213

n2								0.00 362

nn								0.00 362

door-mid								99.57 34

test11								98.87 237

door								99.41 89

HQP5-MVS						96.79 211

HQP-NCC					98.67 252		96.29 269		96.05 243	95.55 321

ACMP_Plane					98.67 252		96.29 269		96.05 243	95.55 321

HQP4-MVS										95.56 320			99.54 287			99.32 176

HQP3-MVS								99.04 208							99.26 231

HQP2-MVS												93.84 249

NP-MVS					98.84 220	97.39 185					96.84 310

ACMMP++_ref															99.77 89

ACMMP++															99.68 130

Test By Simon												96.52 169

High-resolution multi-view benchmark