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		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 8	99.78 6	100.00 1	99.92 1	100.00 1	99.87 9

v1921920		99.56 40	99.57 37	99.55 139	99.75 93	99.11 198	99.05 175	99.61 137	99.15 138	99.88 32	99.71 98	99.08 63	99.87 160	99.90 2	99.97 29	99.66 73

v1240		99.56 40	99.58 34	99.51 149	99.80 55	99.00 209	99.00 184	99.65 122	99.15 138	99.90 22	99.75 78	99.09 60	99.88 147	99.90 2	99.96 40	99.67 63

v10		99.69 21	99.69 18	99.66 90	99.81 49	99.39 143	99.66 38	99.75 70	99.60 72	99.92 18	99.87 30	98.75 107	99.86 180	99.90 2	99.99 12	99.73 40

v1192		99.57 37	99.57 37	99.57 132	99.77 79	99.22 184	99.04 177	99.60 147	99.18 131	99.87 36	99.72 91	99.08 63	99.85 198	99.89 5	99.98 21	99.66 73

v144192		99.55 43	99.54 43	99.58 127	99.78 71	99.20 190	99.11 164	99.62 133	99.18 131	99.89 26	99.72 91	98.66 118	99.87 160	99.88 6	99.97 29	99.66 73

v8		99.68 22	99.69 18	99.65 95	99.80 55	99.40 141	99.66 38	99.76 64	99.64 58	99.93 14	99.85 35	98.66 118	99.84 213	99.88 6	99.99 12	99.71 44

v1144		99.54 45	99.53 47	99.59 123	99.79 65	99.28 167	99.10 165	99.61 137	99.20 129	99.84 41	99.73 85	98.67 116	99.84 213	99.86 8	99.98 21	99.64 87

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 16	99.82 8	99.82 36	99.84 21	99.94 11	99.91 19	99.13 57	99.96 33	99.83 9	99.99 12	99.83 18

v2v482		99.50 48	99.47 51	99.58 127	99.78 71	99.25 175	99.14 151	99.58 163	99.25 121	99.81 55	99.62 156	98.24 166	99.84 213	99.83 9	99.97 29	99.64 87

V42		99.56 40	99.54 43	99.63 106	99.79 65	99.46 121	99.39 81	99.59 154	99.24 123	99.86 37	99.70 105	98.55 130	99.82 234	99.79 11	99.95 47	99.60 115

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 34	99.72 19	99.88 15	99.92 6	99.98 3	99.93 13	99.94 1	99.98 6	99.77 12	100.00 1	99.92 3

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 39	99.68 31	99.85 23	99.95 3	99.98 3	99.92 16	99.28 40	99.98 6	99.75 13	100.00 1	99.94 2

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 37	99.70 22	99.86 19	99.89 11	99.98 3	99.90 21	99.94 1	99.98 6	99.75 13	100.00 1	99.90 4

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 43	100.00 1	99.90 7	100.00 1	99.97 9	99.61 16	99.97 16	99.75 13	100.00 1	99.84 14

pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 4	99.83 6	99.91 8	99.85 19	99.94 11	99.95 11	99.73 8	99.90 119	99.65 16	99.97 29	99.69 50

MIMVSNet1		99.66 24	99.62 25	99.80 29	99.94 10	99.87 7	99.69 28	99.77 59	99.78 32	99.93 14	99.89 25	97.94 191	99.92 84	99.65 16	99.98 21	99.62 104

K. test v3		98.87 196	98.60 205	99.69 80	99.93 13	99.46 121	99.74 15	94.97 345	99.78 32	99.88 32	99.88 28	93.66 289	99.97 16	99.61 18	99.95 47	99.64 87

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 11	99.75 14	99.86 19	99.70 43	99.91 20	99.89 25	99.60 18	99.87 160	99.59 19	99.74 180	99.71 44

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 20	99.83 6	99.85 23	99.80 29	99.93 14	99.93 13	98.54 132	99.93 66	99.59 19	99.98 21	99.76 35

EU-MVSNet		99.39 77	99.62 25	98.72 275	99.88 23	96.44 312	99.56 60	99.85 23	99.90 7	99.90 22	99.85 35	98.09 179	99.83 224	99.58 21	99.95 47	99.90 4

mvs_anonymous		99.28 103	99.39 64	98.94 249	99.19 277	97.81 281	99.02 180	99.55 175	99.78 32	99.85 38	99.80 51	98.24 166	99.86 180	99.57 22	99.50 252	99.15 257

lessismore_v0				99.64 102	99.86 29	99.38 146		90.66 352		99.89 26	99.83 40	94.56 281	99.97 16	99.56 23	99.92 72	99.57 135

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 20	99.76 13	99.87 17	99.73 36	99.89 26	99.87 30	99.63 14	99.87 160	99.54 24	99.92 72	99.63 92

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 11	99.91 15	99.90 4	99.96 1	99.92 5	99.90 7	99.97 6	99.87 30	99.81 5	99.95 42	99.54 24	99.99 12	99.80 23
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
RRT_test8_iter05		97.35 288	97.25 284	97.63 311	98.81 321	93.13 337	99.26 115	99.89 12	99.51 80	99.83 46	99.68 122	79.03 354	99.88 147	99.53 26	99.72 192	99.89 8

DSMNet-mixed		99.48 52	99.65 22	98.95 248	99.71 109	97.27 296	99.50 64	99.82 36	99.59 74	99.41 190	99.85 35	99.62 15	100.00 1	99.53 26	99.89 90	99.59 124

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 90	99.93 4	99.95 10	99.89 25	99.71 9	99.96 33	99.51 28	99.97 29	99.84 14

FC-MVSNet-test		99.70 19	99.65 22	99.86 16	99.88 23	99.86 10	99.72 19	99.78 56	99.90 7	99.82 48	99.83 40	98.45 147	99.87 160	99.51 28	99.97 29	99.86 11

UA-Net		99.78 13	99.76 14	99.86 16	99.72 106	99.71 59	99.91 3	99.95 4	99.96 2	99.71 96	99.91 19	99.15 52	99.97 16	99.50 30	100.00 1	99.90 4

PMMVS2		99.48 52	99.45 55	99.57 132	99.76 83	98.99 210	98.09 283	99.90 11	98.95 160	99.78 66	99.58 180	99.57 19	99.93 66	99.48 31	99.95 47	99.79 29

VPA-MVSNet		99.66 24	99.62 25	99.79 34	99.68 127	99.75 47	99.62 46	99.69 100	99.85 19	99.80 58	99.81 49	98.81 92	99.91 100	99.47 32	99.88 98	99.70 47

nrg030		99.70 19	99.66 21	99.82 23	99.76 83	99.84 16	99.61 50	99.70 94	99.93 4	99.78 66	99.68 122	99.10 58	99.78 262	99.45 33	99.96 40	99.83 18

TAMVS		99.49 50	99.45 55	99.63 106	99.48 201	99.42 136	99.45 71	99.57 165	99.66 54	99.78 66	99.83 40	97.85 199	99.86 180	99.44 34	99.96 40	99.61 111

new-patchmatchnet		99.35 86	99.57 37	98.71 277	99.82 42	96.62 310	98.55 242	99.75 70	99.50 81	99.88 32	99.87 30	99.31 35	99.88 147	99.43 35	100.00 1	99.62 104

test20.03		99.55 43	99.54 43	99.58 127	99.79 65	99.37 149	99.02 180	99.89 12	99.60 72	99.82 48	99.62 156	98.81 92	99.89 132	99.43 35	99.86 113	99.47 182

MVSFormer		99.41 70	99.44 57	99.31 207	99.57 160	98.40 250	99.77 11	99.80 46	99.73 36	99.63 121	99.30 257	98.02 185	99.98 6	99.43 35	99.69 201	99.55 140

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 16	99.77 11	99.80 46	99.73 36	99.97 6	99.92 16	99.77 7	99.98 6	99.43 35	100.00 1	99.90 4

Anonymous20231211		99.62 32	99.57 37	99.76 45	99.61 143	99.60 97	99.81 9	99.73 78	99.82 25	99.90 22	99.90 21	97.97 190	99.86 180	99.42 39	99.96 40	99.80 23

SixPastTwentyTwo		99.42 66	99.30 85	99.76 45	99.92 14	99.67 75	99.70 22	99.14 287	99.65 56	99.89 26	99.90 21	96.20 263	99.94 53	99.42 39	99.92 72	99.67 63

UGNet		99.38 79	99.34 74	99.49 155	98.90 308	98.90 224	99.70 22	99.35 251	99.86 16	98.57 292	99.81 49	98.50 142	99.93 66	99.38 41	99.98 21	99.66 73
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
XXY-MVS		99.71 18	99.67 20	99.81 26	99.89 21	99.72 57	99.59 55	99.82 36	99.39 102	99.82 48	99.84 39	99.38 27	99.91 100	99.38 41	99.93 68	99.80 23

FIs		99.65 29	99.58 34	99.84 19	99.84 33	99.85 11	99.66 38	99.75 70	99.86 16	99.74 86	99.79 57	98.27 164	99.85 198	99.37 43	99.93 68	99.83 18

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 6	99.73 16	99.85 23	99.70 43	99.92 18	99.93 13	99.45 21	99.97 16	99.36 44	100.00 1	99.85 13

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 23	99.66 77	99.69 28	99.92 5	99.67 50	99.77 71	99.75 78	99.61 16	99.98 6	99.35 45	99.98 21	99.72 41
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		99.39 77	99.30 85	99.65 95	99.88 23	99.25 175	98.78 223	99.88 15	98.66 193	99.96 8	99.79 57	97.45 222	99.93 66	99.34 46	99.99 12	99.78 30

CDS-MVSNet		99.22 121	99.13 115	99.50 152	99.35 238	99.11 198	98.96 196	99.54 180	99.46 92	99.61 134	99.70 105	96.31 260	99.83 224	99.34 46	99.88 98	99.55 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 175	99.16 108	98.51 282	99.75 93	95.90 320	98.07 286	99.84 29	99.84 21	99.89 26	99.73 85	96.01 267	99.99 4	99.33 48	100.00 1	99.63 92

HyFIR lowres test		98.91 188	98.64 202	99.73 66	99.85 32	99.47 117	98.07 286	99.83 31	98.64 195	99.89 26	99.60 172	92.57 297	100.00 1	99.33 48	99.97 29	99.72 41

pmmvs5		99.19 131	99.11 121	99.42 174	99.76 83	98.88 225	98.55 242	99.73 78	98.82 178	99.72 91	99.62 156	96.56 250	99.82 234	99.32 50	99.95 47	99.56 137

v148		99.40 73	99.41 62	99.39 186	99.76 83	98.94 216	99.09 169	99.59 154	99.17 134	99.81 55	99.61 165	98.41 150	99.69 294	99.32 50	99.94 60	99.53 150

baseline		99.63 30	99.62 25	99.66 90	99.80 55	99.62 90	99.44 74	99.80 46	99.71 40	99.72 91	99.69 111	99.15 52	99.83 224	99.32 50	99.94 60	99.53 150

testing_2		99.58 36	99.56 41	99.62 115	99.81 49	99.44 128	99.14 151	99.43 227	99.69 46	99.82 48	99.79 57	99.14 54	99.79 258	99.31 53	99.95 47	99.63 92

CVMVSNet		98.61 219	98.88 180	97.80 306	99.58 150	93.60 335	99.26 115	99.64 128	99.66 54	99.72 91	99.67 128	93.26 291	99.93 66	99.30 54	99.81 147	99.87 9

PS-CasMVS		99.66 24	99.58 34	99.89 7	99.80 55	99.85 11	99.66 38	99.73 78	99.62 62	99.84 41	99.71 98	98.62 122	99.96 33	99.30 54	99.96 40	99.86 11

DTE-MVSNet		99.68 22	99.61 29	99.88 11	99.80 55	99.87 7	99.67 35	99.71 90	99.72 39	99.84 41	99.78 64	98.67 116	99.97 16	99.30 54	99.95 47	99.80 23

tmp_tt		95.75 317	95.42 316	96.76 323	89.90 353	94.42 332	98.86 206	97.87 329	78.01 347	99.30 214	99.69 111	97.70 206	95.89 350	99.29 57	98.14 332	99.95 1

PEN-MVS		99.66 24	99.59 32	99.89 7	99.83 36	99.87 7	99.66 38	99.73 78	99.70 43	99.84 41	99.73 85	98.56 129	99.96 33	99.29 57	99.94 60	99.83 18

WR-MVS_H		99.61 34	99.53 47	99.87 14	99.80 55	99.83 20	99.67 35	99.75 70	99.58 75	99.85 38	99.69 111	98.18 175	99.94 53	99.28 59	99.95 47	99.83 18

IterMVS		98.97 179	99.16 108	98.42 286	99.74 99	95.64 323	98.06 288	99.83 31	99.83 24	99.85 38	99.74 81	96.10 266	99.99 4	99.27 60	100.00 1	99.63 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		99.66 24	99.63 24	99.73 66	99.78 71	99.77 39	99.68 31	99.70 94	99.67 50	99.82 48	99.83 40	98.98 73	99.90 119	99.24 61	99.97 29	99.53 150

casdiffmvs		99.63 30	99.61 29	99.67 83	99.79 65	99.59 100	99.13 158	99.85 23	99.79 31	99.76 73	99.72 91	99.33 34	99.82 234	99.21 62	99.94 60	99.59 124

CP-MVSNet		99.54 45	99.43 60	99.87 14	99.76 83	99.82 24	99.57 58	99.61 137	99.54 76	99.80 58	99.64 139	97.79 203	99.95 42	99.21 62	99.94 60	99.84 14

DELS-MVS		99.34 91	99.30 85	99.48 158	99.51 184	99.36 152	98.12 279	99.53 189	99.36 106	99.41 190	99.61 165	99.22 46	99.87 160	99.21 62	99.68 204	99.20 247
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
RRT_MVS		98.75 208	98.54 214	99.41 181	98.14 346	98.61 240	98.98 193	99.66 111	99.31 112	99.84 41	99.75 78	91.98 301	99.98 6	99.20 65	99.95 47	99.62 104

UniMVSNet (Re)		99.37 81	99.26 97	99.68 81	99.51 184	99.58 103	98.98 193	99.60 147	99.43 99	99.70 98	99.36 243	97.70 206	99.88 147	99.20 65	99.87 106	99.59 124

CANet		99.11 152	99.05 141	99.28 212	98.83 317	98.56 241	98.71 230	99.41 230	99.25 121	99.23 222	99.22 275	97.66 215	99.94 53	99.19 67	99.97 29	99.33 222

EI-MVSNet-UG-set		99.48 52	99.50 49	99.42 174	99.57 160	98.65 239	99.24 121	99.46 219	99.68 48	99.80 58	99.66 132	98.99 72	99.89 132	99.19 67	99.90 82	99.72 41

xiu_mvs_v1_base_debu		99.23 113	99.34 74	98.91 255	99.59 147	98.23 258	98.47 251	99.66 111	99.61 66	99.68 103	98.94 310	99.39 23	99.97 16	99.18 69	99.55 240	98.51 313

xiu_mvs_v1_base		99.23 113	99.34 74	98.91 255	99.59 147	98.23 258	98.47 251	99.66 111	99.61 66	99.68 103	98.94 310	99.39 23	99.97 16	99.18 69	99.55 240	98.51 313

xiu_mvs_v1_base_debi		99.23 113	99.34 74	98.91 255	99.59 147	98.23 258	98.47 251	99.66 111	99.61 66	99.68 103	98.94 310	99.39 23	99.97 16	99.18 69	99.55 240	98.51 313

MVS_0304		98.88 194	98.71 196	99.39 186	98.85 315	98.91 223	99.45 71	99.30 263	98.56 202	97.26 337	99.68 122	96.18 264	99.96 33	99.17 72	99.94 60	99.29 231

VPNet		99.46 59	99.37 69	99.71 75	99.82 42	99.59 100	99.48 68	99.70 94	99.81 26	99.69 101	99.58 180	97.66 215	99.86 180	99.17 72	99.44 259	99.67 63

UniMVSNet_NR-MVSNet		99.37 81	99.25 99	99.72 71	99.47 206	99.56 106	98.97 195	99.61 137	99.43 99	99.67 107	99.28 262	97.85 199	99.95 42	99.17 72	99.81 147	99.65 81

DU-MVS		99.33 95	99.21 103	99.71 75	99.43 219	99.56 106	98.83 211	99.53 189	99.38 103	99.67 107	99.36 243	97.67 211	99.95 42	99.17 72	99.81 147	99.63 92

EI-MVSNet-Vis-set		99.47 58	99.49 50	99.42 174	99.57 160	98.66 237	99.24 121	99.46 219	99.67 50	99.79 63	99.65 137	98.97 75	99.89 132	99.15 76	99.89 90	99.71 44

EI-MVSNet		99.38 79	99.44 57	99.21 225	99.58 150	98.09 269	99.26 115	99.46 219	99.62 62	99.75 78	99.67 128	98.54 132	99.85 198	99.15 76	99.92 72	99.68 56

VNet		99.18 135	99.06 137	99.56 136	99.24 269	99.36 152	99.33 93	99.31 260	99.67 50	99.47 171	99.57 187	96.48 253	99.84 213	99.15 76	99.30 281	99.47 182

EG-PatchMatch MVS		99.57 37	99.56 41	99.62 115	99.77 79	99.33 159	99.26 115	99.76 64	99.32 111	99.80 58	99.78 64	99.29 38	99.87 160	99.15 76	99.91 81	99.66 73

PVSNet_Blended_VisFu		99.40 73	99.38 66	99.44 169	99.90 19	98.66 237	98.94 199	99.91 8	97.97 253	99.79 63	99.73 85	99.05 68	99.97 16	99.15 76	99.99 12	99.68 56

IterMVS-LS		99.41 70	99.47 51	99.25 219	99.81 49	98.09 269	98.85 208	99.76 64	99.62 62	99.83 46	99.64 139	98.54 132	99.97 16	99.15 76	99.99 12	99.68 56
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 45	99.47 51	99.76 45	99.58 150	99.64 84	99.30 103	99.63 130	99.61 66	99.71 96	99.56 190	98.76 105	99.96 33	99.14 82	99.92 72	99.68 56

MVSTER		98.47 238	98.22 240	99.24 222	99.06 297	98.35 255	99.08 172	99.46 219	99.27 117	99.75 78	99.66 132	88.61 330	99.85 198	99.14 82	99.92 72	99.52 160

Anonymous20231206		99.35 86	99.31 80	99.47 160	99.74 99	99.06 208	99.28 111	99.74 75	99.23 125	99.72 91	99.53 200	97.63 217	99.88 147	99.11 84	99.84 120	99.48 177

MVS_Test		99.28 103	99.31 80	99.19 228	99.35 238	98.79 230	99.36 89	99.49 209	99.17 134	99.21 228	99.67 128	98.78 101	99.66 314	99.09 85	99.66 215	99.10 267

testgi		99.29 102	99.26 97	99.37 193	99.75 93	98.81 228	98.84 209	99.89 12	98.38 222	99.75 78	99.04 297	99.36 32	99.86 180	99.08 86	99.25 287	99.45 188

1112_ss		99.05 163	98.84 185	99.67 83	99.66 133	99.29 165	98.52 247	99.82 36	97.65 270	99.43 180	99.16 282	96.42 256	99.91 100	99.07 87	99.84 120	99.80 23

CANet_DTU		98.91 188	98.85 183	99.09 236	98.79 323	98.13 264	98.18 272	99.31 260	99.48 83	98.86 266	99.51 205	96.56 250	99.95 42	99.05 88	99.95 47	99.19 249

Baseline_NR-MVSNet		99.49 50	99.37 69	99.82 23	99.91 15	99.84 16	98.83 211	99.86 19	99.68 48	99.65 115	99.88 28	97.67 211	99.87 160	99.03 89	99.86 113	99.76 35

FMVSNet2		99.35 86	99.28 92	99.55 139	99.49 195	99.35 156	99.45 71	99.57 165	99.44 94	99.70 98	99.74 81	97.21 234	99.87 160	99.03 89	99.94 60	99.44 193

Test_1112_low_res		98.95 185	98.73 194	99.63 106	99.68 127	99.15 195	98.09 283	99.80 46	97.14 294	99.46 174	99.40 232	96.11 265	99.89 132	99.01 91	99.84 120	99.84 14

VDD-MVS		99.20 128	99.11 121	99.44 169	99.43 219	98.98 211	99.50 64	98.32 322	99.80 29	99.56 151	99.69 111	96.99 244	99.85 198	98.99 92	99.73 187	99.50 168

DeepC-MVS	98.90 4	99.62 32	99.61 29	99.67 83	99.72 106	99.44 128	99.24 121	99.71 90	99.27 117	99.93 14	99.90 21	99.70 11	99.93 66	98.99 92	99.99 12	99.64 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 52	99.47 51	99.51 149	99.77 79	99.41 140	98.81 216	99.66 111	99.42 101	99.75 78	99.66 132	99.20 47	99.76 272	98.98 94	99.99 12	99.36 216

EPNet_dtu		97.62 278	97.79 272	97.11 322	96.67 351	92.31 341	98.51 248	98.04 324	99.24 123	95.77 345	99.47 220	93.78 288	99.66 314	98.98 94	99.62 223	99.37 213
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 91	99.32 79	99.39 186	99.67 132	98.77 231	98.57 240	99.81 45	99.61 66	99.48 170	99.41 230	98.47 143	99.86 180	98.97 96	99.90 82	99.53 150

NR-MVSNet		99.40 73	99.31 80	99.68 81	99.43 219	99.55 109	99.73 16	99.50 204	99.46 92	99.88 32	99.36 243	97.54 219	99.87 160	98.97 96	99.87 106	99.63 92

GBi-Net		99.42 66	99.31 80	99.73 66	99.49 195	99.77 39	99.68 31	99.70 94	99.44 94	99.62 128	99.83 40	97.21 234	99.90 119	98.96 98	99.90 82	99.53 150

FMVSNet5		97.80 271	97.25 284	99.42 174	98.83 317	98.97 213	99.38 83	99.80 46	98.87 172	99.25 218	99.69 111	80.60 351	99.91 100	98.96 98	99.90 82	99.38 210

test1		99.42 66	99.31 80	99.73 66	99.49 195	99.77 39	99.68 31	99.70 94	99.44 94	99.62 128	99.83 40	97.21 234	99.90 119	98.96 98	99.90 82	99.53 150

FMVSNet3		98.80 203	98.63 204	99.32 204	99.13 285	98.72 233	99.10 165	99.48 211	99.23 125	99.62 128	99.64 139	92.57 297	99.86 180	98.96 98	99.90 82	99.39 208

UnsupCasMVSNet_eth		98.83 199	98.57 211	99.59 123	99.68 127	99.45 126	98.99 189	99.67 107	99.48 83	99.55 156	99.36 243	94.92 275	99.86 180	98.95 102	96.57 342	99.45 188

CHOSEN 280x420		98.41 243	98.41 225	98.40 287	99.34 248	95.89 321	96.94 339	99.44 224	98.80 181	99.25 218	99.52 202	93.51 290	99.98 6	98.94 103	99.98 21	99.32 225

TDRefinement		99.72 17	99.70 17	99.77 39	99.90 19	99.85 11	99.86 5	99.92 5	99.69 46	99.78 66	99.92 16	99.37 29	99.88 147	98.93 104	99.95 47	99.60 115

Regformer-4		99.45 61	99.44 57	99.50 152	99.52 180	98.94 216	99.17 141	99.53 189	99.64 58	99.76 73	99.60 172	98.96 78	99.90 119	98.91 105	99.84 120	99.67 63

Regformer-3		99.41 70	99.41 62	99.40 183	99.52 180	98.70 234	99.17 141	99.44 224	99.62 62	99.75 78	99.60 172	98.90 85	99.85 198	98.89 106	99.84 120	99.65 81

alignmvs		98.28 253	97.96 258	99.25 219	99.12 287	98.93 220	99.03 179	98.42 318	99.64 58	98.72 281	97.85 343	90.86 317	99.62 325	98.88 107	99.13 292	99.19 249

sss		98.90 190	98.77 193	99.27 214	99.48 201	98.44 247	98.72 229	99.32 256	97.94 257	99.37 198	99.35 248	96.31 260	99.91 100	98.85 108	99.63 222	99.47 182

xiu_mvs_v2_base		99.02 169	99.11 121	98.77 272	99.37 234	98.09 269	98.13 278	99.51 201	99.47 88	99.42 182	98.54 331	99.38 27	99.97 16	98.83 109	99.33 278	98.24 324

PS-MVSNAJ		99.00 175	99.08 131	98.76 273	99.37 234	98.10 268	98.00 293	99.51 201	99.47 88	99.41 190	98.50 333	99.28 40	99.97 16	98.83 109	99.34 276	98.20 328

D2MVS		99.22 121	99.19 105	99.29 210	99.69 119	98.74 232	98.81 216	99.41 230	98.55 204	99.68 103	99.69 111	98.13 177	99.87 160	98.82 111	99.98 21	99.24 237

PatchT		98.45 240	98.32 235	98.83 267	98.94 306	98.29 256	99.24 121	98.82 300	99.84 21	99.08 245	99.76 74	91.37 307	99.94 53	98.82 111	99.00 300	98.26 323

Effi-MVS+		99.06 160	98.97 165	99.34 198	99.31 255	98.98 211	98.31 264	99.91 8	98.81 179	98.79 274	98.94 310	99.14 54	99.84 213	98.79 113	98.74 314	99.20 247

canonicalmvs		99.02 169	99.00 156	99.09 236	99.10 293	98.70 234	99.61 50	99.66 111	99.63 61	98.64 286	97.65 345	99.04 69	99.54 334	98.79 113	98.92 303	99.04 281

VDDNet		98.97 179	98.82 188	99.42 174	99.71 109	98.81 228	99.62 46	98.68 306	99.81 26	99.38 197	99.80 51	94.25 283	99.85 198	98.79 113	99.32 279	99.59 124

CR-MVSNet		98.35 250	98.20 242	98.83 267	99.05 298	98.12 265	99.30 103	99.67 107	97.39 284	99.16 235	99.79 57	91.87 304	99.91 100	98.78 116	98.77 310	98.44 316

RPMNet		98.53 231	98.44 221	98.83 267	99.05 298	98.12 265	99.30 103	98.78 302	99.86 16	99.16 235	99.74 81	92.53 299	99.91 100	98.75 117	98.77 310	98.44 316

pmmvs4		99.13 146	99.06 137	99.36 196	99.57 160	99.10 202	98.01 291	99.25 274	98.78 184	99.58 141	99.44 227	98.24 166	99.76 272	98.74 118	99.93 68	99.22 242

tttt0517		97.62 278	97.20 286	98.90 261	99.76 83	97.40 293	99.48 68	94.36 347	99.06 152	99.70 98	99.49 213	84.55 345	99.94 53	98.73 119	99.65 218	99.36 216

EPP-MVSNet		99.17 139	99.00 156	99.66 90	99.80 55	99.43 133	99.70 22	99.24 277	99.48 83	99.56 151	99.77 71	94.89 276	99.93 66	98.72 120	99.89 90	99.63 92

Anonymous20240529		99.42 66	99.34 74	99.65 95	99.53 175	99.60 97	99.63 45	99.39 240	99.47 88	99.76 73	99.78 64	98.13 177	99.86 180	98.70 121	99.68 204	99.49 173

ACMH	98.42 6	99.59 35	99.54 43	99.72 71	99.86 29	99.62 90	99.56 60	99.79 52	98.77 185	99.80 58	99.85 35	99.64 13	99.85 198	98.70 121	99.89 90	99.70 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 95	99.28 92	99.47 160	99.57 160	99.39 143	99.78 10	99.43 227	98.87 172	99.57 144	99.82 46	98.06 182	99.87 160	98.69 123	99.73 187	99.15 257

LFMVS		98.46 239	98.19 245	99.26 216	99.24 269	98.52 243	99.62 46	96.94 338	99.87 14	99.31 210	99.58 180	91.04 312	99.81 250	98.68 124	99.42 264	99.45 188

WR-MVS		99.11 152	98.93 170	99.66 90	99.30 259	99.42 136	98.42 257	99.37 247	99.04 153	99.57 144	99.20 279	96.89 246	99.86 180	98.66 125	99.87 106	99.70 47

Anonymous202405211		98.75 208	98.46 219	99.63 106	99.34 248	99.66 77	99.47 70	97.65 331	99.28 116	99.56 151	99.50 208	93.15 292	99.84 213	98.62 126	99.58 234	99.40 205

EPNet		98.13 261	97.77 273	99.18 230	94.57 352	97.99 273	99.24 121	97.96 326	99.74 35	97.29 336	99.62 156	93.13 293	99.97 16	98.59 127	99.83 130	99.58 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 163	99.09 129	98.91 255	99.21 272	98.36 254	98.82 215	99.47 215	98.85 174	98.90 263	99.56 190	98.78 101	99.09 345	98.57 128	99.68 204	99.26 234

Patchmatch-RL test		98.60 220	98.36 230	99.33 200	99.77 79	99.07 206	98.27 267	99.87 17	98.91 167	99.74 86	99.72 91	90.57 321	99.79 258	98.55 129	99.85 116	99.11 265

pmmvs3		98.08 264	97.80 270	98.91 255	99.41 224	97.69 286	97.87 307	99.66 111	95.87 315	99.50 168	99.51 205	90.35 323	99.97 16	98.55 129	99.47 256	99.08 273

ETV-MVS		99.18 135	99.18 106	99.16 231	99.34 248	99.28 167	99.12 162	99.79 52	99.48 83	98.93 257	98.55 330	99.40 22	99.93 66	98.51 131	99.52 249	98.28 322

jason		99.16 140	99.11 121	99.32 204	99.75 93	98.44 247	98.26 268	99.39 240	98.70 191	99.74 86	99.30 257	98.54 132	99.97 16	98.48 132	99.82 139	99.55 140
jason: jason.
APDe-MVS		99.48 52	99.36 72	99.85 18	99.55 171	99.81 27	99.50 64	99.69 100	98.99 155	99.75 78	99.71 98	98.79 99	99.93 66	98.46 133	99.85 116	99.80 23

our_test_3		98.85 198	99.09 129	98.13 298	99.66 133	94.90 330	97.72 312	99.58 163	99.07 148	99.64 117	99.62 156	98.19 173	99.93 66	98.41 134	99.95 47	99.55 140

Gipuma		99.57 37	99.59 32	99.49 155	99.98 3	99.71 59	99.72 19	99.84 29	99.81 26	99.94 11	99.78 64	98.91 82	99.71 286	98.41 134	99.95 47	99.05 280
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 286	96.91 296	98.74 274	97.72 347	97.57 288	97.60 318	97.36 337	98.00 249	99.21 228	98.02 341	90.04 326	99.79 258	98.37 136	95.89 345	98.86 295

Regformer-1		99.32 97	99.27 95	99.47 160	99.41 224	98.95 215	98.99 189	99.48 211	99.48 83	99.66 111	99.52 202	98.78 101	99.87 160	98.36 137	99.74 180	99.60 115

PM-MVS		99.36 84	99.29 90	99.58 127	99.83 36	99.66 77	98.95 197	99.86 19	98.85 174	99.81 55	99.73 85	98.40 152	99.92 84	98.36 137	99.83 130	99.17 253

baseline1		97.73 274	97.33 281	98.96 247	99.30 259	97.73 284	99.40 79	98.42 318	99.33 110	99.46 174	99.21 277	91.18 310	99.82 234	98.35 139	91.26 347	99.32 225

MVS-HIRNet		97.86 270	98.22 240	96.76 323	99.28 263	91.53 347	98.38 259	92.60 351	99.13 140	99.31 210	99.96 10	97.18 238	99.68 305	98.34 140	99.83 130	99.07 278

GA-MVS		97.99 269	97.68 276	98.93 252	99.52 180	98.04 272	97.19 336	99.05 293	98.32 235	98.81 271	98.97 306	89.89 328	99.41 342	98.33 141	99.05 296	99.34 221

Fast-Effi-MVS+		99.02 169	98.87 181	99.46 163	99.38 231	99.50 113	99.04 177	99.79 52	97.17 292	98.62 287	98.74 323	99.34 33	99.95 42	98.32 142	99.41 265	98.92 290

Regformer-2		99.34 91	99.27 95	99.53 145	99.41 224	99.10 202	98.99 189	99.53 189	99.47 88	99.66 111	99.52 202	98.80 96	99.89 132	98.31 143	99.74 180	99.60 115

CS-MVS		99.09 157	99.03 148	99.25 219	99.45 214	99.49 114	99.41 77	99.82 36	99.10 145	98.03 319	98.48 334	99.30 37	99.89 132	98.30 144	99.41 265	98.35 319

MDA-MVSNet_test_wron		98.95 185	98.99 161	98.85 263	99.64 137	97.16 299	98.23 270	99.33 254	98.93 164	99.56 151	99.66 132	97.39 226	99.83 224	98.29 145	99.88 98	99.55 140

N_pmnet		98.73 212	98.53 216	99.35 197	99.72 106	98.67 236	98.34 260	94.65 346	98.35 229	99.79 63	99.68 122	98.03 183	99.93 66	98.28 146	99.92 72	99.44 193

ET-MVSNet_ETH3D		96.78 298	96.07 306	98.91 255	99.26 266	97.92 279	97.70 314	96.05 342	97.96 256	92.37 349	98.43 335	87.06 334	99.90 119	98.27 147	97.56 339	98.91 291

thisisatest0530		97.45 283	96.95 293	98.94 249	99.68 127	97.73 284	99.09 169	94.19 349	98.61 199	99.56 151	99.30 257	84.30 346	99.93 66	98.27 147	99.54 245	99.16 255

YYNet1		98.95 185	98.99 161	98.84 265	99.64 137	97.14 300	98.22 271	99.32 256	98.92 166	99.59 139	99.66 132	97.40 224	99.83 224	98.27 147	99.90 82	99.55 140

ACMM	98.09 11	99.46 59	99.38 66	99.72 71	99.80 55	99.69 70	99.13 158	99.65 122	98.99 155	99.64 117	99.72 91	99.39 23	99.86 180	98.23 150	99.81 147	99.60 115
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 182	98.87 181	99.24 222	99.57 160	98.40 250	98.12 279	99.18 283	98.28 237	99.63 121	99.13 284	98.02 185	99.97 16	98.22 151	99.69 201	99.35 219

3Dnovator	99.15 2	99.43 63	99.36 72	99.65 95	99.39 228	99.42 136	99.70 22	99.56 170	99.23 125	99.35 201	99.80 51	99.17 50	99.95 42	98.21 152	99.84 120	99.59 124

Fast-Effi-MVS+-dtu		99.20 128	99.12 118	99.43 172	99.25 267	99.69 70	99.05 175	99.82 36	99.50 81	98.97 253	99.05 294	98.98 73	99.98 6	98.20 153	99.24 289	98.62 305

MS-PatchMatch		99.00 175	98.97 165	99.09 236	99.11 292	98.19 261	98.76 225	99.33 254	98.49 212	99.44 176	99.58 180	98.21 170	99.69 294	98.20 153	99.62 223	99.39 208

TSAR-MVS + GP.		99.12 148	99.04 146	99.38 190	99.34 248	99.16 193	98.15 275	99.29 265	98.18 244	99.63 121	99.62 156	99.18 49	99.68 305	98.20 153	99.74 180	99.30 228

DP-MVS		99.48 52	99.39 64	99.74 59	99.57 160	99.62 90	99.29 110	99.61 137	99.87 14	99.74 86	99.76 74	98.69 112	99.87 160	98.20 153	99.80 152	99.75 38

MVP-Stereo		99.16 140	99.08 131	99.43 172	99.48 201	99.07 206	99.08 172	99.55 175	98.63 196	99.31 210	99.68 122	98.19 173	99.78 262	98.18 157	99.58 234	99.45 188
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 63	99.30 85	99.80 29	99.83 36	99.81 27	99.52 62	99.70 94	98.35 229	99.51 167	99.50 208	99.31 35	99.88 147	98.18 157	99.84 120	99.69 50

MDA-MVSNet-bldmvs		99.06 160	99.05 141	99.07 240	99.80 55	97.83 280	98.89 201	99.72 87	99.29 113	99.63 121	99.70 105	96.47 254	99.89 132	98.17 159	99.82 139	99.50 168

JIA-IIPM		98.06 265	97.92 265	98.50 283	98.59 332	97.02 302	98.80 219	98.51 314	99.88 13	97.89 324	99.87 30	91.89 303	99.90 119	98.16 160	97.68 338	98.59 307

EIA-MVS		99.12 148	99.01 153	99.45 167	99.36 236	99.62 90	99.34 91	99.79 52	98.41 218	98.84 268	98.89 315	98.75 107	99.84 213	98.15 161	99.51 250	98.89 292

miper_lstm_enhance		98.65 217	98.60 205	98.82 270	99.20 275	97.33 295	97.78 310	99.66 111	99.01 154	99.59 139	99.50 208	94.62 280	99.85 198	98.12 162	99.90 82	99.26 234

Effi-MVS+-dtu		99.07 159	98.92 174	99.52 146	98.89 311	99.78 37	99.15 149	99.66 111	99.34 107	98.92 260	99.24 273	97.69 208	99.98 6	98.11 163	99.28 283	98.81 299

mvs-test1		98.83 199	98.70 199	99.22 224	98.89 311	99.65 82	98.88 202	99.66 111	99.34 107	98.29 303	98.94 310	97.69 208	99.96 33	98.11 163	98.54 322	98.04 332

tpm		97.15 290	96.95 293	97.75 308	98.91 307	94.24 333	99.32 96	97.96 326	97.71 268	98.29 303	99.32 253	86.72 340	99.92 84	98.10 165	96.24 344	99.09 270

DeepPCF-MVS	98.42 6	99.18 135	99.02 150	99.67 83	99.22 271	99.75 47	97.25 334	99.47 215	98.72 190	99.66 111	99.70 105	99.29 38	99.63 324	98.07 166	99.81 147	99.62 104

ppachtmachnet_test		98.89 193	99.12 118	98.20 296	99.66 133	95.24 327	97.63 316	99.68 103	99.08 146	99.78 66	99.62 156	98.65 120	99.88 147	98.02 167	99.96 40	99.48 177

tpmrst		97.73 274	98.07 252	96.73 325	98.71 329	92.00 342	99.10 165	98.86 297	98.52 208	98.92 260	99.54 198	91.90 302	99.82 234	98.02 167	99.03 298	98.37 318

CSCG		99.37 81	99.29 90	99.60 121	99.71 109	99.46 121	99.43 76	99.85 23	98.79 182	99.41 190	99.60 172	98.92 80	99.92 84	98.02 167	99.92 72	99.43 199

eth_miper_zixun_eth		98.68 215	98.71 196	98.60 279	99.10 293	96.84 307	97.52 324	99.54 180	98.94 161	99.58 141	99.48 215	96.25 262	99.76 272	98.01 170	99.93 68	99.21 244

Patchmtry		98.78 204	98.54 214	99.49 155	98.89 311	99.19 191	99.32 96	99.67 107	99.65 56	99.72 91	99.79 57	91.87 304	99.95 42	98.00 171	99.97 29	99.33 222

PVSNet_BlendedMVS		99.03 167	99.01 153	99.09 236	99.54 172	97.99 273	98.58 236	99.82 36	97.62 271	99.34 204	99.71 98	98.52 139	99.77 270	97.98 172	99.97 29	99.52 160

PVSNet_Blended		98.70 214	98.59 207	99.02 244	99.54 172	97.99 273	97.58 319	99.82 36	95.70 319	99.34 204	98.98 303	98.52 139	99.77 270	97.98 172	99.83 130	99.30 228

cl-mvsnet_		98.54 229	98.41 225	98.92 253	99.03 301	97.80 282	97.46 326	99.59 154	98.90 168	99.60 136	99.46 223	93.85 286	99.78 262	97.97 174	99.89 90	99.17 253

cl-mvsnet1		98.54 229	98.42 224	98.92 253	99.03 301	97.80 282	97.46 326	99.59 154	98.90 168	99.60 136	99.46 223	93.87 285	99.78 262	97.97 174	99.89 90	99.18 251

ambc				99.20 227	99.35 238	98.53 242	99.17 141	99.46 219		99.67 107	99.80 51	98.46 146	99.70 288	97.92 176	99.70 198	99.38 210

USDC		98.96 182	98.93 170	99.05 242	99.54 172	97.99 273	97.07 337	99.80 46	98.21 241	99.75 78	99.77 71	98.43 148	99.64 323	97.90 177	99.88 98	99.51 162

OPM-MVS		99.26 108	99.13 115	99.63 106	99.70 116	99.61 96	98.58 236	99.48 211	98.50 210	99.52 164	99.63 147	99.14 54	99.76 272	97.89 178	99.77 167	99.51 162

MSP-MVS		99.32 97	99.17 107	99.77 39	99.69 119	99.80 32	99.14 151	99.31 260	99.16 136	99.62 128	99.61 165	98.35 156	99.91 100	97.88 179	99.72 192	99.61 111

test_0728_SECOND				99.83 21	99.70 116	99.79 34	99.14 151	99.61 137					99.92 84	97.88 179	99.72 192	99.77 31

cl_fuxian		98.72 213	98.71 196	98.72 275	99.12 287	97.22 298	97.68 315	99.56 170	98.90 168	99.54 158	99.48 215	96.37 259	99.73 280	97.88 179	99.88 98	99.21 244

3Dnovator+	98.92 3	99.35 86	99.24 100	99.67 83	99.35 238	99.47 117	99.62 46	99.50 204	99.44 94	99.12 242	99.78 64	98.77 104	99.94 53	97.87 182	99.72 192	99.62 104

miper_ehance_all_eth		98.59 222	98.59 207	98.59 280	98.98 304	97.07 301	97.49 325	99.52 198	98.50 210	99.52 164	99.37 238	96.41 258	99.71 286	97.86 183	99.62 223	99.00 285

WTY-MVS		98.59 222	98.37 229	99.26 216	99.43 219	98.40 250	98.74 226	99.13 289	98.10 246	99.21 228	99.24 273	94.82 277	99.90 119	97.86 183	98.77 310	99.49 173

SED-MVS		99.40 73	99.28 92	99.77 39	99.69 119	99.82 24	99.20 131	99.54 180	99.13 140	99.82 48	99.63 147	98.91 82	99.92 84	97.85 185	99.70 198	99.58 129

test_241102_TWO								99.54 180	99.13 140	99.76 73	99.63 147	98.32 161	99.92 84	97.85 185	99.69 201	99.75 38

MVS_111021_HR		99.12 148	99.02 150	99.40 183	99.50 190	99.11 198	97.92 304	99.71 90	98.76 188	99.08 245	99.47 220	99.17 50	99.54 334	97.85 185	99.76 169	99.54 147

zzz-MVS		99.30 100	99.14 112	99.80 29	99.81 49	99.81 27	98.73 228	99.53 189	99.27 117	99.42 182	99.63 147	98.21 170	99.95 42	97.83 188	99.79 157	99.65 81

MTAPA		99.35 86	99.20 104	99.80 29	99.81 49	99.81 27	99.33 93	99.53 189	99.27 117	99.42 182	99.63 147	98.21 170	99.95 42	97.83 188	99.79 157	99.65 81

TESTMET0.1,1		96.24 310	95.84 311	97.41 316	98.24 341	93.84 334	97.38 328	95.84 343	98.43 215	97.81 328	98.56 329	79.77 352	99.89 132	97.77 190	98.77 310	98.52 312

ACMH+	98.40 8	99.50 48	99.43 60	99.71 75	99.86 29	99.76 45	99.32 96	99.77 59	99.53 78	99.77 71	99.76 74	99.26 44	99.78 262	97.77 190	99.88 98	99.60 115

IU-MVS					99.69 119	99.77 39		99.22 279	97.50 278	99.69 101				97.75 192	99.70 198	99.77 31

114514_t		98.49 236	98.11 250	99.64 102	99.73 102	99.58 103	99.24 121	99.76 64	89.94 343	99.42 182	99.56 190	97.76 205	99.86 180	97.74 193	99.82 139	99.47 182

test_0728_THIRD									99.18 131	99.62 128	99.61 165	98.58 127	99.91 100	97.72 194	99.80 152	99.77 31

miper_enhance_ethall		98.03 266	97.94 263	98.32 291	98.27 340	96.43 313	96.95 338	99.41 230	96.37 309	99.43 180	98.96 308	94.74 278	99.69 294	97.71 195	99.62 223	98.83 298

TSAR-MVS + MP.		99.34 91	99.24 100	99.63 106	99.82 42	99.37 149	99.26 115	99.35 251	98.77 185	99.57 144	99.70 105	99.27 43	99.88 147	97.71 195	99.75 172	99.65 81
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl-mvsnet2		97.56 281	97.28 282	98.40 287	98.37 338	96.75 308	97.24 335	99.37 247	97.31 288	99.41 190	99.22 275	87.30 332	99.37 343	97.70 197	99.62 223	99.08 273

MP-MVS-pluss		99.14 144	98.92 174	99.80 29	99.83 36	99.83 20	98.61 232	99.63 130	96.84 302	99.44 176	99.58 180	98.81 92	99.91 100	97.70 197	99.82 139	99.67 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 103	99.11 121	99.79 34	99.75 93	99.81 27	98.95 197	99.53 189	98.27 238	99.53 162	99.73 85	98.75 107	99.87 160	97.70 197	99.83 130	99.68 56

UnsupCasMVSNet_bld		98.55 228	98.27 237	99.40 183	99.56 170	99.37 149	97.97 299	99.68 103	97.49 279	99.08 245	99.35 248	95.41 274	99.82 234	97.70 197	98.19 330	99.01 284

MVS_111021_LR		99.13 146	99.03 148	99.42 174	99.58 150	99.32 161	97.91 306	99.73 78	98.68 192	99.31 210	99.48 215	99.09 60	99.66 314	97.70 197	99.77 167	99.29 231

IS-MVSNet		99.03 167	98.85 183	99.55 139	99.80 55	99.25 175	99.73 16	99.15 286	99.37 104	99.61 134	99.71 98	94.73 279	99.81 250	97.70 197	99.88 98	99.58 129

test-LLR		97.15 290	96.95 293	97.74 309	98.18 343	95.02 328	97.38 328	96.10 339	98.00 249	97.81 328	98.58 326	90.04 326	99.91 100	97.69 203	98.78 308	98.31 320

test-mter		96.23 311	95.73 313	97.74 309	98.18 343	95.02 328	97.38 328	96.10 339	97.90 258	97.81 328	98.58 326	79.12 353	99.91 100	97.69 203	98.78 308	98.31 320

XVS		99.27 107	99.11 121	99.75 54	99.71 109	99.71 59	99.37 87	99.61 137	99.29 113	98.76 278	99.47 220	98.47 143	99.88 147	97.62 205	99.73 187	99.67 63

X-MVStestdata		96.09 312	94.87 319	99.75 54	99.71 109	99.71 59	99.37 87	99.61 137	99.29 113	98.76 278	61.30 355	98.47 143	99.88 147	97.62 205	99.73 187	99.67 63

SMA-MVS		99.19 131	99.00 156	99.73 66	99.46 211	99.73 53	99.13 158	99.52 198	97.40 283	99.57 144	99.64 139	98.93 79	99.83 224	97.61 207	99.79 157	99.63 92

CostFormer		96.71 301	96.79 299	96.46 329	98.90 308	90.71 351	99.41 77	98.68 306	94.69 333	98.14 314	99.34 251	86.32 342	99.80 255	97.60 208	98.07 334	98.88 293

PVSNet	97.47 15	98.42 242	98.44 221	98.35 289	99.46 211	96.26 314	96.70 342	99.34 253	97.68 269	99.00 252	99.13 284	97.40 224	99.72 282	97.59 209	99.68 204	99.08 273

new_pmnet		98.88 194	98.89 179	98.84 265	99.70 116	97.62 287	98.15 275	99.50 204	97.98 252	99.62 128	99.54 198	98.15 176	99.94 53	97.55 210	99.84 120	98.95 287

IB-MVS	95.41 20	95.30 320	94.46 322	97.84 305	98.76 327	95.33 326	97.33 331	96.07 341	96.02 313	95.37 347	97.41 348	76.17 355	99.96 33	97.54 211	95.44 346	98.22 325
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
LS3D		99.24 112	99.11 121	99.61 119	98.38 337	99.79 34	99.57 58	99.68 103	99.61 66	99.15 237	99.71 98	98.70 111	99.91 100	97.54 211	99.68 204	99.13 264

ZNCC-MVS		99.22 121	99.04 146	99.77 39	99.76 83	99.73 53	99.28 111	99.56 170	98.19 243	99.14 239	99.29 260	98.84 91	99.92 84	97.53 213	99.80 152	99.64 87

CP-MVS		99.23 113	99.05 141	99.75 54	99.66 133	99.66 77	99.38 83	99.62 133	98.38 222	99.06 249	99.27 264	98.79 99	99.94 53	97.51 214	99.82 139	99.66 73

SD-MVS		99.01 173	99.30 85	98.15 297	99.50 190	99.40 141	98.94 199	99.61 137	99.22 128	99.75 78	99.82 46	99.54 20	95.51 351	97.48 215	99.87 106	99.54 147
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
PMMVS		98.49 236	98.29 236	99.11 234	98.96 305	98.42 249	97.54 320	99.32 256	97.53 276	98.47 299	98.15 340	97.88 197	99.82 234	97.46 216	99.24 289	99.09 270

DeepC-MVS_fast	98.47 5	99.23 113	99.12 118	99.56 136	99.28 263	99.22 184	98.99 189	99.40 237	99.08 146	99.58 141	99.64 139	98.90 85	99.83 224	97.44 217	99.75 172	99.63 92
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 109	99.08 131	99.76 45	99.73 102	99.70 66	99.31 100	99.59 154	98.36 224	99.36 199	99.37 238	98.80 96	99.91 100	97.43 218	99.75 172	99.68 56

ACMMPR		99.23 113	99.06 137	99.76 45	99.74 99	99.69 70	99.31 100	99.59 154	98.36 224	99.35 201	99.38 237	98.61 124	99.93 66	97.43 218	99.75 172	99.67 63

Vis-MVSNet (Re-imp)		98.77 205	98.58 210	99.34 198	99.78 71	98.88 225	99.61 50	99.56 170	99.11 144	99.24 221	99.56 190	93.00 295	99.78 262	97.43 218	99.89 90	99.35 219

MIMVSNet		98.43 241	98.20 242	99.11 234	99.53 175	98.38 253	99.58 57	98.61 310	98.96 159	99.33 206	99.76 74	90.92 314	99.81 250	97.38 221	99.76 169	99.15 257

XVG-OURS-SEG-HR		99.16 140	98.99 161	99.66 90	99.84 33	99.64 84	98.25 269	99.73 78	98.39 221	99.63 121	99.43 228	99.70 11	99.90 119	97.34 222	98.64 318	99.44 193

COLMAP_ROB	98.06 12	99.45 61	99.37 69	99.70 79	99.83 36	99.70 66	99.38 83	99.78 56	99.53 78	99.67 107	99.78 64	99.19 48	99.86 180	97.32 223	99.87 106	99.55 140
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 169	98.81 189	99.65 95	99.58 150	99.49 114	98.58 236	99.07 290	98.40 220	99.04 250	99.25 268	98.51 141	99.80 255	97.31 224	99.51 250	99.65 81

region2R		99.23 113	99.05 141	99.77 39	99.76 83	99.70 66	99.31 100	99.59 154	98.41 218	99.32 208	99.36 243	98.73 110	99.93 66	97.29 225	99.74 180	99.67 63

APD-MVS_3200maxsize		99.31 99	99.16 108	99.74 59	99.53 175	99.75 47	99.27 114	99.61 137	99.19 130	99.57 144	99.64 139	98.76 105	99.90 119	97.29 225	99.62 223	99.56 137

TAPA-MVS	97.92 13	98.03 266	97.55 279	99.46 163	99.47 206	99.44 128	98.50 249	99.62 133	86.79 344	99.07 248	99.26 266	98.26 165	99.62 325	97.28 227	99.73 187	99.31 227
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_yl		98.25 255	97.95 259	99.13 232	99.17 280	98.47 244	99.00 184	98.67 308	98.97 157	99.22 226	99.02 298	91.31 308	99.69 294	97.26 228	98.93 301	99.24 237

DCV-MVSNet		98.25 255	97.95 259	99.13 232	99.17 280	98.47 244	99.00 184	98.67 308	98.97 157	99.22 226	99.02 298	91.31 308	99.69 294	97.26 228	98.93 301	99.24 237

PHI-MVS		99.11 152	98.95 169	99.59 123	99.13 285	99.59 100	99.17 141	99.65 122	97.88 259	99.25 218	99.46 223	98.97 75	99.80 255	97.26 228	99.82 139	99.37 213

tfpnnormal		99.43 63	99.38 66	99.60 121	99.87 27	99.75 47	99.59 55	99.78 56	99.71 40	99.90 22	99.69 111	98.85 90	99.90 119	97.25 231	99.78 163	99.15 257

PatchmatchNet		97.65 277	97.80 270	97.18 320	98.82 320	92.49 340	99.17 141	98.39 320	98.12 245	98.79 274	99.58 180	90.71 319	99.89 132	97.23 232	99.41 265	99.16 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 178	98.80 191	99.56 136	99.25 267	99.43 133	98.54 245	99.27 269	98.58 201	98.80 273	99.43 228	98.53 136	99.70 288	97.22 233	99.59 233	99.54 147

HPM-MVS		99.25 109	99.07 135	99.78 37	99.81 49	99.75 47	99.61 50	99.67 107	97.72 267	99.35 201	99.25 268	99.23 45	99.92 84	97.21 234	99.82 139	99.67 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DWT-MVSNet_test		96.03 314	95.80 312	96.71 327	98.50 335	91.93 343	99.25 120	97.87 329	95.99 314	96.81 340	97.61 346	81.02 349	99.66 314	97.20 235	97.98 335	98.54 311

mPP-MVS		99.19 131	99.00 156	99.76 45	99.76 83	99.68 73	99.38 83	99.54 180	98.34 233	99.01 251	99.50 208	98.53 136	99.93 66	97.18 236	99.78 163	99.66 73

ACMMP		99.25 109	99.08 131	99.74 59	99.79 65	99.68 73	99.50 64	99.65 122	98.07 247	99.52 164	99.69 111	98.57 128	99.92 84	97.18 236	99.79 157	99.63 92
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
abl_6		99.36 84	99.23 102	99.75 54	99.71 109	99.74 52	99.33 93	99.76 64	99.07 148	99.65 115	99.63 147	99.09 60	99.92 84	97.13 238	99.76 169	99.58 129

thisisatest0515		96.98 294	96.42 300	98.66 278	99.42 223	97.47 290	97.27 333	94.30 348	97.24 290	99.15 237	98.86 317	85.01 343	99.87 160	97.10 239	99.39 268	98.63 304

XVG-ACMP-BASELINE		99.23 113	99.10 128	99.63 106	99.82 42	99.58 103	98.83 211	99.72 87	98.36 224	99.60 136	99.71 98	98.92 80	99.91 100	97.08 240	99.84 120	99.40 205

MSDG		99.08 158	98.98 164	99.37 193	99.60 145	99.13 196	97.54 320	99.74 75	98.84 177	99.53 162	99.55 196	99.10 58	99.79 258	97.07 241	99.86 113	99.18 251

SteuartSystems-ACMMP		99.30 100	99.14 112	99.76 45	99.87 27	99.66 77	99.18 136	99.60 147	98.55 204	99.57 144	99.67 128	99.03 70	99.94 53	97.01 242	99.80 152	99.69 50
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		96.53 304	96.32 301	97.17 321	98.18 343	92.97 339	99.39 81	89.95 353	98.21 241	98.61 288	99.59 178	86.69 341	99.72 282	96.99 243	99.23 291	98.81 299

DVP-MVS		99.04 166	98.79 192	99.81 26	99.78 71	99.73 53	99.35 90	99.57 165	98.54 207	99.54 158	98.99 300	96.81 247	99.93 66	96.97 244	99.53 247	99.77 31

HPM-MVS++		98.96 182	98.70 199	99.74 59	99.52 180	99.71 59	98.86 206	99.19 282	98.47 214	98.59 290	99.06 293	98.08 181	99.91 100	96.94 245	99.60 231	99.60 115

SR-MVS		99.19 131	99.00 156	99.74 59	99.51 184	99.72 57	99.18 136	99.60 147	98.85 174	99.47 171	99.58 180	98.38 153	99.92 84	96.92 246	99.54 245	99.57 135

PGM-MVS		99.20 128	99.01 153	99.77 39	99.75 93	99.71 59	99.16 147	99.72 87	97.99 251	99.42 182	99.60 172	98.81 92	99.93 66	96.91 247	99.74 180	99.66 73

HY-MVS	98.23 9	98.21 260	97.95 259	98.99 245	99.03 301	98.24 257	99.61 50	98.72 305	96.81 303	98.73 280	99.51 205	94.06 284	99.86 180	96.91 247	98.20 328	98.86 295

MDTV_nov1_ep13			97.73 274		98.70 330	90.83 350	99.15 149	98.02 325	98.51 209	98.82 270	99.61 165	90.98 313	99.66 314	96.89 249	98.92 303

GST-MVS		99.16 140	98.96 167	99.75 54	99.73 102	99.73 53	99.20 131	99.55 175	98.22 240	99.32 208	99.35 248	98.65 120	99.91 100	96.86 250	99.74 180	99.62 104

test_post1							99.14 151				51.63 357	89.54 329	99.82 234	96.86 250

SCA		98.11 262	98.36 230	97.36 317	99.20 275	92.99 338	98.17 274	98.49 316	98.24 239	99.10 244	99.57 187	96.01 267	99.94 53	96.86 250	99.62 223	99.14 261

#test#		99.12 148	98.90 178	99.76 45	99.73 102	99.70 66	99.10 165	99.59 154	97.60 272	99.36 199	99.37 238	98.80 96	99.91 100	96.84 253	99.75 172	99.68 56

XVG-OURS		99.21 126	99.06 137	99.65 95	99.82 42	99.62 90	97.87 307	99.74 75	98.36 224	99.66 111	99.68 122	99.71 9	99.90 119	96.84 253	99.88 98	99.43 199

LCM-MVSNet-Re		99.28 103	99.15 111	99.67 83	99.33 253	99.76 45	99.34 91	99.97 2	98.93 164	99.91 20	99.79 57	98.68 113	99.93 66	96.80 255	99.56 236	99.30 228

RPSCF		99.18 135	99.02 150	99.64 102	99.83 36	99.85 11	99.44 74	99.82 36	98.33 234	99.50 168	99.78 64	97.90 194	99.65 321	96.78 256	99.83 130	99.44 193

旧先验2							97.94 302		95.33 323	98.94 256			99.88 147	96.75 257

MDTV_nov1_ep13_2view						91.44 348	99.14 151		97.37 285	99.21 228		91.78 306		96.75 257		99.03 282

CLD-MVS		98.76 207	98.57 211	99.33 200	99.57 160	98.97 213	97.53 322	99.55 175	96.41 308	99.27 216	99.13 284	99.07 65	99.78 262	96.73 259	99.89 90	99.23 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 263	97.98 257	98.48 284	99.27 265	96.48 311	99.40 79	99.07 290	98.81 179	99.23 222	99.57 187	90.11 325	99.87 160	96.69 260	99.64 220	99.09 270

baseline2		96.83 297	96.28 302	98.46 285	99.09 295	96.91 305	98.83 211	93.87 350	97.23 291	96.23 344	98.36 336	88.12 331	99.90 119	96.68 261	98.14 332	98.57 310

cascas		96.99 293	96.82 298	97.48 313	97.57 350	95.64 323	96.43 344	99.56 170	91.75 339	97.13 339	97.61 346	95.58 273	98.63 348	96.68 261	99.11 293	98.18 329

LPG-MVS_test		99.22 121	99.05 141	99.74 59	99.82 42	99.63 88	99.16 147	99.73 78	97.56 273	99.64 117	99.69 111	99.37 29	99.89 132	96.66 263	99.87 106	99.69 50

LGP-MVS_train				99.74 59	99.82 42	99.63 88		99.73 78	97.56 273	99.64 117	99.69 111	99.37 29	99.89 132	96.66 263	99.87 106	99.69 50

TinyColmap		98.97 179	98.93 170	99.07 240	99.46 211	98.19 261	97.75 311	99.75 70	98.79 182	99.54 158	99.70 105	98.97 75	99.62 325	96.63 265	99.83 130	99.41 203

LF4IMVS		99.01 173	98.92 174	99.27 214	99.71 109	99.28 167	98.59 235	99.77 59	98.32 235	99.39 196	99.41 230	98.62 122	99.84 213	96.62 266	99.84 120	98.69 303

NCCC		98.82 201	98.57 211	99.58 127	99.21 272	99.31 162	98.61 232	99.25 274	98.65 194	98.43 300	99.26 266	97.86 198	99.81 250	96.55 267	99.27 286	99.61 111

OPU-MVS				99.29 210	99.12 287	99.44 128	99.20 131				99.40 232	99.00 71	98.84 347	96.54 268	99.60 231	99.58 129

F-COLMAP		98.74 210	98.45 220	99.62 115	99.57 160	99.47 117	98.84 209	99.65 122	96.31 310	98.93 257	99.19 281	97.68 210	99.87 160	96.52 269	99.37 273	99.53 150

ADS-MVSNet2		97.78 272	97.66 278	98.12 299	99.14 283	95.36 325	99.22 128	98.75 304	96.97 297	98.25 306	99.64 139	90.90 315	99.94 53	96.51 270	99.56 236	99.08 273

ADS-MVSNet		97.72 276	97.67 277	97.86 304	99.14 283	94.65 331	99.22 128	98.86 297	96.97 297	98.25 306	99.64 139	90.90 315	99.84 213	96.51 270	99.56 236	99.08 273

PatchMatch-RL		98.68 215	98.47 218	99.30 209	99.44 217	99.28 167	98.14 277	99.54 180	97.12 295	99.11 243	99.25 268	97.80 202	99.70 288	96.51 270	99.30 281	98.93 289

CMPMVS	77.52 23	98.50 233	98.19 245	99.41 181	98.33 339	99.56 106	99.01 182	99.59 154	95.44 321	99.57 144	99.80 51	95.64 271	99.46 341	96.47 273	99.92 72	99.21 244
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
xxxxxxxxxxxxxcwj		99.11 152	98.96 167	99.54 143	99.53 175	99.25 175	98.29 265	99.76 64	99.07 148	99.42 182	99.61 165	98.86 88	99.87 160	96.45 274	99.68 204	99.49 173

SF-MVS		99.10 156	98.93 170	99.62 115	99.58 150	99.51 112	99.13 158	99.65 122	97.97 253	99.42 182	99.61 165	98.86 88	99.87 160	96.45 274	99.68 204	99.49 173

DPE-MVS		99.14 144	98.92 174	99.82 23	99.57 160	99.77 39	98.74 226	99.60 147	98.55 204	99.76 73	99.69 111	98.23 169	99.92 84	96.39 276	99.75 172	99.76 35

gm-plane-assit					97.59 348	89.02 354			93.47 335		98.30 337		99.84 213	96.38 277

AllTest		99.21 126	99.07 135	99.63 106	99.78 71	99.64 84	99.12 162	99.83 31	98.63 196	99.63 121	99.72 91	98.68 113	99.75 276	96.38 277	99.83 130	99.51 162

TestCases				99.63 106	99.78 71	99.64 84		99.83 31	98.63 196	99.63 121	99.72 91	98.68 113	99.75 276	96.38 277	99.83 130	99.51 162

testdata				99.42 174	99.51 184	98.93 220		99.30 263	96.20 311	98.87 265	99.40 232	98.33 160	99.89 132	96.29 280	99.28 283	99.44 193

dp		96.86 296	97.07 289	96.24 331	98.68 331	90.30 353	99.19 135	98.38 321	97.35 286	98.23 308	99.59 178	87.23 333	99.82 234	96.27 281	98.73 316	98.59 307

tpmvs		97.39 285	97.69 275	96.52 328	98.41 336	91.76 344	99.30 103	98.94 296	97.74 266	97.85 327	99.55 196	92.40 300	99.73 280	96.25 282	98.73 316	98.06 331

ACMP	97.51 14	99.05 163	98.84 185	99.67 83	99.78 71	99.55 109	98.88 202	99.66 111	97.11 296	99.47 171	99.60 172	99.07 65	99.89 132	96.18 283	99.85 116	99.58 129
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 190	98.72 195	99.44 169	99.39 228	99.42 136	98.58 236	99.64 128	97.31 288	99.44 176	99.62 156	98.59 126	99.69 294	96.17 284	99.79 157	99.22 242

DP-MVS Recon		98.50 233	98.23 239	99.31 207	99.49 195	99.46 121	98.56 241	99.63 130	94.86 330	98.85 267	99.37 238	97.81 201	99.59 331	96.08 285	99.44 259	98.88 293

tpm cat1		96.78 298	96.98 292	96.16 332	98.85 315	90.59 352	99.08 172	99.32 256	92.37 338	97.73 333	99.46 223	91.15 311	99.69 294	96.07 286	98.80 307	98.21 326

tpm2		96.35 307	96.22 303	96.73 325	98.88 314	91.75 345	99.21 130	98.51 314	93.27 336	97.89 324	99.21 277	84.83 344	99.70 288	96.04 287	98.18 331	98.75 302

test_0402		99.22 121	99.14 112	99.45 167	99.79 65	99.43 133	99.28 111	99.68 103	99.54 76	99.40 195	99.56 190	99.07 65	99.82 234	96.01 288	99.96 40	99.11 265

ITE_SJBPF				99.38 190	99.63 139	99.44 128		99.73 78	98.56 202	99.33 206	99.53 200	98.88 87	99.68 305	96.01 288	99.65 218	99.02 283

test_prior3		98.62 218	98.34 233	99.46 163	99.35 238	99.22 184	97.95 300	99.39 240	97.87 260	98.05 316	99.05 294	97.90 194	99.69 294	95.99 290	99.49 254	99.48 177

test_prior2							97.95 300		97.87 260	98.05 316	99.05 294	97.90 194		95.99 290	99.49 254

testdata2													99.89 132	95.99 290

原ACMM1				99.37 193	99.47 206	98.87 227		99.27 269	96.74 305	98.26 305	99.32 253	97.93 192	99.82 234	95.96 293	99.38 269	99.43 199

新几何1				99.52 146	99.50 190	99.22 184		99.26 271	95.66 320	98.60 289	99.28 262	97.67 211	99.89 132	95.95 294	99.32 279	99.45 188

MP-MVS		99.06 160	98.83 187	99.76 45	99.76 83	99.71 59	99.32 96	99.50 204	98.35 229	98.97 253	99.48 215	98.37 154	99.92 84	95.95 294	99.75 172	99.63 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
wuyk23d		97.58 280	99.13 115	92.93 334	99.69 119	99.49 114	99.52 62	99.77 59	97.97 253	99.96 8	99.79 57	99.84 3	99.94 53	95.85 296	99.82 139	79.36 347

HQP_MVS		98.90 190	98.68 201	99.55 139	99.58 150	99.24 180	98.80 219	99.54 180	98.94 161	99.14 239	99.25 268	97.24 232	99.82 234	95.84 297	99.78 163	99.60 115

plane_prior5								99.54 180					99.82 234	95.84 297	99.78 163	99.60 115

无先验							98.01 291	99.23 278	95.83 316				99.85 198	95.79 299		99.44 193

1121		98.56 225	98.24 238	99.52 146	99.49 195	99.24 180	99.30 103	99.22 279	95.77 317	98.52 295	99.29 260	97.39 226	99.85 198	95.79 299	99.34 276	99.46 186

CPTT-MVS		98.74 210	98.44 221	99.64 102	99.61 143	99.38 146	99.18 136	99.55 175	96.49 307	99.27 216	99.37 238	97.11 240	99.92 84	95.74 301	99.67 211	99.62 104

PLC	97.35 16	98.36 247	97.99 255	99.48 158	99.32 254	99.24 180	98.50 249	99.51 201	95.19 326	98.58 291	98.96 308	96.95 245	99.83 224	95.63 302	99.25 287	99.37 213
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 224	98.34 233	99.28 212	99.18 279	99.10 202	98.34 260	99.41 230	98.48 213	98.52 295	98.98 303	97.05 242	99.78 262	95.59 303	99.50 252	98.96 286

1314		98.00 268	97.90 268	98.27 295	98.90 308	97.45 292	99.30 103	99.06 292	94.98 327	97.21 338	99.12 288	98.43 148	99.67 310	95.58 304	98.56 321	97.71 336

agg_prior1		98.33 252	97.92 265	99.57 132	99.35 238	99.36 152	97.99 295	99.39 240	94.85 331	97.76 331	98.98 303	98.03 183	99.85 198	95.49 305	99.44 259	99.51 162

PVSNet_0	95.53 19	95.85 316	95.31 317	97.47 314	98.78 325	93.48 336	95.72 345	99.40 237	96.18 312	97.37 334	97.73 344	95.73 270	99.58 332	95.49 305	81.40 348	99.36 216

MAR-MVS		98.24 257	97.92 265	99.19 228	98.78 325	99.65 82	99.17 141	99.14 287	95.36 322	98.04 318	98.81 320	97.47 221	99.72 282	95.47 307	99.06 295	98.21 326
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
OpenMVS	98.12 10	98.23 258	97.89 269	99.26 216	99.19 277	99.26 171	99.65 43	99.69 100	91.33 341	98.14 314	99.77 71	98.28 163	99.96 33	95.41 308	99.55 240	98.58 309

train_agg		98.35 250	97.95 259	99.57 132	99.35 238	99.35 156	98.11 281	99.41 230	94.90 328	97.92 322	98.99 300	98.02 185	99.85 198	95.38 309	99.44 259	99.50 168

9.14			98.64 202		99.45 214		98.81 216	99.60 147	97.52 277	99.28 215	99.56 190	98.53 136	99.83 224	95.36 310	99.64 220

APD-MVS		98.87 196	98.59 207	99.71 75	99.50 190	99.62 90	99.01 182	99.57 165	96.80 304	99.54 158	99.63 147	98.29 162	99.91 100	95.24 311	99.71 196	99.61 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
AdaColmap		98.60 220	98.35 232	99.38 190	99.12 287	99.22 184	98.67 231	99.42 229	97.84 264	98.81 271	99.27 264	97.32 230	99.81 250	95.14 312	99.53 247	99.10 267

test9_res														95.10 313	99.44 259	99.50 168

CDPH-MVS		98.56 225	98.20 242	99.61 119	99.50 190	99.46 121	98.32 263	99.41 230	95.22 324	99.21 228	99.10 291	98.34 158	99.82 234	95.09 314	99.66 215	99.56 137

ETH3D-3000-0.1		98.77 205	98.50 217	99.59 123	99.47 206	99.53 111	98.77 224	99.60 147	97.33 287	99.23 222	99.50 208	97.91 193	99.83 224	95.02 315	99.67 211	99.41 203

BH-untuned		98.22 259	98.09 251	98.58 281	99.38 231	97.24 297	98.55 242	98.98 295	97.81 265	99.20 233	98.76 322	97.01 243	99.65 321	94.83 316	98.33 326	98.86 295

BP-MVS														94.73 317

HQP-MVS		98.36 247	98.02 254	99.39 186	99.31 255	98.94 216	97.98 296	99.37 247	97.45 280	98.15 310	98.83 318	96.67 248	99.70 288	94.73 317	99.67 211	99.53 150

QAPM		98.40 245	97.99 255	99.65 95	99.39 228	99.47 117	99.67 35	99.52 198	91.70 340	98.78 276	99.80 51	98.55 130	99.95 42	94.71 319	99.75 172	99.53 150

ETH3D cwj APD-0.16		98.50 233	98.16 248	99.51 149	99.04 300	99.39 143	98.47 251	99.47 215	96.70 306	98.78 276	99.33 252	97.62 218	99.86 180	94.69 320	99.38 269	99.28 233

agg_prior2														94.58 321	99.46 258	99.50 168

BH-RMVSNet		98.41 243	98.14 249	99.21 225	99.21 272	98.47 244	98.60 234	98.26 323	98.35 229	98.93 257	99.31 255	97.20 237	99.66 314	94.32 322	99.10 294	99.51 162

E-PMN		97.14 292	97.43 280	96.27 330	98.79 323	91.62 346	95.54 346	99.01 294	99.44 94	98.88 264	99.12 288	92.78 296	99.68 305	94.30 323	99.03 298	97.50 337

MG-MVS		98.52 232	98.39 227	98.94 249	99.15 282	97.39 294	98.18 272	99.21 281	98.89 171	99.23 222	99.63 147	97.37 228	99.74 278	94.22 324	99.61 230	99.69 50

API-MVS		98.38 246	98.39 227	98.35 289	98.83 317	99.26 171	99.14 151	99.18 283	98.59 200	98.66 285	98.78 321	98.61 124	99.57 333	94.14 325	99.56 236	96.21 344

PAPM_NR		98.36 247	98.04 253	99.33 200	99.48 201	98.93 220	98.79 222	99.28 268	97.54 275	98.56 293	98.57 328	97.12 239	99.69 294	94.09 326	98.90 305	99.38 210

DPM-MVS		98.28 253	97.94 263	99.32 204	99.36 236	99.11 198	97.31 332	98.78 302	96.88 299	98.84 268	99.11 290	97.77 204	99.61 329	94.03 327	99.36 274	99.23 240

gg-mvs-nofinetune		95.87 315	95.17 318	97.97 301	98.19 342	96.95 303	99.69 28	89.23 354	99.89 11	96.24 343	99.94 12	81.19 348	99.51 337	93.99 328	98.20 328	97.44 338

PMVS	92.94 21	98.82 201	98.81 189	98.85 263	99.84 33	97.99 273	99.20 131	99.47 215	99.71 40	99.42 182	99.82 46	98.09 179	99.47 339	93.88 329	99.85 116	99.07 278
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testtj		98.56 225	98.17 247	99.72 71	99.45 214	99.60 97	98.88 202	99.50 204	96.88 299	99.18 234	99.48 215	97.08 241	99.92 84	93.69 330	99.38 269	99.63 92

EMVS		96.96 295	97.28 282	95.99 333	98.76 327	91.03 349	95.26 347	98.61 310	99.34 107	98.92 260	98.88 316	93.79 287	99.66 314	92.87 331	99.05 296	97.30 341

BH-w/o		97.20 289	97.01 291	97.76 307	99.08 296	95.69 322	98.03 290	98.52 313	95.76 318	97.96 321	98.02 341	95.62 272	99.47 339	92.82 332	97.25 341	98.12 330

TR-MVS		97.44 284	97.15 288	98.32 291	98.53 334	97.46 291	98.47 251	97.91 328	96.85 301	98.21 309	98.51 332	96.42 256	99.51 337	92.16 333	97.29 340	97.98 333

OpenMVS_ROB	97.31 17	97.36 287	96.84 297	98.89 262	99.29 261	99.45 126	98.87 205	99.48 211	86.54 346	99.44 176	99.74 81	97.34 229	99.86 180	91.61 334	99.28 283	97.37 340

GG-mvs-BLEND				97.36 317	97.59 348	96.87 306	99.70 22	88.49 355		94.64 348	97.26 351	80.66 350	99.12 344	91.50 335	96.50 343	96.08 346

DeepMVS_CX				97.98 300	99.69 119	96.95 303		99.26 271	75.51 348	95.74 346	98.28 338	96.47 254	99.62 325	91.23 336	97.89 336	97.38 339

ETH3 D test6400		97.76 273	97.19 287	99.50 152	99.38 231	99.26 171	98.34 260	99.49 209	92.99 337	98.54 294	99.20 279	95.92 269	99.82 234	91.14 337	99.66 215	99.40 205

PAPR		97.56 281	97.07 289	99.04 243	98.80 322	98.11 267	97.63 316	99.25 274	94.56 334	98.02 320	98.25 339	97.43 223	99.68 305	90.90 338	98.74 314	99.33 222

MVS		95.72 318	94.63 321	98.99 245	98.56 333	97.98 278	99.30 103	98.86 297	72.71 349	97.30 335	99.08 292	98.34 158	99.74 278	89.21 339	98.33 326	99.26 234

thres600view7		96.60 303	96.16 304	97.93 302	99.63 139	96.09 318	99.18 136	97.57 332	98.77 185	98.72 281	97.32 349	87.04 335	99.72 282	88.57 340	98.62 319	97.98 333

FPMVS		96.32 308	95.50 315	98.79 271	99.60 145	98.17 263	98.46 256	98.80 301	97.16 293	96.28 341	99.63 147	82.19 347	99.09 345	88.45 341	98.89 306	99.10 267

PCF-MVS	96.03 18	96.73 300	95.86 310	99.33 200	99.44 217	99.16 193	96.87 340	99.44 224	86.58 345	98.95 255	99.40 232	94.38 282	99.88 147	87.93 342	99.80 152	98.95 287
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 306	96.03 307	97.47 314	99.63 139	95.93 319	99.18 136	97.57 332	98.75 189	98.70 283	97.31 350	87.04 335	99.67 310	87.62 343	98.51 323	96.81 342

tfpn200view9		96.30 309	95.89 308	97.53 312	99.58 150	96.11 316	99.00 184	97.54 335	98.43 215	98.52 295	96.98 352	86.85 337	99.67 310	87.62 343	98.51 323	96.81 342

thres400		96.40 305	95.89 308	97.92 303	99.58 150	96.11 316	99.00 184	97.54 335	98.43 215	98.52 295	96.98 352	86.85 337	99.67 310	87.62 343	98.51 323	97.98 333

thres200		96.09 312	95.68 314	97.33 319	99.48 201	96.22 315	98.53 246	97.57 332	98.06 248	98.37 302	96.73 354	86.84 339	99.61 329	86.99 346	98.57 320	96.16 345

MVE	92.54 22	96.66 302	96.11 305	98.31 293	99.68 127	97.55 289	97.94 302	95.60 344	99.37 104	90.68 350	98.70 324	96.56 250	98.61 349	86.94 347	99.55 240	98.77 301
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM		95.61 319	94.71 320	98.31 293	99.12 287	96.63 309	96.66 343	98.46 317	90.77 342	96.25 342	98.68 325	93.01 294	99.69 294	81.60 348	97.86 337	98.62 305

test123		29.31 321	33.05 325	18.08 335	25.93 355	12.24 355	97.53 322	10.93 357	11.78 350	24.21 351	50.08 359	21.04 356	8.60 352	23.51 349	32.43 350	33.39 348

testmvs		28.94 322	33.33 323	15.79 336	26.03 354	9.81 356	96.77 341	15.67 356	11.55 351	23.87 352	50.74 358	19.03 357	8.53 353	23.21 350	33.07 349	29.03 349

uanet_test		8.33 325	11.11 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_part1				0.00 337		0.00 357	0.00 348	99.53 189				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

cdsmvs_eth3d_5k		24.88 323	33.17 324	0.00 337	0.00 356	0.00 357	0.00 348	99.62 133	0.00 352	0.00 353	99.13 284	99.82 4	0.00 354	0.00 351	0.00 351	0.00 350

pcd_1.5k_mvsjas		16.61 324	22.14 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	99.28 40	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		8.33 325	11.11 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		8.33 325	11.11 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		8.33 325	11.11 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		8.33 325	11.11 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

ab-mvs-re		8.26 331	11.02 333	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	99.16 282	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		8.33 325	11.11 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_241102_ONE					99.69 119	99.82 24		99.54 180	99.12 143	99.82 48	99.49 213	98.91 82	99.52 336

save fliter					99.53 175	99.25 175	98.29 265	99.38 246	99.07 148

test0726					99.69 119	99.80 32	99.24 121	99.57 165	99.16 136	99.73 90	99.65 137	98.35 156

GSMVS																99.14 261

test_part2					99.62 142	99.67 75				99.55 156

sam_mvs1												90.81 318				99.14 261

sam_mvs												90.52 322

MTGPA								99.53 189

test_post											52.41 356	90.25 324	99.86 180

patchmatchnet-post											99.62 156	90.58 320	99.94 53

MTMP							99.09 169	98.59 312

TEST9					99.35 238	99.35 156	98.11 281	99.41 230	94.83 332	97.92 322	98.99 300	98.02 185	99.85 198

test_8					99.34 248	99.31 162	98.08 285	99.40 237	94.90 328	97.87 326	98.97 306	98.02 185	99.84 213

agg_prior					99.35 238	99.36 152		99.39 240		97.76 331			99.85 198

test_prior4						99.19 191	98.00 293

test_prior				99.46 163	99.35 238	99.22 184		99.39 240					99.69 294			99.48 177

新几何2							98.04 289

旧先验1					99.49 195	99.29 165		99.26 271			99.39 236	97.67 211			99.36 274	99.46 186

原ACMM2							97.92 304

test222					99.51 184	99.08 205	97.83 309	99.29 265	95.21 325	98.68 284	99.31 255	97.28 231			99.38 269	99.43 199

segment_acmp												98.37 154

testdata1							97.72 312		97.86 263

test12				99.54 143	99.29 261	99.33 159		99.16 285		98.43 300		97.54 219	99.82 234		99.47 256	99.48 177

plane_prior7					99.58 150	99.38 146

plane_prior6					99.47 206	99.26 171						97.24 232

plane_prior4											99.25 268

plane_prior3						99.31 162			98.36 224	99.14 239

plane_prior2							98.80 219		98.94 161

plane_prior1					99.51 184

plane_prior						99.24 180	98.42 257		97.87 260						99.71 196

n2								0.00 358

nn								0.00 358

door-mid								99.83 31

test11								99.29 265

door								99.77 59

HQP5-MVS						98.94 216

HQP-NCC					99.31 255		97.98 296		97.45 280	98.15 310

ACMP_Plane					99.31 255		97.98 296		97.45 280	98.15 310

HQP4-MVS										98.15 310			99.70 288			99.53 150

HQP3-MVS								99.37 247							99.67 211

HQP2-MVS												96.67 248

NP-MVS					99.40 227	99.13 196					98.83 318

ACMMP++_ref															99.94 60

ACMMP++															99.79 157

Test By Simon												98.41 150

High-resolution multi-view benchmark