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 41	99.57 39	99.55 143	99.75 95	99.11 204	99.05 182	99.61 144	99.15 147	99.88 32	99.71 100	99.08 63	99.87 170	99.90 2	99.97 30	99.66 75

v1240		99.56 41	99.58 36	99.51 153	99.80 56	99.00 215	99.00 191	99.65 126	99.15 147	99.90 22	99.75 80	99.09 60	99.88 157	99.90 2	99.96 42	99.67 65

v10		99.69 21	99.69 18	99.66 95	99.81 51	99.39 149	99.66 39	99.75 73	99.60 78	99.92 18	99.87 31	98.75 107	99.86 190	99.90 2	99.99 12	99.73 42

v1192		99.57 38	99.57 39	99.57 136	99.77 80	99.22 190	99.04 184	99.60 155	99.18 138	99.87 38	99.72 93	99.08 63	99.85 208	99.89 5	99.98 21	99.66 75

v144192		99.55 44	99.54 44	99.58 131	99.78 72	99.20 196	99.11 171	99.62 137	99.18 138	99.89 26	99.72 93	98.66 118	99.87 170	99.88 6	99.97 30	99.66 75

v8		99.68 23	99.69 18	99.65 100	99.80 56	99.40 147	99.66 39	99.76 66	99.64 64	99.93 14	99.85 37	98.66 118	99.84 225	99.88 6	99.99 12	99.71 46

v1144		99.54 46	99.53 49	99.59 127	99.79 66	99.28 173	99.10 172	99.61 144	99.20 136	99.84 43	99.73 87	98.67 116	99.84 225	99.86 8	99.98 21	99.64 90

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 18	99.82 8	99.82 37	99.84 23	99.94 11	99.91 19	99.13 57	99.96 35	99.83 9	99.99 12	99.83 18

v2v482		99.50 50	99.47 53	99.58 131	99.78 72	99.25 181	99.14 159	99.58 171	99.25 128	99.81 57	99.62 160	98.24 170	99.84 225	99.83 9	99.97 30	99.64 90

V42		99.56 41	99.54 44	99.63 111	99.79 66	99.46 128	99.39 84	99.59 162	99.24 130	99.86 39	99.70 107	98.55 131	99.82 246	99.79 11	99.95 49	99.60 119

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 36	99.72 19	99.88 16	99.92 6	99.98 3	99.93 13	99.94 1	99.98 7	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 41	99.68 31	99.85 24	99.95 3	99.98 3	99.92 16	99.28 41	99.98 7	99.75 13	100.00 1	99.94 2

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 39	99.70 22	99.86 20	99.89 11	99.98 3	99.90 21	99.94 1	99.98 7	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 44	100.00 1	99.90 7	100.00 1	99.97 9	99.61 17	99.97 17	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 9	99.85 20	99.94 11	99.95 11	99.73 8	99.90 129	99.65 16	99.97 30	99.69 52

MIMVSNet1		99.66 25	99.62 26	99.80 29	99.94 10	99.87 8	99.69 28	99.77 61	99.78 35	99.93 14	99.89 25	97.94 195	99.92 90	99.65 16	99.98 21	99.62 106

K. test v3		98.87 200	98.60 210	99.69 85	99.93 13	99.46 128	99.74 15	94.97 359	99.78 35	99.88 32	99.88 28	93.66 298	99.97 17	99.61 18	99.95 49	99.64 90

DIV-MVS_2432*1600		99.63 31	99.59 33	99.76 46	99.84 34	99.90 4	99.37 90	99.79 53	99.83 26	99.88 32	99.85 37	98.42 151	99.90 129	99.60 19	99.73 191	99.49 181

Anonymous20240521		99.44 65	99.42 64	99.49 159	99.89 21	98.96 221	99.62 47	99.76 66	99.85 20	99.82 50	99.88 28	96.39 265	99.97 17	99.59 20	99.98 21	99.55 145

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 12	99.75 14	99.86 20	99.70 48	99.91 20	99.89 25	99.60 19	99.87 170	99.59 20	99.74 184	99.71 46

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 22	99.83 6	99.85 24	99.80 32	99.93 14	99.93 13	98.54 133	99.93 71	99.59 20	99.98 21	99.76 37

EU-MVSNet		99.39 80	99.62 26	98.72 285	99.88 24	96.44 324	99.56 64	99.85 24	99.90 7	99.90 22	99.85 37	98.09 183	99.83 236	99.58 23	99.95 49	99.90 4

mvs_anonymous		99.28 106	99.39 67	98.94 259	99.19 289	97.81 293	99.02 187	99.55 184	99.78 35	99.85 40	99.80 54	98.24 170	99.86 190	99.57 24	99.50 260	99.15 266

lessismore_v0				99.64 107	99.86 30	99.38 152		90.66 366		99.89 26	99.83 43	94.56 289	99.97 17	99.56 25	99.92 74	99.57 139

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 22	99.76 13	99.87 18	99.73 40	99.89 26	99.87 31	99.63 14	99.87 170	99.54 26	99.92 74	99.63 95

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 6	99.90 7	99.97 6	99.87 31	99.81 5	99.95 45	99.54 26	99.99 12	99.80 24
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
RRT_test8_iter05		97.35 298	97.25 295	97.63 321	98.81 333	93.13 351	99.26 120	99.89 13	99.51 86	99.83 48	99.68 124	79.03 369	99.88 157	99.53 28	99.72 197	99.89 8

DSMNet-mixed		99.48 54	99.65 23	98.95 258	99.71 111	97.27 308	99.50 68	99.82 37	99.59 80	99.41 198	99.85 37	99.62 16	100.00 1	99.53 28	99.89 92	99.59 128

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 93	99.93 4	99.95 10	99.89 25	99.71 9	99.96 35	99.51 30	99.97 30	99.84 14

FC-MVSNet-test		99.70 19	99.65 23	99.86 16	99.88 24	99.86 11	99.72 19	99.78 58	99.90 7	99.82 50	99.83 43	98.45 148	99.87 170	99.51 30	99.97 30	99.86 11

UA-Net		99.78 13	99.76 14	99.86 16	99.72 108	99.71 65	99.91 3	99.95 4	99.96 2	99.71 100	99.91 19	99.15 53	99.97 17	99.50 32	100.00 1	99.90 4

PMMVS2		99.48 54	99.45 57	99.57 136	99.76 84	98.99 216	98.09 292	99.90 12	98.95 169	99.78 68	99.58 184	99.57 20	99.93 71	99.48 33	99.95 49	99.79 30

VPA-MVSNet		99.66 25	99.62 26	99.79 34	99.68 130	99.75 50	99.62 47	99.69 103	99.85 20	99.80 60	99.81 52	98.81 92	99.91 108	99.47 34	99.88 100	99.70 49

nrg030		99.70 19	99.66 21	99.82 23	99.76 84	99.84 18	99.61 53	99.70 97	99.93 4	99.78 68	99.68 124	99.10 58	99.78 273	99.45 35	99.96 42	99.83 18

TAMVS		99.49 52	99.45 57	99.63 111	99.48 209	99.42 142	99.45 75	99.57 173	99.66 60	99.78 68	99.83 43	97.85 204	99.86 190	99.44 36	99.96 42	99.61 115

GeoE		99.69 21	99.66 21	99.78 37	99.76 84	99.76 47	99.60 58	99.82 37	99.46 98	99.75 80	99.56 195	99.63 14	99.95 45	99.43 37	99.88 100	99.62 106

CS-MVS		99.52 49	99.54 44	99.47 165	99.51 191	99.85 12	99.62 47	99.93 5	99.75 38	99.34 212	99.13 294	99.39 24	99.91 108	99.43 37	99.75 175	98.66 316

new-patchmatchnet		99.35 89	99.57 39	98.71 287	99.82 44	96.62 322	98.55 251	99.75 73	99.50 87	99.88 32	99.87 31	99.31 37	99.88 157	99.43 37	100.00 1	99.62 106

test20.03		99.55 44	99.54 44	99.58 131	99.79 66	99.37 155	99.02 187	99.89 13	99.60 78	99.82 50	99.62 160	98.81 92	99.89 143	99.43 37	99.86 116	99.47 191

MVSFormer		99.41 73	99.44 59	99.31 215	99.57 164	98.40 262	99.77 11	99.80 47	99.73 40	99.63 125	99.30 266	98.02 189	99.98 7	99.43 37	99.69 206	99.55 145

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 18	99.77 11	99.80 47	99.73 40	99.97 6	99.92 16	99.77 7	99.98 7	99.43 37	100.00 1	99.90 4

Anonymous20231211		99.62 34	99.57 39	99.76 46	99.61 146	99.60 104	99.81 9	99.73 81	99.82 28	99.90 22	99.90 21	97.97 194	99.86 190	99.42 43	99.96 42	99.80 24

SixPastTwentyTwo		99.42 69	99.30 88	99.76 46	99.92 14	99.67 81	99.70 22	99.14 299	99.65 62	99.89 26	99.90 21	96.20 270	99.94 57	99.42 43	99.92 74	99.67 65

bset_n11_16_dypcd		98.69 220	98.45 227	99.42 181	99.69 121	98.52 254	96.06 357	96.80 352	99.71 44	99.73 93	99.54 204	95.14 282	99.96 35	99.39 45	99.95 49	99.79 30

UGNet		99.38 82	99.34 77	99.49 159	98.90 320	98.90 231	99.70 22	99.35 261	99.86 16	98.57 306	99.81 52	98.50 143	99.93 71	99.38 46	99.98 21	99.66 75
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 63	99.59 59	99.82 37	99.39 109	99.82 50	99.84 42	99.38 29	99.91 108	99.38 46	99.93 70	99.80 24

FIs		99.65 30	99.58 36	99.84 19	99.84 34	99.85 12	99.66 39	99.75 73	99.86 16	99.74 89	99.79 60	98.27 168	99.85 208	99.37 48	99.93 70	99.83 18

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 7	99.73 16	99.85 24	99.70 48	99.92 18	99.93 13	99.45 22	99.97 17	99.36 49	100.00 1	99.85 13

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 24	99.66 83	99.69 28	99.92 6	99.67 56	99.77 73	99.75 80	99.61 17	99.98 7	99.35 50	99.98 21	99.72 43
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_part1		98.63 224	98.26 247	99.75 56	99.40 235	99.49 121	99.67 35	99.68 106	99.86 16	99.88 32	99.86 36	86.73 352	99.93 71	99.34 51	99.97 30	99.81 23

CHOSEN 1792x2688		99.39 80	99.30 88	99.65 100	99.88 24	99.25 181	98.78 230	99.88 16	98.66 202	99.96 8	99.79 60	97.45 227	99.93 71	99.34 51	99.99 12	99.78 32

CDS-MVSNet		99.22 126	99.13 118	99.50 156	99.35 247	99.11 204	98.96 203	99.54 189	99.46 98	99.61 138	99.70 107	96.31 267	99.83 236	99.34 51	99.88 100	99.55 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 179	99.16 111	98.51 292	99.75 95	95.90 332	98.07 295	99.84 30	99.84 23	99.89 26	99.73 87	96.01 274	99.99 5	99.33 54	100.00 1	99.63 95

HyFIR lowres test		98.91 192	98.64 207	99.73 70	99.85 33	99.47 124	98.07 295	99.83 32	98.64 204	99.89 26	99.60 176	92.57 306	100.00 1	99.33 54	99.97 30	99.72 43

pmmvs5		99.19 136	99.11 125	99.42 181	99.76 84	98.88 232	98.55 251	99.73 81	98.82 187	99.72 95	99.62 160	96.56 256	99.82 246	99.32 56	99.95 49	99.56 142

v148		99.40 76	99.41 65	99.39 194	99.76 84	98.94 223	99.09 176	99.59 162	99.17 141	99.81 57	99.61 169	98.41 152	99.69 306	99.32 56	99.94 62	99.53 158

baseline		99.63 31	99.62 26	99.66 95	99.80 56	99.62 96	99.44 78	99.80 47	99.71 44	99.72 95	99.69 113	99.15 53	99.83 236	99.32 56	99.94 62	99.53 158

CVMVSNet		98.61 226	98.88 185	97.80 316	99.58 154	93.60 349	99.26 120	99.64 132	99.66 60	99.72 95	99.67 130	93.26 300	99.93 71	99.30 59	99.81 150	99.87 9

PS-CasMVS		99.66 25	99.58 36	99.89 7	99.80 56	99.85 12	99.66 39	99.73 81	99.62 68	99.84 43	99.71 100	98.62 122	99.96 35	99.30 59	99.96 42	99.86 11

DTE-MVSNet		99.68 23	99.61 30	99.88 11	99.80 56	99.87 8	99.67 35	99.71 93	99.72 43	99.84 43	99.78 66	98.67 116	99.97 17	99.30 59	99.95 49	99.80 24

tmp_tt		95.75 329	95.42 327	96.76 335	89.90 368	94.42 344	98.86 213	97.87 342	78.01 361	99.30 225	99.69 113	97.70 211	95.89 364	99.29 62	98.14 342	99.95 1

PEN-MVS		99.66 25	99.59 33	99.89 7	99.83 38	99.87 8	99.66 39	99.73 81	99.70 48	99.84 43	99.73 87	98.56 130	99.96 35	99.29 62	99.94 62	99.83 18

WR-MVS_H		99.61 36	99.53 49	99.87 14	99.80 56	99.83 22	99.67 35	99.75 73	99.58 81	99.85 40	99.69 113	98.18 179	99.94 57	99.28 64	99.95 49	99.83 18

IterMVS		98.97 183	99.16 111	98.42 296	99.74 101	95.64 335	98.06 297	99.83 32	99.83 26	99.85 40	99.74 83	96.10 273	99.99 5	99.27 65	100.00 1	99.63 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
hse-mvs3		98.61 226	98.34 240	99.44 175	99.60 148	98.67 243	99.27 118	99.44 233	99.68 52	99.32 217	99.49 221	92.50 309	100.00 1	99.24 66	96.51 357	99.65 83

hse-mvs2		98.52 240	98.30 244	99.16 238	99.29 270	98.60 250	98.77 231	99.02 306	99.68 52	99.32 217	99.04 309	92.50 309	99.85 208	99.24 66	97.87 348	99.03 291

FMVSNet1		99.66 25	99.63 25	99.73 70	99.78 72	99.77 41	99.68 31	99.70 97	99.67 56	99.82 50	99.83 43	98.98 73	99.90 129	99.24 66	99.97 30	99.53 158

casdiffmvs		99.63 31	99.61 30	99.67 88	99.79 66	99.59 107	99.13 165	99.85 24	99.79 34	99.76 75	99.72 93	99.33 36	99.82 246	99.21 69	99.94 62	99.59 128

CP-MVSNet		99.54 46	99.43 62	99.87 14	99.76 84	99.82 26	99.57 62	99.61 144	99.54 82	99.80 60	99.64 141	97.79 208	99.95 45	99.21 69	99.94 62	99.84 14

DELS-MVS		99.34 94	99.30 88	99.48 163	99.51 191	99.36 158	98.12 288	99.53 198	99.36 113	99.41 198	99.61 169	99.22 47	99.87 170	99.21 69	99.68 209	99.20 256
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		98.75 212	98.54 220	99.41 189	98.14 358	98.61 249	98.98 200	99.66 115	99.31 119	99.84 43	99.75 80	91.98 312	99.98 7	99.20 72	99.95 49	99.62 106

UniMVSNet (Re)		99.37 84	99.26 100	99.68 86	99.51 191	99.58 110	98.98 200	99.60 155	99.43 106	99.70 102	99.36 252	97.70 211	99.88 157	99.20 72	99.87 109	99.59 128

CANet		99.11 157	99.05 146	99.28 220	98.83 329	98.56 251	98.71 239	99.41 240	99.25 128	99.23 233	99.22 285	97.66 220	99.94 57	99.19 74	99.97 30	99.33 231

EI-MVSNet-UG-set		99.48 54	99.50 51	99.42 181	99.57 164	98.65 248	99.24 128	99.46 228	99.68 52	99.80 60	99.66 134	98.99 72	99.89 143	99.19 74	99.90 84	99.72 43

xiu_mvs_v1_base_debu		99.23 117	99.34 77	98.91 265	99.59 151	98.23 270	98.47 260	99.66 115	99.61 72	99.68 107	98.94 326	99.39 24	99.97 17	99.18 76	99.55 247	98.51 326

xiu_mvs_v1_base		99.23 117	99.34 77	98.91 265	99.59 151	98.23 270	98.47 260	99.66 115	99.61 72	99.68 107	98.94 326	99.39 24	99.97 17	99.18 76	99.55 247	98.51 326

xiu_mvs_v1_base_debi		99.23 117	99.34 77	98.91 265	99.59 151	98.23 270	98.47 260	99.66 115	99.61 72	99.68 107	98.94 326	99.39 24	99.97 17	99.18 76	99.55 247	98.51 326

MVS_0304		98.88 198	98.71 201	99.39 194	98.85 327	98.91 230	99.45 75	99.30 273	98.56 211	97.26 350	99.68 124	96.18 271	99.96 35	99.17 79	99.94 62	99.29 240

VPNet		99.46 61	99.37 72	99.71 80	99.82 44	99.59 107	99.48 72	99.70 97	99.81 29	99.69 105	99.58 184	97.66 220	99.86 190	99.17 79	99.44 268	99.67 65

UniMVSNet_NR-MVSNet		99.37 84	99.25 102	99.72 76	99.47 214	99.56 113	98.97 202	99.61 144	99.43 106	99.67 111	99.28 271	97.85 204	99.95 45	99.17 79	99.81 150	99.65 83

DU-MVS		99.33 98	99.21 106	99.71 80	99.43 226	99.56 113	98.83 218	99.53 198	99.38 110	99.67 111	99.36 252	97.67 216	99.95 45	99.17 79	99.81 150	99.63 95

EI-MVSNet-Vis-set		99.47 60	99.49 52	99.42 181	99.57 164	98.66 245	99.24 128	99.46 228	99.67 56	99.79 65	99.65 139	98.97 75	99.89 143	99.15 83	99.89 92	99.71 46

EI-MVSNet		99.38 82	99.44 59	99.21 232	99.58 154	98.09 281	99.26 120	99.46 228	99.62 68	99.75 80	99.67 130	98.54 133	99.85 208	99.15 83	99.92 74	99.68 58

VNet		99.18 140	99.06 142	99.56 140	99.24 280	99.36 158	99.33 97	99.31 270	99.67 56	99.47 179	99.57 192	96.48 259	99.84 225	99.15 83	99.30 289	99.47 191

EG-PatchMatch MVS		99.57 38	99.56 43	99.62 120	99.77 80	99.33 165	99.26 120	99.76 66	99.32 118	99.80 60	99.78 66	99.29 39	99.87 170	99.15 83	99.91 83	99.66 75

PVSNet_Blended_VisFu		99.40 76	99.38 69	99.44 175	99.90 19	98.66 245	98.94 206	99.91 9	97.97 264	99.79 65	99.73 87	99.05 68	99.97 17	99.15 83	99.99 12	99.68 58

IterMVS-LS		99.41 73	99.47 53	99.25 227	99.81 51	98.09 281	98.85 215	99.76 66	99.62 68	99.83 48	99.64 141	98.54 133	99.97 17	99.15 83	99.99 12	99.68 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 46	99.47 53	99.76 46	99.58 154	99.64 90	99.30 107	99.63 134	99.61 72	99.71 100	99.56 195	98.76 105	99.96 35	99.14 89	99.92 74	99.68 58

MVSTER		98.47 247	98.22 250	99.24 229	99.06 309	98.35 267	99.08 179	99.46 228	99.27 124	99.75 80	99.66 134	88.61 342	99.85 208	99.14 89	99.92 74	99.52 168

Anonymous20231206		99.35 89	99.31 83	99.47 165	99.74 101	99.06 214	99.28 115	99.74 78	99.23 132	99.72 95	99.53 207	97.63 222	99.88 157	99.11 91	99.84 123	99.48 186

MVS_Test		99.28 106	99.31 83	99.19 235	99.35 247	98.79 237	99.36 93	99.49 218	99.17 141	99.21 239	99.67 130	98.78 101	99.66 326	99.09 92	99.66 220	99.10 276

testgi		99.29 105	99.26 100	99.37 201	99.75 95	98.81 235	98.84 216	99.89 13	98.38 231	99.75 80	99.04 309	99.36 34	99.86 190	99.08 93	99.25 295	99.45 197

1112_ss		99.05 167	98.84 190	99.67 88	99.66 136	99.29 171	98.52 256	99.82 37	97.65 281	99.43 188	99.16 292	96.42 262	99.91 108	99.07 94	99.84 123	99.80 24

CANet_DTU		98.91 192	98.85 188	99.09 246	98.79 335	98.13 276	98.18 281	99.31 270	99.48 89	98.86 280	99.51 213	96.56 256	99.95 45	99.05 95	99.95 49	99.19 258

Baseline_NR-MVSNet		99.49 52	99.37 72	99.82 23	99.91 15	99.84 18	98.83 218	99.86 20	99.68 52	99.65 119	99.88 28	97.67 216	99.87 170	99.03 96	99.86 116	99.76 37

FMVSNet2		99.35 89	99.28 95	99.55 143	99.49 203	99.35 162	99.45 75	99.57 173	99.44 101	99.70 102	99.74 83	97.21 239	99.87 170	99.03 96	99.94 62	99.44 202

Test_1112_low_res		98.95 189	98.73 199	99.63 111	99.68 130	99.15 201	98.09 292	99.80 47	97.14 307	99.46 182	99.40 241	96.11 272	99.89 143	99.01 98	99.84 123	99.84 14

VDD-MVS		99.20 133	99.11 125	99.44 175	99.43 226	98.98 217	99.50 68	98.32 335	99.80 32	99.56 155	99.69 113	96.99 249	99.85 208	98.99 99	99.73 191	99.50 176

DeepC-MVS	98.90 4	99.62 34	99.61 30	99.67 88	99.72 108	99.44 135	99.24 128	99.71 93	99.27 124	99.93 14	99.90 21	99.70 11	99.93 71	98.99 99	99.99 12	99.64 90
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 54	99.47 53	99.51 153	99.77 80	99.41 146	98.81 223	99.66 115	99.42 108	99.75 80	99.66 134	99.20 48	99.76 283	98.98 101	99.99 12	99.36 225

EPNet_dtu		97.62 288	97.79 282	97.11 334	96.67 363	92.31 355	98.51 257	98.04 337	99.24 130	95.77 359	99.47 229	93.78 297	99.66 326	98.98 101	99.62 228	99.37 222
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 94	99.32 82	99.39 194	99.67 135	98.77 238	98.57 249	99.81 46	99.61 72	99.48 177	99.41 239	98.47 144	99.86 190	98.97 103	99.90 84	99.53 158

NR-MVSNet		99.40 76	99.31 83	99.68 86	99.43 226	99.55 116	99.73 16	99.50 213	99.46 98	99.88 32	99.36 252	97.54 224	99.87 170	98.97 103	99.87 109	99.63 95

GBi-Net		99.42 69	99.31 83	99.73 70	99.49 203	99.77 41	99.68 31	99.70 97	99.44 101	99.62 132	99.83 43	97.21 239	99.90 129	98.96 105	99.90 84	99.53 158

FMVSNet5		97.80 281	97.25 295	99.42 181	98.83 329	98.97 219	99.38 86	99.80 47	98.87 181	99.25 229	99.69 113	80.60 364	99.91 108	98.96 105	99.90 84	99.38 219

test1		99.42 69	99.31 83	99.73 70	99.49 203	99.77 41	99.68 31	99.70 97	99.44 101	99.62 132	99.83 43	97.21 239	99.90 129	98.96 105	99.90 84	99.53 158

FMVSNet3		98.80 207	98.63 209	99.32 212	99.13 297	98.72 240	99.10 172	99.48 220	99.23 132	99.62 132	99.64 141	92.57 306	99.86 190	98.96 105	99.90 84	99.39 217

UnsupCasMVSNet_eth		98.83 203	98.57 216	99.59 127	99.68 130	99.45 133	98.99 196	99.67 111	99.48 89	99.55 160	99.36 252	94.92 283	99.86 190	98.95 109	96.57 356	99.45 197

CHOSEN 280x420		98.41 252	98.41 232	98.40 297	99.34 257	95.89 333	96.94 351	99.44 233	98.80 190	99.25 229	99.52 209	93.51 299	99.98 7	98.94 110	99.98 21	99.32 234

TDRefinement		99.72 17	99.70 17	99.77 40	99.90 19	99.85 12	99.86 5	99.92 6	99.69 51	99.78 68	99.92 16	99.37 31	99.88 157	98.93 111	99.95 49	99.60 119

Regformer-4		99.45 63	99.44 59	99.50 156	99.52 186	98.94 223	99.17 149	99.53 198	99.64 64	99.76 75	99.60 176	98.96 78	99.90 129	98.91 112	99.84 123	99.67 65

Regformer-3		99.41 73	99.41 65	99.40 191	99.52 186	98.70 241	99.17 149	99.44 233	99.62 68	99.75 80	99.60 176	98.90 85	99.85 208	98.89 113	99.84 123	99.65 83

alignmvs		98.28 262	97.96 268	99.25 227	99.12 299	98.93 227	99.03 186	98.42 331	99.64 64	98.72 295	97.85 358	90.86 328	99.62 337	98.88 114	99.13 300	99.19 258

sss		98.90 194	98.77 198	99.27 222	99.48 209	98.44 259	98.72 237	99.32 266	97.94 268	99.37 206	99.35 257	96.31 267	99.91 108	98.85 115	99.63 227	99.47 191

xiu_mvs_v2_base		99.02 173	99.11 125	98.77 282	99.37 243	98.09 281	98.13 287	99.51 209	99.47 94	99.42 190	98.54 347	99.38 29	99.97 17	98.83 116	99.33 286	98.24 338

PS-MVSNAJ		99.00 179	99.08 136	98.76 283	99.37 243	98.10 280	98.00 302	99.51 209	99.47 94	99.41 198	98.50 349	99.28 41	99.97 17	98.83 116	99.34 284	98.20 342

D2MVS		99.22 126	99.19 108	99.29 218	99.69 121	98.74 239	98.81 223	99.41 240	98.55 213	99.68 107	99.69 113	98.13 181	99.87 170	98.82 118	99.98 21	99.24 246

PatchT		98.45 249	98.32 243	98.83 277	98.94 318	98.29 268	99.24 128	98.82 314	99.84 23	99.08 257	99.76 76	91.37 318	99.94 57	98.82 118	99.00 308	98.26 337

Effi-MVS+		99.06 164	98.97 170	99.34 206	99.31 264	98.98 217	98.31 273	99.91 9	98.81 188	98.79 288	98.94 326	99.14 55	99.84 225	98.79 120	98.74 323	99.20 256

canonicalmvs		99.02 173	99.00 161	99.09 246	99.10 305	98.70 241	99.61 53	99.66 115	99.63 67	98.64 300	97.65 360	99.04 69	99.54 346	98.79 120	98.92 312	99.04 290

VDDNet		98.97 183	98.82 193	99.42 181	99.71 111	98.81 235	99.62 47	98.68 319	99.81 29	99.38 205	99.80 54	94.25 291	99.85 208	98.79 120	99.32 287	99.59 128

CR-MVSNet		98.35 259	98.20 252	98.83 277	99.05 310	98.12 277	99.30 107	99.67 111	97.39 295	99.16 246	99.79 60	91.87 315	99.91 108	98.78 123	98.77 319	98.44 331

test_method		91.72 333	92.32 336	89.91 347	93.49 367	70.18 369	90.28 361	99.56 178	61.71 364	95.39 361	99.52 209	93.90 293	99.94 57	98.76 124	98.27 337	99.62 106

RPMNet		98.60 228	98.53 222	98.83 277	99.05 310	98.12 277	99.30 107	99.62 137	99.86 16	99.16 246	99.74 83	92.53 308	99.92 90	98.75 125	98.77 319	98.44 331

pmmvs4		99.13 151	99.06 142	99.36 204	99.57 164	99.10 208	98.01 300	99.25 284	98.78 193	99.58 145	99.44 236	98.24 170	99.76 283	98.74 126	99.93 70	99.22 251

tttt0517		97.62 288	97.20 297	98.90 271	99.76 84	97.40 305	99.48 72	94.36 361	99.06 160	99.70 102	99.49 221	84.55 358	99.94 57	98.73 127	99.65 223	99.36 225

EPP-MVSNet		99.17 144	99.00 161	99.66 95	99.80 56	99.43 139	99.70 22	99.24 287	99.48 89	99.56 155	99.77 73	94.89 284	99.93 71	98.72 128	99.89 92	99.63 95

Anonymous20240529		99.42 69	99.34 77	99.65 100	99.53 181	99.60 104	99.63 46	99.39 250	99.47 94	99.76 75	99.78 66	98.13 181	99.86 190	98.70 129	99.68 209	99.49 181

ACMH	98.42 6	99.59 37	99.54 44	99.72 76	99.86 30	99.62 96	99.56 64	99.79 53	98.77 194	99.80 60	99.85 37	99.64 13	99.85 208	98.70 129	99.89 92	99.70 49
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 98	99.28 95	99.47 165	99.57 164	99.39 149	99.78 10	99.43 237	98.87 181	99.57 148	99.82 49	98.06 186	99.87 170	98.69 131	99.73 191	99.15 266

LFMVS		98.46 248	98.19 255	99.26 224	99.24 280	98.52 254	99.62 47	96.94 351	99.87 14	99.31 221	99.58 184	91.04 323	99.81 262	98.68 132	99.42 273	99.45 197

WR-MVS		99.11 157	98.93 175	99.66 95	99.30 268	99.42 142	98.42 266	99.37 257	99.04 161	99.57 148	99.20 289	96.89 251	99.86 190	98.66 133	99.87 109	99.70 49

Anonymous202405211		98.75 212	98.46 226	99.63 111	99.34 257	99.66 83	99.47 74	97.65 344	99.28 123	99.56 155	99.50 216	93.15 301	99.84 225	98.62 134	99.58 241	99.40 214

EPNet		98.13 270	97.77 283	99.18 237	94.57 366	97.99 285	99.24 128	97.96 339	99.74 39	97.29 349	99.62 160	93.13 302	99.97 17	98.59 135	99.83 133	99.58 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 167	99.09 134	98.91 265	99.21 284	98.36 266	98.82 222	99.47 224	98.85 183	98.90 275	99.56 195	98.78 101	99.09 359	98.57 136	99.68 209	99.26 243

Patchmatch-RL test		98.60 228	98.36 237	99.33 208	99.77 80	99.07 212	98.27 276	99.87 18	98.91 176	99.74 89	99.72 93	90.57 332	99.79 270	98.55 137	99.85 119	99.11 274

pmmvs3		98.08 273	97.80 280	98.91 265	99.41 232	97.69 298	97.87 316	99.66 115	95.87 329	99.50 175	99.51 213	90.35 334	99.97 17	98.55 137	99.47 265	99.08 282

ETV-MVS		99.18 140	99.18 109	99.16 238	99.34 257	99.28 173	99.12 169	99.79 53	99.48 89	98.93 269	98.55 346	99.40 23	99.93 71	98.51 139	99.52 257	98.28 336

jason		99.16 145	99.11 125	99.32 212	99.75 95	98.44 259	98.26 277	99.39 250	98.70 200	99.74 89	99.30 266	98.54 133	99.97 17	98.48 140	99.82 142	99.55 145
jason: jason.
APDe-MVS		99.48 54	99.36 75	99.85 18	99.55 175	99.81 29	99.50 68	99.69 103	98.99 163	99.75 80	99.71 100	98.79 99	99.93 71	98.46 141	99.85 119	99.80 24

CL-MVSNet_2432*1600		98.71 218	98.56 219	99.15 240	99.22 282	98.66 245	97.14 346	99.51 209	98.09 257	99.54 162	99.27 273	96.87 252	99.74 289	98.43 142	98.96 309	99.03 291

our_test_3		98.85 202	99.09 134	98.13 308	99.66 136	94.90 342	97.72 321	99.58 171	99.07 156	99.64 121	99.62 160	98.19 177	99.93 71	98.41 143	99.95 49	99.55 145

Gipuma		99.57 38	99.59 33	99.49 159	99.98 3	99.71 65	99.72 19	99.84 30	99.81 29	99.94 11	99.78 66	98.91 82	99.71 298	98.41 143	99.95 49	99.05 289
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 296	96.91 307	98.74 284	97.72 359	97.57 300	97.60 327	97.36 350	98.00 260	99.21 239	98.02 356	90.04 337	99.79 270	98.37 145	95.89 360	98.86 307

Regformer-1		99.32 100	99.27 98	99.47 165	99.41 232	98.95 222	98.99 196	99.48 220	99.48 89	99.66 115	99.52 209	98.78 101	99.87 170	98.36 146	99.74 184	99.60 119

PM-MVS		99.36 87	99.29 93	99.58 131	99.83 38	99.66 83	98.95 204	99.86 20	98.85 183	99.81 57	99.73 87	98.40 156	99.92 90	98.36 146	99.83 133	99.17 262

baseline1		97.73 284	97.33 292	98.96 257	99.30 268	97.73 296	99.40 82	98.42 331	99.33 117	99.46 182	99.21 287	91.18 321	99.82 246	98.35 148	91.26 362	99.32 234

MVS-HIRNet		97.86 279	98.22 250	96.76 335	99.28 273	91.53 361	98.38 268	92.60 365	99.13 149	99.31 221	99.96 10	97.18 243	99.68 317	98.34 149	99.83 133	99.07 287

GA-MVS		97.99 278	97.68 286	98.93 262	99.52 186	98.04 284	97.19 345	99.05 305	98.32 244	98.81 285	98.97 322	89.89 339	99.41 356	98.33 150	99.05 304	99.34 230

Fast-Effi-MVS+		99.02 173	98.87 186	99.46 169	99.38 240	99.50 120	99.04 184	99.79 53	97.17 305	98.62 301	98.74 339	99.34 35	99.95 45	98.32 151	99.41 274	98.92 302

Regformer-2		99.34 94	99.27 98	99.53 149	99.41 232	99.10 208	98.99 196	99.53 198	99.47 94	99.66 115	99.52 209	98.80 96	99.89 143	98.31 152	99.74 184	99.60 119

MDA-MVSNet_test_wron		98.95 189	98.99 166	98.85 273	99.64 140	97.16 311	98.23 279	99.33 264	98.93 173	99.56 155	99.66 134	97.39 231	99.83 236	98.29 153	99.88 100	99.55 145

N_pmnet		98.73 216	98.53 222	99.35 205	99.72 108	98.67 243	98.34 269	94.65 360	98.35 238	99.79 65	99.68 124	98.03 187	99.93 71	98.28 154	99.92 74	99.44 202

ET-MVSNet_ETH3D		96.78 308	96.07 317	98.91 265	99.26 277	97.92 291	97.70 323	96.05 356	97.96 267	92.37 364	98.43 350	87.06 346	99.90 129	98.27 155	97.56 351	98.91 303

thisisatest0530		97.45 293	96.95 304	98.94 259	99.68 130	97.73 296	99.09 176	94.19 363	98.61 208	99.56 155	99.30 266	84.30 359	99.93 71	98.27 155	99.54 253	99.16 264

YYNet1		98.95 189	98.99 166	98.84 275	99.64 140	97.14 312	98.22 280	99.32 266	98.92 175	99.59 143	99.66 134	97.40 229	99.83 236	98.27 155	99.90 84	99.55 145

ACMM	98.09 11	99.46 61	99.38 69	99.72 76	99.80 56	99.69 76	99.13 165	99.65 126	98.99 163	99.64 121	99.72 93	99.39 24	99.86 190	98.23 158	99.81 150	99.60 119
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 186	98.87 186	99.24 229	99.57 164	98.40 262	98.12 288	99.18 295	98.28 246	99.63 125	99.13 294	98.02 189	99.97 17	98.22 159	99.69 206	99.35 228

3Dnovator	99.15 2	99.43 66	99.36 75	99.65 100	99.39 237	99.42 142	99.70 22	99.56 178	99.23 132	99.35 209	99.80 54	99.17 51	99.95 45	98.21 160	99.84 123	99.59 128

Fast-Effi-MVS+-dtu		99.20 133	99.12 122	99.43 179	99.25 278	99.69 76	99.05 182	99.82 37	99.50 87	98.97 265	99.05 306	98.98 73	99.98 7	98.20 161	99.24 297	98.62 318

MS-PatchMatch		99.00 179	98.97 170	99.09 246	99.11 304	98.19 273	98.76 233	99.33 264	98.49 221	99.44 184	99.58 184	98.21 174	99.69 306	98.20 161	99.62 228	99.39 217

TSAR-MVS + GP.		99.12 153	99.04 152	99.38 198	99.34 257	99.16 199	98.15 284	99.29 275	98.18 253	99.63 125	99.62 160	99.18 50	99.68 317	98.20 161	99.74 184	99.30 237

DP-MVS		99.48 54	99.39 67	99.74 62	99.57 164	99.62 96	99.29 114	99.61 144	99.87 14	99.74 89	99.76 76	98.69 112	99.87 170	98.20 161	99.80 155	99.75 40

MVP-Stereo		99.16 145	99.08 136	99.43 179	99.48 209	99.07 212	99.08 179	99.55 184	98.63 205	99.31 221	99.68 124	98.19 177	99.78 273	98.18 165	99.58 241	99.45 197
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 66	99.30 88	99.80 29	99.83 38	99.81 29	99.52 66	99.70 97	98.35 238	99.51 174	99.50 216	99.31 37	99.88 157	98.18 165	99.84 123	99.69 52

MDA-MVSNet-bldmvs		99.06 164	99.05 146	99.07 250	99.80 56	97.83 292	98.89 208	99.72 90	99.29 120	99.63 125	99.70 107	96.47 260	99.89 143	98.17 167	99.82 142	99.50 176

JIA-IIPM		98.06 274	97.92 275	98.50 293	98.59 344	97.02 314	98.80 226	98.51 327	99.88 13	97.89 337	99.87 31	91.89 314	99.90 129	98.16 168	97.68 350	98.59 320

EIA-MVS		99.12 153	99.01 158	99.45 173	99.36 245	99.62 96	99.34 95	99.79 53	98.41 227	98.84 282	98.89 331	98.75 107	99.84 225	98.15 169	99.51 258	98.89 304

miper_lstm_enhance		98.65 223	98.60 210	98.82 280	99.20 287	97.33 307	97.78 319	99.66 115	99.01 162	99.59 143	99.50 216	94.62 288	99.85 208	98.12 170	99.90 84	99.26 243

Effi-MVS+-dtu		99.07 163	98.92 179	99.52 150	98.89 323	99.78 39	99.15 157	99.66 115	99.34 114	98.92 272	99.24 283	97.69 213	99.98 7	98.11 171	99.28 291	98.81 311

mvs-test1		98.83 203	98.70 204	99.22 231	98.89 323	99.65 88	98.88 209	99.66 115	99.34 114	98.29 317	98.94 326	97.69 213	99.96 35	98.11 171	98.54 331	98.04 346

tpm		97.15 300	96.95 304	97.75 318	98.91 319	94.24 345	99.32 100	97.96 339	97.71 279	98.29 317	99.32 262	86.72 353	99.92 90	98.10 173	96.24 359	99.09 279

DeepPCF-MVS	98.42 6	99.18 140	99.02 155	99.67 88	99.22 282	99.75 50	97.25 343	99.47 224	98.72 199	99.66 115	99.70 107	99.29 39	99.63 336	98.07 174	99.81 150	99.62 106

ppachtmachnet_test		98.89 197	99.12 122	98.20 306	99.66 136	95.24 339	97.63 325	99.68 106	99.08 154	99.78 68	99.62 160	98.65 120	99.88 157	98.02 175	99.96 42	99.48 186

tpmrst		97.73 284	98.07 262	96.73 337	98.71 341	92.00 356	99.10 172	98.86 311	98.52 217	98.92 272	99.54 204	91.90 313	99.82 246	98.02 175	99.03 306	98.37 333

CSCG		99.37 84	99.29 93	99.60 125	99.71 111	99.46 128	99.43 80	99.85 24	98.79 191	99.41 198	99.60 176	98.92 80	99.92 90	98.02 175	99.92 74	99.43 208

eth_miper_zixun_eth		98.68 221	98.71 201	98.60 289	99.10 305	96.84 319	97.52 333	99.54 189	98.94 170	99.58 145	99.48 224	96.25 269	99.76 283	98.01 178	99.93 70	99.21 253

Patchmtry		98.78 208	98.54 220	99.49 159	98.89 323	99.19 197	99.32 100	99.67 111	99.65 62	99.72 95	99.79 60	91.87 315	99.95 45	98.00 179	99.97 30	99.33 231

PVSNet_BlendedMVS		99.03 171	99.01 158	99.09 246	99.54 176	97.99 285	98.58 245	99.82 37	97.62 282	99.34 212	99.71 100	98.52 140	99.77 281	97.98 180	99.97 30	99.52 168

PVSNet_Blended		98.70 219	98.59 212	99.02 254	99.54 176	97.99 285	97.58 328	99.82 37	95.70 333	99.34 212	98.98 319	98.52 140	99.77 281	97.98 180	99.83 133	99.30 237

cl-mvsnet____		98.54 238	98.41 232	98.92 263	99.03 313	97.80 294	97.46 335	99.59 162	98.90 177	99.60 140	99.46 232	93.85 295	99.78 273	97.97 182	99.89 92	99.17 262

cl-mvsnet1		98.54 238	98.42 231	98.92 263	99.03 313	97.80 294	97.46 335	99.59 162	98.90 177	99.60 140	99.46 232	93.87 294	99.78 273	97.97 182	99.89 92	99.18 260

AUN-MVS		97.82 280	97.38 291	99.14 241	99.27 275	98.53 252	98.72 237	99.02 306	98.10 255	97.18 352	99.03 313	89.26 341	99.85 208	97.94 184	97.91 346	99.03 291

ambc				99.20 234	99.35 247	98.53 252	99.17 149	99.46 228		99.67 111	99.80 54	98.46 147	99.70 300	97.92 185	99.70 203	99.38 219

USDC		98.96 186	98.93 175	99.05 252	99.54 176	97.99 285	97.07 349	99.80 47	98.21 250	99.75 80	99.77 73	98.43 149	99.64 335	97.90 186	99.88 100	99.51 170

OPM-MVS		99.26 112	99.13 118	99.63 111	99.70 118	99.61 102	98.58 245	99.48 220	98.50 219	99.52 169	99.63 151	99.14 55	99.76 283	97.89 187	99.77 170	99.51 170

DVP-MVS		99.32 100	99.17 110	99.77 40	99.69 121	99.80 34	99.14 159	99.31 270	99.16 143	99.62 132	99.61 169	98.35 160	99.91 108	97.88 188	99.72 197	99.61 115
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.83 21	99.70 118	99.79 36	99.14 159	99.61 144					99.92 90	97.88 188	99.72 197	99.77 33

cl_fuxian		98.72 217	98.71 201	98.72 285	99.12 299	97.22 310	97.68 324	99.56 178	98.90 177	99.54 162	99.48 224	96.37 266	99.73 292	97.88 188	99.88 100	99.21 253

3Dnovator+	98.92 3	99.35 89	99.24 103	99.67 88	99.35 247	99.47 124	99.62 47	99.50 213	99.44 101	99.12 253	99.78 66	98.77 104	99.94 57	97.87 191	99.72 197	99.62 106

miper_ehance_all_eth		98.59 231	98.59 212	98.59 290	98.98 316	97.07 313	97.49 334	99.52 206	98.50 219	99.52 169	99.37 247	96.41 264	99.71 298	97.86 192	99.62 228	99.00 297

WTY-MVS		98.59 231	98.37 236	99.26 224	99.43 226	98.40 262	98.74 234	99.13 301	98.10 255	99.21 239	99.24 283	94.82 285	99.90 129	97.86 192	98.77 319	99.49 181

SED-MVS		99.40 76	99.28 95	99.77 40	99.69 121	99.82 26	99.20 138	99.54 189	99.13 149	99.82 50	99.63 151	98.91 82	99.92 90	97.85 194	99.70 203	99.58 133

test_241102_TWO								99.54 189	99.13 149	99.76 75	99.63 151	98.32 165	99.92 90	97.85 194	99.69 206	99.75 40

MVS_111021_HR		99.12 153	99.02 155	99.40 191	99.50 198	99.11 204	97.92 313	99.71 93	98.76 197	99.08 257	99.47 229	99.17 51	99.54 346	97.85 194	99.76 172	99.54 153

zzz-MVS		99.30 103	99.14 115	99.80 29	99.81 51	99.81 29	98.73 236	99.53 198	99.27 124	99.42 190	99.63 151	98.21 174	99.95 45	97.83 197	99.79 160	99.65 83

MTAPA		99.35 89	99.20 107	99.80 29	99.81 51	99.81 29	99.33 97	99.53 198	99.27 124	99.42 190	99.63 151	98.21 174	99.95 45	97.83 197	99.79 160	99.65 83

TESTMET0.1,1		96.24 320	95.84 322	97.41 326	98.24 353	93.84 348	97.38 337	95.84 357	98.43 224	97.81 341	98.56 345	79.77 365	99.89 143	97.77 199	98.77 319	98.52 325

ACMH+	98.40 8	99.50 50	99.43 62	99.71 80	99.86 30	99.76 47	99.32 100	99.77 61	99.53 84	99.77 73	99.76 76	99.26 45	99.78 273	97.77 199	99.88 100	99.60 119

IU-MVS					99.69 121	99.77 41		99.22 289	97.50 289	99.69 105				97.75 201	99.70 203	99.77 33

114514_t		98.49 245	98.11 260	99.64 107	99.73 104	99.58 110	99.24 128	99.76 66	89.94 357	99.42 190	99.56 195	97.76 210	99.86 190	97.74 202	99.82 142	99.47 191

test_0728_THIRD									99.18 138	99.62 132	99.61 169	98.58 127	99.91 108	97.72 203	99.80 155	99.77 33

miper_enhance_ethall		98.03 275	97.94 273	98.32 301	98.27 352	96.43 325	96.95 350	99.41 240	96.37 323	99.43 188	98.96 324	94.74 286	99.69 306	97.71 204	99.62 228	98.83 310

TSAR-MVS + MP.		99.34 94	99.24 103	99.63 111	99.82 44	99.37 155	99.26 120	99.35 261	98.77 194	99.57 148	99.70 107	99.27 44	99.88 157	97.71 204	99.75 175	99.65 83
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl-mvsnet2		97.56 291	97.28 293	98.40 297	98.37 350	96.75 320	97.24 344	99.37 257	97.31 299	99.41 198	99.22 285	87.30 344	99.37 357	97.70 206	99.62 228	99.08 282

MP-MVS-pluss		99.14 149	98.92 179	99.80 29	99.83 38	99.83 22	98.61 241	99.63 134	96.84 315	99.44 184	99.58 184	98.81 92	99.91 108	97.70 206	99.82 142	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 106	99.11 125	99.79 34	99.75 95	99.81 29	98.95 204	99.53 198	98.27 247	99.53 167	99.73 87	98.75 107	99.87 170	97.70 206	99.83 133	99.68 58

UnsupCasMVSNet_bld		98.55 237	98.27 246	99.40 191	99.56 174	99.37 155	97.97 308	99.68 106	97.49 290	99.08 257	99.35 257	95.41 281	99.82 246	97.70 206	98.19 340	99.01 296

MVS_111021_LR		99.13 151	99.03 154	99.42 181	99.58 154	99.32 167	97.91 315	99.73 81	98.68 201	99.31 221	99.48 224	99.09 60	99.66 326	97.70 206	99.77 170	99.29 240

IS-MVSNet		99.03 171	98.85 188	99.55 143	99.80 56	99.25 181	99.73 16	99.15 298	99.37 111	99.61 138	99.71 100	94.73 287	99.81 262	97.70 206	99.88 100	99.58 133

test-LLR		97.15 300	96.95 304	97.74 319	98.18 355	95.02 340	97.38 337	96.10 353	98.00 260	97.81 341	98.58 342	90.04 337	99.91 108	97.69 212	98.78 317	98.31 334

test-mter		96.23 321	95.73 324	97.74 319	98.18 355	95.02 340	97.38 337	96.10 353	97.90 269	97.81 341	98.58 342	79.12 368	99.91 108	97.69 212	98.78 317	98.31 334

XVS		99.27 110	99.11 125	99.75 56	99.71 111	99.71 65	99.37 90	99.61 144	99.29 120	98.76 292	99.47 229	98.47 144	99.88 157	97.62 214	99.73 191	99.67 65

X-MVStestdata		96.09 322	94.87 332	99.75 56	99.71 111	99.71 65	99.37 90	99.61 144	99.29 120	98.76 292	61.30 370	98.47 144	99.88 157	97.62 214	99.73 191	99.67 65

SMA-MVS		99.19 136	99.00 161	99.73 70	99.46 219	99.73 59	99.13 165	99.52 206	97.40 294	99.57 148	99.64 141	98.93 79	99.83 236	97.61 216	99.79 160	99.63 95
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
CostFormer		96.71 311	96.79 310	96.46 341	98.90 320	90.71 365	99.41 81	98.68 319	94.69 347	98.14 328	99.34 260	86.32 355	99.80 267	97.60 217	98.07 344	98.88 305

PVSNet	97.47 15	98.42 251	98.44 229	98.35 299	99.46 219	96.26 326	96.70 354	99.34 263	97.68 280	99.00 264	99.13 294	97.40 229	99.72 294	97.59 218	99.68 209	99.08 282

new_pmnet		98.88 198	98.89 184	98.84 275	99.70 118	97.62 299	98.15 284	99.50 213	97.98 263	99.62 132	99.54 204	98.15 180	99.94 57	97.55 219	99.84 123	98.95 299

IB-MVS	95.41 20	95.30 332	94.46 335	97.84 315	98.76 339	95.33 338	97.33 340	96.07 355	96.02 327	95.37 362	97.41 363	76.17 370	99.96 35	97.54 220	95.44 361	98.22 339
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		99.24 116	99.11 125	99.61 123	98.38 349	99.79 36	99.57 62	99.68 106	99.61 72	99.15 248	99.71 100	98.70 111	99.91 108	97.54 220	99.68 209	99.13 273

ZNCC-MVS		99.22 126	99.04 152	99.77 40	99.76 84	99.73 59	99.28 115	99.56 178	98.19 252	99.14 250	99.29 269	98.84 91	99.92 90	97.53 222	99.80 155	99.64 90

CP-MVS		99.23 117	99.05 146	99.75 56	99.66 136	99.66 83	99.38 86	99.62 137	98.38 231	99.06 261	99.27 273	98.79 99	99.94 57	97.51 223	99.82 142	99.66 75

SD-MVS		99.01 177	99.30 88	98.15 307	99.50 198	99.40 147	98.94 206	99.61 144	99.22 135	99.75 80	99.82 49	99.54 21	95.51 365	97.48 224	99.87 109	99.54 153
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		98.49 245	98.29 245	99.11 244	98.96 317	98.42 261	97.54 329	99.32 266	97.53 287	98.47 313	98.15 355	97.88 201	99.82 246	97.46 225	99.24 297	99.09 279

DeepC-MVS_fast	98.47 5	99.23 117	99.12 122	99.56 140	99.28 273	99.22 190	98.99 196	99.40 247	99.08 154	99.58 145	99.64 141	98.90 85	99.83 236	97.44 226	99.75 175	99.63 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 113	99.08 136	99.76 46	99.73 104	99.70 72	99.31 104	99.59 162	98.36 233	99.36 207	99.37 247	98.80 96	99.91 108	97.43 227	99.75 175	99.68 58

ACMMPR		99.23 117	99.06 142	99.76 46	99.74 101	99.69 76	99.31 104	99.59 162	98.36 233	99.35 209	99.38 246	98.61 124	99.93 71	97.43 227	99.75 175	99.67 65

Vis-MVSNet (Re-imp)		98.77 209	98.58 215	99.34 206	99.78 72	98.88 232	99.61 53	99.56 178	99.11 153	99.24 232	99.56 195	93.00 304	99.78 273	97.43 227	99.89 92	99.35 228

MIMVSNet		98.43 250	98.20 252	99.11 244	99.53 181	98.38 265	99.58 61	98.61 323	98.96 168	99.33 215	99.76 76	90.92 325	99.81 262	97.38 230	99.76 172	99.15 266

XVG-OURS-SEG-HR		99.16 145	98.99 166	99.66 95	99.84 34	99.64 90	98.25 278	99.73 81	98.39 230	99.63 125	99.43 237	99.70 11	99.90 129	97.34 231	98.64 327	99.44 202

COLMAP_ROB	98.06 12	99.45 63	99.37 72	99.70 84	99.83 38	99.70 72	99.38 86	99.78 58	99.53 84	99.67 111	99.78 66	99.19 49	99.86 190	97.32 232	99.87 109	99.55 145
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 173	98.81 194	99.65 100	99.58 154	99.49 121	98.58 245	99.07 302	98.40 229	99.04 262	99.25 278	98.51 142	99.80 267	97.31 233	99.51 258	99.65 83

region2R		99.23 117	99.05 146	99.77 40	99.76 84	99.70 72	99.31 104	99.59 162	98.41 227	99.32 217	99.36 252	98.73 110	99.93 71	97.29 234	99.74 184	99.67 65

APD-MVS_3200maxsize		99.31 102	99.16 111	99.74 62	99.53 181	99.75 50	99.27 118	99.61 144	99.19 137	99.57 148	99.64 141	98.76 105	99.90 129	97.29 234	99.62 228	99.56 142

TAPA-MVS	97.92 13	98.03 275	97.55 289	99.46 169	99.47 214	99.44 135	98.50 258	99.62 137	86.79 358	99.07 260	99.26 276	98.26 169	99.62 337	97.28 236	99.73 191	99.31 236
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 110	99.11 125	99.73 70	99.54 176	99.74 56	99.26 120	99.62 137	99.16 143	99.52 169	99.64 141	98.41 152	99.91 108	97.27 237	99.61 235	99.54 153

RE-MVS-def			99.13 118		99.54 176	99.74 56	99.26 120	99.62 137	99.16 143	99.52 169	99.64 141	98.57 128		97.27 237	99.61 235	99.54 153

test_yl		98.25 264	97.95 269	99.13 242	99.17 292	98.47 256	99.00 191	98.67 321	98.97 165	99.22 237	99.02 314	91.31 319	99.69 306	97.26 239	98.93 310	99.24 246

DCV-MVSNet		98.25 264	97.95 269	99.13 242	99.17 292	98.47 256	99.00 191	98.67 321	98.97 165	99.22 237	99.02 314	91.31 319	99.69 306	97.26 239	98.93 310	99.24 246

PHI-MVS		99.11 157	98.95 174	99.59 127	99.13 297	99.59 107	99.17 149	99.65 126	97.88 270	99.25 229	99.46 232	98.97 75	99.80 267	97.26 239	99.82 142	99.37 222

tfpnnormal		99.43 66	99.38 69	99.60 125	99.87 28	99.75 50	99.59 59	99.78 58	99.71 44	99.90 22	99.69 113	98.85 90	99.90 129	97.25 242	99.78 166	99.15 266

PatchmatchNet		97.65 287	97.80 280	97.18 332	98.82 332	92.49 354	99.17 149	98.39 333	98.12 254	98.79 288	99.58 184	90.71 330	99.89 143	97.23 243	99.41 274	99.16 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 182	98.80 196	99.56 140	99.25 278	99.43 139	98.54 254	99.27 279	98.58 210	98.80 287	99.43 237	98.53 137	99.70 300	97.22 244	99.59 240	99.54 153

HPM-MVS		99.25 113	99.07 140	99.78 37	99.81 51	99.75 50	99.61 53	99.67 111	97.72 278	99.35 209	99.25 278	99.23 46	99.92 90	97.21 245	99.82 142	99.67 65
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DWT-MVSNet_test		96.03 324	95.80 323	96.71 339	98.50 347	91.93 357	99.25 127	97.87 342	95.99 328	96.81 354	97.61 361	81.02 362	99.66 326	97.20 246	97.98 345	98.54 324

mPP-MVS		99.19 136	99.00 161	99.76 46	99.76 84	99.68 79	99.38 86	99.54 189	98.34 242	99.01 263	99.50 216	98.53 137	99.93 71	97.18 247	99.78 166	99.66 75

ACMMP		99.25 113	99.08 136	99.74 62	99.79 66	99.68 79	99.50 68	99.65 126	98.07 258	99.52 169	99.69 113	98.57 128	99.92 90	97.18 247	99.79 160	99.63 95
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
test1172		99.23 117	99.05 146	99.74 62	99.52 186	99.75 50	99.20 138	99.61 144	98.97 165	99.48 177	99.58 184	98.41 152	99.91 108	97.15 249	99.55 247	99.57 139

abl_6		99.36 87	99.23 105	99.75 56	99.71 111	99.74 56	99.33 97	99.76 66	99.07 156	99.65 119	99.63 151	99.09 60	99.92 90	97.13 250	99.76 172	99.58 133

thisisatest0515		96.98 304	96.42 311	98.66 288	99.42 231	97.47 302	97.27 342	94.30 362	97.24 301	99.15 248	98.86 333	85.01 356	99.87 170	97.10 251	99.39 276	98.63 317

XVG-ACMP-BASELINE		99.23 117	99.10 133	99.63 111	99.82 44	99.58 110	98.83 218	99.72 90	98.36 233	99.60 140	99.71 100	98.92 80	99.91 108	97.08 252	99.84 123	99.40 214

MSDG		99.08 162	98.98 169	99.37 201	99.60 148	99.13 202	97.54 329	99.74 78	98.84 186	99.53 167	99.55 202	99.10 58	99.79 270	97.07 253	99.86 116	99.18 260

SteuartSystems-ACMMP		99.30 103	99.14 115	99.76 46	99.87 28	99.66 83	99.18 144	99.60 155	98.55 213	99.57 148	99.67 130	99.03 70	99.94 57	97.01 254	99.80 155	99.69 52
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		96.53 314	96.32 312	97.17 333	98.18 355	92.97 353	99.39 84	89.95 367	98.21 250	98.61 302	99.59 182	86.69 354	99.72 294	96.99 255	99.23 299	98.81 311

MSP-MVS		99.04 170	98.79 197	99.81 26	99.78 72	99.73 59	99.35 94	99.57 173	98.54 216	99.54 162	98.99 316	96.81 253	99.93 71	96.97 256	99.53 255	99.77 33
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		98.96 186	98.70 204	99.74 62	99.52 186	99.71 65	98.86 213	99.19 294	98.47 223	98.59 304	99.06 305	98.08 185	99.91 108	96.94 257	99.60 238	99.60 119

SR-MVS		99.19 136	99.00 161	99.74 62	99.51 191	99.72 63	99.18 144	99.60 155	98.85 183	99.47 179	99.58 184	98.38 157	99.92 90	96.92 258	99.54 253	99.57 139

PGM-MVS		99.20 133	99.01 158	99.77 40	99.75 95	99.71 65	99.16 155	99.72 90	97.99 262	99.42 190	99.60 176	98.81 92	99.93 71	96.91 259	99.74 184	99.66 75

HY-MVS	98.23 9	98.21 269	97.95 269	98.99 255	99.03 313	98.24 269	99.61 53	98.72 318	96.81 316	98.73 294	99.51 213	94.06 292	99.86 190	96.91 259	98.20 338	98.86 307

MDTV_nov1_ep13			97.73 284		98.70 342	90.83 364	99.15 157	98.02 338	98.51 218	98.82 284	99.61 169	90.98 324	99.66 326	96.89 261	98.92 312

GST-MVS		99.16 145	98.96 172	99.75 56	99.73 104	99.73 59	99.20 138	99.55 184	98.22 249	99.32 217	99.35 257	98.65 120	99.91 108	96.86 262	99.74 184	99.62 106

test_post1							99.14 159				51.63 372	89.54 340	99.82 246	96.86 262

SCA		98.11 271	98.36 237	97.36 327	99.20 287	92.99 352	98.17 283	98.49 329	98.24 248	99.10 256	99.57 192	96.01 274	99.94 57	96.86 262	99.62 228	99.14 270

#test#		99.12 153	98.90 183	99.76 46	99.73 104	99.70 72	99.10 172	99.59 162	97.60 283	99.36 207	99.37 247	98.80 96	99.91 108	96.84 265	99.75 175	99.68 58

XVG-OURS		99.21 131	99.06 142	99.65 100	99.82 44	99.62 96	97.87 316	99.74 78	98.36 233	99.66 115	99.68 124	99.71 9	99.90 129	96.84 265	99.88 100	99.43 208

LCM-MVSNet-Re		99.28 106	99.15 114	99.67 88	99.33 262	99.76 47	99.34 95	99.97 2	98.93 173	99.91 20	99.79 60	98.68 113	99.93 71	96.80 267	99.56 243	99.30 237

RPSCF		99.18 140	99.02 155	99.64 107	99.83 38	99.85 12	99.44 78	99.82 37	98.33 243	99.50 175	99.78 66	97.90 198	99.65 333	96.78 268	99.83 133	99.44 202

旧先验2							97.94 311		95.33 337	98.94 268			99.88 157	96.75 269

MDTV_nov1_ep13_2view						91.44 362	99.14 159		97.37 296	99.21 239		91.78 317		96.75 269		99.03 291

CLD-MVS		98.76 211	98.57 216	99.33 208	99.57 164	98.97 219	97.53 331	99.55 184	96.41 321	99.27 227	99.13 294	99.07 65	99.78 273	96.73 271	99.89 92	99.23 249
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 272	97.98 267	98.48 294	99.27 275	96.48 323	99.40 82	99.07 302	98.81 188	99.23 233	99.57 192	90.11 336	99.87 170	96.69 272	99.64 225	99.09 279

baseline2		96.83 307	96.28 313	98.46 295	99.09 307	96.91 317	98.83 218	93.87 364	97.23 302	96.23 358	98.36 351	88.12 343	99.90 129	96.68 273	98.14 342	98.57 323

cascas		96.99 303	96.82 309	97.48 323	97.57 362	95.64 335	96.43 356	99.56 178	91.75 353	97.13 353	97.61 361	95.58 280	98.63 362	96.68 273	99.11 301	98.18 343

LPG-MVS_test		99.22 126	99.05 146	99.74 62	99.82 44	99.63 94	99.16 155	99.73 81	97.56 284	99.64 121	99.69 113	99.37 31	99.89 143	96.66 275	99.87 109	99.69 52

LGP-MVS_train				99.74 62	99.82 44	99.63 94		99.73 81	97.56 284	99.64 121	99.69 113	99.37 31	99.89 143	96.66 275	99.87 109	99.69 52

TinyColmap		98.97 183	98.93 175	99.07 250	99.46 219	98.19 273	97.75 320	99.75 73	98.79 191	99.54 162	99.70 107	98.97 75	99.62 337	96.63 277	99.83 133	99.41 212

LF4IMVS		99.01 177	98.92 179	99.27 222	99.71 111	99.28 173	98.59 244	99.77 61	98.32 244	99.39 204	99.41 239	98.62 122	99.84 225	96.62 278	99.84 123	98.69 315

NCCC		98.82 205	98.57 216	99.58 131	99.21 284	99.31 168	98.61 241	99.25 284	98.65 203	98.43 314	99.26 276	97.86 202	99.81 262	96.55 279	99.27 294	99.61 115

OPU-MVS				99.29 218	99.12 299	99.44 135	99.20 138				99.40 241	99.00 71	98.84 361	96.54 280	99.60 238	99.58 133

F-COLMAP		98.74 214	98.45 227	99.62 120	99.57 164	99.47 124	98.84 216	99.65 126	96.31 324	98.93 269	99.19 291	97.68 215	99.87 170	96.52 281	99.37 281	99.53 158

ADS-MVSNet2		97.78 282	97.66 288	98.12 309	99.14 295	95.36 337	99.22 135	98.75 317	96.97 310	98.25 320	99.64 141	90.90 326	99.94 57	96.51 282	99.56 243	99.08 282

ADS-MVSNet		97.72 286	97.67 287	97.86 314	99.14 295	94.65 343	99.22 135	98.86 311	96.97 310	98.25 320	99.64 141	90.90 326	99.84 225	96.51 282	99.56 243	99.08 282

PatchMatch-RL		98.68 221	98.47 225	99.30 217	99.44 224	99.28 173	98.14 286	99.54 189	97.12 308	99.11 254	99.25 278	97.80 207	99.70 300	96.51 282	99.30 289	98.93 301

CMPMVS	77.52 23	98.50 242	98.19 255	99.41 189	98.33 351	99.56 113	99.01 189	99.59 162	95.44 335	99.57 148	99.80 54	95.64 278	99.46 355	96.47 285	99.92 74	99.21 253
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
xxxxxxxxxxxxxcwj		99.11 157	98.96 172	99.54 147	99.53 181	99.25 181	98.29 274	99.76 66	99.07 156	99.42 190	99.61 169	98.86 88	99.87 170	96.45 286	99.68 209	99.49 181

SF-MVS		99.10 161	98.93 175	99.62 120	99.58 154	99.51 119	99.13 165	99.65 126	97.97 264	99.42 190	99.61 169	98.86 88	99.87 170	96.45 286	99.68 209	99.49 181

DPE-MVS		99.14 149	98.92 179	99.82 23	99.57 164	99.77 41	98.74 234	99.60 155	98.55 213	99.76 75	99.69 113	98.23 173	99.92 90	96.39 288	99.75 175	99.76 37
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 360	89.02 368			93.47 349		98.30 352		99.84 225	96.38 289

AllTest		99.21 131	99.07 140	99.63 111	99.78 72	99.64 90	99.12 169	99.83 32	98.63 205	99.63 125	99.72 93	98.68 113	99.75 287	96.38 289	99.83 133	99.51 170

TestCases				99.63 111	99.78 72	99.64 90		99.83 32	98.63 205	99.63 125	99.72 93	98.68 113	99.75 287	96.38 289	99.83 133	99.51 170

testdata				99.42 181	99.51 191	98.93 227		99.30 273	96.20 325	98.87 279	99.40 241	98.33 164	99.89 143	96.29 292	99.28 291	99.44 202

dp		96.86 306	97.07 300	96.24 343	98.68 343	90.30 367	99.19 143	98.38 334	97.35 297	98.23 322	99.59 182	87.23 345	99.82 246	96.27 293	98.73 325	98.59 320

tpmvs		97.39 295	97.69 285	96.52 340	98.41 348	91.76 358	99.30 107	98.94 310	97.74 277	97.85 340	99.55 202	92.40 311	99.73 292	96.25 294	98.73 325	98.06 345

KD-MVS_2432*1600		95.89 325	95.41 328	97.31 330	94.96 364	93.89 346	97.09 347	99.22 289	97.23 302	98.88 276	99.04 309	79.23 366	99.54 346	96.24 295	96.81 354	98.50 329

miper_refine_blended		95.89 325	95.41 328	97.31 330	94.96 364	93.89 346	97.09 347	99.22 289	97.23 302	98.88 276	99.04 309	79.23 366	99.54 346	96.24 295	96.81 354	98.50 329

ACMP	97.51 14	99.05 167	98.84 190	99.67 88	99.78 72	99.55 116	98.88 209	99.66 115	97.11 309	99.47 179	99.60 176	99.07 65	99.89 143	96.18 297	99.85 119	99.58 133
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 194	98.72 200	99.44 175	99.39 237	99.42 142	98.58 245	99.64 132	97.31 299	99.44 184	99.62 160	98.59 126	99.69 306	96.17 298	99.79 160	99.22 251

DP-MVS Recon		98.50 242	98.23 249	99.31 215	99.49 203	99.46 128	98.56 250	99.63 134	94.86 344	98.85 281	99.37 247	97.81 206	99.59 343	96.08 299	99.44 268	98.88 305

tpm cat1		96.78 308	96.98 303	96.16 344	98.85 327	90.59 366	99.08 179	99.32 266	92.37 352	97.73 346	99.46 232	91.15 322	99.69 306	96.07 300	98.80 316	98.21 340

tpm2		96.35 317	96.22 314	96.73 337	98.88 326	91.75 359	99.21 137	98.51 327	93.27 350	97.89 337	99.21 287	84.83 357	99.70 300	96.04 301	98.18 341	98.75 314

test_0402		99.22 126	99.14 115	99.45 173	99.79 66	99.43 139	99.28 115	99.68 106	99.54 82	99.40 203	99.56 195	99.07 65	99.82 246	96.01 302	99.96 42	99.11 274

ITE_SJBPF				99.38 198	99.63 142	99.44 135		99.73 81	98.56 211	99.33 215	99.53 207	98.88 87	99.68 317	96.01 302	99.65 223	99.02 295

test_prior3		98.62 225	98.34 240	99.46 169	99.35 247	99.22 190	97.95 309	99.39 250	97.87 271	98.05 330	99.05 306	97.90 198	99.69 306	95.99 304	99.49 262	99.48 186

test_prior2							97.95 309		97.87 271	98.05 330	99.05 306	97.90 198		95.99 304	99.49 262

testdata2													99.89 143	95.99 304

原ACMM1				99.37 201	99.47 214	98.87 234		99.27 279	96.74 318	98.26 319	99.32 262	97.93 196	99.82 246	95.96 307	99.38 277	99.43 208

新几何1				99.52 150	99.50 198	99.22 190		99.26 281	95.66 334	98.60 303	99.28 271	97.67 216	99.89 143	95.95 308	99.32 287	99.45 197

MP-MVS		99.06 164	98.83 192	99.76 46	99.76 84	99.71 65	99.32 100	99.50 213	98.35 238	98.97 265	99.48 224	98.37 158	99.92 90	95.95 308	99.75 175	99.63 95
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
wuyk23d		97.58 290	99.13 118	92.93 346	99.69 121	99.49 121	99.52 66	99.77 61	97.97 264	99.96 8	99.79 60	99.84 3	99.94 57	95.85 310	99.82 142	79.36 361

HQP_MVS		98.90 194	98.68 206	99.55 143	99.58 154	99.24 186	98.80 226	99.54 189	98.94 170	99.14 250	99.25 278	97.24 237	99.82 246	95.84 311	99.78 166	99.60 119

plane_prior5								99.54 189					99.82 246	95.84 311	99.78 166	99.60 119

无先验							98.01 300	99.23 288	95.83 330				99.85 208	95.79 313		99.44 202

1121		98.56 234	98.24 248	99.52 150	99.49 203	99.24 186	99.30 107	99.22 289	95.77 331	98.52 309	99.29 269	97.39 231	99.85 208	95.79 313	99.34 284	99.46 195

CPTT-MVS		98.74 214	98.44 229	99.64 107	99.61 146	99.38 152	99.18 144	99.55 184	96.49 320	99.27 227	99.37 247	97.11 245	99.92 90	95.74 315	99.67 216	99.62 106

PLC	97.35 16	98.36 256	97.99 265	99.48 163	99.32 263	99.24 186	98.50 258	99.51 209	95.19 340	98.58 305	98.96 324	96.95 250	99.83 236	95.63 316	99.25 295	99.37 222
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 233	98.34 240	99.28 220	99.18 291	99.10 208	98.34 269	99.41 240	98.48 222	98.52 309	98.98 319	97.05 247	99.78 273	95.59 317	99.50 260	98.96 298

1314		98.00 277	97.90 278	98.27 305	98.90 320	97.45 304	99.30 107	99.06 304	94.98 341	97.21 351	99.12 299	98.43 149	99.67 322	95.58 318	98.56 330	97.71 350

agg_prior1		98.33 261	97.92 275	99.57 136	99.35 247	99.36 158	97.99 304	99.39 250	94.85 345	97.76 344	98.98 319	98.03 187	99.85 208	95.49 319	99.44 268	99.51 170

PVSNet_0	95.53 19	95.85 328	95.31 330	97.47 324	98.78 337	93.48 350	95.72 358	99.40 247	96.18 326	97.37 347	97.73 359	95.73 277	99.58 344	95.49 319	81.40 363	99.36 225

MAR-MVS		98.24 266	97.92 275	99.19 235	98.78 337	99.65 88	99.17 149	99.14 299	95.36 336	98.04 332	98.81 336	97.47 226	99.72 294	95.47 321	99.06 303	98.21 340
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	98.23 267	97.89 279	99.26 224	99.19 289	99.26 177	99.65 44	99.69 103	91.33 355	98.14 328	99.77 73	98.28 167	99.96 35	95.41 322	99.55 247	98.58 322

train_agg		98.35 259	97.95 269	99.57 136	99.35 247	99.35 162	98.11 290	99.41 240	94.90 342	97.92 335	98.99 316	98.02 189	99.85 208	95.38 323	99.44 268	99.50 176

9.14			98.64 207		99.45 222		98.81 223	99.60 155	97.52 288	99.28 226	99.56 195	98.53 137	99.83 236	95.36 324	99.64 225

APD-MVS		98.87 200	98.59 212	99.71 80	99.50 198	99.62 96	99.01 189	99.57 173	96.80 317	99.54 162	99.63 151	98.29 166	99.91 108	95.24 325	99.71 201	99.61 115
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
AdaColmap		98.60 228	98.35 239	99.38 198	99.12 299	99.22 190	98.67 240	99.42 239	97.84 275	98.81 285	99.27 273	97.32 235	99.81 262	95.14 326	99.53 255	99.10 276

test9_res														95.10 327	99.44 268	99.50 176

CDPH-MVS		98.56 234	98.20 252	99.61 123	99.50 198	99.46 128	98.32 272	99.41 240	95.22 338	99.21 239	99.10 302	98.34 162	99.82 246	95.09 328	99.66 220	99.56 142

ETH3D-3000-0.1		98.77 209	98.50 224	99.59 127	99.47 214	99.53 118	98.77 231	99.60 155	97.33 298	99.23 233	99.50 216	97.91 197	99.83 236	95.02 329	99.67 216	99.41 212

BH-untuned		98.22 268	98.09 261	98.58 291	99.38 240	97.24 309	98.55 251	98.98 309	97.81 276	99.20 244	98.76 338	97.01 248	99.65 333	94.83 330	98.33 335	98.86 307

BP-MVS														94.73 331

HQP-MVS		98.36 256	98.02 264	99.39 194	99.31 264	98.94 223	97.98 305	99.37 257	97.45 291	98.15 324	98.83 334	96.67 254	99.70 300	94.73 331	99.67 216	99.53 158

QAPM		98.40 254	97.99 265	99.65 100	99.39 237	99.47 124	99.67 35	99.52 206	91.70 354	98.78 290	99.80 54	98.55 131	99.95 45	94.71 333	99.75 175	99.53 158

ETH3D cwj APD-0.16		98.50 242	98.16 258	99.51 153	99.04 312	99.39 149	98.47 260	99.47 224	96.70 319	98.78 290	99.33 261	97.62 223	99.86 190	94.69 334	99.38 277	99.28 242

agg_prior2														94.58 335	99.46 267	99.50 176

BH-RMVSNet		98.41 252	98.14 259	99.21 232	99.21 284	98.47 256	98.60 243	98.26 336	98.35 238	98.93 269	99.31 264	97.20 242	99.66 326	94.32 336	99.10 302	99.51 170

E-PMN		97.14 302	97.43 290	96.27 342	98.79 335	91.62 360	95.54 359	99.01 308	99.44 101	98.88 276	99.12 299	92.78 305	99.68 317	94.30 337	99.03 306	97.50 351

MG-MVS		98.52 240	98.39 234	98.94 259	99.15 294	97.39 306	98.18 281	99.21 293	98.89 180	99.23 233	99.63 151	97.37 233	99.74 289	94.22 338	99.61 235	99.69 52

API-MVS		98.38 255	98.39 234	98.35 299	98.83 329	99.26 177	99.14 159	99.18 295	98.59 209	98.66 299	98.78 337	98.61 124	99.57 345	94.14 339	99.56 243	96.21 358

PAPM_NR		98.36 256	98.04 263	99.33 208	99.48 209	98.93 227	98.79 229	99.28 278	97.54 286	98.56 307	98.57 344	97.12 244	99.69 306	94.09 340	98.90 314	99.38 219

ZD-MVS					99.43 226	99.61 102		99.43 237	96.38 322	99.11 254	99.07 304	97.86 202	99.92 90	94.04 341	99.49 262

DPM-MVS		98.28 262	97.94 273	99.32 212	99.36 245	99.11 204	97.31 341	98.78 316	96.88 312	98.84 282	99.11 301	97.77 209	99.61 341	94.03 342	99.36 282	99.23 249

gg-mvs-nofinetune		95.87 327	95.17 331	97.97 311	98.19 354	96.95 315	99.69 28	89.23 368	99.89 11	96.24 357	99.94 12	81.19 361	99.51 351	93.99 343	98.20 338	97.44 352

PMVS	92.94 21	98.82 205	98.81 194	98.85 273	99.84 34	97.99 285	99.20 138	99.47 224	99.71 44	99.42 190	99.82 49	98.09 183	99.47 353	93.88 344	99.85 119	99.07 287
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testtj		98.56 234	98.17 257	99.72 76	99.45 222	99.60 104	98.88 209	99.50 213	96.88 312	99.18 245	99.48 224	97.08 246	99.92 90	93.69 345	99.38 277	99.63 95

EMVS		96.96 305	97.28 293	95.99 345	98.76 339	91.03 363	95.26 360	98.61 323	99.34 114	98.92 272	98.88 332	93.79 296	99.66 326	92.87 346	99.05 304	97.30 355

BH-w/o		97.20 299	97.01 302	97.76 317	99.08 308	95.69 334	98.03 299	98.52 326	95.76 332	97.96 334	98.02 356	95.62 279	99.47 353	92.82 347	97.25 353	98.12 344

TR-MVS		97.44 294	97.15 299	98.32 301	98.53 346	97.46 303	98.47 260	97.91 341	96.85 314	98.21 323	98.51 348	96.42 262	99.51 351	92.16 348	97.29 352	97.98 347

OpenMVS_ROB	97.31 17	97.36 297	96.84 308	98.89 272	99.29 270	99.45 133	98.87 212	99.48 220	86.54 360	99.44 184	99.74 83	97.34 234	99.86 190	91.61 349	99.28 291	97.37 354

GG-mvs-BLEND				97.36 327	97.59 360	96.87 318	99.70 22	88.49 369		94.64 363	97.26 366	80.66 363	99.12 358	91.50 350	96.50 358	96.08 360

DeepMVS_CX				97.98 310	99.69 121	96.95 315		99.26 281	75.51 362	95.74 360	98.28 353	96.47 260	99.62 337	91.23 351	97.89 347	97.38 353

ETH3 D test6400		97.76 283	97.19 298	99.50 156	99.38 240	99.26 177	98.34 269	99.49 218	92.99 351	98.54 308	99.20 289	95.92 276	99.82 246	91.14 352	99.66 220	99.40 214

PAPR		97.56 291	97.07 300	99.04 253	98.80 334	98.11 279	97.63 325	99.25 284	94.56 348	98.02 333	98.25 354	97.43 228	99.68 317	90.90 353	98.74 323	99.33 231

MVS		95.72 330	94.63 334	98.99 255	98.56 345	97.98 290	99.30 107	98.86 311	72.71 363	97.30 348	99.08 303	98.34 162	99.74 289	89.21 354	98.33 335	99.26 243

thres600view7		96.60 313	96.16 315	97.93 312	99.63 142	96.09 330	99.18 144	97.57 345	98.77 194	98.72 295	97.32 364	87.04 347	99.72 294	88.57 355	98.62 328	97.98 347

FPMVS		96.32 318	95.50 326	98.79 281	99.60 148	98.17 275	98.46 265	98.80 315	97.16 306	96.28 355	99.63 151	82.19 360	99.09 359	88.45 356	98.89 315	99.10 276

PCF-MVS	96.03 18	96.73 310	95.86 321	99.33 208	99.44 224	99.16 199	96.87 352	99.44 233	86.58 359	98.95 267	99.40 241	94.38 290	99.88 157	87.93 357	99.80 155	98.95 299
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 316	96.03 318	97.47 324	99.63 142	95.93 331	99.18 144	97.57 345	98.75 198	98.70 297	97.31 365	87.04 347	99.67 322	87.62 358	98.51 332	96.81 356

tfpn200view9		96.30 319	95.89 319	97.53 322	99.58 154	96.11 328	99.00 191	97.54 348	98.43 224	98.52 309	96.98 367	86.85 349	99.67 322	87.62 358	98.51 332	96.81 356

thres400		96.40 315	95.89 319	97.92 313	99.58 154	96.11 328	99.00 191	97.54 348	98.43 224	98.52 309	96.98 367	86.85 349	99.67 322	87.62 358	98.51 332	97.98 347

thres200		96.09 322	95.68 325	97.33 329	99.48 209	96.22 327	98.53 255	97.57 345	98.06 259	98.37 316	96.73 369	86.84 351	99.61 341	86.99 361	98.57 329	96.16 359

MVE	92.54 22	96.66 312	96.11 316	98.31 303	99.68 130	97.55 301	97.94 311	95.60 358	99.37 111	90.68 365	98.70 340	96.56 256	98.61 363	86.94 362	99.55 247	98.77 313
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM		95.61 331	94.71 333	98.31 303	99.12 299	96.63 321	96.66 355	98.46 330	90.77 356	96.25 356	98.68 341	93.01 303	99.69 306	81.60 363	97.86 349	98.62 318

test123		29.31 334	33.05 339	18.08 348	25.93 370	12.24 370	97.53 331	10.93 371	11.78 365	24.21 366	50.08 374	21.04 371	8.60 366	23.51 364	32.43 365	33.39 362

testmvs		28.94 335	33.33 337	15.79 349	26.03 369	9.81 371	96.77 353	15.67 370	11.55 366	23.87 367	50.74 373	19.03 372	8.53 367	23.21 365	33.07 364	29.03 363

uanet_test		8.33 338	11.11 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		24.88 336	33.17 338	0.00 350	0.00 371	0.00 372	0.00 362	99.62 137	0.00 367	0.00 368	99.13 294	99.82 4	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		16.61 337	22.14 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	99.28 41	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		8.33 338	11.11 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		8.33 338	11.11 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		8.33 338	11.11 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		8.33 338	11.11 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		8.26 344	11.02 347	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	99.16 292	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		8.33 338	11.11 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

test_241102_ONE					99.69 121	99.82 26		99.54 189	99.12 152	99.82 50	99.49 221	98.91 82	99.52 350

save fliter					99.53 181	99.25 181	98.29 274	99.38 256	99.07 156

test0726					99.69 121	99.80 34	99.24 128	99.57 173	99.16 143	99.73 93	99.65 139	98.35 160

GSMVS																99.14 270

test_part2					99.62 145	99.67 81				99.55 160

sam_mvs1												90.81 329				99.14 270

sam_mvs												90.52 333

MTGPA								99.53 198

test_post											52.41 371	90.25 335	99.86 190

patchmatchnet-post											99.62 160	90.58 331	99.94 57

MTMP							99.09 176	98.59 325

TEST9					99.35 247	99.35 162	98.11 290	99.41 240	94.83 346	97.92 335	98.99 316	98.02 189	99.85 208

test_8					99.34 257	99.31 168	98.08 294	99.40 247	94.90 342	97.87 339	98.97 322	98.02 189	99.84 225

agg_prior					99.35 247	99.36 158		99.39 250		97.76 344			99.85 208

test_prior4						99.19 197	98.00 302

test_prior				99.46 169	99.35 247	99.22 190		99.39 250					99.69 306			99.48 186

新几何2							98.04 298

旧先验1					99.49 203	99.29 171		99.26 281			99.39 245	97.67 216			99.36 282	99.46 195

原ACMM2							97.92 313

test222					99.51 191	99.08 211	97.83 318	99.29 275	95.21 339	98.68 298	99.31 264	97.28 236			99.38 277	99.43 208

segment_acmp												98.37 158

testdata1							97.72 321		97.86 274

test12				99.54 147	99.29 270	99.33 165		99.16 297		98.43 314		97.54 224	99.82 246		99.47 265	99.48 186

plane_prior7					99.58 154	99.38 152

plane_prior6					99.47 214	99.26 177						97.24 237

plane_prior4											99.25 278

plane_prior3						99.31 168			98.36 233	99.14 250

plane_prior2							98.80 226		98.94 170

plane_prior1					99.51 191

plane_prior						99.24 186	98.42 266		97.87 271						99.71 201

n2								0.00 372

nn								0.00 372

door-mid								99.83 32

test11								99.29 275

door								99.77 61

HQP5-MVS						98.94 223

HQP-NCC					99.31 264		97.98 305		97.45 291	98.15 324

ACMP_Plane					99.31 264		97.98 305		97.45 291	98.15 324

HQP4-MVS										98.15 324			99.70 300			99.53 158

HQP3-MVS								99.37 257							99.67 216

HQP2-MVS												96.67 254

NP-MVS					99.40 235	99.13 202					98.83 334

ACMMP++_ref															99.94 62

ACMMP++															99.79 160

Test By Simon												98.41 152

High-resolution multi-view benchmark