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.

test_vis3_rt		99.89 3	99.90 3	99.87 21	99.98 3	99.75 68	99.70 35	100.00 1	99.73 76	100.00 1	99.89 34	99.79 16	99.88 191	99.98 1	100.00 1	99.98 3

test_fmvs2		99.72 36	99.85 16	99.34 228	99.91 32	98.08 311	99.48 96	100.00 1	99.90 30	99.99 7	99.91 24	99.50 46	99.98 21	99.98 1	99.99 16	99.96 10

test_fmvs3		99.83 19	99.93 2	99.53 175	99.96 7	98.62 274	99.67 49	100.00 1	99.95 20	100.00 1	99.95 13	99.85 10	99.99 8	99.98 1	99.99 16	99.98 3

test_fmvsmconf0.01_n		99.89 3	99.88 6	99.91 2	99.98 3	99.76 62	99.12 197	100.00 1	100.00 1	99.99 7	99.91 24	99.98 1	100.00 1	99.97 4	100.00 1	99.99 1

test_vis1_n_1920		99.72 36	99.88 6	99.27 247	99.93 26	97.84 324	99.34 122	100.00 1	99.99 2	99.99 7	99.82 73	99.87 9	99.99 8	99.97 4	99.99 16	99.97 7

test_vis1_n		99.68 46	99.79 27	99.36 225	99.94 19	98.18 301	99.52 86	100.00 1	99.86 46	100.00 1	99.88 42	98.99 102	99.96 55	99.97 4	99.96 71	99.95 11

test_fmvs1_n		99.68 46	99.81 23	99.28 244	99.95 15	97.93 321	99.49 95	100.00 1	99.82 59	99.99 7	99.89 34	99.21 75	99.98 21	99.97 4	99.98 41	99.93 15

test_f		99.75 32	99.88 6	99.37 221	99.96 7	98.21 298	99.51 90	100.00 1	99.94 24	100.00 1	99.93 17	99.58 36	99.94 78	99.97 4	99.99 16	99.97 7

test_fmvsmconf0.1_n		99.87 8	99.86 12	99.91 2	99.97 6	99.74 74	99.01 228	99.99 10	99.99 2	99.98 13	99.88 42	99.97 2	99.99 8	99.96 9	100.00 1	99.98 3

test_fmvsmvis_n_1920		99.84 15	99.86 12	99.81 41	99.88 45	99.55 139	99.17 177	99.98 11	99.99 2	99.96 23	99.84 62	99.96 3	99.99 8	99.96 9	99.99 16	99.88 25

test_cas_vis1_n_1920		99.76 31	99.86 12	99.45 193	99.93 26	98.40 286	99.30 135	99.98 11	99.94 24	99.99 7	99.89 34	99.80 15	99.97 34	99.96 9	99.97 56	99.97 7

fmvsm_l_conf0.5_n		99.80 23	99.78 31	99.85 27	99.88 45	99.66 102	99.11 201	99.91 32	99.98 14	99.96 23	99.64 179	99.60 34	99.99 8	99.95 12	99.99 16	99.88 25

test_fmvsm_n_1920		99.84 15	99.85 16	99.83 34	99.82 73	99.70 91	99.17 177	99.97 18	99.99 2	99.96 23	99.82 73	99.94 4	100.00 1	99.95 12	100.00 1	99.80 47

test_fmvs1		99.48 88	99.65 50	98.97 287	99.54 216	97.16 347	99.11 201	99.98 11	99.78 70	99.96 23	99.81 79	98.72 137	99.97 34	99.95 12	99.97 56	99.79 54

mvsany_test3		99.85 11	99.88 6	99.75 75	99.95 15	99.37 179	99.53 85	99.98 11	99.77 74	99.99 7	99.95 13	99.85 10	99.94 78	99.95 12	99.98 41	99.94 13

fmvsm_l_conf0.5_n_a		99.80 23	99.79 27	99.84 31	99.88 45	99.64 111	99.12 197	99.91 32	99.98 14	99.95 31	99.67 167	99.67 27	99.99 8	99.94 16	99.99 16	99.88 25

MM		99.18 173	99.05 180	99.55 169	99.35 282	98.81 254	99.05 216	97.79 380	99.99 2	99.48 217	99.59 219	96.29 300	99.95 64	99.94 16	99.98 41	99.88 25

test_fmvsmconf_n		99.85 11	99.84 19	99.88 17	99.91 32	99.73 77	98.97 240	99.98 11	99.99 2	99.96 23	99.85 56	99.93 7	99.99 8	99.94 16	99.99 16	99.93 15

MVS_0304		99.17 178	99.03 188	99.59 153	99.44 260	98.90 248	99.04 219	95.32 397	99.99 2	99.68 143	99.57 231	98.30 197	99.97 34	99.94 16	99.98 41	99.88 25

fmvsm_s_conf0.1_n_a		99.85 11	99.83 20	99.91 2	99.95 15	99.82 35	99.10 204	99.98 11	99.99 2	99.98 13	99.91 24	99.68 26	99.93 95	99.93 20	99.99 16	99.99 1

fmvsm_s_conf0.1_n		99.86 9	99.85 16	99.89 11	99.93 26	99.78 49	99.07 215	99.98 11	99.99 2	99.98 13	99.90 29	99.88 8	99.92 117	99.93 20	99.99 16	99.98 3

fmvsm_s_conf0.5_n_a		99.82 21	99.79 27	99.89 11	99.85 59	99.82 35	99.03 223	99.96 23	99.99 2	99.97 19	99.84 62	99.58 36	99.93 95	99.92 22	99.98 41	99.93 15

fmvsm_s_conf0.5_n		99.83 19	99.81 23	99.87 21	99.85 59	99.78 49	99.03 223	99.96 23	99.99 2	99.97 19	99.84 62	99.78 17	99.92 117	99.92 22	99.99 16	99.92 18

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 18	99.99 2	100.00 1	99.98 10	99.78 17	100.00 1	99.92 22	100.00 1	99.87 30

v1921920		99.56 74	99.57 72	99.55 169	99.75 129	99.11 224	99.05 216	99.61 175	99.15 186	99.88 62	99.71 139	99.08 92	99.87 205	99.90 25	99.97 56	99.66 105

v1240		99.56 74	99.58 69	99.51 179	99.80 87	99.00 235	99.00 231	99.65 157	99.15 186	99.90 50	99.75 117	99.09 89	99.88 191	99.90 25	99.96 71	99.67 96

v10		99.69 43	99.69 43	99.66 117	99.81 81	99.39 174	99.66 53	99.75 104	99.60 116	99.92 41	99.87 47	98.75 132	99.86 224	99.90 25	99.99 16	99.73 72

v1192		99.57 71	99.57 72	99.57 163	99.77 114	99.22 211	99.04 219	99.60 187	99.18 175	99.87 70	99.72 131	99.08 92	99.85 241	99.89 28	99.98 41	99.66 105

v144192		99.55 77	99.54 78	99.58 157	99.78 106	99.20 216	99.11 201	99.62 168	99.18 175	99.89 54	99.72 131	98.66 145	99.87 205	99.88 29	99.97 56	99.66 105

v8		99.68 46	99.69 43	99.65 122	99.80 87	99.40 172	99.66 53	99.76 99	99.64 104	99.93 37	99.85 56	98.66 145	99.84 256	99.88 29	99.99 16	99.71 77

v1144		99.54 79	99.53 82	99.59 153	99.79 99	99.28 197	99.10 204	99.61 175	99.20 173	99.84 76	99.73 124	98.67 143	99.84 256	99.86 31	99.98 41	99.64 123

SSC-MVS		99.52 82	99.42 99	99.83 34	99.86 55	99.65 108	99.52 86	99.81 75	99.87 42	99.81 88	99.79 93	96.78 282	99.99 8	99.83 32	99.51 291	99.86 32

v7n		99.82 21	99.80 26	99.88 17	99.96 7	99.84 24	99.82 8	99.82 66	99.84 54	99.94 34	99.91 24	99.13 86	99.96 55	99.83 32	99.99 16	99.83 40

v2v482		99.50 84	99.47 86	99.58 157	99.78 106	99.25 204	99.14 187	99.58 203	99.25 164	99.81 88	99.62 197	98.24 202	99.84 256	99.83 32	99.97 56	99.64 123

test_vis1_rt		99.45 98	99.46 90	99.41 209	99.71 144	98.63 273	98.99 236	99.96 23	99.03 199	99.95 31	99.12 333	98.75 132	99.84 256	99.82 35	99.82 179	99.77 61

tt0805		99.63 60	99.57 72	99.81 41	99.87 52	99.88 12	99.58 76	98.70 347	99.72 80	99.91 44	99.60 214	99.43 48	99.81 295	99.81 36	99.53 287	99.73 72

V42		99.56 74	99.54 78	99.63 136	99.79 99	99.46 152	99.39 111	99.59 193	99.24 166	99.86 71	99.70 146	98.55 160	99.82 280	99.79 37	99.95 84	99.60 153

mvs_tets		99.90 2	99.90 3	99.90 8	99.96 7	99.79 46	99.72 30	99.88 43	99.92 28	99.98 13	99.93 17	99.94 4	99.98 21	99.77 38	100.00 1	99.92 18

WB-MVS		99.44 100	99.32 117	99.80 46	99.81 81	99.61 124	99.47 99	99.81 75	99.82 59	99.71 133	99.72 131	96.60 286	99.98 21	99.75 39	99.23 331	99.82 46

PS-MVSNAJss		99.84 15	99.82 22	99.89 11	99.96 7	99.77 54	99.68 45	99.85 53	99.95 20	99.98 13	99.92 21	99.28 66	99.98 21	99.75 39	100.00 1	99.94 13

jajsoiax		99.89 3	99.89 5	99.89 11	99.96 7	99.78 49	99.70 35	99.86 48	99.89 36	99.98 13	99.90 29	99.94 4	99.98 21	99.75 39	100.00 1	99.90 20

ANet_high		99.88 6	99.87 10	99.91 2	99.99 1	99.91 4	99.65 59	100.00 1	99.90 30	100.00 1	99.97 11	99.61 32	99.97 34	99.75 39	100.00 1	99.84 36

CS-MVS-test		99.68 46	99.70 39	99.64 129	99.57 202	99.83 29	99.78 12	99.97 18	99.92 28	99.50 214	99.38 284	99.57 38	99.95 64	99.69 43	99.90 116	99.15 297

RRT_MVS		99.67 52	99.59 65	99.91 2	99.94 19	99.88 12	99.78 12	99.27 302	99.87 42	99.91 44	99.87 47	98.04 219	99.96 55	99.68 44	99.99 16	99.90 20

CS-MVS		99.67 52	99.70 39	99.58 157	99.53 222	99.84 24	99.79 11	99.96 23	99.90 30	99.61 175	99.41 274	99.51 45	99.95 64	99.66 45	99.89 125	98.96 335

pmmvs6		99.86 9	99.86 12	99.83 34	99.94 19	99.90 7	99.83 6	99.91 32	99.85 51	99.94 34	99.95 13	99.73 21	99.90 159	99.65 46	99.97 56	99.69 84

MIMVSNet1		99.66 54	99.62 56	99.80 46	99.94 19	99.87 15	99.69 42	99.77 94	99.78 70	99.93 37	99.89 34	97.94 227	99.92 117	99.65 46	99.98 41	99.62 139

EC-MVSNet		99.69 43	99.69 43	99.68 107	99.71 144	99.91 4	99.76 19	99.96 23	99.86 46	99.51 212	99.39 282	99.57 38	99.93 95	99.64 48	99.86 153	99.20 286

K. test v3		98.87 232	98.60 241	99.69 105	99.93 26	99.46 152	99.74 24	94.97 398	99.78 70	99.88 62	99.88 42	93.66 329	99.97 34	99.61 49	99.95 84	99.64 123

KD-MVS_self_test		99.63 60	99.59 65	99.76 65	99.84 62	99.90 7	99.37 117	99.79 85	99.83 57	99.88 62	99.85 56	98.42 181	99.90 159	99.60 50	99.73 223	99.49 212

Anonymous20240521		99.44 100	99.42 99	99.49 182	99.89 40	98.96 241	99.62 63	99.76 99	99.85 51	99.82 81	99.88 42	96.39 296	99.97 34	99.59 51	99.98 41	99.55 175

TransMVSNet (Re)		99.78 27	99.77 33	99.81 41	99.91 32	99.85 19	99.75 22	99.86 48	99.70 87	99.91 44	99.89 34	99.60 34	99.87 205	99.59 51	99.74 218	99.71 77

OurMVSNet-221017-0		99.75 32	99.71 38	99.84 31	99.96 7	99.83 29	99.83 6	99.85 53	99.80 65	99.93 37	99.93 17	98.54 162	99.93 95	99.59 51	99.98 41	99.76 67

EU-MVSNet		99.39 116	99.62 56	98.72 318	99.88 45	96.44 361	99.56 81	99.85 53	99.90 30	99.90 50	99.85 56	98.09 215	99.83 271	99.58 54	99.95 84	99.90 20

mvsmamba		99.74 35	99.70 39	99.85 27	99.93 26	99.83 29	99.76 19	99.81 75	99.96 18	99.91 44	99.81 79	98.60 153	99.94 78	99.58 54	99.98 41	99.77 61

mvs_anonymous		99.28 140	99.39 102	98.94 291	99.19 325	97.81 326	99.02 226	99.55 216	99.78 70	99.85 73	99.80 83	98.24 202	99.86 224	99.57 56	99.50 294	99.15 297

test1111		97.74 312	98.16 286	96.49 380	99.60 183	89.86 410	99.71 34	91.21 406	99.89 36	99.88 62	99.87 47	93.73 328	99.90 159	99.56 57	99.99 16	99.70 80

lessismore_v0				99.64 129	99.86 55	99.38 176		90.66 407		99.89 54	99.83 66	94.56 319	99.97 34	99.56 57	99.92 106	99.57 170

mvsany_test1		99.44 100	99.45 92	99.40 211	99.37 277	98.64 272	97.90 356	99.59 193	99.27 160	99.92 41	99.82 73	99.74 20	99.93 95	99.55 59	99.87 145	99.63 128

bld_raw_dy_0_64		99.70 40	99.65 50	99.85 27	99.95 15	99.77 54	99.66 53	99.71 125	99.95 20	99.91 44	99.77 108	98.35 190	100.00 1	99.54 60	99.99 16	99.79 54

pm-mvs1		99.79 26	99.79 27	99.78 55	99.91 32	99.83 29	99.76 19	99.87 45	99.73 76	99.89 54	99.87 47	99.63 29	99.87 205	99.54 60	99.92 106	99.63 128

LTVRE_ROB	99.19 1	99.88 6	99.87 10	99.88 17	99.91 32	99.90 7	99.96 1	99.92 29	99.90 30	99.97 19	99.87 47	99.81 14	99.95 64	99.54 60	99.99 16	99.80 47
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
DSMNet-mixed		99.48 88	99.65 50	98.95 290	99.71 144	97.27 344	99.50 91	99.82 66	99.59 118	99.41 237	99.85 56	99.62 31	100.00 1	99.53 63	99.89 125	99.59 160

test2506		94.73 370	94.59 371	95.15 386	99.59 187	85.90 412	99.75 22	74.01 412	99.89 36	99.71 133	99.86 54	79.00 403	99.90 159	99.52 64	99.99 16	99.65 113

UniMVSNet_ETH3D		99.85 11	99.83 20	99.90 8	99.89 40	99.91 4	99.89 4	99.71 125	99.93 26	99.95 31	99.89 34	99.71 22	99.96 55	99.51 65	99.97 56	99.84 36

FC-MVSNet-test		99.70 40	99.65 50	99.86 25	99.88 45	99.86 18	99.72 30	99.78 91	99.90 30	99.82 81	99.83 66	98.45 177	99.87 205	99.51 65	99.97 56	99.86 32

UA-Net		99.78 27	99.76 36	99.86 25	99.72 141	99.71 84	99.91 3	99.95 28	99.96 18	99.71 133	99.91 24	99.15 81	99.97 34	99.50 67	100.00 1	99.90 20

PMMVS2		99.48 88	99.45 92	99.57 163	99.76 118	98.99 236	98.09 333	99.90 37	98.95 207	99.78 101	99.58 222	99.57 38	99.93 95	99.48 68	99.95 84	99.79 54

VPA-MVSNet		99.66 54	99.62 56	99.79 52	99.68 164	99.75 68	99.62 63	99.69 137	99.85 51	99.80 92	99.81 79	98.81 120	99.91 141	99.47 69	99.88 134	99.70 80

ECVR-MVS		97.73 313	98.04 292	96.78 374	99.59 187	90.81 406	99.72 30	90.43 408	99.89 36	99.86 71	99.86 54	93.60 330	99.89 177	99.46 70	99.99 16	99.65 113

nrg030		99.70 40	99.66 48	99.82 38	99.76 118	99.84 24	99.61 68	99.70 131	99.93 26	99.78 101	99.68 163	99.10 87	99.78 308	99.45 71	99.96 71	99.83 40

TAMVS		99.49 86	99.45 92	99.63 136	99.48 245	99.42 166	99.45 103	99.57 205	99.66 100	99.78 101	99.83 66	97.85 234	99.86 224	99.44 72	99.96 71	99.61 149

GeoE		99.69 43	99.66 48	99.78 55	99.76 118	99.76 62	99.60 73	99.82 66	99.46 133	99.75 115	99.56 235	99.63 29	99.95 64	99.43 73	99.88 134	99.62 139

new-patchmatchnet		99.35 126	99.57 72	98.71 320	99.82 73	96.62 359	98.55 292	99.75 104	99.50 124	99.88 62	99.87 47	99.31 62	99.88 191	99.43 73	100.00 1	99.62 139

test20.03		99.55 77	99.54 78	99.58 157	99.79 99	99.37 179	99.02 226	99.89 39	99.60 116	99.82 81	99.62 197	98.81 120	99.89 177	99.43 73	99.86 153	99.47 220

MVSFormer		99.41 110	99.44 95	99.31 238	99.57 202	98.40 286	99.77 15	99.80 79	99.73 76	99.63 160	99.30 303	98.02 221	99.98 21	99.43 73	99.69 238	99.55 175

test_djsdf		99.84 15	99.81 23	99.91 2	99.94 19	99.84 24	99.77 15	99.80 79	99.73 76	99.97 19	99.92 21	99.77 19	99.98 21	99.43 73	100.00 1	99.90 20

SDMVSNet		99.77 30	99.77 33	99.76 65	99.80 87	99.65 108	99.63 61	99.86 48	99.97 16	99.89 54	99.89 34	99.52 44	99.99 8	99.42 78	99.96 71	99.65 113

Anonymous20231211		99.62 66	99.57 72	99.76 65	99.61 181	99.60 127	99.81 9	99.73 113	99.82 59	99.90 50	99.90 29	97.97 226	99.86 224	99.42 78	99.96 71	99.80 47

SixPastTwentyTwo		99.42 106	99.30 124	99.76 65	99.92 31	99.67 100	99.70 35	99.14 326	99.65 102	99.89 54	99.90 29	96.20 302	99.94 78	99.42 78	99.92 106	99.67 96

patch_mono-2		99.51 83	99.46 90	99.64 129	99.70 152	99.11 224	99.04 219	99.87 45	99.71 82	99.47 219	99.79 93	98.24 202	99.98 21	99.38 81	99.96 71	99.83 40

UGNet		99.38 118	99.34 112	99.49 182	98.90 359	98.90 248	99.70 35	99.35 285	99.86 46	98.57 344	99.81 79	98.50 172	99.93 95	99.38 81	99.98 41	99.66 105
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 39	99.67 47	99.81 41	99.89 40	99.72 82	99.59 74	99.82 66	99.39 146	99.82 81	99.84 62	99.38 54	99.91 141	99.38 81	99.93 102	99.80 47

iter_conf_final		98.75 242	98.54 251	99.40 211	99.33 296	98.75 260	99.26 149	99.59 193	99.80 65	99.76 108	99.58 222	90.17 368	99.92 117	99.37 84	99.97 56	99.54 183

FIs		99.65 59	99.58 69	99.84 31	99.84 62	99.85 19	99.66 53	99.75 104	99.86 46	99.74 123	99.79 93	98.27 200	99.85 241	99.37 84	99.93 102	99.83 40

sd_testset		99.78 27	99.78 31	99.80 46	99.80 87	99.76 62	99.80 10	99.79 85	99.97 16	99.89 54	99.89 34	99.53 43	99.99 8	99.36 86	99.96 71	99.65 113

anonymousdsp		99.80 23	99.77 33	99.90 8	99.96 7	99.88 12	99.73 27	99.85 53	99.70 87	99.92 41	99.93 17	99.45 47	99.97 34	99.36 86	100.00 1	99.85 35

casdiffmvs_mvg		99.68 46	99.68 46	99.69 105	99.81 81	99.59 129	99.29 142	99.90 37	99.71 82	99.79 97	99.73 124	99.54 41	99.84 256	99.36 86	99.96 71	99.65 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		99.75 32	99.74 37	99.79 52	99.88 45	99.66 102	99.69 42	99.92 29	99.67 96	99.77 106	99.75 117	99.61 32	99.98 21	99.35 89	99.98 41	99.72 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 68	99.64 54	99.53 175	99.79 99	98.82 253	99.58 76	99.97 18	99.95 20	99.96 23	99.76 112	98.44 178	99.99 8	99.34 90	99.96 71	99.78 57

CHOSEN 1792x2688		99.39 116	99.30 124	99.65 122	99.88 45	99.25 204	98.78 268	99.88 43	98.66 246	99.96 23	99.79 93	97.45 256	99.93 95	99.34 90	99.99 16	99.78 57

CDS-MVSNet		99.22 159	99.13 152	99.50 181	99.35 282	99.11 224	98.96 242	99.54 222	99.46 133	99.61 175	99.70 146	96.31 298	99.83 271	99.34 90	99.88 134	99.55 175
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 212	99.16 146	98.51 327	99.75 129	95.90 371	98.07 336	99.84 59	99.84 54	99.89 54	99.73 124	96.01 305	99.99 8	99.33 93	100.00 1	99.63 128

HyFIR lowres test		98.91 225	98.64 238	99.73 89	99.85 59	99.47 148	98.07 336	99.83 61	98.64 248	99.89 54	99.60 214	92.57 339	100.00 1	99.33 93	99.97 56	99.72 74

pmmvs5		99.19 169	99.11 159	99.42 202	99.76 118	98.88 250	98.55 292	99.73 113	98.82 226	99.72 128	99.62 197	96.56 287	99.82 280	99.32 95	99.95 84	99.56 172

v148		99.40 112	99.41 101	99.39 215	99.76 118	98.94 242	99.09 209	99.59 193	99.17 180	99.81 88	99.61 206	98.41 182	99.69 343	99.32 95	99.94 95	99.53 189

baseline		99.63 60	99.62 56	99.66 117	99.80 87	99.62 118	99.44 105	99.80 79	99.71 82	99.72 128	99.69 152	99.15 81	99.83 271	99.32 95	99.94 95	99.53 189

iter_conf05		98.46 274	98.23 277	99.15 265	99.04 348	97.99 314	99.10 204	99.61 175	99.79 68	99.76 108	99.58 222	87.88 378	99.92 117	99.31 98	99.97 56	99.53 189

CVMVSNet		98.61 254	98.88 218	97.80 355	99.58 192	93.60 392	99.26 149	99.64 163	99.66 100	99.72 128	99.67 167	93.26 332	99.93 95	99.30 99	99.81 188	99.87 30

PS-CasMVS		99.66 54	99.58 69	99.89 11	99.80 87	99.85 19	99.66 53	99.73 113	99.62 107	99.84 76	99.71 139	98.62 149	99.96 55	99.30 99	99.96 71	99.86 32

DTE-MVSNet		99.68 46	99.61 60	99.88 17	99.80 87	99.87 15	99.67 49	99.71 125	99.72 80	99.84 76	99.78 101	98.67 143	99.97 34	99.30 99	99.95 84	99.80 47

tmp_tt		95.75 364	95.42 358	96.76 375	89.90 411	94.42 386	98.86 251	97.87 379	78.01 402	99.30 264	99.69 152	97.70 242	95.89 406	99.29 102	98.14 383	99.95 11

PEN-MVS		99.66 54	99.59 65	99.89 11	99.83 66	99.87 15	99.66 53	99.73 113	99.70 87	99.84 76	99.73 124	98.56 159	99.96 55	99.29 102	99.94 95	99.83 40

WR-MVS_H		99.61 68	99.53 82	99.87 21	99.80 87	99.83 29	99.67 49	99.75 104	99.58 119	99.85 73	99.69 152	98.18 211	99.94 78	99.28 104	99.95 84	99.83 40

IterMVS		98.97 216	99.16 146	98.42 331	99.74 135	95.64 374	98.06 338	99.83 61	99.83 57	99.85 73	99.74 120	96.10 304	99.99 8	99.27 105	100.00 1	99.63 128
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		98.61 254	98.34 270	99.44 196	99.60 183	98.67 265	99.27 147	99.44 260	99.68 92	99.32 255	99.49 257	92.50 342	100.00 1	99.24 106	96.51 399	99.65 113

hse-mvs2		98.52 266	98.30 274	99.16 263	99.29 305	98.60 275	98.77 269	99.02 334	99.68 92	99.32 255	99.04 343	92.50 342	99.85 241	99.24 106	97.87 390	99.03 327

FMVSNet1		99.66 54	99.63 55	99.73 89	99.78 106	99.77 54	99.68 45	99.70 131	99.67 96	99.82 81	99.83 66	98.98 104	99.90 159	99.24 106	99.97 56	99.53 189

casdiffmvs		99.63 60	99.61 60	99.67 110	99.79 99	99.59 129	99.13 193	99.85 53	99.79 68	99.76 108	99.72 131	99.33 61	99.82 280	99.21 109	99.94 95	99.59 160
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVSNet		99.54 79	99.43 97	99.87 21	99.76 118	99.82 35	99.57 79	99.61 175	99.54 120	99.80 92	99.64 179	97.79 238	99.95 64	99.21 109	99.94 95	99.84 36

DELS-MVS		99.34 131	99.30 124	99.48 186	99.51 229	99.36 183	98.12 329	99.53 231	99.36 150	99.41 237	99.61 206	99.22 74	99.87 205	99.21 109	99.68 243	99.20 286
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
UniMVSNet (Re)		99.37 121	99.26 135	99.68 107	99.51 229	99.58 133	98.98 239	99.60 187	99.43 141	99.70 137	99.36 290	97.70 242	99.88 191	99.20 112	99.87 145	99.59 160

CANet		99.11 191	99.05 180	99.28 244	98.83 366	98.56 276	98.71 275	99.41 266	99.25 164	99.23 272	99.22 321	97.66 250	99.94 78	99.19 113	99.97 56	99.33 258

EI-MVSNet-UG-set		99.48 88	99.50 84	99.42 202	99.57 202	98.65 271	99.24 157	99.46 255	99.68 92	99.80 92	99.66 172	98.99 102	99.89 177	99.19 113	99.90 116	99.72 74

xiu_mvs_v1_base_debu		99.23 151	99.34 112	98.91 297	99.59 187	98.23 295	98.47 302	99.66 148	99.61 110	99.68 143	98.94 359	99.39 50	99.97 34	99.18 115	99.55 280	98.51 369

xiu_mvs_v1_base		99.23 151	99.34 112	98.91 297	99.59 187	98.23 295	98.47 302	99.66 148	99.61 110	99.68 143	98.94 359	99.39 50	99.97 34	99.18 115	99.55 280	98.51 369

xiu_mvs_v1_base_debi		99.23 151	99.34 112	98.91 297	99.59 187	98.23 295	98.47 302	99.66 148	99.61 110	99.68 143	98.94 359	99.39 50	99.97 34	99.18 115	99.55 280	98.51 369

VPNet		99.46 96	99.37 107	99.71 100	99.82 73	99.59 129	99.48 96	99.70 131	99.81 62	99.69 140	99.58 222	97.66 250	99.86 224	99.17 118	99.44 301	99.67 96

UniMVSNet_NR-MVSNet		99.37 121	99.25 137	99.72 95	99.47 251	99.56 136	98.97 240	99.61 175	99.43 141	99.67 149	99.28 307	97.85 234	99.95 64	99.17 118	99.81 188	99.65 113

DU-MVS		99.33 134	99.21 141	99.71 100	99.43 264	99.56 136	98.83 256	99.53 231	99.38 147	99.67 149	99.36 290	97.67 246	99.95 64	99.17 118	99.81 188	99.63 128

EI-MVSNet-Vis-set		99.47 95	99.49 85	99.42 202	99.57 202	98.66 268	99.24 157	99.46 255	99.67 96	99.79 97	99.65 177	98.97 106	99.89 177	99.15 121	99.89 125	99.71 77

EI-MVSNet		99.38 118	99.44 95	99.21 257	99.58 192	98.09 308	99.26 149	99.46 255	99.62 107	99.75 115	99.67 167	98.54 162	99.85 241	99.15 121	99.92 106	99.68 90

VNet		99.18 173	99.06 176	99.56 166	99.24 315	99.36 183	99.33 125	99.31 294	99.67 96	99.47 219	99.57 231	96.48 290	99.84 256	99.15 121	99.30 320	99.47 220

EG-PatchMatch MVS		99.57 71	99.56 77	99.62 145	99.77 114	99.33 189	99.26 149	99.76 99	99.32 154	99.80 92	99.78 101	99.29 64	99.87 205	99.15 121	99.91 115	99.66 105

PVSNet_Blended_VisFu		99.40 112	99.38 104	99.44 196	99.90 38	98.66 268	98.94 245	99.91 32	97.97 309	99.79 97	99.73 124	99.05 97	99.97 34	99.15 121	99.99 16	99.68 90

IterMVS-LS		99.41 110	99.47 86	99.25 253	99.81 81	98.09 308	98.85 253	99.76 99	99.62 107	99.83 80	99.64 179	98.54 162	99.97 34	99.15 121	99.99 16	99.68 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 79	99.47 86	99.76 65	99.58 192	99.64 111	99.30 135	99.63 165	99.61 110	99.71 133	99.56 235	98.76 130	99.96 55	99.14 127	99.92 106	99.68 90

MVSTER		98.47 273	98.22 279	99.24 255	99.06 345	98.35 292	99.08 212	99.46 255	99.27 160	99.75 115	99.66 172	88.61 376	99.85 241	99.14 127	99.92 106	99.52 200

Anonymous20231206		99.35 126	99.31 119	99.47 188	99.74 135	99.06 234	99.28 144	99.74 109	99.23 168	99.72 128	99.53 246	97.63 252	99.88 191	99.11 129	99.84 162	99.48 216

Syy-MVS		98.17 297	97.85 309	99.15 265	98.50 389	98.79 257	98.60 281	99.21 318	97.89 315	96.76 391	96.37 410	95.47 311	99.57 382	99.10 130	98.73 361	99.09 312

MVS_Test		99.28 140	99.31 119	99.19 260	99.35 282	98.79 257	99.36 120	99.49 248	99.17 180	99.21 277	99.67 167	98.78 127	99.66 364	99.09 131	99.66 252	99.10 308

testgi		99.29 139	99.26 135	99.37 221	99.75 129	98.81 254	98.84 254	99.89 39	98.38 276	99.75 115	99.04 343	99.36 59	99.86 224	99.08 132	99.25 327	99.45 225

1112_ss		99.05 200	98.84 223	99.67 110	99.66 170	99.29 195	98.52 298	99.82 66	97.65 327	99.43 229	99.16 327	96.42 293	99.91 141	99.07 133	99.84 162	99.80 47

CANet_DTU		98.91 225	98.85 221	99.09 275	98.79 372	98.13 303	98.18 322	99.31 294	99.48 126	98.86 317	99.51 250	96.56 287	99.95 64	99.05 134	99.95 84	99.19 289

Baseline_NR-MVSNet		99.49 86	99.37 107	99.82 38	99.91 32	99.84 24	98.83 256	99.86 48	99.68 92	99.65 155	99.88 42	97.67 246	99.87 205	99.03 135	99.86 153	99.76 67

FMVSNet2		99.35 126	99.28 131	99.55 169	99.49 240	99.35 186	99.45 103	99.57 205	99.44 136	99.70 137	99.74 120	97.21 267	99.87 205	99.03 135	99.94 95	99.44 230

Test_1112_low_res		98.95 222	98.73 232	99.63 136	99.68 164	99.15 221	98.09 333	99.80 79	97.14 353	99.46 223	99.40 278	96.11 303	99.89 177	99.01 137	99.84 162	99.84 36

VDD-MVS		99.20 166	99.11 159	99.44 196	99.43 264	98.98 237	99.50 91	98.32 369	99.80 65	99.56 193	99.69 152	96.99 277	99.85 241	98.99 138	99.73 223	99.50 207

DeepC-MVS	98.90 4	99.62 66	99.61 60	99.67 110	99.72 141	99.44 159	99.24 157	99.71 125	99.27 160	99.93 37	99.90 29	99.70 24	99.93 95	98.99 138	99.99 16	99.64 123
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 88	99.47 86	99.51 179	99.77 114	99.41 171	98.81 261	99.66 148	99.42 145	99.75 115	99.66 172	99.20 76	99.76 318	98.98 140	99.99 16	99.36 251

EPNet_dtu		97.62 318	97.79 312	97.11 373	96.67 406	92.31 397	98.51 299	98.04 373	99.24 166	95.77 400	99.47 264	93.78 327	99.66 364	98.98 140	99.62 259	99.37 248
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 131	99.32 117	99.39 215	99.67 169	98.77 259	98.57 290	99.81 75	99.61 110	99.48 217	99.41 274	98.47 173	99.86 224	98.97 142	99.90 116	99.53 189
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NR-MVSNet		99.40 112	99.31 119	99.68 107	99.43 264	99.55 139	99.73 27	99.50 244	99.46 133	99.88 62	99.36 290	97.54 253	99.87 205	98.97 142	99.87 145	99.63 128

GBi-Net		99.42 106	99.31 119	99.73 89	99.49 240	99.77 54	99.68 45	99.70 131	99.44 136	99.62 169	99.83 66	97.21 267	99.90 159	98.96 144	99.90 116	99.53 189

FMVSNet5		97.80 310	97.25 326	99.42 202	98.83 366	98.97 239	99.38 113	99.80 79	98.87 219	99.25 268	99.69 152	80.60 397	99.91 141	98.96 144	99.90 116	99.38 245

test1		99.42 106	99.31 119	99.73 89	99.49 240	99.77 54	99.68 45	99.70 131	99.44 136	99.62 169	99.83 66	97.21 267	99.90 159	98.96 144	99.90 116	99.53 189

FMVSNet3		98.80 238	98.63 240	99.32 235	99.13 333	98.72 263	99.10 204	99.48 249	99.23 168	99.62 169	99.64 179	92.57 339	99.86 224	98.96 144	99.90 116	99.39 243

UnsupCasMVSNet_eth		98.83 235	98.57 247	99.59 153	99.68 164	99.45 157	98.99 236	99.67 144	99.48 126	99.55 198	99.36 290	94.92 313	99.86 224	98.95 148	96.57 398	99.45 225

CHOSEN 280x420		98.41 279	98.41 262	98.40 332	99.34 291	95.89 372	96.94 392	99.44 260	98.80 230	99.25 268	99.52 248	93.51 331	99.98 21	98.94 149	99.98 41	99.32 261

TDRefinement		99.72 36	99.70 39	99.77 58	99.90 38	99.85 19	99.86 5	99.92 29	99.69 90	99.78 101	99.92 21	99.37 56	99.88 191	98.93 150	99.95 84	99.60 153

alignmvs		98.28 289	97.96 298	99.25 253	99.12 335	98.93 245	99.03 223	98.42 363	99.64 104	98.72 332	97.85 394	90.86 360	99.62 374	98.88 151	99.13 333	99.19 289

sss		98.90 227	98.77 231	99.27 247	99.48 245	98.44 283	98.72 273	99.32 290	97.94 313	99.37 245	99.35 295	96.31 298	99.91 141	98.85 152	99.63 258	99.47 220

xiu_mvs_v2_base		99.02 206	99.11 159	98.77 315	99.37 277	98.09 308	98.13 328	99.51 240	99.47 130	99.42 231	98.54 380	99.38 54	99.97 34	98.83 153	99.33 316	98.24 381

PS-MVSNAJ		99.00 212	99.08 170	98.76 316	99.37 277	98.10 307	98.00 344	99.51 240	99.47 130	99.41 237	98.50 382	99.28 66	99.97 34	98.83 153	99.34 315	98.20 385

D2MVS		99.22 159	99.19 143	99.29 242	99.69 156	98.74 262	98.81 261	99.41 266	98.55 257	99.68 143	99.69 152	98.13 213	99.87 205	98.82 155	99.98 41	99.24 275

PatchT		98.45 276	98.32 272	98.83 310	98.94 357	98.29 293	99.24 157	98.82 342	99.84 54	99.08 294	99.76 112	91.37 350	99.94 78	98.82 155	99.00 343	98.26 380

testf1		99.63 60	99.60 63	99.72 95	99.94 19	99.95 2	99.47 99	99.89 39	99.43 141	99.88 62	99.80 83	99.26 70	99.90 159	98.81 157	99.88 134	99.32 261

APD_test2		99.63 60	99.60 63	99.72 95	99.94 19	99.95 2	99.47 99	99.89 39	99.43 141	99.88 62	99.80 83	99.26 70	99.90 159	98.81 157	99.88 134	99.32 261

Effi-MVS+		99.06 197	98.97 205	99.34 228	99.31 299	98.98 237	98.31 314	99.91 32	98.81 228	98.79 326	98.94 359	99.14 84	99.84 256	98.79 159	98.74 359	99.20 286

canonicalmvs		99.02 206	99.00 196	99.09 275	99.10 341	98.70 264	99.61 68	99.66 148	99.63 106	98.64 338	97.65 397	99.04 98	99.54 385	98.79 159	98.92 348	99.04 326

VDDNet		98.97 216	98.82 226	99.42 202	99.71 144	98.81 254	99.62 63	98.68 348	99.81 62	99.38 244	99.80 83	94.25 321	99.85 241	98.79 159	99.32 318	99.59 160

CR-MVSNet		98.35 286	98.20 281	98.83 310	99.05 346	98.12 304	99.30 135	99.67 144	97.39 341	99.16 283	99.79 93	91.87 347	99.91 141	98.78 162	98.77 355	98.44 374

test_method		91.72 371	92.32 374	89.91 388	93.49 410	70.18 413	90.28 401	99.56 210	61.71 405	95.39 402	99.52 248	93.90 323	99.94 78	98.76 163	98.27 376	99.62 139

RPMNet		98.60 256	98.53 253	98.83 310	99.05 346	98.12 304	99.30 135	99.62 168	99.86 46	99.16 283	99.74 120	92.53 341	99.92 117	98.75 164	98.77 355	98.44 374

pmmvs4		99.13 186	99.06 176	99.36 225	99.57 202	99.10 229	98.01 342	99.25 308	98.78 233	99.58 183	99.44 271	98.24 202	99.76 318	98.74 165	99.93 102	99.22 280

tttt0517		97.62 318	97.20 327	98.90 303	99.76 118	97.40 341	99.48 96	94.36 400	99.06 197	99.70 137	99.49 257	84.55 392	99.94 78	98.73 166	99.65 254	99.36 251

EPP-MVSNet		99.17 178	99.00 196	99.66 117	99.80 87	99.43 163	99.70 35	99.24 311	99.48 126	99.56 193	99.77 108	94.89 314	99.93 95	98.72 167	99.89 125	99.63 128

Anonymous20240529		99.42 106	99.34 112	99.65 122	99.53 222	99.60 127	99.63 61	99.39 276	99.47 130	99.76 108	99.78 101	98.13 213	99.86 224	98.70 168	99.68 243	99.49 212

ACMH	98.42 6	99.59 70	99.54 78	99.72 95	99.86 55	99.62 118	99.56 81	99.79 85	98.77 235	99.80 92	99.85 56	99.64 28	99.85 241	98.70 168	99.89 125	99.70 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 134	99.28 131	99.47 188	99.57 202	99.39 174	99.78 12	99.43 263	98.87 219	99.57 186	99.82 73	98.06 218	99.87 205	98.69 170	99.73 223	99.15 297

LFMVS		98.46 274	98.19 284	99.26 250	99.24 315	98.52 279	99.62 63	96.94 389	99.87 42	99.31 259	99.58 222	91.04 355	99.81 295	98.68 171	99.42 305	99.45 225

WR-MVS		99.11 191	98.93 209	99.66 117	99.30 303	99.42 166	98.42 307	99.37 281	99.04 198	99.57 186	99.20 325	96.89 279	99.86 224	98.66 172	99.87 145	99.70 80

Anonymous202405211		98.75 242	98.46 257	99.63 136	99.34 291	99.66 102	99.47 99	97.65 381	99.28 159	99.56 193	99.50 253	93.15 333	99.84 256	98.62 173	99.58 274	99.40 241

EPNet		98.13 298	97.77 313	99.18 262	94.57 409	97.99 314	99.24 157	97.96 375	99.74 75	97.29 384	99.62 197	93.13 334	99.97 34	98.59 174	99.83 170	99.58 165
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 200	99.09 168	98.91 297	99.21 320	98.36 291	98.82 260	99.47 252	98.85 222	98.90 312	99.56 235	98.78 127	99.09 399	98.57 175	99.68 243	99.26 272

Patchmatch-RL test		98.60 256	98.36 267	99.33 231	99.77 114	99.07 232	98.27 316	99.87 45	98.91 214	99.74 123	99.72 131	90.57 364	99.79 305	98.55 176	99.85 157	99.11 306

pmmvs3		98.08 301	97.80 310	98.91 297	99.41 270	97.69 332	97.87 357	99.66 148	95.87 372	99.50 214	99.51 250	90.35 366	99.97 34	98.55 176	99.47 298	99.08 318

ETV-MVS		99.18 173	99.18 144	99.16 263	99.34 291	99.28 197	99.12 197	99.79 85	99.48 126	98.93 306	98.55 379	99.40 49	99.93 95	98.51 178	99.52 290	98.28 379

jason		99.16 180	99.11 159	99.32 235	99.75 129	98.44 283	98.26 318	99.39 276	98.70 243	99.74 123	99.30 303	98.54 162	99.97 34	98.48 179	99.82 179	99.55 175
jason: jason.
APDe-MVS		99.48 88	99.36 110	99.85 27	99.55 214	99.81 40	99.50 91	99.69 137	98.99 201	99.75 115	99.71 139	98.79 125	99.93 95	98.46 180	99.85 157	99.80 47
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CL-MVSNet_self_test		98.71 248	98.56 250	99.15 265	99.22 318	98.66 268	97.14 387	99.51 240	98.09 302	99.54 200	99.27 309	96.87 280	99.74 325	98.43 181	98.96 345	99.03 327

our_test_3		98.85 234	99.09 168	98.13 344	99.66 170	94.90 384	97.72 362	99.58 203	99.07 195	99.64 156	99.62 197	98.19 209	99.93 95	98.41 182	99.95 84	99.55 175

Gipuma		99.57 71	99.59 65	99.49 182	99.98 3	99.71 84	99.72 30	99.84 59	99.81 62	99.94 34	99.78 101	98.91 112	99.71 334	98.41 182	99.95 84	99.05 325
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 326	96.91 336	98.74 317	97.72 402	97.57 334	97.60 368	97.36 387	98.00 305	99.21 277	98.02 390	90.04 370	99.79 305	98.37 184	95.89 402	98.86 348

PM-MVS		99.36 124	99.29 129	99.58 157	99.83 66	99.66 102	98.95 243	99.86 48	98.85 222	99.81 88	99.73 124	98.40 186	99.92 117	98.36 185	99.83 170	99.17 293

baseline1		97.73 313	97.33 323	98.96 288	99.30 303	97.73 330	99.40 109	98.42 363	99.33 153	99.46 223	99.21 323	91.18 353	99.82 280	98.35 186	91.26 404	99.32 261

MVS-HIRNet		97.86 307	98.22 279	96.76 375	99.28 308	91.53 402	98.38 309	92.60 405	99.13 188	99.31 259	99.96 12	97.18 271	99.68 355	98.34 187	99.83 170	99.07 323

GA-MVS		97.99 306	97.68 316	98.93 294	99.52 227	98.04 312	97.19 386	99.05 333	98.32 289	98.81 322	98.97 355	89.89 372	99.41 395	98.33 188	99.05 339	99.34 257

Fast-Effi-MVS+		99.02 206	98.87 219	99.46 190	99.38 275	99.50 145	99.04 219	99.79 85	97.17 351	98.62 339	98.74 371	99.34 60	99.95 64	98.32 189	99.41 306	98.92 341

MDA-MVSNet_test_wron		98.95 222	98.99 201	98.85 306	99.64 174	97.16 347	98.23 320	99.33 288	98.93 211	99.56 193	99.66 172	97.39 260	99.83 271	98.29 190	99.88 134	99.55 175

N_pmnet		98.73 246	98.53 253	99.35 227	99.72 141	98.67 265	98.34 311	94.65 399	98.35 283	99.79 97	99.68 163	98.03 220	99.93 95	98.28 191	99.92 106	99.44 230

ET-MVSNet_ETH3D		96.78 338	96.07 347	98.91 297	99.26 312	97.92 322	97.70 364	96.05 394	97.96 312	92.37 405	98.43 383	87.06 381	99.90 159	98.27 192	97.56 393	98.91 342

thisisatest0530		97.45 323	96.95 333	98.94 291	99.68 164	97.73 330	99.09 209	94.19 402	98.61 253	99.56 193	99.30 303	84.30 393	99.93 95	98.27 192	99.54 285	99.16 295

YYNet1		98.95 222	98.99 201	98.84 308	99.64 174	97.14 349	98.22 321	99.32 290	98.92 213	99.59 181	99.66 172	97.40 258	99.83 271	98.27 192	99.90 116	99.55 175

ACMM	98.09 11	99.46 96	99.38 104	99.72 95	99.80 87	99.69 95	99.13 193	99.65 157	98.99 201	99.64 156	99.72 131	99.39 50	99.86 224	98.23 195	99.81 188	99.60 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 219	98.87 219	99.24 255	99.57 202	98.40 286	98.12 329	99.18 322	98.28 291	99.63 160	99.13 329	98.02 221	99.97 34	98.22 196	99.69 238	99.35 254

3Dnovator	99.15 2	99.43 103	99.36 110	99.65 122	99.39 272	99.42 166	99.70 35	99.56 210	99.23 168	99.35 247	99.80 83	99.17 79	99.95 64	98.21 197	99.84 162	99.59 160

Fast-Effi-MVS+-dtu		99.20 166	99.12 156	99.43 200	99.25 313	99.69 95	99.05 216	99.82 66	99.50 124	98.97 302	99.05 341	98.98 104	99.98 21	98.20 198	99.24 329	98.62 361

MS-PatchMatch		99.00 212	98.97 205	99.09 275	99.11 340	98.19 299	98.76 270	99.33 288	98.49 266	99.44 225	99.58 222	98.21 207	99.69 343	98.20 198	99.62 259	99.39 243

TSAR-MVS + GP.		99.12 188	99.04 186	99.38 218	99.34 291	99.16 219	98.15 325	99.29 298	98.18 298	99.63 160	99.62 197	99.18 78	99.68 355	98.20 198	99.74 218	99.30 267

DP-MVS		99.48 88	99.39 102	99.74 80	99.57 202	99.62 118	99.29 142	99.61 175	99.87 42	99.74 123	99.76 112	98.69 139	99.87 205	98.20 198	99.80 193	99.75 70

MVP-Stereo		99.16 180	99.08 170	99.43 200	99.48 245	99.07 232	99.08 212	99.55 216	98.63 249	99.31 259	99.68 163	98.19 209	99.78 308	98.18 202	99.58 274	99.45 225
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 103	99.30 124	99.80 46	99.83 66	99.81 40	99.52 86	99.70 131	98.35 283	99.51 212	99.50 253	99.31 62	99.88 191	98.18 202	99.84 162	99.69 84

MDA-MVSNet-bldmvs		99.06 197	99.05 180	99.07 279	99.80 87	97.83 325	98.89 248	99.72 122	99.29 156	99.63 160	99.70 146	96.47 291	99.89 177	98.17 204	99.82 179	99.50 207

JIA-IIPM		98.06 302	97.92 305	98.50 328	98.59 385	97.02 351	98.80 264	98.51 358	99.88 41	97.89 372	99.87 47	91.89 346	99.90 159	98.16 205	97.68 392	98.59 363

EIA-MVS		99.12 188	99.01 193	99.45 193	99.36 280	99.62 118	99.34 122	99.79 85	98.41 272	98.84 319	98.89 363	98.75 132	99.84 256	98.15 206	99.51 291	98.89 345

miper_lstm_enhance		98.65 253	98.60 241	98.82 313	99.20 323	97.33 343	97.78 360	99.66 148	99.01 200	99.59 181	99.50 253	94.62 318	99.85 241	98.12 207	99.90 116	99.26 272

Effi-MVS+-dtu		99.07 196	98.92 213	99.52 177	98.89 362	99.78 49	99.15 185	99.66 148	99.34 151	98.92 309	99.24 319	97.69 244	99.98 21	98.11 208	99.28 323	98.81 352

tpm		97.15 330	96.95 333	97.75 357	98.91 358	94.24 387	99.32 127	97.96 375	97.71 325	98.29 354	99.32 299	86.72 387	99.92 117	98.10 209	96.24 401	99.09 312

DeepPCF-MVS	98.42 6	99.18 173	99.02 190	99.67 110	99.22 318	99.75 68	97.25 384	99.47 252	98.72 240	99.66 153	99.70 146	99.29 64	99.63 373	98.07 210	99.81 188	99.62 139

ppachtmachnet_test		98.89 230	99.12 156	98.20 342	99.66 170	95.24 380	97.63 366	99.68 140	99.08 193	99.78 101	99.62 197	98.65 147	99.88 191	98.02 211	99.96 71	99.48 216

tpmrst		97.73 313	98.07 291	96.73 377	98.71 381	92.00 398	99.10 204	98.86 339	98.52 262	98.92 309	99.54 244	91.90 345	99.82 280	98.02 211	99.03 341	98.37 376

CSCG		99.37 121	99.29 129	99.60 151	99.71 144	99.46 152	99.43 107	99.85 53	98.79 231	99.41 237	99.60 214	98.92 110	99.92 117	98.02 211	99.92 106	99.43 236

eth_miper_zixun_eth		98.68 251	98.71 234	98.60 323	99.10 341	96.84 356	97.52 374	99.54 222	98.94 208	99.58 183	99.48 260	96.25 301	99.76 318	98.01 214	99.93 102	99.21 282

Patchmtry		98.78 239	98.54 251	99.49 182	98.89 362	99.19 217	99.32 127	99.67 144	99.65 102	99.72 128	99.79 93	91.87 347	99.95 64	98.00 215	99.97 56	99.33 258

PVSNet_BlendedMVS		99.03 204	99.01 193	99.09 275	99.54 216	97.99 314	98.58 286	99.82 66	97.62 328	99.34 250	99.71 139	98.52 169	99.77 316	97.98 216	99.97 56	99.52 200

PVSNet_Blended		98.70 249	98.59 243	99.02 283	99.54 216	97.99 314	97.58 369	99.82 66	95.70 376	99.34 250	98.98 353	98.52 169	99.77 316	97.98 216	99.83 170	99.30 267

cl____		98.54 264	98.41 262	98.92 295	99.03 349	97.80 328	97.46 376	99.59 193	98.90 215	99.60 178	99.46 267	93.85 325	99.78 308	97.97 218	99.89 125	99.17 293

DIV-MVS_self_test		98.54 264	98.42 261	98.92 295	99.03 349	97.80 328	97.46 376	99.59 193	98.90 215	99.60 178	99.46 267	93.87 324	99.78 308	97.97 218	99.89 125	99.18 291

AUN-MVS		97.82 309	97.38 322	99.14 269	99.27 310	98.53 277	98.72 273	99.02 334	98.10 300	97.18 387	99.03 347	89.26 374	99.85 241	97.94 220	97.91 388	99.03 327

FA-MVS(test-final)		98.52 266	98.32 272	99.10 274	99.48 245	98.67 265	99.77 15	98.60 355	97.35 343	99.63 160	99.80 83	93.07 335	99.84 256	97.92 221	99.30 320	98.78 355

ambc				99.20 259	99.35 282	98.53 277	99.17 177	99.46 255		99.67 149	99.80 83	98.46 176	99.70 337	97.92 221	99.70 234	99.38 245

USDC		98.96 219	98.93 209	99.05 281	99.54 216	97.99 314	97.07 390	99.80 79	98.21 295	99.75 115	99.77 108	98.43 179	99.64 372	97.90 223	99.88 134	99.51 202

OPM-MVS		99.26 146	99.13 152	99.63 136	99.70 152	99.61 124	98.58 286	99.48 249	98.50 264	99.52 207	99.63 190	99.14 84	99.76 318	97.89 224	99.77 207	99.51 202
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 136	99.17 145	99.77 58	99.69 156	99.80 44	99.14 187	99.31 294	99.16 182	99.62 169	99.61 206	98.35 190	99.91 141	97.88 225	99.72 229	99.61 149
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 34	99.70 152	99.79 46	99.14 187	99.61 175					99.92 117	97.88 225	99.72 229	99.77 61

c3_l		98.72 247	98.71 234	98.72 318	99.12 335	97.22 346	97.68 365	99.56 210	98.90 215	99.54 200	99.48 260	96.37 297	99.73 328	97.88 225	99.88 134	99.21 282

3Dnovator+	98.92 3	99.35 126	99.24 139	99.67 110	99.35 282	99.47 148	99.62 63	99.50 244	99.44 136	99.12 290	99.78 101	98.77 129	99.94 78	97.87 228	99.72 229	99.62 139

miper_ehance_all_eth		98.59 259	98.59 243	98.59 324	98.98 355	97.07 350	97.49 375	99.52 236	98.50 264	99.52 207	99.37 286	96.41 295	99.71 334	97.86 229	99.62 259	99.00 333

WTY-MVS		98.59 259	98.37 266	99.26 250	99.43 264	98.40 286	98.74 271	99.13 328	98.10 300	99.21 277	99.24 319	94.82 315	99.90 159	97.86 229	98.77 355	99.49 212

APD_test1		99.36 124	99.28 131	99.61 148	99.89 40	99.89 10	99.32 127	99.74 109	99.18 175	99.69 140	99.75 117	98.41 182	99.84 256	97.85 231	99.70 234	99.10 308

SED-MVS		99.40 112	99.28 131	99.77 58	99.69 156	99.82 35	99.20 167	99.54 222	99.13 188	99.82 81	99.63 190	98.91 112	99.92 117	97.85 231	99.70 234	99.58 165

test_241102_TWO								99.54 222	99.13 188	99.76 108	99.63 190	98.32 196	99.92 117	97.85 231	99.69 238	99.75 70

MVS_111021_HR		99.12 188	99.02 190	99.40 211	99.50 235	99.11 224	97.92 353	99.71 125	98.76 238	99.08 294	99.47 264	99.17 79	99.54 385	97.85 231	99.76 209	99.54 183

MTAPA		99.35 126	99.20 142	99.80 46	99.81 81	99.81 40	99.33 125	99.53 231	99.27 160	99.42 231	99.63 190	98.21 207	99.95 64	97.83 235	99.79 198	99.65 113

MSC_two_6792asdad				99.74 80	99.03 349	99.53 142		99.23 312					99.92 117	97.77 236	99.69 238	99.78 57

No_MVS				99.74 80	99.03 349	99.53 142		99.23 312					99.92 117	97.77 236	99.69 238	99.78 57

TESTMET0.1,1		96.24 351	95.84 352	97.41 365	98.24 396	93.84 390	97.38 378	95.84 395	98.43 269	97.81 377	98.56 378	79.77 399	99.89 177	97.77 236	98.77 355	98.52 368

ACMH+	98.40 8	99.50 84	99.43 97	99.71 100	99.86 55	99.76 62	99.32 127	99.77 94	99.53 122	99.77 106	99.76 112	99.26 70	99.78 308	97.77 236	99.88 134	99.60 153

IU-MVS					99.69 156	99.77 54		99.22 315	97.50 335	99.69 140				97.75 240	99.70 234	99.77 61

114514_t		98.49 271	98.11 289	99.64 129	99.73 138	99.58 133	99.24 157	99.76 99	89.94 398	99.42 231	99.56 235	97.76 241	99.86 224	97.74 241	99.82 179	99.47 220

DVP-MVS++		99.38 118	99.25 137	99.77 58	99.03 349	99.77 54	99.74 24	99.61 175	99.18 175	99.76 108	99.61 206	99.00 100	99.92 117	97.72 242	99.60 269	99.62 139

test_0728_THIRD									99.18 175	99.62 169	99.61 206	98.58 156	99.91 141	97.72 242	99.80 193	99.77 61

EGC-MVSNET		89.05 372	85.52 375	99.64 129	99.89 40	99.78 49	99.56 81	99.52 236	24.19 406	49.96 407	99.83 66	99.15 81	99.92 117	97.71 244	99.85 157	99.21 282

miper_enhance_ethall		98.03 303	97.94 303	98.32 337	98.27 395	96.43 362	96.95 391	99.41 266	96.37 367	99.43 229	98.96 357	94.74 316	99.69 343	97.71 244	99.62 259	98.83 351

TSAR-MVS + MP.		99.34 131	99.24 139	99.63 136	99.82 73	99.37 179	99.26 149	99.35 285	98.77 235	99.57 186	99.70 146	99.27 69	99.88 191	97.71 244	99.75 211	99.65 113
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 321	97.28 324	98.40 332	98.37 393	96.75 357	97.24 385	99.37 281	97.31 345	99.41 237	99.22 321	87.30 379	99.37 396	97.70 247	99.62 259	99.08 318

MP-MVS-pluss		99.14 184	98.92 213	99.80 46	99.83 66	99.83 29	98.61 279	99.63 165	96.84 360	99.44 225	99.58 222	98.81 120	99.91 141	97.70 247	99.82 179	99.67 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 140	99.11 159	99.79 52	99.75 129	99.81 40	98.95 243	99.53 231	98.27 292	99.53 205	99.73 124	98.75 132	99.87 205	97.70 247	99.83 170	99.68 90

UnsupCasMVSNet_bld		98.55 263	98.27 276	99.40 211	99.56 213	99.37 179	97.97 349	99.68 140	97.49 336	99.08 294	99.35 295	95.41 312	99.82 280	97.70 247	98.19 380	99.01 332

MVS_111021_LR		99.13 186	99.03 188	99.42 202	99.58 192	99.32 191	97.91 355	99.73 113	98.68 244	99.31 259	99.48 260	99.09 89	99.66 364	97.70 247	99.77 207	99.29 270

IS-MVSNet		99.03 204	98.85 221	99.55 169	99.80 87	99.25 204	99.73 27	99.15 325	99.37 148	99.61 175	99.71 139	94.73 317	99.81 295	97.70 247	99.88 134	99.58 165

test-LLR		97.15 330	96.95 333	97.74 358	98.18 398	95.02 382	97.38 378	96.10 391	98.00 305	97.81 377	98.58 375	90.04 370	99.91 141	97.69 253	98.78 353	98.31 377

test-mter		96.23 352	95.73 354	97.74 358	98.18 398	95.02 382	97.38 378	96.10 391	97.90 314	97.81 377	98.58 375	79.12 402	99.91 141	97.69 253	98.78 353	98.31 377

XVS		99.27 144	99.11 159	99.75 75	99.71 144	99.71 84	99.37 117	99.61 175	99.29 156	98.76 329	99.47 264	98.47 173	99.88 191	97.62 255	99.73 223	99.67 96

X-MVStestdata		96.09 355	94.87 367	99.75 75	99.71 144	99.71 84	99.37 117	99.61 175	99.29 156	98.76 329	61.30 413	98.47 173	99.88 191	97.62 255	99.73 223	99.67 96

SMA-MVS		99.19 169	99.00 196	99.73 89	99.46 255	99.73 77	99.13 193	99.52 236	97.40 340	99.57 186	99.64 179	98.93 109	99.83 271	97.61 257	99.79 198	99.63 128
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		96.71 341	96.79 340	96.46 381	98.90 359	90.71 407	99.41 108	98.68 348	94.69 389	98.14 364	99.34 298	86.32 389	99.80 302	97.60 258	98.07 386	98.88 346

PVSNet	97.47 15	98.42 278	98.44 259	98.35 334	99.46 255	96.26 365	96.70 395	99.34 287	97.68 326	99.00 301	99.13 329	97.40 258	99.72 330	97.59 259	99.68 243	99.08 318

new_pmnet		98.88 231	98.89 217	98.84 308	99.70 152	97.62 333	98.15 325	99.50 244	97.98 308	99.62 169	99.54 244	98.15 212	99.94 78	97.55 260	99.84 162	98.95 337

IB-MVS	95.41 20	95.30 369	94.46 373	97.84 354	98.76 377	95.33 378	97.33 381	96.07 393	96.02 371	95.37 403	97.41 399	76.17 404	99.96 55	97.54 261	95.44 403	98.22 382
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		99.24 150	99.11 159	99.61 148	98.38 392	99.79 46	99.57 79	99.68 140	99.61 110	99.15 285	99.71 139	98.70 138	99.91 141	97.54 261	99.68 243	99.13 305

ZNCC-MVS		99.22 159	99.04 186	99.77 58	99.76 118	99.73 77	99.28 144	99.56 210	98.19 297	99.14 287	99.29 306	98.84 119	99.92 117	97.53 263	99.80 193	99.64 123

CP-MVS		99.23 151	99.05 180	99.75 75	99.66 170	99.66 102	99.38 113	99.62 168	98.38 276	99.06 298	99.27 309	98.79 125	99.94 78	97.51 264	99.82 179	99.66 105

SD-MVS		99.01 210	99.30 124	98.15 343	99.50 235	99.40 172	98.94 245	99.61 175	99.22 172	99.75 115	99.82 73	99.54 41	95.51 407	97.48 265	99.87 145	99.54 183
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		98.49 271	98.29 275	99.11 272	98.96 356	98.42 285	97.54 370	99.32 290	97.53 333	98.47 349	98.15 389	97.88 231	99.82 280	97.46 266	99.24 329	99.09 312

DeepC-MVS_fast	98.47 5	99.23 151	99.12 156	99.56 166	99.28 308	99.22 211	98.99 236	99.40 273	99.08 193	99.58 183	99.64 179	98.90 115	99.83 271	97.44 267	99.75 211	99.63 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 147	99.08 170	99.76 65	99.73 138	99.70 91	99.31 132	99.59 193	98.36 278	99.36 246	99.37 286	98.80 124	99.91 141	97.43 268	99.75 211	99.68 90

ACMMPR		99.23 151	99.06 176	99.76 65	99.74 135	99.69 95	99.31 132	99.59 193	98.36 278	99.35 247	99.38 284	98.61 151	99.93 95	97.43 268	99.75 211	99.67 96

Vis-MVSNet (Re-imp)		98.77 240	98.58 246	99.34 228	99.78 106	98.88 250	99.61 68	99.56 210	99.11 192	99.24 271	99.56 235	93.00 337	99.78 308	97.43 268	99.89 125	99.35 254

MIMVSNet		98.43 277	98.20 281	99.11 272	99.53 222	98.38 290	99.58 76	98.61 353	98.96 205	99.33 252	99.76 112	90.92 357	99.81 295	97.38 271	99.76 209	99.15 297

WB-MVSnew		98.34 288	98.14 287	98.96 288	98.14 401	97.90 323	98.27 316	97.26 388	98.63 249	98.80 324	98.00 392	97.77 239	99.90 159	97.37 272	98.98 344	99.09 312

XVG-OURS-SEG-HR		99.16 180	98.99 201	99.66 117	99.84 62	99.64 111	98.25 319	99.73 113	98.39 275	99.63 160	99.43 272	99.70 24	99.90 159	97.34 273	98.64 365	99.44 230

COLMAP_ROB	98.06 12	99.45 98	99.37 107	99.70 104	99.83 66	99.70 91	99.38 113	99.78 91	99.53 122	99.67 149	99.78 101	99.19 77	99.86 224	97.32 274	99.87 145	99.55 175
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 206	98.81 227	99.65 122	99.58 192	99.49 146	98.58 286	99.07 330	98.40 274	99.04 299	99.25 314	98.51 171	99.80 302	97.31 275	99.51 291	99.65 113

region2R		99.23 151	99.05 180	99.77 58	99.76 118	99.70 91	99.31 132	99.59 193	98.41 272	99.32 255	99.36 290	98.73 136	99.93 95	97.29 276	99.74 218	99.67 96

APD-MVS_3200maxsize		99.31 137	99.16 146	99.74 80	99.53 222	99.75 68	99.27 147	99.61 175	99.19 174	99.57 186	99.64 179	98.76 130	99.90 159	97.29 276	99.62 259	99.56 172

TAPA-MVS	97.92 13	98.03 303	97.55 319	99.46 190	99.47 251	99.44 159	98.50 300	99.62 168	86.79 399	99.07 297	99.26 312	98.26 201	99.62 374	97.28 278	99.73 223	99.31 265
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 144	99.11 159	99.73 89	99.54 216	99.74 74	99.26 149	99.62 168	99.16 182	99.52 207	99.64 179	98.41 182	99.91 141	97.27 279	99.61 266	99.54 183

RE-MVS-def			99.13 152		99.54 216	99.74 74	99.26 149	99.62 168	99.16 182	99.52 207	99.64 179	98.57 157		97.27 279	99.61 266	99.54 183

testing11		96.05 357	95.41 359	97.97 348	98.78 374	95.27 379	98.59 284	98.23 371	98.86 221	96.56 394	96.91 405	75.20 405	99.69 343	97.26 281	98.29 375	98.93 339

test_yl		98.25 291	97.95 299	99.13 270	99.17 328	98.47 280	99.00 231	98.67 350	98.97 203	99.22 275	99.02 348	91.31 351	99.69 343	97.26 281	98.93 346	99.24 275

DCV-MVSNet		98.25 291	97.95 299	99.13 270	99.17 328	98.47 280	99.00 231	98.67 350	98.97 203	99.22 275	99.02 348	91.31 351	99.69 343	97.26 281	98.93 346	99.24 275

PHI-MVS		99.11 191	98.95 208	99.59 153	99.13 333	99.59 129	99.17 177	99.65 157	97.88 317	99.25 268	99.46 267	98.97 106	99.80 302	97.26 281	99.82 179	99.37 248

tfpnnormal		99.43 103	99.38 104	99.60 151	99.87 52	99.75 68	99.59 74	99.78 91	99.71 82	99.90 50	99.69 152	98.85 118	99.90 159	97.25 285	99.78 203	99.15 297

PatchmatchNet		97.65 317	97.80 310	97.18 371	98.82 369	92.49 396	99.17 177	98.39 365	98.12 299	98.79 326	99.58 222	90.71 362	99.89 177	97.23 286	99.41 306	99.16 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 215	98.80 229	99.56 166	99.25 313	99.43 163	98.54 295	99.27 302	98.58 255	98.80 324	99.43 272	98.53 166	99.70 337	97.22 287	99.59 273	99.54 183

testing3		96.48 345	95.63 356	99.01 284	99.23 317	97.81 326	98.90 247	99.10 329	98.72 240	97.84 376	97.92 393	72.44 409	99.85 241	97.21 288	99.33 316	99.35 254

HPM-MVS		99.25 147	99.07 174	99.78 55	99.81 81	99.75 68	99.61 68	99.67 144	97.72 324	99.35 247	99.25 314	99.23 73	99.92 117	97.21 288	99.82 179	99.67 96
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 169	99.00 196	99.76 65	99.76 118	99.68 98	99.38 113	99.54 222	98.34 287	99.01 300	99.50 253	98.53 166	99.93 95	97.18 290	99.78 203	99.66 105

ACMMP		99.25 147	99.08 170	99.74 80	99.79 99	99.68 98	99.50 91	99.65 157	98.07 303	99.52 207	99.69 152	98.57 157	99.92 117	97.18 290	99.79 198	99.63 128
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
thisisatest0515		96.98 334	96.42 341	98.66 321	99.42 269	97.47 337	97.27 383	94.30 401	97.24 347	99.15 285	98.86 365	85.01 390	99.87 205	97.10 292	99.39 308	98.63 360

XVG-ACMP-BASELINE		99.23 151	99.10 167	99.63 136	99.82 73	99.58 133	98.83 256	99.72 122	98.36 278	99.60 178	99.71 139	98.92 110	99.91 141	97.08 293	99.84 162	99.40 241

MSDG		99.08 195	98.98 204	99.37 221	99.60 183	99.13 222	97.54 370	99.74 109	98.84 225	99.53 205	99.55 242	99.10 87	99.79 305	97.07 294	99.86 153	99.18 291

SteuartSystems-ACMMP		99.30 138	99.14 150	99.76 65	99.87 52	99.66 102	99.18 172	99.60 187	98.55 257	99.57 186	99.67 167	99.03 99	99.94 78	97.01 295	99.80 193	99.69 84
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 353	95.78 353	97.49 361	98.53 387	93.83 391	98.04 339	93.94 403	98.96 205	98.46 350	98.17 388	79.86 398	99.87 205	96.99 296	99.06 337	98.78 355

EPMVS		96.53 344	96.32 342	97.17 372	98.18 398	92.97 395	99.39 111	89.95 409	98.21 295	98.61 340	99.59 219	86.69 388	99.72 330	96.99 296	99.23 331	98.81 352

MSP-MVS		99.04 203	98.79 230	99.81 41	99.78 106	99.73 77	99.35 121	99.57 205	98.54 260	99.54 200	98.99 350	96.81 281	99.93 95	96.97 298	99.53 287	99.77 61
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++		98.96 219	98.70 236	99.74 80	99.52 227	99.71 84	98.86 251	99.19 321	98.47 268	98.59 342	99.06 340	98.08 217	99.91 141	96.94 299	99.60 269	99.60 153

SR-MVS		99.19 169	99.00 196	99.74 80	99.51 229	99.72 82	99.18 172	99.60 187	98.85 222	99.47 219	99.58 222	98.38 187	99.92 117	96.92 300	99.54 285	99.57 170

PGM-MVS		99.20 166	99.01 193	99.77 58	99.75 129	99.71 84	99.16 183	99.72 122	97.99 307	99.42 231	99.60 214	98.81 120	99.93 95	96.91 301	99.74 218	99.66 105

HY-MVS	98.23 9	98.21 296	97.95 299	98.99 285	99.03 349	98.24 294	99.61 68	98.72 346	96.81 361	98.73 331	99.51 250	94.06 322	99.86 224	96.91 301	98.20 378	98.86 348

MDTV_nov1_ep13			97.73 314		98.70 382	90.83 405	99.15 185	98.02 374	98.51 263	98.82 321	99.61 206	90.98 356	99.66 364	96.89 303	98.92 348

GST-MVS		99.16 180	98.96 207	99.75 75	99.73 138	99.73 77	99.20 167	99.55 216	98.22 294	99.32 255	99.35 295	98.65 147	99.91 141	96.86 304	99.74 218	99.62 139

test_post1							99.14 187				51.63 415	89.54 373	99.82 280	96.86 304

SCA		98.11 299	98.36 267	97.36 366	99.20 323	92.99 394	98.17 324	98.49 360	98.24 293	99.10 293	99.57 231	96.01 305	99.94 78	96.86 304	99.62 259	99.14 302

XVG-OURS		99.21 164	99.06 176	99.65 122	99.82 73	99.62 118	97.87 357	99.74 109	98.36 278	99.66 153	99.68 163	99.71 22	99.90 159	96.84 307	99.88 134	99.43 236

LCM-MVSNet-Re		99.28 140	99.15 149	99.67 110	99.33 296	99.76 62	99.34 122	99.97 18	98.93 211	99.91 44	99.79 93	98.68 140	99.93 95	96.80 308	99.56 276	99.30 267

RPSCF		99.18 173	99.02 190	99.64 129	99.83 66	99.85 19	99.44 105	99.82 66	98.33 288	99.50 214	99.78 101	97.90 229	99.65 370	96.78 309	99.83 170	99.44 230

旧先验2							97.94 351		95.33 380	98.94 305			99.88 191	96.75 310

MDTV_nov1_ep13_2view						91.44 403	99.14 187		97.37 342	99.21 277		91.78 349		96.75 310		99.03 327

CLD-MVS		98.76 241	98.57 247	99.33 231	99.57 202	98.97 239	97.53 372	99.55 216	96.41 365	99.27 266	99.13 329	99.07 94	99.78 308	96.73 312	99.89 125	99.23 278
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 300	97.98 297	98.48 329	99.27 310	96.48 360	99.40 109	99.07 330	98.81 228	99.23 272	99.57 231	90.11 369	99.87 205	96.69 313	99.64 256	99.09 312

baseline2		96.83 337	96.28 343	98.46 330	99.09 343	96.91 354	98.83 256	93.87 404	97.23 348	96.23 399	98.36 384	88.12 377	99.90 159	96.68 314	98.14 383	98.57 366

cascas		96.99 333	96.82 339	97.48 362	97.57 405	95.64 374	96.43 397	99.56 210	91.75 394	97.13 389	97.61 398	95.58 310	98.63 403	96.68 314	99.11 335	98.18 386

PC_three_1452									97.56 329	99.68 143	99.41 274	99.09 89	97.09 405	96.66 316	99.60 269	99.62 139

LPG-MVS_test		99.22 159	99.05 180	99.74 80	99.82 73	99.63 116	99.16 183	99.73 113	97.56 329	99.64 156	99.69 152	99.37 56	99.89 177	96.66 316	99.87 145	99.69 84

LGP-MVS_train				99.74 80	99.82 73	99.63 116		99.73 113	97.56 329	99.64 156	99.69 152	99.37 56	99.89 177	96.66 316	99.87 145	99.69 84

ETVMVS		96.14 354	95.22 364	98.89 304	98.80 370	98.01 313	98.66 277	98.35 368	98.71 242	97.18 387	96.31 412	74.23 408	99.75 322	96.64 319	98.13 385	98.90 343

TinyColmap		98.97 216	98.93 209	99.07 279	99.46 255	98.19 299	97.75 361	99.75 104	98.79 231	99.54 200	99.70 146	98.97 106	99.62 374	96.63 320	99.83 170	99.41 240

LF4IMVS		99.01 210	98.92 213	99.27 247	99.71 144	99.28 197	98.59 284	99.77 94	98.32 289	99.39 243	99.41 274	98.62 149	99.84 256	96.62 321	99.84 162	98.69 359

NCCC		98.82 236	98.57 247	99.58 157	99.21 320	99.31 192	98.61 279	99.25 308	98.65 247	98.43 351	99.26 312	97.86 232	99.81 295	96.55 322	99.27 326	99.61 149

OPU-MVS				99.29 242	99.12 335	99.44 159	99.20 167				99.40 278	99.00 100	98.84 402	96.54 323	99.60 269	99.58 165

F-COLMAP		98.74 244	98.45 258	99.62 145	99.57 202	99.47 148	98.84 254	99.65 157	96.31 368	98.93 306	99.19 326	97.68 245	99.87 205	96.52 324	99.37 311	99.53 189

testing99		95.86 362	95.19 365	97.87 352	98.76 377	95.03 381	98.62 278	98.44 362	98.68 244	96.67 393	96.66 408	74.31 407	99.69 343	96.51 325	98.03 387	98.90 343

ADS-MVSNet2		97.78 311	97.66 318	98.12 345	99.14 331	95.36 377	99.22 164	98.75 345	96.97 356	98.25 356	99.64 179	90.90 358	99.94 78	96.51 325	99.56 276	99.08 318

ADS-MVSNet		97.72 316	97.67 317	97.86 353	99.14 331	94.65 385	99.22 164	98.86 339	96.97 356	98.25 356	99.64 179	90.90 358	99.84 256	96.51 325	99.56 276	99.08 318

PatchMatch-RL		98.68 251	98.47 256	99.30 241	99.44 260	99.28 197	98.14 327	99.54 222	97.12 354	99.11 291	99.25 314	97.80 237	99.70 337	96.51 325	99.30 320	98.93 339

CMPMVS	77.52 23	98.50 269	98.19 284	99.41 209	98.33 394	99.56 136	99.01 228	99.59 193	95.44 378	99.57 186	99.80 83	95.64 308	99.46 394	96.47 329	99.92 106	99.21 282
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 358	95.32 362	98.02 346	98.76 377	95.39 376	98.38 309	98.65 352	98.82 226	96.84 390	96.71 407	75.06 406	99.71 334	96.46 330	98.23 377	98.98 334

SF-MVS		99.10 194	98.93 209	99.62 145	99.58 192	99.51 144	99.13 193	99.65 157	97.97 309	99.42 231	99.61 206	98.86 117	99.87 205	96.45 331	99.68 243	99.49 212

FE-MVS		97.85 308	97.42 321	99.15 265	99.44 260	98.75 260	99.77 15	98.20 372	95.85 373	99.33 252	99.80 83	88.86 375	99.88 191	96.40 332	99.12 334	98.81 352

DPE-MVS		99.14 184	98.92 213	99.82 38	99.57 202	99.77 54	98.74 271	99.60 187	98.55 257	99.76 108	99.69 152	98.23 206	99.92 117	96.39 333	99.75 211	99.76 67
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 403	89.02 411			93.47 391		98.30 385		99.84 256	96.38 334

AllTest		99.21 164	99.07 174	99.63 136	99.78 106	99.64 111	99.12 197	99.83 61	98.63 249	99.63 160	99.72 131	98.68 140	99.75 322	96.38 334	99.83 170	99.51 202

TestCases				99.63 136	99.78 106	99.64 111		99.83 61	98.63 249	99.63 160	99.72 131	98.68 140	99.75 322	96.38 334	99.83 170	99.51 202

testdata				99.42 202	99.51 229	98.93 245		99.30 297	96.20 369	98.87 316	99.40 278	98.33 195	99.89 177	96.29 337	99.28 323	99.44 230

dp		96.86 336	97.07 329	96.24 383	98.68 383	90.30 409	99.19 171	98.38 366	97.35 343	98.23 358	99.59 219	87.23 380	99.82 280	96.27 338	98.73 361	98.59 363

tpmvs		97.39 325	97.69 315	96.52 379	98.41 391	91.76 399	99.30 135	98.94 338	97.74 323	97.85 375	99.55 242	92.40 344	99.73 328	96.25 339	98.73 361	98.06 388

KD-MVS_2432*1600		95.89 359	95.41 359	97.31 369	94.96 407	93.89 388	97.09 388	99.22 315	97.23 348	98.88 313	99.04 343	79.23 400	99.54 385	96.24 340	96.81 396	98.50 372

miper_refine_blended		95.89 359	95.41 359	97.31 369	94.96 407	93.89 388	97.09 388	99.22 315	97.23 348	98.88 313	99.04 343	79.23 400	99.54 385	96.24 340	96.81 396	98.50 372

ACMP	97.51 14	99.05 200	98.84 223	99.67 110	99.78 106	99.55 139	98.88 249	99.66 148	97.11 355	99.47 219	99.60 214	99.07 94	99.89 177	96.18 342	99.85 157	99.58 165
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 227	98.72 233	99.44 196	99.39 272	99.42 166	98.58 286	99.64 163	97.31 345	99.44 225	99.62 197	98.59 154	99.69 343	96.17 343	99.79 198	99.22 280

DP-MVS Recon		98.50 269	98.23 277	99.31 238	99.49 240	99.46 152	98.56 291	99.63 165	94.86 387	98.85 318	99.37 286	97.81 236	99.59 380	96.08 344	99.44 301	98.88 346

tpm cat1		96.78 338	96.98 332	96.16 384	98.85 365	90.59 408	99.08 212	99.32 290	92.37 393	97.73 381	99.46 267	91.15 354	99.69 343	96.07 345	98.80 352	98.21 383

tpm2		96.35 348	96.22 344	96.73 377	98.88 364	91.75 400	99.21 166	98.51 358	93.27 392	97.89 372	99.21 323	84.83 391	99.70 337	96.04 346	98.18 381	98.75 358

dmvs_re		98.69 250	98.48 255	99.31 238	99.55 214	99.42 166	99.54 84	98.38 366	99.32 154	98.72 332	98.71 372	96.76 283	99.21 397	96.01 347	99.35 314	99.31 265

test_0402		99.22 159	99.14 150	99.45 193	99.79 99	99.43 163	99.28 144	99.68 140	99.54 120	99.40 242	99.56 235	99.07 94	99.82 280	96.01 347	99.96 71	99.11 306

ITE_SJBPF				99.38 218	99.63 176	99.44 159		99.73 113	98.56 256	99.33 252	99.53 246	98.88 116	99.68 355	96.01 347	99.65 254	99.02 331

test_prior2							97.95 350		97.87 318	98.05 366	99.05 341	97.90 229		95.99 350	99.49 296

testdata2													99.89 177	95.99 350

原ACMM1				99.37 221	99.47 251	98.87 252		99.27 302	96.74 363	98.26 355	99.32 299	97.93 228	99.82 280	95.96 352	99.38 309	99.43 236

新几何1				99.52 177	99.50 235	99.22 211		99.26 305	95.66 377	98.60 341	99.28 307	97.67 246	99.89 177	95.95 353	99.32 318	99.45 225

MP-MVS		99.06 197	98.83 225	99.76 65	99.76 118	99.71 84	99.32 127	99.50 244	98.35 283	98.97 302	99.48 260	98.37 188	99.92 117	95.95 353	99.75 211	99.63 128
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 368	94.59 371	98.61 322	98.66 384	97.45 339	98.54 295	97.90 378	98.53 261	96.54 395	96.47 409	70.62 411	99.81 295	95.91 355	98.15 382	98.56 367

wuyk23d		97.58 320	99.13 152	92.93 387	99.69 156	99.49 146	99.52 86	99.77 94	97.97 309	99.96 23	99.79 93	99.84 12	99.94 78	95.85 356	99.82 179	79.36 403

HQP_MVS		98.90 227	98.68 237	99.55 169	99.58 192	99.24 208	98.80 264	99.54 222	98.94 208	99.14 287	99.25 314	97.24 265	99.82 280	95.84 357	99.78 203	99.60 153

plane_prior5								99.54 222					99.82 280	95.84 357	99.78 203	99.60 153

无先验							98.01 342	99.23 312	95.83 374				99.85 241	95.79 359		99.44 230

CPTT-MVS		98.74 244	98.44 259	99.64 129	99.61 181	99.38 176	99.18 172	99.55 216	96.49 364	99.27 266	99.37 286	97.11 273	99.92 117	95.74 360	99.67 249	99.62 139

PLC	97.35 16	98.36 283	97.99 295	99.48 186	99.32 298	99.24 208	98.50 300	99.51 240	95.19 383	98.58 343	98.96 357	96.95 278	99.83 271	95.63 361	99.25 327	99.37 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 261	98.34 270	99.28 244	99.18 327	99.10 229	98.34 311	99.41 266	98.48 267	98.52 346	98.98 353	97.05 275	99.78 308	95.59 362	99.50 294	98.96 335

1314		98.00 305	97.90 307	98.27 341	98.90 359	97.45 339	99.30 135	99.06 332	94.98 384	97.21 386	99.12 333	98.43 179	99.67 360	95.58 363	98.56 368	97.71 392

PVSNet_0	95.53 19	95.85 363	95.31 363	97.47 363	98.78 374	93.48 393	95.72 398	99.40 273	96.18 370	97.37 382	97.73 395	95.73 307	99.58 381	95.49 364	81.40 405	99.36 251

MAR-MVS		98.24 293	97.92 305	99.19 260	98.78 374	99.65 108	99.17 177	99.14 326	95.36 379	98.04 367	98.81 368	97.47 255	99.72 330	95.47 365	99.06 337	98.21 383
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	98.23 294	97.89 308	99.26 250	99.19 325	99.26 201	99.65 59	99.69 137	91.33 396	98.14 364	99.77 108	98.28 199	99.96 55	95.41 366	99.55 280	98.58 365

train_agg		98.35 286	97.95 299	99.57 163	99.35 282	99.35 186	98.11 331	99.41 266	94.90 385	97.92 370	98.99 350	98.02 221	99.85 241	95.38 367	99.44 301	99.50 207

9.14			98.64 238		99.45 259		98.81 261	99.60 187	97.52 334	99.28 265	99.56 235	98.53 166	99.83 271	95.36 368	99.64 256

APD-MVS		98.87 232	98.59 243	99.71 100	99.50 235	99.62 118	99.01 228	99.57 205	96.80 362	99.54 200	99.63 190	98.29 198	99.91 141	95.24 369	99.71 232	99.61 149
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 363								95.20 370

AdaColmap		98.60 256	98.35 269	99.38 218	99.12 335	99.22 211	98.67 276	99.42 265	97.84 321	98.81 322	99.27 309	97.32 263	99.81 295	95.14 371	99.53 287	99.10 308

test9_res														95.10 372	99.44 301	99.50 207

CDPH-MVS		98.56 262	98.20 281	99.61 148	99.50 235	99.46 152	98.32 313	99.41 266	95.22 381	99.21 277	99.10 337	98.34 193	99.82 280	95.09 373	99.66 252	99.56 172

BH-untuned		98.22 295	98.09 290	98.58 326	99.38 275	97.24 345	98.55 292	98.98 337	97.81 322	99.20 282	98.76 370	97.01 276	99.65 370	94.83 374	98.33 373	98.86 348

BP-MVS														94.73 375

HQP-MVS		98.36 283	98.02 294	99.39 215	99.31 299	98.94 242	97.98 346	99.37 281	97.45 337	98.15 360	98.83 366	96.67 284	99.70 337	94.73 375	99.67 249	99.53 189

QAPM		98.40 281	97.99 295	99.65 122	99.39 272	99.47 148	99.67 49	99.52 236	91.70 395	98.78 328	99.80 83	98.55 160	99.95 64	94.71 377	99.75 211	99.53 189

agg_prior2														94.58 378	99.46 300	99.50 207

myMVS_eth3d		95.63 366	94.73 368	98.34 336	98.50 389	96.36 363	98.60 281	99.21 318	97.89 315	96.76 391	96.37 410	72.10 410	99.57 382	94.38 379	98.73 361	99.09 312

BH-RMVSNet		98.41 279	98.14 287	99.21 257	99.21 320	98.47 280	98.60 281	98.26 370	98.35 283	98.93 306	99.31 301	97.20 270	99.66 364	94.32 380	99.10 336	99.51 202

E-PMN		97.14 332	97.43 320	96.27 382	98.79 372	91.62 401	95.54 399	99.01 336	99.44 136	98.88 313	99.12 333	92.78 338	99.68 355	94.30 381	99.03 341	97.50 393

MG-MVS		98.52 266	98.39 264	98.94 291	99.15 330	97.39 342	98.18 322	99.21 318	98.89 218	99.23 272	99.63 190	97.37 261	99.74 325	94.22 382	99.61 266	99.69 84

API-MVS		98.38 282	98.39 264	98.35 334	98.83 366	99.26 201	99.14 187	99.18 322	98.59 254	98.66 337	98.78 369	98.61 151	99.57 382	94.14 383	99.56 276	96.21 400

PAPM_NR		98.36 283	98.04 292	99.33 231	99.48 245	98.93 245	98.79 267	99.28 301	97.54 332	98.56 345	98.57 377	97.12 272	99.69 343	94.09 384	98.90 350	99.38 245

ZD-MVS					99.43 264	99.61 124		99.43 263	96.38 366	99.11 291	99.07 339	97.86 232	99.92 117	94.04 385	99.49 296

DPM-MVS		98.28 289	97.94 303	99.32 235	99.36 280	99.11 224	97.31 382	98.78 344	96.88 358	98.84 319	99.11 336	97.77 239	99.61 378	94.03 386	99.36 312	99.23 278

gg-mvs-nofinetune		95.87 361	95.17 366	97.97 348	98.19 397	96.95 352	99.69 42	89.23 410	99.89 36	96.24 398	99.94 16	81.19 395	99.51 390	93.99 387	98.20 378	97.44 394

PMVS	92.94 21	98.82 236	98.81 227	98.85 306	99.84 62	97.99 314	99.20 167	99.47 252	99.71 82	99.42 231	99.82 73	98.09 215	99.47 392	93.88 388	99.85 157	99.07 323
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 335	97.28 324	95.99 385	98.76 377	91.03 404	95.26 400	98.61 353	99.34 151	98.92 309	98.88 364	93.79 326	99.66 364	92.87 389	99.05 339	97.30 397

BH-w/o		97.20 329	97.01 331	97.76 356	99.08 344	95.69 373	98.03 341	98.52 357	95.76 375	97.96 369	98.02 390	95.62 309	99.47 392	92.82 390	97.25 395	98.12 387

TR-MVS		97.44 324	97.15 328	98.32 337	98.53 387	97.46 338	98.47 302	97.91 377	96.85 359	98.21 359	98.51 381	96.42 293	99.51 390	92.16 391	97.29 394	97.98 389

OpenMVS_ROB	97.31 17	97.36 327	96.84 337	98.89 304	99.29 305	99.45 157	98.87 250	99.48 249	86.54 401	99.44 225	99.74 120	97.34 262	99.86 224	91.61 392	99.28 323	97.37 396

GG-mvs-BLEND				97.36 366	97.59 403	96.87 355	99.70 35	88.49 411		94.64 404	97.26 402	80.66 396	99.12 398	91.50 393	96.50 400	96.08 402

DeepMVS_CX				97.98 347	99.69 156	96.95 352		99.26 305	75.51 403	95.74 401	98.28 386	96.47 291	99.62 374	91.23 394	97.89 389	97.38 395

PAPR		97.56 321	97.07 329	99.04 282	98.80 370	98.11 306	97.63 366	99.25 308	94.56 390	98.02 368	98.25 387	97.43 257	99.68 355	90.90 395	98.74 359	99.33 258

MVS		95.72 365	94.63 370	98.99 285	98.56 386	97.98 320	99.30 135	98.86 339	72.71 404	97.30 383	99.08 338	98.34 193	99.74 325	89.21 396	98.33 373	99.26 272

thres600view7		96.60 343	96.16 345	97.93 350	99.63 176	96.09 369	99.18 172	97.57 382	98.77 235	98.72 332	97.32 400	87.04 382	99.72 330	88.57 397	98.62 366	97.98 389

FPMVS		96.32 349	95.50 357	98.79 314	99.60 183	98.17 302	98.46 306	98.80 343	97.16 352	96.28 396	99.63 190	82.19 394	99.09 399	88.45 398	98.89 351	99.10 308

PCF-MVS	96.03 18	96.73 340	95.86 351	99.33 231	99.44 260	99.16 219	96.87 393	99.44 260	86.58 400	98.95 304	99.40 278	94.38 320	99.88 191	87.93 399	99.80 193	98.95 337
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 347	96.03 348	97.47 363	99.63 176	95.93 370	99.18 172	97.57 382	98.75 239	98.70 335	97.31 401	87.04 382	99.67 360	87.62 400	98.51 370	96.81 398

tfpn200view9		96.30 350	95.89 349	97.53 360	99.58 192	96.11 367	99.00 231	97.54 385	98.43 269	98.52 346	96.98 403	86.85 384	99.67 360	87.62 400	98.51 370	96.81 398

thres400		96.40 346	95.89 349	97.92 351	99.58 192	96.11 367	99.00 231	97.54 385	98.43 269	98.52 346	96.98 403	86.85 384	99.67 360	87.62 400	98.51 370	97.98 389

thres200		96.09 355	95.68 355	97.33 368	99.48 245	96.22 366	98.53 297	97.57 382	98.06 304	98.37 353	96.73 406	86.84 386	99.61 378	86.99 403	98.57 367	96.16 401

MVE	92.54 22	96.66 342	96.11 346	98.31 339	99.68 164	97.55 335	97.94 351	95.60 396	99.37 148	90.68 406	98.70 373	96.56 287	98.61 404	86.94 404	99.55 280	98.77 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 328	96.83 338	98.59 324	99.46 255	97.55 335	99.25 156	96.84 390	98.78 233	97.24 385	97.67 396	97.11 273	98.97 401	86.59 405	98.54 369	99.27 271

PAPM		95.61 367	94.71 369	98.31 339	99.12 335	96.63 358	96.66 396	98.46 361	90.77 397	96.25 397	98.68 374	93.01 336	99.69 343	81.60 406	97.86 391	98.62 361

test123		29.31 373	33.05 378	18.08 389	25.93 413	12.24 414	97.53 372	10.93 414	11.78 407	24.21 408	50.08 417	21.04 412	8.60 408	23.51 407	32.43 407	33.39 404

testmvs		28.94 374	33.33 376	15.79 390	26.03 412	9.81 415	96.77 394	15.67 413	11.55 408	23.87 409	50.74 416	19.03 413	8.53 409	23.21 408	33.07 406	29.03 405

test_blank		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		24.88 375	33.17 377	0.00 391	0.00 414	0.00 416	0.00 402	99.62 168	0.00 409	0.00 410	99.13 329	99.82 13	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		16.61 376	22.14 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	99.28 66	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		8.26 385	11.02 388	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	99.16 327	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		8.33 377	11.11 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

FOURS1					99.83 66	99.89 10	99.74 24	99.71 125	99.69 90	99.63 160

test_one_0601					99.63 176	99.76 62		99.55 216	99.23 168	99.31 259	99.61 206	98.59 154

eth-test2					0.00 414

eth-test					0.00 414

test_241102_ONE					99.69 156	99.82 35		99.54 222	99.12 191	99.82 81	99.49 257	98.91 112	99.52 389

save fliter					99.53 222	99.25 204	98.29 315	99.38 280	99.07 195

test0726					99.69 156	99.80 44	99.24 157	99.57 205	99.16 182	99.73 127	99.65 177	98.35 190

GSMVS																99.14 302

test_part2					99.62 180	99.67 100				99.55 198

sam_mvs1												90.81 361				99.14 302

sam_mvs												90.52 365

MTGPA								99.53 231

test_post											52.41 414	90.25 367	99.86 224

patchmatchnet-post											99.62 197	90.58 363	99.94 78

MTMP							99.09 209	98.59 356

TEST9					99.35 282	99.35 186	98.11 331	99.41 266	94.83 388	97.92 370	98.99 350	98.02 221	99.85 241

test_8					99.34 291	99.31 192	98.08 335	99.40 273	94.90 385	97.87 374	98.97 355	98.02 221	99.84 256

agg_prior					99.35 282	99.36 183		99.39 276		97.76 380			99.85 241

test_prior4						99.19 217	98.00 344

test_prior				99.46 190	99.35 282	99.22 211		99.39 276					99.69 343			99.48 216

新几何2							98.04 339

旧先验1					99.49 240	99.29 195		99.26 305			99.39 282	97.67 246			99.36 312	99.46 224

原ACMM2							97.92 353

test222					99.51 229	99.08 231	97.83 359	99.29 298	95.21 382	98.68 336	99.31 301	97.28 264			99.38 309	99.43 236

segment_acmp												98.37 188

testdata1							97.72 362		97.86 320

test12				99.54 174	99.29 305	99.33 189		99.16 324		98.43 351		97.54 253	99.82 280		99.47 298	99.48 216

plane_prior7					99.58 192	99.38 176

plane_prior6					99.47 251	99.26 201						97.24 265

plane_prior4											99.25 314

plane_prior3						99.31 192			98.36 278	99.14 287

plane_prior2							98.80 264		98.94 208

plane_prior1					99.51 229

plane_prior						99.24 208	98.42 307		97.87 318						99.71 232

n2								0.00 415

nn								0.00 415

door-mid								99.83 61

test11								99.29 298

door								99.77 94

HQP5-MVS						98.94 242

HQP-NCC					99.31 299		97.98 346		97.45 337	98.15 360

ACMP_Plane					99.31 299		97.98 346		97.45 337	98.15 360

HQP4-MVS										98.15 360			99.70 337			99.53 189

HQP3-MVS								99.37 281							99.67 249

HQP2-MVS												96.67 284

NP-MVS					99.40 271	99.13 222					98.83 366

ACMMP++_ref															99.94 95

ACMMP++															99.79 198

Test By Simon												98.41 182

High-resolution multi-view benchmark