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	97.84 323	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	97.93 320	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	97.16 346	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				99.55 169		98.81 254	99.05 216	97.79 376	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		99.17 177	99.03 187	99.59 153	99.44 260	98.90 248	99.04 219	95.32 393	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	99.86 223	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	99.85 240	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	99.84 255	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	99.84 255	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	99.84 255	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	99.84 255	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	99.81 294	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	99.82 279	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	98.96 334

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		98.87 231	98.60 240	99.69 105	99.93 26	99.46 152	99.74 24	94.97 394	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	98.72 317	99.88 45	96.44 360	99.56 81	99.85 53	99.90 30	99.90 50	99.85 56	98.09 215	99.83 270	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	99.19 324	97.81 325	99.02 226	99.55 216	99.78 70	99.85 73	99.80 83	98.24 202	99.86 223	99.57 56	99.50 294	99.15 297

test1111		97.74 311	98.16 285	96.49 375	99.60 183	89.86 405	99.71 34	91.21 401	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		90.66 402		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	97.90 351	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	97.27 343	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		94.73 364	94.59 365	95.15 381	99.59 187	85.90 407	99.75 22	74.01 407	99.89 36	99.71 133	99.86 54	79.00 402	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	98.09 329	99.90 37	98.95 206	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		97.73 312	98.04 291	96.78 369	99.59 187	90.81 401	99.72 30	90.43 403	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	99.78 307	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	99.86 223	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	98.71 319	99.82 73	96.62 358	98.55 289	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	99.86 223	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	98.90 358	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		98.75 241	98.54 250	99.40 211	99.33 295	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	99.85 240	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	99.84 255	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	98.66 241	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	99.83 270	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		99.00 211	99.16 146	98.51 326	99.75 129	95.90 370	98.07 332	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		98.91 224	98.64 237	99.73 89	99.85 59	99.47 148	98.07 332	99.83 61	98.64 243	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	98.55 289	99.73 113	98.82 224	99.72 128	99.62 197	96.56 287	99.82 279	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	99.69 340	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	99.83 270	99.32 95	99.94 95	99.53 189

iter_conf05		98.46 273	98.23 276	99.15 265	99.04 347	97.99 313	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		98.61 253	98.88 217	97.80 351	99.58 192	93.60 387	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		95.75 358	95.42 356	96.76 370	89.90 405	94.42 382	98.86 251	97.87 375	78.01 397	99.30 264	99.69 152	97.70 242	95.89 401	99.29 102	98.14 380	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		98.97 215	99.16 146	98.42 330	99.74 135	95.64 373	98.06 334	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		98.61 253	98.34 269	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	96.51 394	99.65 113

hse-mvs2		98.52 265	98.30 273	99.16 263	99.29 304	98.60 275	98.77 269	99.02 334	99.68 92	99.32 255	99.04 343	92.50 342	99.85 240	99.24 106	97.87 385	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	99.82 279	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	98.12 325	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		99.11 190	99.05 180	99.28 244	98.83 365	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	98.47 299	99.66 148	99.61 110	99.68 143	98.94 359	99.39 50	99.97 34	99.18 115	99.55 280	98.51 364

xiu_mvs_v1_base		99.23 151	99.34 112	98.91 297	99.59 187	98.23 295	98.47 299	99.66 148	99.61 110	99.68 143	98.94 359	99.39 50	99.97 34	99.18 115	99.55 280	98.51 364

xiu_mvs_v1_base_debi		99.23 151	99.34 112	98.91 297	99.59 187	98.23 295	98.47 299	99.66 148	99.61 110	99.68 143	98.94 359	99.39 50	99.97 34	99.18 115	99.55 280	98.51 364

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	99.86 223	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	99.85 240	99.15 121	99.92 106	99.68 90

VNet		99.18 173	99.06 176	99.56 166	99.24 314	99.36 183	99.33 125	99.31 294	99.67 96	99.47 219	99.57 231	96.48 290	99.84 255	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	97.97 304	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		98.47 272	98.22 278	99.24 255	99.06 344	98.35 292	99.08 212	99.46 255	99.27 160	99.75 115	99.66 172	88.61 376	99.85 240	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		98.17 296	97.85 308	99.15 265	98.50 383	98.79 257	98.60 279	99.21 318	97.89 310	96.76 388	96.37 406	95.47 311	99.57 377	99.10 130	98.73 360	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	99.66 359	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	98.38 271	99.75 115	99.04 343	99.36 59	99.86 223	99.08 132	99.25 327	99.45 225

1112_ss		99.05 199	98.84 222	99.67 110	99.66 170	99.29 195	98.52 295	99.82 66	97.65 322	99.43 229	99.16 327	96.42 293	99.91 141	99.07 133	99.84 162	99.80 47

CANet_DTU		98.91 224	98.85 220	99.09 275	98.79 370	98.13 303	98.18 318	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		98.95 221	98.73 231	99.63 136	99.68 164	99.15 221	98.09 329	99.80 79	97.14 348	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	98.32 366	99.80 65	99.56 193	99.69 152	96.99 277	99.85 240	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	99.76 317	98.98 140	99.99 16	99.36 251

EPNet_dtu		97.62 317	97.79 311	97.11 368	96.67 400	92.31 392	98.51 296	98.04 369	99.24 166	95.77 395	99.47 264	93.78 327	99.66 359	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	98.57 287	99.81 75	99.61 110	99.48 217	99.41 274	98.47 173	99.86 223	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		97.80 309	97.25 325	99.42 202	98.83 365	98.97 239	99.38 113	99.80 79	98.87 218	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		98.80 237	98.63 239	99.32 235	99.13 332	98.72 263	99.10 204	99.48 249	99.23 168	99.62 169	99.64 179	92.57 339	99.86 223	98.96 144	99.90 116	99.39 243

UnsupCasMVSNet_eth		98.83 234	98.57 246	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	99.86 223	98.95 148	96.57 393	99.45 225

CHOSEN 280x420		98.41 278	98.41 261	98.40 331	99.34 290	95.89 371	96.94 387	99.44 260	98.80 227	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		98.28 288	97.96 297	99.25 253	99.12 334	98.93 245	99.03 223	98.42 361	99.64 104	98.72 332	97.85 393	90.86 360	99.62 369	98.88 151	99.13 333	99.19 289

sss		98.90 226	98.77 230	99.27 247	99.48 245	98.44 283	98.72 273	99.32 290	97.94 308	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		99.02 205	99.11 159	98.77 314	99.37 277	98.09 308	98.13 324	99.51 240	99.47 130	99.42 231	98.54 380	99.38 54	99.97 34	98.83 153	99.33 316	98.24 376

PS-MVSNAJ		99.00 211	99.08 170	98.76 315	99.37 277	98.10 307	98.00 339	99.51 240	99.47 130	99.41 237	98.50 382	99.28 66	99.97 34	98.83 153	99.34 315	98.20 380

D2MVS		99.22 159	99.19 143	99.29 242	99.69 156	98.74 262	98.81 261	99.41 266	98.55 252	99.68 143	99.69 152	98.13 213	99.87 205	98.82 155	99.98 41	99.24 275

PatchT		98.45 275	98.32 271	98.83 309	98.94 356	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	99.00 342	98.26 375

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+		99.06 196	98.97 204	99.34 228	99.31 298	98.98 237	98.31 310	99.91 32	98.81 225	98.79 326	98.94 359	99.14 84	99.84 255	98.79 159	98.74 358	99.20 286

canonicalmvs		99.02 205	99.00 195	99.09 275	99.10 340	98.70 264	99.61 68	99.66 148	99.63 106	98.64 338	97.65 396	99.04 98	99.54 380	98.79 159	98.92 347	99.04 326

VDDNet		98.97 215	98.82 225	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	99.85 240	98.79 159	99.32 318	99.59 160

CR-MVSNet		98.35 285	98.20 280	98.83 309	99.05 345	98.12 304	99.30 135	99.67 144	97.39 336	99.16 283	99.79 93	91.87 347	99.91 141	98.78 162	98.77 354	98.44 369

test_method		91.72 365	92.32 368	89.91 383	93.49 404	70.18 408	90.28 396	99.56 210	61.71 400	95.39 397	99.52 248	93.90 323	99.94 78	98.76 163	98.27 374	99.62 139

RPMNet		98.60 255	98.53 252	98.83 309	99.05 345	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	98.77 354	98.44 369

pmmvs4		99.13 185	99.06 176	99.36 225	99.57 202	99.10 229	98.01 337	99.25 308	98.78 230	99.58 183	99.44 271	98.24 202	99.76 317	98.74 165	99.93 102	99.22 280

tttt0517		97.62 317	97.20 326	98.90 303	99.76 118	97.40 340	99.48 96	94.36 396	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		99.17 177	99.00 195	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	99.86 223	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	98.77 232	99.80 92	99.85 56	99.64 28	99.85 240	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	98.87 218	99.57 186	99.82 73	98.06 218	99.87 205	98.69 170	99.73 223	99.15 297

LFMVS		98.46 273	98.19 283	99.26 250	99.24 314	98.52 279	99.62 63	96.94 385	99.87 42	99.31 259	99.58 222	91.04 355	99.81 294	98.68 171	99.42 305	99.45 225

WR-MVS		99.11 190	98.93 208	99.66 117	99.30 302	99.42 166	98.42 304	99.37 281	99.04 198	99.57 186	99.20 325	96.89 279	99.86 223	98.66 172	99.87 145	99.70 80

Anonymous202405211		98.75 241	98.46 256	99.63 136	99.34 290	99.66 102	99.47 99	97.65 377	99.28 159	99.56 193	99.50 253	93.15 333	99.84 255	98.62 173	99.58 274	99.40 241

EPNet		98.13 297	97.77 312	99.18 262	94.57 403	97.99 313	99.24 157	97.96 371	99.74 75	97.29 383	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++		99.05 199	99.09 168	98.91 297	99.21 319	98.36 291	98.82 260	99.47 252	98.85 220	98.90 312	99.56 235	98.78 127	99.09 394	98.57 175	99.68 243	99.26 272

Patchmatch-RL test		98.60 255	98.36 266	99.33 231	99.77 114	99.07 232	98.27 312	99.87 45	98.91 213	99.74 123	99.72 131	90.57 364	99.79 304	98.55 176	99.85 157	99.11 306

pmmvs3		98.08 300	97.80 309	98.91 297	99.41 270	97.69 331	97.87 352	99.66 148	95.87 367	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	99.34 290	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	98.28 374

jason		99.16 179	99.11 159	99.32 235	99.75 129	98.44 283	98.26 314	99.39 276	98.70 239	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		98.71 247	98.56 249	99.15 265	99.22 317	98.66 268	97.14 382	99.51 240	98.09 297	99.54 200	99.27 309	96.87 280	99.74 323	98.43 181	98.96 344	99.03 327

our_test_3		98.85 233	99.09 168	98.13 343	99.66 170	94.90 380	97.72 357	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	99.71 332	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		97.37 325	96.91 335	98.74 316	97.72 396	97.57 333	97.60 363	97.36 383	98.00 300	99.21 277	98.02 389	90.04 370	99.79 304	98.37 184	95.89 397	98.86 344

PM-MVS		99.36 124	99.29 129	99.58 157	99.83 66	99.66 102	98.95 243	99.86 48	98.85 220	99.81 88	99.73 124	98.40 186	99.92 117	98.36 185	99.83 170	99.17 293

baseline1		97.73 312	97.33 322	98.96 288	99.30 302	97.73 329	99.40 109	98.42 361	99.33 153	99.46 223	99.21 323	91.18 353	99.82 279	98.35 186	91.26 399	99.32 261

MVS-HIRNet		97.86 306	98.22 278	96.76 370	99.28 307	91.53 397	98.38 306	92.60 400	99.13 188	99.31 259	99.96 12	97.18 271	99.68 350	98.34 187	99.83 170	99.07 323

GA-MVS		97.99 305	97.68 315	98.93 294	99.52 227	98.04 312	97.19 381	99.05 333	98.32 284	98.81 322	98.97 355	89.89 372	99.41 390	98.33 188	99.05 338	99.34 257

Fast-Effi-MVS+		99.02 205	98.87 218	99.46 190	99.38 275	99.50 145	99.04 219	99.79 85	97.17 346	98.62 339	98.74 371	99.34 60	99.95 64	98.32 189	99.41 306	98.92 339

MDA-MVSNet_test_wron		98.95 221	98.99 200	98.85 305	99.64 174	97.16 346	98.23 316	99.33 288	98.93 210	99.56 193	99.66 172	97.39 260	99.83 270	98.29 190	99.88 134	99.55 175

N_pmnet		98.73 245	98.53 252	99.35 227	99.72 141	98.67 265	98.34 307	94.65 395	98.35 278	99.79 97	99.68 163	98.03 220	99.93 95	98.28 191	99.92 106	99.44 230

ET-MVSNet_ETH3D		96.78 337	96.07 346	98.91 297	99.26 311	97.92 321	97.70 359	96.05 390	97.96 307	92.37 400	98.43 383	87.06 381	99.90 159	98.27 192	97.56 388	98.91 340

thisisatest0530		97.45 322	96.95 332	98.94 291	99.68 164	97.73 329	99.09 209	94.19 398	98.61 248	99.56 193	99.30 303	84.30 393	99.93 95	98.27 192	99.54 285	99.16 295

YYNet1		98.95 221	98.99 200	98.84 307	99.64 174	97.14 348	98.22 317	99.32 290	98.92 212	99.59 181	99.66 172	97.40 258	99.83 270	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	99.86 223	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		98.96 218	98.87 218	99.24 255	99.57 202	98.40 286	98.12 325	99.18 322	98.28 286	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	99.25 312	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	98.62 356

MS-PatchMatch		99.00 211	98.97 204	99.09 275	99.11 339	98.19 299	98.76 270	99.33 288	98.49 261	99.44 225	99.58 222	98.21 207	99.69 340	98.20 198	99.62 259	99.39 243

TSAR-MVS + GP.		99.12 187	99.04 185	99.38 218	99.34 290	99.16 219	98.15 321	99.29 298	98.18 293	99.63 160	99.62 197	99.18 78	99.68 350	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		99.16 179	99.08 170	99.43 200	99.48 245	99.07 232	99.08 212	99.55 216	98.63 244	99.31 259	99.68 163	98.19 209	99.78 307	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	98.35 278	99.51 212	99.50 253	99.31 62	99.88 191	98.18 202	99.84 162	99.69 84

MDA-MVSNet-bldmvs		99.06 196	99.05 180	99.07 279	99.80 87	97.83 324	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		98.06 301	97.92 304	98.50 327	98.59 380	97.02 350	98.80 264	98.51 357	99.88 41	97.89 371	99.87 47	91.89 346	99.90 159	98.16 205	97.68 387	98.59 358

EIA-MVS		99.12 187	99.01 192	99.45 193	99.36 280	99.62 118	99.34 122	99.79 85	98.41 267	98.84 319	98.89 363	98.75 132	99.84 255	98.15 206	99.51 291	98.89 341

miper_lstm_enhance		98.65 252	98.60 240	98.82 312	99.20 322	97.33 342	97.78 355	99.66 148	99.01 200	99.59 181	99.50 253	94.62 318	99.85 240	98.12 207	99.90 116	99.26 272

Effi-MVS+-dtu		99.07 195	98.92 212	99.52 177	98.89 361	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	98.81 348

tpm		97.15 329	96.95 332	97.75 353	98.91 357	94.24 383	99.32 127	97.96 371	97.71 320	98.29 353	99.32 299	86.72 387	99.92 117	98.10 209	96.24 396	99.09 312

DeepPCF-MVS	98.42 6	99.18 173	99.02 189	99.67 110	99.22 317	99.75 68	97.25 379	99.47 252	98.72 237	99.66 153	99.70 146	99.29 64	99.63 368	98.07 210	99.81 188	99.62 139

ppachtmachnet_test		98.89 229	99.12 156	98.20 341	99.66 170	95.24 377	97.63 361	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		97.73 312	98.07 290	96.73 372	98.71 376	92.00 393	99.10 204	98.86 339	98.52 257	98.92 309	99.54 244	91.90 345	99.82 279	98.02 211	99.03 340	98.37 371

CSCG		99.37 121	99.29 129	99.60 151	99.71 144	99.46 152	99.43 107	99.85 53	98.79 228	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		98.68 250	98.71 233	98.60 322	99.10 340	96.84 355	97.52 369	99.54 222	98.94 207	99.58 183	99.48 260	96.25 301	99.76 317	98.01 214	99.93 102	99.21 282

Patchmtry		98.78 238	98.54 250	99.49 182	98.89 361	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		99.03 203	99.01 192	99.09 275	99.54 216	97.99 313	98.58 283	99.82 66	97.62 323	99.34 250	99.71 139	98.52 169	99.77 315	97.98 216	99.97 56	99.52 200

PVSNet_Blended		98.70 248	98.59 242	99.02 283	99.54 216	97.99 313	97.58 364	99.82 66	95.70 371	99.34 250	98.98 353	98.52 169	99.77 315	97.98 216	99.83 170	99.30 267

cl____		98.54 263	98.41 261	98.92 295	99.03 348	97.80 327	97.46 371	99.59 193	98.90 214	99.60 178	99.46 267	93.85 325	99.78 307	97.97 218	99.89 125	99.17 293

DIV-MVS_self_test		98.54 263	98.42 260	98.92 295	99.03 348	97.80 327	97.46 371	99.59 193	98.90 214	99.60 178	99.46 267	93.87 324	99.78 307	97.97 218	99.89 125	99.18 291

AUN-MVS		97.82 308	97.38 321	99.14 269	99.27 309	98.53 277	98.72 273	99.02 334	98.10 295	97.18 386	99.03 347	89.26 374	99.85 240	97.94 220	97.91 383	99.03 327

FA-MVS(test-final)		98.52 265	98.32 271	99.10 274	99.48 245	98.67 265	99.77 15	98.60 354	97.35 338	99.63 160	99.80 83	93.07 335	99.84 255	97.92 221	99.30 320	98.78 351

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	99.70 334	97.92 221	99.70 234	99.38 245

USDC		98.96 218	98.93 208	99.05 281	99.54 216	97.99 313	97.07 385	99.80 79	98.21 290	99.75 115	99.77 108	98.43 179	99.64 367	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	98.58 283	99.48 249	98.50 259	99.52 207	99.63 190	99.14 84	99.76 317	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		98.72 246	98.71 233	98.72 317	99.12 334	97.22 345	97.68 360	99.56 210	98.90 214	99.54 200	99.48 260	96.37 297	99.73 326	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		98.59 258	98.59 242	98.59 323	98.98 354	97.07 349	97.49 370	99.52 236	98.50 259	99.52 207	99.37 286	96.41 295	99.71 332	97.86 229	99.62 259	99.00 333

WTY-MVS		98.59 258	98.37 265	99.26 250	99.43 264	98.40 286	98.74 271	99.13 328	98.10 295	99.21 277	99.24 319	94.82 315	99.90 159	97.86 229	98.77 354	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	99.84 255	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		99.12 187	99.02 189	99.40 211	99.50 235	99.11 224	97.92 348	99.71 125	98.76 235	99.08 294	99.47 264	99.17 79	99.54 380	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	99.03 348	99.53 142		99.23 312					99.92 117	97.77 236	99.69 238	99.78 57

No_MVS				99.74 80	99.03 348	99.53 142		99.23 312					99.92 117	97.77 236	99.69 238	99.78 57

TESTMET0.1,1		96.24 350	95.84 351	97.41 360	98.24 390	93.84 386	97.38 373	95.84 391	98.43 264	97.81 376	98.56 378	79.77 398	99.89 177	97.77 236	98.77 354	98.52 363

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	99.78 307	97.77 236	99.88 134	99.60 153

IU-MVS					99.69 156	99.77 54		99.22 315	97.50 330	99.69 140				97.75 240	99.70 234	99.77 61

114514_t		98.49 270	98.11 288	99.64 129	99.73 138	99.58 133	99.24 157	99.76 99	89.94 393	99.42 231	99.56 235	97.76 241	99.86 223	97.74 241	99.82 179	99.47 220

DVP-MVS++		99.38 118	99.25 137	99.77 58	99.03 348	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		89.05 366	85.52 369	99.64 129	99.89 40	99.78 49	99.56 81	99.52 236	24.19 401	49.96 402	99.83 66	99.15 81	99.92 117	97.71 244	99.85 157	99.21 282

miper_enhance_ethall		98.03 302	97.94 302	98.32 336	98.27 389	96.43 361	96.95 386	99.41 266	96.37 362	99.43 229	98.96 357	94.74 316	99.69 340	97.71 244	99.62 259	98.83 347

TSAR-MVS + MP.		99.34 131	99.24 139	99.63 136	99.82 73	99.37 179	99.26 149	99.35 285	98.77 232	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		97.56 320	97.28 323	98.40 331	98.37 387	96.75 356	97.24 380	99.37 281	97.31 340	99.41 237	99.22 321	87.30 379	99.37 391	97.70 247	99.62 259	99.08 318

MP-MVS-pluss		99.14 183	98.92 212	99.80 46	99.83 66	99.83 29	98.61 277	99.63 165	96.84 355	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	98.27 287	99.53 205	99.73 124	98.75 132	99.87 205	97.70 247	99.83 170	99.68 90

UnsupCasMVSNet_bld		98.55 262	98.27 275	99.40 211	99.56 213	99.37 179	97.97 344	99.68 140	97.49 331	99.08 294	99.35 295	95.41 312	99.82 279	97.70 247	98.19 377	99.01 332

MVS_111021_LR		99.13 185	99.03 187	99.42 202	99.58 192	99.32 191	97.91 350	99.73 113	98.68 240	99.31 259	99.48 260	99.09 89	99.66 359	97.70 247	99.77 207	99.29 270

IS-MVSNet		99.03 203	98.85 220	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	99.81 294	97.70 247	99.88 134	99.58 165

test-LLR		97.15 329	96.95 332	97.74 354	98.18 392	95.02 378	97.38 373	96.10 387	98.00 300	97.81 376	98.58 375	90.04 370	99.91 141	97.69 253	98.78 352	98.31 372

test-mter		96.23 351	95.73 352	97.74 354	98.18 392	95.02 378	97.38 373	96.10 387	97.90 309	97.81 376	98.58 375	79.12 401	99.91 141	97.69 253	98.78 352	98.31 372

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		96.09 352	94.87 361	99.75 75	99.71 144	99.71 84	99.37 117	99.61 175	99.29 156	98.76 329	61.30 408	98.47 173	99.88 191	97.62 255	99.73 223	99.67 96

SMA-MVS		99.19 169	99.00 195	99.73 89	99.46 255	99.73 77	99.13 193	99.52 236	97.40 335	99.57 186	99.64 179	98.93 109	99.83 270	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		96.71 340	96.79 339	96.46 376	98.90 358	90.71 402	99.41 108	98.68 348	94.69 384	98.14 363	99.34 298	86.32 389	99.80 301	97.60 258	98.07 382	98.88 342

PVSNet	97.47 15	98.42 277	98.44 258	98.35 333	99.46 255	96.26 364	96.70 390	99.34 287	97.68 321	99.00 301	99.13 329	97.40 258	99.72 328	97.59 259	99.68 243	99.08 318

new_pmnet		98.88 230	98.89 216	98.84 307	99.70 152	97.62 332	98.15 321	99.50 244	97.98 303	99.62 169	99.54 244	98.15 212	99.94 78	97.55 260	99.84 162	98.95 336

IB-MVS	95.41 20	95.30 363	94.46 367	97.84 350	98.76 374	95.33 376	97.33 376	96.07 389	96.02 366	95.37 398	97.41 398	76.17 403	99.96 55	97.54 261	95.44 398	98.22 377
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	98.38 386	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	99.04 185	99.77 58	99.76 118	99.73 77	99.28 144	99.56 210	98.19 292	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	98.38 271	99.06 298	99.27 309	98.79 125	99.94 78	97.51 264	99.82 179	99.66 105

SD-MVS		99.01 209	99.30 124	98.15 342	99.50 235	99.40 172	98.94 245	99.61 175	99.22 172	99.75 115	99.82 73	99.54 41	95.51 402	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		98.49 270	98.29 274	99.11 272	98.96 355	98.42 285	97.54 365	99.32 290	97.53 328	98.47 349	98.15 388	97.88 231	99.82 279	97.46 266	99.24 329	99.09 312

DeepC-MVS_fast	98.47 5	99.23 151	99.12 156	99.56 166	99.28 307	99.22 211	98.99 236	99.40 273	99.08 193	99.58 183	99.64 179	98.90 115	99.83 270	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	98.36 273	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	98.36 273	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)		98.77 239	98.58 245	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	99.78 307	97.43 268	99.89 125	99.35 254

MIMVSNet		98.43 276	98.20 280	99.11 272	99.53 222	98.38 290	99.58 76	98.61 352	98.96 205	99.33 252	99.76 112	90.92 357	99.81 294	97.38 271	99.76 209	99.15 297

WB-MVSnew		98.34 287	98.14 286	98.96 288	98.14 395	97.90 322	98.27 312	97.26 384	98.63 244	98.80 324	98.00 391	97.77 239	99.90 159	97.37 272	98.98 343	99.09 312

XVG-OURS-SEG-HR		99.16 179	98.99 200	99.66 117	99.84 62	99.64 111	98.25 315	99.73 113	98.39 270	99.63 160	99.43 272	99.70 24	99.90 159	97.34 273	98.64 364	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	99.86 223	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		99.02 205	98.81 226	99.65 122	99.58 192	99.49 146	98.58 283	99.07 330	98.40 269	99.04 299	99.25 314	98.51 171	99.80 301	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	98.41 267	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	98.03 302	97.55 318	99.46 190	99.47 251	99.44 159	98.50 297	99.62 168	86.79 394	99.07 297	99.26 312	98.26 201	99.62 369	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

test_yl		98.25 290	97.95 298	99.13 270	99.17 327	98.47 280	99.00 231	98.67 350	98.97 203	99.22 275	99.02 348	91.31 351	99.69 340	97.26 281	98.93 345	99.24 275

DCV-MVSNet		98.25 290	97.95 298	99.13 270	99.17 327	98.47 280	99.00 231	98.67 350	98.97 203	99.22 275	99.02 348	91.31 351	99.69 340	97.26 281	98.93 345	99.24 275

PHI-MVS		99.11 190	98.95 207	99.59 153	99.13 332	99.59 129	99.17 177	99.65 157	97.88 312	99.25 268	99.46 267	98.97 106	99.80 301	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	97.25 284	99.78 203	99.15 297

PatchmatchNet		97.65 316	97.80 309	97.18 366	98.82 368	92.49 391	99.17 177	98.39 363	98.12 294	98.79 326	99.58 222	90.71 362	99.89 177	97.23 285	99.41 306	99.16 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 214	98.80 228	99.56 166	99.25 312	99.43 163	98.54 292	99.27 302	98.58 250	98.80 324	99.43 272	98.53 166	99.70 334	97.22 286	99.59 273	99.54 183

testing3		96.48 344	95.63 354	99.01 284	99.23 316	97.81 325	98.90 247	99.10 329	98.72 237	97.84 375	97.92 392	72.44 404	99.85 240	97.21 287	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	97.72 319	99.35 247	99.25 314	99.23 73	99.92 117	97.21 287	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	99.00 195	99.76 65	99.76 118	99.68 98	99.38 113	99.54 222	98.34 282	99.01 300	99.50 253	98.53 166	99.93 95	97.18 289	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	98.07 298	99.52 207	99.69 152	98.57 157	99.92 117	97.18 289	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		96.98 333	96.42 340	98.66 320	99.42 269	97.47 336	97.27 378	94.30 397	97.24 342	99.15 285	98.86 365	85.01 390	99.87 205	97.10 291	99.39 308	98.63 355

XVG-ACMP-BASELINE		99.23 151	99.10 167	99.63 136	99.82 73	99.58 133	98.83 256	99.72 122	98.36 273	99.60 178	99.71 139	98.92 110	99.91 141	97.08 292	99.84 162	99.40 241

MSDG		99.08 194	98.98 203	99.37 221	99.60 183	99.13 222	97.54 365	99.74 109	98.84 223	99.53 205	99.55 242	99.10 87	99.79 304	97.07 293	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	98.55 252	99.57 186	99.67 167	99.03 99	99.94 78	97.01 294	99.80 193	99.69 84
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		96.53 343	96.32 341	97.17 367	98.18 392	92.97 390	99.39 111	89.95 404	98.21 290	98.61 340	99.59 219	86.69 388	99.72 328	96.99 295	99.23 331	98.81 348

MSP-MVS		99.04 202	98.79 229	99.81 41	99.78 106	99.73 77	99.35 121	99.57 205	98.54 255	99.54 200	98.99 350	96.81 281	99.93 95	96.97 296	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++		98.96 218	98.70 235	99.74 80	99.52 227	99.71 84	98.86 251	99.19 321	98.47 263	98.59 342	99.06 340	98.08 217	99.91 141	96.94 297	99.60 269	99.60 153

SR-MVS		99.19 169	99.00 195	99.74 80	99.51 229	99.72 82	99.18 172	99.60 187	98.85 220	99.47 219	99.58 222	98.38 187	99.92 117	96.92 298	99.54 285	99.57 170

PGM-MVS		99.20 166	99.01 192	99.77 58	99.75 129	99.71 84	99.16 183	99.72 122	97.99 302	99.42 231	99.60 214	98.81 120	99.93 95	96.91 299	99.74 218	99.66 105

HY-MVS	98.23 9	98.21 295	97.95 298	98.99 285	99.03 348	98.24 294	99.61 68	98.72 346	96.81 356	98.73 331	99.51 250	94.06 322	99.86 223	96.91 299	98.20 375	98.86 344

MDTV_nov1_ep13			97.73 313		98.70 377	90.83 400	99.15 185	98.02 370	98.51 258	98.82 321	99.61 206	90.98 356	99.66 359	96.89 301	98.92 347

GST-MVS		99.16 179	98.96 206	99.75 75	99.73 138	99.73 77	99.20 167	99.55 216	98.22 289	99.32 255	99.35 295	98.65 147	99.91 141	96.86 302	99.74 218	99.62 139

test_post1							99.14 187				51.63 410	89.54 373	99.82 279	96.86 302

SCA		98.11 298	98.36 266	97.36 361	99.20 322	92.99 389	98.17 320	98.49 359	98.24 288	99.10 293	99.57 231	96.01 305	99.94 78	96.86 302	99.62 259	99.14 302

XVG-OURS		99.21 164	99.06 176	99.65 122	99.82 73	99.62 118	97.87 352	99.74 109	98.36 273	99.66 153	99.68 163	99.71 22	99.90 159	96.84 305	99.88 134	99.43 236

LCM-MVSNet-Re		99.28 140	99.15 149	99.67 110	99.33 295	99.76 62	99.34 122	99.97 18	98.93 210	99.91 44	99.79 93	98.68 140	99.93 95	96.80 306	99.56 276	99.30 267

RPSCF		99.18 173	99.02 189	99.64 129	99.83 66	99.85 19	99.44 105	99.82 66	98.33 283	99.50 214	99.78 101	97.90 229	99.65 365	96.78 307	99.83 170	99.44 230

旧先验2							97.94 346		95.33 375	98.94 305			99.88 191	96.75 308

MDTV_nov1_ep13_2view						91.44 398	99.14 187		97.37 337	99.21 277		91.78 349		96.75 308		99.03 327

CLD-MVS		98.76 240	98.57 246	99.33 231	99.57 202	98.97 239	97.53 367	99.55 216	96.41 360	99.27 266	99.13 329	99.07 94	99.78 307	96.73 310	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		98.10 299	97.98 296	98.48 328	99.27 309	96.48 359	99.40 109	99.07 330	98.81 225	99.23 272	99.57 231	90.11 369	99.87 205	96.69 311	99.64 256	99.09 312

baseline2		96.83 336	96.28 342	98.46 329	99.09 342	96.91 353	98.83 256	93.87 399	97.23 343	96.23 394	98.36 384	88.12 377	99.90 159	96.68 312	98.14 380	98.57 361

cascas		96.99 332	96.82 338	97.48 357	97.57 399	95.64 373	96.43 392	99.56 210	91.75 389	97.13 387	97.61 397	95.58 310	98.63 398	96.68 312	99.11 335	98.18 381

PC_three_1452									97.56 324	99.68 143	99.41 274	99.09 89	97.09 400	96.66 314	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	97.56 324	99.64 156	99.69 152	99.37 56	99.89 177	96.66 314	99.87 145	99.69 84

LGP-MVS_train				99.74 80	99.82 73	99.63 116		99.73 113	97.56 324	99.64 156	99.69 152	99.37 56	99.89 177	96.66 314	99.87 145	99.69 84

TinyColmap		98.97 215	98.93 208	99.07 279	99.46 255	98.19 299	97.75 356	99.75 104	98.79 228	99.54 200	99.70 146	98.97 106	99.62 369	96.63 317	99.83 170	99.41 240

LF4IMVS		99.01 209	98.92 212	99.27 247	99.71 144	99.28 197	98.59 282	99.77 94	98.32 284	99.39 243	99.41 274	98.62 149	99.84 255	96.62 318	99.84 162	98.69 354

NCCC		98.82 235	98.57 246	99.58 157	99.21 319	99.31 192	98.61 277	99.25 308	98.65 242	98.43 350	99.26 312	97.86 232	99.81 294	96.55 319	99.27 326	99.61 149

OPU-MVS				99.29 242	99.12 334	99.44 159	99.20 167				99.40 278	99.00 100	98.84 397	96.54 320	99.60 269	99.58 165

F-COLMAP		98.74 243	98.45 257	99.62 145	99.57 202	99.47 148	98.84 254	99.65 157	96.31 363	98.93 306	99.19 326	97.68 245	99.87 205	96.52 321	99.37 311	99.53 189

ADS-MVSNet2		97.78 310	97.66 317	98.12 344	99.14 330	95.36 375	99.22 164	98.75 345	96.97 351	98.25 355	99.64 179	90.90 358	99.94 78	96.51 322	99.56 276	99.08 318

ADS-MVSNet		97.72 315	97.67 316	97.86 349	99.14 330	94.65 381	99.22 164	98.86 339	96.97 351	98.25 355	99.64 179	90.90 358	99.84 255	96.51 322	99.56 276	99.08 318

PatchMatch-RL		98.68 250	98.47 255	99.30 241	99.44 260	99.28 197	98.14 323	99.54 222	97.12 349	99.11 291	99.25 314	97.80 237	99.70 334	96.51 322	99.30 320	98.93 338

CMPMVS	77.52 23	98.50 268	98.19 283	99.41 209	98.33 388	99.56 136	99.01 228	99.59 193	95.44 373	99.57 186	99.80 83	95.64 308	99.46 389	96.47 325	99.92 106	99.21 282
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SF-MVS		99.10 193	98.93 208	99.62 145	99.58 192	99.51 144	99.13 193	99.65 157	97.97 304	99.42 231	99.61 206	98.86 117	99.87 205	96.45 326	99.68 243	99.49 212

FE-MVS		97.85 307	97.42 320	99.15 265	99.44 260	98.75 260	99.77 15	98.20 368	95.85 368	99.33 252	99.80 83	88.86 375	99.88 191	96.40 327	99.12 334	98.81 348

DPE-MVS		99.14 183	98.92 212	99.82 38	99.57 202	99.77 54	98.74 271	99.60 187	98.55 252	99.76 108	99.69 152	98.23 206	99.92 117	96.39 328	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					97.59 397	89.02 406			93.47 386		98.30 385		99.84 255	96.38 329

AllTest		99.21 164	99.07 174	99.63 136	99.78 106	99.64 111	99.12 197	99.83 61	98.63 244	99.63 160	99.72 131	98.68 140	99.75 321	96.38 329	99.83 170	99.51 202

TestCases				99.63 136	99.78 106	99.64 111		99.83 61	98.63 244	99.63 160	99.72 131	98.68 140	99.75 321	96.38 329	99.83 170	99.51 202

testdata				99.42 202	99.51 229	98.93 245		99.30 297	96.20 364	98.87 316	99.40 278	98.33 195	99.89 177	96.29 332	99.28 323	99.44 230

dp		96.86 335	97.07 328	96.24 378	98.68 378	90.30 404	99.19 171	98.38 364	97.35 338	98.23 357	99.59 219	87.23 380	99.82 279	96.27 333	98.73 360	98.59 358

tpmvs		97.39 324	97.69 314	96.52 374	98.41 385	91.76 394	99.30 135	98.94 338	97.74 318	97.85 374	99.55 242	92.40 344	99.73 326	96.25 334	98.73 360	98.06 383

KD-MVS_2432*1600		95.89 354	95.41 357	97.31 364	94.96 401	93.89 384	97.09 383	99.22 315	97.23 343	98.88 313	99.04 343	79.23 399	99.54 380	96.24 335	96.81 391	98.50 367

miper_refine_blended		95.89 354	95.41 357	97.31 364	94.96 401	93.89 384	97.09 383	99.22 315	97.23 343	98.88 313	99.04 343	79.23 399	99.54 380	96.24 335	96.81 391	98.50 367

ACMP	97.51 14	99.05 199	98.84 222	99.67 110	99.78 106	99.55 139	98.88 249	99.66 148	97.11 350	99.47 219	99.60 214	99.07 94	99.89 177	96.18 337	99.85 157	99.58 165
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 226	98.72 232	99.44 196	99.39 272	99.42 166	98.58 283	99.64 163	97.31 340	99.44 225	99.62 197	98.59 154	99.69 340	96.17 338	99.79 198	99.22 280

DP-MVS Recon		98.50 268	98.23 276	99.31 238	99.49 240	99.46 152	98.56 288	99.63 165	94.86 382	98.85 318	99.37 286	97.81 236	99.59 375	96.08 339	99.44 301	98.88 342

tpm cat1		96.78 337	96.98 331	96.16 379	98.85 364	90.59 403	99.08 212	99.32 290	92.37 388	97.73 380	99.46 267	91.15 354	99.69 340	96.07 340	98.80 351	98.21 378

tpm2		96.35 347	96.22 343	96.73 372	98.88 363	91.75 395	99.21 166	98.51 357	93.27 387	97.89 371	99.21 323	84.83 391	99.70 334	96.04 341	98.18 378	98.75 353

dmvs_re		98.69 249	98.48 254	99.31 238	99.55 214	99.42 166	99.54 84	98.38 364	99.32 154	98.72 332	98.71 372	96.76 283	99.21 392	96.01 342	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	99.82 279	96.01 342	99.96 71	99.11 306

ITE_SJBPF				99.38 218	99.63 176	99.44 159		99.73 113	98.56 251	99.33 252	99.53 246	98.88 116	99.68 350	96.01 342	99.65 254	99.02 331

test_prior2							97.95 345		97.87 313	98.05 365	99.05 341	97.90 229		95.99 345	99.49 296

testdata2													99.89 177	95.99 345

原ACMM1				99.37 221	99.47 251	98.87 252		99.27 302	96.74 358	98.26 354	99.32 299	97.93 228	99.82 279	95.96 347	99.38 309	99.43 236

新几何1				99.52 177	99.50 235	99.22 211		99.26 305	95.66 372	98.60 341	99.28 307	97.67 246	99.89 177	95.95 348	99.32 318	99.45 225

MP-MVS		99.06 196	98.83 224	99.76 65	99.76 118	99.71 84	99.32 127	99.50 244	98.35 278	98.97 302	99.48 260	98.37 188	99.92 117	95.95 348	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		95.60 362	94.59 365	98.61 321	98.66 379	97.45 338	98.54 292	97.90 374	98.53 256	96.54 390	96.47 405	70.62 406	99.81 294	95.91 350	98.15 379	98.56 362

wuyk23d		97.58 319	99.13 152	92.93 382	99.69 156	99.49 146	99.52 86	99.77 94	97.97 304	99.96 23	99.79 93	99.84 12	99.94 78	95.85 351	99.82 179	79.36 398

HQP_MVS		98.90 226	98.68 236	99.55 169	99.58 192	99.24 208	98.80 264	99.54 222	98.94 207	99.14 287	99.25 314	97.24 265	99.82 279	95.84 352	99.78 203	99.60 153

plane_prior5								99.54 222					99.82 279	95.84 352	99.78 203	99.60 153

无先验							98.01 337	99.23 312	95.83 369				99.85 240	95.79 354		99.44 230

CPTT-MVS		98.74 243	98.44 258	99.64 129	99.61 181	99.38 176	99.18 172	99.55 216	96.49 359	99.27 266	99.37 286	97.11 273	99.92 117	95.74 355	99.67 249	99.62 139

PLC	97.35 16	98.36 282	97.99 294	99.48 186	99.32 297	99.24 208	98.50 297	99.51 240	95.19 378	98.58 343	98.96 357	96.95 278	99.83 270	95.63 356	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		98.57 260	98.34 269	99.28 244	99.18 326	99.10 229	98.34 307	99.41 266	98.48 262	98.52 346	98.98 353	97.05 275	99.78 307	95.59 357	99.50 294	98.96 334

1314		98.00 304	97.90 306	98.27 340	98.90 358	97.45 338	99.30 135	99.06 332	94.98 379	97.21 385	99.12 333	98.43 179	99.67 355	95.58 358	98.56 367	97.71 387

PVSNet_0	95.53 19	95.85 357	95.31 359	97.47 358	98.78 372	93.48 388	95.72 393	99.40 273	96.18 365	97.37 381	97.73 394	95.73 307	99.58 376	95.49 359	81.40 400	99.36 251

MAR-MVS		98.24 292	97.92 304	99.19 260	98.78 372	99.65 108	99.17 177	99.14 326	95.36 374	98.04 366	98.81 368	97.47 255	99.72 328	95.47 360	99.06 337	98.21 378
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	98.23 293	97.89 307	99.26 250	99.19 324	99.26 201	99.65 59	99.69 137	91.33 391	98.14 363	99.77 108	98.28 199	99.96 55	95.41 361	99.55 280	98.58 360

train_agg		98.35 285	97.95 298	99.57 163	99.35 282	99.35 186	98.11 327	99.41 266	94.90 380	97.92 369	98.99 350	98.02 221	99.85 240	95.38 362	99.44 301	99.50 207

9.14			98.64 237		99.45 259		98.81 261	99.60 187	97.52 329	99.28 265	99.56 235	98.53 166	99.83 270	95.36 363	99.64 256

APD-MVS		98.87 231	98.59 242	99.71 100	99.50 235	99.62 118	99.01 228	99.57 205	96.80 357	99.54 200	99.63 190	98.29 198	99.91 141	95.24 364	99.71 232	99.61 149
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 362								95.20 365

AdaColmap		98.60 255	98.35 268	99.38 218	99.12 334	99.22 211	98.67 276	99.42 265	97.84 316	98.81 322	99.27 309	97.32 263	99.81 294	95.14 366	99.53 287	99.10 308

test9_res														95.10 367	99.44 301	99.50 207

CDPH-MVS		98.56 261	98.20 280	99.61 148	99.50 235	99.46 152	98.32 309	99.41 266	95.22 376	99.21 277	99.10 337	98.34 193	99.82 279	95.09 368	99.66 252	99.56 172

BH-untuned		98.22 294	98.09 289	98.58 325	99.38 275	97.24 344	98.55 289	98.98 337	97.81 317	99.20 282	98.76 370	97.01 276	99.65 365	94.83 369	98.33 372	98.86 344

BP-MVS														94.73 370

HQP-MVS		98.36 282	98.02 293	99.39 215	99.31 298	98.94 242	97.98 341	99.37 281	97.45 332	98.15 359	98.83 366	96.67 284	99.70 334	94.73 370	99.67 249	99.53 189

QAPM		98.40 280	97.99 294	99.65 122	99.39 272	99.47 148	99.67 49	99.52 236	91.70 390	98.78 328	99.80 83	98.55 160	99.95 64	94.71 372	99.75 211	99.53 189

agg_prior2														94.58 373	99.46 300	99.50 207

myMVS_eth3d		95.63 360	94.73 362	98.34 335	98.50 383	96.36 362	98.60 279	99.21 318	97.89 310	96.76 388	96.37 406	72.10 405	99.57 377	94.38 374	98.73 360	99.09 312

BH-RMVSNet		98.41 278	98.14 286	99.21 257	99.21 319	98.47 280	98.60 279	98.26 367	98.35 278	98.93 306	99.31 301	97.20 270	99.66 359	94.32 375	99.10 336	99.51 202

E-PMN		97.14 331	97.43 319	96.27 377	98.79 370	91.62 396	95.54 394	99.01 336	99.44 136	98.88 313	99.12 333	92.78 338	99.68 350	94.30 376	99.03 340	97.50 388

MG-MVS		98.52 265	98.39 263	98.94 291	99.15 329	97.39 341	98.18 318	99.21 318	98.89 217	99.23 272	99.63 190	97.37 261	99.74 323	94.22 377	99.61 266	99.69 84

API-MVS		98.38 281	98.39 263	98.35 333	98.83 365	99.26 201	99.14 187	99.18 322	98.59 249	98.66 337	98.78 369	98.61 151	99.57 377	94.14 378	99.56 276	96.21 395

PAPM_NR		98.36 282	98.04 291	99.33 231	99.48 245	98.93 245	98.79 267	99.28 301	97.54 327	98.56 345	98.57 377	97.12 272	99.69 340	94.09 379	98.90 349	99.38 245

ZD-MVS					99.43 264	99.61 124		99.43 263	96.38 361	99.11 291	99.07 339	97.86 232	99.92 117	94.04 380	99.49 296

DPM-MVS		98.28 288	97.94 302	99.32 235	99.36 280	99.11 224	97.31 377	98.78 344	96.88 353	98.84 319	99.11 336	97.77 239	99.61 373	94.03 381	99.36 312	99.23 278

gg-mvs-nofinetune		95.87 356	95.17 360	97.97 346	98.19 391	96.95 351	99.69 42	89.23 405	99.89 36	96.24 393	99.94 16	81.19 395	99.51 385	93.99 382	98.20 375	97.44 389

PMVS	92.94 21	98.82 235	98.81 226	98.85 305	99.84 62	97.99 313	99.20 167	99.47 252	99.71 82	99.42 231	99.82 73	98.09 215	99.47 387	93.88 383	99.85 157	99.07 323
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 334	97.28 323	95.99 380	98.76 374	91.03 399	95.26 395	98.61 352	99.34 151	98.92 309	98.88 364	93.79 326	99.66 359	92.87 384	99.05 338	97.30 392

BH-w/o		97.20 328	97.01 330	97.76 352	99.08 343	95.69 372	98.03 336	98.52 356	95.76 370	97.96 368	98.02 389	95.62 309	99.47 387	92.82 385	97.25 390	98.12 382

TR-MVS		97.44 323	97.15 327	98.32 336	98.53 382	97.46 337	98.47 299	97.91 373	96.85 354	98.21 358	98.51 381	96.42 293	99.51 385	92.16 386	97.29 389	97.98 384

OpenMVS_ROB	97.31 17	97.36 326	96.84 336	98.89 304	99.29 304	99.45 157	98.87 250	99.48 249	86.54 396	99.44 225	99.74 120	97.34 262	99.86 223	91.61 387	99.28 323	97.37 391

GG-mvs-BLEND				97.36 361	97.59 397	96.87 354	99.70 35	88.49 406		94.64 399	97.26 401	80.66 396	99.12 393	91.50 388	96.50 395	96.08 397

DeepMVS_CX				97.98 345	99.69 156	96.95 351		99.26 305	75.51 398	95.74 396	98.28 386	96.47 291	99.62 369	91.23 389	97.89 384	97.38 390

PAPR		97.56 320	97.07 328	99.04 282	98.80 369	98.11 306	97.63 361	99.25 308	94.56 385	98.02 367	98.25 387	97.43 257	99.68 350	90.90 390	98.74 358	99.33 258

MVS		95.72 359	94.63 364	98.99 285	98.56 381	97.98 319	99.30 135	98.86 339	72.71 399	97.30 382	99.08 338	98.34 193	99.74 323	89.21 391	98.33 372	99.26 272

thres600view7		96.60 342	96.16 344	97.93 347	99.63 176	96.09 368	99.18 172	97.57 378	98.77 232	98.72 332	97.32 399	87.04 382	99.72 328	88.57 392	98.62 365	97.98 384

FPMVS		96.32 348	95.50 355	98.79 313	99.60 183	98.17 302	98.46 303	98.80 343	97.16 347	96.28 391	99.63 190	82.19 394	99.09 394	88.45 393	98.89 350	99.10 308

PCF-MVS	96.03 18	96.73 339	95.86 350	99.33 231	99.44 260	99.16 219	96.87 388	99.44 260	86.58 395	98.95 304	99.40 278	94.38 320	99.88 191	87.93 394	99.80 193	98.95 336
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 346	96.03 347	97.47 358	99.63 176	95.93 369	99.18 172	97.57 378	98.75 236	98.70 335	97.31 400	87.04 382	99.67 355	87.62 395	98.51 369	96.81 393

tfpn200view9		96.30 349	95.89 348	97.53 356	99.58 192	96.11 366	99.00 231	97.54 381	98.43 264	98.52 346	96.98 402	86.85 384	99.67 355	87.62 395	98.51 369	96.81 393

thres400		96.40 345	95.89 348	97.92 348	99.58 192	96.11 366	99.00 231	97.54 381	98.43 264	98.52 346	96.98 402	86.85 384	99.67 355	87.62 395	98.51 369	97.98 384

thres200		96.09 352	95.68 353	97.33 363	99.48 245	96.22 365	98.53 294	97.57 378	98.06 299	98.37 352	96.73 404	86.84 386	99.61 373	86.99 398	98.57 366	96.16 396

MVE	92.54 22	96.66 341	96.11 345	98.31 338	99.68 164	97.55 334	97.94 346	95.60 392	99.37 148	90.68 401	98.70 373	96.56 287	98.61 399	86.94 399	99.55 280	98.77 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 327	96.83 337	98.59 323	99.46 255	97.55 334	99.25 156	96.84 386	98.78 230	97.24 384	97.67 395	97.11 273	98.97 396	86.59 400	98.54 368	99.27 271

PAPM		95.61 361	94.71 363	98.31 338	99.12 334	96.63 357	96.66 391	98.46 360	90.77 392	96.25 392	98.68 374	93.01 336	99.69 340	81.60 401	97.86 386	98.62 356

test123		29.31 367	33.05 372	18.08 384	25.93 407	12.24 409	97.53 367	10.93 409	11.78 402	24.21 403	50.08 412	21.04 407	8.60 403	23.51 402	32.43 402	33.39 399

testmvs		28.94 368	33.33 370	15.79 385	26.03 406	9.81 410	96.77 389	15.67 408	11.55 403	23.87 404	50.74 411	19.03 408	8.53 404	23.21 403	33.07 401	29.03 400

test_blank		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet_test		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

cdsmvs_eth3d_5k		24.88 369	33.17 371	0.00 386	0.00 408	0.00 411	0.00 397	99.62 168	0.00 404	0.00 405	99.13 329	99.82 13	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		16.61 370	22.14 373	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	99.28 66	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

ab-mvs-re		8.26 379	11.02 382	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	99.16 327	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		8.33 371	11.11 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

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					0.00 408

eth-test					0.00 408

test_241102_ONE					99.69 156	99.82 35		99.54 222	99.12 191	99.82 81	99.49 257	98.91 112	99.52 384

save fliter					99.53 222	99.25 204	98.29 311	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											52.41 409	90.25 367	99.86 223

patchmatchnet-post											99.62 197	90.58 363	99.94 78

MTMP							99.09 209	98.59 355

TEST9					99.35 282	99.35 186	98.11 327	99.41 266	94.83 383	97.92 369	98.99 350	98.02 221	99.85 240

test_8					99.34 290	99.31 192	98.08 331	99.40 273	94.90 380	97.87 373	98.97 355	98.02 221	99.84 255

agg_prior					99.35 282	99.36 183		99.39 276		97.76 379			99.85 240

test_prior4						99.19 217	98.00 339

test_prior				99.46 190	99.35 282	99.22 211		99.39 276					99.69 340			99.48 216

新几何2							98.04 335

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							97.92 348

test222					99.51 229	99.08 231	97.83 354	99.29 298	95.21 377	98.68 336	99.31 301	97.28 264			99.38 309	99.43 236

segment_acmp												98.37 188

testdata1							97.72 357		97.86 315

test12				99.54 174	99.29 304	99.33 189		99.16 324		98.43 350		97.54 253	99.82 279		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			98.36 273	99.14 287

plane_prior2							98.80 264		98.94 207

plane_prior1					99.51 229

plane_prior						99.24 208	98.42 304		97.87 313						99.71 232

n2								0.00 410

nn								0.00 410

door-mid								99.83 61

test11								99.29 298

door								99.77 94

HQP5-MVS						98.94 242

HQP-NCC					99.31 298		97.98 341		97.45 332	98.15 359

ACMP_Plane					99.31 298		97.98 341		97.45 332	98.15 359

HQP4-MVS										98.15 359			99.70 334			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