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 20	99.98 3	99.75 66	99.70 34	100.00 1	99.73 76	100.00 1	99.89 34	99.79 16	99.88 192	99.98 1	100.00 1	99.98 3

test_fmvs2		99.72 36	99.85 16	99.34 230	99.91 29	98.08 316	99.48 100	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	99.53 173	99.96 7	98.62 277	99.67 49	100.00 1	99.95 18	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 60	99.12 203	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	99.27 250	99.93 24	97.84 328	99.34 127	100.00 1	99.99 2	99.99 7	99.82 77	99.87 9	99.99 8	99.97 4	99.99 16	99.97 7

test_vis1_n		99.68 45	99.79 27	99.36 227	99.94 18	98.18 305	99.52 87	100.00 1	99.86 45	100.00 1	99.88 43	98.99 106	99.96 54	99.97 4	99.96 66	99.95 11

test_fmvs1_n		99.68 45	99.81 23	99.28 247	99.95 15	97.93 325	99.49 99	100.00 1	99.82 60	99.99 7	99.89 34	99.21 75	99.98 21	99.97 4	99.98 39	99.93 16

test_f		99.75 32	99.88 6	99.37 223	99.96 7	98.21 302	99.51 94	100.00 1	99.94 21	100.00 1	99.93 17	99.58 36	99.94 79	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 72	99.01 232	99.99 10	99.99 2	99.98 13	99.88 43	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 38	99.88 42	99.55 138	99.17 183	99.98 11	99.99 2	99.96 24	99.84 66	99.96 3	99.99 8	99.96 9	99.99 16	99.88 26

test_cas_vis1_n_1920		99.76 31	99.86 12	99.45 194	99.93 24	98.40 290	99.30 141	99.98 11	99.94 21	99.99 7	99.89 34	99.80 15	99.97 34	99.96 9	99.97 53	99.97 7

fmvsm_l_conf0.5_n		99.80 23	99.78 31	99.85 26	99.88 42	99.66 100	99.11 207	99.91 35	99.98 13	99.96 24	99.64 184	99.60 34	99.99 8	99.95 12	99.99 16	99.88 26

test_fmvsm_n_1920		99.84 15	99.85 16	99.83 31	99.82 70	99.70 89	99.17 183	99.97 18	99.99 2	99.96 24	99.82 77	99.94 4	100.00 1	99.95 12	100.00 1	99.80 47

test_fmvs1		99.48 89	99.65 50	98.97 291	99.54 217	97.16 351	99.11 207	99.98 11	99.78 70	99.96 24	99.81 83	98.72 141	99.97 34	99.95 12	99.97 53	99.79 54

mvsany_test3		99.85 11	99.88 6	99.75 72	99.95 15	99.37 181	99.53 86	99.98 11	99.77 74	99.99 7	99.95 13	99.85 10	99.94 79	99.95 12	99.98 39	99.94 14

fmvsm_l_conf0.5_n_a		99.80 23	99.79 27	99.84 28	99.88 42	99.64 109	99.12 203	99.91 35	99.98 13	99.95 32	99.67 172	99.67 27	99.99 8	99.94 16	99.99 16	99.88 26

MM		99.18 176	99.05 183	99.55 167	99.35 285	98.81 257	99.05 221	97.79 387	99.99 2	99.48 218	99.59 224	96.29 305	99.95 64	99.94 16	99.98 39	99.88 26

test_fmvsmconf_n		99.85 11	99.84 19	99.88 16	99.91 29	99.73 75	98.97 244	99.98 11	99.99 2	99.96 24	99.85 60	99.93 7	99.99 8	99.94 16	99.99 16	99.93 16

fmvsm_s_conf0.1_n_a		99.85 11	99.83 20	99.91 2	99.95 15	99.82 34	99.10 210	99.98 11	99.99 2	99.98 13	99.91 24	99.68 26	99.93 96	99.93 19	99.99 16	99.99 1

fmvsm_s_conf0.1_n		99.86 9	99.85 16	99.89 10	99.93 24	99.78 48	99.07 220	99.98 11	99.99 2	99.98 13	99.90 29	99.88 8	99.92 118	99.93 19	99.99 16	99.98 3

fmvsm_s_conf0.5_n_a		99.82 21	99.79 27	99.89 10	99.85 56	99.82 34	99.03 227	99.96 23	99.99 2	99.97 20	99.84 66	99.58 36	99.93 96	99.92 21	99.98 39	99.93 16

fmvsm_s_conf0.5_n		99.83 19	99.81 23	99.87 20	99.85 56	99.78 48	99.03 227	99.96 23	99.99 2	99.97 20	99.84 66	99.78 17	99.92 118	99.92 21	99.99 16	99.92 20

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 21	100.00 1	99.87 30

MVStest1		98.22 299	98.09 294	98.62 326	99.04 353	96.23 371	99.20 172	99.92 31	99.44 140	99.98 13	99.87 48	85.87 394	99.67 363	99.91 24	99.57 279	99.95 11

v1921920		99.56 73	99.57 72	99.55 167	99.75 127	99.11 227	99.05 221	99.61 182	99.15 192	99.88 62	99.71 142	99.08 93	99.87 206	99.90 25	99.97 53	99.66 105

v1240		99.56 73	99.58 68	99.51 178	99.80 84	99.00 238	99.00 235	99.65 164	99.15 192	99.90 50	99.75 119	99.09 90	99.88 192	99.90 25	99.96 66	99.67 96

v10		99.69 42	99.69 43	99.66 115	99.81 78	99.39 176	99.66 53	99.75 107	99.60 119	99.92 44	99.87 48	98.75 136	99.86 225	99.90 25	99.99 16	99.73 70

v1192		99.57 70	99.57 72	99.57 161	99.77 111	99.22 213	99.04 224	99.60 193	99.18 181	99.87 70	99.72 134	99.08 93	99.85 243	99.89 28	99.98 39	99.66 105

v144192		99.55 76	99.54 79	99.58 155	99.78 103	99.20 218	99.11 207	99.62 175	99.18 181	99.89 54	99.72 134	98.66 149	99.87 206	99.88 29	99.97 53	99.66 105

v8		99.68 45	99.69 43	99.65 120	99.80 84	99.40 172	99.66 53	99.76 102	99.64 105	99.93 38	99.85 60	98.66 149	99.84 258	99.88 29	99.99 16	99.71 75

v1144		99.54 79	99.53 83	99.59 152	99.79 96	99.28 199	99.10 210	99.61 182	99.20 179	99.84 76	99.73 126	98.67 147	99.84 258	99.86 31	99.98 39	99.64 123

SSC-MVS		99.52 82	99.42 102	99.83 31	99.86 52	99.65 106	99.52 87	99.81 79	99.87 42	99.81 88	99.79 96	96.78 286	99.99 8	99.83 32	99.51 295	99.86 32

v7n		99.82 21	99.80 26	99.88 16	99.96 7	99.84 24	99.82 8	99.82 70	99.84 54	99.94 35	99.91 24	99.13 86	99.96 54	99.83 32	99.99 16	99.83 40

v2v482		99.50 84	99.47 88	99.58 155	99.78 103	99.25 206	99.14 193	99.58 208	99.25 170	99.81 88	99.62 202	98.24 205	99.84 258	99.83 32	99.97 53	99.64 123

test_vis1_rt		99.45 100	99.46 92	99.41 212	99.71 142	98.63 276	98.99 240	99.96 23	99.03 205	99.95 32	99.12 337	98.75 136	99.84 258	99.82 35	99.82 180	99.77 60

tt0805		99.63 59	99.57 72	99.81 38	99.87 49	99.88 12	99.58 77	98.70 353	99.72 81	99.91 47	99.60 219	99.43 48	99.81 297	99.81 36	99.53 291	99.73 70

V42		99.56 73	99.54 79	99.63 134	99.79 96	99.46 152	99.39 115	99.59 199	99.24 172	99.86 71	99.70 150	98.55 163	99.82 282	99.79 37	99.95 79	99.60 153

mvs_tets		99.90 2	99.90 3	99.90 7	99.96 7	99.79 45	99.72 29	99.88 47	99.92 26	99.98 13	99.93 17	99.94 4	99.98 21	99.77 38	100.00 1	99.92 20

WB-MVS		99.44 102	99.32 120	99.80 43	99.81 78	99.61 123	99.47 103	99.81 79	99.82 60	99.71 134	99.72 134	96.60 290	99.98 21	99.75 39	99.23 335	99.82 46

PS-MVSNAJss		99.84 15	99.82 22	99.89 10	99.96 7	99.77 53	99.68 45	99.85 57	99.95 18	99.98 13	99.92 21	99.28 66	99.98 21	99.75 39	100.00 1	99.94 14

jajsoiax		99.89 3	99.89 5	99.89 10	99.96 7	99.78 48	99.70 34	99.86 52	99.89 36	99.98 13	99.90 29	99.94 4	99.98 21	99.75 39	100.00 1	99.90 22

ANet_high		99.88 6	99.87 10	99.91 2	99.99 1	99.91 4	99.65 58	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 45	99.70 40	99.64 127	99.57 203	99.83 29	99.78 13	99.97 18	99.92 26	99.50 215	99.38 288	99.57 38	99.95 64	99.69 43	99.90 114	99.15 299

MVS_0304		98.61 258	98.30 278	99.52 175	97.88 410	98.95 245	98.76 274	94.11 409	99.84 54	99.32 258	99.57 234	95.57 316	99.95 64	99.68 44	99.98 39	99.68 88

CS-MVS		99.67 51	99.70 40	99.58 155	99.53 223	99.84 24	99.79 12	99.96 23	99.90 30	99.61 175	99.41 278	99.51 45	99.95 64	99.66 45	99.89 125	98.96 342

mamv4		99.73 35	99.74 37	99.70 101	99.66 170	99.87 14	99.69 41	99.93 30	99.93 23	99.93 38	99.86 55	99.07 95	100.00 1	99.66 45	99.92 102	99.24 274

pmmvs6		99.86 9	99.86 12	99.83 31	99.94 18	99.90 7	99.83 6	99.91 35	99.85 51	99.94 35	99.95 13	99.73 21	99.90 159	99.65 47	99.97 53	99.69 82

MIMVSNet1		99.66 52	99.62 56	99.80 43	99.94 18	99.87 14	99.69 41	99.77 97	99.78 70	99.93 38	99.89 34	97.94 231	99.92 118	99.65 47	99.98 39	99.62 139

EC-MVSNet		99.69 42	99.69 43	99.68 105	99.71 142	99.91 4	99.76 19	99.96 23	99.86 45	99.51 213	99.39 286	99.57 38	99.93 96	99.64 49	99.86 154	99.20 288

K. test v3		98.87 237	98.60 246	99.69 103	99.93 24	99.46 152	99.74 23	94.97 404	99.78 70	99.88 62	99.88 43	93.66 335	99.97 34	99.61 50	99.95 79	99.64 123

KD-MVS_self_test		99.63 59	99.59 65	99.76 62	99.84 59	99.90 7	99.37 122	99.79 88	99.83 58	99.88 62	99.85 60	98.42 184	99.90 159	99.60 51	99.73 224	99.49 210

Anonymous20240521		99.44 102	99.42 102	99.49 182	99.89 37	98.96 244	99.62 63	99.76 102	99.85 51	99.82 81	99.88 43	96.39 300	99.97 34	99.59 52	99.98 39	99.55 175

TransMVSNet (Re)		99.78 27	99.77 33	99.81 38	99.91 29	99.85 19	99.75 21	99.86 52	99.70 88	99.91 47	99.89 34	99.60 34	99.87 206	99.59 52	99.74 219	99.71 75

OurMVSNet-221017-0		99.75 32	99.71 39	99.84 28	99.96 7	99.83 29	99.83 6	99.85 57	99.80 66	99.93 38	99.93 17	98.54 165	99.93 96	99.59 52	99.98 39	99.76 65

EU-MVSNet		99.39 119	99.62 56	98.72 322	99.88 42	96.44 365	99.56 82	99.85 57	99.90 30	99.90 50	99.85 60	98.09 219	99.83 273	99.58 55	99.95 79	99.90 22

mvs_anonymous		99.28 143	99.39 105	98.94 295	99.19 327	97.81 330	99.02 230	99.55 221	99.78 70	99.85 73	99.80 86	98.24 205	99.86 225	99.57 56	99.50 298	99.15 299

test1111		97.74 317	98.16 290	96.49 387	99.60 184	89.86 417	99.71 33	91.21 413	99.89 36	99.88 62	99.87 48	93.73 334	99.90 159	99.56 57	99.99 16	99.70 78

lessismore_v0				99.64 127	99.86 52	99.38 178		90.66 414		99.89 54	99.83 70	94.56 325	99.97 34	99.56 57	99.92 102	99.57 170

mvsany_test1		99.44 102	99.45 94	99.40 214	99.37 279	98.64 275	97.90 363	99.59 199	99.27 166	99.92 44	99.82 77	99.74 20	99.93 96	99.55 59	99.87 146	99.63 128

MVSMamba_PlusPlus		99.55 76	99.58 68	99.47 188	99.68 162	99.40 172	99.52 87	99.70 134	99.92 26	99.77 107	99.86 55	98.28 200	99.96 54	99.54 60	99.90 114	99.05 328

iter_conf05		99.64 58	99.65 50	99.60 149	99.68 162	99.62 116	99.82 8	99.89 42	99.92 26	99.93 38	99.86 55	98.28 200	99.96 54	99.54 60	99.91 112	99.23 278

pm-mvs1		99.79 26	99.79 27	99.78 52	99.91 29	99.83 29	99.76 19	99.87 49	99.73 76	99.89 54	99.87 48	99.63 29	99.87 206	99.54 60	99.92 102	99.63 128

LTVRE_ROB	99.19 1	99.88 6	99.87 10	99.88 16	99.91 29	99.90 7	99.96 1	99.92 31	99.90 30	99.97 20	99.87 48	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 89	99.65 50	98.95 294	99.71 142	97.27 348	99.50 95	99.82 70	99.59 121	99.41 238	99.85 60	99.62 31	100.00 1	99.53 64	99.89 125	99.59 160

test2506		94.73 377	94.59 378	95.15 393	99.59 188	85.90 419	99.75 21	74.01 421	99.89 36	99.71 134	99.86 55	79.00 410	99.90 159	99.52 65	99.99 16	99.65 113

UniMVSNet_ETH3D		99.85 11	99.83 20	99.90 7	99.89 37	99.91 4	99.89 4	99.71 129	99.93 23	99.95 32	99.89 34	99.71 22	99.96 54	99.51 66	99.97 53	99.84 36

FC-MVSNet-test		99.70 40	99.65 50	99.86 24	99.88 42	99.86 18	99.72 29	99.78 94	99.90 30	99.82 81	99.83 70	98.45 180	99.87 206	99.51 66	99.97 53	99.86 32

UA-Net		99.78 27	99.76 36	99.86 24	99.72 139	99.71 82	99.91 3	99.95 28	99.96 17	99.71 134	99.91 24	99.15 81	99.97 34	99.50 68	100.00 1	99.90 22

PMMVS2		99.48 89	99.45 94	99.57 161	99.76 115	98.99 239	98.09 340	99.90 40	98.95 213	99.78 101	99.58 227	99.57 38	99.93 96	99.48 69	99.95 79	99.79 54

VPA-MVSNet		99.66 52	99.62 56	99.79 49	99.68 162	99.75 66	99.62 63	99.69 142	99.85 51	99.80 92	99.81 83	98.81 124	99.91 140	99.47 70	99.88 134	99.70 78

ECVR-MVS		97.73 318	98.04 297	96.78 381	99.59 188	90.81 413	99.72 29	90.43 415	99.89 36	99.86 71	99.86 55	93.60 336	99.89 178	99.46 71	99.99 16	99.65 113

nrg030		99.70 40	99.66 48	99.82 35	99.76 115	99.84 24	99.61 68	99.70 134	99.93 23	99.78 101	99.68 168	99.10 88	99.78 310	99.45 72	99.96 66	99.83 40

TAMVS		99.49 87	99.45 94	99.63 134	99.48 246	99.42 166	99.45 107	99.57 210	99.66 101	99.78 101	99.83 70	97.85 238	99.86 225	99.44 73	99.96 66	99.61 149

GeoE		99.69 42	99.66 48	99.78 52	99.76 115	99.76 60	99.60 74	99.82 70	99.46 137	99.75 116	99.56 238	99.63 29	99.95 64	99.43 74	99.88 134	99.62 139

new-patchmatchnet		99.35 129	99.57 72	98.71 324	99.82 70	96.62 363	98.55 297	99.75 107	99.50 128	99.88 62	99.87 48	99.31 62	99.88 192	99.43 74	100.00 1	99.62 139

test20.03		99.55 76	99.54 79	99.58 155	99.79 96	99.37 181	99.02 230	99.89 42	99.60 119	99.82 81	99.62 202	98.81 124	99.89 178	99.43 74	99.86 154	99.47 218

MVSFormer		99.41 113	99.44 97	99.31 240	99.57 203	98.40 290	99.77 15	99.80 82	99.73 76	99.63 160	99.30 307	98.02 225	99.98 21	99.43 74	99.69 239	99.55 175

test_djsdf		99.84 15	99.81 23	99.91 2	99.94 18	99.84 24	99.77 15	99.80 82	99.73 76	99.97 20	99.92 21	99.77 19	99.98 21	99.43 74	100.00 1	99.90 22

SDMVSNet		99.77 30	99.77 33	99.76 62	99.80 84	99.65 106	99.63 60	99.86 52	99.97 15	99.89 54	99.89 34	99.52 44	99.99 8	99.42 79	99.96 66	99.65 113

Anonymous20231211		99.62 65	99.57 72	99.76 62	99.61 182	99.60 126	99.81 10	99.73 117	99.82 60	99.90 50	99.90 29	97.97 230	99.86 225	99.42 79	99.96 66	99.80 47

SixPastTwentyTwo		99.42 109	99.30 127	99.76 62	99.92 28	99.67 98	99.70 34	99.14 331	99.65 103	99.89 54	99.90 29	96.20 307	99.94 79	99.42 79	99.92 102	99.67 96

balanced_conf03		99.50 84	99.50 85	99.50 180	99.42 269	99.49 145	99.52 87	99.75 107	99.86 45	99.78 101	99.71 142	98.20 212	99.90 159	99.39 82	99.88 134	99.10 310

patch_mono-2		99.51 83	99.46 92	99.64 127	99.70 150	99.11 227	99.04 224	99.87 49	99.71 83	99.47 220	99.79 96	98.24 205	99.98 21	99.38 83	99.96 66	99.83 40

UGNet		99.38 121	99.34 115	99.49 182	98.90 364	98.90 252	99.70 34	99.35 291	99.86 45	98.57 350	99.81 83	98.50 175	99.93 96	99.38 83	99.98 39	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 38	99.89 37	99.72 80	99.59 75	99.82 70	99.39 151	99.82 81	99.84 66	99.38 54	99.91 140	99.38 83	99.93 98	99.80 47

FIs		99.65 57	99.58 68	99.84 28	99.84 59	99.85 19	99.66 53	99.75 107	99.86 45	99.74 124	99.79 96	98.27 203	99.85 243	99.37 86	99.93 98	99.83 40

sd_testset		99.78 27	99.78 31	99.80 43	99.80 84	99.76 60	99.80 11	99.79 88	99.97 15	99.89 54	99.89 34	99.53 43	99.99 8	99.36 87	99.96 66	99.65 113

anonymousdsp		99.80 23	99.77 33	99.90 7	99.96 7	99.88 12	99.73 26	99.85 57	99.70 88	99.92 44	99.93 17	99.45 47	99.97 34	99.36 87	100.00 1	99.85 35

casdiffmvs_mvg		99.68 45	99.68 46	99.69 103	99.81 78	99.59 128	99.29 148	99.90 40	99.71 83	99.79 97	99.73 126	99.54 41	99.84 258	99.36 87	99.96 66	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 49	99.88 42	99.66 100	99.69 41	99.92 31	99.67 97	99.77 107	99.75 119	99.61 32	99.98 21	99.35 90	99.98 39	99.72 72
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 67	99.64 54	99.53 173	99.79 96	98.82 256	99.58 77	99.97 18	99.95 18	99.96 24	99.76 114	98.44 181	99.99 8	99.34 91	99.96 66	99.78 56

CHOSEN 1792x2688		99.39 119	99.30 127	99.65 120	99.88 42	99.25 206	98.78 272	99.88 47	98.66 253	99.96 24	99.79 96	97.45 260	99.93 96	99.34 91	99.99 16	99.78 56

CDS-MVSNet		99.22 162	99.13 155	99.50 180	99.35 285	99.11 227	98.96 246	99.54 227	99.46 137	99.61 175	99.70 150	96.31 303	99.83 273	99.34 91	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		99.00 217	99.16 149	98.51 332	99.75 127	95.90 377	98.07 343	99.84 63	99.84 54	99.89 54	99.73 126	96.01 310	99.99 8	99.33 94	100.00 1	99.63 128

HyFIR lowres test		98.91 230	98.64 243	99.73 86	99.85 56	99.47 148	98.07 343	99.83 65	98.64 255	99.89 54	99.60 219	92.57 345	100.00 1	99.33 94	99.97 53	99.72 72

pmmvs5		99.19 172	99.11 162	99.42 205	99.76 115	98.88 253	98.55 297	99.73 117	98.82 233	99.72 129	99.62 202	96.56 291	99.82 282	99.32 96	99.95 79	99.56 172

v148		99.40 115	99.41 104	99.39 217	99.76 115	98.94 246	99.09 214	99.59 199	99.17 186	99.81 88	99.61 211	98.41 185	99.69 346	99.32 96	99.94 91	99.53 188

baseline		99.63 59	99.62 56	99.66 115	99.80 84	99.62 116	99.44 109	99.80 82	99.71 83	99.72 129	99.69 157	99.15 81	99.83 273	99.32 96	99.94 91	99.53 188

CVMVSNet		98.61 258	98.88 223	97.80 362	99.58 193	93.60 399	99.26 155	99.64 170	99.66 101	99.72 129	99.67 172	93.26 338	99.93 96	99.30 99	99.81 189	99.87 30

PS-CasMVS		99.66 52	99.58 68	99.89 10	99.80 84	99.85 19	99.66 53	99.73 117	99.62 110	99.84 76	99.71 142	98.62 153	99.96 54	99.30 99	99.96 66	99.86 32

DTE-MVSNet		99.68 45	99.61 60	99.88 16	99.80 84	99.87 14	99.67 49	99.71 129	99.72 81	99.84 76	99.78 104	98.67 147	99.97 34	99.30 99	99.95 79	99.80 47

tmp_tt		95.75 371	95.42 365	96.76 382	89.90 420	94.42 393	98.86 255	97.87 386	78.01 411	99.30 268	99.69 157	97.70 246	95.89 413	99.29 102	98.14 390	99.95 11

PEN-MVS		99.66 52	99.59 65	99.89 10	99.83 63	99.87 14	99.66 53	99.73 117	99.70 88	99.84 76	99.73 126	98.56 162	99.96 54	99.29 102	99.94 91	99.83 40

WR-MVS_H		99.61 67	99.53 83	99.87 20	99.80 84	99.83 29	99.67 49	99.75 107	99.58 122	99.85 73	99.69 157	98.18 215	99.94 79	99.28 104	99.95 79	99.83 40

IterMVS		98.97 221	99.16 149	98.42 337	99.74 133	95.64 381	98.06 345	99.83 65	99.83 58	99.85 73	99.74 122	96.10 309	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.
WBMVS		97.50 328	97.18 333	98.48 334	98.85 371	95.89 378	98.44 313	99.52 241	99.53 125	99.52 207	99.42 277	80.10 403	99.86 225	99.24 106	99.95 79	99.68 88

h-mvs33		98.61 258	98.34 274	99.44 199	99.60 184	98.67 268	99.27 153	99.44 266	99.68 93	99.32 258	99.49 260	92.50 348	100.00 1	99.24 106	96.51 407	99.65 113

hse-mvs2		98.52 271	98.30 278	99.16 266	99.29 307	98.60 278	98.77 273	99.02 339	99.68 93	99.32 258	99.04 347	92.50 348	99.85 243	99.24 106	97.87 397	99.03 333

FMVSNet1		99.66 52	99.63 55	99.73 86	99.78 103	99.77 53	99.68 45	99.70 134	99.67 97	99.82 81	99.83 70	98.98 108	99.90 159	99.24 106	99.97 53	99.53 188

casdiffmvs		99.63 59	99.61 60	99.67 108	99.79 96	99.59 128	99.13 199	99.85 57	99.79 68	99.76 111	99.72 134	99.33 61	99.82 282	99.21 110	99.94 91	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 99	99.87 20	99.76 115	99.82 34	99.57 80	99.61 182	99.54 123	99.80 92	99.64 184	97.79 242	99.95 64	99.21 110	99.94 91	99.84 36

DELS-MVS		99.34 134	99.30 127	99.48 186	99.51 230	99.36 185	98.12 336	99.53 236	99.36 156	99.41 238	99.61 211	99.22 74	99.87 206	99.21 110	99.68 244	99.20 288
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)		99.37 124	99.26 138	99.68 105	99.51 230	99.58 132	98.98 243	99.60 193	99.43 146	99.70 138	99.36 294	97.70 246	99.88 192	99.20 113	99.87 146	99.59 160

CANet		99.11 193	99.05 183	99.28 247	98.83 373	98.56 280	98.71 280	99.41 272	99.25 170	99.23 276	99.22 325	97.66 254	99.94 79	99.19 114	99.97 53	99.33 256

EI-MVSNet-UG-set		99.48 89	99.50 85	99.42 205	99.57 203	98.65 274	99.24 162	99.46 261	99.68 93	99.80 92	99.66 177	98.99 106	99.89 178	99.19 114	99.90 114	99.72 72

xiu_mvs_v1_base_debu		99.23 154	99.34 115	98.91 301	99.59 188	98.23 299	98.47 308	99.66 154	99.61 113	99.68 144	98.94 363	99.39 50	99.97 34	99.18 116	99.55 284	98.51 378

xiu_mvs_v1_base		99.23 154	99.34 115	98.91 301	99.59 188	98.23 299	98.47 308	99.66 154	99.61 113	99.68 144	98.94 363	99.39 50	99.97 34	99.18 116	99.55 284	98.51 378

xiu_mvs_v1_base_debi		99.23 154	99.34 115	98.91 301	99.59 188	98.23 299	98.47 308	99.66 154	99.61 113	99.68 144	98.94 363	99.39 50	99.97 34	99.18 116	99.55 284	98.51 378

VPNet		99.46 98	99.37 110	99.71 97	99.82 70	99.59 128	99.48 100	99.70 134	99.81 63	99.69 141	99.58 227	97.66 254	99.86 225	99.17 119	99.44 305	99.67 96

UniMVSNet_NR-MVSNet		99.37 124	99.25 140	99.72 92	99.47 252	99.56 135	98.97 244	99.61 182	99.43 146	99.67 149	99.28 311	97.85 238	99.95 64	99.17 119	99.81 189	99.65 113

DU-MVS		99.33 137	99.21 144	99.71 97	99.43 264	99.56 135	98.83 260	99.53 236	99.38 152	99.67 149	99.36 294	97.67 250	99.95 64	99.17 119	99.81 189	99.63 128

EI-MVSNet-Vis-set		99.47 97	99.49 87	99.42 205	99.57 203	98.66 271	99.24 162	99.46 261	99.67 97	99.79 97	99.65 182	98.97 110	99.89 178	99.15 122	99.89 125	99.71 75

EI-MVSNet		99.38 121	99.44 97	99.21 260	99.58 193	98.09 313	99.26 155	99.46 261	99.62 110	99.75 116	99.67 172	98.54 165	99.85 243	99.15 122	99.92 102	99.68 88

VNet		99.18 176	99.06 179	99.56 164	99.24 317	99.36 185	99.33 130	99.31 300	99.67 97	99.47 220	99.57 234	96.48 294	99.84 258	99.15 122	99.30 324	99.47 218

EG-PatchMatch MVS		99.57 70	99.56 77	99.62 143	99.77 111	99.33 191	99.26 155	99.76 102	99.32 160	99.80 92	99.78 104	99.29 64	99.87 206	99.15 122	99.91 112	99.66 105

PVSNet_Blended_VisFu		99.40 115	99.38 107	99.44 199	99.90 35	98.66 271	98.94 249	99.91 35	97.97 316	99.79 97	99.73 126	99.05 100	99.97 34	99.15 122	99.99 16	99.68 88

IterMVS-LS		99.41 113	99.47 88	99.25 256	99.81 78	98.09 313	98.85 257	99.76 102	99.62 110	99.83 80	99.64 184	98.54 165	99.97 34	99.15 122	99.99 16	99.68 88
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 88	99.76 62	99.58 193	99.64 109	99.30 141	99.63 172	99.61 113	99.71 134	99.56 238	98.76 134	99.96 54	99.14 128	99.92 102	99.68 88

MVSTER		98.47 278	98.22 283	99.24 258	99.06 350	98.35 296	99.08 217	99.46 261	99.27 166	99.75 116	99.66 177	88.61 381	99.85 243	99.14 128	99.92 102	99.52 198

Anonymous20231206		99.35 129	99.31 122	99.47 188	99.74 133	99.06 237	99.28 150	99.74 113	99.23 174	99.72 129	99.53 249	97.63 256	99.88 192	99.11 130	99.84 163	99.48 214

Syy-MVS		98.17 302	97.85 314	99.15 268	98.50 396	98.79 260	98.60 286	99.21 323	97.89 322	96.76 398	96.37 419	95.47 317	99.57 387	99.10 131	98.73 368	99.09 315

m2depth		99.48 89	99.55 78	99.29 244	99.76 115	98.16 307	99.33 130	99.95 28	99.79 68	99.36 247	99.89 34	99.13 86	99.77 318	99.09 132	99.64 257	99.93 16

MVS_Test		99.28 143	99.31 122	99.19 263	99.35 285	98.79 260	99.36 125	99.49 254	99.17 186	99.21 281	99.67 172	98.78 131	99.66 368	99.09 132	99.66 253	99.10 310

testgi		99.29 142	99.26 138	99.37 223	99.75 127	98.81 257	98.84 258	99.89 42	98.38 283	99.75 116	99.04 347	99.36 59	99.86 225	99.08 134	99.25 331	99.45 223

1112_ss		99.05 203	98.84 228	99.67 108	99.66 170	99.29 197	98.52 303	99.82 70	97.65 334	99.43 230	99.16 331	96.42 297	99.91 140	99.07 135	99.84 163	99.80 47

CANet_DTU		98.91 230	98.85 226	99.09 277	98.79 379	98.13 308	98.18 329	99.31 300	99.48 130	98.86 321	99.51 253	96.56 291	99.95 64	99.05 136	99.95 79	99.19 291

bld_raw_conf03		99.43 105	99.43 99	99.45 194	99.42 269	99.40 172	99.52 87	99.70 134	99.73 76	99.77 107	99.73 126	98.05 223	99.91 140	99.04 137	99.90 114	99.05 328

Baseline_NR-MVSNet		99.49 87	99.37 110	99.82 35	99.91 29	99.84 24	98.83 260	99.86 52	99.68 93	99.65 155	99.88 43	97.67 250	99.87 206	99.03 138	99.86 154	99.76 65

FMVSNet2		99.35 129	99.28 134	99.55 167	99.49 241	99.35 188	99.45 107	99.57 210	99.44 140	99.70 138	99.74 122	97.21 271	99.87 206	99.03 138	99.94 91	99.44 228

Test_1112_low_res		98.95 227	98.73 237	99.63 134	99.68 162	99.15 224	98.09 340	99.80 82	97.14 360	99.46 224	99.40 282	96.11 308	99.89 178	99.01 140	99.84 163	99.84 36

VDD-MVS		99.20 169	99.11 162	99.44 199	99.43 264	98.98 240	99.50 95	98.32 375	99.80 66	99.56 193	99.69 157	96.99 281	99.85 243	98.99 141	99.73 224	99.50 205

DeepC-MVS	98.90 4	99.62 65	99.61 60	99.67 108	99.72 139	99.44 159	99.24 162	99.71 129	99.27 166	99.93 38	99.90 29	99.70 24	99.93 96	98.99 141	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 89	99.47 88	99.51 178	99.77 111	99.41 171	98.81 265	99.66 154	99.42 150	99.75 116	99.66 177	99.20 76	99.76 321	98.98 143	99.99 16	99.36 249

EPNet_dtu		97.62 323	97.79 317	97.11 380	96.67 415	92.31 404	98.51 304	98.04 380	99.24 172	95.77 407	99.47 267	93.78 333	99.66 368	98.98 143	99.62 261	99.37 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 134	99.32 120	99.39 217	99.67 169	98.77 262	98.57 295	99.81 79	99.61 113	99.48 218	99.41 278	98.47 176	99.86 225	98.97 145	99.90 114	99.53 188
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		99.40 115	99.31 122	99.68 105	99.43 264	99.55 138	99.73 26	99.50 250	99.46 137	99.88 62	99.36 294	97.54 257	99.87 206	98.97 145	99.87 146	99.63 128

GBi-Net		99.42 109	99.31 122	99.73 86	99.49 241	99.77 53	99.68 45	99.70 134	99.44 140	99.62 169	99.83 70	97.21 271	99.90 159	98.96 147	99.90 114	99.53 188

FMVSNet5		97.80 315	97.25 331	99.42 205	98.83 373	98.97 242	99.38 118	99.80 82	98.87 225	99.25 272	99.69 157	80.60 402	99.91 140	98.96 147	99.90 114	99.38 243

test1		99.42 109	99.31 122	99.73 86	99.49 241	99.77 53	99.68 45	99.70 134	99.44 140	99.62 169	99.83 70	97.21 271	99.90 159	98.96 147	99.90 114	99.53 188

FMVSNet3		98.80 243	98.63 245	99.32 237	99.13 336	98.72 265	99.10 210	99.48 255	99.23 174	99.62 169	99.64 184	92.57 345	99.86 225	98.96 147	99.90 114	99.39 241

UnsupCasMVSNet_eth		98.83 240	98.57 252	99.59 152	99.68 162	99.45 157	98.99 240	99.67 149	99.48 130	99.55 198	99.36 294	94.92 319	99.86 225	98.95 151	96.57 406	99.45 223

CHOSEN 280x420		98.41 283	98.41 266	98.40 338	99.34 294	95.89 378	96.94 399	99.44 266	98.80 237	99.25 272	99.52 251	93.51 337	99.98 21	98.94 152	99.98 39	99.32 259

TDRefinement		99.72 36	99.70 40	99.77 55	99.90 35	99.85 19	99.86 5	99.92 31	99.69 91	99.78 101	99.92 21	99.37 56	99.88 192	98.93 153	99.95 79	99.60 153

alignmvs		98.28 293	97.96 303	99.25 256	99.12 338	98.93 249	99.03 227	98.42 369	99.64 105	98.72 336	97.85 399	90.86 366	99.62 378	98.88 154	99.13 337	99.19 291

MGCFI-Net		99.02 209	99.01 195	99.06 284	99.11 343	98.60 278	99.63 60	99.67 149	99.63 107	98.58 348	97.65 402	99.07 95	99.57 387	98.85 155	98.92 352	99.03 333

sss		98.90 232	98.77 236	99.27 250	99.48 246	98.44 287	98.72 278	99.32 296	97.94 320	99.37 246	99.35 299	96.31 303	99.91 140	98.85 155	99.63 260	99.47 218

xiu_mvs_v2_base		99.02 209	99.11 162	98.77 319	99.37 279	98.09 313	98.13 335	99.51 246	99.47 134	99.42 232	98.54 385	99.38 54	99.97 34	98.83 157	99.33 320	98.24 390

PS-MVSNAJ		99.00 217	99.08 173	98.76 320	99.37 279	98.10 312	98.00 351	99.51 246	99.47 134	99.41 238	98.50 387	99.28 66	99.97 34	98.83 157	99.34 319	98.20 394

D2MVS		99.22 162	99.19 146	99.29 244	99.69 154	98.74 264	98.81 265	99.41 272	98.55 264	99.68 144	99.69 157	98.13 217	99.87 206	98.82 159	99.98 39	99.24 274

PatchT		98.45 280	98.32 276	98.83 314	98.94 362	98.29 297	99.24 162	98.82 347	99.84 54	99.08 298	99.76 114	91.37 356	99.94 79	98.82 159	99.00 347	98.26 389

testf1		99.63 59	99.60 63	99.72 92	99.94 18	99.95 2	99.47 103	99.89 42	99.43 146	99.88 62	99.80 86	99.26 70	99.90 159	98.81 161	99.88 134	99.32 259

APD_test2		99.63 59	99.60 63	99.72 92	99.94 18	99.95 2	99.47 103	99.89 42	99.43 146	99.88 62	99.80 86	99.26 70	99.90 159	98.81 161	99.88 134	99.32 259

sasdasda		99.02 209	99.00 199	99.09 277	99.10 345	98.70 266	99.61 68	99.66 154	99.63 107	98.64 342	97.65 402	99.04 101	99.54 391	98.79 163	98.92 352	99.04 331

Effi-MVS+		99.06 200	98.97 209	99.34 230	99.31 301	98.98 240	98.31 321	99.91 35	98.81 235	98.79 330	98.94 363	99.14 84	99.84 258	98.79 163	98.74 365	99.20 288

canonicalmvs		99.02 209	99.00 199	99.09 277	99.10 345	98.70 266	99.61 68	99.66 154	99.63 107	98.64 342	97.65 402	99.04 101	99.54 391	98.79 163	98.92 352	99.04 331

VDDNet		98.97 221	98.82 231	99.42 205	99.71 142	98.81 257	99.62 63	98.68 354	99.81 63	99.38 245	99.80 86	94.25 327	99.85 243	98.79 163	99.32 322	99.59 160

CR-MVSNet		98.35 290	98.20 285	98.83 314	99.05 351	98.12 309	99.30 141	99.67 149	97.39 348	99.16 287	99.79 96	91.87 353	99.91 140	98.78 167	98.77 361	98.44 383

test_method		91.72 378	92.32 381	89.91 396	93.49 419	70.18 422	90.28 410	99.56 215	61.71 414	95.39 409	99.52 251	93.90 329	99.94 79	98.76 168	98.27 383	99.62 139

RPMNet		98.60 261	98.53 257	98.83 314	99.05 351	98.12 309	99.30 141	99.62 175	99.86 45	99.16 287	99.74 122	92.53 347	99.92 118	98.75 169	98.77 361	98.44 383

pmmvs4		99.13 188	99.06 179	99.36 227	99.57 203	99.10 232	98.01 349	99.25 313	98.78 240	99.58 183	99.44 274	98.24 205	99.76 321	98.74 170	99.93 98	99.22 281

tttt0517		97.62 323	97.20 332	98.90 307	99.76 115	97.40 345	99.48 100	94.36 406	99.06 203	99.70 138	99.49 260	84.55 397	99.94 79	98.73 171	99.65 255	99.36 249

EPP-MVSNet		99.17 181	99.00 199	99.66 115	99.80 84	99.43 163	99.70 34	99.24 316	99.48 130	99.56 193	99.77 111	94.89 320	99.93 96	98.72 172	99.89 125	99.63 128

Anonymous20240529		99.42 109	99.34 115	99.65 120	99.53 223	99.60 126	99.63 60	99.39 282	99.47 134	99.76 111	99.78 104	98.13 217	99.86 225	98.70 173	99.68 244	99.49 210

ACMH	98.42 6	99.59 69	99.54 79	99.72 92	99.86 52	99.62 116	99.56 82	99.79 88	98.77 242	99.80 92	99.85 60	99.64 28	99.85 243	98.70 173	99.89 125	99.70 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 137	99.28 134	99.47 188	99.57 203	99.39 176	99.78 13	99.43 269	98.87 225	99.57 186	99.82 77	98.06 222	99.87 206	98.69 175	99.73 224	99.15 299

LFMVS		98.46 279	98.19 288	99.26 253	99.24 317	98.52 283	99.62 63	96.94 396	99.87 42	99.31 263	99.58 227	91.04 361	99.81 297	98.68 176	99.42 309	99.45 223

WR-MVS		99.11 193	98.93 214	99.66 115	99.30 305	99.42 166	98.42 314	99.37 287	99.04 204	99.57 186	99.20 329	96.89 283	99.86 225	98.66 177	99.87 146	99.70 78

mvsmamba		99.08 197	98.95 212	99.45 194	99.36 282	99.18 221	99.39 115	98.81 348	99.37 153	99.35 249	99.70 150	96.36 302	99.94 79	98.66 177	99.59 275	99.22 281

Anonymous202405211		98.75 247	98.46 261	99.63 134	99.34 294	99.66 100	99.47 103	97.65 388	99.28 165	99.56 193	99.50 256	93.15 339	99.84 258	98.62 179	99.58 277	99.40 239

EPNet		98.13 303	97.77 318	99.18 265	94.57 418	97.99 319	99.24 162	97.96 382	99.74 75	97.29 391	99.62 202	93.13 340	99.97 34	98.59 180	99.83 171	99.58 165
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 203	99.09 171	98.91 301	99.21 322	98.36 295	98.82 264	99.47 258	98.85 228	98.90 316	99.56 238	98.78 131	99.09 406	98.57 181	99.68 244	99.26 271

Patchmatch-RL test		98.60 261	98.36 271	99.33 233	99.77 111	99.07 235	98.27 323	99.87 49	98.91 220	99.74 124	99.72 134	90.57 370	99.79 307	98.55 182	99.85 158	99.11 308

pmmvs3		98.08 306	97.80 315	98.91 301	99.41 272	97.69 336	97.87 364	99.66 154	95.87 379	99.50 215	99.51 253	90.35 372	99.97 34	98.55 182	99.47 302	99.08 321

ETV-MVS		99.18 176	99.18 147	99.16 266	99.34 294	99.28 199	99.12 203	99.79 88	99.48 130	98.93 310	98.55 384	99.40 49	99.93 96	98.51 184	99.52 294	98.28 388

jason		99.16 182	99.11 162	99.32 237	99.75 127	98.44 287	98.26 325	99.39 282	98.70 250	99.74 124	99.30 307	98.54 165	99.97 34	98.48 185	99.82 180	99.55 175
jason: jason.
APDe-MVS		99.48 89	99.36 113	99.85 26	99.55 215	99.81 39	99.50 95	99.69 142	98.99 207	99.75 116	99.71 142	98.79 129	99.93 96	98.46 186	99.85 158	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		98.71 252	98.56 255	99.15 268	99.22 320	98.66 271	97.14 394	99.51 246	98.09 309	99.54 200	99.27 313	96.87 284	99.74 328	98.43 187	98.96 349	99.03 333

our_test_3		98.85 239	99.09 171	98.13 351	99.66 170	94.90 391	97.72 369	99.58 208	99.07 201	99.64 156	99.62 202	98.19 213	99.93 96	98.41 188	99.95 79	99.55 175

Gipuma		99.57 70	99.59 65	99.49 182	99.98 3	99.71 82	99.72 29	99.84 63	99.81 63	99.94 35	99.78 104	98.91 116	99.71 337	98.41 188	99.95 79	99.05 328
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 332	96.91 342	98.74 321	97.72 411	97.57 338	97.60 375	97.36 394	98.00 312	99.21 281	98.02 395	90.04 375	99.79 307	98.37 190	95.89 410	98.86 356

PM-MVS		99.36 127	99.29 132	99.58 155	99.83 63	99.66 100	98.95 247	99.86 52	98.85 228	99.81 88	99.73 126	98.40 189	99.92 118	98.36 191	99.83 171	99.17 295

baseline1		97.73 318	97.33 328	98.96 292	99.30 305	97.73 334	99.40 113	98.42 369	99.33 159	99.46 224	99.21 327	91.18 359	99.82 282	98.35 192	91.26 413	99.32 259

MVS-HIRNet		97.86 312	98.22 283	96.76 382	99.28 310	91.53 409	98.38 316	92.60 412	99.13 194	99.31 263	99.96 12	97.18 275	99.68 358	98.34 193	99.83 171	99.07 326

GA-MVS		97.99 311	97.68 321	98.93 298	99.52 228	98.04 317	97.19 393	99.05 338	98.32 296	98.81 326	98.97 359	89.89 377	99.41 402	98.33 194	99.05 343	99.34 255

Fast-Effi-MVS+		99.02 209	98.87 224	99.46 191	99.38 277	99.50 144	99.04 224	99.79 88	97.17 358	98.62 344	98.74 376	99.34 60	99.95 64	98.32 195	99.41 310	98.92 349

MDA-MVSNet_test_wron		98.95 227	98.99 205	98.85 310	99.64 175	97.16 351	98.23 327	99.33 294	98.93 217	99.56 193	99.66 177	97.39 264	99.83 273	98.29 196	99.88 134	99.55 175

N_pmnet		98.73 250	98.53 257	99.35 229	99.72 139	98.67 268	98.34 318	94.65 405	98.35 290	99.79 97	99.68 168	98.03 224	99.93 96	98.28 197	99.92 102	99.44 228

ET-MVSNet_ETH3D		96.78 344	96.07 353	98.91 301	99.26 314	97.92 326	97.70 371	96.05 401	97.96 319	92.37 413	98.43 388	87.06 385	99.90 159	98.27 198	97.56 400	98.91 350

thisisatest0530		97.45 329	96.95 339	98.94 295	99.68 162	97.73 334	99.09 214	94.19 408	98.61 260	99.56 193	99.30 307	84.30 398	99.93 96	98.27 198	99.54 289	99.16 297

YYNet1		98.95 227	98.99 205	98.84 312	99.64 175	97.14 353	98.22 328	99.32 296	98.92 219	99.59 181	99.66 177	97.40 262	99.83 273	98.27 198	99.90 114	99.55 175

ACMM	98.09 11	99.46 98	99.38 107	99.72 92	99.80 84	99.69 93	99.13 199	99.65 164	98.99 207	99.64 156	99.72 134	99.39 50	99.86 225	98.23 201	99.81 189	99.60 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 224	98.87 224	99.24 258	99.57 203	98.40 290	98.12 336	99.18 327	98.28 298	99.63 160	99.13 333	98.02 225	99.97 34	98.22 202	99.69 239	99.35 252

3Dnovator	99.15 2	99.43 105	99.36 113	99.65 120	99.39 274	99.42 166	99.70 34	99.56 215	99.23 174	99.35 249	99.80 86	99.17 79	99.95 64	98.21 203	99.84 163	99.59 160

Fast-Effi-MVS+-dtu		99.20 169	99.12 159	99.43 203	99.25 315	99.69 93	99.05 221	99.82 70	99.50 128	98.97 306	99.05 345	98.98 108	99.98 21	98.20 204	99.24 333	98.62 369

MS-PatchMatch		99.00 217	98.97 209	99.09 277	99.11 343	98.19 303	98.76 274	99.33 294	98.49 273	99.44 226	99.58 227	98.21 210	99.69 346	98.20 204	99.62 261	99.39 241

TSAR-MVS + GP.		99.12 190	99.04 189	99.38 220	99.34 294	99.16 222	98.15 332	99.29 304	98.18 305	99.63 160	99.62 202	99.18 78	99.68 358	98.20 204	99.74 219	99.30 265

DP-MVS		99.48 89	99.39 105	99.74 77	99.57 203	99.62 116	99.29 148	99.61 182	99.87 42	99.74 124	99.76 114	98.69 143	99.87 206	98.20 204	99.80 194	99.75 68

MVP-Stereo		99.16 182	99.08 173	99.43 203	99.48 246	99.07 235	99.08 217	99.55 221	98.63 256	99.31 263	99.68 168	98.19 213	99.78 310	98.18 208	99.58 277	99.45 223
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 105	99.30 127	99.80 43	99.83 63	99.81 39	99.52 87	99.70 134	98.35 290	99.51 213	99.50 256	99.31 62	99.88 192	98.18 208	99.84 163	99.69 82

MDA-MVSNet-bldmvs		99.06 200	99.05 183	99.07 282	99.80 84	97.83 329	98.89 252	99.72 126	99.29 162	99.63 160	99.70 150	96.47 295	99.89 178	98.17 210	99.82 180	99.50 205

JIA-IIPM		98.06 307	97.92 310	98.50 333	98.59 392	97.02 355	98.80 268	98.51 364	99.88 41	97.89 378	99.87 48	91.89 352	99.90 159	98.16 211	97.68 399	98.59 372

EIA-MVS		99.12 190	99.01 195	99.45 194	99.36 282	99.62 116	99.34 127	99.79 88	98.41 279	98.84 323	98.89 367	98.75 136	99.84 258	98.15 212	99.51 295	98.89 353

miper_lstm_enhance		98.65 257	98.60 246	98.82 317	99.20 325	97.33 347	97.78 367	99.66 154	99.01 206	99.59 181	99.50 256	94.62 324	99.85 243	98.12 213	99.90 114	99.26 271

Effi-MVS+-dtu		99.07 199	98.92 218	99.52 175	98.89 367	99.78 48	99.15 191	99.66 154	99.34 157	98.92 313	99.24 323	97.69 248	99.98 21	98.11 214	99.28 327	98.81 360

tpm		97.15 336	96.95 339	97.75 364	98.91 363	94.24 394	99.32 133	97.96 382	97.71 332	98.29 360	99.32 303	86.72 391	99.92 118	98.10 215	96.24 409	99.09 315

DeepPCF-MVS	98.42 6	99.18 176	99.02 192	99.67 108	99.22 320	99.75 66	97.25 391	99.47 258	98.72 247	99.66 153	99.70 150	99.29 64	99.63 377	98.07 216	99.81 189	99.62 139

ppachtmachnet_test		98.89 235	99.12 159	98.20 349	99.66 170	95.24 387	97.63 373	99.68 145	99.08 199	99.78 101	99.62 202	98.65 151	99.88 192	98.02 217	99.96 66	99.48 214

tpmrst		97.73 318	98.07 296	96.73 384	98.71 388	92.00 405	99.10 210	98.86 344	98.52 269	98.92 313	99.54 247	91.90 351	99.82 282	98.02 217	99.03 345	98.37 385

CSCG		99.37 124	99.29 132	99.60 149	99.71 142	99.46 152	99.43 111	99.85 57	98.79 238	99.41 238	99.60 219	98.92 114	99.92 118	98.02 217	99.92 102	99.43 234

eth_miper_zixun_eth		98.68 255	98.71 239	98.60 328	99.10 345	96.84 360	97.52 381	99.54 227	98.94 214	99.58 183	99.48 263	96.25 306	99.76 321	98.01 220	99.93 98	99.21 284

Patchmtry		98.78 244	98.54 256	99.49 182	98.89 367	99.19 219	99.32 133	99.67 149	99.65 103	99.72 129	99.79 96	91.87 353	99.95 64	98.00 221	99.97 53	99.33 256

PVSNet_BlendedMVS		99.03 207	99.01 195	99.09 277	99.54 217	97.99 319	98.58 291	99.82 70	97.62 335	99.34 253	99.71 142	98.52 172	99.77 318	97.98 222	99.97 53	99.52 198

PVSNet_Blended		98.70 253	98.59 248	99.02 287	99.54 217	97.99 319	97.58 376	99.82 70	95.70 383	99.34 253	98.98 357	98.52 172	99.77 318	97.98 222	99.83 171	99.30 265

cl____		98.54 269	98.41 266	98.92 299	99.03 354	97.80 332	97.46 383	99.59 199	98.90 221	99.60 178	99.46 270	93.85 331	99.78 310	97.97 224	99.89 125	99.17 295

DIV-MVS_self_test		98.54 269	98.42 265	98.92 299	99.03 354	97.80 332	97.46 383	99.59 199	98.90 221	99.60 178	99.46 270	93.87 330	99.78 310	97.97 224	99.89 125	99.18 293

AUN-MVS		97.82 314	97.38 327	99.14 271	99.27 312	98.53 281	98.72 278	99.02 339	98.10 307	97.18 394	99.03 351	89.26 379	99.85 243	97.94 226	97.91 395	99.03 333

FA-MVS(test-final)		98.52 271	98.32 276	99.10 276	99.48 246	98.67 268	99.77 15	98.60 361	97.35 350	99.63 160	99.80 86	93.07 341	99.84 258	97.92 227	99.30 324	98.78 363

ambc				99.20 262	99.35 285	98.53 281	99.17 183	99.46 261		99.67 149	99.80 86	98.46 179	99.70 340	97.92 227	99.70 235	99.38 243

USDC		98.96 224	98.93 214	99.05 285	99.54 217	97.99 319	97.07 397	99.80 82	98.21 302	99.75 116	99.77 111	98.43 182	99.64 376	97.90 229	99.88 134	99.51 200

OPM-MVS		99.26 149	99.13 155	99.63 134	99.70 150	99.61 123	98.58 291	99.48 255	98.50 271	99.52 207	99.63 195	99.14 84	99.76 321	97.89 230	99.77 208	99.51 200
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 139	99.17 148	99.77 55	99.69 154	99.80 43	99.14 193	99.31 300	99.16 188	99.62 169	99.61 211	98.35 193	99.91 140	97.88 231	99.72 230	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 31	99.70 150	99.79 45	99.14 193	99.61 182					99.92 118	97.88 231	99.72 230	99.77 60

c3_l		98.72 251	98.71 239	98.72 322	99.12 338	97.22 350	97.68 372	99.56 215	98.90 221	99.54 200	99.48 263	96.37 301	99.73 331	97.88 231	99.88 134	99.21 284

3Dnovator+	98.92 3	99.35 129	99.24 142	99.67 108	99.35 285	99.47 148	99.62 63	99.50 250	99.44 140	99.12 294	99.78 104	98.77 133	99.94 79	97.87 234	99.72 230	99.62 139

miper_ehance_all_eth		98.59 264	98.59 248	98.59 329	98.98 360	97.07 354	97.49 382	99.52 241	98.50 271	99.52 207	99.37 290	96.41 299	99.71 337	97.86 235	99.62 261	99.00 340

WTY-MVS		98.59 264	98.37 270	99.26 253	99.43 264	98.40 290	98.74 276	99.13 333	98.10 307	99.21 281	99.24 323	94.82 321	99.90 159	97.86 235	98.77 361	99.49 210

APD_test1		99.36 127	99.28 134	99.61 146	99.89 37	99.89 10	99.32 133	99.74 113	99.18 181	99.69 141	99.75 119	98.41 185	99.84 258	97.85 237	99.70 235	99.10 310

SED-MVS		99.40 115	99.28 134	99.77 55	99.69 154	99.82 34	99.20 172	99.54 227	99.13 194	99.82 81	99.63 195	98.91 116	99.92 118	97.85 237	99.70 235	99.58 165

test_241102_TWO								99.54 227	99.13 194	99.76 111	99.63 195	98.32 198	99.92 118	97.85 237	99.69 239	99.75 68

MVS_111021_HR		99.12 190	99.02 192	99.40 214	99.50 236	99.11 227	97.92 360	99.71 129	98.76 245	99.08 298	99.47 267	99.17 79	99.54 391	97.85 237	99.76 210	99.54 183

MTAPA		99.35 129	99.20 145	99.80 43	99.81 78	99.81 39	99.33 130	99.53 236	99.27 166	99.42 232	99.63 195	98.21 210	99.95 64	97.83 241	99.79 199	99.65 113

MSC_two_6792asdad				99.74 77	99.03 354	99.53 141		99.23 317					99.92 118	97.77 242	99.69 239	99.78 56

No_MVS				99.74 77	99.03 354	99.53 141		99.23 317					99.92 118	97.77 242	99.69 239	99.78 56

TESTMET0.1,1		96.24 358	95.84 359	97.41 372	98.24 403	93.84 397	97.38 385	95.84 402	98.43 276	97.81 383	98.56 383	79.77 406	99.89 178	97.77 242	98.77 361	98.52 377

ACMH+	98.40 8	99.50 84	99.43 99	99.71 97	99.86 52	99.76 60	99.32 133	99.77 97	99.53 125	99.77 107	99.76 114	99.26 70	99.78 310	97.77 242	99.88 134	99.60 153

IU-MVS					99.69 154	99.77 53		99.22 320	97.50 342	99.69 141				97.75 246	99.70 235	99.77 60

114514_t		98.49 276	98.11 293	99.64 127	99.73 136	99.58 132	99.24 162	99.76 102	89.94 406	99.42 232	99.56 238	97.76 245	99.86 225	97.74 247	99.82 180	99.47 218

DVP-MVS++		99.38 121	99.25 140	99.77 55	99.03 354	99.77 53	99.74 23	99.61 182	99.18 181	99.76 111	99.61 211	99.00 104	99.92 118	97.72 248	99.60 271	99.62 139

test_0728_THIRD									99.18 181	99.62 169	99.61 211	98.58 159	99.91 140	97.72 248	99.80 194	99.77 60

EGC-MVSNET		89.05 380	85.52 383	99.64 127	99.89 37	99.78 48	99.56 82	99.52 241	24.19 415	49.96 416	99.83 70	99.15 81	99.92 118	97.71 250	99.85 158	99.21 284

miper_enhance_ethall		98.03 308	97.94 308	98.32 343	98.27 402	96.43 366	96.95 398	99.41 272	96.37 374	99.43 230	98.96 361	94.74 322	99.69 346	97.71 250	99.62 261	98.83 359

TSAR-MVS + MP.		99.34 134	99.24 142	99.63 134	99.82 70	99.37 181	99.26 155	99.35 291	98.77 242	99.57 186	99.70 150	99.27 69	99.88 192	97.71 250	99.75 212	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		97.56 326	97.28 329	98.40 338	98.37 400	96.75 361	97.24 392	99.37 287	97.31 352	99.41 238	99.22 325	87.30 383	99.37 403	97.70 253	99.62 261	99.08 321

MP-MVS-pluss		99.14 186	98.92 218	99.80 43	99.83 63	99.83 29	98.61 284	99.63 172	96.84 367	99.44 226	99.58 227	98.81 124	99.91 140	97.70 253	99.82 180	99.67 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 143	99.11 162	99.79 49	99.75 127	99.81 39	98.95 247	99.53 236	98.27 299	99.53 205	99.73 126	98.75 136	99.87 206	97.70 253	99.83 171	99.68 88

UnsupCasMVSNet_bld		98.55 268	98.27 281	99.40 214	99.56 214	99.37 181	97.97 356	99.68 145	97.49 343	99.08 298	99.35 299	95.41 318	99.82 282	97.70 253	98.19 387	99.01 339

MVS_111021_LR		99.13 188	99.03 191	99.42 205	99.58 193	99.32 193	97.91 362	99.73 117	98.68 251	99.31 263	99.48 263	99.09 90	99.66 368	97.70 253	99.77 208	99.29 268

IS-MVSNet		99.03 207	98.85 226	99.55 167	99.80 84	99.25 206	99.73 26	99.15 330	99.37 153	99.61 175	99.71 142	94.73 323	99.81 297	97.70 253	99.88 134	99.58 165

test-LLR		97.15 336	96.95 339	97.74 365	98.18 405	95.02 389	97.38 385	96.10 398	98.00 312	97.81 383	98.58 380	90.04 375	99.91 140	97.69 259	98.78 359	98.31 386

test-mter		96.23 359	95.73 361	97.74 365	98.18 405	95.02 389	97.38 385	96.10 398	97.90 321	97.81 383	98.58 380	79.12 409	99.91 140	97.69 259	98.78 359	98.31 386

XVS		99.27 147	99.11 162	99.75 72	99.71 142	99.71 82	99.37 122	99.61 182	99.29 162	98.76 333	99.47 267	98.47 176	99.88 192	97.62 261	99.73 224	99.67 96

X-MVStestdata		96.09 362	94.87 374	99.75 72	99.71 142	99.71 82	99.37 122	99.61 182	99.29 162	98.76 333	61.30 422	98.47 176	99.88 192	97.62 261	99.73 224	99.67 96

SMA-MVS		99.19 172	99.00 199	99.73 86	99.46 256	99.73 75	99.13 199	99.52 241	97.40 347	99.57 186	99.64 184	98.93 113	99.83 273	97.61 263	99.79 199	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		96.71 347	96.79 346	96.46 388	98.90 364	90.71 414	99.41 112	98.68 354	94.69 396	98.14 370	99.34 302	86.32 393	99.80 304	97.60 264	98.07 393	98.88 354

PVSNet	97.47 15	98.42 282	98.44 263	98.35 340	99.46 256	96.26 370	96.70 402	99.34 293	97.68 333	99.00 305	99.13 333	97.40 262	99.72 333	97.59 265	99.68 244	99.08 321

new_pmnet		98.88 236	98.89 222	98.84 312	99.70 150	97.62 337	98.15 332	99.50 250	97.98 315	99.62 169	99.54 247	98.15 216	99.94 79	97.55 266	99.84 163	98.95 344

IB-MVS	95.41 20	95.30 376	94.46 380	97.84 361	98.76 384	95.33 385	97.33 388	96.07 400	96.02 378	95.37 410	97.41 406	76.17 411	99.96 54	97.54 267	95.44 412	98.22 391
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		99.24 153	99.11 162	99.61 146	98.38 399	99.79 45	99.57 80	99.68 145	99.61 113	99.15 289	99.71 142	98.70 142	99.91 140	97.54 267	99.68 244	99.13 307

ZNCC-MVS		99.22 162	99.04 189	99.77 55	99.76 115	99.73 75	99.28 150	99.56 215	98.19 304	99.14 291	99.29 310	98.84 123	99.92 118	97.53 269	99.80 194	99.64 123

CP-MVS		99.23 154	99.05 183	99.75 72	99.66 170	99.66 100	99.38 118	99.62 175	98.38 283	99.06 302	99.27 313	98.79 129	99.94 79	97.51 270	99.82 180	99.66 105

SD-MVS		99.01 215	99.30 127	98.15 350	99.50 236	99.40 172	98.94 249	99.61 182	99.22 178	99.75 116	99.82 77	99.54 41	95.51 415	97.48 271	99.87 146	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		98.49 276	98.29 280	99.11 274	98.96 361	98.42 289	97.54 377	99.32 296	97.53 340	98.47 355	98.15 394	97.88 235	99.82 282	97.46 272	99.24 333	99.09 315

DeepC-MVS_fast	98.47 5	99.23 154	99.12 159	99.56 164	99.28 310	99.22 213	98.99 240	99.40 279	99.08 199	99.58 183	99.64 184	98.90 119	99.83 273	97.44 273	99.75 212	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		99.25 150	99.08 173	99.76 62	99.73 136	99.70 89	99.31 138	99.59 199	98.36 285	99.36 247	99.37 290	98.80 128	99.91 140	97.43 274	99.75 212	99.68 88

ACMMPR		99.23 154	99.06 179	99.76 62	99.74 133	99.69 93	99.31 138	99.59 199	98.36 285	99.35 249	99.38 288	98.61 155	99.93 96	97.43 274	99.75 212	99.67 96

Vis-MVSNet (Re-imp)		98.77 245	98.58 251	99.34 230	99.78 103	98.88 253	99.61 68	99.56 215	99.11 198	99.24 275	99.56 238	93.00 343	99.78 310	97.43 274	99.89 125	99.35 252

MIMVSNet		98.43 281	98.20 285	99.11 274	99.53 223	98.38 294	99.58 77	98.61 359	98.96 211	99.33 255	99.76 114	90.92 363	99.81 297	97.38 277	99.76 210	99.15 299

WB-MVSnew		98.34 292	98.14 291	98.96 292	98.14 408	97.90 327	98.27 323	97.26 395	98.63 256	98.80 328	98.00 397	97.77 243	99.90 159	97.37 278	98.98 348	99.09 315

XVG-OURS-SEG-HR		99.16 182	98.99 205	99.66 115	99.84 59	99.64 109	98.25 326	99.73 117	98.39 282	99.63 160	99.43 275	99.70 24	99.90 159	97.34 279	98.64 372	99.44 228

COLMAP_ROB	98.06 12	99.45 100	99.37 110	99.70 101	99.83 63	99.70 89	99.38 118	99.78 94	99.53 125	99.67 149	99.78 104	99.19 77	99.86 225	97.32 280	99.87 146	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		99.02 209	98.81 232	99.65 120	99.58 193	99.49 145	98.58 291	99.07 335	98.40 281	99.04 303	99.25 318	98.51 174	99.80 304	97.31 281	99.51 295	99.65 113

region2R		99.23 154	99.05 183	99.77 55	99.76 115	99.70 89	99.31 138	99.59 199	98.41 279	99.32 258	99.36 294	98.73 140	99.93 96	97.29 282	99.74 219	99.67 96

APD-MVS_3200maxsize		99.31 140	99.16 149	99.74 77	99.53 223	99.75 66	99.27 153	99.61 182	99.19 180	99.57 186	99.64 184	98.76 134	99.90 159	97.29 282	99.62 261	99.56 172

TAPA-MVS	97.92 13	98.03 308	97.55 324	99.46 191	99.47 252	99.44 159	98.50 305	99.62 175	86.79 407	99.07 301	99.26 316	98.26 204	99.62 378	97.28 284	99.73 224	99.31 263
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 147	99.11 162	99.73 86	99.54 217	99.74 72	99.26 155	99.62 175	99.16 188	99.52 207	99.64 184	98.41 185	99.91 140	97.27 285	99.61 268	99.54 183

RE-MVS-def			99.13 155		99.54 217	99.74 72	99.26 155	99.62 175	99.16 188	99.52 207	99.64 184	98.57 160		97.27 285	99.61 268	99.54 183

testing11		96.05 364	95.41 366	97.97 355	98.78 381	95.27 386	98.59 289	98.23 377	98.86 227	96.56 401	96.91 412	75.20 412	99.69 346	97.26 287	98.29 382	98.93 347

test_yl		98.25 295	97.95 304	99.13 272	99.17 331	98.47 284	99.00 235	98.67 356	98.97 209	99.22 279	99.02 352	91.31 357	99.69 346	97.26 287	98.93 350	99.24 274

DCV-MVSNet		98.25 295	97.95 304	99.13 272	99.17 331	98.47 284	99.00 235	98.67 356	98.97 209	99.22 279	99.02 352	91.31 357	99.69 346	97.26 287	98.93 350	99.24 274

PHI-MVS		99.11 193	98.95 212	99.59 152	99.13 336	99.59 128	99.17 183	99.65 164	97.88 324	99.25 272	99.46 270	98.97 110	99.80 304	97.26 287	99.82 180	99.37 246

tfpnnormal		99.43 105	99.38 107	99.60 149	99.87 49	99.75 66	99.59 75	99.78 94	99.71 83	99.90 50	99.69 157	98.85 122	99.90 159	97.25 291	99.78 204	99.15 299

PatchmatchNet		97.65 322	97.80 315	97.18 378	98.82 376	92.49 403	99.17 183	98.39 371	98.12 306	98.79 330	99.58 227	90.71 368	99.89 178	97.23 292	99.41 310	99.16 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 220	98.80 234	99.56 164	99.25 315	99.43 163	98.54 300	99.27 308	98.58 262	98.80 328	99.43 275	98.53 169	99.70 340	97.22 293	99.59 275	99.54 183

testing3		96.48 352	95.63 363	99.01 288	99.23 319	97.81 330	98.90 251	99.10 334	98.72 247	97.84 382	97.92 398	72.44 416	99.85 243	97.21 294	99.33 320	99.35 252

HPM-MVS		99.25 150	99.07 177	99.78 52	99.81 78	99.75 66	99.61 68	99.67 149	97.72 331	99.35 249	99.25 318	99.23 73	99.92 118	97.21 294	99.82 180	99.67 96
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 172	99.00 199	99.76 62	99.76 115	99.68 96	99.38 118	99.54 227	98.34 294	99.01 304	99.50 256	98.53 169	99.93 96	97.18 296	99.78 204	99.66 105

ACMMP		99.25 150	99.08 173	99.74 77	99.79 96	99.68 96	99.50 95	99.65 164	98.07 310	99.52 207	99.69 157	98.57 160	99.92 118	97.18 296	99.79 199	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		96.98 340	96.42 347	98.66 325	99.42 269	97.47 341	97.27 390	94.30 407	97.24 354	99.15 289	98.86 369	85.01 395	99.87 206	97.10 298	99.39 312	98.63 368

XVG-ACMP-BASELINE		99.23 154	99.10 170	99.63 134	99.82 70	99.58 132	98.83 260	99.72 126	98.36 285	99.60 178	99.71 142	98.92 114	99.91 140	97.08 299	99.84 163	99.40 239

MSDG		99.08 197	98.98 208	99.37 223	99.60 184	99.13 225	97.54 377	99.74 113	98.84 231	99.53 205	99.55 245	99.10 88	99.79 307	97.07 300	99.86 154	99.18 293

SteuartSystems-ACMMP		99.30 141	99.14 153	99.76 62	99.87 49	99.66 100	99.18 178	99.60 193	98.55 264	99.57 186	99.67 172	99.03 103	99.94 79	97.01 301	99.80 194	99.69 82
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 360	95.78 360	97.49 368	98.53 394	93.83 398	98.04 346	93.94 410	98.96 211	98.46 356	98.17 393	79.86 404	99.87 206	96.99 302	99.06 341	98.78 363

EPMVS		96.53 350	96.32 348	97.17 379	98.18 405	92.97 402	99.39 115	89.95 416	98.21 302	98.61 345	99.59 224	86.69 392	99.72 333	96.99 302	99.23 335	98.81 360

MSP-MVS		99.04 206	98.79 235	99.81 38	99.78 103	99.73 75	99.35 126	99.57 210	98.54 267	99.54 200	98.99 354	96.81 285	99.93 96	96.97 304	99.53 291	99.77 60
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++		98.96 224	98.70 241	99.74 77	99.52 228	99.71 82	98.86 255	99.19 326	98.47 275	98.59 347	99.06 344	98.08 221	99.91 140	96.94 305	99.60 271	99.60 153

SR-MVS		99.19 172	99.00 199	99.74 77	99.51 230	99.72 80	99.18 178	99.60 193	98.85 228	99.47 220	99.58 227	98.38 190	99.92 118	96.92 306	99.54 289	99.57 170

PGM-MVS		99.20 169	99.01 195	99.77 55	99.75 127	99.71 82	99.16 189	99.72 126	97.99 314	99.42 232	99.60 219	98.81 124	99.93 96	96.91 307	99.74 219	99.66 105

HY-MVS	98.23 9	98.21 301	97.95 304	98.99 289	99.03 354	98.24 298	99.61 68	98.72 352	96.81 368	98.73 335	99.51 253	94.06 328	99.86 225	96.91 307	98.20 385	98.86 356

MDTV_nov1_ep13			97.73 319		98.70 389	90.83 412	99.15 191	98.02 381	98.51 270	98.82 325	99.61 211	90.98 362	99.66 368	96.89 309	98.92 352

GST-MVS		99.16 182	98.96 211	99.75 72	99.73 136	99.73 75	99.20 172	99.55 221	98.22 301	99.32 258	99.35 299	98.65 151	99.91 140	96.86 310	99.74 219	99.62 139

test_post1							99.14 193				51.63 424	89.54 378	99.82 282	96.86 310

SCA		98.11 304	98.36 271	97.36 373	99.20 325	92.99 401	98.17 331	98.49 366	98.24 300	99.10 297	99.57 234	96.01 310	99.94 79	96.86 310	99.62 261	99.14 304

UBG		96.53 350	95.95 355	98.29 347	98.87 370	96.31 369	98.48 307	98.07 379	98.83 232	97.32 389	96.54 417	79.81 405	99.62 378	96.84 313	98.74 365	98.95 344

XVG-OURS		99.21 167	99.06 179	99.65 120	99.82 70	99.62 116	97.87 364	99.74 113	98.36 285	99.66 153	99.68 168	99.71 22	99.90 159	96.84 313	99.88 134	99.43 234

LCM-MVSNet-Re		99.28 143	99.15 152	99.67 108	99.33 299	99.76 60	99.34 127	99.97 18	98.93 217	99.91 47	99.79 96	98.68 144	99.93 96	96.80 315	99.56 280	99.30 265

RPSCF		99.18 176	99.02 192	99.64 127	99.83 63	99.85 19	99.44 109	99.82 70	98.33 295	99.50 215	99.78 104	97.90 233	99.65 374	96.78 316	99.83 171	99.44 228

旧先验2							97.94 358		95.33 387	98.94 309			99.88 192	96.75 317

MDTV_nov1_ep13_2view						91.44 410	99.14 193		97.37 349	99.21 281		91.78 355		96.75 317		99.03 333

CLD-MVS		98.76 246	98.57 252	99.33 233	99.57 203	98.97 242	97.53 379	99.55 221	96.41 372	99.27 270	99.13 333	99.07 95	99.78 310	96.73 319	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		98.10 305	97.98 302	98.48 334	99.27 312	96.48 364	99.40 113	99.07 335	98.81 235	99.23 276	99.57 234	90.11 374	99.87 206	96.69 320	99.64 257	99.09 315

baseline2		96.83 343	96.28 349	98.46 336	99.09 348	96.91 358	98.83 260	93.87 411	97.23 355	96.23 406	98.36 389	88.12 382	99.90 159	96.68 321	98.14 390	98.57 375

cascas		96.99 339	96.82 345	97.48 369	97.57 414	95.64 381	96.43 404	99.56 215	91.75 402	97.13 396	97.61 405	95.58 315	98.63 410	96.68 321	99.11 339	98.18 395

PC_three_1452									97.56 336	99.68 144	99.41 278	99.09 90	97.09 412	96.66 323	99.60 271	99.62 139

LPG-MVS_test		99.22 162	99.05 183	99.74 77	99.82 70	99.63 114	99.16 189	99.73 117	97.56 336	99.64 156	99.69 157	99.37 56	99.89 178	96.66 323	99.87 146	99.69 82

LGP-MVS_train				99.74 77	99.82 70	99.63 114		99.73 117	97.56 336	99.64 156	99.69 157	99.37 56	99.89 178	96.66 323	99.87 146	99.69 82

ETVMVS		96.14 361	95.22 371	98.89 308	98.80 377	98.01 318	98.66 282	98.35 374	98.71 249	97.18 394	96.31 421	74.23 415	99.75 325	96.64 326	98.13 392	98.90 351

TinyColmap		98.97 221	98.93 214	99.07 282	99.46 256	98.19 303	97.75 368	99.75 107	98.79 238	99.54 200	99.70 150	98.97 110	99.62 378	96.63 327	99.83 171	99.41 238

LF4IMVS		99.01 215	98.92 218	99.27 250	99.71 142	99.28 199	98.59 289	99.77 97	98.32 296	99.39 244	99.41 278	98.62 153	99.84 258	96.62 328	99.84 163	98.69 367

NCCC		98.82 241	98.57 252	99.58 155	99.21 322	99.31 194	98.61 284	99.25 313	98.65 254	98.43 357	99.26 316	97.86 236	99.81 297	96.55 329	99.27 330	99.61 149

OPU-MVS				99.29 244	99.12 338	99.44 159	99.20 172				99.40 282	99.00 104	98.84 409	96.54 330	99.60 271	99.58 165

F-COLMAP		98.74 248	98.45 262	99.62 143	99.57 203	99.47 148	98.84 258	99.65 164	96.31 375	98.93 310	99.19 330	97.68 249	99.87 206	96.52 331	99.37 315	99.53 188

testing99		95.86 369	95.19 372	97.87 359	98.76 384	95.03 388	98.62 283	98.44 368	98.68 251	96.67 400	96.66 416	74.31 414	99.69 346	96.51 332	98.03 394	98.90 351

ADS-MVSNet2		97.78 316	97.66 323	98.12 352	99.14 334	95.36 384	99.22 169	98.75 351	96.97 363	98.25 362	99.64 184	90.90 364	99.94 79	96.51 332	99.56 280	99.08 321

ADS-MVSNet		97.72 321	97.67 322	97.86 360	99.14 334	94.65 392	99.22 169	98.86 344	96.97 363	98.25 362	99.64 184	90.90 364	99.84 258	96.51 332	99.56 280	99.08 321

PatchMatch-RL		98.68 255	98.47 260	99.30 243	99.44 261	99.28 199	98.14 334	99.54 227	97.12 361	99.11 295	99.25 318	97.80 241	99.70 340	96.51 332	99.30 324	98.93 347

CMPMVS	77.52 23	98.50 274	98.19 288	99.41 212	98.33 401	99.56 135	99.01 232	99.59 199	95.44 385	99.57 186	99.80 86	95.64 313	99.46 401	96.47 336	99.92 102	99.21 284
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 365	95.32 369	98.02 353	98.76 384	95.39 383	98.38 316	98.65 358	98.82 233	96.84 397	96.71 415	75.06 413	99.71 337	96.46 337	98.23 384	98.98 341

SF-MVS		99.10 196	98.93 214	99.62 143	99.58 193	99.51 143	99.13 199	99.65 164	97.97 316	99.42 232	99.61 211	98.86 121	99.87 206	96.45 338	99.68 244	99.49 210

FE-MVS		97.85 313	97.42 326	99.15 268	99.44 261	98.75 263	99.77 15	98.20 378	95.85 380	99.33 255	99.80 86	88.86 380	99.88 192	96.40 339	99.12 338	98.81 360

DPE-MVS		99.14 186	98.92 218	99.82 35	99.57 203	99.77 53	98.74 276	99.60 193	98.55 264	99.76 111	99.69 157	98.23 209	99.92 118	96.39 340	99.75 212	99.76 65
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 412	89.02 418			93.47 398		98.30 390		99.84 258	96.38 341

AllTest		99.21 167	99.07 177	99.63 134	99.78 103	99.64 109	99.12 203	99.83 65	98.63 256	99.63 160	99.72 134	98.68 144	99.75 325	96.38 341	99.83 171	99.51 200

TestCases				99.63 134	99.78 103	99.64 109		99.83 65	98.63 256	99.63 160	99.72 134	98.68 144	99.75 325	96.38 341	99.83 171	99.51 200

testdata				99.42 205	99.51 230	98.93 249		99.30 303	96.20 376	98.87 320	99.40 282	98.33 197	99.89 178	96.29 344	99.28 327	99.44 228

dp		96.86 342	97.07 335	96.24 390	98.68 390	90.30 416	99.19 177	98.38 372	97.35 350	98.23 364	99.59 224	87.23 384	99.82 282	96.27 345	98.73 368	98.59 372

tpmvs		97.39 331	97.69 320	96.52 386	98.41 398	91.76 406	99.30 141	98.94 343	97.74 330	97.85 381	99.55 245	92.40 350	99.73 331	96.25 346	98.73 368	98.06 397

KD-MVS_2432*1600		95.89 366	95.41 366	97.31 376	94.96 416	93.89 395	97.09 395	99.22 320	97.23 355	98.88 317	99.04 347	79.23 407	99.54 391	96.24 347	96.81 404	98.50 381

miper_refine_blended		95.89 366	95.41 366	97.31 376	94.96 416	93.89 395	97.09 395	99.22 320	97.23 355	98.88 317	99.04 347	79.23 407	99.54 391	96.24 347	96.81 404	98.50 381

ACMP	97.51 14	99.05 203	98.84 228	99.67 108	99.78 103	99.55 138	98.88 253	99.66 154	97.11 362	99.47 220	99.60 219	99.07 95	99.89 178	96.18 349	99.85 158	99.58 165
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 232	98.72 238	99.44 199	99.39 274	99.42 166	98.58 291	99.64 170	97.31 352	99.44 226	99.62 202	98.59 157	99.69 346	96.17 350	99.79 199	99.22 281

DP-MVS Recon		98.50 274	98.23 282	99.31 240	99.49 241	99.46 152	98.56 296	99.63 172	94.86 394	98.85 322	99.37 290	97.81 240	99.59 385	96.08 351	99.44 305	98.88 354

tpm cat1		96.78 344	96.98 338	96.16 391	98.85 371	90.59 415	99.08 217	99.32 296	92.37 400	97.73 387	99.46 270	91.15 360	99.69 346	96.07 352	98.80 358	98.21 392

tpm2		96.35 355	96.22 350	96.73 384	98.88 369	91.75 407	99.21 171	98.51 364	93.27 399	97.89 378	99.21 327	84.83 396	99.70 340	96.04 353	98.18 388	98.75 366

dmvs_re		98.69 254	98.48 259	99.31 240	99.55 215	99.42 166	99.54 85	98.38 372	99.32 160	98.72 336	98.71 377	96.76 287	99.21 404	96.01 354	99.35 318	99.31 263

test_0402		99.22 162	99.14 153	99.45 194	99.79 96	99.43 163	99.28 150	99.68 145	99.54 123	99.40 243	99.56 238	99.07 95	99.82 282	96.01 354	99.96 66	99.11 308

ITE_SJBPF				99.38 220	99.63 177	99.44 159		99.73 117	98.56 263	99.33 255	99.53 249	98.88 120	99.68 358	96.01 354	99.65 255	99.02 338

test_prior2							97.95 357		97.87 325	98.05 372	99.05 345	97.90 233		95.99 357	99.49 300

testdata2													99.89 178	95.99 357

原ACMM1				99.37 223	99.47 252	98.87 255		99.27 308	96.74 370	98.26 361	99.32 303	97.93 232	99.82 282	95.96 359	99.38 313	99.43 234

新几何1				99.52 175	99.50 236	99.22 213		99.26 310	95.66 384	98.60 346	99.28 311	97.67 250	99.89 178	95.95 360	99.32 322	99.45 223

MP-MVS		99.06 200	98.83 230	99.76 62	99.76 115	99.71 82	99.32 133	99.50 250	98.35 290	98.97 306	99.48 263	98.37 191	99.92 118	95.95 360	99.75 212	99.63 128
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 375	94.59 378	98.61 327	98.66 391	97.45 343	98.54 300	97.90 385	98.53 268	96.54 402	96.47 418	70.62 419	99.81 297	95.91 362	98.15 389	98.56 376

wuyk23d		97.58 325	99.13 155	92.93 394	99.69 154	99.49 145	99.52 87	99.77 97	97.97 316	99.96 24	99.79 96	99.84 12	99.94 79	95.85 363	99.82 180	79.36 412

HQP_MVS		98.90 232	98.68 242	99.55 167	99.58 193	99.24 210	98.80 268	99.54 227	98.94 214	99.14 291	99.25 318	97.24 269	99.82 282	95.84 364	99.78 204	99.60 153

plane_prior5								99.54 227					99.82 282	95.84 364	99.78 204	99.60 153

无先验							98.01 349	99.23 317	95.83 381				99.85 243	95.79 366		99.44 228

CPTT-MVS		98.74 248	98.44 263	99.64 127	99.61 182	99.38 178	99.18 178	99.55 221	96.49 371	99.27 270	99.37 290	97.11 277	99.92 118	95.74 367	99.67 250	99.62 139

PLC	97.35 16	98.36 287	97.99 300	99.48 186	99.32 300	99.24 210	98.50 305	99.51 246	95.19 390	98.58 348	98.96 361	96.95 282	99.83 273	95.63 368	99.25 331	99.37 246
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 266	98.34 274	99.28 247	99.18 330	99.10 232	98.34 318	99.41 272	98.48 274	98.52 352	98.98 357	97.05 279	99.78 310	95.59 369	99.50 298	98.96 342

1314		98.00 310	97.90 312	98.27 348	98.90 364	97.45 343	99.30 141	99.06 337	94.98 391	97.21 393	99.12 337	98.43 182	99.67 363	95.58 370	98.56 375	97.71 401

PVSNet_0	95.53 19	95.85 370	95.31 370	97.47 370	98.78 381	93.48 400	95.72 406	99.40 279	96.18 377	97.37 388	97.73 400	95.73 312	99.58 386	95.49 371	81.40 414	99.36 249

MAR-MVS		98.24 297	97.92 310	99.19 263	98.78 381	99.65 106	99.17 183	99.14 331	95.36 386	98.04 373	98.81 373	97.47 259	99.72 333	95.47 372	99.06 341	98.21 392
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	98.23 298	97.89 313	99.26 253	99.19 327	99.26 203	99.65 58	99.69 142	91.33 404	98.14 370	99.77 111	98.28 200	99.96 54	95.41 373	99.55 284	98.58 374

train_agg		98.35 290	97.95 304	99.57 161	99.35 285	99.35 188	98.11 338	99.41 272	94.90 392	97.92 376	98.99 354	98.02 225	99.85 243	95.38 374	99.44 305	99.50 205

9.14			98.64 243		99.45 260		98.81 265	99.60 193	97.52 341	99.28 269	99.56 238	98.53 169	99.83 273	95.36 375	99.64 257

APD-MVS		98.87 237	98.59 248	99.71 97	99.50 236	99.62 116	99.01 232	99.57 210	96.80 369	99.54 200	99.63 195	98.29 199	99.91 140	95.24 376	99.71 233	99.61 149
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 367								95.20 377

AdaColmap		98.60 261	98.35 273	99.38 220	99.12 338	99.22 213	98.67 281	99.42 271	97.84 328	98.81 326	99.27 313	97.32 267	99.81 297	95.14 378	99.53 291	99.10 310

test9_res														95.10 379	99.44 305	99.50 205

CDPH-MVS		98.56 267	98.20 285	99.61 146	99.50 236	99.46 152	98.32 320	99.41 272	95.22 388	99.21 281	99.10 341	98.34 195	99.82 282	95.09 380	99.66 253	99.56 172

BH-untuned		98.22 299	98.09 294	98.58 331	99.38 277	97.24 349	98.55 297	98.98 342	97.81 329	99.20 286	98.76 375	97.01 280	99.65 374	94.83 381	98.33 380	98.86 356

BP-MVS														94.73 382

HQP-MVS		98.36 287	98.02 299	99.39 217	99.31 301	98.94 246	97.98 353	99.37 287	97.45 344	98.15 366	98.83 370	96.67 288	99.70 340	94.73 382	99.67 250	99.53 188

QAPM		98.40 285	97.99 300	99.65 120	99.39 274	99.47 148	99.67 49	99.52 241	91.70 403	98.78 332	99.80 86	98.55 163	99.95 64	94.71 384	99.75 212	99.53 188

agg_prior2														94.58 385	99.46 304	99.50 205

myMVS_eth3d		95.63 373	94.73 375	98.34 342	98.50 396	96.36 367	98.60 286	99.21 323	97.89 322	96.76 398	96.37 419	72.10 417	99.57 387	94.38 386	98.73 368	99.09 315

BH-RMVSNet		98.41 283	98.14 291	99.21 260	99.21 322	98.47 284	98.60 286	98.26 376	98.35 290	98.93 310	99.31 305	97.20 274	99.66 368	94.32 387	99.10 340	99.51 200

E-PMN		97.14 338	97.43 325	96.27 389	98.79 379	91.62 408	95.54 407	99.01 341	99.44 140	98.88 317	99.12 337	92.78 344	99.68 358	94.30 388	99.03 345	97.50 402

MG-MVS		98.52 271	98.39 268	98.94 295	99.15 333	97.39 346	98.18 329	99.21 323	98.89 224	99.23 276	99.63 195	97.37 265	99.74 328	94.22 389	99.61 268	99.69 82

API-MVS		98.38 286	98.39 268	98.35 340	98.83 373	99.26 203	99.14 193	99.18 327	98.59 261	98.66 341	98.78 374	98.61 155	99.57 387	94.14 390	99.56 280	96.21 409

PAPM_NR		98.36 287	98.04 297	99.33 233	99.48 246	98.93 249	98.79 271	99.28 307	97.54 339	98.56 351	98.57 382	97.12 276	99.69 346	94.09 391	98.90 356	99.38 243

ZD-MVS					99.43 264	99.61 123		99.43 269	96.38 373	99.11 295	99.07 343	97.86 236	99.92 118	94.04 392	99.49 300

DPM-MVS		98.28 293	97.94 308	99.32 237	99.36 282	99.11 227	97.31 389	98.78 350	96.88 365	98.84 323	99.11 340	97.77 243	99.61 383	94.03 393	99.36 316	99.23 278

gg-mvs-nofinetune		95.87 368	95.17 373	97.97 355	98.19 404	96.95 356	99.69 41	89.23 417	99.89 36	96.24 405	99.94 16	81.19 400	99.51 397	93.99 394	98.20 385	97.44 403

PMVS	92.94 21	98.82 241	98.81 232	98.85 310	99.84 59	97.99 319	99.20 172	99.47 258	99.71 83	99.42 232	99.82 77	98.09 219	99.47 399	93.88 395	99.85 158	99.07 326
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 341	97.28 329	95.99 392	98.76 384	91.03 411	95.26 409	98.61 359	99.34 157	98.92 313	98.88 368	93.79 332	99.66 368	92.87 396	99.05 343	97.30 406

BH-w/o		97.20 335	97.01 337	97.76 363	99.08 349	95.69 380	98.03 348	98.52 363	95.76 382	97.96 375	98.02 395	95.62 314	99.47 399	92.82 397	97.25 403	98.12 396

TR-MVS		97.44 330	97.15 334	98.32 343	98.53 394	97.46 342	98.47 308	97.91 384	96.85 366	98.21 365	98.51 386	96.42 297	99.51 397	92.16 398	97.29 402	97.98 398

OpenMVS_ROB	97.31 17	97.36 333	96.84 343	98.89 308	99.29 307	99.45 157	98.87 254	99.48 255	86.54 409	99.44 226	99.74 122	97.34 266	99.86 225	91.61 399	99.28 327	97.37 405

GG-mvs-BLEND				97.36 373	97.59 412	96.87 359	99.70 34	88.49 418		94.64 411	97.26 409	80.66 401	99.12 405	91.50 400	96.50 408	96.08 411

DeepMVS_CX				97.98 354	99.69 154	96.95 356		99.26 310	75.51 412	95.74 408	98.28 391	96.47 295	99.62 378	91.23 401	97.89 396	97.38 404

PAPR		97.56 326	97.07 335	99.04 286	98.80 377	98.11 311	97.63 373	99.25 313	94.56 397	98.02 374	98.25 392	97.43 261	99.68 358	90.90 402	98.74 365	99.33 256

MVS		95.72 372	94.63 377	98.99 289	98.56 393	97.98 324	99.30 141	98.86 344	72.71 413	97.30 390	99.08 342	98.34 195	99.74 328	89.21 403	98.33 380	99.26 271

thres600view7		96.60 349	96.16 351	97.93 357	99.63 177	96.09 375	99.18 178	97.57 389	98.77 242	98.72 336	97.32 407	87.04 386	99.72 333	88.57 404	98.62 373	97.98 398

FPMVS		96.32 356	95.50 364	98.79 318	99.60 184	98.17 306	98.46 312	98.80 349	97.16 359	96.28 403	99.63 195	82.19 399	99.09 406	88.45 405	98.89 357	99.10 310

PCF-MVS	96.03 18	96.73 346	95.86 358	99.33 233	99.44 261	99.16 222	96.87 400	99.44 266	86.58 408	98.95 308	99.40 282	94.38 326	99.88 192	87.93 406	99.80 194	98.95 344
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 354	96.03 354	97.47 370	99.63 177	95.93 376	99.18 178	97.57 389	98.75 246	98.70 339	97.31 408	87.04 386	99.67 363	87.62 407	98.51 377	96.81 407

tfpn200view9		96.30 357	95.89 356	97.53 367	99.58 193	96.11 373	99.00 235	97.54 392	98.43 276	98.52 352	96.98 410	86.85 388	99.67 363	87.62 407	98.51 377	96.81 407

thres400		96.40 353	95.89 356	97.92 358	99.58 193	96.11 373	99.00 235	97.54 392	98.43 276	98.52 352	96.98 410	86.85 388	99.67 363	87.62 407	98.51 377	97.98 398

thres200		96.09 362	95.68 362	97.33 375	99.48 246	96.22 372	98.53 302	97.57 389	98.06 311	98.37 359	96.73 414	86.84 390	99.61 383	86.99 410	98.57 374	96.16 410

MVE	92.54 22	96.66 348	96.11 352	98.31 345	99.68 162	97.55 339	97.94 358	95.60 403	99.37 153	90.68 414	98.70 378	96.56 291	98.61 411	86.94 411	99.55 284	98.77 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 334	96.83 344	98.59 329	99.46 256	97.55 339	99.25 161	96.84 397	98.78 240	97.24 392	97.67 401	97.11 277	98.97 408	86.59 412	98.54 376	99.27 269

PAPM		95.61 374	94.71 376	98.31 345	99.12 338	96.63 362	96.66 403	98.46 367	90.77 405	96.25 404	98.68 379	93.01 342	99.69 346	81.60 413	97.86 398	98.62 369

dongtai		89.37 379	88.91 382	90.76 395	99.19 327	77.46 420	95.47 408	87.82 419	92.28 401	94.17 412	98.82 372	71.22 418	95.54 414	63.85 414	97.34 401	99.27 269

kuosan		85.65 381	84.57 384	88.90 397	97.91 409	77.11 421	96.37 405	87.62 420	85.24 410	85.45 415	96.83 413	69.94 420	90.98 416	45.90 415	95.83 411	98.62 369

test123		29.31 382	33.05 387	18.08 398	25.93 422	12.24 423	97.53 379	10.93 423	11.78 416	24.21 417	50.08 426	21.04 421	8.60 417	23.51 416	32.43 416	33.39 413

testmvs		28.94 383	33.33 385	15.79 399	26.03 421	9.81 424	96.77 401	15.67 422	11.55 417	23.87 418	50.74 425	19.03 422	8.53 418	23.21 417	33.07 415	29.03 414

test_blank		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

uanet_test		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

DCPMVS		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

cdsmvs_eth3d_5k		24.88 384	33.17 386	0.00 400	0.00 423	0.00 425	0.00 411	99.62 175	0.00 418	0.00 419	99.13 333	99.82 13	0.00 419	0.00 418	0.00 417	0.00 415

pcd_1.5k_mvsjas		16.61 385	22.14 388	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	99.28 66	0.00 419	0.00 418	0.00 417	0.00 415

sosnet-low-res		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

sosnet		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

uncertanet		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

Regformer		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

ab-mvs-re		8.26 394	11.02 397	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	99.16 331	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

uanet		8.33 386	11.11 389	0.00 400	0.00 423	0.00 425	0.00 411	0.00 424	0.00 418	0.00 419	100.00 1	0.00 423	0.00 419	0.00 418	0.00 417	0.00 415

FOURS1					99.83 63	99.89 10	99.74 23	99.71 129	99.69 91	99.63 160

test_one_0601					99.63 177	99.76 60		99.55 221	99.23 174	99.31 263	99.61 211	98.59 157

eth-test2					0.00 423

eth-test					0.00 423

test_241102_ONE					99.69 154	99.82 34		99.54 227	99.12 197	99.82 81	99.49 260	98.91 116	99.52 396

save fliter					99.53 223	99.25 206	98.29 322	99.38 286	99.07 201

test0726					99.69 154	99.80 43	99.24 162	99.57 210	99.16 188	99.73 128	99.65 182	98.35 193

GSMVS																99.14 304

test_part2					99.62 181	99.67 98				99.55 198

sam_mvs1												90.81 367				99.14 304

sam_mvs												90.52 371

MTGPA								99.53 236

test_post											52.41 423	90.25 373	99.86 225

patchmatchnet-post											99.62 202	90.58 369	99.94 79

MTMP							99.09 214	98.59 362

TEST9					99.35 285	99.35 188	98.11 338	99.41 272	94.83 395	97.92 376	98.99 354	98.02 225	99.85 243

test_8					99.34 294	99.31 194	98.08 342	99.40 279	94.90 392	97.87 380	98.97 359	98.02 225	99.84 258

agg_prior					99.35 285	99.36 185		99.39 282		97.76 386			99.85 243

test_prior4						99.19 219	98.00 351

test_prior				99.46 191	99.35 285	99.22 213		99.39 282					99.69 346			99.48 214

新几何2							98.04 346

旧先验1					99.49 241	99.29 197		99.26 310			99.39 286	97.67 250			99.36 316	99.46 222

原ACMM2							97.92 360

test222					99.51 230	99.08 234	97.83 366	99.29 304	95.21 389	98.68 340	99.31 305	97.28 268			99.38 313	99.43 234

segment_acmp												98.37 191

testdata1							97.72 369		97.86 327

test12				99.54 172	99.29 307	99.33 191		99.16 329		98.43 357		97.54 257	99.82 282		99.47 302	99.48 214

plane_prior7					99.58 193	99.38 178

plane_prior6					99.47 252	99.26 203						97.24 269

plane_prior4											99.25 318

plane_prior3						99.31 194			98.36 285	99.14 291

plane_prior2							98.80 268		98.94 214

plane_prior1					99.51 230

plane_prior						99.24 210	98.42 314		97.87 325						99.71 233

n2								0.00 424

nn								0.00 424

door-mid								99.83 65

test11								99.29 304

door								99.77 97

HQP5-MVS						98.94 246

HQP-NCC					99.31 301		97.98 353		97.45 344	98.15 366

ACMP_Plane					99.31 301		97.98 353		97.45 344	98.15 366

HQP4-MVS										98.15 366			99.70 340			99.53 188

HQP3-MVS								99.37 287							99.67 250

HQP2-MVS												96.67 288

NP-MVS					99.40 273	99.13 225					98.83 370

ACMMP++_ref															99.94 91

ACMMP++															99.79 199

Test By Simon												98.41 185

High-resolution multi-view benchmark