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 67	99.70 35	100.00 1	99.73 74	100.00 1	99.89 34	99.79 16	99.88 189	99.98 1	100.00 1	99.98 3

test_fmvs2		99.72 36	99.85 16	99.34 226	99.91 31	98.08 313	99.48 97	100.00 1	99.90 29	99.99 7	99.91 24	99.50 46	99.98 20	99.98 1	99.99 16	99.96 10

test_fmvs3		99.83 19	99.93 2	99.53 174	99.96 7	98.62 275	99.67 49	100.00 1	99.95 20	100.00 1	99.95 13	99.85 10	99.99 7	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 61	99.12 198	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	99.27 245	99.93 25	97.84 326	99.34 123	100.00 1	99.99 2	99.99 7	99.82 73	99.87 9	99.99 7	99.97 4	99.99 16	99.97 7

test_vis1_n		99.68 45	99.79 27	99.36 223	99.94 18	98.18 303	99.52 87	100.00 1	99.86 45	100.00 1	99.88 42	98.99 104	99.96 54	99.97 4	99.96 70	99.95 11

test_fmvs1_n		99.68 45	99.81 23	99.28 242	99.95 15	97.93 323	99.49 96	100.00 1	99.82 58	99.99 7	99.89 34	99.21 75	99.98 20	99.97 4	99.98 41	99.93 15

test_f		99.75 32	99.88 6	99.37 219	99.96 7	98.21 300	99.51 91	100.00 1	99.94 23	100.00 1	99.93 17	99.58 36	99.94 77	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 73	99.01 229	99.99 10	99.99 2	99.98 13	99.88 42	99.97 2	99.99 7	99.96 9	100.00 1	99.98 3

test_fmvsmvis_n_1920		99.84 15	99.86 12	99.81 40	99.88 44	99.55 138	99.17 178	99.98 11	99.99 2	99.96 23	99.84 62	99.96 3	99.99 7	99.96 9	99.99 16	99.88 25

test_cas_vis1_n_1920		99.76 31	99.86 12	99.45 192	99.93 25	98.40 288	99.30 136	99.98 11	99.94 23	99.99 7	99.89 34	99.80 15	99.97 33	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 44	99.66 101	99.11 202	99.91 33	99.98 14	99.96 23	99.64 178	99.60 34	99.99 7	99.95 12	99.99 16	99.88 25

test_fmvsm_n_1920		99.84 15	99.85 16	99.83 33	99.82 72	99.70 90	99.17 178	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 87	99.65 50	98.97 289	99.54 215	97.16 349	99.11 202	99.98 11	99.78 68	99.96 23	99.81 79	98.72 139	99.97 33	99.95 12	99.97 56	99.79 54

mvsany_test3		99.85 11	99.88 6	99.75 74	99.95 15	99.37 178	99.53 86	99.98 11	99.77 72	99.99 7	99.95 13	99.85 10	99.94 77	99.95 12	99.98 41	99.94 13

fmvsm_l_conf0.5_n_a		99.80 23	99.79 27	99.84 30	99.88 44	99.64 110	99.12 198	99.91 33	99.98 14	99.95 31	99.67 166	99.67 27	99.99 7	99.94 16	99.99 16	99.88 25

MM		99.18 172	99.05 179	99.55 168	99.35 281	98.81 255	99.05 217	97.79 381	99.99 2	99.48 216	99.59 218	96.29 301	99.95 63	99.94 16	99.98 41	99.88 25

test_fmvsmconf_n		99.85 11	99.84 19	99.88 17	99.91 31	99.73 76	98.97 241	99.98 11	99.99 2	99.96 23	99.85 56	99.93 7	99.99 7	99.94 16	99.99 16	99.93 15

MVS_0304		99.17 177	99.03 187	99.59 152	99.44 259	98.90 249	99.04 220	95.32 399	99.99 2	99.68 142	99.57 229	98.30 198	99.97 33	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	99.10 205	99.98 11	99.99 2	99.98 13	99.91 24	99.68 26	99.93 94	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 25	99.78 49	99.07 216	99.98 11	99.99 2	99.98 13	99.90 29	99.88 8	99.92 116	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 58	99.82 35	99.03 224	99.96 23	99.99 2	99.97 19	99.84 62	99.58 36	99.93 94	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 58	99.78 49	99.03 224	99.96 23	99.99 2	99.97 19	99.84 62	99.78 17	99.92 116	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 73	99.57 71	99.55 168	99.75 128	99.11 225	99.05 217	99.61 177	99.15 188	99.88 61	99.71 138	99.08 92	99.87 203	99.90 25	99.97 56	99.66 104

v1240		99.56 73	99.58 68	99.51 178	99.80 86	99.00 236	99.00 232	99.65 159	99.15 188	99.90 49	99.75 116	99.09 89	99.88 189	99.90 25	99.96 70	99.67 95

v10		99.69 42	99.69 43	99.66 116	99.81 80	99.39 173	99.66 53	99.75 105	99.60 116	99.92 41	99.87 47	98.75 134	99.86 222	99.90 25	99.99 16	99.73 71

v1192		99.57 70	99.57 71	99.57 162	99.77 113	99.22 211	99.04 220	99.60 189	99.18 176	99.87 69	99.72 130	99.08 92	99.85 240	99.89 28	99.98 41	99.66 104

v144192		99.55 76	99.54 77	99.58 156	99.78 105	99.20 216	99.11 202	99.62 170	99.18 176	99.89 53	99.72 130	98.66 147	99.87 203	99.88 29	99.97 56	99.66 104

v8		99.68 45	99.69 43	99.65 121	99.80 86	99.40 171	99.66 53	99.76 100	99.64 102	99.93 37	99.85 56	98.66 147	99.84 255	99.88 29	99.99 16	99.71 76

v1144		99.54 78	99.53 81	99.59 152	99.79 98	99.28 196	99.10 205	99.61 177	99.20 174	99.84 76	99.73 123	98.67 145	99.84 255	99.86 31	99.98 41	99.64 122

SSC-MVS		99.52 81	99.42 98	99.83 33	99.86 54	99.65 107	99.52 87	99.81 76	99.87 41	99.81 88	99.79 93	96.78 283	99.99 7	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 67	99.84 53	99.94 34	99.91 24	99.13 86	99.96 54	99.83 32	99.99 16	99.83 40

v2v482		99.50 83	99.47 85	99.58 156	99.78 105	99.25 203	99.14 188	99.58 204	99.25 164	99.81 88	99.62 196	98.24 203	99.84 255	99.83 32	99.97 56	99.64 122

test_vis1_rt		99.45 97	99.46 89	99.41 208	99.71 143	98.63 274	98.99 237	99.96 23	99.03 201	99.95 31	99.12 331	98.75 134	99.84 255	99.82 35	99.82 179	99.77 60

tt0805		99.63 59	99.57 71	99.81 40	99.87 51	99.88 12	99.58 77	98.70 348	99.72 78	99.91 44	99.60 213	99.43 48	99.81 294	99.81 36	99.53 287	99.73 71

V42		99.56 73	99.54 77	99.63 135	99.79 98	99.46 151	99.39 112	99.59 195	99.24 166	99.86 71	99.70 145	98.55 162	99.82 279	99.79 37	99.95 83	99.60 152

mvs_tets		99.90 2	99.90 3	99.90 8	99.96 7	99.79 46	99.72 30	99.88 44	99.92 27	99.98 13	99.93 17	99.94 4	99.98 20	99.77 38	100.00 1	99.92 18

WB-MVS		99.44 99	99.32 116	99.80 45	99.81 80	99.61 123	99.47 100	99.81 76	99.82 58	99.71 132	99.72 130	96.60 287	99.98 20	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 54	99.95 20	99.98 13	99.92 21	99.28 66	99.98 20	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 49	99.89 35	99.98 13	99.90 29	99.94 4	99.98 20	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 58	100.00 1	99.90 29	100.00 1	99.97 11	99.61 32	99.97 33	99.75 39	100.00 1	99.84 36

CS-MVS-test		99.68 45	99.70 39	99.64 128	99.57 201	99.83 29	99.78 12	99.97 18	99.92 27	99.50 213	99.38 282	99.57 38	99.95 63	99.69 43	99.90 115	99.15 295

RRT_MVS		99.67 51	99.59 64	99.91 2	99.94 18	99.88 12	99.78 12	99.27 303	99.87 41	99.91 44	99.87 47	98.04 220	99.96 54	99.68 44	99.99 16	99.90 20

CS-MVS		99.67 51	99.70 39	99.58 156	99.53 221	99.84 24	99.79 11	99.96 23	99.90 29	99.61 174	99.41 272	99.51 45	99.95 63	99.66 45	99.89 124	98.96 335

pmmvs6		99.86 9	99.86 12	99.83 33	99.94 18	99.90 7	99.83 6	99.91 33	99.85 50	99.94 34	99.95 13	99.73 21	99.90 157	99.65 46	99.97 56	99.69 83

MIMVSNet1		99.66 53	99.62 55	99.80 45	99.94 18	99.87 15	99.69 42	99.77 95	99.78 68	99.93 37	99.89 34	97.94 228	99.92 116	99.65 46	99.98 41	99.62 138

EC-MVSNet		99.69 42	99.69 43	99.68 106	99.71 143	99.91 4	99.76 19	99.96 23	99.86 45	99.51 211	99.39 280	99.57 38	99.93 94	99.64 48	99.86 153	99.20 284

K. test v3		98.87 234	98.60 243	99.69 104	99.93 25	99.46 151	99.74 24	94.97 400	99.78 68	99.88 61	99.88 42	93.66 332	99.97 33	99.61 49	99.95 83	99.64 122

KD-MVS_self_test		99.63 59	99.59 64	99.76 64	99.84 61	99.90 7	99.37 118	99.79 86	99.83 56	99.88 61	99.85 56	98.42 183	99.90 157	99.60 50	99.73 223	99.49 210

Anonymous20240521		99.44 99	99.42 98	99.49 181	99.89 39	98.96 242	99.62 63	99.76 100	99.85 50	99.82 81	99.88 42	96.39 297	99.97 33	99.59 51	99.98 41	99.55 174

TransMVSNet (Re)		99.78 27	99.77 33	99.81 40	99.91 31	99.85 19	99.75 22	99.86 49	99.70 85	99.91 44	99.89 34	99.60 34	99.87 203	99.59 51	99.74 218	99.71 76

OurMVSNet-221017-0		99.75 32	99.71 38	99.84 30	99.96 7	99.83 29	99.83 6	99.85 54	99.80 64	99.93 37	99.93 17	98.54 164	99.93 94	99.59 51	99.98 41	99.76 66

EU-MVSNet		99.39 115	99.62 55	98.72 320	99.88 44	96.44 363	99.56 82	99.85 54	99.90 29	99.90 49	99.85 56	98.09 216	99.83 270	99.58 54	99.95 83	99.90 20

mvsmamba		99.74 35	99.70 39	99.85 27	99.93 25	99.83 29	99.76 19	99.81 76	99.96 18	99.91 44	99.81 79	98.60 155	99.94 77	99.58 54	99.98 41	99.77 60

mvs_anonymous		99.28 139	99.39 101	98.94 293	99.19 323	97.81 328	99.02 227	99.55 217	99.78 68	99.85 73	99.80 83	98.24 203	99.86 222	99.57 56	99.50 294	99.15 295

test1111		97.74 314	98.16 288	96.49 382	99.60 182	89.86 412	99.71 34	91.21 408	99.89 35	99.88 61	99.87 47	93.73 331	99.90 157	99.56 57	99.99 16	99.70 79

lessismore_v0				99.64 128	99.86 54	99.38 175		90.66 409		99.89 53	99.83 66	94.56 322	99.97 33	99.56 57	99.92 105	99.57 169

mvsany_test1		99.44 99	99.45 91	99.40 210	99.37 276	98.64 273	97.90 358	99.59 195	99.27 160	99.92 41	99.82 73	99.74 20	99.93 94	99.55 59	99.87 145	99.63 127

pm-mvs1		99.79 26	99.79 27	99.78 54	99.91 31	99.83 29	99.76 19	99.87 46	99.73 74	99.89 53	99.87 47	99.63 29	99.87 203	99.54 60	99.92 105	99.63 127

LTVRE_ROB	99.19 1	99.88 6	99.87 10	99.88 17	99.91 31	99.90 7	99.96 1	99.92 30	99.90 29	99.97 19	99.87 47	99.81 14	99.95 63	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 87	99.65 50	98.95 292	99.71 143	97.27 346	99.50 92	99.82 67	99.59 118	99.41 236	99.85 56	99.62 31	100.00 1	99.53 62	99.89 124	99.59 159

test2506		94.73 372	94.59 373	95.15 388	99.59 186	85.90 414	99.75 22	74.01 414	99.89 35	99.71 132	99.86 54	79.00 405	99.90 157	99.52 63	99.99 16	99.65 112

UniMVSNet_ETH3D		99.85 11	99.83 20	99.90 8	99.89 39	99.91 4	99.89 4	99.71 126	99.93 25	99.95 31	99.89 34	99.71 22	99.96 54	99.51 64	99.97 56	99.84 36

FC-MVSNet-test		99.70 40	99.65 50	99.86 25	99.88 44	99.86 18	99.72 30	99.78 92	99.90 29	99.82 81	99.83 66	98.45 179	99.87 203	99.51 64	99.97 56	99.86 32

UA-Net		99.78 27	99.76 36	99.86 25	99.72 140	99.71 83	99.91 3	99.95 28	99.96 18	99.71 132	99.91 24	99.15 81	99.97 33	99.50 66	100.00 1	99.90 20

PMMVS2		99.48 87	99.45 91	99.57 162	99.76 117	98.99 237	98.09 334	99.90 38	98.95 209	99.78 101	99.58 221	99.57 38	99.93 94	99.48 67	99.95 83	99.79 54

VPA-MVSNet		99.66 53	99.62 55	99.79 51	99.68 163	99.75 67	99.62 63	99.69 137	99.85 50	99.80 92	99.81 79	98.81 122	99.91 139	99.47 68	99.88 134	99.70 79

ECVR-MVS		97.73 315	98.04 294	96.78 376	99.59 186	90.81 408	99.72 30	90.43 410	99.89 35	99.86 71	99.86 54	93.60 333	99.89 175	99.46 69	99.99 16	99.65 112

nrg030		99.70 40	99.66 48	99.82 37	99.76 117	99.84 24	99.61 68	99.70 131	99.93 25	99.78 101	99.68 162	99.10 87	99.78 308	99.45 70	99.96 70	99.83 40

TAMVS		99.49 85	99.45 91	99.63 135	99.48 244	99.42 165	99.45 104	99.57 206	99.66 98	99.78 101	99.83 66	97.85 235	99.86 222	99.44 71	99.96 70	99.61 148

GeoE		99.69 42	99.66 48	99.78 54	99.76 117	99.76 61	99.60 74	99.82 67	99.46 133	99.75 114	99.56 233	99.63 29	99.95 63	99.43 72	99.88 134	99.62 138

new-patchmatchnet		99.35 125	99.57 71	98.71 322	99.82 72	96.62 361	98.55 293	99.75 105	99.50 124	99.88 61	99.87 47	99.31 62	99.88 189	99.43 72	100.00 1	99.62 138

test20.03		99.55 76	99.54 77	99.58 156	99.79 98	99.37 178	99.02 227	99.89 40	99.60 116	99.82 81	99.62 196	98.81 122	99.89 175	99.43 72	99.86 153	99.47 218

MVSFormer		99.41 109	99.44 94	99.31 236	99.57 201	98.40 288	99.77 15	99.80 80	99.73 74	99.63 159	99.30 301	98.02 222	99.98 20	99.43 72	99.69 238	99.55 174

test_djsdf		99.84 15	99.81 23	99.91 2	99.94 18	99.84 24	99.77 15	99.80 80	99.73 74	99.97 19	99.92 21	99.77 19	99.98 20	99.43 72	100.00 1	99.90 20

SDMVSNet		99.77 30	99.77 33	99.76 64	99.80 86	99.65 107	99.63 60	99.86 49	99.97 16	99.89 53	99.89 34	99.52 44	99.99 7	99.42 77	99.96 70	99.65 112

Anonymous20231211		99.62 65	99.57 71	99.76 64	99.61 180	99.60 126	99.81 9	99.73 114	99.82 58	99.90 49	99.90 29	97.97 227	99.86 222	99.42 77	99.96 70	99.80 47

SixPastTwentyTwo		99.42 105	99.30 123	99.76 64	99.92 30	99.67 99	99.70 35	99.14 327	99.65 100	99.89 53	99.90 29	96.20 303	99.94 77	99.42 77	99.92 105	99.67 95

patch_mono-2		99.51 82	99.46 89	99.64 128	99.70 151	99.11 225	99.04 220	99.87 46	99.71 80	99.47 218	99.79 93	98.24 203	99.98 20	99.38 80	99.96 70	99.83 40

UGNet		99.38 117	99.34 111	99.49 181	98.90 361	98.90 249	99.70 35	99.35 286	99.86 45	98.57 346	99.81 79	98.50 174	99.93 94	99.38 80	99.98 41	99.66 104
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 40	99.89 39	99.72 81	99.59 75	99.82 67	99.39 146	99.82 81	99.84 62	99.38 54	99.91 139	99.38 80	99.93 101	99.80 47

FIs		99.65 58	99.58 68	99.84 30	99.84 61	99.85 19	99.66 53	99.75 105	99.86 45	99.74 122	99.79 93	98.27 201	99.85 240	99.37 83	99.93 101	99.83 40

sd_testset		99.78 27	99.78 31	99.80 45	99.80 86	99.76 61	99.80 10	99.79 86	99.97 16	99.89 53	99.89 34	99.53 43	99.99 7	99.36 84	99.96 70	99.65 112

anonymousdsp		99.80 23	99.77 33	99.90 8	99.96 7	99.88 12	99.73 27	99.85 54	99.70 85	99.92 41	99.93 17	99.45 47	99.97 33	99.36 84	100.00 1	99.85 35

casdiffmvs_mvg		99.68 45	99.68 46	99.69 104	99.81 80	99.59 128	99.29 143	99.90 38	99.71 80	99.79 97	99.73 123	99.54 41	99.84 255	99.36 84	99.96 70	99.65 112
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 51	99.88 44	99.66 101	99.69 42	99.92 30	99.67 94	99.77 106	99.75 116	99.61 32	99.98 20	99.35 87	99.98 41	99.72 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 67	99.64 53	99.53 174	99.79 98	98.82 254	99.58 77	99.97 18	99.95 20	99.96 23	99.76 111	98.44 180	99.99 7	99.34 88	99.96 70	99.78 56

CHOSEN 1792x2688		99.39 115	99.30 123	99.65 121	99.88 44	99.25 203	98.78 269	99.88 44	98.66 248	99.96 23	99.79 93	97.45 257	99.93 94	99.34 88	99.99 16	99.78 56

CDS-MVSNet		99.22 158	99.13 151	99.50 180	99.35 281	99.11 225	98.96 243	99.54 223	99.46 133	99.61 174	99.70 145	96.31 299	99.83 270	99.34 88	99.88 134	99.55 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 213	99.16 145	98.51 329	99.75 128	95.90 373	98.07 337	99.84 60	99.84 53	99.89 53	99.73 123	96.01 306	99.99 7	99.33 91	100.00 1	99.63 127

HyFIR lowres test		98.91 227	98.64 240	99.73 88	99.85 58	99.47 147	98.07 337	99.83 62	98.64 250	99.89 53	99.60 213	92.57 342	100.00 1	99.33 91	99.97 56	99.72 73

pmmvs5		99.19 168	99.11 158	99.42 201	99.76 117	98.88 251	98.55 293	99.73 114	98.82 228	99.72 127	99.62 196	96.56 288	99.82 279	99.32 93	99.95 83	99.56 171

v148		99.40 111	99.41 100	99.39 213	99.76 117	98.94 243	99.09 210	99.59 195	99.17 181	99.81 88	99.61 205	98.41 184	99.69 343	99.32 93	99.94 94	99.53 187

baseline		99.63 59	99.62 55	99.66 116	99.80 86	99.62 117	99.44 106	99.80 80	99.71 80	99.72 127	99.69 151	99.15 81	99.83 270	99.32 93	99.94 94	99.53 187

iter_conf05		98.46 276	98.23 279	99.15 265	99.04 350	97.99 316	99.10 205	99.61 177	99.79 66	99.76 108	99.58 221	87.88 380	99.92 116	99.31 96	99.97 56	99.53 187

CVMVSNet		98.61 255	98.88 220	97.80 357	99.58 191	93.60 394	99.26 150	99.64 165	99.66 98	99.72 127	99.67 166	93.26 335	99.93 94	99.30 97	99.81 188	99.87 30

PS-CasMVS		99.66 53	99.58 68	99.89 11	99.80 86	99.85 19	99.66 53	99.73 114	99.62 107	99.84 76	99.71 138	98.62 151	99.96 54	99.30 97	99.96 70	99.86 32

DTE-MVSNet		99.68 45	99.61 59	99.88 17	99.80 86	99.87 15	99.67 49	99.71 126	99.72 78	99.84 76	99.78 101	98.67 145	99.97 33	99.30 97	99.95 83	99.80 47

tmp_tt		95.75 366	95.42 360	96.76 377	89.90 413	94.42 388	98.86 252	97.87 380	78.01 404	99.30 263	99.69 151	97.70 243	95.89 408	99.29 100	98.14 385	99.95 11

PEN-MVS		99.66 53	99.59 64	99.89 11	99.83 65	99.87 15	99.66 53	99.73 114	99.70 85	99.84 76	99.73 123	98.56 161	99.96 54	99.29 100	99.94 94	99.83 40

WR-MVS_H		99.61 67	99.53 81	99.87 21	99.80 86	99.83 29	99.67 49	99.75 105	99.58 119	99.85 73	99.69 151	98.18 212	99.94 77	99.28 102	99.95 83	99.83 40

IterMVS		98.97 217	99.16 145	98.42 333	99.74 134	95.64 376	98.06 339	99.83 62	99.83 56	99.85 73	99.74 119	96.10 305	99.99 7	99.27 103	100.00 1	99.63 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		98.61 255	98.34 271	99.44 195	99.60 182	98.67 266	99.27 148	99.44 261	99.68 90	99.32 254	99.49 255	92.50 345	100.00 1	99.24 104	96.51 401	99.65 112

hse-mvs2		98.52 268	98.30 276	99.16 263	99.29 303	98.60 276	98.77 270	99.02 335	99.68 90	99.32 254	99.04 341	92.50 345	99.85 240	99.24 104	97.87 392	99.03 326

FMVSNet1		99.66 53	99.63 54	99.73 88	99.78 105	99.77 54	99.68 45	99.70 131	99.67 94	99.82 81	99.83 66	98.98 106	99.90 157	99.24 104	99.97 56	99.53 187

casdiffmvs		99.63 59	99.61 59	99.67 109	99.79 98	99.59 128	99.13 194	99.85 54	99.79 66	99.76 108	99.72 130	99.33 61	99.82 279	99.21 107	99.94 94	99.59 159
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 78	99.43 96	99.87 21	99.76 117	99.82 35	99.57 80	99.61 177	99.54 120	99.80 92	99.64 178	97.79 239	99.95 63	99.21 107	99.94 94	99.84 36

DELS-MVS		99.34 130	99.30 123	99.48 185	99.51 228	99.36 182	98.12 330	99.53 232	99.36 150	99.41 236	99.61 205	99.22 74	99.87 203	99.21 107	99.68 243	99.20 284
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)		99.37 120	99.26 134	99.68 106	99.51 228	99.58 132	98.98 240	99.60 189	99.43 141	99.70 136	99.36 288	97.70 243	99.88 189	99.20 110	99.87 145	99.59 159

CANet		99.11 190	99.05 179	99.28 242	98.83 368	98.56 278	98.71 276	99.41 267	99.25 164	99.23 272	99.22 319	97.66 251	99.94 77	99.19 111	99.97 56	99.33 256

EI-MVSNet-UG-set		99.48 87	99.50 83	99.42 201	99.57 201	98.65 272	99.24 157	99.46 256	99.68 90	99.80 92	99.66 171	98.99 104	99.89 175	99.19 111	99.90 115	99.72 73

xiu_mvs_v1_base_debu		99.23 150	99.34 111	98.91 299	99.59 186	98.23 297	98.47 303	99.66 149	99.61 110	99.68 142	98.94 358	99.39 50	99.97 33	99.18 113	99.55 280	98.51 369

xiu_mvs_v1_base		99.23 150	99.34 111	98.91 299	99.59 186	98.23 297	98.47 303	99.66 149	99.61 110	99.68 142	98.94 358	99.39 50	99.97 33	99.18 113	99.55 280	98.51 369

xiu_mvs_v1_base_debi		99.23 150	99.34 111	98.91 299	99.59 186	98.23 297	98.47 303	99.66 149	99.61 110	99.68 142	98.94 358	99.39 50	99.97 33	99.18 113	99.55 280	98.51 369

VPNet		99.46 95	99.37 106	99.71 99	99.82 72	99.59 128	99.48 97	99.70 131	99.81 61	99.69 139	99.58 221	97.66 251	99.86 222	99.17 116	99.44 301	99.67 95

UniMVSNet_NR-MVSNet		99.37 120	99.25 136	99.72 94	99.47 250	99.56 135	98.97 241	99.61 177	99.43 141	99.67 148	99.28 305	97.85 235	99.95 63	99.17 116	99.81 188	99.65 112

DU-MVS		99.33 133	99.21 140	99.71 99	99.43 263	99.56 135	98.83 257	99.53 232	99.38 147	99.67 148	99.36 288	97.67 247	99.95 63	99.17 116	99.81 188	99.63 127

EI-MVSNet-Vis-set		99.47 94	99.49 84	99.42 201	99.57 201	98.66 269	99.24 157	99.46 256	99.67 94	99.79 97	99.65 176	98.97 108	99.89 175	99.15 119	99.89 124	99.71 76

EI-MVSNet		99.38 117	99.44 94	99.21 255	99.58 191	98.09 310	99.26 150	99.46 256	99.62 107	99.75 114	99.67 166	98.54 164	99.85 240	99.15 119	99.92 105	99.68 89

VNet		99.18 172	99.06 175	99.56 165	99.24 313	99.36 182	99.33 126	99.31 295	99.67 94	99.47 218	99.57 229	96.48 291	99.84 255	99.15 119	99.30 320	99.47 218

EG-PatchMatch MVS		99.57 70	99.56 76	99.62 144	99.77 113	99.33 188	99.26 150	99.76 100	99.32 154	99.80 92	99.78 101	99.29 64	99.87 203	99.15 119	99.91 114	99.66 104

PVSNet_Blended_VisFu		99.40 111	99.38 103	99.44 195	99.90 37	98.66 269	98.94 246	99.91 33	97.97 311	99.79 97	99.73 123	99.05 98	99.97 33	99.15 119	99.99 16	99.68 89

IterMVS-LS		99.41 109	99.47 85	99.25 251	99.81 80	98.09 310	98.85 254	99.76 100	99.62 107	99.83 80	99.64 178	98.54 164	99.97 33	99.15 119	99.99 16	99.68 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 78	99.47 85	99.76 64	99.58 191	99.64 110	99.30 136	99.63 167	99.61 110	99.71 132	99.56 233	98.76 132	99.96 54	99.14 125	99.92 105	99.68 89

MVSTER		98.47 275	98.22 281	99.24 253	99.06 347	98.35 294	99.08 213	99.46 256	99.27 160	99.75 114	99.66 171	88.61 378	99.85 240	99.14 125	99.92 105	99.52 198

Anonymous20231206		99.35 125	99.31 118	99.47 187	99.74 134	99.06 235	99.28 145	99.74 110	99.23 168	99.72 127	99.53 244	97.63 253	99.88 189	99.11 127	99.84 162	99.48 214

Syy-MVS		98.17 299	97.85 311	99.15 265	98.50 391	98.79 258	98.60 282	99.21 319	97.89 317	96.76 393	96.37 412	95.47 313	99.57 382	99.10 128	98.73 363	99.09 310

MVS_Test		99.28 139	99.31 118	99.19 258	99.35 281	98.79 258	99.36 121	99.49 249	99.17 181	99.21 277	99.67 166	98.78 129	99.66 364	99.09 129	99.66 252	99.10 306

testgi		99.29 138	99.26 134	99.37 219	99.75 128	98.81 255	98.84 255	99.89 40	98.38 278	99.75 114	99.04 341	99.36 59	99.86 222	99.08 130	99.25 327	99.45 223

1112_ss		99.05 199	98.84 225	99.67 109	99.66 169	99.29 194	98.52 299	99.82 67	97.65 329	99.43 228	99.16 325	96.42 294	99.91 139	99.07 131	99.84 162	99.80 47

CANet_DTU		98.91 227	98.85 223	99.09 275	98.79 374	98.13 305	98.18 323	99.31 295	99.48 126	98.86 317	99.51 248	96.56 288	99.95 63	99.05 132	99.95 83	99.19 287

Baseline_NR-MVSNet		99.49 85	99.37 106	99.82 37	99.91 31	99.84 24	98.83 257	99.86 49	99.68 90	99.65 154	99.88 42	97.67 247	99.87 203	99.03 133	99.86 153	99.76 66

FMVSNet2		99.35 125	99.28 130	99.55 168	99.49 239	99.35 185	99.45 104	99.57 206	99.44 136	99.70 136	99.74 119	97.21 268	99.87 203	99.03 133	99.94 94	99.44 228

Test_1112_low_res		98.95 224	98.73 234	99.63 135	99.68 163	99.15 222	98.09 334	99.80 80	97.14 355	99.46 222	99.40 276	96.11 304	99.89 175	99.01 135	99.84 162	99.84 36

VDD-MVS		99.20 165	99.11 158	99.44 195	99.43 263	98.98 238	99.50 92	98.32 370	99.80 64	99.56 192	99.69 151	96.99 278	99.85 240	98.99 136	99.73 223	99.50 205

DeepC-MVS	98.90 4	99.62 65	99.61 59	99.67 109	99.72 140	99.44 158	99.24 157	99.71 126	99.27 160	99.93 37	99.90 29	99.70 24	99.93 94	98.99 136	99.99 16	99.64 122
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 87	99.47 85	99.51 178	99.77 113	99.41 170	98.81 262	99.66 149	99.42 145	99.75 114	99.66 171	99.20 76	99.76 318	98.98 138	99.99 16	99.36 249

EPNet_dtu		97.62 320	97.79 314	97.11 375	96.67 408	92.31 399	98.51 300	98.04 374	99.24 166	95.77 402	99.47 262	93.78 330	99.66 364	98.98 138	99.62 259	99.37 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 130	99.32 116	99.39 213	99.67 168	98.77 260	98.57 291	99.81 76	99.61 110	99.48 216	99.41 272	98.47 175	99.86 222	98.97 140	99.90 115	99.53 187
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		99.40 111	99.31 118	99.68 106	99.43 263	99.55 138	99.73 27	99.50 245	99.46 133	99.88 61	99.36 288	97.54 254	99.87 203	98.97 140	99.87 145	99.63 127

GBi-Net		99.42 105	99.31 118	99.73 88	99.49 239	99.77 54	99.68 45	99.70 131	99.44 136	99.62 168	99.83 66	97.21 268	99.90 157	98.96 142	99.90 115	99.53 187

FMVSNet5		97.80 312	97.25 328	99.42 201	98.83 368	98.97 240	99.38 114	99.80 80	98.87 221	99.25 268	99.69 151	80.60 399	99.91 139	98.96 142	99.90 115	99.38 243

test1		99.42 105	99.31 118	99.73 88	99.49 239	99.77 54	99.68 45	99.70 131	99.44 136	99.62 168	99.83 66	97.21 268	99.90 157	98.96 142	99.90 115	99.53 187

FMVSNet3		98.80 240	98.63 242	99.32 233	99.13 331	98.72 263	99.10 205	99.48 250	99.23 168	99.62 168	99.64 178	92.57 342	99.86 222	98.96 142	99.90 115	99.39 241

UnsupCasMVSNet_eth		98.83 237	98.57 249	99.59 152	99.68 163	99.45 156	98.99 237	99.67 144	99.48 126	99.55 197	99.36 288	94.92 315	99.86 222	98.95 146	96.57 400	99.45 223

CHOSEN 280x420		98.41 281	98.41 263	98.40 334	99.34 290	95.89 374	96.94 394	99.44 261	98.80 232	99.25 268	99.52 246	93.51 334	99.98 20	98.94 147	99.98 41	99.32 259

TDRefinement		99.72 36	99.70 39	99.77 57	99.90 37	99.85 19	99.86 5	99.92 30	99.69 88	99.78 101	99.92 21	99.37 56	99.88 189	98.93 148	99.95 83	99.60 152

alignmvs		98.28 291	97.96 300	99.25 251	99.12 333	98.93 246	99.03 224	98.42 364	99.64 102	98.72 332	97.85 394	90.86 363	99.62 374	98.88 149	99.13 333	99.19 287

sdadasadasd		99.02 205	99.01 192	99.06 282	99.11 338	98.60 276	99.63 60	99.67 144	99.63 104	98.58 344	97.65 397	99.07 94	99.57 382	98.85 150	98.92 348	99.03 326

sss		98.90 229	98.77 233	99.27 245	99.48 244	98.44 285	98.72 274	99.32 291	97.94 315	99.37 244	99.35 293	96.31 299	99.91 139	98.85 150	99.63 258	99.47 218

xiu_mvs_v2_base		99.02 205	99.11 158	98.77 317	99.37 276	98.09 310	98.13 329	99.51 241	99.47 130	99.42 230	98.54 380	99.38 54	99.97 33	98.83 152	99.33 316	98.24 383

PS-MVSNAJ		99.00 213	99.08 169	98.76 318	99.37 276	98.10 309	98.00 345	99.51 241	99.47 130	99.41 236	98.50 382	99.28 66	99.97 33	98.83 152	99.34 315	98.20 387

D2MVS		99.22 158	99.19 142	99.29 240	99.69 155	98.74 262	98.81 262	99.41 267	98.55 259	99.68 142	99.69 151	98.13 214	99.87 203	98.82 154	99.98 41	99.24 273

PatchT		98.45 278	98.32 274	98.83 312	98.94 359	98.29 295	99.24 157	98.82 343	99.84 53	99.08 294	99.76 111	91.37 353	99.94 77	98.82 154	99.00 343	98.26 382

testf1		99.63 59	99.60 62	99.72 94	99.94 18	99.95 2	99.47 100	99.89 40	99.43 141	99.88 61	99.80 83	99.26 70	99.90 157	98.81 156	99.88 134	99.32 259

APD_test2		99.63 59	99.60 62	99.72 94	99.94 18	99.95 2	99.47 100	99.89 40	99.43 141	99.88 61	99.80 83	99.26 70	99.90 157	98.81 156	99.88 134	99.32 259

sasdasda		99.02 205	99.00 196	99.09 275	99.10 340	98.70 264	99.61 68	99.66 149	99.63 104	98.64 338	97.65 397	99.04 99	99.54 386	98.79 158	98.92 348	99.04 324

Effi-MVS+		99.06 196	98.97 206	99.34 226	99.31 297	98.98 238	98.31 315	99.91 33	98.81 230	98.79 326	98.94 358	99.14 84	99.84 255	98.79 158	98.74 361	99.20 284

canonicalmvs		99.02 205	99.00 196	99.09 275	99.10 340	98.70 264	99.61 68	99.66 149	99.63 104	98.64 338	97.65 397	99.04 99	99.54 386	98.79 158	98.92 348	99.04 324

VDDNet		98.97 217	98.82 228	99.42 201	99.71 143	98.81 255	99.62 63	98.68 349	99.81 61	99.38 243	99.80 83	94.25 324	99.85 240	98.79 158	99.32 318	99.59 159

CR-MVSNet		98.35 288	98.20 283	98.83 312	99.05 348	98.12 306	99.30 136	99.67 144	97.39 343	99.16 283	99.79 93	91.87 350	99.91 139	98.78 162	98.77 357	98.44 376

test_method		91.72 373	92.32 376	89.91 390	93.49 412	70.18 415	90.28 403	99.56 211	61.71 407	95.39 404	99.52 246	93.90 326	99.94 77	98.76 163	98.27 378	99.62 138

RPMNet		98.60 257	98.53 254	98.83 312	99.05 348	98.12 306	99.30 136	99.62 170	99.86 45	99.16 283	99.74 119	92.53 344	99.92 116	98.75 164	98.77 357	98.44 376

pmmvs4		99.13 185	99.06 175	99.36 223	99.57 201	99.10 230	98.01 343	99.25 309	98.78 235	99.58 182	99.44 269	98.24 203	99.76 318	98.74 165	99.93 101	99.22 278

tttt0517		97.62 320	97.20 329	98.90 305	99.76 117	97.40 343	99.48 97	94.36 402	99.06 199	99.70 136	99.49 255	84.55 394	99.94 77	98.73 166	99.65 254	99.36 249

EPP-MVSNet		99.17 177	99.00 196	99.66 116	99.80 86	99.43 162	99.70 35	99.24 312	99.48 126	99.56 192	99.77 108	94.89 316	99.93 94	98.72 167	99.89 124	99.63 127

Anonymous20240529		99.42 105	99.34 111	99.65 121	99.53 221	99.60 126	99.63 60	99.39 277	99.47 130	99.76 108	99.78 101	98.13 214	99.86 222	98.70 168	99.68 243	99.49 210

ACMH	98.42 6	99.59 69	99.54 77	99.72 94	99.86 54	99.62 117	99.56 82	99.79 86	98.77 237	99.80 92	99.85 56	99.64 28	99.85 240	98.70 168	99.89 124	99.70 79
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 133	99.28 130	99.47 187	99.57 201	99.39 173	99.78 12	99.43 264	98.87 221	99.57 185	99.82 73	98.06 219	99.87 203	98.69 170	99.73 223	99.15 295

LFMVS		98.46 276	98.19 286	99.26 248	99.24 313	98.52 281	99.62 63	96.94 391	99.87 41	99.31 258	99.58 221	91.04 358	99.81 294	98.68 171	99.42 305	99.45 223

WR-MVS		99.11 190	98.93 210	99.66 116	99.30 301	99.42 165	98.42 308	99.37 282	99.04 200	99.57 185	99.20 323	96.89 280	99.86 222	98.66 172	99.87 145	99.70 79

Anonymous202405211		98.75 244	98.46 258	99.63 135	99.34 290	99.66 101	99.47 100	97.65 382	99.28 159	99.56 192	99.50 251	93.15 336	99.84 255	98.62 173	99.58 274	99.40 239

EPNet		98.13 300	97.77 315	99.18 260	94.57 411	97.99 316	99.24 157	97.96 376	99.74 73	97.29 386	99.62 196	93.13 337	99.97 33	98.59 174	99.83 170	99.58 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 199	99.09 167	98.91 299	99.21 318	98.36 293	98.82 261	99.47 253	98.85 224	98.90 312	99.56 233	98.78 129	99.09 401	98.57 175	99.68 243	99.26 270

Patchmatch-RL test		98.60 257	98.36 268	99.33 229	99.77 113	99.07 233	98.27 317	99.87 46	98.91 216	99.74 122	99.72 130	90.57 367	99.79 305	98.55 176	99.85 157	99.11 304

pmmvs3		98.08 303	97.80 312	98.91 299	99.41 269	97.69 334	97.87 359	99.66 149	95.87 374	99.50 213	99.51 248	90.35 369	99.97 33	98.55 176	99.47 298	99.08 316

ETV-MVS		99.18 172	99.18 143	99.16 263	99.34 290	99.28 196	99.12 198	99.79 86	99.48 126	98.93 306	98.55 379	99.40 49	99.93 94	98.51 178	99.52 290	98.28 381

jason		99.16 179	99.11 158	99.32 233	99.75 128	98.44 285	98.26 319	99.39 277	98.70 245	99.74 122	99.30 301	98.54 164	99.97 33	98.48 179	99.82 179	99.55 174
jason: jason.
APDe-MVS		99.48 87	99.36 109	99.85 27	99.55 213	99.81 40	99.50 92	99.69 137	98.99 203	99.75 114	99.71 138	98.79 127	99.93 94	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		98.71 249	98.56 252	99.15 265	99.22 316	98.66 269	97.14 389	99.51 241	98.09 304	99.54 199	99.27 307	96.87 281	99.74 325	98.43 181	98.96 345	99.03 326

our_test_3		98.85 236	99.09 167	98.13 346	99.66 169	94.90 386	97.72 364	99.58 204	99.07 197	99.64 155	99.62 196	98.19 210	99.93 94	98.41 182	99.95 83	99.55 174

Gipuma		99.57 70	99.59 64	99.49 181	99.98 3	99.71 83	99.72 30	99.84 60	99.81 61	99.94 34	99.78 101	98.91 114	99.71 334	98.41 182	99.95 83	99.05 323
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 328	96.91 338	98.74 319	97.72 404	97.57 336	97.60 370	97.36 389	98.00 307	99.21 277	98.02 390	90.04 372	99.79 305	98.37 184	95.89 404	98.86 348

PM-MVS		99.36 123	99.29 128	99.58 156	99.83 65	99.66 101	98.95 244	99.86 49	98.85 224	99.81 88	99.73 123	98.40 188	99.92 116	98.36 185	99.83 170	99.17 291

baseline1		97.73 315	97.33 325	98.96 290	99.30 301	97.73 332	99.40 110	98.42 364	99.33 153	99.46 222	99.21 321	91.18 356	99.82 279	98.35 186	91.26 406	99.32 259

MVS-HIRNet		97.86 309	98.22 281	96.76 377	99.28 306	91.53 404	98.38 310	92.60 407	99.13 190	99.31 258	99.96 12	97.18 272	99.68 355	98.34 187	99.83 170	99.07 321

GA-MVS		97.99 308	97.68 318	98.93 296	99.52 226	98.04 314	97.19 388	99.05 334	98.32 291	98.81 322	98.97 354	89.89 374	99.41 397	98.33 188	99.05 339	99.34 255

Fast-Effi-MVS+		99.02 205	98.87 221	99.46 189	99.38 274	99.50 144	99.04 220	99.79 86	97.17 353	98.62 340	98.74 371	99.34 60	99.95 63	98.32 189	99.41 306	98.92 341

iter_conf05_11		98.54 265	98.33 273	99.18 260	99.07 345	99.20 216	97.94 352	97.59 383	99.17 181	99.30 263	98.92 362	94.79 318	99.86 222	98.29 190	99.89 124	98.47 374

bld_raw_dy_0_64		98.97 217	98.90 218	99.17 262	99.07 345	99.24 207	99.24 157	99.93 29	99.23 168	99.87 69	99.03 345	95.48 312	99.81 294	98.29 190	99.99 16	98.47 374

MDA-MVSNet_test_wron		98.95 224	98.99 202	98.85 308	99.64 173	97.16 349	98.23 321	99.33 289	98.93 213	99.56 192	99.66 171	97.39 261	99.83 270	98.29 190	99.88 134	99.55 174

N_pmnet		98.73 247	98.53 254	99.35 225	99.72 140	98.67 266	98.34 312	94.65 401	98.35 285	99.79 97	99.68 162	98.03 221	99.93 94	98.28 193	99.92 105	99.44 228

ET-MVSNet_ETH3D		96.78 340	96.07 349	98.91 299	99.26 310	97.92 324	97.70 366	96.05 396	97.96 314	92.37 407	98.43 383	87.06 383	99.90 157	98.27 194	97.56 395	98.91 342

thisisatest0530		97.45 325	96.95 335	98.94 293	99.68 163	97.73 332	99.09 210	94.19 404	98.61 255	99.56 192	99.30 301	84.30 395	99.93 94	98.27 194	99.54 285	99.16 293

YYNet1		98.95 224	98.99 202	98.84 310	99.64 173	97.14 351	98.22 322	99.32 291	98.92 215	99.59 180	99.66 171	97.40 259	99.83 270	98.27 194	99.90 115	99.55 174

ACMM	98.09 11	99.46 95	99.38 103	99.72 94	99.80 86	99.69 94	99.13 194	99.65 159	98.99 203	99.64 155	99.72 130	99.39 50	99.86 222	98.23 197	99.81 188	99.60 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 221	98.87 221	99.24 253	99.57 201	98.40 288	98.12 330	99.18 323	98.28 293	99.63 159	99.13 327	98.02 222	99.97 33	98.22 198	99.69 238	99.35 252

3Dnovator	99.15 2	99.43 102	99.36 109	99.65 121	99.39 271	99.42 165	99.70 35	99.56 211	99.23 168	99.35 246	99.80 83	99.17 79	99.95 63	98.21 199	99.84 162	99.59 159

Fast-Effi-MVS+-dtu		99.20 165	99.12 155	99.43 199	99.25 311	99.69 94	99.05 217	99.82 67	99.50 124	98.97 302	99.05 339	98.98 106	99.98 20	98.20 200	99.24 329	98.62 361

MS-PatchMatch		99.00 213	98.97 206	99.09 275	99.11 338	98.19 301	98.76 271	99.33 289	98.49 268	99.44 224	99.58 221	98.21 208	99.69 343	98.20 200	99.62 259	99.39 241

TSAR-MVS + GP.		99.12 187	99.04 185	99.38 216	99.34 290	99.16 220	98.15 326	99.29 299	98.18 300	99.63 159	99.62 196	99.18 78	99.68 355	98.20 200	99.74 218	99.30 265

DP-MVS		99.48 87	99.39 101	99.74 79	99.57 201	99.62 117	99.29 143	99.61 177	99.87 41	99.74 122	99.76 111	98.69 141	99.87 203	98.20 200	99.80 193	99.75 69

MVP-Stereo		99.16 179	99.08 169	99.43 199	99.48 244	99.07 233	99.08 213	99.55 217	98.63 251	99.31 258	99.68 162	98.19 210	99.78 308	98.18 204	99.58 274	99.45 223
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 102	99.30 123	99.80 45	99.83 65	99.81 40	99.52 87	99.70 131	98.35 285	99.51 211	99.50 251	99.31 62	99.88 189	98.18 204	99.84 162	99.69 83

MDA-MVSNet-bldmvs		99.06 196	99.05 179	99.07 280	99.80 86	97.83 327	98.89 249	99.72 123	99.29 156	99.63 159	99.70 145	96.47 292	99.89 175	98.17 206	99.82 179	99.50 205

JIA-IIPM		98.06 304	97.92 307	98.50 330	98.59 387	97.02 353	98.80 265	98.51 359	99.88 40	97.89 374	99.87 47	91.89 349	99.90 157	98.16 207	97.68 394	98.59 363

EIA-MVS		99.12 187	99.01 192	99.45 192	99.36 279	99.62 117	99.34 123	99.79 86	98.41 274	98.84 319	98.89 363	98.75 134	99.84 255	98.15 208	99.51 291	98.89 345

miper_lstm_enhance		98.65 254	98.60 243	98.82 315	99.20 321	97.33 345	97.78 362	99.66 149	99.01 202	99.59 180	99.50 251	94.62 321	99.85 240	98.12 209	99.90 115	99.26 270

Effi-MVS+-dtu		99.07 195	98.92 214	99.52 176	98.89 364	99.78 49	99.15 186	99.66 149	99.34 151	98.92 309	99.24 317	97.69 245	99.98 20	98.11 210	99.28 323	98.81 352

tpm		97.15 332	96.95 335	97.75 359	98.91 360	94.24 389	99.32 128	97.96 376	97.71 327	98.29 356	99.32 297	86.72 389	99.92 116	98.10 211	96.24 403	99.09 310

DeepPCF-MVS	98.42 6	99.18 172	99.02 189	99.67 109	99.22 316	99.75 67	97.25 386	99.47 253	98.72 242	99.66 152	99.70 145	99.29 64	99.63 373	98.07 212	99.81 188	99.62 138

ppachtmachnet_test		98.89 232	99.12 155	98.20 344	99.66 169	95.24 382	97.63 368	99.68 140	99.08 195	99.78 101	99.62 196	98.65 149	99.88 189	98.02 213	99.96 70	99.48 214

tpmrst		97.73 315	98.07 293	96.73 379	98.71 383	92.00 400	99.10 205	98.86 340	98.52 264	98.92 309	99.54 242	91.90 348	99.82 279	98.02 213	99.03 341	98.37 378

CSCG		99.37 120	99.29 128	99.60 150	99.71 143	99.46 151	99.43 108	99.85 54	98.79 233	99.41 236	99.60 213	98.92 112	99.92 116	98.02 213	99.92 105	99.43 234

eth_miper_zixun_eth		98.68 252	98.71 236	98.60 325	99.10 340	96.84 358	97.52 376	99.54 223	98.94 210	99.58 182	99.48 258	96.25 302	99.76 318	98.01 216	99.93 101	99.21 280

Patchmtry		98.78 241	98.54 253	99.49 181	98.89 364	99.19 218	99.32 128	99.67 144	99.65 100	99.72 127	99.79 93	91.87 350	99.95 63	98.00 217	99.97 56	99.33 256

PVSNet_BlendedMVS		99.03 203	99.01 192	99.09 275	99.54 215	97.99 316	98.58 287	99.82 67	97.62 330	99.34 249	99.71 138	98.52 171	99.77 316	97.98 218	99.97 56	99.52 198

PVSNet_Blended		98.70 250	98.59 245	99.02 285	99.54 215	97.99 316	97.58 371	99.82 67	95.70 378	99.34 249	98.98 352	98.52 171	99.77 316	97.98 218	99.83 170	99.30 265

cl____		98.54 265	98.41 263	98.92 297	99.03 351	97.80 330	97.46 378	99.59 195	98.90 217	99.60 177	99.46 265	93.85 328	99.78 308	97.97 220	99.89 124	99.17 291

DIV-MVS_self_test		98.54 265	98.42 262	98.92 297	99.03 351	97.80 330	97.46 378	99.59 195	98.90 217	99.60 177	99.46 265	93.87 327	99.78 308	97.97 220	99.89 124	99.18 289

AUN-MVS		97.82 311	97.38 324	99.14 269	99.27 308	98.53 279	98.72 274	99.02 335	98.10 302	97.18 389	99.03 345	89.26 376	99.85 240	97.94 222	97.91 390	99.03 326

FA-MVS(test-final)		98.52 268	98.32 274	99.10 274	99.48 244	98.67 266	99.77 15	98.60 356	97.35 345	99.63 159	99.80 83	93.07 338	99.84 255	97.92 223	99.30 320	98.78 355

ambc				99.20 257	99.35 281	98.53 279	99.17 178	99.46 256		99.67 148	99.80 83	98.46 178	99.70 337	97.92 223	99.70 234	99.38 243

USDC		98.96 221	98.93 210	99.05 283	99.54 215	97.99 316	97.07 392	99.80 80	98.21 297	99.75 114	99.77 108	98.43 181	99.64 372	97.90 225	99.88 134	99.51 200

OPM-MVS		99.26 145	99.13 151	99.63 135	99.70 151	99.61 123	98.58 287	99.48 250	98.50 266	99.52 206	99.63 189	99.14 84	99.76 318	97.89 226	99.77 207	99.51 200
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 135	99.17 144	99.77 57	99.69 155	99.80 44	99.14 188	99.31 295	99.16 184	99.62 168	99.61 205	98.35 192	99.91 139	97.88 227	99.72 229	99.61 148
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 33	99.70 151	99.79 46	99.14 188	99.61 177					99.92 116	97.88 227	99.72 229	99.77 60

c3_l		98.72 248	98.71 236	98.72 320	99.12 333	97.22 348	97.68 367	99.56 211	98.90 217	99.54 199	99.48 258	96.37 298	99.73 328	97.88 227	99.88 134	99.21 280

3Dnovator+	98.92 3	99.35 125	99.24 138	99.67 109	99.35 281	99.47 147	99.62 63	99.50 245	99.44 136	99.12 290	99.78 101	98.77 131	99.94 77	97.87 230	99.72 229	99.62 138

miper_ehance_all_eth		98.59 260	98.59 245	98.59 326	98.98 357	97.07 352	97.49 377	99.52 237	98.50 266	99.52 206	99.37 284	96.41 296	99.71 334	97.86 231	99.62 259	99.00 333

WTY-MVS		98.59 260	98.37 267	99.26 248	99.43 263	98.40 288	98.74 272	99.13 329	98.10 302	99.21 277	99.24 317	94.82 317	99.90 157	97.86 231	98.77 357	99.49 210

APD_test1		99.36 123	99.28 130	99.61 147	99.89 39	99.89 10	99.32 128	99.74 110	99.18 176	99.69 139	99.75 116	98.41 184	99.84 255	97.85 233	99.70 234	99.10 306

SED-MVS		99.40 111	99.28 130	99.77 57	99.69 155	99.82 35	99.20 168	99.54 223	99.13 190	99.82 81	99.63 189	98.91 114	99.92 116	97.85 233	99.70 234	99.58 164

test_241102_TWO								99.54 223	99.13 190	99.76 108	99.63 189	98.32 197	99.92 116	97.85 233	99.69 238	99.75 69

MVS_111021_HR		99.12 187	99.02 189	99.40 210	99.50 234	99.11 225	97.92 355	99.71 126	98.76 240	99.08 294	99.47 262	99.17 79	99.54 386	97.85 233	99.76 209	99.54 182

MTAPA		99.35 125	99.20 141	99.80 45	99.81 80	99.81 40	99.33 126	99.53 232	99.27 160	99.42 230	99.63 189	98.21 208	99.95 63	97.83 237	99.79 198	99.65 112

MSC_two_6792asdad				99.74 79	99.03 351	99.53 141		99.23 313					99.92 116	97.77 238	99.69 238	99.78 56

No_MVS				99.74 79	99.03 351	99.53 141		99.23 313					99.92 116	97.77 238	99.69 238	99.78 56

TESTMET0.1,1		96.24 353	95.84 354	97.41 367	98.24 398	93.84 392	97.38 380	95.84 397	98.43 271	97.81 379	98.56 378	79.77 401	99.89 175	97.77 238	98.77 357	98.52 368

ACMH+	98.40 8	99.50 83	99.43 96	99.71 99	99.86 54	99.76 61	99.32 128	99.77 95	99.53 122	99.77 106	99.76 111	99.26 70	99.78 308	97.77 238	99.88 134	99.60 152

IU-MVS					99.69 155	99.77 54		99.22 316	97.50 337	99.69 139				97.75 242	99.70 234	99.77 60

114514_t		98.49 273	98.11 291	99.64 128	99.73 137	99.58 132	99.24 157	99.76 100	89.94 400	99.42 230	99.56 233	97.76 242	99.86 222	97.74 243	99.82 179	99.47 218

DVP-MVS++		99.38 117	99.25 136	99.77 57	99.03 351	99.77 54	99.74 24	99.61 177	99.18 176	99.76 108	99.61 205	99.00 102	99.92 116	97.72 244	99.60 269	99.62 138

test_0728_THIRD									99.18 176	99.62 168	99.61 205	98.58 158	99.91 139	97.72 244	99.80 193	99.77 60

EGC-MVSNET		89.05 374	85.52 377	99.64 128	99.89 39	99.78 49	99.56 82	99.52 237	24.19 408	49.96 409	99.83 66	99.15 81	99.92 116	97.71 246	99.85 157	99.21 280

miper_enhance_ethall		98.03 305	97.94 305	98.32 339	98.27 397	96.43 364	96.95 393	99.41 267	96.37 369	99.43 228	98.96 356	94.74 319	99.69 343	97.71 246	99.62 259	98.83 351

TSAR-MVS + MP.		99.34 130	99.24 138	99.63 135	99.82 72	99.37 178	99.26 150	99.35 286	98.77 237	99.57 185	99.70 145	99.27 69	99.88 189	97.71 246	99.75 211	99.65 112
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 323	97.28 326	98.40 334	98.37 395	96.75 359	97.24 387	99.37 282	97.31 347	99.41 236	99.22 319	87.30 381	99.37 398	97.70 249	99.62 259	99.08 316

MP-MVS-pluss		99.14 183	98.92 214	99.80 45	99.83 65	99.83 29	98.61 280	99.63 167	96.84 362	99.44 224	99.58 221	98.81 122	99.91 139	97.70 249	99.82 179	99.67 95
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 139	99.11 158	99.79 51	99.75 128	99.81 40	98.95 244	99.53 232	98.27 294	99.53 204	99.73 123	98.75 134	99.87 203	97.70 249	99.83 170	99.68 89

UnsupCasMVSNet_bld		98.55 264	98.27 278	99.40 210	99.56 212	99.37 178	97.97 350	99.68 140	97.49 338	99.08 294	99.35 293	95.41 314	99.82 279	97.70 249	98.19 382	99.01 332

MVS_111021_LR		99.13 185	99.03 187	99.42 201	99.58 191	99.32 190	97.91 357	99.73 114	98.68 246	99.31 258	99.48 258	99.09 89	99.66 364	97.70 249	99.77 207	99.29 268

IS-MVSNet		99.03 203	98.85 223	99.55 168	99.80 86	99.25 203	99.73 27	99.15 326	99.37 148	99.61 174	99.71 138	94.73 320	99.81 294	97.70 249	99.88 134	99.58 164

test-LLR		97.15 332	96.95 335	97.74 360	98.18 400	95.02 384	97.38 380	96.10 393	98.00 307	97.81 379	98.58 375	90.04 372	99.91 139	97.69 255	98.78 355	98.31 379

test-mter		96.23 354	95.73 356	97.74 360	98.18 400	95.02 384	97.38 380	96.10 393	97.90 316	97.81 379	98.58 375	79.12 404	99.91 139	97.69 255	98.78 355	98.31 379

XVS		99.27 143	99.11 158	99.75 74	99.71 143	99.71 83	99.37 118	99.61 177	99.29 156	98.76 329	99.47 262	98.47 175	99.88 189	97.62 257	99.73 223	99.67 95

X-MVStestdata		96.09 357	94.87 369	99.75 74	99.71 143	99.71 83	99.37 118	99.61 177	99.29 156	98.76 329	61.30 415	98.47 175	99.88 189	97.62 257	99.73 223	99.67 95

SMA-MVS		99.19 168	99.00 196	99.73 88	99.46 254	99.73 76	99.13 194	99.52 237	97.40 342	99.57 185	99.64 178	98.93 111	99.83 270	97.61 259	99.79 198	99.63 127
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		96.71 343	96.79 342	96.46 383	98.90 361	90.71 409	99.41 109	98.68 349	94.69 391	98.14 366	99.34 296	86.32 391	99.80 302	97.60 260	98.07 388	98.88 346

PVSNet	97.47 15	98.42 280	98.44 260	98.35 336	99.46 254	96.26 367	96.70 397	99.34 288	97.68 328	99.00 301	99.13 327	97.40 259	99.72 330	97.59 261	99.68 243	99.08 316

new_pmnet		98.88 233	98.89 219	98.84 310	99.70 151	97.62 335	98.15 326	99.50 245	97.98 310	99.62 168	99.54 242	98.15 213	99.94 77	97.55 262	99.84 162	98.95 337

IB-MVS	95.41 20	95.30 371	94.46 375	97.84 356	98.76 379	95.33 380	97.33 383	96.07 395	96.02 373	95.37 405	97.41 401	76.17 406	99.96 54	97.54 263	95.44 405	98.22 384
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		99.24 149	99.11 158	99.61 147	98.38 394	99.79 46	99.57 80	99.68 140	99.61 110	99.15 285	99.71 138	98.70 140	99.91 139	97.54 263	99.68 243	99.13 303

ZNCC-MVS		99.22 158	99.04 185	99.77 57	99.76 117	99.73 76	99.28 145	99.56 211	98.19 299	99.14 287	99.29 304	98.84 121	99.92 116	97.53 265	99.80 193	99.64 122

CP-MVS		99.23 150	99.05 179	99.75 74	99.66 169	99.66 101	99.38 114	99.62 170	98.38 278	99.06 298	99.27 307	98.79 127	99.94 77	97.51 266	99.82 179	99.66 104

SD-MVS		99.01 211	99.30 123	98.15 345	99.50 234	99.40 171	98.94 246	99.61 177	99.22 173	99.75 114	99.82 73	99.54 41	95.51 409	97.48 267	99.87 145	99.54 182
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		98.49 273	98.29 277	99.11 272	98.96 358	98.42 287	97.54 372	99.32 291	97.53 335	98.47 351	98.15 389	97.88 232	99.82 279	97.46 268	99.24 329	99.09 310

DeepC-MVS_fast	98.47 5	99.23 150	99.12 155	99.56 165	99.28 306	99.22 211	98.99 237	99.40 274	99.08 195	99.58 182	99.64 178	98.90 117	99.83 270	97.44 269	99.75 211	99.63 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 146	99.08 169	99.76 64	99.73 137	99.70 90	99.31 133	99.59 195	98.36 280	99.36 245	99.37 284	98.80 126	99.91 139	97.43 270	99.75 211	99.68 89

ACMMPR		99.23 150	99.06 175	99.76 64	99.74 134	99.69 94	99.31 133	99.59 195	98.36 280	99.35 246	99.38 282	98.61 153	99.93 94	97.43 270	99.75 211	99.67 95

Vis-MVSNet (Re-imp)		98.77 242	98.58 248	99.34 226	99.78 105	98.88 251	99.61 68	99.56 211	99.11 194	99.24 271	99.56 233	93.00 340	99.78 308	97.43 270	99.89 124	99.35 252

MIMVSNet		98.43 279	98.20 283	99.11 272	99.53 221	98.38 292	99.58 77	98.61 354	98.96 207	99.33 251	99.76 111	90.92 360	99.81 294	97.38 273	99.76 209	99.15 295

WB-MVSnew		98.34 290	98.14 289	98.96 290	98.14 403	97.90 325	98.27 317	97.26 390	98.63 251	98.80 324	98.00 392	97.77 240	99.90 157	97.37 274	98.98 344	99.09 310

XVG-OURS-SEG-HR		99.16 179	98.99 202	99.66 116	99.84 61	99.64 110	98.25 320	99.73 114	98.39 277	99.63 159	99.43 270	99.70 24	99.90 157	97.34 275	98.64 367	99.44 228

COLMAP_ROB	98.06 12	99.45 97	99.37 106	99.70 103	99.83 65	99.70 90	99.38 114	99.78 92	99.53 122	99.67 148	99.78 101	99.19 77	99.86 222	97.32 276	99.87 145	99.55 174
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	98.81 229	99.65 121	99.58 191	99.49 145	98.58 287	99.07 331	98.40 276	99.04 299	99.25 312	98.51 173	99.80 302	97.31 277	99.51 291	99.65 112

region2R		99.23 150	99.05 179	99.77 57	99.76 117	99.70 90	99.31 133	99.59 195	98.41 274	99.32 254	99.36 288	98.73 138	99.93 94	97.29 278	99.74 218	99.67 95

APD-MVS_3200maxsize		99.31 136	99.16 145	99.74 79	99.53 221	99.75 67	99.27 148	99.61 177	99.19 175	99.57 185	99.64 178	98.76 132	99.90 157	97.29 278	99.62 259	99.56 171

TAPA-MVS	97.92 13	98.03 305	97.55 321	99.46 189	99.47 250	99.44 158	98.50 301	99.62 170	86.79 401	99.07 297	99.26 310	98.26 202	99.62 374	97.28 280	99.73 223	99.31 263
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 143	99.11 158	99.73 88	99.54 215	99.74 73	99.26 150	99.62 170	99.16 184	99.52 206	99.64 178	98.41 184	99.91 139	97.27 281	99.61 266	99.54 182

RE-MVS-def			99.13 151		99.54 215	99.74 73	99.26 150	99.62 170	99.16 184	99.52 206	99.64 178	98.57 159		97.27 281	99.61 266	99.54 182

testing11		96.05 359	95.41 361	97.97 350	98.78 376	95.27 381	98.59 285	98.23 372	98.86 223	96.56 396	96.91 407	75.20 407	99.69 343	97.26 283	98.29 377	98.93 339

test_yl		98.25 293	97.95 301	99.13 270	99.17 326	98.47 282	99.00 232	98.67 351	98.97 205	99.22 275	99.02 347	91.31 354	99.69 343	97.26 283	98.93 346	99.24 273

DCV-MVSNet		98.25 293	97.95 301	99.13 270	99.17 326	98.47 282	99.00 232	98.67 351	98.97 205	99.22 275	99.02 347	91.31 354	99.69 343	97.26 283	98.93 346	99.24 273

PHI-MVS		99.11 190	98.95 209	99.59 152	99.13 331	99.59 128	99.17 178	99.65 159	97.88 319	99.25 268	99.46 265	98.97 108	99.80 302	97.26 283	99.82 179	99.37 246

tfpnnormal		99.43 102	99.38 103	99.60 150	99.87 51	99.75 67	99.59 75	99.78 92	99.71 80	99.90 49	99.69 151	98.85 120	99.90 157	97.25 287	99.78 203	99.15 295

PatchmatchNet		97.65 319	97.80 312	97.18 373	98.82 371	92.49 398	99.17 178	98.39 366	98.12 301	98.79 326	99.58 221	90.71 365	99.89 175	97.23 288	99.41 306	99.16 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 216	98.80 231	99.56 165	99.25 311	99.43 162	98.54 296	99.27 303	98.58 257	98.80 324	99.43 270	98.53 168	99.70 337	97.22 289	99.59 273	99.54 182

testing3		96.48 347	95.63 358	99.01 286	99.23 315	97.81 328	98.90 248	99.10 330	98.72 242	97.84 378	97.92 393	72.44 411	99.85 240	97.21 290	99.33 316	99.35 252

HPM-MVS		99.25 146	99.07 173	99.78 54	99.81 80	99.75 67	99.61 68	99.67 144	97.72 326	99.35 246	99.25 312	99.23 73	99.92 116	97.21 290	99.82 179	99.67 95
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 168	99.00 196	99.76 64	99.76 117	99.68 97	99.38 114	99.54 223	98.34 289	99.01 300	99.50 251	98.53 168	99.93 94	97.18 292	99.78 203	99.66 104

ACMMP		99.25 146	99.08 169	99.74 79	99.79 98	99.68 97	99.50 92	99.65 159	98.07 305	99.52 206	99.69 151	98.57 159	99.92 116	97.18 292	99.79 198	99.63 127
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		96.98 336	96.42 343	98.66 323	99.42 268	97.47 339	97.27 385	94.30 403	97.24 349	99.15 285	98.86 365	85.01 392	99.87 203	97.10 294	99.39 308	98.63 360

XVG-ACMP-BASELINE		99.23 150	99.10 166	99.63 135	99.82 72	99.58 132	98.83 257	99.72 123	98.36 280	99.60 177	99.71 138	98.92 112	99.91 139	97.08 295	99.84 162	99.40 239

MSDG		99.08 194	98.98 205	99.37 219	99.60 182	99.13 223	97.54 372	99.74 110	98.84 227	99.53 204	99.55 240	99.10 87	99.79 305	97.07 296	99.86 153	99.18 289

SteuartSystems-ACMMP		99.30 137	99.14 149	99.76 64	99.87 51	99.66 101	99.18 173	99.60 189	98.55 259	99.57 185	99.67 166	99.03 101	99.94 77	97.01 297	99.80 193	99.69 83
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 355	95.78 355	97.49 363	98.53 389	93.83 393	98.04 340	93.94 405	98.96 207	98.46 352	98.17 388	79.86 400	99.87 203	96.99 298	99.06 337	98.78 355

EPMVS		96.53 346	96.32 344	97.17 374	98.18 400	92.97 397	99.39 112	89.95 411	98.21 297	98.61 341	99.59 218	86.69 390	99.72 330	96.99 298	99.23 331	98.81 352

MSP-MVS		99.04 202	98.79 232	99.81 40	99.78 105	99.73 76	99.35 122	99.57 206	98.54 262	99.54 199	98.99 349	96.81 282	99.93 94	96.97 300	99.53 287	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++		98.96 221	98.70 238	99.74 79	99.52 226	99.71 83	98.86 252	99.19 322	98.47 270	98.59 343	99.06 338	98.08 218	99.91 139	96.94 301	99.60 269	99.60 152

SR-MVS		99.19 168	99.00 196	99.74 79	99.51 228	99.72 81	99.18 173	99.60 189	98.85 224	99.47 218	99.58 221	98.38 189	99.92 116	96.92 302	99.54 285	99.57 169

PGM-MVS		99.20 165	99.01 192	99.77 57	99.75 128	99.71 83	99.16 184	99.72 123	97.99 309	99.42 230	99.60 213	98.81 122	99.93 94	96.91 303	99.74 218	99.66 104

HY-MVS	98.23 9	98.21 298	97.95 301	98.99 287	99.03 351	98.24 296	99.61 68	98.72 347	96.81 363	98.73 331	99.51 248	94.06 325	99.86 222	96.91 303	98.20 380	98.86 348

MDTV_nov1_ep13			97.73 316		98.70 384	90.83 407	99.15 186	98.02 375	98.51 265	98.82 321	99.61 205	90.98 359	99.66 364	96.89 305	98.92 348

GST-MVS		99.16 179	98.96 208	99.75 74	99.73 137	99.73 76	99.20 168	99.55 217	98.22 296	99.32 254	99.35 293	98.65 149	99.91 139	96.86 306	99.74 218	99.62 138

test_post1							99.14 188				51.63 417	89.54 375	99.82 279	96.86 306

SCA		98.11 301	98.36 268	97.36 368	99.20 321	92.99 396	98.17 325	98.49 361	98.24 295	99.10 293	99.57 229	96.01 306	99.94 77	96.86 306	99.62 259	99.14 300

XVG-OURS		99.21 163	99.06 175	99.65 121	99.82 72	99.62 117	97.87 359	99.74 110	98.36 280	99.66 152	99.68 162	99.71 22	99.90 157	96.84 309	99.88 134	99.43 234

LCM-MVSNet-Re		99.28 139	99.15 148	99.67 109	99.33 295	99.76 61	99.34 123	99.97 18	98.93 213	99.91 44	99.79 93	98.68 142	99.93 94	96.80 310	99.56 276	99.30 265

RPSCF		99.18 172	99.02 189	99.64 128	99.83 65	99.85 19	99.44 106	99.82 67	98.33 290	99.50 213	99.78 101	97.90 230	99.65 370	96.78 311	99.83 170	99.44 228

旧先验2							97.94 352		95.33 382	98.94 305			99.88 189	96.75 312

MDTV_nov1_ep13_2view						91.44 405	99.14 188		97.37 344	99.21 277		91.78 352		96.75 312		99.03 326

CLD-MVS		98.76 243	98.57 249	99.33 229	99.57 201	98.97 240	97.53 374	99.55 217	96.41 367	99.27 266	99.13 327	99.07 94	99.78 308	96.73 314	99.89 124	99.23 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 302	97.98 299	98.48 331	99.27 308	96.48 362	99.40 110	99.07 331	98.81 230	99.23 272	99.57 229	90.11 371	99.87 203	96.69 315	99.64 256	99.09 310

baseline2		96.83 339	96.28 345	98.46 332	99.09 343	96.91 356	98.83 257	93.87 406	97.23 350	96.23 401	98.36 384	88.12 379	99.90 157	96.68 316	98.14 385	98.57 366

cascas		96.99 335	96.82 341	97.48 364	97.57 407	95.64 376	96.43 399	99.56 211	91.75 396	97.13 391	97.61 400	95.58 311	98.63 405	96.68 316	99.11 335	98.18 388

PC_three_1452									97.56 331	99.68 142	99.41 272	99.09 89	97.09 407	96.66 318	99.60 269	99.62 138

LPG-MVS_test		99.22 158	99.05 179	99.74 79	99.82 72	99.63 115	99.16 184	99.73 114	97.56 331	99.64 155	99.69 151	99.37 56	99.89 175	96.66 318	99.87 145	99.69 83

LGP-MVS_train				99.74 79	99.82 72	99.63 115		99.73 114	97.56 331	99.64 155	99.69 151	99.37 56	99.89 175	96.66 318	99.87 145	99.69 83

ETVMVS		96.14 356	95.22 366	98.89 306	98.80 372	98.01 315	98.66 278	98.35 369	98.71 244	97.18 389	96.31 414	74.23 410	99.75 322	96.64 321	98.13 387	98.90 343

TinyColmap		98.97 217	98.93 210	99.07 280	99.46 254	98.19 301	97.75 363	99.75 105	98.79 233	99.54 199	99.70 145	98.97 108	99.62 374	96.63 322	99.83 170	99.41 238

LF4IMVS		99.01 211	98.92 214	99.27 245	99.71 143	99.28 196	98.59 285	99.77 95	98.32 291	99.39 242	99.41 272	98.62 151	99.84 255	96.62 323	99.84 162	98.69 359

NCCC		98.82 238	98.57 249	99.58 156	99.21 318	99.31 191	98.61 280	99.25 309	98.65 249	98.43 353	99.26 310	97.86 233	99.81 294	96.55 324	99.27 326	99.61 148

OPU-MVS				99.29 240	99.12 333	99.44 158	99.20 168				99.40 276	99.00 102	98.84 404	96.54 325	99.60 269	99.58 164

F-COLMAP		98.74 245	98.45 259	99.62 144	99.57 201	99.47 147	98.84 255	99.65 159	96.31 370	98.93 306	99.19 324	97.68 246	99.87 203	96.52 326	99.37 311	99.53 187

testing99		95.86 364	95.19 367	97.87 354	98.76 379	95.03 383	98.62 279	98.44 363	98.68 246	96.67 395	96.66 410	74.31 409	99.69 343	96.51 327	98.03 389	98.90 343

ADS-MVSNet2		97.78 313	97.66 320	98.12 347	99.14 329	95.36 379	99.22 165	98.75 346	96.97 358	98.25 358	99.64 178	90.90 361	99.94 77	96.51 327	99.56 276	99.08 316

ADS-MVSNet		97.72 318	97.67 319	97.86 355	99.14 329	94.65 387	99.22 165	98.86 340	96.97 358	98.25 358	99.64 178	90.90 361	99.84 255	96.51 327	99.56 276	99.08 316

PatchMatch-RL		98.68 252	98.47 257	99.30 239	99.44 259	99.28 196	98.14 328	99.54 223	97.12 356	99.11 291	99.25 312	97.80 238	99.70 337	96.51 327	99.30 320	98.93 339

CMPMVS	77.52 23	98.50 271	98.19 286	99.41 208	98.33 396	99.56 135	99.01 229	99.59 195	95.44 380	99.57 185	99.80 83	95.64 309	99.46 396	96.47 331	99.92 105	99.21 280
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 360	95.32 364	98.02 348	98.76 379	95.39 378	98.38 310	98.65 353	98.82 228	96.84 392	96.71 409	75.06 408	99.71 334	96.46 332	98.23 379	98.98 334

SF-MVS		99.10 193	98.93 210	99.62 144	99.58 191	99.51 143	99.13 194	99.65 159	97.97 311	99.42 230	99.61 205	98.86 119	99.87 203	96.45 333	99.68 243	99.49 210

FE-MVS		97.85 310	97.42 323	99.15 265	99.44 259	98.75 261	99.77 15	98.20 373	95.85 375	99.33 251	99.80 83	88.86 377	99.88 189	96.40 334	99.12 334	98.81 352

DPE-MVS		99.14 183	98.92 214	99.82 37	99.57 201	99.77 54	98.74 272	99.60 189	98.55 259	99.76 108	99.69 151	98.23 207	99.92 116	96.39 335	99.75 211	99.76 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 405	89.02 413			93.47 393		98.30 385		99.84 255	96.38 336

AllTest		99.21 163	99.07 173	99.63 135	99.78 105	99.64 110	99.12 198	99.83 62	98.63 251	99.63 159	99.72 130	98.68 142	99.75 322	96.38 336	99.83 170	99.51 200

TestCases				99.63 135	99.78 105	99.64 110		99.83 62	98.63 251	99.63 159	99.72 130	98.68 142	99.75 322	96.38 336	99.83 170	99.51 200

testdata				99.42 201	99.51 228	98.93 246		99.30 298	96.20 371	98.87 316	99.40 276	98.33 196	99.89 175	96.29 339	99.28 323	99.44 228

dp		96.86 338	97.07 331	96.24 385	98.68 385	90.30 411	99.19 172	98.38 367	97.35 345	98.23 360	99.59 218	87.23 382	99.82 279	96.27 340	98.73 363	98.59 363

tpmvs		97.39 327	97.69 317	96.52 381	98.41 393	91.76 401	99.30 136	98.94 339	97.74 325	97.85 377	99.55 240	92.40 347	99.73 328	96.25 341	98.73 363	98.06 390

KD-MVS_2432*1600		95.89 361	95.41 361	97.31 371	94.96 409	93.89 390	97.09 390	99.22 316	97.23 350	98.88 313	99.04 341	79.23 402	99.54 386	96.24 342	96.81 398	98.50 372

miper_refine_blended		95.89 361	95.41 361	97.31 371	94.96 409	93.89 390	97.09 390	99.22 316	97.23 350	98.88 313	99.04 341	79.23 402	99.54 386	96.24 342	96.81 398	98.50 372

ACMP	97.51 14	99.05 199	98.84 225	99.67 109	99.78 105	99.55 138	98.88 250	99.66 149	97.11 357	99.47 218	99.60 213	99.07 94	99.89 175	96.18 344	99.85 157	99.58 164
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 229	98.72 235	99.44 195	99.39 271	99.42 165	98.58 287	99.64 165	97.31 347	99.44 224	99.62 196	98.59 156	99.69 343	96.17 345	99.79 198	99.22 278

DP-MVS Recon		98.50 271	98.23 279	99.31 236	99.49 239	99.46 151	98.56 292	99.63 167	94.86 389	98.85 318	99.37 284	97.81 237	99.59 380	96.08 346	99.44 301	98.88 346

tpm cat1		96.78 340	96.98 334	96.16 386	98.85 367	90.59 410	99.08 213	99.32 291	92.37 395	97.73 383	99.46 265	91.15 357	99.69 343	96.07 347	98.80 354	98.21 385

tpm2		96.35 350	96.22 346	96.73 379	98.88 366	91.75 402	99.21 167	98.51 359	93.27 394	97.89 374	99.21 321	84.83 393	99.70 337	96.04 348	98.18 383	98.75 358

dmvs_re		98.69 251	98.48 256	99.31 236	99.55 213	99.42 165	99.54 85	98.38 367	99.32 154	98.72 332	98.71 372	96.76 284	99.21 399	96.01 349	99.35 314	99.31 263

test_0402		99.22 158	99.14 149	99.45 192	99.79 98	99.43 162	99.28 145	99.68 140	99.54 120	99.40 241	99.56 233	99.07 94	99.82 279	96.01 349	99.96 70	99.11 304

ITE_SJBPF				99.38 216	99.63 175	99.44 158		99.73 114	98.56 258	99.33 251	99.53 244	98.88 118	99.68 355	96.01 349	99.65 254	99.02 331

test_prior2							97.95 351		97.87 320	98.05 368	99.05 339	97.90 230		95.99 352	99.49 296

testdata2													99.89 175	95.99 352

原ACMM1				99.37 219	99.47 250	98.87 253		99.27 303	96.74 365	98.26 357	99.32 297	97.93 229	99.82 279	95.96 354	99.38 309	99.43 234

新几何1				99.52 176	99.50 234	99.22 211		99.26 306	95.66 379	98.60 342	99.28 305	97.67 247	99.89 175	95.95 355	99.32 318	99.45 223

MP-MVS		99.06 196	98.83 227	99.76 64	99.76 117	99.71 83	99.32 128	99.50 245	98.35 285	98.97 302	99.48 258	98.37 190	99.92 116	95.95 355	99.75 211	99.63 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 370	94.59 373	98.61 324	98.66 386	97.45 341	98.54 296	97.90 379	98.53 263	96.54 397	96.47 411	70.62 413	99.81 294	95.91 357	98.15 384	98.56 367

wuyk23d		97.58 322	99.13 151	92.93 389	99.69 155	99.49 145	99.52 87	99.77 95	97.97 311	99.96 23	99.79 93	99.84 12	99.94 77	95.85 358	99.82 179	79.36 405

HQP_MVS		98.90 229	98.68 239	99.55 168	99.58 191	99.24 207	98.80 265	99.54 223	98.94 210	99.14 287	99.25 312	97.24 266	99.82 279	95.84 359	99.78 203	99.60 152

plane_prior5								99.54 223					99.82 279	95.84 359	99.78 203	99.60 152

无先验							98.01 343	99.23 313	95.83 376				99.85 240	95.79 361		99.44 228

CPTT-MVS		98.74 245	98.44 260	99.64 128	99.61 180	99.38 175	99.18 173	99.55 217	96.49 366	99.27 266	99.37 284	97.11 274	99.92 116	95.74 362	99.67 249	99.62 138

PLC	97.35 16	98.36 285	97.99 297	99.48 185	99.32 296	99.24 207	98.50 301	99.51 241	95.19 385	98.58 344	98.96 356	96.95 279	99.83 270	95.63 363	99.25 327	99.37 246
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 262	98.34 271	99.28 242	99.18 325	99.10 230	98.34 312	99.41 267	98.48 269	98.52 348	98.98 352	97.05 276	99.78 308	95.59 364	99.50 294	98.96 335

1314		98.00 307	97.90 309	98.27 343	98.90 361	97.45 341	99.30 136	99.06 333	94.98 386	97.21 388	99.12 331	98.43 181	99.67 360	95.58 365	98.56 370	97.71 394

PVSNet_0	95.53 19	95.85 365	95.31 365	97.47 365	98.78 376	93.48 395	95.72 400	99.40 274	96.18 372	97.37 384	97.73 395	95.73 308	99.58 381	95.49 366	81.40 407	99.36 249

MAR-MVS		98.24 295	97.92 307	99.19 258	98.78 376	99.65 107	99.17 178	99.14 327	95.36 381	98.04 369	98.81 368	97.47 256	99.72 330	95.47 367	99.06 337	98.21 385
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	98.23 296	97.89 310	99.26 248	99.19 323	99.26 200	99.65 58	99.69 137	91.33 398	98.14 366	99.77 108	98.28 200	99.96 54	95.41 368	99.55 280	98.58 365

train_agg		98.35 288	97.95 301	99.57 162	99.35 281	99.35 185	98.11 332	99.41 267	94.90 387	97.92 372	98.99 349	98.02 222	99.85 240	95.38 369	99.44 301	99.50 205

9.14			98.64 240		99.45 258		98.81 262	99.60 189	97.52 336	99.28 265	99.56 233	98.53 168	99.83 270	95.36 370	99.64 256

APD-MVS		98.87 234	98.59 245	99.71 99	99.50 234	99.62 117	99.01 229	99.57 206	96.80 364	99.54 199	99.63 189	98.29 199	99.91 139	95.24 371	99.71 232	99.61 148
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 365								95.20 372

AdaColmap		98.60 257	98.35 270	99.38 216	99.12 333	99.22 211	98.67 277	99.42 266	97.84 323	98.81 322	99.27 307	97.32 264	99.81 294	95.14 373	99.53 287	99.10 306

test9_res														95.10 374	99.44 301	99.50 205

CDPH-MVS		98.56 263	98.20 283	99.61 147	99.50 234	99.46 151	98.32 314	99.41 267	95.22 383	99.21 277	99.10 335	98.34 194	99.82 279	95.09 375	99.66 252	99.56 171

BH-untuned		98.22 297	98.09 292	98.58 328	99.38 274	97.24 347	98.55 293	98.98 338	97.81 324	99.20 282	98.76 370	97.01 277	99.65 370	94.83 376	98.33 375	98.86 348

BP-MVS														94.73 377

HQP-MVS		98.36 285	98.02 296	99.39 213	99.31 297	98.94 243	97.98 347	99.37 282	97.45 339	98.15 362	98.83 366	96.67 285	99.70 337	94.73 377	99.67 249	99.53 187

QAPM		98.40 283	97.99 297	99.65 121	99.39 271	99.47 147	99.67 49	99.52 237	91.70 397	98.78 328	99.80 83	98.55 162	99.95 63	94.71 379	99.75 211	99.53 187

agg_prior2														94.58 380	99.46 300	99.50 205

myMVS_eth3d		95.63 368	94.73 370	98.34 338	98.50 391	96.36 365	98.60 282	99.21 319	97.89 317	96.76 393	96.37 412	72.10 412	99.57 382	94.38 381	98.73 363	99.09 310

BH-RMVSNet		98.41 281	98.14 289	99.21 255	99.21 318	98.47 282	98.60 282	98.26 371	98.35 285	98.93 306	99.31 299	97.20 271	99.66 364	94.32 382	99.10 336	99.51 200

E-PMN		97.14 334	97.43 322	96.27 384	98.79 374	91.62 403	95.54 401	99.01 337	99.44 136	98.88 313	99.12 331	92.78 341	99.68 355	94.30 383	99.03 341	97.50 395

MG-MVS		98.52 268	98.39 265	98.94 293	99.15 328	97.39 344	98.18 323	99.21 319	98.89 220	99.23 272	99.63 189	97.37 262	99.74 325	94.22 384	99.61 266	99.69 83

API-MVS		98.38 284	98.39 265	98.35 336	98.83 368	99.26 200	99.14 188	99.18 323	98.59 256	98.66 337	98.78 369	98.61 153	99.57 382	94.14 385	99.56 276	96.21 402

PAPM_NR		98.36 285	98.04 294	99.33 229	99.48 244	98.93 246	98.79 268	99.28 302	97.54 334	98.56 347	98.57 377	97.12 273	99.69 343	94.09 386	98.90 352	99.38 243

ZD-MVS					99.43 263	99.61 123		99.43 264	96.38 368	99.11 291	99.07 337	97.86 233	99.92 116	94.04 387	99.49 296

DPM-MVS		98.28 291	97.94 305	99.32 233	99.36 279	99.11 225	97.31 384	98.78 345	96.88 360	98.84 319	99.11 334	97.77 240	99.61 378	94.03 388	99.36 312	99.23 276

gg-mvs-nofinetune		95.87 363	95.17 368	97.97 350	98.19 399	96.95 354	99.69 42	89.23 412	99.89 35	96.24 400	99.94 16	81.19 397	99.51 392	93.99 389	98.20 380	97.44 396

PMVS	92.94 21	98.82 238	98.81 229	98.85 308	99.84 61	97.99 316	99.20 168	99.47 253	99.71 80	99.42 230	99.82 73	98.09 216	99.47 394	93.88 390	99.85 157	99.07 321
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 337	97.28 326	95.99 387	98.76 379	91.03 406	95.26 402	98.61 354	99.34 151	98.92 309	98.88 364	93.79 329	99.66 364	92.87 391	99.05 339	97.30 399

BH-w/o		97.20 331	97.01 333	97.76 358	99.08 344	95.69 375	98.03 342	98.52 358	95.76 377	97.96 371	98.02 390	95.62 310	99.47 394	92.82 392	97.25 397	98.12 389

TR-MVS		97.44 326	97.15 330	98.32 339	98.53 389	97.46 340	98.47 303	97.91 378	96.85 361	98.21 361	98.51 381	96.42 294	99.51 392	92.16 393	97.29 396	97.98 391

OpenMVS_ROB	97.31 17	97.36 329	96.84 339	98.89 306	99.29 303	99.45 156	98.87 251	99.48 250	86.54 403	99.44 224	99.74 119	97.34 263	99.86 222	91.61 394	99.28 323	97.37 398

GG-mvs-BLEND				97.36 368	97.59 405	96.87 357	99.70 35	88.49 413		94.64 406	97.26 404	80.66 398	99.12 400	91.50 395	96.50 402	96.08 404

DeepMVS_CX				97.98 349	99.69 155	96.95 354		99.26 306	75.51 405	95.74 403	98.28 386	96.47 292	99.62 374	91.23 396	97.89 391	97.38 397

PAPR		97.56 323	97.07 331	99.04 284	98.80 372	98.11 308	97.63 368	99.25 309	94.56 392	98.02 370	98.25 387	97.43 258	99.68 355	90.90 397	98.74 361	99.33 256

MVS		95.72 367	94.63 372	98.99 287	98.56 388	97.98 322	99.30 136	98.86 340	72.71 406	97.30 385	99.08 336	98.34 194	99.74 325	89.21 398	98.33 375	99.26 270

thres600view7		96.60 345	96.16 347	97.93 352	99.63 175	96.09 371	99.18 173	97.57 384	98.77 237	98.72 332	97.32 402	87.04 384	99.72 330	88.57 399	98.62 368	97.98 391

FPMVS		96.32 351	95.50 359	98.79 316	99.60 182	98.17 304	98.46 307	98.80 344	97.16 354	96.28 398	99.63 189	82.19 396	99.09 401	88.45 400	98.89 353	99.10 306

PCF-MVS	96.03 18	96.73 342	95.86 353	99.33 229	99.44 259	99.16 220	96.87 395	99.44 261	86.58 402	98.95 304	99.40 276	94.38 323	99.88 189	87.93 401	99.80 193	98.95 337
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 349	96.03 350	97.47 365	99.63 175	95.93 372	99.18 173	97.57 384	98.75 241	98.70 335	97.31 403	87.04 384	99.67 360	87.62 402	98.51 372	96.81 400

tfpn200view9		96.30 352	95.89 351	97.53 362	99.58 191	96.11 369	99.00 232	97.54 387	98.43 271	98.52 348	96.98 405	86.85 386	99.67 360	87.62 402	98.51 372	96.81 400

thres400		96.40 348	95.89 351	97.92 353	99.58 191	96.11 369	99.00 232	97.54 387	98.43 271	98.52 348	96.98 405	86.85 386	99.67 360	87.62 402	98.51 372	97.98 391

thres200		96.09 357	95.68 357	97.33 370	99.48 244	96.22 368	98.53 298	97.57 384	98.06 306	98.37 355	96.73 408	86.84 388	99.61 378	86.99 405	98.57 369	96.16 403

MVE	92.54 22	96.66 344	96.11 348	98.31 341	99.68 163	97.55 337	97.94 352	95.60 398	99.37 148	90.68 408	98.70 373	96.56 288	98.61 406	86.94 406	99.55 280	98.77 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 330	96.83 340	98.59 326	99.46 254	97.55 337	99.25 156	96.84 392	98.78 235	97.24 387	97.67 396	97.11 274	98.97 403	86.59 407	98.54 371	99.27 269

PAPM		95.61 369	94.71 371	98.31 341	99.12 333	96.63 360	96.66 398	98.46 362	90.77 399	96.25 399	98.68 374	93.01 339	99.69 343	81.60 408	97.86 393	98.62 361

test123		29.31 375	33.05 380	18.08 391	25.93 415	12.24 416	97.53 374	10.93 416	11.78 409	24.21 410	50.08 419	21.04 414	8.60 410	23.51 409	32.43 409	33.39 406

testmvs		28.94 376	33.33 378	15.79 392	26.03 414	9.81 417	96.77 396	15.67 415	11.55 410	23.87 411	50.74 418	19.03 415	8.53 411	23.21 410	33.07 408	29.03 407

test_blank		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet_test		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

DCPMVS		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

cdsmvs_eth3d_5k		24.88 377	33.17 379	0.00 393	0.00 416	0.00 418	0.00 404	99.62 170	0.00 411	0.00 412	99.13 327	99.82 13	0.00 412	0.00 411	0.00 410	0.00 408

pcd_1.5k_mvsjas		16.61 378	22.14 381	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	99.28 66	0.00 412	0.00 411	0.00 410	0.00 408

sosnet-low-res		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

sosnet		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uncertanet		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

Regformer		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

ab-mvs-re		8.26 387	11.02 390	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	99.16 325	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet		8.33 379	11.11 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

FOURS1					99.83 65	99.89 10	99.74 24	99.71 126	99.69 88	99.63 159

test_one_0601					99.63 175	99.76 61		99.55 217	99.23 168	99.31 258	99.61 205	98.59 156

eth-test2					0.00 416

eth-test					0.00 416

test_241102_ONE					99.69 155	99.82 35		99.54 223	99.12 193	99.82 81	99.49 255	98.91 114	99.52 391

save fliter					99.53 221	99.25 203	98.29 316	99.38 281	99.07 197

test0726					99.69 155	99.80 44	99.24 157	99.57 206	99.16 184	99.73 126	99.65 176	98.35 192

GSMVS																99.14 300

test_part2					99.62 179	99.67 99				99.55 197

sam_mvs1												90.81 364				99.14 300

sam_mvs												90.52 368

MTGPA								99.53 232

test_post											52.41 416	90.25 370	99.86 222

patchmatchnet-post											99.62 196	90.58 366	99.94 77

MTMP							99.09 210	98.59 357

TEST9					99.35 281	99.35 185	98.11 332	99.41 267	94.83 390	97.92 372	98.99 349	98.02 222	99.85 240

test_8					99.34 290	99.31 191	98.08 336	99.40 274	94.90 387	97.87 376	98.97 354	98.02 222	99.84 255

agg_prior					99.35 281	99.36 182		99.39 277		97.76 382			99.85 240

test_prior4						99.19 218	98.00 345

test_prior				99.46 189	99.35 281	99.22 211		99.39 277					99.69 343			99.48 214

新几何2							98.04 340

旧先验1					99.49 239	99.29 194		99.26 306			99.39 280	97.67 247			99.36 312	99.46 222

原ACMM2							97.92 355

test222					99.51 228	99.08 232	97.83 361	99.29 299	95.21 384	98.68 336	99.31 299	97.28 265			99.38 309	99.43 234

segment_acmp												98.37 190

testdata1							97.72 364		97.86 322

test12				99.54 173	99.29 303	99.33 188		99.16 325		98.43 353		97.54 254	99.82 279		99.47 298	99.48 214

plane_prior7					99.58 191	99.38 175

plane_prior6					99.47 250	99.26 200						97.24 266

plane_prior4											99.25 312

plane_prior3						99.31 191			98.36 280	99.14 287

plane_prior2							98.80 265		98.94 210

plane_prior1					99.51 228

plane_prior						99.24 207	98.42 308		97.87 320						99.71 232

n2								0.00 417

nn								0.00 417

door-mid								99.83 62

test11								99.29 299

door								99.77 95

HQP5-MVS						98.94 243

HQP-NCC					99.31 297		97.98 347		97.45 339	98.15 362

ACMP_Plane					99.31 297		97.98 347		97.45 339	98.15 362

HQP4-MVS										98.15 362			99.70 337			99.53 187

HQP3-MVS								99.37 282							99.67 249

HQP2-MVS												96.67 285

NP-MVS					99.40 270	99.13 223					98.83 366

ACMMP++_ref															99.94 94

ACMMP++															99.79 198

Test By Simon												98.41 184

High-resolution multi-view benchmark