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 15	99.98 3	99.75 63	99.70 35	100.00 1	99.73 64	100.00 1	99.89 31	99.79 12	99.88 178	99.98 1	100.00 1	99.98 1

test_fmvs2		99.72 27	99.85 14	99.34 216	99.91 28	98.08 298	99.48 95	100.00 1	99.90 20	99.99 7	99.91 24	99.50 37	99.98 15	99.98 1	99.99 14	99.96 6

test_fmvs3		99.83 14	99.93 2	99.53 163	99.96 5	98.62 261	99.67 49	100.00 1	99.95 10	100.00 1	99.95 13	99.85 6	99.99 7	99.98 1	99.99 14	99.98 1

test_vis1_n_1920		99.72 27	99.88 6	99.27 235	99.93 23	97.84 309	99.34 120	100.00 1	99.99 1	99.99 7	99.82 66	99.87 5	99.99 7	99.97 4	99.99 14	99.97 3

test_vis1_n		99.68 37	99.79 21	99.36 213	99.94 16	98.18 288	99.52 86	100.00 1	99.86 35	100.00 1	99.88 39	98.99 93	99.96 48	99.97 4	99.96 61	99.95 7

test_fmvs1_n		99.68 37	99.81 18	99.28 232	99.95 13	97.93 307	99.49 94	100.00 1	99.82 48	99.99 7	99.89 31	99.21 66	99.98 15	99.97 4	99.98 33	99.93 11

test_f		99.75 23	99.88 6	99.37 209	99.96 5	98.21 285	99.51 89	100.00 1	99.94 14	100.00 1	99.93 17	99.58 28	99.94 70	99.97 4	99.99 14	99.97 3

test_fmvsmvis_n_1920		99.84 10	99.86 11	99.81 31	99.88 40	99.55 128	99.17 175	99.98 9	99.99 1	99.96 17	99.84 57	99.96 1	99.99 7	99.96 8	99.99 14	99.88 18

test_cas_vis1_n_1920		99.76 22	99.86 11	99.45 181	99.93 23	98.40 273	99.30 133	99.98 9	99.94 14	99.99 7	99.89 31	99.80 11	99.97 27	99.96 8	99.97 46	99.97 3

test_fmvsm_n_1920		99.84 10	99.85 14	99.83 25	99.82 63	99.70 84	99.17 175	99.97 13	99.99 1	99.96 17	99.82 66	99.94 2	100.00 1	99.95 10	100.00 1	99.80 35

test_fmvs1		99.48 78	99.65 41	98.97 273	99.54 205	97.16 330	99.11 197	99.98 9	99.78 58	99.96 17	99.81 72	98.72 128	99.97 27	99.95 10	99.97 46	99.79 42

mvsany_test3		99.85 8	99.88 6	99.75 64	99.95 13	99.37 168	99.53 85	99.98 9	99.77 62	99.99 7	99.95 13	99.85 6	99.94 70	99.95 10	99.98 33	99.94 9

MVS_0304		99.17 166	99.03 176	99.59 142	99.44 249	98.90 237	99.04 211	95.32 376	99.99 1	99.68 132	99.57 219	98.30 188	99.97 27	99.94 13	99.98 33	99.88 18

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 13	99.99 1	100.00 1	99.98 10	99.78 13	100.00 1	99.92 14	100.00 1	99.87 20

v1921920		99.56 65	99.57 63	99.55 158	99.75 118	99.11 213	99.05 209	99.61 164	99.15 174	99.88 53	99.71 130	99.08 83	99.87 192	99.90 15	99.97 46	99.66 93

v1240		99.56 65	99.58 60	99.51 167	99.80 76	99.00 224	99.00 220	99.65 146	99.15 174	99.90 41	99.75 109	99.09 80	99.88 178	99.90 15	99.96 61	99.67 84

v10		99.69 34	99.69 34	99.66 106	99.81 71	99.39 163	99.66 53	99.75 93	99.60 104	99.92 32	99.87 43	98.75 123	99.86 210	99.90 15	99.99 14	99.73 60

v1192		99.57 62	99.57 63	99.57 152	99.77 103	99.22 200	99.04 211	99.60 176	99.18 163	99.87 61	99.72 123	99.08 83	99.85 227	99.89 18	99.98 33	99.66 93

v144192		99.55 68	99.54 69	99.58 146	99.78 95	99.20 205	99.11 197	99.62 157	99.18 163	99.89 45	99.72 123	98.66 136	99.87 192	99.88 19	99.97 46	99.66 93

v8		99.68 37	99.69 34	99.65 111	99.80 76	99.40 161	99.66 53	99.76 88	99.64 92	99.93 28	99.85 52	98.66 136	99.84 241	99.88 19	99.99 14	99.71 65

v1144		99.54 70	99.53 73	99.59 142	99.79 88	99.28 186	99.10 199	99.61 164	99.20 161	99.84 67	99.73 116	98.67 134	99.84 241	99.86 21	99.98 33	99.64 111

v7n		99.82 15	99.80 20	99.88 12	99.96 5	99.84 24	99.82 8	99.82 57	99.84 43	99.94 25	99.91 24	99.13 77	99.96 48	99.83 22	99.99 14	99.83 29

v2v482		99.50 74	99.47 77	99.58 146	99.78 95	99.25 193	99.14 185	99.58 192	99.25 152	99.81 79	99.62 186	98.24 193	99.84 241	99.83 22	99.97 46	99.64 111

test_vis1_rt		99.45 88	99.46 81	99.41 197	99.71 133	98.63 260	98.99 225	99.96 18	99.03 187	99.95 23	99.12 321	98.75 123	99.84 241	99.82 24	99.82 169	99.77 49

tt0805		99.63 51	99.57 63	99.81 31	99.87 45	99.88 12	99.58 76	98.70 333	99.72 68	99.91 35	99.60 203	99.43 39	99.81 280	99.81 25	99.53 277	99.73 60

V42		99.56 65	99.54 69	99.63 125	99.79 88	99.46 141	99.39 109	99.59 182	99.24 154	99.86 62	99.70 137	98.55 151	99.82 265	99.79 26	99.95 74	99.60 141

mvs_tets		99.90 2	99.90 3	99.90 5	99.96 5	99.79 44	99.72 30	99.88 34	99.92 18	99.98 12	99.93 17	99.94 2	99.98 15	99.77 27	100.00 1	99.92 12

PS-MVSNAJss		99.84 10	99.82 17	99.89 8	99.96 5	99.77 50	99.68 45	99.85 44	99.95 10	99.98 12	99.92 21	99.28 57	99.98 15	99.75 28	100.00 1	99.94 9

jajsoiax		99.89 3	99.89 5	99.89 8	99.96 5	99.78 47	99.70 35	99.86 39	99.89 26	99.98 12	99.90 27	99.94 2	99.98 15	99.75 28	100.00 1	99.90 13

ANet_high		99.88 5	99.87 9	99.91 2	99.99 1	99.91 4	99.65 59	100.00 1	99.90 20	100.00 1	99.97 11	99.61 25	99.97 27	99.75 28	100.00 1	99.84 25

CS-MVS-test		99.68 37	99.70 30	99.64 118	99.57 191	99.83 29	99.78 12	99.97 13	99.92 18	99.50 203	99.38 272	99.57 29	99.95 57	99.69 31	99.90 106	99.15 284

RRT_MVS		99.67 43	99.59 56	99.91 2	99.94 16	99.88 12	99.78 12	99.27 291	99.87 32	99.91 35	99.87 43	98.04 210	99.96 48	99.68 32	99.99 14	99.90 13

CS-MVS		99.67 43	99.70 30	99.58 146	99.53 211	99.84 24	99.79 11	99.96 18	99.90 20	99.61 164	99.41 262	99.51 36	99.95 57	99.66 33	99.89 115	98.96 318

pmmvs6		99.86 7	99.86 11	99.83 25	99.94 16	99.90 7	99.83 6	99.91 25	99.85 40	99.94 25	99.95 13	99.73 16	99.90 147	99.65 34	99.97 46	99.69 72

MIMVSNet1		99.66 45	99.62 47	99.80 36	99.94 16	99.87 15	99.69 42	99.77 83	99.78 58	99.93 28	99.89 31	97.94 218	99.92 107	99.65 34	99.98 33	99.62 127

EC-MVSNet		99.69 34	99.69 34	99.68 96	99.71 133	99.91 4	99.76 19	99.96 18	99.86 35	99.51 201	99.39 270	99.57 29	99.93 87	99.64 36	99.86 143	99.20 273

K. test v3		98.87 220	98.60 229	99.69 94	99.93 23	99.46 141	99.74 24	94.97 377	99.78 58	99.88 53	99.88 39	93.66 315	99.97 27	99.61 37	99.95 74	99.64 111

KD-MVS_self_test		99.63 51	99.59 56	99.76 54	99.84 52	99.90 7	99.37 115	99.79 74	99.83 46	99.88 53	99.85 52	98.42 172	99.90 147	99.60 38	99.73 213	99.49 200

Anonymous20240521		99.44 90	99.42 90	99.49 170	99.89 35	98.96 230	99.62 63	99.76 88	99.85 40	99.82 72	99.88 39	96.39 284	99.97 27	99.59 39	99.98 33	99.55 163

TransMVSNet (Re)		99.78 18	99.77 24	99.81 31	99.91 28	99.85 19	99.75 22	99.86 39	99.70 75	99.91 35	99.89 31	99.60 27	99.87 192	99.59 39	99.74 208	99.71 65

OurMVSNet-221017-0		99.75 23	99.71 29	99.84 23	99.96 5	99.83 29	99.83 6	99.85 44	99.80 53	99.93 28	99.93 17	98.54 153	99.93 87	99.59 39	99.98 33	99.76 55

EU-MVSNet		99.39 105	99.62 47	98.72 302	99.88 40	96.44 344	99.56 81	99.85 44	99.90 20	99.90 41	99.85 52	98.09 206	99.83 256	99.58 42	99.95 74	99.90 13

mvsmamba		99.74 26	99.70 30	99.85 20	99.93 23	99.83 29	99.76 19	99.81 66	99.96 8	99.91 35	99.81 72	98.60 144	99.94 70	99.58 42	99.98 33	99.77 49

mvs_anonymous		99.28 129	99.39 92	98.94 276	99.19 312	97.81 311	99.02 216	99.55 205	99.78 58	99.85 64	99.80 76	98.24 193	99.86 210	99.57 44	99.50 283	99.15 284

test1111		97.74 298	98.16 274	96.49 358	99.60 172	89.86 387	99.71 34	91.21 384	99.89 26	99.88 53	99.87 43	93.73 314	99.90 147	99.56 45	99.99 14	99.70 68

lessismore_v0				99.64 118	99.86 48	99.38 165		90.66 385		99.89 45	99.83 59	94.56 305	99.97 27	99.56 45	99.92 96	99.57 158

mvsany_test1		99.44 90	99.45 83	99.40 199	99.37 266	98.64 259	97.90 334	99.59 182	99.27 148	99.92 32	99.82 66	99.74 15	99.93 87	99.55 47	99.87 135	99.63 116

bld_raw_dy_0_64		99.70 31	99.65 41	99.85 20	99.95 13	99.77 50	99.66 53	99.71 114	99.95 10	99.91 35	99.77 100	98.35 181	100.00 1	99.54 48	99.99 14	99.79 42

pm-mvs1		99.79 17	99.79 21	99.78 44	99.91 28	99.83 29	99.76 19	99.87 36	99.73 64	99.89 45	99.87 43	99.63 22	99.87 192	99.54 48	99.92 96	99.63 116

LTVRE_ROB	99.19 1	99.88 5	99.87 9	99.88 12	99.91 28	99.90 7	99.96 1	99.92 22	99.90 20	99.97 15	99.87 43	99.81 10	99.95 57	99.54 48	99.99 14	99.80 35
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 78	99.65 41	98.95 275	99.71 133	97.27 327	99.50 90	99.82 57	99.59 106	99.41 225	99.85 52	99.62 24	100.00 1	99.53 51	99.89 115	99.59 148

test2506		94.73 348	94.59 350	95.15 364	99.59 176	85.90 389	99.75 22	74.01 390	99.89 26	99.71 123	99.86 50	79.00 388	99.90 147	99.52 52	99.99 14	99.65 101

UniMVSNet_ETH3D		99.85 8	99.83 16	99.90 5	99.89 35	99.91 4	99.89 4	99.71 114	99.93 16	99.95 23	99.89 31	99.71 17	99.96 48	99.51 53	99.97 46	99.84 25

FC-MVSNet-test		99.70 31	99.65 41	99.86 18	99.88 40	99.86 18	99.72 30	99.78 80	99.90 20	99.82 72	99.83 59	98.45 168	99.87 192	99.51 53	99.97 46	99.86 22

UA-Net		99.78 18	99.76 27	99.86 18	99.72 130	99.71 77	99.91 3	99.95 21	99.96 8	99.71 123	99.91 24	99.15 72	99.97 27	99.50 55	100.00 1	99.90 13

PMMVS2		99.48 78	99.45 83	99.57 152	99.76 107	98.99 225	98.09 312	99.90 28	98.95 194	99.78 91	99.58 210	99.57 29	99.93 87	99.48 56	99.95 74	99.79 42

VPA-MVSNet		99.66 45	99.62 47	99.79 41	99.68 153	99.75 63	99.62 63	99.69 126	99.85 40	99.80 82	99.81 72	98.81 111	99.91 129	99.47 57	99.88 124	99.70 68

ECVR-MVS		97.73 299	98.04 279	96.78 352	99.59 176	90.81 383	99.72 30	90.43 386	99.89 26	99.86 62	99.86 50	93.60 316	99.89 164	99.46 58	99.99 14	99.65 101

nrg030		99.70 31	99.66 39	99.82 28	99.76 107	99.84 24	99.61 68	99.70 120	99.93 16	99.78 91	99.68 154	99.10 78	99.78 292	99.45 59	99.96 61	99.83 29

TAMVS		99.49 76	99.45 83	99.63 125	99.48 234	99.42 155	99.45 101	99.57 194	99.66 88	99.78 91	99.83 59	97.85 225	99.86 210	99.44 60	99.96 61	99.61 137

GeoE		99.69 34	99.66 39	99.78 44	99.76 107	99.76 58	99.60 73	99.82 57	99.46 121	99.75 105	99.56 223	99.63 22	99.95 57	99.43 61	99.88 124	99.62 127

new-patchmatchnet		99.35 115	99.57 63	98.71 304	99.82 63	96.62 342	98.55 274	99.75 93	99.50 112	99.88 53	99.87 43	99.31 53	99.88 178	99.43 61	100.00 1	99.62 127

test20.03		99.55 68	99.54 69	99.58 146	99.79 88	99.37 168	99.02 216	99.89 30	99.60 104	99.82 72	99.62 186	98.81 111	99.89 164	99.43 61	99.86 143	99.47 208

MVSFormer		99.41 99	99.44 86	99.31 226	99.57 191	98.40 273	99.77 15	99.80 68	99.73 64	99.63 149	99.30 291	98.02 212	99.98 15	99.43 61	99.69 228	99.55 163

test_djsdf		99.84 10	99.81 18	99.91 2	99.94 16	99.84 24	99.77 15	99.80 68	99.73 64	99.97 15	99.92 21	99.77 14	99.98 15	99.43 61	100.00 1	99.90 13

SDMVSNet		99.77 21	99.77 24	99.76 54	99.80 76	99.65 100	99.63 61	99.86 39	99.97 6	99.89 45	99.89 31	99.52 35	99.99 7	99.42 66	99.96 61	99.65 101

Anonymous20231211		99.62 57	99.57 63	99.76 54	99.61 170	99.60 116	99.81 9	99.73 102	99.82 48	99.90 41	99.90 27	97.97 217	99.86 210	99.42 66	99.96 61	99.80 35

SixPastTwentyTwo		99.42 95	99.30 113	99.76 54	99.92 27	99.67 93	99.70 35	99.14 313	99.65 90	99.89 45	99.90 27	96.20 289	99.94 70	99.42 66	99.92 96	99.67 84

patch_mono-2		99.51 73	99.46 81	99.64 118	99.70 141	99.11 213	99.04 211	99.87 36	99.71 70	99.47 207	99.79 86	98.24 193	99.98 15	99.38 69	99.96 61	99.83 29

UGNet		99.38 107	99.34 102	99.49 170	98.90 346	98.90 237	99.70 35	99.35 274	99.86 35	98.57 331	99.81 72	98.50 163	99.93 87	99.38 69	99.98 33	99.66 93
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 30	99.67 38	99.81 31	99.89 35	99.72 75	99.59 74	99.82 57	99.39 134	99.82 72	99.84 57	99.38 45	99.91 129	99.38 69	99.93 92	99.80 35

iter_conf_final		98.75 230	98.54 239	99.40 199	99.33 284	98.75 247	99.26 147	99.59 182	99.80 53	99.76 98	99.58 210	90.17 354	99.92 107	99.37 72	99.97 46	99.54 171

FIs		99.65 50	99.58 60	99.84 23	99.84 52	99.85 19	99.66 53	99.75 93	99.86 35	99.74 113	99.79 86	98.27 191	99.85 227	99.37 72	99.93 92	99.83 29

sd_testset		99.78 18	99.78 23	99.80 36	99.80 76	99.76 58	99.80 10	99.79 74	99.97 6	99.89 45	99.89 31	99.53 34	99.99 7	99.36 74	99.96 61	99.65 101

anonymousdsp		99.80 16	99.77 24	99.90 5	99.96 5	99.88 12	99.73 27	99.85 44	99.70 75	99.92 32	99.93 17	99.45 38	99.97 27	99.36 74	100.00 1	99.85 24

casdiffmvs_mvg		99.68 37	99.68 37	99.69 94	99.81 71	99.59 118	99.29 140	99.90 28	99.71 70	99.79 87	99.73 116	99.54 32	99.84 241	99.36 74	99.96 61	99.65 101
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 23	99.74 28	99.79 41	99.88 40	99.66 95	99.69 42	99.92 22	99.67 84	99.77 96	99.75 109	99.61 25	99.98 15	99.35 77	99.98 33	99.72 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 59	99.64 45	99.53 163	99.79 88	98.82 242	99.58 76	99.97 13	99.95 10	99.96 17	99.76 104	98.44 169	99.99 7	99.34 78	99.96 61	99.78 45

CHOSEN 1792x2688		99.39 105	99.30 113	99.65 111	99.88 40	99.25 193	98.78 255	99.88 34	98.66 228	99.96 17	99.79 86	97.45 246	99.93 87	99.34 78	99.99 14	99.78 45

CDS-MVSNet		99.22 148	99.13 141	99.50 169	99.35 271	99.11 213	98.96 230	99.54 211	99.46 121	99.61 164	99.70 137	96.31 286	99.83 256	99.34 78	99.88 124	99.55 163
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 200	99.16 135	98.51 310	99.75 118	95.90 352	98.07 315	99.84 50	99.84 43	99.89 45	99.73 116	96.01 292	99.99 7	99.33 81	100.00 1	99.63 116

HyFIR lowres test		98.91 213	98.64 226	99.73 78	99.85 51	99.47 137	98.07 315	99.83 52	98.64 230	99.89 45	99.60 203	92.57 325	100.00 1	99.33 81	99.97 46	99.72 62

pmmvs5		99.19 158	99.11 148	99.42 190	99.76 107	98.88 239	98.55 274	99.73 102	98.82 212	99.72 118	99.62 186	96.56 275	99.82 265	99.32 83	99.95 74	99.56 160

v148		99.40 101	99.41 91	99.39 203	99.76 107	98.94 231	99.09 203	99.59 182	99.17 168	99.81 79	99.61 195	98.41 173	99.69 325	99.32 83	99.94 85	99.53 177

baseline		99.63 51	99.62 47	99.66 106	99.80 76	99.62 108	99.44 103	99.80 68	99.71 70	99.72 118	99.69 143	99.15 72	99.83 256	99.32 83	99.94 85	99.53 177

iter_conf05		98.46 262	98.23 265	99.15 253	99.04 335	97.99 300	99.10 199	99.61 164	99.79 56	99.76 98	99.58 210	87.88 364	99.92 107	99.31 86	99.97 46	99.53 177

CVMVSNet		98.61 242	98.88 206	97.80 334	99.58 181	93.60 369	99.26 147	99.64 152	99.66 88	99.72 118	99.67 158	93.26 318	99.93 87	99.30 87	99.81 178	99.87 20

PS-CasMVS		99.66 45	99.58 60	99.89 8	99.80 76	99.85 19	99.66 53	99.73 102	99.62 95	99.84 67	99.71 130	98.62 140	99.96 48	99.30 87	99.96 61	99.86 22

DTE-MVSNet		99.68 37	99.61 51	99.88 12	99.80 76	99.87 15	99.67 49	99.71 114	99.72 68	99.84 67	99.78 93	98.67 134	99.97 27	99.30 87	99.95 74	99.80 35

tmp_tt		95.75 344	95.42 342	96.76 353	89.90 389	94.42 364	98.86 238	97.87 360	78.01 380	99.30 252	99.69 143	97.70 232	95.89 384	99.29 90	98.14 363	99.95 7

PEN-MVS		99.66 45	99.59 56	99.89 8	99.83 56	99.87 15	99.66 53	99.73 102	99.70 75	99.84 67	99.73 116	98.56 150	99.96 48	99.29 90	99.94 85	99.83 29

WR-MVS_H		99.61 59	99.53 73	99.87 15	99.80 76	99.83 29	99.67 49	99.75 93	99.58 107	99.85 64	99.69 143	98.18 202	99.94 70	99.28 92	99.95 74	99.83 29

IterMVS		98.97 204	99.16 135	98.42 314	99.74 124	95.64 355	98.06 317	99.83 52	99.83 46	99.85 64	99.74 112	96.10 291	99.99 7	99.27 93	100.00 1	99.63 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		98.61 242	98.34 258	99.44 184	99.60 172	98.67 252	99.27 145	99.44 249	99.68 80	99.32 243	99.49 245	92.50 328	100.00 1	99.24 94	96.51 377	99.65 101

hse-mvs2		98.52 254	98.30 262	99.16 251	99.29 293	98.60 262	98.77 256	99.02 320	99.68 80	99.32 243	99.04 331	92.50 328	99.85 227	99.24 94	97.87 368	99.03 311

FMVSNet1		99.66 45	99.63 46	99.73 78	99.78 95	99.77 50	99.68 45	99.70 120	99.67 84	99.82 72	99.83 59	98.98 95	99.90 147	99.24 94	99.97 46	99.53 177

casdiffmvs		99.63 51	99.61 51	99.67 99	99.79 88	99.59 118	99.13 191	99.85 44	99.79 56	99.76 98	99.72 123	99.33 52	99.82 265	99.21 97	99.94 85	99.59 148
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 70	99.43 88	99.87 15	99.76 107	99.82 35	99.57 79	99.61 164	99.54 108	99.80 82	99.64 169	97.79 229	99.95 57	99.21 97	99.94 85	99.84 25

DELS-MVS		99.34 120	99.30 113	99.48 174	99.51 218	99.36 172	98.12 308	99.53 220	99.36 138	99.41 225	99.61 195	99.22 65	99.87 192	99.21 97	99.68 233	99.20 273
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)		99.37 110	99.26 124	99.68 96	99.51 218	99.58 122	98.98 228	99.60 176	99.43 129	99.70 126	99.36 278	97.70 232	99.88 178	99.20 100	99.87 135	99.59 148

CANet		99.11 179	99.05 169	99.28 232	98.83 353	98.56 263	98.71 262	99.41 255	99.25 152	99.23 260	99.22 309	97.66 240	99.94 70	99.19 101	99.97 46	99.33 245

EI-MVSNet-UG-set		99.48 78	99.50 75	99.42 190	99.57 191	98.65 258	99.24 155	99.46 244	99.68 80	99.80 82	99.66 162	98.99 93	99.89 164	99.19 101	99.90 106	99.72 62

xiu_mvs_v1_base_debu		99.23 140	99.34 102	98.91 282	99.59 176	98.23 282	98.47 283	99.66 137	99.61 98	99.68 132	98.94 347	99.39 41	99.97 27	99.18 103	99.55 270	98.51 347

xiu_mvs_v1_base		99.23 140	99.34 102	98.91 282	99.59 176	98.23 282	98.47 283	99.66 137	99.61 98	99.68 132	98.94 347	99.39 41	99.97 27	99.18 103	99.55 270	98.51 347

xiu_mvs_v1_base_debi		99.23 140	99.34 102	98.91 282	99.59 176	98.23 282	98.47 283	99.66 137	99.61 98	99.68 132	98.94 347	99.39 41	99.97 27	99.18 103	99.55 270	98.51 347

VPNet		99.46 86	99.37 97	99.71 89	99.82 63	99.59 118	99.48 95	99.70 120	99.81 50	99.69 129	99.58 210	97.66 240	99.86 210	99.17 106	99.44 290	99.67 84

UniMVSNet_NR-MVSNet		99.37 110	99.25 126	99.72 84	99.47 240	99.56 125	98.97 229	99.61 164	99.43 129	99.67 138	99.28 295	97.85 225	99.95 57	99.17 106	99.81 178	99.65 101

DU-MVS		99.33 123	99.21 130	99.71 89	99.43 253	99.56 125	98.83 243	99.53 220	99.38 135	99.67 138	99.36 278	97.67 236	99.95 57	99.17 106	99.81 178	99.63 116

EI-MVSNet-Vis-set		99.47 85	99.49 76	99.42 190	99.57 191	98.66 255	99.24 155	99.46 244	99.67 84	99.79 87	99.65 167	98.97 97	99.89 164	99.15 109	99.89 115	99.71 65

EI-MVSNet		99.38 107	99.44 86	99.21 245	99.58 181	98.09 295	99.26 147	99.46 244	99.62 95	99.75 105	99.67 158	98.54 153	99.85 227	99.15 109	99.92 96	99.68 78

VNet		99.18 162	99.06 165	99.56 155	99.24 303	99.36 172	99.33 123	99.31 283	99.67 84	99.47 207	99.57 219	96.48 278	99.84 241	99.15 109	99.30 308	99.47 208

EG-PatchMatch MVS		99.57 62	99.56 68	99.62 134	99.77 103	99.33 178	99.26 147	99.76 88	99.32 142	99.80 82	99.78 93	99.29 55	99.87 192	99.15 109	99.91 105	99.66 93

PVSNet_Blended_VisFu		99.40 101	99.38 94	99.44 184	99.90 33	98.66 255	98.94 233	99.91 25	97.97 289	99.79 87	99.73 116	99.05 88	99.97 27	99.15 109	99.99 14	99.68 78

IterMVS-LS		99.41 99	99.47 77	99.25 241	99.81 71	98.09 295	98.85 240	99.76 88	99.62 95	99.83 71	99.64 169	98.54 153	99.97 27	99.15 109	99.99 14	99.68 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 70	99.47 77	99.76 54	99.58 181	99.64 102	99.30 133	99.63 154	99.61 98	99.71 123	99.56 223	98.76 121	99.96 48	99.14 115	99.92 96	99.68 78

MVSTER		98.47 261	98.22 267	99.24 243	99.06 332	98.35 279	99.08 206	99.46 244	99.27 148	99.75 105	99.66 162	88.61 362	99.85 227	99.14 115	99.92 96	99.52 188

Anonymous20231206		99.35 115	99.31 108	99.47 176	99.74 124	99.06 223	99.28 142	99.74 98	99.23 156	99.72 118	99.53 234	97.63 242	99.88 178	99.11 117	99.84 152	99.48 204

MVS_Test		99.28 129	99.31 108	99.19 248	99.35 271	98.79 245	99.36 118	99.49 237	99.17 168	99.21 265	99.67 158	98.78 118	99.66 344	99.09 118	99.66 242	99.10 295

testgi		99.29 128	99.26 124	99.37 209	99.75 118	98.81 243	98.84 241	99.89 30	98.38 256	99.75 105	99.04 331	99.36 50	99.86 210	99.08 119	99.25 315	99.45 213

1112_ss		99.05 188	98.84 211	99.67 99	99.66 159	99.29 184	98.52 279	99.82 57	97.65 305	99.43 217	99.16 315	96.42 281	99.91 129	99.07 120	99.84 152	99.80 35

CANet_DTU		98.91 213	98.85 209	99.09 262	98.79 358	98.13 290	98.18 301	99.31 283	99.48 114	98.86 305	99.51 238	96.56 275	99.95 57	99.05 121	99.95 74	99.19 276

Baseline_NR-MVSNet		99.49 76	99.37 97	99.82 28	99.91 28	99.84 24	98.83 243	99.86 39	99.68 80	99.65 144	99.88 39	97.67 236	99.87 192	99.03 122	99.86 143	99.76 55

FMVSNet2		99.35 115	99.28 120	99.55 158	99.49 229	99.35 175	99.45 101	99.57 194	99.44 124	99.70 126	99.74 112	97.21 257	99.87 192	99.03 122	99.94 85	99.44 218

Test_1112_low_res		98.95 210	98.73 220	99.63 125	99.68 153	99.15 210	98.09 312	99.80 68	97.14 331	99.46 211	99.40 266	96.11 290	99.89 164	99.01 124	99.84 152	99.84 25

VDD-MVS		99.20 155	99.11 148	99.44 184	99.43 253	98.98 226	99.50 90	98.32 352	99.80 53	99.56 182	99.69 143	96.99 267	99.85 227	98.99 125	99.73 213	99.50 195

DeepC-MVS	98.90 4	99.62 57	99.61 51	99.67 99	99.72 130	99.44 148	99.24 155	99.71 114	99.27 148	99.93 28	99.90 27	99.70 19	99.93 87	98.99 125	99.99 14	99.64 111
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 78	99.47 77	99.51 167	99.77 103	99.41 160	98.81 248	99.66 137	99.42 133	99.75 105	99.66 162	99.20 67	99.76 302	98.98 127	99.99 14	99.36 239

EPNet_dtu		97.62 304	97.79 298	97.11 351	96.67 384	92.31 374	98.51 280	98.04 355	99.24 154	95.77 378	99.47 252	93.78 313	99.66 344	98.98 127	99.62 249	99.37 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 120	99.32 107	99.39 203	99.67 158	98.77 246	98.57 272	99.81 66	99.61 98	99.48 206	99.41 262	98.47 164	99.86 210	98.97 129	99.90 106	99.53 177
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		99.40 101	99.31 108	99.68 96	99.43 253	99.55 128	99.73 27	99.50 233	99.46 121	99.88 53	99.36 278	97.54 243	99.87 192	98.97 129	99.87 135	99.63 116

GBi-Net		99.42 95	99.31 108	99.73 78	99.49 229	99.77 50	99.68 45	99.70 120	99.44 124	99.62 158	99.83 59	97.21 257	99.90 147	98.96 131	99.90 106	99.53 177

FMVSNet5		97.80 296	97.25 312	99.42 190	98.83 353	98.97 228	99.38 111	99.80 68	98.87 206	99.25 256	99.69 143	80.60 383	99.91 129	98.96 131	99.90 106	99.38 233

test1		99.42 95	99.31 108	99.73 78	99.49 229	99.77 50	99.68 45	99.70 120	99.44 124	99.62 158	99.83 59	97.21 257	99.90 147	98.96 131	99.90 106	99.53 177

FMVSNet3		98.80 226	98.63 228	99.32 223	99.13 320	98.72 250	99.10 199	99.48 238	99.23 156	99.62 158	99.64 169	92.57 325	99.86 210	98.96 131	99.90 106	99.39 231

UnsupCasMVSNet_eth		98.83 223	98.57 235	99.59 142	99.68 153	99.45 146	98.99 225	99.67 133	99.48 114	99.55 187	99.36 278	94.92 299	99.86 210	98.95 135	96.57 376	99.45 213

CHOSEN 280x420		98.41 267	98.41 250	98.40 315	99.34 279	95.89 353	96.94 370	99.44 249	98.80 215	99.25 256	99.52 236	93.51 317	99.98 15	98.94 136	99.98 33	99.32 248

TDRefinement		99.72 27	99.70 30	99.77 47	99.90 33	99.85 19	99.86 5	99.92 22	99.69 78	99.78 91	99.92 21	99.37 47	99.88 178	98.93 137	99.95 74	99.60 141

alignmvs		98.28 276	97.96 285	99.25 241	99.12 322	98.93 234	99.03 215	98.42 347	99.64 92	98.72 319	97.85 379	90.86 346	99.62 354	98.88 138	99.13 320	99.19 276

sss		98.90 215	98.77 219	99.27 235	99.48 234	98.44 270	98.72 260	99.32 279	97.94 293	99.37 233	99.35 283	96.31 286	99.91 129	98.85 139	99.63 248	99.47 208

xiu_mvs_v2_base		99.02 194	99.11 148	98.77 299	99.37 266	98.09 295	98.13 307	99.51 229	99.47 118	99.42 219	98.54 368	99.38 45	99.97 27	98.83 140	99.33 305	98.24 359

PS-MVSNAJ		99.00 200	99.08 159	98.76 300	99.37 266	98.10 294	98.00 322	99.51 229	99.47 118	99.41 225	98.50 370	99.28 57	99.97 27	98.83 140	99.34 304	98.20 363

D2MVS		99.22 148	99.19 132	99.29 230	99.69 145	98.74 249	98.81 248	99.41 255	98.55 238	99.68 132	99.69 143	98.13 204	99.87 192	98.82 142	99.98 33	99.24 262

PatchT		98.45 264	98.32 260	98.83 294	98.94 344	98.29 280	99.24 155	98.82 328	99.84 43	99.08 282	99.76 104	91.37 336	99.94 70	98.82 142	99.00 329	98.26 358

testf1		99.63 51	99.60 54	99.72 84	99.94 16	99.95 2	99.47 98	99.89 30	99.43 129	99.88 53	99.80 76	99.26 61	99.90 147	98.81 144	99.88 124	99.32 248

APD_test2		99.63 51	99.60 54	99.72 84	99.94 16	99.95 2	99.47 98	99.89 30	99.43 129	99.88 53	99.80 76	99.26 61	99.90 147	98.81 144	99.88 124	99.32 248

Effi-MVS+		99.06 185	98.97 193	99.34 216	99.31 287	98.98 226	98.31 294	99.91 25	98.81 213	98.79 313	98.94 347	99.14 75	99.84 241	98.79 146	98.74 344	99.20 273

canonicalmvs		99.02 194	99.00 184	99.09 262	99.10 328	98.70 251	99.61 68	99.66 137	99.63 94	98.64 325	97.65 382	99.04 89	99.54 363	98.79 146	98.92 333	99.04 310

VDDNet		98.97 204	98.82 214	99.42 190	99.71 133	98.81 243	99.62 63	98.68 334	99.81 50	99.38 232	99.80 76	94.25 307	99.85 227	98.79 146	99.32 306	99.59 148

CR-MVSNet		98.35 274	98.20 269	98.83 294	99.05 333	98.12 291	99.30 133	99.67 133	97.39 319	99.16 271	99.79 86	91.87 333	99.91 129	98.78 149	98.77 340	98.44 352

test_method		91.72 349	92.32 352	89.91 366	93.49 388	70.18 390	90.28 379	99.56 199	61.71 383	95.39 380	99.52 236	93.90 309	99.94 70	98.76 150	98.27 358	99.62 127

RPMNet		98.60 244	98.53 241	98.83 294	99.05 333	98.12 291	99.30 133	99.62 157	99.86 35	99.16 271	99.74 112	92.53 327	99.92 107	98.75 151	98.77 340	98.44 352

pmmvs4		99.13 174	99.06 165	99.36 213	99.57 191	99.10 218	98.01 320	99.25 297	98.78 218	99.58 172	99.44 259	98.24 193	99.76 302	98.74 152	99.93 92	99.22 267

tttt0517		97.62 304	97.20 313	98.90 288	99.76 107	97.40 324	99.48 95	94.36 379	99.06 185	99.70 126	99.49 245	84.55 378	99.94 70	98.73 153	99.65 244	99.36 239

EPP-MVSNet		99.17 166	99.00 184	99.66 106	99.80 76	99.43 152	99.70 35	99.24 300	99.48 114	99.56 182	99.77 100	94.89 300	99.93 87	98.72 154	99.89 115	99.63 116

Anonymous20240529		99.42 95	99.34 102	99.65 111	99.53 211	99.60 116	99.63 61	99.39 265	99.47 118	99.76 98	99.78 93	98.13 204	99.86 210	98.70 155	99.68 233	99.49 200

ACMH	98.42 6	99.59 61	99.54 69	99.72 84	99.86 48	99.62 108	99.56 81	99.79 74	98.77 220	99.80 82	99.85 52	99.64 21	99.85 227	98.70 155	99.89 115	99.70 68
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 123	99.28 120	99.47 176	99.57 191	99.39 163	99.78 12	99.43 252	98.87 206	99.57 175	99.82 66	98.06 209	99.87 192	98.69 157	99.73 213	99.15 284

LFMVS		98.46 262	98.19 272	99.26 238	99.24 303	98.52 266	99.62 63	96.94 368	99.87 32	99.31 247	99.58 210	91.04 341	99.81 280	98.68 158	99.42 294	99.45 213

WR-MVS		99.11 179	98.93 197	99.66 106	99.30 291	99.42 155	98.42 288	99.37 270	99.04 186	99.57 175	99.20 313	96.89 269	99.86 210	98.66 159	99.87 135	99.70 68

Anonymous202405211		98.75 230	98.46 245	99.63 125	99.34 279	99.66 95	99.47 98	97.65 361	99.28 147	99.56 182	99.50 241	93.15 319	99.84 241	98.62 160	99.58 264	99.40 229

EPNet		98.13 284	97.77 299	99.18 250	94.57 387	97.99 300	99.24 155	97.96 357	99.74 63	97.29 369	99.62 186	93.13 320	99.97 27	98.59 161	99.83 160	99.58 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 188	99.09 157	98.91 282	99.21 307	98.36 278	98.82 247	99.47 241	98.85 208	98.90 300	99.56 223	98.78 118	99.09 377	98.57 162	99.68 233	99.26 259

Patchmatch-RL test		98.60 244	98.36 255	99.33 219	99.77 103	99.07 221	98.27 296	99.87 36	98.91 201	99.74 113	99.72 123	90.57 350	99.79 289	98.55 163	99.85 147	99.11 293

pmmvs3		98.08 287	97.80 296	98.91 282	99.41 259	97.69 316	97.87 335	99.66 137	95.87 350	99.50 203	99.51 238	90.35 352	99.97 27	98.55 163	99.47 287	99.08 302

ETV-MVS		99.18 162	99.18 133	99.16 251	99.34 279	99.28 186	99.12 195	99.79 74	99.48 114	98.93 294	98.55 367	99.40 40	99.93 87	98.51 165	99.52 280	98.28 357

jason		99.16 168	99.11 148	99.32 223	99.75 118	98.44 270	98.26 297	99.39 265	98.70 226	99.74 113	99.30 291	98.54 153	99.97 27	98.48 166	99.82 169	99.55 163
jason: jason.
APDe-MVS		99.48 78	99.36 100	99.85 20	99.55 203	99.81 38	99.50 90	99.69 126	98.99 189	99.75 105	99.71 130	98.79 116	99.93 87	98.46 167	99.85 147	99.80 35

CL-MVSNet_self_test		98.71 236	98.56 238	99.15 253	99.22 305	98.66 255	97.14 365	99.51 229	98.09 282	99.54 189	99.27 297	96.87 270	99.74 308	98.43 168	98.96 330	99.03 311

our_test_3		98.85 222	99.09 157	98.13 326	99.66 159	94.90 362	97.72 340	99.58 192	99.07 183	99.64 145	99.62 186	98.19 200	99.93 87	98.41 169	99.95 74	99.55 163

Gipuma		99.57 62	99.59 56	99.49 170	99.98 3	99.71 77	99.72 30	99.84 50	99.81 50	99.94 25	99.78 93	98.91 103	99.71 317	98.41 169	99.95 74	99.05 309
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 312	96.91 322	98.74 301	97.72 380	97.57 318	97.60 346	97.36 367	98.00 285	99.21 265	98.02 377	90.04 356	99.79 289	98.37 171	95.89 380	98.86 328

PM-MVS		99.36 113	99.29 118	99.58 146	99.83 56	99.66 95	98.95 231	99.86 39	98.85 208	99.81 79	99.73 116	98.40 177	99.92 107	98.36 172	99.83 160	99.17 280

baseline1		97.73 299	97.33 309	98.96 274	99.30 291	97.73 314	99.40 107	98.42 347	99.33 141	99.46 211	99.21 311	91.18 339	99.82 265	98.35 173	91.26 382	99.32 248

MVS-HIRNet		97.86 293	98.22 267	96.76 353	99.28 296	91.53 379	98.38 290	92.60 383	99.13 176	99.31 247	99.96 12	97.18 261	99.68 335	98.34 174	99.83 160	99.07 307

GA-MVS		97.99 292	97.68 302	98.93 279	99.52 216	98.04 299	97.19 364	99.05 319	98.32 269	98.81 310	98.97 343	89.89 358	99.41 373	98.33 175	99.05 325	99.34 244

Fast-Effi-MVS+		99.02 194	98.87 207	99.46 178	99.38 264	99.50 134	99.04 211	99.79 74	97.17 329	98.62 326	98.74 359	99.34 51	99.95 57	98.32 176	99.41 295	98.92 323

MDA-MVSNet_test_wron		98.95 210	98.99 189	98.85 290	99.64 163	97.16 330	98.23 299	99.33 277	98.93 198	99.56 182	99.66 162	97.39 250	99.83 256	98.29 177	99.88 124	99.55 163

N_pmnet		98.73 234	98.53 241	99.35 215	99.72 130	98.67 252	98.34 291	94.65 378	98.35 263	99.79 87	99.68 154	98.03 211	99.93 87	98.28 178	99.92 96	99.44 218

ET-MVSNet_ETH3D		96.78 324	96.07 333	98.91 282	99.26 300	97.92 308	97.70 342	96.05 373	97.96 292	92.37 383	98.43 371	87.06 367	99.90 147	98.27 179	97.56 371	98.91 324

thisisatest0530		97.45 309	96.95 319	98.94 276	99.68 153	97.73 314	99.09 203	94.19 381	98.61 234	99.56 182	99.30 291	84.30 379	99.93 87	98.27 179	99.54 275	99.16 282

YYNet1		98.95 210	98.99 189	98.84 292	99.64 163	97.14 332	98.22 300	99.32 279	98.92 200	99.59 170	99.66 162	97.40 248	99.83 256	98.27 179	99.90 106	99.55 163

ACMM	98.09 11	99.46 86	99.38 94	99.72 84	99.80 76	99.69 88	99.13 191	99.65 146	98.99 189	99.64 145	99.72 123	99.39 41	99.86 210	98.23 182	99.81 178	99.60 141
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 207	98.87 207	99.24 243	99.57 191	98.40 273	98.12 308	99.18 309	98.28 271	99.63 149	99.13 317	98.02 212	99.97 27	98.22 183	99.69 228	99.35 242

3Dnovator	99.15 2	99.43 92	99.36 100	99.65 111	99.39 261	99.42 155	99.70 35	99.56 199	99.23 156	99.35 235	99.80 76	99.17 70	99.95 57	98.21 184	99.84 152	99.59 148

Fast-Effi-MVS+-dtu		99.20 155	99.12 145	99.43 188	99.25 301	99.69 88	99.05 209	99.82 57	99.50 112	98.97 290	99.05 329	98.98 95	99.98 15	98.20 185	99.24 317	98.62 340

MS-PatchMatch		99.00 200	98.97 193	99.09 262	99.11 327	98.19 286	98.76 257	99.33 277	98.49 246	99.44 213	99.58 210	98.21 198	99.69 325	98.20 185	99.62 249	99.39 231

TSAR-MVS + GP.		99.12 176	99.04 174	99.38 206	99.34 279	99.16 208	98.15 304	99.29 287	98.18 278	99.63 149	99.62 186	99.18 69	99.68 335	98.20 185	99.74 208	99.30 254

DP-MVS		99.48 78	99.39 92	99.74 69	99.57 191	99.62 108	99.29 140	99.61 164	99.87 32	99.74 113	99.76 104	98.69 130	99.87 192	98.20 185	99.80 183	99.75 58

MVP-Stereo		99.16 168	99.08 159	99.43 188	99.48 234	99.07 221	99.08 206	99.55 205	98.63 231	99.31 247	99.68 154	98.19 200	99.78 292	98.18 189	99.58 264	99.45 213
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 92	99.30 113	99.80 36	99.83 56	99.81 38	99.52 86	99.70 120	98.35 263	99.51 201	99.50 241	99.31 53	99.88 178	98.18 189	99.84 152	99.69 72

MDA-MVSNet-bldmvs		99.06 185	99.05 169	99.07 266	99.80 76	97.83 310	98.89 235	99.72 111	99.29 144	99.63 149	99.70 137	96.47 279	99.89 164	98.17 191	99.82 169	99.50 195

JIA-IIPM		98.06 288	97.92 292	98.50 311	98.59 367	97.02 334	98.80 251	98.51 343	99.88 31	97.89 358	99.87 43	91.89 332	99.90 147	98.16 192	97.68 370	98.59 342

EIA-MVS		99.12 176	99.01 181	99.45 181	99.36 269	99.62 108	99.34 120	99.79 74	98.41 252	98.84 307	98.89 351	98.75 123	99.84 241	98.15 193	99.51 281	98.89 325

miper_lstm_enhance		98.65 241	98.60 229	98.82 297	99.20 310	97.33 326	97.78 338	99.66 137	99.01 188	99.59 170	99.50 241	94.62 304	99.85 227	98.12 194	99.90 106	99.26 259

Effi-MVS+-dtu		99.07 184	98.92 201	99.52 165	98.89 349	99.78 47	99.15 183	99.66 137	99.34 139	98.92 297	99.24 307	97.69 234	99.98 15	98.11 195	99.28 311	98.81 332

tpm		97.15 316	96.95 319	97.75 336	98.91 345	94.24 365	99.32 125	97.96 357	97.71 303	98.29 340	99.32 287	86.72 373	99.92 107	98.10 196	96.24 379	99.09 299

DeepPCF-MVS	98.42 6	99.18 162	99.02 178	99.67 99	99.22 305	99.75 63	97.25 362	99.47 241	98.72 225	99.66 142	99.70 137	99.29 55	99.63 353	98.07 197	99.81 178	99.62 127

ppachtmachnet_test		98.89 218	99.12 145	98.20 324	99.66 159	95.24 359	97.63 344	99.68 129	99.08 181	99.78 91	99.62 186	98.65 138	99.88 178	98.02 198	99.96 61	99.48 204

tpmrst		97.73 299	98.07 278	96.73 355	98.71 364	92.00 375	99.10 199	98.86 325	98.52 242	98.92 297	99.54 232	91.90 331	99.82 265	98.02 198	99.03 327	98.37 354

CSCG		99.37 110	99.29 118	99.60 140	99.71 133	99.46 141	99.43 105	99.85 44	98.79 216	99.41 225	99.60 203	98.92 101	99.92 107	98.02 198	99.92 96	99.43 224

eth_miper_zixun_eth		98.68 239	98.71 222	98.60 306	99.10 328	96.84 339	97.52 352	99.54 211	98.94 195	99.58 172	99.48 248	96.25 288	99.76 302	98.01 201	99.93 92	99.21 269

Patchmtry		98.78 227	98.54 239	99.49 170	98.89 349	99.19 206	99.32 125	99.67 133	99.65 90	99.72 118	99.79 86	91.87 333	99.95 57	98.00 202	99.97 46	99.33 245

PVSNet_BlendedMVS		99.03 192	99.01 181	99.09 262	99.54 205	97.99 300	98.58 268	99.82 57	97.62 306	99.34 238	99.71 130	98.52 160	99.77 300	97.98 203	99.97 46	99.52 188

PVSNet_Blended		98.70 237	98.59 231	99.02 270	99.54 205	97.99 300	97.58 347	99.82 57	95.70 354	99.34 238	98.98 341	98.52 160	99.77 300	97.98 203	99.83 160	99.30 254

cl____		98.54 252	98.41 250	98.92 280	99.03 336	97.80 312	97.46 354	99.59 182	98.90 202	99.60 167	99.46 255	93.85 311	99.78 292	97.97 205	99.89 115	99.17 280

DIV-MVS_self_test		98.54 252	98.42 249	98.92 280	99.03 336	97.80 312	97.46 354	99.59 182	98.90 202	99.60 167	99.46 255	93.87 310	99.78 292	97.97 205	99.89 115	99.18 278

AUN-MVS		97.82 295	97.38 308	99.14 256	99.27 298	98.53 264	98.72 260	99.02 320	98.10 280	97.18 372	99.03 335	89.26 360	99.85 227	97.94 207	97.91 366	99.03 311

FA-MVS(test-final)		98.52 254	98.32 260	99.10 261	99.48 234	98.67 252	99.77 15	98.60 340	97.35 321	99.63 149	99.80 76	93.07 321	99.84 241	97.92 208	99.30 308	98.78 335

ambc				99.20 247	99.35 271	98.53 264	99.17 175	99.46 244		99.67 138	99.80 76	98.46 167	99.70 319	97.92 208	99.70 224	99.38 233

USDC		98.96 207	98.93 197	99.05 268	99.54 205	97.99 300	97.07 368	99.80 68	98.21 275	99.75 105	99.77 100	98.43 170	99.64 352	97.90 210	99.88 124	99.51 190

OPM-MVS		99.26 135	99.13 141	99.63 125	99.70 141	99.61 114	98.58 268	99.48 238	98.50 244	99.52 196	99.63 179	99.14 75	99.76 302	97.89 211	99.77 197	99.51 190
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 125	99.17 134	99.77 47	99.69 145	99.80 42	99.14 185	99.31 283	99.16 170	99.62 158	99.61 195	98.35 181	99.91 129	97.88 212	99.72 219	99.61 137
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 25	99.70 141	99.79 44	99.14 185	99.61 164					99.92 107	97.88 212	99.72 219	99.77 49

c3_l		98.72 235	98.71 222	98.72 302	99.12 322	97.22 329	97.68 343	99.56 199	98.90 202	99.54 189	99.48 248	96.37 285	99.73 311	97.88 212	99.88 124	99.21 269

3Dnovator+	98.92 3	99.35 115	99.24 128	99.67 99	99.35 271	99.47 137	99.62 63	99.50 233	99.44 124	99.12 278	99.78 93	98.77 120	99.94 70	97.87 215	99.72 219	99.62 127

miper_ehance_all_eth		98.59 247	98.59 231	98.59 307	98.98 342	97.07 333	97.49 353	99.52 225	98.50 244	99.52 196	99.37 274	96.41 283	99.71 317	97.86 216	99.62 249	99.00 317

WTY-MVS		98.59 247	98.37 254	99.26 238	99.43 253	98.40 273	98.74 258	99.13 315	98.10 280	99.21 265	99.24 307	94.82 301	99.90 147	97.86 216	98.77 340	99.49 200

APD_test1		99.36 113	99.28 120	99.61 137	99.89 35	99.89 10	99.32 125	99.74 98	99.18 163	99.69 129	99.75 109	98.41 173	99.84 241	97.85 218	99.70 224	99.10 295

SED-MVS		99.40 101	99.28 120	99.77 47	99.69 145	99.82 35	99.20 165	99.54 211	99.13 176	99.82 72	99.63 179	98.91 103	99.92 107	97.85 218	99.70 224	99.58 153

test_241102_TWO								99.54 211	99.13 176	99.76 98	99.63 179	98.32 187	99.92 107	97.85 218	99.69 228	99.75 58

MVS_111021_HR		99.12 176	99.02 178	99.40 199	99.50 224	99.11 213	97.92 331	99.71 114	98.76 223	99.08 282	99.47 252	99.17 70	99.54 363	97.85 218	99.76 199	99.54 171

MTAPA		99.35 115	99.20 131	99.80 36	99.81 71	99.81 38	99.33 123	99.53 220	99.27 148	99.42 219	99.63 179	98.21 198	99.95 57	97.83 222	99.79 188	99.65 101

MSC_two_6792asdad				99.74 69	99.03 336	99.53 131		99.23 301					99.92 107	97.77 223	99.69 228	99.78 45

No_MVS				99.74 69	99.03 336	99.53 131		99.23 301					99.92 107	97.77 223	99.69 228	99.78 45

TESTMET0.1,1		96.24 336	95.84 338	97.41 343	98.24 375	93.84 368	97.38 356	95.84 374	98.43 249	97.81 362	98.56 366	79.77 384	99.89 164	97.77 223	98.77 340	98.52 346

ACMH+	98.40 8	99.50 74	99.43 88	99.71 89	99.86 48	99.76 58	99.32 125	99.77 83	99.53 110	99.77 96	99.76 104	99.26 61	99.78 292	97.77 223	99.88 124	99.60 141

IU-MVS					99.69 145	99.77 50		99.22 304	97.50 313	99.69 129				97.75 227	99.70 224	99.77 49

114514_t		98.49 259	98.11 276	99.64 118	99.73 127	99.58 122	99.24 155	99.76 88	89.94 376	99.42 219	99.56 223	97.76 231	99.86 210	97.74 228	99.82 169	99.47 208

DVP-MVS++		99.38 107	99.25 126	99.77 47	99.03 336	99.77 50	99.74 24	99.61 164	99.18 163	99.76 98	99.61 195	99.00 91	99.92 107	97.72 229	99.60 259	99.62 127

test_0728_THIRD									99.18 163	99.62 158	99.61 195	98.58 147	99.91 129	97.72 229	99.80 183	99.77 49

EGC-MVSNET		89.05 350	85.52 353	99.64 118	99.89 35	99.78 47	99.56 81	99.52 225	24.19 384	49.96 385	99.83 59	99.15 72	99.92 107	97.71 231	99.85 147	99.21 269

miper_enhance_ethall		98.03 289	97.94 290	98.32 319	98.27 374	96.43 345	96.95 369	99.41 255	96.37 345	99.43 217	98.96 345	94.74 302	99.69 325	97.71 231	99.62 249	98.83 331

TSAR-MVS + MP.		99.34 120	99.24 128	99.63 125	99.82 63	99.37 168	99.26 147	99.35 274	98.77 220	99.57 175	99.70 137	99.27 60	99.88 178	97.71 231	99.75 201	99.65 101
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 307	97.28 310	98.40 315	98.37 372	96.75 340	97.24 363	99.37 270	97.31 323	99.41 225	99.22 309	87.30 365	99.37 374	97.70 234	99.62 249	99.08 302

MP-MVS-pluss		99.14 172	98.92 201	99.80 36	99.83 56	99.83 29	98.61 264	99.63 154	96.84 338	99.44 213	99.58 210	98.81 111	99.91 129	97.70 234	99.82 169	99.67 84
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 129	99.11 148	99.79 41	99.75 118	99.81 38	98.95 231	99.53 220	98.27 272	99.53 194	99.73 116	98.75 123	99.87 192	97.70 234	99.83 160	99.68 78

UnsupCasMVSNet_bld		98.55 251	98.27 264	99.40 199	99.56 202	99.37 168	97.97 327	99.68 129	97.49 314	99.08 282	99.35 283	95.41 298	99.82 265	97.70 234	98.19 361	99.01 316

MVS_111021_LR		99.13 174	99.03 176	99.42 190	99.58 181	99.32 180	97.91 333	99.73 102	98.68 227	99.31 247	99.48 248	99.09 80	99.66 344	97.70 234	99.77 197	99.29 257

IS-MVSNet		99.03 192	98.85 209	99.55 158	99.80 76	99.25 193	99.73 27	99.15 312	99.37 136	99.61 164	99.71 130	94.73 303	99.81 280	97.70 234	99.88 124	99.58 153

test-LLR		97.15 316	96.95 319	97.74 337	98.18 377	95.02 360	97.38 356	96.10 370	98.00 285	97.81 362	98.58 363	90.04 356	99.91 129	97.69 240	98.78 338	98.31 355

test-mter		96.23 337	95.73 339	97.74 337	98.18 377	95.02 360	97.38 356	96.10 370	97.90 294	97.81 362	98.58 363	79.12 387	99.91 129	97.69 240	98.78 338	98.31 355

XVS		99.27 133	99.11 148	99.75 64	99.71 133	99.71 77	99.37 115	99.61 164	99.29 144	98.76 316	99.47 252	98.47 164	99.88 178	97.62 242	99.73 213	99.67 84

X-MVStestdata		96.09 338	94.87 347	99.75 64	99.71 133	99.71 77	99.37 115	99.61 164	99.29 144	98.76 316	61.30 391	98.47 164	99.88 178	97.62 242	99.73 213	99.67 84

SMA-MVS		99.19 158	99.00 184	99.73 78	99.46 244	99.73 71	99.13 191	99.52 225	97.40 318	99.57 175	99.64 169	98.93 100	99.83 256	97.61 244	99.79 188	99.63 116
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		96.71 327	96.79 326	96.46 359	98.90 346	90.71 384	99.41 106	98.68 334	94.69 367	98.14 350	99.34 286	86.32 375	99.80 286	97.60 245	98.07 365	98.88 326

PVSNet	97.47 15	98.42 266	98.44 247	98.35 317	99.46 244	96.26 346	96.70 373	99.34 276	97.68 304	99.00 289	99.13 317	97.40 248	99.72 313	97.59 246	99.68 233	99.08 302

new_pmnet		98.88 219	98.89 205	98.84 292	99.70 141	97.62 317	98.15 304	99.50 233	97.98 288	99.62 158	99.54 232	98.15 203	99.94 70	97.55 247	99.84 152	98.95 320

IB-MVS	95.41 20	95.30 347	94.46 351	97.84 333	98.76 362	95.33 358	97.33 359	96.07 372	96.02 349	95.37 381	97.41 384	76.17 389	99.96 48	97.54 248	95.44 381	98.22 360
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		99.24 139	99.11 148	99.61 137	98.38 371	99.79 44	99.57 79	99.68 129	99.61 98	99.15 273	99.71 130	98.70 129	99.91 129	97.54 248	99.68 233	99.13 292

ZNCC-MVS		99.22 148	99.04 174	99.77 47	99.76 107	99.73 71	99.28 142	99.56 199	98.19 277	99.14 275	99.29 294	98.84 110	99.92 107	97.53 250	99.80 183	99.64 111

CP-MVS		99.23 140	99.05 169	99.75 64	99.66 159	99.66 95	99.38 111	99.62 157	98.38 256	99.06 286	99.27 297	98.79 116	99.94 70	97.51 251	99.82 169	99.66 93

SD-MVS		99.01 198	99.30 113	98.15 325	99.50 224	99.40 161	98.94 233	99.61 164	99.22 160	99.75 105	99.82 66	99.54 32	95.51 385	97.48 252	99.87 135	99.54 171
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		98.49 259	98.29 263	99.11 259	98.96 343	98.42 272	97.54 348	99.32 279	97.53 311	98.47 336	98.15 376	97.88 222	99.82 265	97.46 253	99.24 317	99.09 299

DeepC-MVS_fast	98.47 5	99.23 140	99.12 145	99.56 155	99.28 296	99.22 200	98.99 225	99.40 262	99.08 181	99.58 172	99.64 169	98.90 106	99.83 256	97.44 254	99.75 201	99.63 116
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 136	99.08 159	99.76 54	99.73 127	99.70 84	99.31 130	99.59 182	98.36 258	99.36 234	99.37 274	98.80 115	99.91 129	97.43 255	99.75 201	99.68 78

ACMMPR		99.23 140	99.06 165	99.76 54	99.74 124	99.69 88	99.31 130	99.59 182	98.36 258	99.35 235	99.38 272	98.61 142	99.93 87	97.43 255	99.75 201	99.67 84

Vis-MVSNet (Re-imp)		98.77 228	98.58 234	99.34 216	99.78 95	98.88 239	99.61 68	99.56 199	99.11 180	99.24 259	99.56 223	93.00 323	99.78 292	97.43 255	99.89 115	99.35 242

MIMVSNet		98.43 265	98.20 269	99.11 259	99.53 211	98.38 277	99.58 76	98.61 338	98.96 193	99.33 240	99.76 104	90.92 343	99.81 280	97.38 258	99.76 199	99.15 284

XVG-OURS-SEG-HR		99.16 168	98.99 189	99.66 106	99.84 52	99.64 102	98.25 298	99.73 102	98.39 255	99.63 149	99.43 260	99.70 19	99.90 147	97.34 259	98.64 348	99.44 218

COLMAP_ROB	98.06 12	99.45 88	99.37 97	99.70 93	99.83 56	99.70 84	99.38 111	99.78 80	99.53 110	99.67 138	99.78 93	99.19 68	99.86 210	97.32 260	99.87 135	99.55 163
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 194	98.81 215	99.65 111	99.58 181	99.49 135	98.58 268	99.07 316	98.40 254	99.04 287	99.25 302	98.51 162	99.80 286	97.31 261	99.51 281	99.65 101

region2R		99.23 140	99.05 169	99.77 47	99.76 107	99.70 84	99.31 130	99.59 182	98.41 252	99.32 243	99.36 278	98.73 127	99.93 87	97.29 262	99.74 208	99.67 84

APD-MVS_3200maxsize		99.31 126	99.16 135	99.74 69	99.53 211	99.75 63	99.27 145	99.61 164	99.19 162	99.57 175	99.64 169	98.76 121	99.90 147	97.29 262	99.62 249	99.56 160

TAPA-MVS	97.92 13	98.03 289	97.55 305	99.46 178	99.47 240	99.44 148	98.50 281	99.62 157	86.79 377	99.07 285	99.26 300	98.26 192	99.62 354	97.28 264	99.73 213	99.31 252
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 133	99.11 148	99.73 78	99.54 205	99.74 69	99.26 147	99.62 157	99.16 170	99.52 196	99.64 169	98.41 173	99.91 129	97.27 265	99.61 256	99.54 171

RE-MVS-def			99.13 141		99.54 205	99.74 69	99.26 147	99.62 157	99.16 170	99.52 196	99.64 169	98.57 148		97.27 265	99.61 256	99.54 171

test_yl		98.25 278	97.95 286	99.13 257	99.17 315	98.47 267	99.00 220	98.67 336	98.97 191	99.22 263	99.02 336	91.31 337	99.69 325	97.26 267	98.93 331	99.24 262

DCV-MVSNet		98.25 278	97.95 286	99.13 257	99.17 315	98.47 267	99.00 220	98.67 336	98.97 191	99.22 263	99.02 336	91.31 337	99.69 325	97.26 267	98.93 331	99.24 262

PHI-MVS		99.11 179	98.95 196	99.59 142	99.13 320	99.59 118	99.17 175	99.65 146	97.88 295	99.25 256	99.46 255	98.97 97	99.80 286	97.26 267	99.82 169	99.37 236

tfpnnormal		99.43 92	99.38 94	99.60 140	99.87 45	99.75 63	99.59 74	99.78 80	99.71 70	99.90 41	99.69 143	98.85 109	99.90 147	97.25 270	99.78 193	99.15 284

PatchmatchNet		97.65 303	97.80 296	97.18 349	98.82 356	92.49 373	99.17 175	98.39 349	98.12 279	98.79 313	99.58 210	90.71 348	99.89 164	97.23 271	99.41 295	99.16 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 203	98.80 217	99.56 155	99.25 301	99.43 152	98.54 277	99.27 291	98.58 236	98.80 312	99.43 260	98.53 157	99.70 319	97.22 272	99.59 263	99.54 171

HPM-MVS		99.25 136	99.07 163	99.78 44	99.81 71	99.75 63	99.61 68	99.67 133	97.72 302	99.35 235	99.25 302	99.23 64	99.92 107	97.21 273	99.82 169	99.67 84
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 158	99.00 184	99.76 54	99.76 107	99.68 91	99.38 111	99.54 211	98.34 267	99.01 288	99.50 241	98.53 157	99.93 87	97.18 274	99.78 193	99.66 93

ACMMP		99.25 136	99.08 159	99.74 69	99.79 88	99.68 91	99.50 90	99.65 146	98.07 283	99.52 196	99.69 143	98.57 148	99.92 107	97.18 274	99.79 188	99.63 116
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		96.98 320	96.42 327	98.66 305	99.42 258	97.47 321	97.27 361	94.30 380	97.24 325	99.15 273	98.86 353	85.01 376	99.87 192	97.10 276	99.39 297	98.63 339

XVG-ACMP-BASELINE		99.23 140	99.10 156	99.63 125	99.82 63	99.58 122	98.83 243	99.72 111	98.36 258	99.60 167	99.71 130	98.92 101	99.91 129	97.08 277	99.84 152	99.40 229

MSDG		99.08 183	98.98 192	99.37 209	99.60 172	99.13 211	97.54 348	99.74 98	98.84 211	99.53 194	99.55 230	99.10 78	99.79 289	97.07 278	99.86 143	99.18 278

SteuartSystems-ACMMP		99.30 127	99.14 139	99.76 54	99.87 45	99.66 95	99.18 170	99.60 176	98.55 238	99.57 175	99.67 158	99.03 90	99.94 70	97.01 279	99.80 183	99.69 72
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		96.53 330	96.32 328	97.17 350	98.18 377	92.97 372	99.39 109	89.95 387	98.21 275	98.61 327	99.59 208	86.69 374	99.72 313	96.99 280	99.23 319	98.81 332

MSP-MVS		99.04 191	98.79 218	99.81 31	99.78 95	99.73 71	99.35 119	99.57 194	98.54 241	99.54 189	98.99 338	96.81 271	99.93 87	96.97 281	99.53 277	99.77 49
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++		98.96 207	98.70 224	99.74 69	99.52 216	99.71 77	98.86 238	99.19 308	98.47 248	98.59 329	99.06 328	98.08 208	99.91 129	96.94 282	99.60 259	99.60 141

SR-MVS		99.19 158	99.00 184	99.74 69	99.51 218	99.72 75	99.18 170	99.60 176	98.85 208	99.47 207	99.58 210	98.38 178	99.92 107	96.92 283	99.54 275	99.57 158

PGM-MVS		99.20 155	99.01 181	99.77 47	99.75 118	99.71 77	99.16 181	99.72 111	97.99 287	99.42 219	99.60 203	98.81 111	99.93 87	96.91 284	99.74 208	99.66 93

HY-MVS	98.23 9	98.21 283	97.95 286	98.99 271	99.03 336	98.24 281	99.61 68	98.72 332	96.81 339	98.73 318	99.51 238	94.06 308	99.86 210	96.91 284	98.20 359	98.86 328

MDTV_nov1_ep13			97.73 300		98.70 365	90.83 382	99.15 183	98.02 356	98.51 243	98.82 309	99.61 195	90.98 342	99.66 344	96.89 286	98.92 333

GST-MVS		99.16 168	98.96 195	99.75 64	99.73 127	99.73 71	99.20 165	99.55 205	98.22 274	99.32 243	99.35 283	98.65 138	99.91 129	96.86 287	99.74 208	99.62 127

test_post1							99.14 185				51.63 393	89.54 359	99.82 265	96.86 287

SCA		98.11 285	98.36 255	97.36 344	99.20 310	92.99 371	98.17 303	98.49 345	98.24 273	99.10 281	99.57 219	96.01 292	99.94 70	96.86 287	99.62 249	99.14 289

XVG-OURS		99.21 153	99.06 165	99.65 111	99.82 63	99.62 108	97.87 335	99.74 98	98.36 258	99.66 142	99.68 154	99.71 17	99.90 147	96.84 290	99.88 124	99.43 224

LCM-MVSNet-Re		99.28 129	99.15 138	99.67 99	99.33 284	99.76 58	99.34 120	99.97 13	98.93 198	99.91 35	99.79 86	98.68 131	99.93 87	96.80 291	99.56 266	99.30 254

RPSCF		99.18 162	99.02 178	99.64 118	99.83 56	99.85 19	99.44 103	99.82 57	98.33 268	99.50 203	99.78 93	97.90 220	99.65 350	96.78 292	99.83 160	99.44 218

旧先验2							97.94 329		95.33 358	98.94 293			99.88 178	96.75 293

MDTV_nov1_ep13_2view						91.44 380	99.14 185		97.37 320	99.21 265		91.78 335		96.75 293		99.03 311

CLD-MVS		98.76 229	98.57 235	99.33 219	99.57 191	98.97 228	97.53 350	99.55 205	96.41 343	99.27 254	99.13 317	99.07 85	99.78 292	96.73 295	99.89 115	99.23 265
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 286	97.98 284	98.48 312	99.27 298	96.48 343	99.40 107	99.07 316	98.81 213	99.23 260	99.57 219	90.11 355	99.87 192	96.69 296	99.64 246	99.09 299

baseline2		96.83 323	96.28 329	98.46 313	99.09 330	96.91 337	98.83 243	93.87 382	97.23 326	96.23 377	98.36 372	88.12 363	99.90 147	96.68 297	98.14 363	98.57 345

cascas		96.99 319	96.82 325	97.48 340	97.57 383	95.64 355	96.43 375	99.56 199	91.75 372	97.13 373	97.61 383	95.58 297	98.63 381	96.68 297	99.11 322	98.18 364

PC_three_1452									97.56 307	99.68 132	99.41 262	99.09 80	97.09 383	96.66 299	99.60 259	99.62 127

LPG-MVS_test		99.22 148	99.05 169	99.74 69	99.82 63	99.63 106	99.16 181	99.73 102	97.56 307	99.64 145	99.69 143	99.37 47	99.89 164	96.66 299	99.87 135	99.69 72

LGP-MVS_train				99.74 69	99.82 63	99.63 106		99.73 102	97.56 307	99.64 145	99.69 143	99.37 47	99.89 164	96.66 299	99.87 135	99.69 72

TinyColmap		98.97 204	98.93 197	99.07 266	99.46 244	98.19 286	97.75 339	99.75 93	98.79 216	99.54 189	99.70 137	98.97 97	99.62 354	96.63 302	99.83 160	99.41 228

LF4IMVS		99.01 198	98.92 201	99.27 235	99.71 133	99.28 186	98.59 267	99.77 83	98.32 269	99.39 231	99.41 262	98.62 140	99.84 241	96.62 303	99.84 152	98.69 338

NCCC		98.82 224	98.57 235	99.58 146	99.21 307	99.31 181	98.61 264	99.25 297	98.65 229	98.43 337	99.26 300	97.86 223	99.81 280	96.55 304	99.27 314	99.61 137

OPU-MVS				99.29 230	99.12 322	99.44 148	99.20 165				99.40 266	99.00 91	98.84 380	96.54 305	99.60 259	99.58 153

F-COLMAP		98.74 232	98.45 246	99.62 134	99.57 191	99.47 137	98.84 241	99.65 146	96.31 346	98.93 294	99.19 314	97.68 235	99.87 192	96.52 306	99.37 300	99.53 177

ADS-MVSNet2		97.78 297	97.66 304	98.12 327	99.14 318	95.36 357	99.22 162	98.75 331	96.97 334	98.25 342	99.64 169	90.90 344	99.94 70	96.51 307	99.56 266	99.08 302

ADS-MVSNet		97.72 302	97.67 303	97.86 332	99.14 318	94.65 363	99.22 162	98.86 325	96.97 334	98.25 342	99.64 169	90.90 344	99.84 241	96.51 307	99.56 266	99.08 302

PatchMatch-RL		98.68 239	98.47 244	99.30 229	99.44 249	99.28 186	98.14 306	99.54 211	97.12 332	99.11 279	99.25 302	97.80 228	99.70 319	96.51 307	99.30 308	98.93 322

CMPMVS	77.52 23	98.50 257	98.19 272	99.41 197	98.33 373	99.56 125	99.01 218	99.59 182	95.44 356	99.57 175	99.80 76	95.64 295	99.46 372	96.47 310	99.92 96	99.21 269
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SF-MVS		99.10 182	98.93 197	99.62 134	99.58 181	99.51 133	99.13 191	99.65 146	97.97 289	99.42 219	99.61 195	98.86 108	99.87 192	96.45 311	99.68 233	99.49 200

FE-MVS		97.85 294	97.42 307	99.15 253	99.44 249	98.75 247	99.77 15	98.20 354	95.85 351	99.33 240	99.80 76	88.86 361	99.88 178	96.40 312	99.12 321	98.81 332

DPE-MVS		99.14 172	98.92 201	99.82 28	99.57 191	99.77 50	98.74 258	99.60 176	98.55 238	99.76 98	99.69 143	98.23 197	99.92 107	96.39 313	99.75 201	99.76 55
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 381	89.02 388			93.47 369		98.30 373		99.84 241	96.38 314

AllTest		99.21 153	99.07 163	99.63 125	99.78 95	99.64 102	99.12 195	99.83 52	98.63 231	99.63 149	99.72 123	98.68 131	99.75 306	96.38 314	99.83 160	99.51 190

TestCases				99.63 125	99.78 95	99.64 102		99.83 52	98.63 231	99.63 149	99.72 123	98.68 131	99.75 306	96.38 314	99.83 160	99.51 190

testdata				99.42 190	99.51 218	98.93 234		99.30 286	96.20 347	98.87 304	99.40 266	98.33 186	99.89 164	96.29 317	99.28 311	99.44 218

dp		96.86 322	97.07 315	96.24 361	98.68 366	90.30 386	99.19 169	98.38 350	97.35 321	98.23 344	99.59 208	87.23 366	99.82 265	96.27 318	98.73 346	98.59 342

tpmvs		97.39 311	97.69 301	96.52 357	98.41 370	91.76 376	99.30 133	98.94 324	97.74 301	97.85 361	99.55 230	92.40 330	99.73 311	96.25 319	98.73 346	98.06 366

KD-MVS_2432*1600		95.89 340	95.41 343	97.31 347	94.96 385	93.89 366	97.09 366	99.22 304	97.23 326	98.88 301	99.04 331	79.23 385	99.54 363	96.24 320	96.81 374	98.50 350

miper_refine_blended		95.89 340	95.41 343	97.31 347	94.96 385	93.89 366	97.09 366	99.22 304	97.23 326	98.88 301	99.04 331	79.23 385	99.54 363	96.24 320	96.81 374	98.50 350

ACMP	97.51 14	99.05 188	98.84 211	99.67 99	99.78 95	99.55 128	98.88 236	99.66 137	97.11 333	99.47 207	99.60 203	99.07 85	99.89 164	96.18 322	99.85 147	99.58 153
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 215	98.72 221	99.44 184	99.39 261	99.42 155	98.58 268	99.64 152	97.31 323	99.44 213	99.62 186	98.59 145	99.69 325	96.17 323	99.79 188	99.22 267

DP-MVS Recon		98.50 257	98.23 265	99.31 226	99.49 229	99.46 141	98.56 273	99.63 154	94.86 365	98.85 306	99.37 274	97.81 227	99.59 360	96.08 324	99.44 290	98.88 326

tpm cat1		96.78 324	96.98 318	96.16 362	98.85 352	90.59 385	99.08 206	99.32 279	92.37 371	97.73 366	99.46 255	91.15 340	99.69 325	96.07 325	98.80 337	98.21 361

tpm2		96.35 333	96.22 330	96.73 355	98.88 351	91.75 377	99.21 164	98.51 343	93.27 370	97.89 358	99.21 311	84.83 377	99.70 319	96.04 326	98.18 362	98.75 337

dmvs_re		98.69 238	98.48 243	99.31 226	99.55 203	99.42 155	99.54 84	98.38 350	99.32 142	98.72 319	98.71 360	96.76 272	99.21 375	96.01 327	99.35 303	99.31 252

test_0402		99.22 148	99.14 139	99.45 181	99.79 88	99.43 152	99.28 142	99.68 129	99.54 108	99.40 230	99.56 223	99.07 85	99.82 265	96.01 327	99.96 61	99.11 293

ITE_SJBPF				99.38 206	99.63 165	99.44 148		99.73 102	98.56 237	99.33 240	99.53 234	98.88 107	99.68 335	96.01 327	99.65 244	99.02 315

test_prior2							97.95 328		97.87 296	98.05 352	99.05 329	97.90 220		95.99 330	99.49 285

testdata2													99.89 164	95.99 330

原ACMM1				99.37 209	99.47 240	98.87 241		99.27 291	96.74 341	98.26 341	99.32 287	97.93 219	99.82 265	95.96 332	99.38 298	99.43 224

新几何1				99.52 165	99.50 224	99.22 200		99.26 294	95.66 355	98.60 328	99.28 295	97.67 236	99.89 164	95.95 333	99.32 306	99.45 213

MP-MVS		99.06 185	98.83 213	99.76 54	99.76 107	99.71 77	99.32 125	99.50 233	98.35 263	98.97 290	99.48 248	98.37 179	99.92 107	95.95 333	99.75 201	99.63 116
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
wuyk23d		97.58 306	99.13 141	92.93 365	99.69 145	99.49 135	99.52 86	99.77 83	97.97 289	99.96 17	99.79 86	99.84 8	99.94 70	95.85 335	99.82 169	79.36 381

HQP_MVS		98.90 215	98.68 225	99.55 158	99.58 181	99.24 197	98.80 251	99.54 211	98.94 195	99.14 275	99.25 302	97.24 255	99.82 265	95.84 336	99.78 193	99.60 141

plane_prior5								99.54 211					99.82 265	95.84 336	99.78 193	99.60 141

无先验							98.01 320	99.23 301	95.83 352				99.85 227	95.79 338		99.44 218

CPTT-MVS		98.74 232	98.44 247	99.64 118	99.61 170	99.38 165	99.18 170	99.55 205	96.49 342	99.27 254	99.37 274	97.11 263	99.92 107	95.74 339	99.67 239	99.62 127

PLC	97.35 16	98.36 271	97.99 282	99.48 174	99.32 286	99.24 197	98.50 281	99.51 229	95.19 361	98.58 330	98.96 345	96.95 268	99.83 256	95.63 340	99.25 315	99.37 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 249	98.34 258	99.28 232	99.18 314	99.10 218	98.34 291	99.41 255	98.48 247	98.52 333	98.98 341	97.05 265	99.78 292	95.59 341	99.50 283	98.96 318

1314		98.00 291	97.90 294	98.27 323	98.90 346	97.45 323	99.30 133	99.06 318	94.98 362	97.21 371	99.12 321	98.43 170	99.67 340	95.58 342	98.56 351	97.71 370

PVSNet_0	95.53 19	95.85 343	95.31 345	97.47 341	98.78 360	93.48 370	95.72 376	99.40 262	96.18 348	97.37 367	97.73 380	95.73 294	99.58 361	95.49 343	81.40 383	99.36 239

MAR-MVS		98.24 280	97.92 292	99.19 248	98.78 360	99.65 100	99.17 175	99.14 313	95.36 357	98.04 353	98.81 356	97.47 245	99.72 313	95.47 344	99.06 324	98.21 361
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	98.23 281	97.89 295	99.26 238	99.19 312	99.26 190	99.65 59	99.69 126	91.33 374	98.14 350	99.77 100	98.28 190	99.96 48	95.41 345	99.55 270	98.58 344

train_agg		98.35 274	97.95 286	99.57 152	99.35 271	99.35 175	98.11 310	99.41 255	94.90 363	97.92 356	98.99 338	98.02 212	99.85 227	95.38 346	99.44 290	99.50 195

9.14			98.64 226		99.45 248		98.81 248	99.60 176	97.52 312	99.28 253	99.56 223	98.53 157	99.83 256	95.36 347	99.64 246

APD-MVS		98.87 220	98.59 231	99.71 89	99.50 224	99.62 108	99.01 218	99.57 194	96.80 340	99.54 189	99.63 179	98.29 189	99.91 129	95.24 348	99.71 222	99.61 137
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
AdaColmap		98.60 244	98.35 257	99.38 206	99.12 322	99.22 200	98.67 263	99.42 254	97.84 299	98.81 310	99.27 297	97.32 253	99.81 280	95.14 349	99.53 277	99.10 295

test9_res														95.10 350	99.44 290	99.50 195

CDPH-MVS		98.56 250	98.20 269	99.61 137	99.50 224	99.46 141	98.32 293	99.41 255	95.22 359	99.21 265	99.10 325	98.34 184	99.82 265	95.09 351	99.66 242	99.56 160

BH-untuned		98.22 282	98.09 277	98.58 309	99.38 264	97.24 328	98.55 274	98.98 323	97.81 300	99.20 270	98.76 358	97.01 266	99.65 350	94.83 352	98.33 356	98.86 328

BP-MVS														94.73 353

HQP-MVS		98.36 271	98.02 281	99.39 203	99.31 287	98.94 231	97.98 324	99.37 270	97.45 315	98.15 346	98.83 354	96.67 273	99.70 319	94.73 353	99.67 239	99.53 177

QAPM		98.40 269	97.99 282	99.65 111	99.39 261	99.47 137	99.67 49	99.52 225	91.70 373	98.78 315	99.80 76	98.55 151	99.95 57	94.71 355	99.75 201	99.53 177

agg_prior2														94.58 356	99.46 289	99.50 195

BH-RMVSNet		98.41 267	98.14 275	99.21 245	99.21 307	98.47 267	98.60 266	98.26 353	98.35 263	98.93 294	99.31 289	97.20 260	99.66 344	94.32 357	99.10 323	99.51 190

E-PMN		97.14 318	97.43 306	96.27 360	98.79 358	91.62 378	95.54 377	99.01 322	99.44 124	98.88 301	99.12 321	92.78 324	99.68 335	94.30 358	99.03 327	97.50 371

MG-MVS		98.52 254	98.39 252	98.94 276	99.15 317	97.39 325	98.18 301	99.21 307	98.89 205	99.23 260	99.63 179	97.37 251	99.74 308	94.22 359	99.61 256	99.69 72

API-MVS		98.38 270	98.39 252	98.35 317	98.83 353	99.26 190	99.14 185	99.18 309	98.59 235	98.66 324	98.78 357	98.61 142	99.57 362	94.14 360	99.56 266	96.21 378

PAPM_NR		98.36 271	98.04 279	99.33 219	99.48 234	98.93 234	98.79 254	99.28 290	97.54 310	98.56 332	98.57 365	97.12 262	99.69 325	94.09 361	98.90 335	99.38 233

ZD-MVS					99.43 253	99.61 114		99.43 252	96.38 344	99.11 279	99.07 327	97.86 223	99.92 107	94.04 362	99.49 285

DPM-MVS		98.28 276	97.94 290	99.32 223	99.36 269	99.11 213	97.31 360	98.78 330	96.88 336	98.84 307	99.11 324	97.77 230	99.61 358	94.03 363	99.36 301	99.23 265

gg-mvs-nofinetune		95.87 342	95.17 346	97.97 329	98.19 376	96.95 335	99.69 42	89.23 388	99.89 26	96.24 376	99.94 16	81.19 381	99.51 368	93.99 364	98.20 359	97.44 372

PMVS	92.94 21	98.82 224	98.81 215	98.85 290	99.84 52	97.99 300	99.20 165	99.47 241	99.71 70	99.42 219	99.82 66	98.09 206	99.47 370	93.88 365	99.85 147	99.07 307
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 321	97.28 310	95.99 363	98.76 362	91.03 381	95.26 378	98.61 338	99.34 139	98.92 297	98.88 352	93.79 312	99.66 344	92.87 366	99.05 325	97.30 375

BH-w/o		97.20 315	97.01 317	97.76 335	99.08 331	95.69 354	98.03 319	98.52 342	95.76 353	97.96 355	98.02 377	95.62 296	99.47 370	92.82 367	97.25 373	98.12 365

TR-MVS		97.44 310	97.15 314	98.32 319	98.53 369	97.46 322	98.47 283	97.91 359	96.85 337	98.21 345	98.51 369	96.42 281	99.51 368	92.16 368	97.29 372	97.98 367

OpenMVS_ROB	97.31 17	97.36 313	96.84 323	98.89 289	99.29 293	99.45 146	98.87 237	99.48 238	86.54 379	99.44 213	99.74 112	97.34 252	99.86 210	91.61 369	99.28 311	97.37 374

GG-mvs-BLEND				97.36 344	97.59 381	96.87 338	99.70 35	88.49 389		94.64 382	97.26 387	80.66 382	99.12 376	91.50 370	96.50 378	96.08 380

DeepMVS_CX				97.98 328	99.69 145	96.95 335		99.26 294	75.51 381	95.74 379	98.28 374	96.47 279	99.62 354	91.23 371	97.89 367	97.38 373

PAPR		97.56 307	97.07 315	99.04 269	98.80 357	98.11 293	97.63 344	99.25 297	94.56 368	98.02 354	98.25 375	97.43 247	99.68 335	90.90 372	98.74 344	99.33 245

MVS		95.72 345	94.63 349	98.99 271	98.56 368	97.98 306	99.30 133	98.86 325	72.71 382	97.30 368	99.08 326	98.34 184	99.74 308	89.21 373	98.33 356	99.26 259

thres600view7		96.60 329	96.16 331	97.93 330	99.63 165	96.09 350	99.18 170	97.57 362	98.77 220	98.72 319	97.32 385	87.04 368	99.72 313	88.57 374	98.62 349	97.98 367

FPMVS		96.32 334	95.50 341	98.79 298	99.60 172	98.17 289	98.46 287	98.80 329	97.16 330	96.28 374	99.63 179	82.19 380	99.09 377	88.45 375	98.89 336	99.10 295

PCF-MVS	96.03 18	96.73 326	95.86 337	99.33 219	99.44 249	99.16 208	96.87 371	99.44 249	86.58 378	98.95 292	99.40 266	94.38 306	99.88 178	87.93 376	99.80 183	98.95 320
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 332	96.03 334	97.47 341	99.63 165	95.93 351	99.18 170	97.57 362	98.75 224	98.70 322	97.31 386	87.04 368	99.67 340	87.62 377	98.51 353	96.81 376

tfpn200view9		96.30 335	95.89 335	97.53 339	99.58 181	96.11 348	99.00 220	97.54 365	98.43 249	98.52 333	96.98 388	86.85 370	99.67 340	87.62 377	98.51 353	96.81 376

thres400		96.40 331	95.89 335	97.92 331	99.58 181	96.11 348	99.00 220	97.54 365	98.43 249	98.52 333	96.98 388	86.85 370	99.67 340	87.62 377	98.51 353	97.98 367

thres200		96.09 338	95.68 340	97.33 346	99.48 234	96.22 347	98.53 278	97.57 362	98.06 284	98.37 339	96.73 390	86.84 372	99.61 358	86.99 380	98.57 350	96.16 379

MVE	92.54 22	96.66 328	96.11 332	98.31 321	99.68 153	97.55 319	97.94 329	95.60 375	99.37 136	90.68 384	98.70 361	96.56 275	98.61 382	86.94 381	99.55 270	98.77 336
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 314	96.83 324	98.59 307	99.46 244	97.55 319	99.25 154	96.84 369	98.78 218	97.24 370	97.67 381	97.11 263	98.97 379	86.59 382	98.54 352	99.27 258

PAPM		95.61 346	94.71 348	98.31 321	99.12 322	96.63 341	96.66 374	98.46 346	90.77 375	96.25 375	98.68 362	93.01 322	99.69 325	81.60 383	97.86 369	98.62 340

test123		29.31 351	33.05 356	18.08 367	25.93 391	12.24 391	97.53 350	10.93 392	11.78 385	24.21 386	50.08 395	21.04 390	8.60 386	23.51 384	32.43 385	33.39 382

testmvs		28.94 352	33.33 354	15.79 368	26.03 390	9.81 392	96.77 372	15.67 391	11.55 386	23.87 387	50.74 394	19.03 391	8.53 387	23.21 385	33.07 384	29.03 383

test_blank		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		24.88 353	33.17 355	0.00 369	0.00 392	0.00 393	0.00 380	99.62 157	0.00 387	0.00 388	99.13 317	99.82 9	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		16.61 354	22.14 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	99.28 57	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		8.26 363	11.02 366	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	99.16 315	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

FOURS1					99.83 56	99.89 10	99.74 24	99.71 114	99.69 78	99.63 149

test_one_0601					99.63 165	99.76 58		99.55 205	99.23 156	99.31 247	99.61 195	98.59 145

eth-test2					0.00 392

eth-test					0.00 392

test_241102_ONE					99.69 145	99.82 35		99.54 211	99.12 179	99.82 72	99.49 245	98.91 103	99.52 367

save fliter					99.53 211	99.25 193	98.29 295	99.38 269	99.07 183

test0726					99.69 145	99.80 42	99.24 155	99.57 194	99.16 170	99.73 117	99.65 167	98.35 181

GSMVS																99.14 289

test_part2					99.62 169	99.67 93				99.55 187

sam_mvs1												90.81 347				99.14 289

sam_mvs												90.52 351

MTGPA								99.53 220

test_post											52.41 392	90.25 353	99.86 210

patchmatchnet-post											99.62 186	90.58 349	99.94 70

MTMP							99.09 203	98.59 341

TEST9					99.35 271	99.35 175	98.11 310	99.41 255	94.83 366	97.92 356	98.99 338	98.02 212	99.85 227

test_8					99.34 279	99.31 181	98.08 314	99.40 262	94.90 363	97.87 360	98.97 343	98.02 212	99.84 241

agg_prior					99.35 271	99.36 172		99.39 265		97.76 365			99.85 227

test_prior4						99.19 206	98.00 322

test_prior				99.46 178	99.35 271	99.22 200		99.39 265					99.69 325			99.48 204

新几何2							98.04 318

旧先验1					99.49 229	99.29 184		99.26 294			99.39 270	97.67 236			99.36 301	99.46 212

原ACMM2							97.92 331

test222					99.51 218	99.08 220	97.83 337	99.29 287	95.21 360	98.68 323	99.31 289	97.28 254			99.38 298	99.43 224

segment_acmp												98.37 179

testdata1							97.72 340		97.86 298

test12				99.54 162	99.29 293	99.33 178		99.16 311		98.43 337		97.54 243	99.82 265		99.47 287	99.48 204

plane_prior7					99.58 181	99.38 165

plane_prior6					99.47 240	99.26 190						97.24 255

plane_prior4											99.25 302

plane_prior3						99.31 181			98.36 258	99.14 275

plane_prior2							98.80 251		98.94 195

plane_prior1					99.51 218

plane_prior						99.24 197	98.42 288		97.87 296						99.71 222

n2								0.00 393

nn								0.00 393

door-mid								99.83 52

test11								99.29 287

door								99.77 83

HQP5-MVS						98.94 231

HQP-NCC					99.31 287		97.98 324		97.45 315	98.15 346

ACMP_Plane					99.31 287		97.98 324		97.45 315	98.15 346

HQP4-MVS										98.15 346			99.70 319			99.53 177

HQP3-MVS								99.37 270							99.67 239

HQP2-MVS												96.67 273

NP-MVS					99.40 260	99.13 211					98.83 354

ACMMP++_ref															99.94 85

ACMMP++															99.79 188

Test By Simon												98.41 173

High-resolution multi-view benchmark