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.

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 14	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 16	100.00 1	99.85 30

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 103	99.90 3	99.86 24	99.78 13	99.58 6	99.95 25	99.00 87	99.95 39	99.78 50

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 26	99.53 9	99.76 39	99.64 26	99.84 30	99.83 4	99.50 9	99.87 135	99.36 57	99.92 71	99.64 86

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 36	99.63 28	99.78 39	99.67 30	99.48 10	99.81 227	99.30 62	99.97 21	99.77 53
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
tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 45	98.98 72	99.51 10	99.85 19	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 39	99.61 100

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 104	99.34 23	99.71 48	99.27 74	99.90 14	99.74 18	99.68 4	99.97 6	99.55 43	99.99 5	99.88 20

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 82	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 52	99.93 57	99.60 102

tt0320		99.61 8	99.65 9	99.48 57	99.71 49	98.94 79	99.54 8	99.83 26	99.87 5	99.89 18	99.82 5	98.75 47	99.90 81	99.54 44	99.95 39	99.59 109

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 104	99.28 40	99.66 71	99.09 110	99.89 18	99.68 25	99.53 7	99.97 6	99.50 50	99.99 5	99.87 22

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 152	98.08 197	99.95 2	99.45 50	99.98 2	99.75 16	99.80 1	99.97 6	99.82 12	99.99 5	99.99 2

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 158	99.14 58	99.93 6	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 61	100.00 1	99.82 36

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 112	99.45 14	99.61 92	99.66 23	99.68 57	99.66 32	98.44 84	99.95 25	99.73 28	99.96 28	99.75 62

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 92	99.39 20	99.56 121	99.11 100	99.70 51	99.73 20	99.00 27	99.97 6	99.26 65	99.98 12	99.89 16

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 57	98.93 132	99.65 63	99.72 21	98.93 33	99.95 25	99.11 77	100.00 1	99.82 36

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 154	97.77 255	99.90 12	99.33 66	99.97 3	99.66 32	99.71 3	99.96 13	99.79 19	99.99 5	99.96 8

UA-Net		99.47 16	99.40 27	99.70 2	99.49 150	99.29 23	99.80 4	99.72 46	99.82 8	99.04 203	99.81 8	98.05 131	99.96 13	98.85 98	99.99 5	99.86 28

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 149	99.20 49	99.65 77	99.48 44	99.92 8	99.71 22	98.07 128	99.96 13	99.53 48	100.00 1	99.93 11

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 92	98.21 146	97.82 246	99.84 23	99.41 57	99.92 8	99.41 94	99.51 8	99.95 25	99.84 9	99.97 21	99.87 22

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 77	98.10 157	97.68 270	99.84 23	99.29 72	99.92 8	99.57 49	99.60 5	99.96 13	99.74 27	99.98 12	99.89 16

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 101	99.29 36	99.63 82	99.30 71	99.65 63	99.60 45	99.16 22	99.82 210	99.07 80	99.83 126	99.56 130

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 107	99.27 42	99.57 111	99.39 58	99.75 44	99.62 40	99.17 20	99.83 198	99.06 82	99.62 267	99.66 80

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 49	99.30 21	99.31 30	99.51 144	99.64 26	99.56 74	99.46 80	98.23 110	99.97 6	98.78 102	99.93 57	99.72 64

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 48	99.38 59	99.53 83	99.61 43	98.64 61	99.80 236	98.24 147	99.84 114	99.52 161

PEN-MVS		99.41 24	99.34 35	99.62 9	99.73 38	99.14 57	99.29 36	99.54 132	99.62 32	99.56 74	99.42 89	98.16 122	99.96 13	98.78 102	99.93 57	99.77 53

nrg030		99.40 25	99.35 33	99.54 31	99.58 94	99.13 60	98.98 76	99.48 159	99.68 19	99.46 101	99.26 138	98.62 64	99.73 299	99.17 74	99.92 71	99.76 58

PS-CasMVS		99.40 25	99.33 37	99.62 9	99.71 49	99.10 65	99.29 36	99.53 136	99.53 41	99.46 101	99.41 94	98.23 110	99.95 25	98.89 96	99.95 39	99.81 41

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 41	99.41 17	99.59 100	99.59 36	99.71 49	99.57 49	97.12 214	99.90 81	99.21 70	99.87 100	99.54 143

OurMVSNet-221017-0		99.37 28	99.31 41	99.53 38	99.91 3	98.98 72	99.63 7	99.58 103	99.44 52	99.78 39	99.76 15	96.39 265	99.92 65	99.44 54	99.92 71	99.68 73

Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 38	98.26 138	99.17 54	99.78 36	99.11 100	99.27 153	99.48 75	98.82 38	99.95 25	98.94 91	99.93 57	99.59 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsm_n_1920		99.33 30	99.45 23	98.99 156	99.57 103	97.73 214	97.93 230	99.83 26	99.22 80	99.93 6	99.30 126	99.42 11	99.96 13	99.85 6	99.99 5	99.29 284

WR-MVS_H		99.33 30	99.22 54	99.65 8	99.71 49	99.24 29	99.32 26	99.55 126	99.46 49	99.50 93	99.34 116	97.30 201	99.93 53	98.90 94	99.93 57	99.77 53

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 160	99.59 92	97.18 271	97.44 312	99.83 26	99.56 39	99.91 12	99.34 116	99.36 13	99.93 53	99.83 10	99.98 12	99.85 30

mmtdpeth		99.30 33	99.42 25	98.92 173	99.58 94	96.89 293	99.48 13	99.92 8	99.92 2	98.26 342	99.80 11	98.33 96	99.91 74	99.56 41	99.95 39	99.97 4

mvs5depth		99.30 33	99.59 12	98.44 281	99.65 71	95.35 374	99.82 3	99.94 3	99.83 7	99.42 112	99.94 2	98.13 125	99.96 13	99.63 36	99.96 28	100.00 1

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 150	98.36 129	99.00 73	99.45 179	99.63 28	99.52 87	99.44 85	98.25 107	99.88 115	99.09 79	99.84 114	99.62 92

sd_testset		99.28 36	99.31 41	99.19 112	99.68 64	98.06 168	99.41 17	99.30 254	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 249	99.92 71	99.57 124

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 215	98.18 147	99.49 12	99.51 144	99.70 15	99.80 37	99.68 25	96.84 232	99.83 198	99.21 70	99.91 80	99.77 53

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 126	99.30 35	99.57 111	99.61 34	99.40 117	99.50 68	97.12 214	99.85 159	99.02 86	99.94 51	99.80 45

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 265	99.66 70	96.97 285	98.00 216	99.85 19	99.24 77	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 409

lecture		99.25 40	99.12 71	99.62 9	99.64 77	99.40 11	98.89 88	99.51 144	99.19 89	99.37 125	99.25 143	98.36 90	99.88 115	98.23 149	99.67 246	99.59 109

testf1		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 57	98.90 136	99.43 108	99.35 112	98.86 35	99.67 348	97.81 191	99.81 140	99.24 302

APD_test2		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 57	98.90 136	99.43 108	99.35 112	98.86 35	99.67 348	97.81 191	99.81 140	99.24 302

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 83	99.06 70	98.69 108	99.54 132	99.31 69	99.62 69	99.53 64	97.36 198	99.86 145	99.24 69	99.71 217	99.39 232

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 45	98.38 124	99.07 65	99.55 126	98.30 195	99.65 63	99.45 84	99.22 17	99.76 273	98.44 131	99.77 172	99.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SDMVSNet		99.23 45	99.32 39	98.96 164	99.68 64	97.35 245	98.84 95	99.48 159	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 177	99.92 71	99.57 124

fmvsm_s_conf0.5_n_3		99.22 46	99.37 31	98.78 204	99.46 164	96.58 311	97.65 276	99.72 46	99.47 47	99.86 24	99.50 68	98.94 31	99.89 97	99.75 26	99.97 21	99.86 28

fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 197	99.48 158	96.56 313	97.97 228	99.69 57	99.63 28	99.84 30	99.54 62	98.21 115	99.94 41	99.76 23	99.95 39	99.88 20

fmvsm_l_conf0.5_n		99.21 47	99.28 46	99.02 151	99.64 77	97.28 257	97.82 246	99.76 39	98.73 151	99.82 34	99.09 198	98.81 39	99.95 25	99.86 4	99.96 28	99.83 33

CP-MVSNet		99.21 47	99.09 82	99.56 26	99.65 71	98.96 78	99.13 59	99.34 233	99.42 55	99.33 138	99.26 138	97.01 223	99.94 41	98.74 107	99.93 57	99.79 47

fmvsm_s_conf0.1_n_2		99.20 50	99.38 28	98.65 234	99.69 61	96.08 336	97.49 303	99.90 12	99.53 41	99.88 21	99.64 37	98.51 76	99.90 81	99.83 10	99.98 12	99.97 4

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 167	99.65 71	97.05 280	97.80 250	99.76 39	98.70 159	99.78 39	99.11 189	98.79 43	99.95 25	99.85 6	99.96 28	99.83 33

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 267	99.51 134	95.82 349	97.62 281	99.78 36	99.72 14	99.90 14	99.48 75	98.66 59	99.89 97	99.85 6	99.93 57	99.89 16

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 197	99.75 34	96.59 308	97.97 228	99.86 17	98.22 204	99.88 21	99.71 22	98.59 67	99.84 180	99.73 28	99.98 12	99.98 3

TranMVSNet+NR-MVSNet		99.17 52	99.07 85	99.46 63	99.37 193	98.87 85	98.39 157	99.42 200	99.42 55	99.36 128	99.06 201	98.38 89	99.95 25	98.34 142	99.90 88	99.57 124

FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 117	98.24 140	99.20 49	99.44 187	99.21 82	99.43 108	99.55 56	97.82 154	99.86 145	98.42 137	99.89 94	99.41 222

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 236	99.71 49	96.10 331	97.87 241	99.85 19	98.56 177	99.90 14	99.68 25	98.69 57	99.85 159	99.72 30	99.98 12	99.97 4

FE-MVSNET2		99.15 57	99.22 54	98.94 167	99.70 57	97.49 232	98.62 118	99.67 70	98.85 145	99.34 135	99.54 62	98.47 77	99.81 227	98.93 92	99.91 80	99.51 165

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 204	99.47 161	96.56 313	97.75 261	99.71 48	99.60 35	99.74 46	99.44 85	97.96 139	99.95 25	99.86 4	99.94 51	99.82 36

Elysia		99.15 57	99.14 68	99.18 113	99.63 83	97.92 186	98.50 137	99.43 193	99.67 20	99.70 51	99.13 183	96.66 249	99.98 4	99.54 44	99.96 28	99.64 86

StellarMVS		99.15 57	99.14 68	99.18 113	99.63 83	97.92 186	98.50 137	99.43 193	99.67 20	99.70 51	99.13 183	96.66 249	99.98 4	99.54 44	99.96 28	99.64 86

reproduce_model		99.15 57	98.97 98	99.67 4	99.33 205	99.44 9	98.15 185	99.47 170	99.12 99	99.52 87	99.32 124	98.31 97	99.90 81	97.78 194	99.73 199	99.66 80

fmvsm_s_conf0.5_n_2		99.14 62	99.31 41	98.63 240	99.49 150	96.08 336	97.38 317	99.81 32	99.48 44	99.84 30	99.57 49	98.46 82	99.89 97	99.82 12	99.97 21	99.91 13

test_vis3_rt		99.14 62	99.17 60	99.07 138	99.78 24	98.38 124	98.92 83	99.94 3	97.80 248	99.91 12	99.67 30	97.15 212	98.91 504	99.76 23	99.56 293	99.92 12

FIs		99.14 62	99.09 82	99.29 95	99.70 57	98.28 136	99.13 59	99.52 142	99.48 44	99.24 167	99.41 94	96.79 239	99.82 210	98.69 112	99.88 95	99.76 58

XXY-MVS		99.14 62	99.15 67	99.10 130	99.76 30	97.74 212	98.85 93	99.62 89	98.48 181	99.37 125	99.49 74	98.75 47	99.86 145	98.20 152	99.80 152	99.71 65

fmvsm_s_conf0.5_n_8		99.13 66	99.26 50	98.74 217	99.51 134	96.44 321	97.65 276	99.65 77	99.66 23	99.78 39	99.48 75	97.92 142	99.93 53	99.72 30	99.95 39	99.87 22

CS-MVS		99.13 66	99.10 80	99.24 106	99.06 287	99.15 52	99.36 22	99.88 15	99.36 63	98.21 344	98.46 358	98.68 58	99.93 53	99.03 85	99.85 109	98.64 420

SPE-MVS-test		99.13 66	99.09 82	99.26 101	99.13 270	98.97 74	99.31 30	99.88 15	99.44 52	98.16 348	98.51 349	98.64 61	99.93 53	98.91 93	99.85 109	98.88 383

Casviewmamba		99.12 69	99.12 71	99.09 134	99.53 127	98.08 162	98.34 164	99.66 71	99.35 64	99.35 130	99.23 151	98.39 88	99.72 310	98.46 129	99.81 140	99.47 197

test_fmvs3		99.12 69	99.41 26	98.25 305	99.76 30	95.07 390	99.05 68	99.94 3	97.78 251	99.82 34	99.84 3	98.56 73	99.71 312	99.96 1	99.96 28	99.97 4

casdiffmvs_mvg		99.12 69	99.16 62	98.99 156	99.43 176	97.73 214	98.00 216	99.62 89	99.22 80	99.55 77	99.22 153	98.93 33	99.75 285	98.66 113	99.81 140	99.50 169
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_a		99.10 72	99.20 58	98.78 204	99.55 117	96.59 308	97.79 251	99.82 31	98.21 206	99.81 36	99.53 64	98.46 82	99.84 180	99.70 33	99.97 21	99.90 15

casdiffseed414692147		99.09 73	99.12 71	99.01 153	99.55 117	97.91 188	98.30 166	99.68 64	99.04 119	99.19 176	99.37 105	98.98 28	99.61 389	98.13 156	99.83 126	99.50 169

TestfortrainingZip a		99.09 73	98.92 102	99.61 13	99.58 94	99.17 43	98.68 109	99.27 269	98.85 145	99.61 70	99.16 171	97.14 213	99.86 145	98.39 138	99.57 289	99.81 41

reproduce-ours		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 216	99.42 200	99.05 117	99.48 96	99.27 132	98.29 99	99.89 97	97.61 216	99.71 217	99.62 92

our_new_method		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 216	99.42 200	99.05 117	99.48 96	99.27 132	98.29 99	99.89 97	97.61 216	99.71 217	99.62 92

fmvsm_s_conf0.5_n		99.09 73	99.26 50	98.61 246	99.55 117	96.09 334	97.74 263	99.81 32	98.55 178	99.85 27	99.55 56	98.60 66	99.84 180	99.69 35	99.98 12	99.89 16

EC-MVSNet		99.09 73	99.05 86	99.20 110	99.28 218	98.93 80	99.24 44	99.84 23	99.08 114	98.12 353	98.37 368	98.72 50	99.90 81	99.05 83	99.77 172	98.77 402

hybridcas		99.08 79	99.13 70	98.92 173	99.54 123	97.61 226	98.22 178	99.66 71	99.27 74	99.40 117	99.24 145	98.47 77	99.70 321	98.59 118	99.80 152	99.46 200

fmvsm_s_conf0.5_n_6		99.08 79	99.21 57	98.69 227	99.36 194	96.51 315	97.62 281	99.68 64	98.43 184	99.85 27	99.10 192	99.12 23	99.88 115	99.77 22	99.92 71	99.67 78

ACMH+	96.62 9	99.08 79	99.00 94	99.33 89	99.71 49	98.83 87	98.60 121	99.58 103	99.11 100	99.53 83	99.18 164	98.81 39	99.67 348	96.71 307	99.77 172	99.50 169

fmvsm_s_conf0.5_n_5		99.07 82	99.10 80	98.99 156	99.47 161	97.22 264	97.40 314	99.83 26	97.61 266	99.85 27	99.30 126	98.80 41	99.95 25	99.71 32	99.90 88	99.78 50

E5new		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

E6new		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

E6		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

E5		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

GeoE		99.05 83	98.99 96	99.25 104	99.44 171	98.35 130	98.73 103	99.56 121	98.42 185	98.91 236	98.81 285	98.94 31	99.91 74	98.35 141	99.73 199	99.49 177

KinetiMVS		99.03 88	99.02 90	99.03 148	99.70 57	97.48 235	98.43 148	99.29 262	99.70 15	99.60 71	99.07 200	96.13 281	99.94 41	99.42 55	99.87 100	99.68 73

Gipuma		99.03 88	99.16 62	98.64 236	99.94 2	98.51 114	99.32 26	99.75 43	99.58 38	98.60 300	99.62 40	98.22 113	99.51 434	97.70 208	99.73 199	97.89 472
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MED-MVS		99.01 90	98.84 119	99.52 44	99.58 94	98.93 80	98.68 109	99.60 94	98.85 145	99.53 83	99.16 171	97.87 149	99.83 198	96.67 312	99.62 267	99.81 41

fmvsm_s_conf0.5_n_4		99.01 90	99.22 54	98.38 289	99.31 209	95.48 365	97.56 292	99.73 45	98.87 140	99.75 44	99.27 132	98.80 41	99.86 145	99.80 17	99.90 88	99.81 41

v8		99.01 90	99.16 62	98.57 253	99.47 161	96.31 326	98.90 84	99.47 170	99.03 121	99.52 87	99.57 49	96.93 228	99.81 227	99.60 37	99.98 12	99.60 102

HPM-MVS_fast		99.01 90	98.82 121	99.57 21	99.71 49	99.35 16	99.00 73	99.50 149	97.33 301	98.94 232	98.86 269	98.75 47	99.82 210	97.53 225	99.71 217	99.56 130

usedtu_dtu_shiyan2		98.99 94	98.86 115	99.39 72	99.73 38	98.71 98	99.05 68	99.47 170	99.16 94	99.49 94	99.12 187	96.34 271	99.93 53	98.05 166	99.36 348	99.54 143

APDe-MVS		98.99 94	98.79 124	99.60 16	99.21 241	99.15 52	98.87 89	99.48 159	97.57 270	99.35 130	99.24 145	97.83 151	99.89 97	97.88 184	99.70 228	99.75 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
EG-PatchMatch MVS		98.99 94	99.01 92	98.94 167	99.50 141	97.47 236	98.04 206	99.59 100	98.15 222	99.40 117	99.36 111	98.58 72	99.76 273	98.78 102	99.68 240	99.59 109

COLMAP_ROB	96.50 10	98.99 94	98.85 118	99.41 69	99.58 94	99.10 65	98.74 99	99.56 121	99.09 110	99.33 138	99.19 160	98.40 86	99.72 310	95.98 366	99.76 188	99.42 219
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		98.98 98	98.86 115	99.36 74	99.82 19	98.55 109	97.47 308	99.57 111	99.37 60	99.21 174	99.61 43	96.76 242	99.83 198	98.06 164	99.83 126	99.71 65

v10		98.97 99	99.11 74	98.55 260	99.44 171	96.21 330	98.90 84	99.55 126	98.73 151	99.48 96	99.60 45	96.63 253	99.83 198	99.70 33	99.99 5	99.61 100

DeepC-MVS	97.60 4	98.97 99	98.93 101	99.10 130	99.35 199	97.98 177	98.01 214	99.46 175	97.56 272	99.54 79	99.50 68	98.97 29	99.84 180	98.06 164	99.92 71	99.49 177
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		98.96 101	99.02 90	98.76 211	99.38 187	97.26 259	98.49 140	99.50 149	98.86 142	99.19 176	99.06 201	98.23 110	99.69 331	98.71 110	99.76 188	99.33 268

casdiffmvs		98.95 102	99.00 94	98.81 194	99.38 187	97.33 247	97.82 246	99.57 111	99.17 93	99.35 130	99.17 169	98.35 94	99.69 331	98.46 129	99.73 199	99.41 222
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		98.95 102	98.82 121	99.36 74	99.16 261	98.72 97	99.22 46	99.20 290	99.10 107	99.72 47	98.76 297	96.38 267	99.86 145	98.00 172	99.82 133	99.50 169

Anonymous20240529		98.93 104	98.87 111	99.12 126	99.19 249	98.22 145	99.01 71	98.99 340	99.25 76	99.54 79	99.37 105	97.04 218	99.80 236	97.89 181	99.52 308	99.35 258

DP-MVS		98.93 104	98.81 123	99.28 96	99.21 241	98.45 118	98.46 145	99.33 239	99.63 28	99.48 96	99.15 177	97.23 207	99.75 285	97.17 255	99.66 254	99.63 91

SED-MVS		98.91 106	98.72 132	99.49 55	99.49 150	99.17 43	98.10 194	99.31 246	98.03 228	99.66 60	99.02 214	98.36 90	99.88 115	96.91 282	99.62 267	99.41 222

ACMM	96.08 12	98.91 106	98.73 130	99.48 57	99.55 117	99.14 57	98.07 201	99.37 217	97.62 263	99.04 203	98.96 243	98.84 37	99.79 249	97.43 236	99.65 256	99.49 177
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SSM_0404		98.90 108	99.01 92	98.57 253	99.42 178	96.59 308	98.13 187	99.66 71	99.09 110	99.30 148	99.02 214	98.79 43	99.89 97	97.87 186	99.80 152	99.23 304

DVP-MVS++		98.90 108	98.70 138	99.51 49	98.43 414	99.15 52	99.43 15	99.32 241	98.17 214	99.26 157	99.02 214	98.18 118	99.88 115	97.07 267	99.45 328	99.49 177

tfpnnormal		98.90 108	98.90 105	98.91 175	99.67 68	97.82 202	99.00 73	99.44 187	99.45 50	99.51 92	99.24 145	98.20 117	99.86 145	95.92 368	99.69 234	99.04 350

MTAPA		98.88 111	98.64 150	99.61 13	99.67 68	99.36 15	98.43 148	99.20 290	98.83 149	98.89 240	98.90 258	96.98 225	99.92 65	97.16 256	99.70 228	99.56 130

E4		98.87 112	98.88 108	98.81 194	99.52 131	97.23 261	97.62 281	99.61 92	98.58 172	99.18 181	99.33 119	98.29 99	99.69 331	97.99 175	99.83 126	99.52 161

mvsany_test3		98.87 112	98.92 102	98.74 217	99.38 187	96.94 289	98.58 123	99.10 316	96.49 367	99.96 4	99.81 8	98.18 118	99.45 453	98.97 89	99.79 159	99.83 33

VPNet		98.87 112	98.83 120	99.01 153	99.70 57	97.62 225	98.43 148	99.35 227	99.47 47	99.28 151	99.05 208	96.72 246	99.82 210	98.09 161	99.36 348	99.59 109

UniMVSNet (Re)		98.87 112	98.71 135	99.35 80	99.24 233	98.73 95	97.73 265	99.38 213	98.93 132	99.12 185	98.73 301	96.77 240	99.86 145	98.63 116	99.80 152	99.46 200

viewmacassd2359aftdt		98.86 116	98.87 111	98.83 190	99.53 127	97.32 250	97.70 268	99.64 79	98.22 204	99.25 165	99.27 132	98.40 86	99.61 389	97.98 176	99.87 100	99.55 137

SSM_0407		98.86 116	98.96 100	98.55 260	99.27 221	96.50 316	98.04 206	99.66 71	99.09 110	99.22 171	99.02 214	98.79 43	99.87 135	97.87 186	99.72 208	99.27 291

UniMVSNet_NR-MVSNet		98.86 116	98.68 141	99.40 71	99.17 259	98.74 92	97.68 270	99.40 209	99.14 98	99.06 193	98.59 339	96.71 247	99.93 53	98.57 121	99.77 172	99.53 157

viewdifsd2359ckpt11		98.84 119	99.04 87	98.24 307	99.56 111	95.51 360	97.38 317	99.70 54	99.16 94	99.57 72	99.40 98	98.26 105	99.71 312	98.55 125	99.82 133	99.50 169

viewmsd2359difaftdt		98.84 119	99.04 87	98.24 307	99.56 111	95.51 360	97.38 317	99.70 54	99.16 94	99.57 72	99.40 98	98.26 105	99.71 312	98.55 125	99.82 133	99.50 169

APD-MVS_3200maxsize		98.84 119	98.61 158	99.53 38	99.19 249	99.27 26	98.49 140	99.33 239	98.64 161	99.03 206	98.98 237	97.89 147	99.85 159	96.54 331	99.42 340	99.46 200

fmvsm_s_conf0.5_n_7		98.83 122	99.04 87	98.20 312	99.30 213	94.83 400	97.23 335	99.36 221	98.64 161	99.84 30	99.43 88	98.10 127	99.91 74	99.56 41	99.96 28	99.87 22

MVSMamba_PlusPlus		98.83 122	98.98 97	98.36 293	99.32 207	96.58 311	98.90 84	99.41 204	99.75 10	98.72 275	99.50 68	96.17 278	99.94 41	99.27 64	99.78 164	98.57 428

APD_test1		98.83 122	98.66 146	99.34 83	99.78 24	99.47 8	98.42 151	99.45 179	98.28 200	98.98 214	99.19 160	97.76 158	99.58 405	96.57 323	99.55 298	98.97 364

PM-MVS		98.82 125	98.72 132	99.12 126	99.64 77	98.54 112	97.98 224	99.68 64	97.62 263	99.34 135	99.18 164	97.54 180	99.77 267	97.79 193	99.74 195	99.04 350

DU-MVS		98.82 125	98.63 152	99.39 72	99.16 261	98.74 92	97.54 296	99.25 277	98.84 148	99.06 193	98.76 297	96.76 242	99.93 53	98.57 121	99.77 172	99.50 169

SR-MVS-dyc-post		98.81 127	98.55 165	99.57 21	99.20 245	99.38 12	98.48 143	99.30 254	98.64 161	98.95 224	98.96 243	97.49 189	99.86 145	96.56 327	99.39 344	99.45 206

3Dnovator	98.27 2	98.81 127	98.73 130	99.05 145	98.76 355	97.81 205	99.25 43	99.30 254	98.57 174	98.55 310	99.33 119	97.95 140	99.90 81	97.16 256	99.67 246	99.44 210

mamba_0408		98.80 129	98.88 108	98.55 260	99.27 221	96.50 316	98.00 216	99.60 94	98.93 132	99.22 171	98.84 277	98.59 67	99.89 97	97.74 203	99.72 208	99.27 291

SSM_04072		98.80 129	98.88 108	98.56 258	99.27 221	96.50 316	98.00 216	99.60 94	98.93 132	99.22 171	98.84 277	98.59 67	99.90 81	97.74 203	99.72 208	99.27 291

HPM-MVS		98.79 131	98.53 169	99.59 20	99.65 71	99.29 23	99.16 55	99.43 193	96.74 354	98.61 297	98.38 367	98.62 64	99.87 135	96.47 335	99.67 246	99.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		98.79 131	98.54 167	99.54 31	99.73 38	99.16 48	98.23 174	99.31 246	97.92 238	98.90 237	98.90 258	98.00 134	99.88 115	96.15 358	99.72 208	99.58 117
Skip Steuart: Steuart Systems R&D Blog.
dcpmvs_2		98.78 133	99.11 74	97.78 355	99.56 111	93.67 450	99.06 66	99.86 17	99.50 43	99.66 60	99.26 138	97.21 209	99.99 2	98.00 172	99.91 80	99.68 73

V42		98.78 133	98.78 126	98.76 211	99.44 171	97.04 281	98.27 171	99.19 294	97.87 242	99.25 165	99.16 171	96.84 232	99.78 261	99.21 70	99.84 114	99.46 200

test20.03		98.78 133	98.77 127	98.78 204	99.46 164	97.20 267	97.78 252	99.24 283	99.04 119	99.41 114	98.90 258	97.65 165	99.76 273	97.70 208	99.79 159	99.39 232

DVP-MVS		98.77 136	98.52 170	99.52 44	99.50 141	99.21 32	98.02 211	98.84 369	97.97 232	99.08 191	99.02 214	97.61 172	99.88 115	96.99 274	99.63 263	99.48 188
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_0402		98.76 137	98.71 135	98.93 170	99.56 111	98.14 151	98.45 147	99.34 233	99.28 73	98.95 224	98.91 255	98.34 95	99.79 249	95.63 384	99.91 80	98.86 385

ACMMP_NAP		98.75 138	98.48 181	99.57 21	99.58 94	99.29 23	97.82 246	99.25 277	96.94 336	98.78 265	99.12 187	98.02 132	99.84 180	97.13 263	99.67 246	99.59 109

SixPastTwentyTwo		98.75 138	98.62 154	99.16 118	99.83 18	97.96 181	99.28 40	98.20 426	99.37 60	99.70 51	99.65 36	92.65 400	99.93 53	99.04 84	99.84 114	99.60 102

ACMMP		98.75 138	98.50 175	99.52 44	99.56 111	99.16 48	98.87 89	99.37 217	97.16 324	98.82 259	99.01 226	97.71 161	99.87 135	96.29 350	99.69 234	99.54 143
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
XVS		98.72 141	98.45 186	99.53 38	99.46 164	99.21 32	98.65 114	99.34 233	98.62 166	97.54 402	98.63 331	97.50 186	99.83 198	96.79 295	99.53 305	99.56 130

viewdifsd2359ckpt07		98.71 142	98.86 115	98.26 303	99.43 176	95.65 354	97.20 340	99.66 71	99.20 84	99.29 149	99.01 226	98.29 99	99.73 299	97.92 180	99.75 192	99.39 232

SSC-MVS		98.71 142	98.74 128	98.62 242	99.72 45	96.08 336	98.74 99	98.64 397	99.74 12	99.67 59	99.24 145	94.57 346	99.95 25	99.11 77	99.24 374	99.82 36

SR-MVS		98.71 142	98.43 189	99.57 21	99.18 257	99.35 16	98.36 160	99.29 262	98.29 198	98.88 244	98.85 272	97.53 182	99.87 135	96.14 359	99.31 360	99.48 188

HFP-MVS		98.71 142	98.44 188	99.51 49	99.49 150	99.16 48	98.52 130	99.31 246	97.47 283	98.58 304	98.50 353	97.97 138	99.85 159	96.57 323	99.59 280	99.53 157

LPG-MVS_test		98.71 142	98.46 185	99.47 61	99.57 103	98.97 74	98.23 174	99.48 159	96.60 361	99.10 189	99.06 201	98.71 51	99.83 198	95.58 388	99.78 164	99.62 92

E2		98.70 147	98.68 141	98.73 219	99.40 183	97.10 278	97.48 304	99.57 111	98.09 225	99.00 209	99.20 157	97.90 143	99.67 348	97.73 205	99.77 172	99.43 214

test_fmvs2		98.70 147	98.97 98	97.89 346	99.54 123	94.05 427	98.55 126	99.92 8	96.78 352	99.72 47	99.78 13	96.60 254	99.67 348	99.91 2	99.90 88	99.94 10

ACMMPR		98.70 147	98.42 191	99.54 31	99.52 131	99.14 57	98.52 130	99.31 246	97.47 283	98.56 308	98.54 344	97.75 159	99.88 115	96.57 323	99.59 280	99.58 117

CP-MVS		98.70 147	98.42 191	99.52 44	99.36 194	99.12 62	98.72 104	99.36 221	97.54 277	98.30 336	98.40 364	97.86 150	99.89 97	96.53 332	99.72 208	99.56 130

E3		98.69 151	98.68 141	98.73 219	99.40 183	97.10 278	97.48 304	99.57 111	98.09 225	99.00 209	99.20 157	97.90 143	99.67 348	97.73 205	99.77 172	99.43 214

tt0805		98.69 151	98.62 154	98.90 178	99.75 34	99.30 21	99.15 57	96.97 470	98.86 142	98.87 249	97.62 438	98.63 63	98.96 500	99.41 56	98.29 461	98.45 435

Anonymous20240521		98.69 151	98.87 111	98.16 318	99.77 27	95.11 389	99.08 62	99.44 187	99.34 65	99.33 138	99.55 56	94.10 367	99.94 41	99.25 67	99.96 28	99.42 219

region2R		98.69 151	98.40 193	99.54 31	99.53 127	99.17 43	98.52 130	99.31 246	97.46 288	98.44 324	98.51 349	97.83 151	99.88 115	96.46 336	99.58 285	99.58 117

EI-MVSNet-UG-set		98.69 151	98.71 135	98.62 242	99.10 275	96.37 323	97.23 335	98.87 360	99.20 84	99.19 176	98.99 232	97.30 201	99.85 159	98.77 105	99.79 159	99.65 85

3Dnovator+	97.89 3	98.69 151	98.51 172	99.24 106	98.81 348	98.40 121	99.02 70	99.19 294	98.99 124	98.07 358	99.28 130	97.11 216	99.84 180	96.84 293	99.32 358	99.47 197

RoMa-HiRes		98.68 157	98.52 170	99.16 118	99.50 141	98.35 130	98.01 214	99.71 48	96.94 336	99.35 130	98.66 322	96.38 267	99.63 376	98.39 138	99.71 217	99.48 188

ZNCC-MVS		98.68 157	98.40 193	99.54 31	99.57 103	99.21 32	98.46 145	99.29 262	97.28 308	98.11 354	98.39 365	98.00 134	99.87 135	96.86 292	99.64 258	99.55 137

EI-MVSNet-Vis-set		98.68 157	98.70 138	98.63 240	99.09 278	96.40 322	97.23 335	98.86 365	99.20 84	99.18 181	98.97 239	97.29 203	99.85 159	98.72 109	99.78 164	99.64 86

CSCG		98.68 157	98.50 175	99.20 110	99.45 169	98.63 101	98.56 125	99.57 111	97.87 242	98.85 251	98.04 406	97.66 164	99.84 180	96.72 305	99.81 140	99.13 339

test_f		98.67 161	98.87 111	98.05 333	99.72 45	95.59 355	98.51 135	99.81 32	96.30 378	99.78 39	99.82 5	96.14 279	98.63 512	99.82 12	99.93 57	99.95 9

PGM-MVS		98.66 162	98.37 202	99.55 28	99.53 127	99.18 42	98.23 174	99.49 157	97.01 333	98.69 280	98.88 266	98.00 134	99.89 97	95.87 372	99.59 280	99.58 117

GBi-Net		98.65 163	98.47 183	99.17 115	98.90 327	98.24 140	99.20 49	99.44 187	98.59 169	98.95 224	99.55 56	94.14 363	99.86 145	97.77 197	99.69 234	99.41 222

test1		98.65 163	98.47 183	99.17 115	98.90 327	98.24 140	99.20 49	99.44 187	98.59 169	98.95 224	99.55 56	94.14 363	99.86 145	97.77 197	99.69 234	99.41 222

LCM-MVSNet-Re		98.64 165	98.48 181	99.11 128	98.85 339	98.51 114	98.49 140	99.83 26	98.37 186	99.69 55	99.46 80	98.21 115	99.92 65	94.13 430	99.30 364	98.91 378

mPP-MVS		98.64 165	98.34 208	99.54 31	99.54 123	99.17 43	98.63 116	99.24 283	97.47 283	98.09 356	98.68 316	97.62 170	99.89 97	96.22 353	99.62 267	99.57 124

BridgeMVS		98.63 167	98.72 132	98.38 289	98.66 384	96.68 307	98.90 84	99.42 200	98.99 124	98.97 218	99.19 160	95.81 301	99.85 159	98.77 105	99.77 172	98.60 424

TSAR-MVS + MP.		98.63 167	98.49 180	99.06 144	99.64 77	97.90 190	98.51 135	98.94 344	96.96 334	99.24 167	98.89 264	97.83 151	99.81 227	96.88 289	99.49 323	99.48 188
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
LS3D		98.63 167	98.38 200	99.36 74	97.25 501	99.38 12	99.12 61	99.32 241	99.21 82	98.44 324	98.88 266	97.31 200	99.80 236	96.58 321	99.34 353	98.92 374

RPSCF		98.62 170	98.36 204	99.42 67	99.65 71	99.42 10	98.55 126	99.57 111	97.72 256	98.90 237	99.26 138	96.12 283	99.52 428	95.72 379	99.71 217	99.32 273

aaEdge-Enhanced		98.61 171	98.33 213	99.44 65	99.24 233	98.93 80	97.45 310	99.06 322	98.14 223	99.06 193	98.77 292	96.97 226	99.82 210	96.67 312	99.64 258	99.58 117

GST-MVS		98.61 171	98.30 216	99.52 44	99.51 134	99.20 38	98.26 172	99.25 277	97.44 291	98.67 284	98.39 365	97.68 162	99.85 159	96.00 364	99.51 311	99.52 161

v1192		98.60 173	98.66 146	98.41 285	99.27 221	95.88 345	97.52 298	99.36 221	97.41 293	99.33 138	99.20 157	96.37 269	99.82 210	99.57 39	99.92 71	99.55 137

v1144		98.60 173	98.66 146	98.41 285	99.36 194	95.90 343	97.58 290	99.34 233	97.51 279	99.27 153	99.15 177	96.34 271	99.80 236	99.47 53	99.93 57	99.51 165

FE-MVSNET		98.59 175	98.50 175	98.87 179	99.58 94	97.30 251	98.08 197	99.74 44	96.94 336	98.97 218	99.10 192	96.94 227	99.74 292	97.33 242	99.86 107	99.55 137

DPE-MVS		98.59 175	98.26 225	99.57 21	99.27 221	99.15 52	97.01 351	99.39 211	97.67 259	99.44 107	98.99 232	97.53 182	99.89 97	95.40 393	99.68 240	99.66 80
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
viewmanbaseed2359cas		98.58 177	98.54 167	98.70 225	99.28 218	97.13 277	97.47 308	99.55 126	97.55 274	98.96 223	98.92 252	97.77 157	99.59 398	97.59 219	99.77 172	99.39 232

viewmamba		98.57 178	98.66 146	98.31 298	99.20 245	95.89 344	96.92 361	99.57 111	98.71 158	99.02 207	99.04 210	97.48 190	99.71 312	98.28 146	99.70 228	99.35 258

MP-MVS-pluss		98.57 178	98.23 230	99.60 16	99.69 61	99.35 16	97.16 345	99.38 213	94.87 444	98.97 218	98.99 232	98.01 133	99.88 115	97.29 246	99.70 228	99.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 180	98.32 214	99.25 104	99.41 181	98.73 95	97.13 347	99.18 298	97.10 327	98.75 271	98.92 252	98.18 118	99.65 368	96.68 311	99.56 293	99.37 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VDD-MVS		98.56 180	98.39 196	99.07 138	99.13 270	98.07 165	98.59 122	97.01 467	99.59 36	99.11 186	99.27 132	94.82 335	99.79 249	98.34 142	99.63 263	99.34 262

v2v482		98.56 180	98.62 154	98.37 292	99.42 178	95.81 350	97.58 290	99.16 305	97.90 240	99.28 151	99.01 226	95.98 293	99.79 249	99.33 59	99.90 88	99.51 165

XVG-ACMP-BASELINE		98.56 180	98.34 208	99.22 109	99.54 123	98.59 106	97.71 266	99.46 175	97.25 312	98.98 214	98.99 232	97.54 180	99.84 180	95.88 369	99.74 195	99.23 304

viewcassd2359sk11		98.55 184	98.51 172	98.67 230	99.29 215	96.99 284	97.39 315	99.54 132	97.73 254	98.81 261	99.08 199	97.55 178	99.66 361	97.52 227	99.67 246	99.36 252

v1240		98.55 184	98.62 154	98.32 296	99.22 239	95.58 357	97.51 300	99.45 179	97.16 324	99.45 106	99.24 145	96.12 283	99.85 159	99.60 37	99.88 95	99.55 137

IterMVS-LS		98.55 184	98.70 138	98.09 325	99.48 158	94.73 405	97.22 339	99.39 211	98.97 127	99.38 121	99.31 125	96.00 288	99.93 53	98.58 119	99.97 21	99.60 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 187	98.57 163	98.45 279	99.21 241	95.98 339	97.63 280	99.36 221	97.15 326	99.32 144	99.18 164	95.84 300	99.84 180	99.50 50	99.91 80	99.54 143

v1921920		98.54 187	98.60 159	98.38 289	99.20 245	95.76 353	97.56 292	99.36 221	97.23 318	99.38 121	99.17 169	96.02 286	99.84 180	99.57 39	99.90 88	99.54 143

SSC-MVS3.2		98.53 189	98.79 124	97.74 362	99.46 164	93.62 453	96.45 397	99.34 233	99.33 66	98.93 233	98.70 312	97.90 143	99.90 81	99.12 76	99.92 71	99.69 72

SF-MVS		98.53 189	98.27 222	99.32 91	99.31 209	98.75 91	98.19 179	99.41 204	96.77 353	98.83 256	98.90 258	97.80 155	99.82 210	95.68 382	99.52 308	99.38 241

XVG-OURS		98.53 189	98.34 208	99.11 128	99.50 141	98.82 89	95.97 433	99.50 149	97.30 306	99.05 201	98.98 237	99.35 14	99.32 474	95.72 379	99.68 240	99.18 324

UGNet		98.53 189	98.45 186	98.79 201	97.94 459	96.96 287	99.08 62	98.54 406	99.10 107	96.82 450	99.47 78	96.55 257	99.84 180	98.56 124	99.94 51	99.55 137
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
WB-MVS		98.52 193	98.55 165	98.43 282	99.65 71	95.59 355	98.52 130	98.77 380	99.65 25	99.52 87	99.00 230	94.34 356	99.93 53	98.65 114	98.83 424	99.76 58

patch_mono-2		98.51 194	98.63 152	98.17 315	99.38 187	94.78 402	97.36 322	99.69 57	98.16 217	98.49 317	99.29 129	97.06 217	99.97 6	98.29 145	99.91 80	99.76 58

diffmvs_AUTHOR		98.50 195	98.59 161	98.23 310	99.35 199	95.48 365	96.61 386	99.60 94	98.37 186	98.90 237	99.00 230	97.37 197	99.76 273	98.22 150	99.85 109	99.46 200

XVG-OURS-SEG-HR		98.49 196	98.28 219	99.14 124	99.49 150	98.83 87	96.54 390	99.48 159	97.32 303	99.11 186	98.61 336	99.33 15	99.30 477	96.23 352	98.38 455	99.28 287

FMVSNet2		98.49 196	98.40 193	98.75 213	98.90 327	97.14 276	98.61 120	99.13 312	98.59 169	99.19 176	99.28 130	94.14 363	99.82 210	97.97 177	99.80 152	99.29 284

pmmvs-eth3d		98.47 198	98.34 208	98.86 181	99.30 213	97.76 210	97.16 345	99.28 266	95.54 420	99.42 112	99.19 160	97.27 204	99.63 376	97.89 181	99.97 21	99.20 314

RoMa-SfM		98.46 199	98.27 222	99.02 151	99.35 199	98.32 133	97.56 292	99.70 54	95.88 400	99.38 121	98.65 325	96.41 263	99.46 450	97.78 194	99.71 217	99.28 287

MP-MVS		98.46 199	98.09 249	99.54 31	99.57 103	99.22 31	98.50 137	99.19 294	97.61 266	97.58 398	98.66 322	97.40 195	99.88 115	94.72 410	99.60 276	99.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.45 201	98.60 159	98.00 337	99.44 171	94.98 392	97.44 312	99.06 322	98.30 195	99.32 144	98.97 239	96.65 251	99.62 381	98.37 140	99.85 109	99.39 232

AllTest		98.44 202	98.20 232	99.16 118	99.50 141	98.55 109	98.25 173	99.58 103	96.80 350	98.88 244	99.06 201	97.65 165	99.57 407	94.45 417	99.61 274	99.37 244

VNet		98.42 203	98.30 216	98.79 201	98.79 354	97.29 256	98.23 174	98.66 394	99.31 69	98.85 251	98.80 286	94.80 339	99.78 261	98.13 156	99.13 394	99.31 278

E3new		98.41 204	98.34 208	98.62 242	99.19 249	96.90 292	97.32 325	99.50 149	97.40 295	98.63 291	98.92 252	97.21 209	99.65 368	97.34 240	99.52 308	99.31 278

ab-mvs		98.41 204	98.36 204	98.59 249	99.19 249	97.23 261	99.32 26	98.81 374	97.66 260	98.62 295	99.40 98	96.82 235	99.80 236	95.88 369	99.51 311	98.75 405

ACMP	95.32 15	98.41 204	98.09 249	99.36 74	99.51 134	98.79 90	97.68 270	99.38 213	95.76 410	98.81 261	98.82 282	98.36 90	99.82 210	94.75 407	99.77 172	99.48 188
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
onestephybrid01		98.40 207	98.39 196	98.42 283	99.05 290	96.23 328	96.73 373	99.41 204	98.18 213	98.65 287	99.02 214	97.02 221	99.69 331	97.73 205	99.70 228	99.33 268

test_vis1_n_1920		98.40 207	98.92 102	96.81 436	99.74 37	90.76 509	98.15 185	99.91 10	98.33 191	99.89 18	99.55 56	95.07 328	99.88 115	99.76 23	99.93 57	99.79 47

SMA-MVS		98.40 207	98.03 257	99.51 49	99.16 261	99.21 32	98.05 204	99.22 286	94.16 466	98.98 214	99.10 192	97.52 184	99.79 249	96.45 337	99.64 258	99.53 157
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
MSP-MVS		98.40 207	98.00 260	99.61 13	99.57 103	99.25 28	98.57 124	99.35 227	97.55 274	99.31 147	97.71 431	94.61 345	99.88 115	96.14 359	99.19 386	99.70 70
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
SD-MVS		98.40 207	98.68 141	97.54 390	98.96 315	97.99 174	97.88 238	99.36 221	98.20 210	99.63 66	99.04 210	98.76 46	95.33 547	96.56 327	99.74 195	99.31 278
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
EI-MVSNet		98.40 207	98.51 172	98.04 334	99.10 275	94.73 405	97.20 340	98.87 360	98.97 127	99.06 193	99.02 214	96.00 288	99.80 236	98.58 119	99.82 133	99.60 102

WR-MVS		98.40 207	98.19 236	99.03 148	99.00 307	97.65 221	96.85 364	98.94 344	98.57 174	98.89 240	98.50 353	95.60 309	99.85 159	97.54 224	99.85 109	99.59 109

viewdifsd2359ckpt13		98.39 214	98.29 218	98.70 225	99.26 230	97.19 268	97.51 300	99.48 159	96.94 336	98.58 304	98.82 282	97.47 192	99.55 415	97.21 252	99.33 355	99.34 262

IMVS_0407		98.39 214	98.64 150	97.66 372	99.03 295	94.03 430	98.10 194	99.45 179	98.16 217	99.06 193	98.71 305	98.27 103	99.71 312	97.50 228	99.45 328	99.22 309

LuminaMVS		98.39 214	98.20 232	98.98 160	99.50 141	97.49 232	97.78 252	97.69 442	98.75 150	99.49 94	99.25 143	92.30 406	99.94 41	99.14 75	99.88 95	99.50 169

new-patchmatchnet		98.35 217	98.74 128	97.18 412	99.24 233	92.23 482	96.42 401	99.48 159	98.30 195	99.69 55	99.53 64	97.44 193	99.82 210	98.84 99	99.77 172	99.49 177

IMVS_0403		98.34 218	98.56 164	97.66 372	99.03 295	94.03 430	97.98 224	99.45 179	98.16 217	98.89 240	98.71 305	97.90 143	99.74 292	97.50 228	99.45 328	99.22 309

MGCFI-Net		98.34 218	98.28 219	98.51 270	98.47 407	97.59 227	98.96 78	99.48 159	99.18 92	97.40 416	95.50 500	98.66 59	99.50 435	98.18 153	98.71 435	98.44 438

sasdasda		98.34 218	98.26 225	98.58 250	98.46 409	97.82 202	98.96 78	99.46 175	99.19 89	97.46 409	95.46 503	98.59 67	99.46 450	98.08 162	98.71 435	98.46 432

canonicalmvs		98.34 218	98.26 225	98.58 250	98.46 409	97.82 202	98.96 78	99.46 175	99.19 89	97.46 409	95.46 503	98.59 67	99.46 450	98.08 162	98.71 435	98.46 432

test_cas_vis1_n_1920		98.33 222	98.68 141	97.27 407	99.69 61	92.29 480	98.03 208	99.85 19	97.62 263	99.96 4	99.62 40	93.98 368	99.74 292	99.52 49	99.86 107	99.79 47

hybridnocas07		98.32 223	98.37 202	98.17 315	99.14 267	95.51 360	96.67 379	99.56 121	97.85 244	98.75 271	98.95 247	96.65 251	99.63 376	98.00 172	99.78 164	99.37 244

dtuplus		98.32 223	98.39 196	98.10 323	99.15 265	95.29 378	96.68 377	99.51 144	97.32 303	99.18 181	99.15 177	97.61 172	99.62 381	97.19 253	99.74 195	99.38 241

testgi		98.32 223	98.39 196	98.13 320	99.57 103	95.54 358	97.78 252	99.49 157	97.37 298	99.19 176	97.65 435	98.96 30	99.49 439	96.50 334	98.99 412	99.34 262

DeepPCF-MVS	96.93 5	98.32 223	98.01 259	99.23 108	98.39 419	98.97 74	95.03 474	99.18 298	96.88 344	99.33 138	98.78 290	98.16 122	99.28 481	96.74 302	99.62 267	99.44 210

test_vis1_n		98.31 227	98.50 175	97.73 365	99.76 30	94.17 422	98.68 109	99.91 10	96.31 376	99.79 38	99.57 49	92.85 396	99.42 459	99.79 19	99.84 114	99.60 102

MVS_111021_LR		98.30 228	98.12 247	98.83 190	99.16 261	98.03 170	96.09 426	99.30 254	97.58 269	98.10 355	98.24 387	98.25 107	99.34 470	96.69 310	99.65 256	99.12 340

EPP-MVSNet		98.30 228	98.04 256	99.07 138	99.56 111	97.83 197	99.29 36	98.07 433	99.03 121	98.59 302	99.13 183	92.16 408	99.90 81	96.87 290	99.68 240	99.49 177

DeepC-MVS_fast	96.85 6	98.30 228	98.15 244	98.75 213	98.61 389	97.23 261	97.76 258	99.09 318	97.31 305	98.75 271	98.66 322	97.56 177	99.64 373	96.10 363	99.55 298	99.39 232
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 231	97.95 266	99.34 83	98.44 412	99.16 48	98.12 191	99.38 213	96.01 393	98.06 359	98.43 361	97.80 155	99.67 348	95.69 381	99.58 285	99.20 314

balanced_ft_v1		98.28 232	98.35 207	98.10 323	98.08 451	96.23 328	99.23 45	99.26 275	98.34 189	97.46 409	99.42 89	95.38 319	99.88 115	98.60 117	99.34 353	98.17 457

Fast-Effi-MVS+-dtu		98.27 233	98.09 249	98.81 194	98.43 414	98.11 154	97.61 286	99.50 149	98.64 161	97.39 418	97.52 445	98.12 126	99.95 25	96.90 287	98.71 435	98.38 445

DELS-MVS		98.27 233	98.20 232	98.48 276	98.86 336	96.70 305	95.60 453	99.20 290	97.73 254	98.45 323	98.71 305	97.50 186	99.82 210	98.21 151	99.59 280	98.93 373
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
NormalMVS		98.26 235	97.97 265	99.15 123	99.64 77	97.83 197	98.28 168	99.43 193	99.24 77	98.80 263	98.85 272	89.76 439	99.94 41	98.04 167	99.67 246	99.68 73

Effi-MVS+-dtu		98.26 235	97.90 275	99.35 80	98.02 454	99.49 5	98.02 211	99.16 305	98.29 198	97.64 392	97.99 410	96.44 262	99.95 25	96.66 315	98.93 420	98.60 424

MVSFormer		98.26 235	98.43 189	97.77 356	98.88 333	93.89 443	99.39 20	99.56 121	99.11 100	98.16 348	98.13 396	93.81 372	99.97 6	99.26 65	99.57 289	99.43 214

MVS_111021_HR		98.25 238	98.08 252	98.75 213	99.09 278	97.46 238	95.97 433	99.27 269	97.60 268	97.99 366	98.25 385	98.15 124	99.38 465	96.87 290	99.57 289	99.42 219

TAMVS		98.24 239	98.05 255	98.80 197	99.07 282	97.18 271	97.88 238	98.81 374	96.66 360	99.17 184	99.21 155	94.81 338	99.77 267	96.96 279	99.88 95	99.44 210

hybrid		98.22 240	98.27 222	98.08 328	99.13 270	95.24 380	96.61 386	99.53 136	97.43 292	98.46 321	98.97 239	96.75 245	99.65 368	97.84 189	99.69 234	99.35 258

MM		98.22 240	97.99 261	98.91 175	98.66 384	96.97 285	97.89 237	94.44 517	99.54 40	98.95 224	99.14 181	93.50 379	99.92 65	99.80 17	99.96 28	99.85 30

diffmvs		98.22 240	98.24 229	98.17 315	99.00 307	95.44 369	96.38 403	99.58 103	97.79 250	98.53 313	98.50 353	96.76 242	99.74 292	97.95 179	99.64 258	99.34 262
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Anonymous20231206		98.21 243	98.21 231	98.20 312	99.51 134	95.43 370	98.13 187	99.32 241	96.16 385	98.93 233	98.82 282	96.00 288	99.83 198	97.32 244	99.73 199	99.36 252

VDDNet		98.21 243	97.95 266	99.01 153	99.58 94	97.74 212	99.01 71	97.29 458	99.67 20	98.97 218	99.50 68	90.45 433	99.80 236	97.88 184	99.20 383	99.48 188

icg_test_0407_2		98.20 245	98.38 200	97.65 374	99.03 295	94.03 430	95.78 447	99.45 179	98.16 217	99.06 193	98.71 305	98.27 103	99.68 343	97.50 228	99.45 328	99.22 309

viewmambaseed2359dif		98.19 246	98.26 225	97.99 339	99.02 303	95.03 391	96.59 389	99.53 136	96.21 380	99.00 209	98.99 232	97.62 170	99.61 389	97.62 215	99.72 208	99.33 268

IS-MVSNet		98.19 246	97.90 275	99.08 136	99.57 103	97.97 178	99.31 30	98.32 419	99.01 123	98.98 214	99.03 213	91.59 417	99.79 249	95.49 391	99.80 152	99.48 188

DKM		98.18 248	97.95 266	98.85 182	99.35 199	98.31 134	96.68 377	99.69 57	96.90 342	98.61 297	98.77 292	94.41 351	98.93 502	97.32 244	99.84 114	99.32 273

MVS_Test		98.18 248	98.36 204	97.67 370	98.48 406	94.73 405	98.18 180	99.02 334	97.69 257	98.04 362	99.11 189	97.22 208	99.56 410	98.57 121	98.90 422	98.71 409

TSAR-MVS + GP.		98.18 248	97.98 262	98.77 209	98.71 365	97.88 192	96.32 408	98.66 394	96.33 374	99.23 169	98.51 349	97.48 190	99.40 461	97.16 256	99.46 326	99.02 353

CNVR-MVS		98.17 251	97.87 277	99.07 138	98.67 379	98.24 140	97.01 351	98.93 347	97.25 312	97.62 394	98.34 372	97.27 204	99.57 407	96.42 339	99.33 355	99.39 232

PVSNet_Blended_VisFu		98.17 251	98.15 244	98.22 311	99.73 38	95.15 386	97.36 322	99.68 64	94.45 458	98.99 213	99.27 132	96.87 231	99.94 41	97.13 263	99.91 80	99.57 124

AstraMVS		98.16 253	98.07 254	98.41 285	99.51 134	95.86 346	98.00 216	95.14 511	98.97 127	99.43 108	99.24 145	93.25 383	99.84 180	99.21 70	99.87 100	99.54 143

DKM-HiRes		98.14 254	97.80 282	99.16 118	99.51 134	98.40 121	96.70 375	99.63 82	97.55 274	97.45 412	98.74 299	93.27 382	99.54 421	97.78 194	99.55 298	99.53 157

viewdifsd2359ckpt09		98.13 255	97.92 272	98.77 209	99.18 257	97.35 245	97.29 329	99.53 136	95.81 407	98.09 356	98.47 357	96.34 271	99.66 361	97.02 270	99.51 311	99.29 284

DenseAffine		98.10 256	97.86 278	98.84 188	99.32 207	97.93 185	96.62 385	99.76 39	96.68 359	98.65 287	98.72 303	94.46 349	99.33 472	96.76 299	99.75 192	99.25 298

HPM-MVS++		98.10 256	97.64 300	99.48 57	99.09 278	99.13 60	97.52 298	98.75 386	97.46 288	96.90 444	97.83 424	96.01 287	99.84 180	95.82 376	99.35 351	99.46 200

APD-MVS		98.10 256	97.67 295	99.42 67	99.11 273	98.93 80	97.76 258	99.28 266	94.97 441	98.72 275	98.77 292	97.04 218	99.85 159	93.79 440	99.54 301	99.49 177
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_fmvs1_n		98.09 259	98.28 219	97.52 392	99.68 64	93.47 456	98.63 116	99.93 6	95.41 429	99.68 57	99.64 37	91.88 415	99.48 443	99.82 12	99.87 100	99.62 92

MVP-Stereo		98.08 260	97.92 272	98.57 253	98.96 315	96.79 299	97.90 236	99.18 298	96.41 372	98.46 321	98.95 247	95.93 297	99.60 393	96.51 333	98.98 415	99.31 278
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IMVS_0404		98.07 261	98.20 232	97.69 367	99.03 295	94.03 430	96.67 379	99.45 179	98.16 217	98.03 363	98.71 305	96.80 238	99.82 210	97.50 228	99.45 328	99.22 309

PMMVS2		98.07 261	98.08 252	98.04 334	99.41 181	94.59 411	94.59 491	99.40 209	97.50 280	98.82 259	98.83 279	96.83 234	99.84 180	97.50 228	99.81 140	99.71 65

SymmetryMVS		98.05 263	97.71 293	99.09 134	99.29 215	97.83 197	98.28 168	97.64 447	99.24 77	98.80 263	98.85 272	89.76 439	99.94 41	98.04 167	99.50 319	99.49 177

ETV-MVS		98.03 264	97.86 278	98.56 258	98.69 374	98.07 165	97.51 300	99.50 149	98.10 224	97.50 406	95.51 499	98.41 85	99.88 115	96.27 351	99.24 374	97.71 486

Effi-MVS+		98.02 265	97.82 281	98.62 242	98.53 403	97.19 268	97.33 324	99.68 64	97.30 306	96.68 457	97.46 451	98.56 73	99.80 236	96.63 317	98.20 464	98.86 385

MSLP-MVS++		98.02 265	98.14 246	97.64 377	98.58 396	95.19 385	97.48 304	99.23 285	97.47 283	97.90 373	98.62 334	97.04 218	98.81 507	97.55 222	99.41 341	98.94 372

guyue		98.01 267	97.93 271	98.26 303	99.45 169	95.48 365	98.08 197	96.24 489	98.89 138	99.34 135	99.14 181	91.32 424	99.82 210	99.07 80	99.83 126	99.48 188

EIA-MVS		98.00 268	97.74 287	98.80 197	98.72 361	98.09 158	98.05 204	99.60 94	97.39 296	96.63 459	95.55 498	97.68 162	99.80 236	96.73 304	99.27 368	98.52 430

MCST-MVS		98.00 268	97.63 302	99.10 130	99.24 233	98.17 148	96.89 363	98.73 389	95.66 412	97.92 371	97.70 433	97.17 211	99.66 361	96.18 357	99.23 377	99.47 197

K. test v3		98.00 268	97.66 298	99.03 148	99.79 23	97.56 228	99.19 53	92.47 533	99.62 32	99.52 87	99.66 32	89.61 441	99.96 13	99.25 67	99.81 140	99.56 130

HQP_MVS		97.99 271	97.67 295	98.93 170	99.19 249	97.65 221	97.77 255	99.27 269	98.20 210	97.79 384	97.98 411	94.90 331	99.70 321	94.42 420	99.51 311	99.45 206

VortexMVS		97.98 272	98.31 215	97.02 422	98.88 333	91.45 491	98.03 208	99.47 170	98.65 160	99.55 77	99.47 78	91.49 421	99.81 227	99.32 60	99.91 80	99.80 45

LoFTR		97.97 273	97.79 283	98.53 267	98.80 351	97.47 236	97.01 351	99.55 126	95.55 418	99.46 101	99.22 153	94.22 361	99.44 455	96.45 337	99.82 133	98.68 417

ArgMatch-SfM		97.96 274	97.72 291	98.66 232	99.02 303	97.33 247	96.49 395	99.52 142	95.46 424	98.71 279	98.29 382	96.14 279	99.69 331	96.30 348	99.56 293	98.97 364

PRO-TEST		97.94 275	98.16 242	97.26 408	98.17 441	93.56 455	98.36 160	99.22 286	98.46 182	97.93 370	99.41 94	94.82 335	99.87 135	97.64 212	99.45 328	98.35 450

MDA-MVSNet-bldmvs		97.94 275	97.91 274	98.06 331	99.44 171	94.96 393	96.63 384	99.15 310	98.35 188	98.83 256	99.11 189	94.31 358	99.85 159	96.60 320	98.72 433	99.37 244

ttmdpeth		97.91 277	98.02 258	97.58 383	98.69 374	94.10 426	98.13 187	98.90 354	97.95 234	97.32 421	99.58 47	95.95 296	98.75 509	96.41 340	99.22 378	99.87 22

Anonymous202405211		97.90 278	97.50 310	99.08 136	98.90 327	98.25 139	98.53 129	96.16 490	98.87 140	99.11 186	98.86 269	90.40 434	99.78 261	97.36 239	99.31 360	99.19 320

LF4IMVS		97.90 278	97.69 294	98.52 269	99.17 259	97.66 219	97.19 344	99.47 170	96.31 376	97.85 380	98.20 391	96.71 247	99.52 428	94.62 411	99.72 208	98.38 445

PMatch-SfM		97.89 280	97.64 300	98.66 232	99.26 230	97.44 241	96.08 427	99.51 144	96.72 355	98.47 320	99.13 183	93.62 378	99.70 321	97.14 260	98.80 427	98.83 387

UnsupCasMVSNet_eth		97.89 280	97.60 304	98.75 213	99.31 209	97.17 273	97.62 281	99.35 227	98.72 157	98.76 270	98.68 316	92.57 401	99.74 292	97.76 201	95.60 532	99.34 262

TinyColmap		97.89 280	97.98 262	97.60 381	98.86 336	94.35 416	96.21 416	99.44 187	97.45 290	99.06 193	98.88 266	97.99 137	99.28 481	94.38 424	99.58 285	99.18 324

RRT-MVS		97.88 283	97.98 262	97.61 380	98.15 444	93.77 447	98.97 77	99.64 79	99.16 94	98.69 280	99.42 89	91.60 416	99.89 97	97.63 214	98.52 452	99.16 334

OMC-MVS		97.88 283	97.49 311	99.04 147	98.89 332	98.63 101	96.94 357	99.25 277	95.02 439	98.53 313	98.51 349	97.27 204	99.47 446	93.50 451	99.51 311	99.01 355

CANet		97.87 285	97.76 285	98.19 314	97.75 471	95.51 360	96.76 370	99.05 326	97.74 253	96.93 438	98.21 390	95.59 310	99.89 97	97.86 188	99.93 57	99.19 320

xiu_mvs_v1_base_debu		97.86 286	98.17 239	96.92 429	98.98 311	93.91 440	96.45 397	99.17 302	97.85 244	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 505

xiu_mvs_v1_base		97.86 286	98.17 239	96.92 429	98.98 311	93.91 440	96.45 397	99.17 302	97.85 244	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 505

xiu_mvs_v1_base_debi		97.86 286	98.17 239	96.92 429	98.98 311	93.91 440	96.45 397	99.17 302	97.85 244	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 505

NCCC		97.86 286	97.47 315	99.05 145	98.61 389	98.07 165	96.98 354	98.90 354	97.63 262	97.04 434	97.93 417	95.99 292	99.66 361	95.31 394	98.82 426	99.43 214

PMVS	91.26 20	97.86 286	97.94 269	97.65 374	99.71 49	97.94 184	98.52 130	98.68 392	98.99 124	97.52 404	99.35 112	97.41 194	98.18 519	91.59 495	99.67 246	96.82 509
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IterMVS-SCA-FT		97.85 291	98.18 238	96.87 432	99.27 221	91.16 501	95.53 455	99.25 277	99.10 107	99.41 114	99.35 112	93.10 389	99.96 13	98.65 114	99.94 51	99.49 177

D2MVS		97.84 292	97.84 280	97.83 351	99.14 267	94.74 404	96.94 357	98.88 358	95.84 402	98.89 240	98.96 243	94.40 353	99.69 331	97.55 222	99.95 39	99.05 346

CPTT-MVS		97.84 292	97.36 320	99.27 99	99.31 209	98.46 117	98.29 167	99.27 269	94.90 443	97.83 381	98.37 368	94.90 331	99.84 180	93.85 439	99.54 301	99.51 165

ArgMatch-Sym		97.83 294	97.54 306	98.71 223	98.98 311	97.65 221	96.25 415	99.43 193	95.60 415	98.85 251	97.98 411	95.72 304	99.56 410	95.54 390	99.50 319	98.92 374

mvs_anonymous		97.83 294	98.16 242	96.87 432	98.18 439	91.89 484	97.31 327	98.90 354	97.37 298	98.83 256	99.46 80	96.28 274	99.79 249	98.90 94	98.16 468	98.95 368

ELoFTR		97.81 296	97.74 287	98.04 334	99.39 185	95.79 351	97.28 333	99.58 103	94.13 467	99.38 121	99.37 105	93.31 381	99.60 393	97.23 250	99.96 28	98.74 407

PMatch-Up-SfM		97.79 297	97.48 314	98.72 221	99.03 295	97.78 207	96.05 429	99.48 159	96.90 342	98.72 275	99.18 164	92.00 413	99.71 312	97.15 259	98.77 428	98.69 413

h-mvs33		97.77 298	97.33 323	99.10 130	99.21 241	97.84 196	98.35 162	98.57 403	99.11 100	98.58 304	99.02 214	88.65 450	99.96 13	98.11 158	96.34 518	99.49 177

test_vis1_rt		97.75 299	97.72 291	97.83 351	98.81 348	96.35 324	97.30 328	99.69 57	94.61 451	97.87 376	98.05 405	96.26 275	98.32 516	98.74 107	98.18 465	98.82 389

IterMVS		97.73 300	98.11 248	96.57 445	99.24 233	90.28 512	95.52 457	99.21 288	98.86 142	99.33 138	99.33 119	93.11 388	99.94 41	98.49 128	99.94 51	99.48 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_fmvs1		97.72 301	97.94 269	97.07 420	98.66 384	92.39 477	97.68 270	99.81 32	95.20 436	99.54 79	99.44 85	91.56 419	99.41 460	99.78 21	99.77 172	99.40 231

MSDG		97.71 302	97.52 309	98.28 302	98.91 326	96.82 297	94.42 496	99.37 217	97.65 261	98.37 333	98.29 382	97.40 195	99.33 472	94.09 431	99.22 378	98.68 417

dtuonlycased		97.70 303	98.19 236	96.24 458	99.75 34	89.51 520	94.69 486	99.64 79	98.23 202	99.46 101	98.57 341	98.25 107	99.85 159	95.65 383	99.44 336	99.36 252

CDS-MVSNet		97.69 304	97.35 321	98.69 227	98.73 359	97.02 283	96.92 361	98.75 386	95.89 399	98.59 302	98.67 318	92.08 412	99.74 292	96.72 305	99.81 140	99.32 273
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 305	97.75 286	97.45 399	98.23 436	93.78 446	97.29 329	98.84 369	96.10 388	98.64 290	98.65 325	96.04 285	99.36 466	96.84 293	99.14 392	99.20 314

Fast-Effi-MVS+		97.67 306	97.38 318	98.57 253	98.71 365	97.43 242	97.23 335	99.45 179	94.82 446	96.13 477	96.51 476	98.52 75	99.91 74	96.19 355	98.83 424	98.37 447

EU-MVSNet		97.66 307	98.50 175	95.13 499	99.63 83	85.84 535	98.35 162	98.21 425	98.23 202	99.54 79	99.46 80	95.02 329	99.68 343	98.24 147	99.87 100	99.87 22

pmmvs5		97.64 308	97.49 311	98.08 328	99.14 267	95.12 388	96.70 375	99.05 326	93.77 476	98.62 295	98.83 279	93.23 384	99.75 285	98.33 144	99.76 188	99.36 252

N_pmnet		97.63 309	97.17 332	98.99 156	99.27 221	97.86 194	95.98 432	93.41 530	95.25 433	99.47 100	98.90 258	95.63 307	99.85 159	96.91 282	99.73 199	99.27 291

mvsany_test1		97.60 310	97.54 306	97.77 356	97.72 472	95.35 374	95.36 463	97.13 465	94.13 467	99.71 49	99.33 119	97.93 141	99.30 477	97.60 218	98.94 419	98.67 419

YYNet1		97.60 310	97.67 295	97.39 403	99.04 292	93.04 463	95.27 466	98.38 418	97.25 312	98.92 235	98.95 247	95.48 315	99.73 299	96.99 274	98.74 431	99.41 222

MDA-MVSNet_test_wron		97.60 310	97.66 298	97.41 402	99.04 292	93.09 459	95.27 466	98.42 415	97.26 311	98.88 244	98.95 247	95.43 317	99.73 299	97.02 270	98.72 433	99.41 222

pmmvs4		97.58 313	97.28 324	98.51 270	98.84 340	96.93 290	95.40 462	98.52 409	93.60 478	98.61 297	98.65 325	95.10 327	99.60 393	96.97 278	99.79 159	98.99 359

mvsmamba		97.57 314	97.26 326	98.51 270	98.69 374	96.73 304	98.74 99	97.25 459	97.03 332	97.88 375	99.23 151	90.95 427	99.87 135	96.61 319	99.00 410	98.91 378

PVSNet_BlendedMVS		97.55 315	97.53 308	97.60 381	98.92 323	93.77 447	96.64 383	99.43 193	94.49 453	97.62 394	99.18 164	96.82 235	99.67 348	94.73 408	99.93 57	99.36 252

GDP-MVS		97.50 316	97.11 339	98.67 230	99.02 303	96.85 296	98.16 184	99.71 48	98.32 193	98.52 315	98.54 344	83.39 494	99.95 25	98.79 101	99.56 293	99.19 320

ppachtmachnet_test		97.50 316	97.74 287	96.78 439	98.70 369	91.23 500	94.55 492	99.05 326	96.36 373	99.21 174	98.79 288	96.39 265	99.78 261	96.74 302	99.82 133	99.34 262

FMVSNet3		97.50 316	97.24 328	98.29 301	98.08 451	95.83 348	97.86 242	98.91 353	97.89 241	98.95 224	98.95 247	87.06 459	99.81 227	97.77 197	99.69 234	99.23 304

CHOSEN 1792x2688		97.49 319	97.14 336	98.54 265	99.68 64	96.09 334	96.50 394	99.62 89	91.58 506	98.84 254	98.97 239	92.36 403	99.88 115	96.76 299	99.95 39	99.67 78

CLD-MVS		97.49 319	97.16 333	98.48 276	99.07 282	97.03 282	94.71 482	99.21 288	94.46 455	98.06 359	97.16 463	97.57 176	99.48 443	94.46 416	99.78 164	98.95 368
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		97.46 321	97.07 340	98.64 236	98.73 359	97.33 247	97.45 310	97.64 447	99.11 100	98.58 304	97.98 411	88.65 450	99.79 249	98.11 158	97.39 498	98.81 394

Vis-MVSNet (Re-imp)		97.46 321	97.16 333	98.34 295	99.55 117	96.10 331	98.94 81	98.44 412	98.32 193	98.16 348	98.62 334	88.76 446	99.73 299	93.88 437	99.79 159	99.18 324

jason		97.45 323	97.35 321	97.76 359	99.24 233	93.93 439	95.86 442	98.42 415	94.24 463	98.50 316	98.13 396	94.82 335	99.91 74	97.22 251	99.73 199	99.43 214
jason: jason.
CL-MVSNet_self_test		97.44 324	97.22 330	98.08 328	98.57 398	95.78 352	94.30 500	98.79 377	96.58 363	98.60 300	98.19 392	94.74 342	99.64 373	96.41 340	98.84 423	98.82 389

MGCNet		97.44 324	97.01 344	98.72 221	96.42 529	96.74 303	97.20 340	91.97 540	98.46 182	98.30 336	98.79 288	92.74 398	99.91 74	99.30 62	99.94 51	99.52 161

DSMNet-mixed		97.42 326	97.60 304	96.87 432	99.15 265	91.46 490	98.54 128	99.12 313	92.87 493	97.58 398	99.63 39	96.21 277	99.90 81	95.74 378	99.54 301	99.27 291

USDC		97.41 327	97.40 316	97.44 400	98.94 317	93.67 450	95.17 470	99.53 136	94.03 472	98.97 218	99.10 192	95.29 320	99.34 470	95.84 375	99.73 199	99.30 282

BP-MVS1		97.40 328	96.97 346	98.71 223	99.07 282	96.81 298	98.34 164	97.18 462	98.58 172	98.17 345	98.61 336	84.01 490	99.94 41	98.97 89	99.78 164	99.37 244

our_test_3		97.39 329	97.73 290	96.34 453	98.70 369	89.78 518	94.61 490	98.97 343	96.50 366	99.04 203	98.85 272	95.98 293	99.84 180	97.26 248	99.67 246	99.41 222

usedtu_dtu_shiyan1		97.37 330	97.13 337	98.11 321	99.03 295	95.40 371	94.47 494	98.99 340	96.87 345	97.97 367	97.81 425	92.12 409	99.75 285	97.49 233	99.43 338	99.16 334

FE-MVSNET3		97.37 330	97.13 337	98.11 321	99.03 295	95.40 371	94.47 494	98.99 340	96.87 345	97.97 367	97.81 425	92.12 409	99.75 285	97.49 233	99.43 338	99.16 334

c3_l		97.36 332	97.37 319	97.31 404	98.09 450	93.25 458	95.01 475	99.16 305	97.05 329	98.77 268	98.72 303	92.88 394	99.64 373	96.93 281	99.76 188	99.05 346

alignmvs		97.35 333	96.88 354	98.78 204	98.54 401	98.09 158	97.71 266	97.69 442	99.20 84	97.59 397	95.90 491	88.12 456	99.55 415	98.18 153	98.96 417	98.70 412

Patchmtry		97.35 333	96.97 346	98.50 274	97.31 500	96.47 319	98.18 180	98.92 351	98.95 131	98.78 265	99.37 105	85.44 477	99.85 159	95.96 367	99.83 126	99.17 328

DP-MVS Recon		97.33 335	96.92 350	98.57 253	99.09 278	97.99 174	96.79 366	99.35 227	93.18 484	97.71 388	98.07 404	95.00 330	99.31 475	93.97 433	99.13 394	98.42 442

SP-SuperGlue		97.31 336	97.23 329	97.57 388	96.96 511	97.24 260	96.26 414	98.76 382	97.68 258	96.88 447	97.85 422	94.32 357	98.01 521	97.76 201	98.57 449	97.45 495

QAPM		97.31 336	96.81 361	98.82 192	98.80 351	97.49 232	99.06 66	99.19 294	90.22 519	97.69 390	99.16 171	96.91 229	99.90 81	90.89 510	99.41 341	99.07 344

UnsupCasMVSNet_bld		97.30 338	96.92 350	98.45 279	99.28 218	96.78 302	96.20 417	99.27 269	95.42 426	98.28 340	98.30 379	93.16 386	99.71 312	94.99 401	97.37 499	98.87 384

F-COLMAP		97.30 338	96.68 369	99.14 124	99.19 249	98.39 123	97.27 334	99.30 254	92.93 490	96.62 460	98.00 409	95.73 303	99.68 343	92.62 476	98.46 453	99.35 258

1112_ss		97.29 340	96.86 355	98.58 250	99.34 204	96.32 325	96.75 371	99.58 103	93.14 485	96.89 445	97.48 448	92.11 411	99.86 145	96.91 282	99.54 301	99.57 124

CANet_DTU		97.26 341	97.06 341	97.84 350	97.57 483	94.65 409	96.19 418	98.79 377	97.23 318	95.14 502	98.24 387	93.22 385	99.84 180	97.34 240	99.84 114	99.04 350

Patchmatch-RL test		97.26 341	97.02 343	97.99 339	99.52 131	95.53 359	96.13 423	99.71 48	97.47 283	99.27 153	99.16 171	84.30 488	99.62 381	97.89 181	99.77 172	98.81 394

CDPH-MVS		97.26 341	96.66 373	99.07 138	99.00 307	98.15 149	96.03 430	99.01 337	91.21 512	97.79 384	97.85 422	96.89 230	99.69 331	92.75 473	99.38 347	99.39 232

PatchMatch-RL		97.24 344	96.78 362	98.61 246	99.03 295	97.83 197	96.36 405	99.06 322	93.49 481	97.36 420	97.78 427	95.75 302	99.49 439	93.44 453	98.77 428	98.52 430

eth_miper_zixun_eth		97.23 345	97.25 327	97.17 414	98.00 455	92.77 470	94.71 482	99.18 298	97.27 310	98.56 308	98.74 299	91.89 414	99.69 331	97.06 269	99.81 140	99.05 346

SP-LightGlue		97.22 346	97.01 344	97.88 347	97.33 499	97.19 268	96.38 403	99.08 320	97.28 308	96.53 465	97.50 446	92.36 403	98.70 511	97.84 189	98.76 430	97.74 483

sss		97.21 347	96.93 348	98.06 331	98.83 342	95.22 384	96.75 371	98.48 411	94.49 453	97.27 422	97.90 418	92.77 397	99.80 236	96.57 323	99.32 358	99.16 334

LFMVS		97.20 348	96.72 366	98.64 236	98.72 361	96.95 288	98.93 82	94.14 525	99.74 12	98.78 265	99.01 226	84.45 485	99.73 299	97.44 235	99.27 368	99.25 298

HyFIR lowres test		97.19 349	96.60 380	98.96 164	99.62 87	97.28 257	95.17 470	99.50 149	94.21 464	99.01 208	98.32 377	86.61 462	99.99 2	97.10 265	99.84 114	99.60 102

miper_lstm_enhance		97.18 350	97.16 333	97.25 410	98.16 443	92.85 468	95.15 472	99.31 246	97.25 312	98.74 274	98.78 290	90.07 435	99.78 261	97.19 253	99.80 152	99.11 341

CNLPA		97.17 351	96.71 367	98.55 260	98.56 399	98.05 169	96.33 407	98.93 347	96.91 341	97.06 432	97.39 454	94.38 354	99.45 453	91.66 492	99.18 388	98.14 459

xiu_mvs_v2_base		97.16 352	97.49 311	96.17 464	98.54 401	92.46 475	95.45 459	98.84 369	97.25 312	97.48 408	96.49 477	98.31 97	99.90 81	96.34 345	98.68 440	96.15 521

AdaColmap		97.14 353	96.71 367	98.46 278	98.34 422	97.80 206	96.95 356	98.93 347	95.58 417	96.92 439	97.66 434	95.87 299	99.53 424	90.97 506	99.14 392	98.04 464

ALIKED-LG		97.10 354	96.63 375	98.50 274	97.96 456	98.68 100	97.75 261	99.68 64	95.86 401	98.36 335	98.33 376	91.58 418	99.04 494	90.87 511	99.31 360	97.77 481

train_agg		97.10 354	96.45 389	99.07 138	98.71 365	98.08 162	95.96 435	99.03 331	91.64 504	95.85 485	97.53 442	96.47 260	99.76 273	93.67 443	99.16 389	99.36 252

OpenMVS	96.65 7	97.09 356	96.68 369	98.32 296	98.32 423	97.16 274	98.86 92	99.37 217	89.48 524	96.29 475	99.15 177	96.56 256	99.90 81	92.90 466	99.20 383	97.89 472

PS-MVSNAJ		97.08 357	97.39 317	96.16 466	98.56 399	92.46 475	95.24 468	98.85 368	97.25 312	97.49 407	95.99 488	98.07 128	99.90 81	96.37 342	98.67 441	96.12 522

MatchFormer		97.07 358	96.92 350	97.49 395	98.44 412	95.92 342	96.79 366	99.14 311	93.08 487	99.32 144	99.10 192	93.89 369	99.03 495	92.78 472	99.78 164	97.52 492

miper_ehance_all_eth		97.06 359	97.03 342	97.16 416	97.83 466	93.06 460	94.66 487	99.09 318	95.99 395	98.69 280	98.45 359	92.73 399	99.61 389	96.79 295	99.03 404	98.82 389

lupinMVS		97.06 359	96.86 355	97.65 374	98.88 333	93.89 443	95.48 458	97.97 435	93.53 479	98.16 348	97.58 439	93.81 372	99.91 74	96.77 298	99.57 289	99.17 328

API-MVS		97.04 361	96.91 353	97.42 401	97.88 462	98.23 144	98.18 180	98.50 410	97.57 270	97.39 418	96.75 472	96.77 240	99.15 491	90.16 515	99.02 407	94.88 528

cl____		97.02 362	96.83 358	97.58 383	97.82 467	94.04 429	94.66 487	99.16 305	97.04 330	98.63 291	98.71 305	88.68 449	99.69 331	97.00 272	99.81 140	99.00 358

DIV-MVS_self_test		97.02 362	96.84 357	97.58 383	97.82 467	94.03 430	94.66 487	99.16 305	97.04 330	98.63 291	98.71 305	88.69 447	99.69 331	97.00 272	99.81 140	99.01 355

RPMNet		97.02 362	96.93 348	97.30 405	97.71 475	94.22 418	98.11 192	99.30 254	99.37 60	96.91 441	99.34 116	86.72 461	99.87 135	97.53 225	97.36 501	97.81 477

HQP-MVS		97.00 365	96.49 385	98.55 260	98.67 379	96.79 299	96.29 410	99.04 329	96.05 389	95.55 492	96.84 469	93.84 370	99.54 421	92.82 469	99.26 372	99.32 273

FA-MVS(test-final)		96.99 366	96.82 359	97.50 394	98.70 369	94.78 402	99.34 23	96.99 468	95.07 438	98.48 319	99.33 119	88.41 453	99.65 368	96.13 361	98.92 421	98.07 463

new_pmnet		96.99 366	96.76 363	97.67 370	98.72 361	94.89 397	95.95 437	98.20 426	92.62 496	98.55 310	98.54 344	94.88 334	99.52 428	93.96 434	99.44 336	98.59 427

Test_1112_low_res		96.99 366	96.55 382	98.31 298	99.35 199	95.47 368	95.84 445	99.53 136	91.51 508	96.80 451	98.48 356	91.36 423	99.83 198	96.58 321	99.53 305	99.62 92

PVSNet_Blended		96.88 369	96.68 369	97.47 398	98.92 323	93.77 447	94.71 482	99.43 193	90.98 515	97.62 394	97.36 457	96.82 235	99.67 348	94.73 408	99.56 293	98.98 360

SP-DiffGlue		96.87 370	96.76 363	97.21 411	95.17 540	96.88 295	96.12 424	98.93 347	96.51 364	98.37 333	97.55 441	93.65 377	97.83 524	96.11 362	98.45 454	96.92 505

MVSTER		96.86 371	96.55 382	97.79 354	97.91 461	94.21 420	97.56 292	98.87 360	97.49 282	99.06 193	99.05 208	80.72 503	99.80 236	98.44 131	99.82 133	99.37 244

BH-untuned		96.83 372	96.75 365	97.08 418	98.74 358	93.33 457	96.71 374	98.26 422	96.72 355	98.44 324	97.37 456	95.20 322	99.47 446	91.89 488	97.43 496	98.44 438

BH-RMVSNet		96.83 372	96.58 381	97.58 383	98.47 407	94.05 427	96.67 379	97.36 452	96.70 358	97.87 376	97.98 411	95.14 326	99.44 455	90.47 514	98.58 448	99.25 298

PAPM_NR		96.82 374	96.32 393	98.30 300	99.07 282	96.69 306	97.48 304	98.76 382	95.81 407	96.61 461	96.47 479	94.12 366	99.17 489	90.82 512	97.78 484	99.06 345

MG-MVS		96.77 375	96.61 378	97.26 408	98.31 424	93.06 460	95.93 438	98.12 431	96.45 371	97.92 371	98.73 301	93.77 374	99.39 463	91.19 503	99.04 403	99.33 268

test_yl		96.69 376	96.29 395	97.90 344	98.28 428	95.24 380	97.29 329	97.36 452	98.21 206	98.17 345	97.86 420	86.27 464	99.55 415	94.87 405	98.32 457	98.89 380

DCV-MVSNet		96.69 376	96.29 395	97.90 344	98.28 428	95.24 380	97.29 329	97.36 452	98.21 206	98.17 345	97.86 420	86.27 464	99.55 415	94.87 405	98.32 457	98.89 380

WTY-MVS		96.67 378	96.27 397	97.87 349	98.81 348	94.61 410	96.77 369	97.92 437	94.94 442	97.12 427	97.74 430	91.11 426	99.82 210	93.89 436	98.15 469	99.18 324

PatchT		96.65 379	96.35 391	97.54 390	97.40 496	95.32 377	97.98 224	96.64 482	99.33 66	96.89 445	99.42 89	84.32 487	99.81 227	97.69 210	97.49 492	97.48 493

TAPA-MVS	96.21 11	96.63 380	95.95 403	98.65 234	98.93 319	98.09 158	96.93 359	99.28 266	83.58 542	98.13 352	97.78 427	96.13 281	99.40 461	93.52 449	99.29 366	98.45 435
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 381	96.25 398	97.71 366	99.04 292	94.66 408	99.16 55	96.92 475	97.23 318	97.87 376	99.10 192	86.11 468	99.65 368	91.65 493	99.21 381	98.82 389

SIFT-ConvMatch		96.57 382	96.62 376	96.43 449	98.20 437	98.27 137	93.88 514	96.88 476	95.29 431	98.88 244	98.25 385	95.18 324	97.43 531	93.22 459	99.83 126	93.59 532

SIFT-NCM-Cal		96.56 383	96.68 369	96.20 462	98.27 430	98.44 119	94.40 497	96.67 480	95.29 431	97.63 393	98.17 393	96.40 264	96.59 542	93.61 444	99.66 254	93.57 533

Patchmatch-test		96.55 384	96.34 392	97.17 414	98.35 421	93.06 460	98.40 156	97.79 438	97.33 301	98.41 327	98.67 318	83.68 493	99.69 331	95.16 399	99.31 360	98.77 402

PMMVS		96.51 385	95.98 401	98.09 325	97.53 488	95.84 347	94.92 477	98.84 369	91.58 506	96.05 482	95.58 497	95.68 306	99.66 361	95.59 387	98.09 472	98.76 404

PLC	94.65 16	96.51 385	95.73 409	98.85 182	98.75 357	97.91 188	96.42 401	99.06 322	90.94 516	95.59 489	97.38 455	94.41 351	99.59 398	90.93 508	98.04 478	99.05 346
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 387	95.77 407	98.69 227	99.48 158	97.43 242	97.84 245	99.55 126	81.42 545	96.51 469	98.58 340	95.53 311	99.67 348	93.41 454	99.58 285	98.98 360

dtuonly		96.49 388	97.28 324	94.10 511	98.80 351	83.27 547	93.66 519	99.48 159	95.10 437	97.87 376	98.30 379	95.61 308	99.68 343	96.98 277	99.75 192	99.33 268

SIFT-UM-Cal		96.49 388	96.62 376	96.12 469	98.13 448	97.89 191	93.35 525	98.44 412	95.48 423	98.63 291	98.34 372	95.45 316	97.45 530	92.22 484	99.50 319	93.02 540

test1111		96.49 388	96.82 359	95.52 490	99.42 178	87.08 532	99.22 46	87.14 550	99.11 100	99.46 101	99.58 47	88.69 447	99.86 145	98.80 100	99.95 39	99.62 92

MAR-MVS		96.47 391	95.70 410	98.79 201	97.92 460	99.12 62	98.28 168	98.60 399	92.16 501	95.54 495	96.17 485	94.77 341	99.52 428	89.62 518	98.23 462	97.72 485
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
SP-MNN		96.46 392	96.24 399	97.10 417	96.71 519	95.98 339	96.00 431	97.33 456	95.82 406	94.93 506	97.10 468	93.70 376	98.01 521	96.30 348	98.30 460	97.30 499

SIFT-PointCN		96.45 393	96.47 386	96.39 451	98.13 448	97.54 230	93.31 526	97.23 461	94.67 450	98.68 283	98.32 377	94.64 344	97.81 525	93.50 451	99.77 172	93.83 530

ECVR-MVS		96.42 394	96.61 378	95.85 479	99.38 187	88.18 527	99.22 46	86.00 552	99.08 114	99.36 128	99.57 49	88.47 452	99.82 210	98.52 127	99.95 39	99.54 143

SCA		96.41 395	96.66 373	95.67 485	98.24 433	88.35 525	95.85 444	96.88 476	96.11 387	97.67 391	98.67 318	93.10 389	99.85 159	94.16 426	99.22 378	98.81 394

SIFT-PCN-Cal		96.34 396	96.46 388	96.01 473	98.17 441	96.89 293	93.48 523	97.35 455	94.84 445	99.35 130	98.30 379	94.70 343	97.92 523	92.03 485	99.88 95	93.21 539

SIFT-UMatch		96.33 397	96.47 386	95.89 477	98.29 426	97.95 182	93.84 515	97.24 460	95.78 409	98.72 275	98.04 406	93.45 380	96.81 538	93.14 461	99.73 199	92.91 542

DPM-MVS		96.32 398	95.59 417	98.51 270	98.76 355	97.21 266	94.54 493	98.26 422	91.94 503	96.37 473	97.25 461	93.06 391	99.43 457	91.42 498	98.74 431	98.89 380

SIFT-NCMNet		96.30 399	96.40 390	96.03 472	97.80 469	97.68 218	92.34 534	96.94 473	95.55 418	98.84 254	98.63 331	94.17 362	97.63 528	93.57 448	99.71 217	92.77 544

CMPMVS	75.91 23	96.29 400	95.44 424	98.84 188	96.25 532	98.69 99	97.02 350	99.12 313	88.90 528	97.83 381	98.86 269	89.51 442	98.90 505	91.92 487	99.51 311	98.92 374
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SIFT-CM-Cal		96.28 401	96.31 394	96.16 466	98.39 419	98.11 154	93.46 524	96.47 486	94.81 447	98.49 317	98.43 361	94.48 348	97.34 533	92.60 478	99.70 228	93.02 540

SD_0403		96.28 401	95.83 405	97.64 377	98.72 361	94.30 417	98.87 89	98.77 380	97.80 248	96.53 465	98.02 408	97.34 199	99.47 446	76.93 547	99.48 324	99.16 334

CR-MVSNet		96.28 401	95.95 403	97.28 406	97.71 475	94.22 418	98.11 192	98.92 351	92.31 499	96.91 441	99.37 105	85.44 477	99.81 227	97.39 238	97.36 501	97.81 477

MonoMVSNet		96.25 404	96.53 384	95.39 494	96.57 522	91.01 503	98.82 97	97.68 444	98.57 174	98.03 363	99.37 105	90.92 428	97.78 526	94.99 401	93.88 540	97.38 497

CVMVSNet		96.25 404	97.21 331	93.38 523	99.10 275	80.56 555	97.20 340	98.19 428	96.94 336	99.00 209	99.02 214	89.50 443	99.80 236	96.36 344	99.59 280	99.78 50

AUN-MVS		96.24 406	95.45 423	98.60 248	98.70 369	97.22 264	97.38 317	97.65 445	95.95 397	95.53 496	97.96 416	82.11 502	99.79 249	96.31 346	97.44 495	98.80 399

usedtu_blend_shiyan5		96.20 407	95.62 413	97.94 342	96.53 523	94.93 394	98.83 96	99.59 100	98.89 138	96.71 454	91.16 542	86.05 469	99.73 299	96.70 308	96.09 523	99.17 328

EPNet		96.14 408	95.44 424	98.25 305	90.76 554	95.50 364	97.92 233	94.65 514	98.97 127	92.98 530	98.85 272	89.12 445	99.87 135	95.99 365	99.68 240	99.39 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SIFT-NN-PointCN		96.06 409	96.11 400	95.91 476	97.88 462	97.73 214	93.49 522	97.51 449	93.22 483	96.57 462	98.26 384	96.23 276	96.60 541	92.54 479	99.27 368	93.40 535

wuyk23d		96.06 409	97.62 303	91.38 527	98.65 388	98.57 108	98.85 93	96.95 472	96.86 348	99.90 14	99.16 171	99.18 19	98.40 515	89.23 521	99.77 172	77.18 550

Syy-MVS		96.04 411	95.56 419	97.49 395	97.10 505	94.48 412	96.18 420	96.58 483	95.65 413	94.77 509	92.29 539	91.27 425	99.36 466	98.17 155	98.05 476	98.63 421

MASt3R-SfM		96.02 412	95.82 406	96.60 444	97.03 510	94.90 396	94.26 503	98.53 407	88.40 533	98.41 327	98.67 318	92.39 402	97.62 529	95.31 394	99.41 341	97.29 500

miper_enhance_ethall		96.01 413	95.74 408	96.81 436	96.41 530	92.27 481	93.69 518	98.89 357	91.14 513	98.30 336	97.35 458	90.58 432	99.58 405	96.31 346	99.03 404	98.60 424

FMVSNet5		96.01 413	95.20 439	98.41 285	97.53 488	96.10 331	98.74 99	99.50 149	97.22 321	98.03 363	99.04 210	69.80 529	99.88 115	97.27 247	99.71 217	99.25 298

blended_shiyan6		95.99 415	95.33 430	97.95 341	97.06 507	94.89 397	95.34 464	98.58 401	96.17 381	97.06 432	92.41 536	87.64 457	99.76 273	97.64 212	96.09 523	99.19 320

blended_shiyan8		95.98 416	95.33 430	97.94 342	97.05 509	94.87 399	95.34 464	98.59 400	96.17 381	97.09 430	92.39 537	87.62 458	99.76 273	97.65 211	96.05 529	99.20 314

dmvs_re		95.98 416	95.39 427	97.74 362	98.86 336	97.45 239	98.37 159	95.69 505	97.95 234	96.56 463	95.95 489	90.70 431	97.68 527	88.32 523	96.13 522	98.11 460

ALIKED-MNN		95.97 418	95.30 433	98.00 337	97.66 482	98.12 153	96.98 354	99.41 204	91.11 514	94.04 522	97.30 459	91.56 419	98.61 513	89.99 516	99.63 263	97.28 501

baseline1		95.96 419	95.44 424	97.52 392	98.51 405	93.99 437	98.39 157	96.09 494	98.21 206	98.40 332	97.76 429	86.88 460	99.63 376	95.42 392	89.27 545	98.95 368

SIFT-MNN		95.92 420	95.97 402	95.74 484	98.18 439	98.00 172	94.17 505	96.99 468	95.74 411	97.16 426	97.90 418	90.71 430	95.79 544	93.71 442	99.21 381	93.44 534

HY-MVS	95.94 13	95.90 421	95.35 429	97.55 389	97.95 457	94.79 401	98.81 98	96.94 473	92.28 500	95.17 501	98.57 341	89.90 437	99.75 285	91.20 502	97.33 503	98.10 461

MVStest1		95.86 422	95.60 415	96.63 443	95.87 538	91.70 486	97.93 230	98.94 344	98.03 228	99.56 74	99.66 32	71.83 526	98.26 517	99.35 58	99.24 374	99.91 13

GA-MVS		95.86 422	95.32 432	97.49 395	98.60 391	94.15 423	93.83 516	97.93 436	95.49 422	96.68 457	97.42 453	83.21 495	99.30 477	96.22 353	98.55 450	99.01 355

OpenMVS_ROB	95.38 14	95.84 424	95.18 440	97.81 353	98.41 418	97.15 275	97.37 321	98.62 398	83.86 541	98.65 287	98.37 368	94.29 359	99.68 343	88.41 522	98.62 446	96.60 513

cl22		95.79 425	95.39 427	96.98 425	96.77 518	92.79 469	94.40 497	98.53 407	94.59 452	97.89 374	98.17 393	82.82 499	99.24 483	96.37 342	99.03 404	98.92 374

1314		95.74 426	95.60 415	96.17 464	97.53 488	92.75 471	98.07 201	98.31 420	91.22 511	94.25 516	96.68 473	95.53 311	99.03 495	91.64 494	97.18 505	96.74 511

WB-MVSnew		95.73 427	95.57 418	96.23 460	96.70 520	90.70 510	96.07 428	93.86 527	95.60 415	97.04 434	95.45 507	96.00 288	99.55 415	91.04 504	98.31 459	98.43 440

PVSNet	93.40 17	95.67 428	95.70 410	95.57 488	98.83 342	88.57 523	92.50 532	97.72 440	92.69 495	96.49 472	96.44 480	93.72 375	99.43 457	93.61 444	99.28 367	98.71 409

FE-MVS		95.66 429	94.95 445	97.77 356	98.53 403	95.28 379	99.40 19	96.09 494	93.11 486	97.96 369	99.26 138	79.10 512	99.77 267	92.40 482	98.71 435	98.27 453

tttt0517		95.64 430	94.98 443	97.64 377	99.36 194	93.81 445	98.72 104	90.47 544	98.08 227	98.67 284	98.34 372	73.88 524	99.92 65	97.77 197	99.51 311	99.20 314

SIFT-NN-CMatch		95.63 431	95.48 420	96.08 470	98.24 433	98.00 172	92.71 530	94.29 520	94.20 465	95.85 485	97.26 460	95.72 304	97.01 535	91.99 486	99.02 407	93.23 537

PatchmatchNet		95.58 432	95.67 412	95.30 498	97.34 498	87.32 531	97.65 276	96.65 481	95.30 430	97.07 431	98.69 314	84.77 482	99.75 285	94.97 403	98.64 442	98.83 387
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 433	95.12 441	96.86 435	97.54 486	93.94 438	96.49 395	96.53 485	94.36 462	97.03 436	96.61 475	94.26 360	99.16 490	86.91 529	96.31 519	97.47 494

JIA-IIPM		95.52 434	95.03 442	97.00 423	96.85 515	94.03 430	96.93 359	95.82 500	99.20 84	94.63 513	99.71 22	83.09 496	99.60 393	94.42 420	94.64 536	97.36 498

CHOSEN 280x420		95.51 435	95.47 421	95.65 487	98.25 431	88.27 526	93.25 527	98.88 358	93.53 479	94.65 512	97.15 464	86.17 466	99.93 53	97.41 237	99.93 57	98.73 408

wanda-best-256-512		95.48 436	94.74 450	97.68 368	96.53 523	94.12 424	94.17 505	98.57 403	95.84 402	96.71 454	91.16 542	86.05 469	99.76 273	97.57 220	96.09 523	99.17 328

FE-blended-shiyan7		95.48 436	94.74 450	97.68 368	96.53 523	94.12 424	94.17 505	98.57 403	95.84 402	96.71 454	91.16 542	86.05 469	99.76 273	97.57 220	96.09 523	99.17 328

gbinet_0.2-2-1-0.02		95.44 438	94.55 453	98.14 319	95.99 537	95.34 376	94.71 482	98.29 421	96.00 394	96.05 482	90.50 546	84.99 479	99.79 249	97.33 242	97.07 509	99.28 287

ADS-MVSNet2		95.43 439	94.98 443	96.76 440	98.14 445	91.74 485	97.92 233	97.76 439	90.23 517	96.51 469	98.91 255	85.61 474	99.85 159	92.88 467	96.90 510	98.69 413

SIFT-NN-NCMNet		95.39 440	95.22 437	95.92 475	98.29 426	98.34 132	93.58 521	94.60 516	94.07 471	94.84 508	97.53 442	94.37 355	96.62 540	91.01 505	98.64 442	92.80 543

SIFT-NN-UMatch		95.38 441	95.26 434	95.75 482	98.25 431	97.78 207	93.24 528	95.66 507	94.01 473	95.10 503	97.47 450	93.12 387	96.78 539	92.42 481	98.04 478	92.69 545

PAPR		95.29 442	94.47 454	97.75 360	97.50 494	95.14 387	94.89 479	98.71 391	91.39 510	95.35 499	95.48 502	94.57 346	99.14 492	84.95 534	97.37 499	98.97 364

thisisatest0530		95.27 443	94.45 455	97.74 362	99.19 249	94.37 415	97.86 242	90.20 545	97.17 323	98.22 343	97.65 435	73.53 525	99.90 81	96.90 287	99.35 351	98.95 368

ADS-MVSNet		95.24 444	94.93 446	96.18 463	98.14 445	90.10 515	97.92 233	97.32 457	90.23 517	96.51 469	98.91 255	85.61 474	99.74 292	92.88 467	96.90 510	98.69 413

PDCNetPlus		95.22 445	94.73 452	96.70 442	97.85 464	91.14 502	93.94 513	99.97 1	93.06 488	98.95 224	98.89 264	74.32 523	99.14 492	95.63 384	99.93 57	99.82 36

WBMVS		95.18 446	94.78 448	96.37 452	97.68 480	89.74 519	95.80 446	98.73 389	97.54 277	98.30 336	98.44 360	70.06 528	99.82 210	96.62 318	99.87 100	99.54 143

BH-w/o		95.13 447	94.89 447	95.86 478	98.20 437	91.31 495	95.65 451	97.37 451	93.64 477	96.52 468	95.70 496	93.04 392	99.02 497	88.10 524	95.82 530	97.24 502

tpmrst		95.07 448	95.46 422	93.91 514	97.11 504	84.36 543	97.62 281	96.96 471	94.98 440	96.35 474	98.80 286	85.46 476	99.59 398	95.60 386	96.23 520	97.79 480

pmmvs3		95.03 449	94.40 457	96.93 428	97.70 477	92.53 474	95.08 473	97.71 441	88.57 531	97.71 388	98.08 403	79.39 510	99.82 210	96.19 355	99.11 398	98.43 440

tpmvs		95.02 450	95.25 435	94.33 507	96.39 531	85.87 534	98.08 197	96.83 478	95.46 424	95.51 497	98.69 314	85.91 472	99.53 424	94.16 426	96.23 520	97.58 490

reproduce_monomvs		95.00 451	95.25 435	94.22 509	97.51 493	83.34 546	97.86 242	98.44 412	98.51 179	99.29 149	99.30 126	67.68 535	99.56 410	98.89 96	99.81 140	99.77 53

EPNet_dtu		94.93 452	94.78 448	95.38 495	93.58 545	87.68 529	96.78 368	95.69 505	97.35 300	89.14 545	98.09 402	88.15 455	99.49 439	94.95 404	99.30 364	98.98 360
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		94.79 453	94.33 460	96.15 468	96.02 536	92.36 479	92.34 534	99.26 275	85.34 540	95.08 504	94.96 513	92.96 393	98.53 514	94.41 423	98.59 447	97.56 491

SP-NN		94.67 454	94.44 456	95.36 496	95.12 541	95.23 383	94.27 502	96.10 493	94.46 455	90.91 540	95.76 495	91.47 422	93.87 549	95.23 397	96.62 515	97.00 504

tpm		94.67 454	94.34 459	95.66 486	97.68 480	88.42 524	97.88 238	94.90 512	94.46 455	96.03 484	98.56 343	78.66 514	99.79 249	95.88 369	95.01 535	98.78 401

test0.0.03 1		94.51 456	93.69 466	96.99 424	96.05 534	93.61 454	94.97 476	93.49 529	96.17 381	97.57 400	94.88 514	82.30 500	99.01 499	93.60 446	94.17 539	98.37 447

thres600view7		94.45 457	93.83 464	96.29 455	99.06 287	91.53 489	97.99 223	94.24 523	98.34 189	97.44 414	95.01 510	79.84 506	99.67 348	84.33 535	98.23 462	97.66 487

PCF-MVS	92.86 18	94.36 458	93.00 478	98.42 283	98.70 369	97.56 228	93.16 529	99.11 315	79.59 546	97.55 401	97.43 452	92.19 407	99.73 299	79.85 544	99.45 328	97.97 469
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		94.32 459	92.59 482	99.53 38	99.46 164	99.21 32	98.65 114	99.34 233	98.62 166	97.54 402	45.85 554	97.50 186	99.83 198	96.79 295	99.53 305	99.56 130

MVS-HIRNet		94.32 459	95.62 413	90.42 530	98.46 409	75.36 556	96.29 410	89.13 547	95.25 433	95.38 498	99.75 16	92.88 394	99.19 487	94.07 432	99.39 344	96.72 512

ET-MVSNet_ETH3D		94.30 461	93.21 473	97.58 383	98.14 445	94.47 413	94.78 481	93.24 532	94.72 448	89.56 543	95.87 492	78.57 516	99.81 227	96.91 282	97.11 508	98.46 432

ALIKED-NN		94.29 462	93.41 471	96.94 427	96.18 533	97.66 219	94.90 478	98.68 392	88.85 529	90.43 541	96.81 471	89.82 438	96.59 542	86.67 530	98.33 456	96.58 514

thres100view900		94.19 463	93.67 467	95.75 482	99.06 287	91.35 494	98.03 208	94.24 523	98.33 191	97.40 416	94.98 512	79.84 506	99.62 381	83.05 538	98.08 473	96.29 517

E-PMN		94.17 464	94.37 458	93.58 518	96.86 514	85.71 537	90.11 541	97.07 466	98.17 214	97.82 383	97.19 462	84.62 484	98.94 501	89.77 517	97.68 487	96.09 523

thres400		94.14 465	93.44 469	96.24 458	98.93 319	91.44 492	97.60 287	94.29 520	97.94 236	97.10 428	94.31 521	79.67 508	99.62 381	83.05 538	98.08 473	97.66 487

thisisatest0515		94.12 466	93.16 474	96.97 426	98.60 391	92.90 466	93.77 517	90.61 543	94.10 469	96.91 441	95.87 492	74.99 522	99.80 236	94.52 414	99.12 397	98.20 455

nomal-1		94.03 467	93.02 477	97.07 420	97.95 457	92.86 467	96.66 382	95.37 508	96.16 385	94.89 507	94.68 518	69.16 531	99.73 299	94.43 419	97.86 483	98.62 423

tfpn200view9		94.03 467	93.44 469	95.78 481	98.93 319	91.44 492	97.60 287	94.29 520	97.94 236	97.10 428	94.31 521	79.67 508	99.62 381	83.05 538	98.08 473	96.29 517

CostFormer		93.97 469	93.78 465	94.51 506	97.53 488	85.83 536	97.98 224	95.96 496	89.29 526	94.99 505	98.63 331	78.63 515	99.62 381	94.54 413	96.50 516	98.09 462

test-LLR		93.90 470	93.85 463	94.04 512	96.53 523	84.62 541	94.05 510	92.39 534	96.17 381	94.12 518	95.07 508	82.30 500	99.67 348	95.87 372	98.18 465	97.82 475

EMVS		93.83 471	94.02 461	93.23 524	96.83 516	84.96 538	89.77 542	96.32 488	97.92 238	97.43 415	96.36 483	86.17 466	98.93 502	87.68 525	97.73 486	95.81 524

testing3-2		93.78 472	93.91 462	93.39 522	98.82 345	81.72 553	97.76 258	95.28 509	98.60 168	96.54 464	96.66 474	65.85 542	99.62 381	96.65 316	98.99 412	98.82 389

baseline2		93.73 473	92.83 480	96.42 450	97.70 477	91.28 497	96.84 365	89.77 546	93.96 475	92.44 535	95.93 490	79.14 511	99.77 267	92.94 464	96.76 514	98.21 454

thres200		93.72 474	93.14 475	95.46 493	98.66 384	91.29 496	96.61 386	94.63 515	97.39 296	96.83 449	93.71 524	79.88 505	99.56 410	82.40 541	98.13 470	95.54 526

EPMVS		93.72 474	93.27 472	95.09 501	96.04 535	87.76 528	98.13 187	85.01 553	94.69 449	96.92 439	98.64 329	78.47 518	99.31 475	95.04 400	96.46 517	98.20 455

testing3		93.51 476	92.09 489	97.75 360	98.60 391	94.40 414	97.32 325	95.26 510	97.56 272	96.79 452	95.50 500	53.57 555	99.77 267	95.26 396	98.97 416	99.08 342

dp		93.47 477	93.59 468	93.13 525	96.64 521	81.62 554	97.66 274	96.42 487	92.80 494	96.11 478	98.64 329	78.55 517	99.59 398	93.31 455	92.18 544	98.16 458

FPMVS		93.44 478	92.23 487	97.08 418	99.25 232	97.86 194	95.61 452	97.16 464	92.90 492	93.76 527	98.65 325	75.94 521	95.66 545	79.30 545	97.49 492	97.73 484

XFeat-MNN		93.41 479	92.98 479	94.68 504	92.63 547	92.92 465	89.72 543	95.81 501	92.10 502	97.23 425	96.29 484	84.95 480	97.31 534	89.60 519	98.54 451	93.81 531

testing91		93.32 480	92.27 486	96.47 448	97.54 486	91.25 498	96.17 422	96.76 479	97.18 322	93.65 528	93.50 526	65.11 545	99.63 376	93.04 462	97.45 494	98.53 429

tpm cat1		93.29 481	93.13 476	93.75 516	97.39 497	84.74 539	97.39 315	97.65 445	83.39 543	94.16 517	98.41 363	82.86 498	99.39 463	91.56 496	95.35 534	97.14 503

UBG		93.25 482	92.32 484	96.04 471	97.72 472	90.16 513	95.92 440	95.91 499	96.03 392	93.95 525	93.04 531	69.60 530	99.52 428	90.72 513	97.98 480	98.45 435

MVS		93.19 483	92.09 489	96.50 447	96.91 513	94.03 430	98.07 201	98.06 434	68.01 549	94.56 514	96.48 478	95.96 295	99.30 477	83.84 536	96.89 512	96.17 519

FBQ-MVS		93.12 484	91.90 496	96.81 436	97.80 469	92.96 464	97.12 348	95.93 498	95.83 405	94.07 520	93.03 532	65.21 544	99.18 488	90.94 507	97.13 506	98.28 451

tpm2		93.09 485	92.58 483	94.62 505	97.56 484	86.53 533	97.66 274	95.79 502	86.15 538	94.07 520	98.23 389	75.95 520	99.53 424	90.91 509	96.86 513	97.81 477

testing11		93.08 486	92.02 491	96.26 457	97.56 484	90.83 507	96.32 408	95.70 503	96.47 369	92.66 533	93.73 523	64.36 546	99.59 398	93.77 441	97.57 488	98.37 447

testing99		93.04 487	91.98 494	96.23 460	97.53 488	90.70 510	96.35 406	95.94 497	96.87 345	93.41 529	93.43 528	63.84 547	99.59 398	93.24 458	97.19 504	98.40 443

SIFT-NN		92.96 488	92.79 481	93.46 519	96.92 512	96.45 320	91.89 536	94.39 518	92.91 491	92.54 534	95.46 503	88.26 454	90.71 552	85.22 533	97.52 490	93.22 538

dmvs_testset		92.94 489	92.21 488	95.13 499	98.59 394	90.99 504	97.65 276	92.09 536	96.95 335	94.00 523	93.55 525	92.34 405	96.97 537	72.20 548	92.52 542	97.43 496

myMVS_eth3d28		92.92 490	92.31 485	94.77 502	97.84 465	87.59 530	96.19 418	96.11 492	97.08 328	94.27 515	93.49 527	66.07 541	98.78 508	91.78 490	97.93 482	97.92 471

KD-MVS_2432*1600		92.87 491	91.99 492	95.51 491	91.37 550	89.27 521	94.07 508	98.14 429	95.42 426	97.25 423	96.44 480	67.86 533	99.24 483	91.28 500	96.08 527	98.02 465

miper_refine_blended		92.87 491	91.99 492	95.51 491	91.37 550	89.27 521	94.07 508	98.14 429	95.42 426	97.25 423	96.44 480	67.86 533	99.24 483	91.28 500	96.08 527	98.02 465

ETVMVS		92.60 493	91.08 502	97.18 412	97.70 477	93.65 452	96.54 390	95.70 503	96.51 364	94.68 511	92.39 537	61.80 551	99.50 435	86.97 527	97.41 497	98.40 443

MVE	83.40 22	92.50 494	91.92 495	94.25 508	98.83 342	91.64 487	92.71 530	83.52 554	95.92 398	86.46 548	95.46 503	95.20 322	95.40 546	80.51 543	98.64 442	95.73 525
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		92.39 495	91.89 497	93.89 515	99.38 187	82.28 551	99.32 26	66.03 559	99.08 114	98.77 268	99.57 49	66.26 539	99.84 180	98.71 110	99.95 39	99.54 143

UWE-MVS		92.38 496	91.76 499	94.21 510	97.16 503	84.65 540	95.42 461	88.45 548	95.96 396	96.17 476	95.84 494	66.36 538	99.71 312	91.87 489	98.64 442	98.28 451

gg-mvs-nofinetune		92.37 497	91.20 501	95.85 479	95.80 539	92.38 478	99.31 30	81.84 555	99.75 10	91.83 538	99.74 18	68.29 532	99.02 497	87.15 526	97.12 507	96.16 520

test-mter		92.33 498	91.76 499	94.04 512	96.53 523	84.62 541	94.05 510	92.39 534	94.00 474	94.12 518	95.07 508	65.63 543	99.67 348	95.87 372	98.18 465	97.82 475

IB-MVS	91.63 19	92.24 499	90.90 503	96.27 456	97.22 502	91.24 499	94.36 499	93.33 531	92.37 498	92.24 537	94.58 520	66.20 540	99.89 97	93.16 460	94.63 537	97.66 487
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
TESTMET0.1,1		92.19 500	91.77 498	93.46 519	96.48 528	82.80 550	94.05 510	91.52 542	94.45 458	94.00 523	94.88 514	66.65 537	99.56 410	95.78 377	98.11 471	98.02 465

blend_shiyan4		92.09 501	90.16 508	97.88 347	96.78 517	94.93 394	95.24 468	98.58 401	96.22 379	96.07 480	91.42 541	63.46 550	99.73 299	96.70 308	76.98 551	98.98 360

testing222		91.96 502	90.37 505	96.72 441	97.47 495	92.59 472	96.11 425	94.76 513	96.83 349	92.90 531	92.87 533	57.92 553	99.55 415	86.93 528	97.52 490	98.00 468

myMVS_eth3d		91.92 503	90.45 504	96.30 454	97.10 505	90.90 505	96.18 420	96.58 483	95.65 413	94.77 509	92.29 539	53.88 554	99.36 466	89.59 520	98.05 476	98.63 421

PAPM		91.88 504	90.34 506	96.51 446	98.06 453	92.56 473	92.44 533	97.17 463	86.35 537	90.38 542	96.01 487	86.61 462	99.21 486	70.65 550	95.43 533	97.75 482

PVSNet_0	89.98 21	91.15 505	90.30 507	93.70 517	97.72 472	84.34 544	90.24 539	97.42 450	90.20 520	93.79 526	93.09 530	90.90 429	98.89 506	86.57 531	72.76 553	97.87 474

UWE-MVS-28		90.22 506	89.28 509	93.02 526	94.50 544	82.87 549	96.52 393	87.51 549	95.21 435	92.36 536	96.04 486	71.57 527	98.25 518	72.04 549	97.77 485	97.94 470

XFeat-NN		89.63 507	89.13 510	91.14 528	90.93 553	90.02 517	84.90 546	94.05 526	88.10 534	92.89 532	93.33 529	78.74 513	90.89 551	83.46 537	95.72 531	92.52 546

0.4-1-1-0.1		88.42 508	85.91 511	95.94 474	93.08 546	91.54 488	90.99 538	92.04 538	89.96 523	84.83 550	83.25 548	63.75 548	99.52 428	93.25 457	82.07 546	96.75 510

0.4-1-1-0.2		87.49 509	84.89 512	95.31 497	91.33 552	90.08 516	88.47 545	92.07 537	88.70 530	84.06 551	81.08 550	63.62 549	99.49 439	92.93 465	81.71 547	96.37 516

0.3-1-1-0.015		87.27 510	84.50 514	95.57 488	91.70 549	90.77 508	89.41 544	92.04 538	88.98 527	82.46 552	81.35 549	60.36 552	99.50 435	92.96 463	81.23 548	96.45 515

GLUNet-SfM		86.26 511	84.68 513	91.01 529	80.58 556	83.56 545	78.04 547	93.59 528	76.70 547	95.29 500	94.72 517	77.51 519	94.26 548	66.39 551	99.33 355	95.20 527

EGC-MVSNET		85.24 512	80.54 515	99.34 83	99.77 27	99.20 38	99.08 62	99.29 262	12.08 555	20.84 558	99.42 89	97.55 178	99.85 159	97.08 266	99.72 208	98.96 367

test_method		79.78 513	79.50 516	80.62 531	80.21 557	45.76 562	70.82 548	98.41 417	31.08 553	80.89 553	97.71 431	84.85 481	97.37 532	91.51 497	80.03 549	98.75 405

tmp_tt		78.77 514	78.73 517	78.90 532	58.45 559	74.76 558	94.20 504	78.26 557	39.16 552	86.71 547	92.82 534	80.50 504	75.19 554	86.16 532	92.29 543	86.74 547

dongtai		76.24 515	75.95 518	77.12 533	92.39 548	67.91 559	90.16 540	59.44 561	82.04 544	89.42 544	94.67 519	49.68 556	81.74 553	48.06 554	77.66 550	81.72 548

kuosan		69.30 516	68.95 519	70.34 534	87.68 555	65.00 560	91.11 537	59.90 560	69.02 548	74.46 554	88.89 547	48.58 558	68.03 555	28.61 555	72.33 554	77.99 549

VLMVS_CLIP		57.57 517	58.80 521	53.85 535	47.22 560	42.89 563	60.06 550	76.87 558	39.44 551	65.76 555	80.47 551	36.24 559	64.75 556	58.06 553	65.11 555	53.91 552

MVS_clip		56.94 518	60.93 520	44.97 536	71.47 558	51.70 561	61.73 549	21.77 562	28.88 554	86.09 549	92.75 535	48.89 557	27.00 557	61.70 552	75.08 552	56.23 551

VLMVS		32.15 519	34.06 522	26.43 537	35.38 561	29.60 564	32.69 551	19.27 563	3.29 558	44.01 557	60.07 553	35.02 560	20.44 558	22.64 556	54.15 557	29.25 553

MVS_baseline		25.61 520	31.27 524	8.63 538	32.09 562	3.00 567	22.13 552	5.43 565	1.36 559	58.03 556	69.99 552	18.40 561	0.00 561	18.79 557	55.18 556	22.88 554

cdsmvs_eth3d_5k		24.66 521	32.88 523	0.00 541	0.00 565	0.00 568	0.00 553	99.10 316	0.00 560	0.00 561	97.58 439	99.21 18	0.00 561	0.00 560	0.00 560	0.00 557

testmvs		17.12 522	20.53 525	6.87 540	12.05 563	4.20 566	93.62 520	6.73 564	4.62 557	10.41 559	24.33 555	8.28 563	3.56 560	9.69 559	15.07 558	12.86 556

test123		17.04 523	20.11 526	7.82 539	10.25 564	4.91 565	94.80 480	4.47 566	4.93 556	10.00 560	24.28 556	9.69 562	3.64 559	10.14 558	12.43 559	14.92 555

pcd_1.5k_mvsjas		8.17 524	10.90 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	98.07 128	0.00 561	0.00 560	0.00 560	0.00 557

ab-mvs-re		8.12 525	10.83 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	97.48 448	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

PatchmatchNet2					0.00 565	90.12 514	94.29 501	98.12 431	94.40 460
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														96.95 280	99.71 217	99.28 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.85 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					99.33 205	99.02 71		99.25 277		99.23 169		96.59 255	99.85 159	98.10 160	99.62 267

aaatest				99.45 64	99.58 94	98.93 80	98.68 109	99.60 94	96.46 370	99.53 83	98.77 292		99.83 198	96.67 312	99.64 258	99.58 117

TestfortrainingZip				98.97 162	98.30 425	98.43 120	98.68 109	98.26 422	97.76 252	98.86 250	98.16 395	95.15 325	99.47 446		97.55 489	99.02 353

WAC-MVS						90.90 505								91.37 499

FOURS1					99.73 38	99.67 2	99.43 15	99.54 132	99.43 54	99.26 157

MSC_two_6792asdad				99.32 91	98.43 414	98.37 126		98.86 365					99.89 97	97.14 260	99.60 276	99.71 65

PC_three_1452									93.27 482	99.40 117	98.54 344	98.22 113	97.00 536	95.17 398	99.45 328	99.49 177

No_MVS				99.32 91	98.43 414	98.37 126		98.86 365					99.89 97	97.14 260	99.60 276	99.71 65

test_one_0601					99.39 185	99.20 38		99.31 246	98.49 180	98.66 286	99.02 214	97.64 168

eth-test2					0.00 565

eth-test					0.00 565

ZD-MVS					99.01 306	98.84 86		99.07 321	94.10 469	98.05 361	98.12 398	96.36 270	99.86 145	92.70 475	99.19 386

RE-MVS-def			98.58 162		99.20 245	99.38 12	98.48 143	99.30 254	98.64 161	98.95 224	98.96 243	97.75 159		96.56 327	99.39 344	99.45 206

IU-MVS					99.49 150	99.15 52		98.87 360	92.97 489	99.41 114				96.76 299	99.62 267	99.66 80

OPU-MVS				98.82 192	98.59 394	98.30 135	98.10 194				98.52 348	98.18 118	98.75 509	94.62 411	99.48 324	99.41 222

test_241102_TWO								99.30 254	98.03 228	99.26 157	99.02 214	97.51 185	99.88 115	96.91 282	99.60 276	99.66 80

test_241102_ONE					99.49 150	99.17 43		99.31 246	97.98 231	99.66 60	98.90 258	98.36 90	99.48 443

9.14			97.78 284		99.07 282		97.53 297	99.32 241	95.53 421	98.54 312	98.70 312	97.58 175	99.76 273	94.32 425	99.46 326

save fliter					99.11 273	97.97 178	96.53 392	99.02 334	98.24 201

test_0728_THIRD									98.17 214	99.08 191	99.02 214	97.89 147	99.88 115	97.07 267	99.71 217	99.70 70

test_0728_SECOND				99.60 16	99.50 141	99.23 30	98.02 211	99.32 241					99.88 115	96.99 274	99.63 263	99.68 73

test0726					99.50 141	99.21 32	98.17 183	99.35 227	97.97 232	99.26 157	99.06 201	97.61 172

GSMVS																98.81 394

test_part2					99.36 194	99.10 65				99.05 201

sam_mvs1												84.74 483				98.81 394

sam_mvs												84.29 489

ambc				98.24 307	98.82 345	95.97 341	98.62 118	99.00 339		99.27 153	99.21 155	96.99 224	99.50 435	96.55 330	99.50 319	99.26 297

MTGPA								99.20 290

test_post1							97.59 289				20.48 558	83.07 497	99.66 361	94.16 426

test_post											21.25 557	83.86 492	99.70 321

patchmatchnet-post											98.77 292	84.37 486	99.85 159

GG-mvs-BLEND				94.76 503	94.54 543	92.13 483	99.31 30	80.47 556		88.73 546	91.01 545	67.59 536	98.16 520	82.30 542	94.53 538	93.98 529

MTMP							97.93 230	91.91 541

gm-plane-assit					94.83 542	81.97 552			88.07 535		94.99 511		99.60 393	91.76 491

test9_res														93.28 456	99.15 391	99.38 241

TEST9					98.71 365	98.08 162	95.96 435	99.03 331	91.40 509	95.85 485	97.53 442	96.52 258	99.76 273

test_8					98.67 379	98.01 171	95.91 441	99.02 334	91.64 504	95.79 488	97.50 446	96.47 260	99.76 273

agg_prior2														92.50 480	99.16 389	99.37 244

agg_prior					98.68 378	97.99 174		99.01 337		95.59 489			99.77 267

TestCases				99.16 118	99.50 141	98.55 109		99.58 103	96.80 350	98.88 244	99.06 201	97.65 165	99.57 407	94.45 417	99.61 274	99.37 244

test_prior4						97.97 178	95.86 442

test_prior2							95.74 449		96.48 368	96.11 478	97.63 437	95.92 298		94.16 426	99.20 383

test_prior				98.95 166	98.69 374	97.95 182		99.03 331					99.59 398			99.30 282

旧先验2							95.76 448		88.56 532	97.52 404			99.66 361	94.48 415

新几何2							95.93 438

新几何1				98.91 175	98.94 317	97.76 210		98.76 382	87.58 536	96.75 453	98.10 400	94.80 339	99.78 261	92.73 474	99.00 410	99.20 314

旧先验1					98.82 345	97.45 239		98.76 382			98.34 372	95.50 314			99.01 409	99.23 304

无先验							95.74 449	98.74 388	89.38 525				99.73 299	92.38 483		99.22 309

原ACMM2							95.53 455

原ACMM1				98.35 294	98.90 327	96.25 327		98.83 373	92.48 497	96.07 480	98.10 400	95.39 318	99.71 312	92.61 477	98.99 412	99.08 342

test222					98.92 323	96.93 290	95.54 454	98.78 379	85.72 539	96.86 448	98.11 399	94.43 350			99.10 399	99.23 304

testdata2													99.79 249	92.80 471

segment_acmp												97.02 221

testdata				98.09 325	98.93 319	95.40 371		98.80 376	90.08 521	97.45 412	98.37 368	95.26 321	99.70 321	93.58 447	98.95 418	99.17 328

testdata1							95.44 460		96.32 375

test12				98.93 170	98.58 396	97.83 197		98.66 394		96.53 465		95.51 313	99.69 331		99.13 394	99.27 291

plane_prior7					99.19 249	97.87 193

plane_prior6					98.99 310	97.70 217						94.90 331

plane_prior5								99.27 269					99.70 321	94.42 420	99.51 311	99.45 206

plane_prior4											97.98 411

plane_prior3						97.78 207			97.41 293	97.79 384

plane_prior2							97.77 255		98.20 210

plane_prior1					99.05 290

plane_prior						97.65 221	97.07 349		96.72 355						99.36 348

n2								0.00 567

nn								0.00 567

door-mid								99.57 111

lessismore_v0				98.97 162	99.73 38	97.53 231		86.71 551		99.37 125	99.52 67	89.93 436	99.92 65	98.99 88	99.72 208	99.44 210

LGP-MVS_train				99.47 61	99.57 103	98.97 74		99.48 159	96.60 361	99.10 189	99.06 201	98.71 51	99.83 198	95.58 388	99.78 164	99.62 92

test11								98.87 360

door								99.41 204

HQP5-MVS						96.79 299

HQP-NCC					98.67 379		96.29 410		96.05 389	95.55 492

ACMP_Plane					98.67 379		96.29 410		96.05 389	95.55 492

BP-MVS														92.82 469

HQP4-MVS										95.56 491			99.54 421			99.32 273

HQP3-MVS								99.04 329							99.26 372

HQP2-MVS												93.84 370

NP-MVS					98.84 340	97.39 244					96.84 469

MDTV_nov1_ep13_2view						74.92 557	97.69 269		90.06 522	97.75 387		85.78 473		93.52 449		98.69 413

MDTV_nov1_ep13			95.22 437		97.06 507	83.20 548	97.74 263	96.16 490	94.37 461	96.99 437	98.83 279	83.95 491	99.53 424	93.90 435	97.95 481

ACMMP++_ref															99.77 172

ACMMP++															99.68 240

Test By Simon												96.52 258

ITE_SJBPF				98.87 179	99.22 239	98.48 116		99.35 227	97.50 280	98.28 340	98.60 338	97.64 168	99.35 469	93.86 438	99.27 368	98.79 400

DeepMVS_CX				93.44 521	98.24 433	94.21 420		94.34 519	64.28 550	91.34 539	94.87 516	89.45 444	92.77 550	77.54 546	93.14 541	93.35 536

High-resolution multi-view benchmark