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	99.81 225	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	98.08 196	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	97.77 254	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	97.82 245	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	97.68 269	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	99.82 208	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	99.83 196	99.06 82	99.62 266	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	99.80 234	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	99.26 137	98.62 64	99.73 297	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	97.93 229	99.83 26	99.22 80	99.93 6	99.30 125	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	99.34 115	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	97.44 311	99.83 26	99.56 39	99.91 12	99.34 115	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	97.25 248	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	99.83 196	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	99.85 158	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	98.00 215	99.85 19	99.24 77	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 408

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	99.25 142	98.36 90	99.88 115	98.23 149	99.67 245	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	99.35 111	98.86 35	99.67 345	97.81 191	99.81 140	99.24 301

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	99.35 111	98.86 35	99.67 345	97.81 191	99.81 140	99.24 301

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	99.86 144	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	98.30 194	99.65 63	99.45 84	99.22 17	99.76 271	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	97.65 275	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	97.97 227	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	97.82 245	99.76 39	98.73 151	99.82 34	99.09 197	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	99.26 137	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	97.49 302	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	97.80 249	99.76 39	98.70 159	99.78 39	99.11 188	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	97.62 280	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	97.97 227	99.86 17	98.22 203	99.88 21	99.71 22	98.59 67	99.84 178	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	99.06 200	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	99.86 144	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	97.87 240	99.85 19	98.56 177	99.90 14	99.68 25	98.69 57	99.85 158	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	99.81 225	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	97.75 260	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	99.13 182	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	99.13 182	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	98.15 184	99.47 170	99.12 99	99.52 87	99.32 123	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	97.38 316	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	97.80 247	99.91 12	99.67 30	97.15 212	98.91 500	99.76 23	99.56 292	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	99.82 208	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	99.86 144	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	97.65 275	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	98.46 357	98.68 58	99.93 53	99.03 85	99.85 109	98.64 419

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	98.51 348	98.64 61	99.93 53	98.91 93	99.85 109	98.88 382

Casviewmamba		99.12 69	99.12 71	99.09 134	99.53 127	98.08 162	98.34 163	99.66 71	99.35 64	99.35 130	99.23 150	98.39 88	99.72 307	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	97.78 250	99.82 34	99.84 3	98.56 73	99.71 309	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	98.00 215	99.62 89	99.22 80	99.55 77	99.22 152	98.93 33	99.75 283	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	97.79 250	99.82 31	98.21 205	99.81 36	99.53 64	98.46 82	99.84 178	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	98.30 165	99.68 64	99.04 119	99.19 176	99.37 104	98.98 28	99.61 386	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	99.16 170	97.14 213	99.86 144	98.39 138	99.57 288	99.81 41

reproduce-ours		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 215	99.42 200	99.05 117	99.48 96	99.27 131	98.29 99	99.89 97	97.61 215	99.71 217	99.62 92

our_new_method		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 215	99.42 200	99.05 117	99.48 96	99.27 131	98.29 99	99.89 97	97.61 215	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	97.74 262	99.81 32	98.55 178	99.85 27	99.55 56	98.60 66	99.84 178	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	98.37 367	98.72 50	99.90 81	99.05 83	99.77 172	98.77 401

hybridcas		99.08 79	99.13 70	98.92 173	99.54 123	97.61 226	98.22 177	99.66 71	99.27 74	99.40 117	99.24 144	98.47 77	99.70 318	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	97.62 280	99.68 64	98.43 183	99.85 27	99.10 191	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	99.18 163	98.81 39	99.67 345	96.71 305	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	97.40 313	99.83 26	97.61 265	99.85 27	99.30 125	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	99.39 100	98.71 51	99.70 318	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	99.39 100	98.71 51	99.70 318	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	99.39 100	98.71 51	99.70 318	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	99.39 100	98.71 51	99.70 318	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	98.42 184	98.91 236	98.81 284	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	99.07 199	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	99.51 431	97.70 208	99.73 199	97.89 468
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	99.16 170	97.87 149	99.83 196	96.67 310	99.62 266	99.81 41

fmvsm_s_conf0.5_n_4		99.01 90	99.22 54	98.38 289	99.31 209	95.48 365	97.56 291	99.73 45	98.87 140	99.75 44	99.27 131	98.80 41	99.86 144	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	99.81 225	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	97.33 300	98.94 232	98.86 268	98.75 47	99.82 208	97.53 224	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	99.12 186	96.34 271	99.93 53	98.05 166	99.36 346	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	97.57 269	99.35 130	99.24 144	97.83 151	99.89 97	97.88 184	99.70 227	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	98.04 205	99.59 100	98.15 221	99.40 117	99.36 110	98.58 72	99.76 271	98.78 102	99.68 239	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	99.19 159	98.40 86	99.72 307	95.98 364	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	97.47 307	99.57 111	99.37 60	99.21 174	99.61 43	96.76 242	99.83 196	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	99.83 196	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	98.01 213	99.46 175	97.56 271	99.54 79	99.50 68	98.97 29	99.84 178	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	99.06 200	98.23 110	99.69 328	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	97.82 245	99.57 111	99.17 93	99.35 130	99.17 168	98.35 94	99.69 328	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	99.20 289	99.10 107	99.72 47	98.76 296	96.38 267	99.86 144	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	98.99 339	99.25 76	99.54 79	99.37 104	97.04 218	99.80 234	97.89 181	99.52 307	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	99.15 176	97.23 207	99.75 283	97.17 254	99.66 253	99.63 91

SED-MVS		98.91 106	98.72 132	99.49 55	99.49 150	99.17 43	98.10 193	99.31 246	98.03 227	99.66 60	99.02 213	98.36 90	99.88 115	96.91 280	99.62 266	99.41 222

ACMM	96.08 12	98.91 106	98.73 130	99.48 57	99.55 117	99.14 57	98.07 200	99.37 217	97.62 262	99.04 203	98.96 242	98.84 37	99.79 247	97.43 235	99.65 255	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	98.13 186	99.66 71	99.09 110	99.30 148	99.02 213	98.79 43	99.89 97	97.87 186	99.80 152	99.23 303

DVP-MVS++		98.90 108	98.70 138	99.51 49	98.43 414	99.15 52	99.43 15	99.32 241	98.17 213	99.26 157	99.02 213	98.18 118	99.88 115	97.07 266	99.45 327	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	99.24 144	98.20 117	99.86 144	95.92 366	99.69 233	99.04 349

MTAPA		98.88 111	98.64 150	99.61 13	99.67 68	99.36 15	98.43 148	99.20 289	98.83 149	98.89 240	98.90 257	96.98 225	99.92 65	97.16 255	99.70 227	99.56 130

E4		98.87 112	98.88 108	98.81 194	99.52 131	97.23 261	97.62 280	99.61 92	98.58 172	99.18 181	99.33 118	98.29 99	99.69 328	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	99.10 315	96.49 366	99.96 4	99.81 8	98.18 118	99.45 450	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	99.05 207	96.72 246	99.82 208	98.09 161	99.36 346	99.59 109

UniMVSNet (Re)		98.87 112	98.71 135	99.35 80	99.24 233	98.73 95	97.73 264	99.38 213	98.93 132	99.12 185	98.73 300	96.77 240	99.86 144	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	97.70 267	99.64 79	98.22 203	99.25 165	99.27 131	98.40 86	99.61 386	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	98.04 205	99.66 71	99.09 110	99.22 171	99.02 213	98.79 43	99.87 135	97.87 186	99.72 208	99.27 290

UniMVSNet_NR-MVSNet		98.86 116	98.68 141	99.40 71	99.17 259	98.74 92	97.68 269	99.40 209	99.14 98	99.06 193	98.59 338	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	97.38 316	99.70 54	99.16 94	99.57 72	99.40 97	98.26 105	99.71 309	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	97.38 316	99.70 54	99.16 94	99.57 72	99.40 97	98.26 105	99.71 309	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	98.98 236	97.89 147	99.85 158	96.54 329	99.42 338	99.46 200

fmvsm_s_conf0.5_n_7		98.83 122	99.04 87	98.20 312	99.30 213	94.83 400	97.23 334	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	98.57 426

APD_test1		98.83 122	98.66 146	99.34 83	99.78 24	99.47 8	98.42 151	99.45 179	98.28 199	98.98 214	99.19 159	97.76 158	99.58 402	96.57 321	99.55 297	98.97 363

PM-MVS		98.82 125	98.72 132	99.12 126	99.64 77	98.54 112	97.98 223	99.68 64	97.62 262	99.34 135	99.18 163	97.54 180	99.77 265	97.79 193	99.74 195	99.04 349

DU-MVS		98.82 125	98.63 152	99.39 72	99.16 261	98.74 92	97.54 295	99.25 277	98.84 148	99.06 193	98.76 296	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	98.96 242	97.49 189	99.86 144	96.56 325	99.39 342	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	99.33 118	97.95 140	99.90 81	97.16 255	99.67 245	99.44 210

mamba_0408		98.80 129	98.88 108	98.55 260	99.27 221	96.50 316	98.00 215	99.60 94	98.93 132	99.22 171	98.84 276	98.59 67	99.89 97	97.74 203	99.72 208	99.27 290

SSM_04072		98.80 129	98.88 108	98.56 258	99.27 221	96.50 316	98.00 215	99.60 94	98.93 132	99.22 171	98.84 276	98.59 67	99.90 81	97.74 203	99.72 208	99.27 290

HPM-MVS		98.79 131	98.53 169	99.59 20	99.65 71	99.29 23	99.16 55	99.43 193	96.74 353	98.61 297	98.38 366	98.62 64	99.87 135	96.47 333	99.67 245	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	98.23 173	99.31 246	97.92 237	98.90 237	98.90 257	98.00 134	99.88 115	96.15 356	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	99.26 137	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	98.27 170	99.19 293	97.87 241	99.25 165	99.16 170	96.84 232	99.78 259	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	97.78 251	99.24 283	99.04 119	99.41 114	98.90 257	97.65 165	99.76 271	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	98.02 210	98.84 368	97.97 231	99.08 191	99.02 213	97.61 172	99.88 115	96.99 273	99.63 262	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	98.91 254	98.34 95	99.79 247	95.63 382	99.91 80	98.86 384

ACMMP_NAP		98.75 138	98.48 181	99.57 21	99.58 94	99.29 23	97.82 245	99.25 277	96.94 335	98.78 265	99.12 186	98.02 132	99.84 178	97.13 262	99.67 245	99.59 109

SixPastTwentyTwo		98.75 138	98.62 154	99.16 118	99.83 18	97.96 181	99.28 40	98.20 425	99.37 60	99.70 51	99.65 36	92.65 399	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	97.16 323	98.82 259	99.01 225	97.71 161	99.87 135	96.29 348	99.69 233	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	97.54 401	98.63 330	97.50 186	99.83 196	96.79 293	99.53 304	99.56 130

viewdifsd2359ckpt07		98.71 142	98.86 115	98.26 303	99.43 176	95.65 354	97.20 339	99.66 71	99.20 84	99.29 149	99.01 225	98.29 99	99.73 297	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	98.64 396	99.74 12	99.67 59	99.24 144	94.57 345	99.95 25	99.11 77	99.24 372	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	98.29 197	98.88 244	98.85 271	97.53 182	99.87 135	96.14 357	99.31 358	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	97.47 282	98.58 304	98.50 352	97.97 138	99.85 158	96.57 321	99.59 279	99.53 157

LPG-MVS_test		98.71 142	98.46 185	99.47 61	99.57 103	98.97 74	98.23 173	99.48 159	96.60 360	99.10 189	99.06 200	98.71 51	99.83 196	95.58 386	99.78 164	99.62 92

E2		98.70 147	98.68 141	98.73 219	99.40 183	97.10 278	97.48 303	99.57 111	98.09 224	99.00 209	99.20 156	97.90 143	99.67 345	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	96.78 351	99.72 47	99.78 13	96.60 254	99.67 345	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	97.47 282	98.56 308	98.54 343	97.75 159	99.88 115	96.57 321	99.59 279	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	97.54 276	98.30 336	98.40 363	97.86 150	99.89 97	96.53 330	99.72 208	99.56 130

E3		98.69 151	98.68 141	98.73 219	99.40 183	97.10 278	97.48 303	99.57 111	98.09 224	99.00 209	99.20 156	97.90 143	99.67 345	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	96.97 468	98.86 142	98.87 249	97.62 437	98.63 63	98.96 496	99.41 56	98.29 459	98.45 433

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	94.10 366	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	97.46 287	98.44 324	98.51 348	97.83 151	99.88 115	96.46 334	99.58 284	99.58 117

EI-MVSNet-UG-set		98.69 151	98.71 135	98.62 242	99.10 275	96.37 323	97.23 334	98.87 359	99.20 84	99.19 176	98.99 231	97.30 201	99.85 158	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	99.19 293	98.99 124	98.07 358	99.28 129	97.11 216	99.84 178	96.84 291	99.32 356	99.47 197

RoMa-HiRes		98.68 157	98.52 170	99.16 118	99.50 141	98.35 130	98.01 213	99.71 48	96.94 335	99.35 130	98.66 321	96.38 267	99.63 373	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	97.28 307	98.11 354	98.39 364	98.00 134	99.87 135	96.86 290	99.64 257	99.55 137

EI-MVSNet-Vis-set		98.68 157	98.70 138	98.63 240	99.09 278	96.40 322	97.23 334	98.86 364	99.20 84	99.18 181	98.97 238	97.29 203	99.85 158	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	97.87 241	98.85 251	98.04 405	97.66 164	99.84 178	96.72 303	99.81 140	99.13 338

test_f		98.67 161	98.87 111	98.05 333	99.72 45	95.59 355	98.51 135	99.81 32	96.30 377	99.78 39	99.82 5	96.14 279	98.63 508	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	98.23 173	99.49 157	97.01 332	98.69 280	98.88 265	98.00 134	99.89 97	95.87 370	99.59 279	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	94.14 362	99.86 144	97.77 197	99.69 233	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	94.14 362	99.86 144	97.77 197	99.69 233	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	98.37 185	99.69 55	99.46 80	98.21 115	99.92 65	94.13 427	99.30 362	98.91 377

mPP-MVS		98.64 165	98.34 208	99.54 31	99.54 123	99.17 43	98.63 116	99.24 283	97.47 282	98.09 356	98.68 315	97.62 170	99.89 97	96.22 351	99.62 266	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	99.19 159	95.81 301	99.85 158	98.77 105	99.77 172	98.60 422

TSAR-MVS + MP.		98.63 167	98.49 180	99.06 144	99.64 77	97.90 190	98.51 135	98.94 343	96.96 333	99.24 167	98.89 263	97.83 151	99.81 225	96.88 287	99.49 322	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	97.25 498	99.38 12	99.12 61	99.32 241	99.21 82	98.44 324	98.88 265	97.31 200	99.80 234	96.58 319	99.34 351	98.92 373

RPSCF		98.62 170	98.36 204	99.42 67	99.65 71	99.42 10	98.55 126	99.57 111	97.72 255	98.90 237	99.26 137	96.12 283	99.52 425	95.72 377	99.71 217	99.32 273

ME-MVS		98.61 171	98.33 213	99.44 65	99.24 233	98.93 80	97.45 309	99.06 321	98.14 222	99.06 193	98.77 291	96.97 226	99.82 208	96.67 310	99.64 257	99.58 117

GST-MVS		98.61 171	98.30 216	99.52 44	99.51 134	99.20 38	98.26 171	99.25 277	97.44 290	98.67 284	98.39 364	97.68 162	99.85 158	96.00 362	99.51 310	99.52 161

v1192		98.60 173	98.66 146	98.41 285	99.27 221	95.88 345	97.52 297	99.36 221	97.41 292	99.33 138	99.20 156	96.37 269	99.82 208	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	97.58 289	99.34 233	97.51 278	99.27 153	99.15 176	96.34 271	99.80 234	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	98.08 196	99.74 44	96.94 335	98.97 218	99.10 191	96.94 227	99.74 290	97.33 241	99.86 107	99.55 137

DPE-MVS		98.59 175	98.26 225	99.57 21	99.27 221	99.15 52	97.01 349	99.39 211	97.67 258	99.44 107	98.99 231	97.53 182	99.89 97	95.40 391	99.68 239	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	97.47 307	99.55 126	97.55 273	98.96 223	98.92 251	97.77 157	99.59 395	97.59 218	99.77 172	99.39 232

viewmamba		98.57 178	98.66 146	98.31 298	99.20 245	95.89 344	96.92 359	99.57 111	98.71 158	99.02 207	99.04 209	97.48 190	99.71 309	98.28 146	99.70 227	99.35 258

MP-MVS-pluss		98.57 178	98.23 230	99.60 16	99.69 61	99.35 16	97.16 344	99.38 213	94.87 441	98.97 218	98.99 231	98.01 133	99.88 115	97.29 245	99.70 227	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	97.13 346	99.18 297	97.10 326	98.75 271	98.92 251	98.18 118	99.65 365	96.68 309	99.56 292	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	97.01 465	99.59 36	99.11 186	99.27 131	94.82 335	99.79 247	98.34 142	99.63 262	99.34 262

v2v482		98.56 180	98.62 154	98.37 292	99.42 178	95.81 350	97.58 289	99.16 304	97.90 239	99.28 151	99.01 225	95.98 293	99.79 247	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	97.71 265	99.46 175	97.25 311	98.98 214	98.99 231	97.54 180	99.84 178	95.88 367	99.74 195	99.23 303

viewcassd2359sk11		98.55 184	98.51 172	98.67 230	99.29 215	96.99 284	97.39 314	99.54 132	97.73 253	98.81 261	99.08 198	97.55 178	99.66 358	97.52 226	99.67 245	99.36 252

v1240		98.55 184	98.62 154	98.32 296	99.22 239	95.58 357	97.51 299	99.45 179	97.16 323	99.45 106	99.24 144	96.12 283	99.85 158	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	97.22 338	99.39 211	98.97 127	99.38 121	99.31 124	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	97.63 279	99.36 221	97.15 325	99.32 144	99.18 163	95.84 300	99.84 178	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	97.56 291	99.36 221	97.23 317	99.38 121	99.17 168	96.02 286	99.84 178	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	96.45 394	99.34 233	99.33 66	98.93 233	98.70 311	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	98.19 178	99.41 204	96.77 352	98.83 256	98.90 257	97.80 155	99.82 208	95.68 380	99.52 307	99.38 241

XVG-OURS		98.53 189	98.34 208	99.11 128	99.50 141	98.82 89	95.97 430	99.50 149	97.30 305	99.05 201	98.98 236	99.35 14	99.32 471	95.72 377	99.68 239	99.18 323

UGNet		98.53 189	98.45 186	98.79 201	97.94 457	96.96 287	99.08 62	98.54 405	99.10 107	96.82 449	99.47 78	96.55 257	99.84 178	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	98.77 379	99.65 25	99.52 87	99.00 229	94.34 355	99.93 53	98.65 114	98.83 422	99.76 58

patch_mono-2		98.51 194	98.63 152	98.17 315	99.38 187	94.78 402	97.36 321	99.69 57	98.16 216	98.49 317	99.29 128	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	96.61 383	99.60 94	98.37 185	98.90 237	99.00 229	97.37 197	99.76 271	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	96.54 387	99.48 159	97.32 302	99.11 186	98.61 335	99.33 15	99.30 474	96.23 350	98.38 453	99.28 287

FMVSNet2		98.49 196	98.40 193	98.75 213	98.90 327	97.14 276	98.61 120	99.13 311	98.59 169	99.19 176	99.28 129	94.14 362	99.82 208	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	97.16 344	99.28 266	95.54 417	99.42 112	99.19 159	97.27 204	99.63 373	97.89 181	99.97 21	99.20 313

RoMa-SfM		98.46 199	98.27 222	99.02 151	99.35 199	98.32 133	97.56 291	99.70 54	95.88 398	99.38 121	98.65 324	96.41 263	99.46 447	97.78 194	99.71 217	99.28 287

MP-MVS		98.46 199	98.09 248	99.54 31	99.57 103	99.22 31	98.50 137	99.19 293	97.61 265	97.58 397	98.66 321	97.40 195	99.88 115	94.72 408	99.60 275	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	97.44 311	99.06 321	98.30 194	99.32 144	98.97 238	96.65 251	99.62 378	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	98.25 172	99.58 103	96.80 349	98.88 244	99.06 200	97.65 165	99.57 404	94.45 415	99.61 273	99.37 244

VNet		98.42 203	98.30 216	98.79 201	98.79 354	97.29 256	98.23 173	98.66 393	99.31 69	98.85 251	98.80 285	94.80 338	99.78 259	98.13 156	99.13 392	99.31 278

E3new		98.41 204	98.34 208	98.62 242	99.19 249	96.90 292	97.32 324	99.50 149	97.40 294	98.63 291	98.92 251	97.21 209	99.65 365	97.34 239	99.52 307	99.31 278

ab-mvs		98.41 204	98.36 204	98.59 249	99.19 249	97.23 261	99.32 26	98.81 373	97.66 259	98.62 295	99.40 97	96.82 235	99.80 234	95.88 367	99.51 310	98.75 404

ACMP	95.32 15	98.41 204	98.09 248	99.36 74	99.51 134	98.79 90	97.68 269	99.38 213	95.76 407	98.81 261	98.82 281	98.36 90	99.82 208	94.75 405	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	96.73 371	99.41 204	98.18 212	98.65 287	99.02 213	97.02 221	99.69 328	97.73 205	99.70 227	99.33 268

test_vis1_n_1920		98.40 207	98.92 102	96.81 434	99.74 37	90.76 506	98.15 184	99.91 10	98.33 190	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	98.03 256	99.51 49	99.16 261	99.21 32	98.05 203	99.22 286	94.16 462	98.98 214	99.10 191	97.52 184	99.79 247	96.45 335	99.64 257	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	98.00 259	99.61 13	99.57 103	99.25 28	98.57 124	99.35 227	97.55 273	99.31 147	97.71 430	94.61 344	99.88 115	96.14 357	99.19 384	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	97.88 237	99.36 221	98.20 209	99.63 66	99.04 209	98.76 46	95.33 543	96.56 325	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	97.20 339	98.87 359	98.97 127	99.06 193	99.02 213	96.00 288	99.80 234	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	96.85 362	98.94 343	98.57 174	98.89 240	98.50 352	95.60 309	99.85 158	97.54 223	99.85 109	99.59 109

viewdifsd2359ckpt13		98.39 214	98.29 218	98.70 225	99.26 230	97.19 268	97.51 299	99.48 159	96.94 335	98.58 304	98.82 281	97.47 192	99.55 412	97.21 251	99.33 353	99.34 262

IMVS_0407		98.39 214	98.64 150	97.66 372	99.03 295	94.03 430	98.10 193	99.45 179	98.16 216	99.06 193	98.71 304	98.27 103	99.71 309	97.50 227	99.45 327	99.22 308

LuminaMVS		98.39 214	98.20 232	98.98 160	99.50 141	97.49 232	97.78 251	97.69 440	98.75 150	99.49 94	99.25 142	92.30 405	99.94 41	99.14 75	99.88 95	99.50 169

new-patchmatchnet		98.35 217	98.74 128	97.18 411	99.24 233	92.23 479	96.42 398	99.48 159	98.30 194	99.69 55	99.53 64	97.44 193	99.82 208	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	97.98 223	99.45 179	98.16 216	98.89 240	98.71 304	97.90 143	99.74 290	97.50 227	99.45 327	99.22 308

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	97.40 415	95.50 499	98.66 59	99.50 432	98.18 153	98.71 433	98.44 436

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	97.46 408	95.46 502	98.59 67	99.46 447	98.08 162	98.71 433	98.46 430

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	97.46 408	95.46 502	98.59 67	99.46 447	98.08 162	98.71 433	98.46 430

test_cas_vis1_n_1920		98.33 222	98.68 141	97.27 407	99.69 61	92.29 477	98.03 207	99.85 19	97.62 262	99.96 4	99.62 40	93.98 367	99.74 290	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	96.67 377	99.56 121	97.85 243	98.75 271	98.95 246	96.65 251	99.63 373	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	96.68 375	99.51 144	97.32 302	99.18 181	99.15 176	97.61 172	99.62 378	97.19 252	99.74 195	99.38 241

testgi		98.32 223	98.39 196	98.13 320	99.57 103	95.54 358	97.78 251	99.49 157	97.37 297	99.19 176	97.65 434	98.96 30	99.49 436	96.50 332	98.99 410	99.34 262

DeepPCF-MVS	96.93 5	98.32 223	98.01 258	99.23 108	98.39 419	98.97 74	95.03 471	99.18 297	96.88 343	99.33 138	98.78 289	98.16 122	99.28 478	96.74 300	99.62 266	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	96.31 375	99.79 38	99.57 49	92.85 395	99.42 456	99.79 19	99.84 114	99.60 102

MVS_111021_LR		98.30 228	98.12 246	98.83 190	99.16 261	98.03 170	96.09 423	99.30 254	97.58 268	98.10 355	98.24 386	98.25 107	99.34 467	96.69 308	99.65 255	99.12 339

EPP-MVSNet		98.30 228	98.04 255	99.07 138	99.56 111	97.83 197	99.29 36	98.07 431	99.03 121	98.59 302	99.13 182	92.16 407	99.90 81	96.87 288	99.68 239	99.49 177

DeepC-MVS_fast	96.85 6	98.30 228	98.15 243	98.75 213	98.61 389	97.23 261	97.76 257	99.09 317	97.31 304	98.75 271	98.66 321	97.56 177	99.64 370	96.10 361	99.55 297	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	97.95 265	99.34 83	98.44 412	99.16 48	98.12 190	99.38 213	96.01 391	98.06 359	98.43 360	97.80 155	99.67 345	95.69 379	99.58 284	99.20 313

balanced_ft_v1		98.28 232	98.35 207	98.10 323	98.08 450	96.23 328	99.23 45	99.26 275	98.34 188	97.46 408	99.42 89	95.38 319	99.88 115	98.60 117	99.34 351	98.17 453

Fast-Effi-MVS+-dtu		98.27 233	98.09 248	98.81 194	98.43 414	98.11 154	97.61 285	99.50 149	98.64 161	97.39 417	97.52 444	98.12 126	99.95 25	96.90 285	98.71 433	98.38 443

DELS-MVS		98.27 233	98.20 232	98.48 276	98.86 336	96.70 305	95.60 450	99.20 289	97.73 253	98.45 323	98.71 304	97.50 186	99.82 208	98.21 151	99.59 279	98.93 372
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	97.97 264	99.15 123	99.64 77	97.83 197	98.28 167	99.43 193	99.24 77	98.80 263	98.85 271	89.76 438	99.94 41	98.04 167	99.67 245	99.68 73

Effi-MVS+-dtu		98.26 235	97.90 274	99.35 80	98.02 453	99.49 5	98.02 210	99.16 304	98.29 197	97.64 391	97.99 409	96.44 262	99.95 25	96.66 313	98.93 418	98.60 422

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	98.13 395	93.81 371	99.97 6	99.26 65	99.57 288	99.43 214

MVS_111021_HR		98.25 238	98.08 251	98.75 213	99.09 278	97.46 238	95.97 430	99.27 269	97.60 267	97.99 366	98.25 384	98.15 124	99.38 462	96.87 288	99.57 288	99.42 219

TAMVS		98.24 239	98.05 254	98.80 197	99.07 282	97.18 271	97.88 237	98.81 373	96.66 359	99.17 184	99.21 154	94.81 337	99.77 265	96.96 278	99.88 95	99.44 210

hybrid		98.22 240	98.27 222	98.08 328	99.13 270	95.24 380	96.61 383	99.53 136	97.43 291	98.46 321	98.97 238	96.75 245	99.65 365	97.84 189	99.69 233	99.35 258

MM		98.22 240	97.99 260	98.91 175	98.66 384	96.97 285	97.89 236	94.44 513	99.54 40	98.95 224	99.14 180	93.50 378	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	96.38 400	99.58 103	97.79 249	98.53 313	98.50 352	96.76 242	99.74 290	97.95 179	99.64 257	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	98.13 186	99.32 241	96.16 384	98.93 233	98.82 281	96.00 288	99.83 196	97.32 243	99.73 199	99.36 252

VDDNet		98.21 243	97.95 265	99.01 153	99.58 94	97.74 212	99.01 71	97.29 456	99.67 20	98.97 218	99.50 68	90.45 432	99.80 234	97.88 184	99.20 381	99.48 188

icg_test_0407_2		98.20 245	98.38 200	97.65 374	99.03 295	94.03 430	95.78 444	99.45 179	98.16 216	99.06 193	98.71 304	98.27 103	99.68 340	97.50 227	99.45 327	99.22 308

viewmambaseed2359dif		98.19 246	98.26 225	97.99 339	99.02 303	95.03 391	96.59 386	99.53 136	96.21 379	99.00 209	98.99 231	97.62 170	99.61 386	97.62 214	99.72 208	99.33 268

IS-MVSNet		98.19 246	97.90 274	99.08 136	99.57 103	97.97 178	99.31 30	98.32 418	99.01 123	98.98 214	99.03 212	91.59 416	99.79 247	95.49 389	99.80 152	99.48 188

DKM		98.18 248	97.95 265	98.85 182	99.35 199	98.31 134	96.68 375	99.69 57	96.90 341	98.61 297	98.77 291	94.41 350	98.93 498	97.32 243	99.84 114	99.32 273

MVS_Test		98.18 248	98.36 204	97.67 370	98.48 406	94.73 405	98.18 179	99.02 333	97.69 256	98.04 362	99.11 188	97.22 208	99.56 407	98.57 121	98.90 420	98.71 408

TSAR-MVS + GP.		98.18 248	97.98 261	98.77 209	98.71 365	97.88 192	96.32 405	98.66 393	96.33 373	99.23 169	98.51 348	97.48 190	99.40 458	97.16 255	99.46 325	99.02 352

CNVR-MVS		98.17 251	97.87 276	99.07 138	98.67 379	98.24 140	97.01 349	98.93 346	97.25 311	97.62 393	98.34 371	97.27 204	99.57 404	96.42 337	99.33 353	99.39 232

PVSNet_Blended_VisFu		98.17 251	98.15 243	98.22 311	99.73 38	95.15 386	97.36 321	99.68 64	94.45 455	98.99 213	99.27 131	96.87 231	99.94 41	97.13 262	99.91 80	99.57 124

AstraMVS		98.16 253	98.07 253	98.41 285	99.51 134	95.86 346	98.00 215	95.14 507	98.97 127	99.43 108	99.24 144	93.25 382	99.84 178	99.21 70	99.87 100	99.54 143

DKM-HiRes		98.14 254	97.80 281	99.16 118	99.51 134	98.40 121	96.70 373	99.63 82	97.55 273	97.45 411	98.74 298	93.27 381	99.54 418	97.78 194	99.55 297	99.53 157

viewdifsd2359ckpt09		98.13 255	97.92 271	98.77 209	99.18 257	97.35 245	97.29 328	99.53 136	95.81 404	98.09 356	98.47 356	96.34 271	99.66 358	97.02 269	99.51 310	99.29 284

DenseAffine		98.10 256	97.86 277	98.84 188	99.32 207	97.93 185	96.62 382	99.76 39	96.68 358	98.65 287	98.72 302	94.46 348	99.33 469	96.76 297	99.75 192	99.25 297

HPM-MVS++		98.10 256	97.64 299	99.48 57	99.09 278	99.13 60	97.52 297	98.75 385	97.46 287	96.90 443	97.83 423	96.01 287	99.84 178	95.82 374	99.35 349	99.46 200

APD-MVS		98.10 256	97.67 294	99.42 67	99.11 273	98.93 80	97.76 257	99.28 266	94.97 438	98.72 275	98.77 291	97.04 218	99.85 158	93.79 437	99.54 300	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	93.47 455	98.63 116	99.93 6	95.41 426	99.68 57	99.64 37	91.88 414	99.48 440	99.82 12	99.87 100	99.62 92

MVP-Stereo		98.08 260	97.92 271	98.57 253	98.96 315	96.79 299	97.90 235	99.18 297	96.41 371	98.46 321	98.95 246	95.93 297	99.60 390	96.51 331	98.98 413	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	96.67 377	99.45 179	98.16 216	98.03 363	98.71 304	96.80 238	99.82 208	97.50 227	99.45 327	99.22 308

PMMVS2		98.07 261	98.08 251	98.04 334	99.41 181	94.59 411	94.59 488	99.40 209	97.50 279	98.82 259	98.83 278	96.83 234	99.84 178	97.50 227	99.81 140	99.71 65

SymmetryMVS		98.05 263	97.71 292	99.09 134	99.29 215	97.83 197	98.28 167	97.64 445	99.24 77	98.80 263	98.85 271	89.76 438	99.94 41	98.04 167	99.50 318	99.49 177

ETV-MVS		98.03 264	97.86 277	98.56 258	98.69 374	98.07 165	97.51 299	99.50 149	98.10 223	97.50 405	95.51 498	98.41 85	99.88 115	96.27 349	99.24 372	97.71 482

Effi-MVS+		98.02 265	97.82 280	98.62 242	98.53 403	97.19 268	97.33 323	99.68 64	97.30 305	96.68 456	97.46 450	98.56 73	99.80 234	96.63 315	98.20 462	98.86 384

MSLP-MVS++		98.02 265	98.14 245	97.64 377	98.58 396	95.19 385	97.48 303	99.23 285	97.47 282	97.90 372	98.62 333	97.04 218	98.81 503	97.55 221	99.41 339	98.94 371

guyue		98.01 267	97.93 270	98.26 303	99.45 169	95.48 365	98.08 196	96.24 487	98.89 138	99.34 135	99.14 180	91.32 423	99.82 208	99.07 80	99.83 126	99.48 188

EIA-MVS		98.00 268	97.74 286	98.80 197	98.72 361	98.09 158	98.05 203	99.60 94	97.39 295	96.63 458	95.55 497	97.68 162	99.80 234	96.73 302	99.27 366	98.52 428

MCST-MVS		98.00 268	97.63 301	99.10 130	99.24 233	98.17 148	96.89 361	98.73 388	95.66 409	97.92 370	97.70 432	97.17 211	99.66 358	96.18 355	99.23 375	99.47 197

K. test v3		98.00 268	97.66 297	99.03 148	99.79 23	97.56 228	99.19 53	92.47 529	99.62 32	99.52 87	99.66 32	89.61 440	99.96 13	99.25 67	99.81 140	99.56 130

HQP_MVS		97.99 271	97.67 294	98.93 170	99.19 249	97.65 221	97.77 254	99.27 269	98.20 209	97.79 383	97.98 410	94.90 331	99.70 318	94.42 417	99.51 310	99.45 206

VortexMVS		97.98 272	98.31 215	97.02 420	98.88 333	91.45 488	98.03 207	99.47 170	98.65 160	99.55 77	99.47 78	91.49 420	99.81 225	99.32 60	99.91 80	99.80 45

LoFTR		97.97 273	97.79 282	98.53 267	98.80 351	97.47 236	97.01 349	99.55 126	95.55 415	99.46 101	99.22 152	94.22 360	99.44 452	96.45 335	99.82 133	98.68 416

ArgMatch-SfM		97.96 274	97.72 290	98.66 232	99.02 303	97.33 247	96.49 392	99.52 142	95.46 421	98.71 279	98.29 381	96.14 279	99.69 328	96.30 346	99.56 292	98.97 363

MDA-MVSNet-bldmvs		97.94 275	97.91 273	98.06 331	99.44 171	94.96 393	96.63 381	99.15 309	98.35 187	98.83 256	99.11 188	94.31 357	99.85 158	96.60 318	98.72 431	99.37 244

ttmdpeth		97.91 276	98.02 257	97.58 383	98.69 374	94.10 426	98.13 186	98.90 353	97.95 233	97.32 420	99.58 47	95.95 296	98.75 505	96.41 338	99.22 376	99.87 22

Anonymous202405211		97.90 277	97.50 309	99.08 136	98.90 327	98.25 139	98.53 129	96.16 488	98.87 140	99.11 186	98.86 268	90.40 433	99.78 259	97.36 238	99.31 358	99.19 319

LF4IMVS		97.90 277	97.69 293	98.52 269	99.17 259	97.66 219	97.19 343	99.47 170	96.31 375	97.85 379	98.20 390	96.71 247	99.52 425	94.62 409	99.72 208	98.38 443

PMatch-SfM		97.89 279	97.64 299	98.66 232	99.26 230	97.44 241	96.08 424	99.51 144	96.72 354	98.47 320	99.13 182	93.62 377	99.70 318	97.14 259	98.80 425	98.83 386

UnsupCasMVSNet_eth		97.89 279	97.60 303	98.75 213	99.31 209	97.17 273	97.62 280	99.35 227	98.72 157	98.76 270	98.68 315	92.57 400	99.74 290	97.76 201	95.60 528	99.34 262

TinyColmap		97.89 279	97.98 261	97.60 381	98.86 336	94.35 416	96.21 413	99.44 187	97.45 289	99.06 193	98.88 265	97.99 137	99.28 478	94.38 421	99.58 284	99.18 323

RRT-MVS		97.88 282	97.98 261	97.61 380	98.15 443	93.77 447	98.97 77	99.64 79	99.16 94	98.69 280	99.42 89	91.60 415	99.89 97	97.63 213	98.52 450	99.16 333

OMC-MVS		97.88 282	97.49 310	99.04 147	98.89 332	98.63 101	96.94 355	99.25 277	95.02 436	98.53 313	98.51 348	97.27 204	99.47 443	93.50 448	99.51 310	99.01 354

CANet		97.87 284	97.76 284	98.19 314	97.75 468	95.51 360	96.76 368	99.05 325	97.74 252	96.93 437	98.21 389	95.59 310	99.89 97	97.86 188	99.93 57	99.19 319

xiu_mvs_v1_base_debu		97.86 285	98.17 239	96.92 427	98.98 311	93.91 440	96.45 394	99.17 301	97.85 243	98.41 327	97.14 464	98.47 77	99.92 65	98.02 169	99.05 398	96.92 501

xiu_mvs_v1_base		97.86 285	98.17 239	96.92 427	98.98 311	93.91 440	96.45 394	99.17 301	97.85 243	98.41 327	97.14 464	98.47 77	99.92 65	98.02 169	99.05 398	96.92 501

xiu_mvs_v1_base_debi		97.86 285	98.17 239	96.92 427	98.98 311	93.91 440	96.45 394	99.17 301	97.85 243	98.41 327	97.14 464	98.47 77	99.92 65	98.02 169	99.05 398	96.92 501

NCCC		97.86 285	97.47 314	99.05 145	98.61 389	98.07 165	96.98 352	98.90 353	97.63 261	97.04 433	97.93 416	95.99 292	99.66 358	95.31 392	98.82 424	99.43 214

PMVS	91.26 20	97.86 285	97.94 268	97.65 374	99.71 49	97.94 184	98.52 130	98.68 391	98.99 124	97.52 403	99.35 111	97.41 194	98.18 515	91.59 492	99.67 245	96.82 505
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IterMVS-SCA-FT		97.85 290	98.18 238	96.87 430	99.27 221	91.16 498	95.53 452	99.25 277	99.10 107	99.41 114	99.35 111	93.10 388	99.96 13	98.65 114	99.94 51	99.49 177

D2MVS		97.84 291	97.84 279	97.83 351	99.14 267	94.74 404	96.94 355	98.88 357	95.84 400	98.89 240	98.96 242	94.40 352	99.69 328	97.55 221	99.95 39	99.05 345

CPTT-MVS		97.84 291	97.36 319	99.27 99	99.31 209	98.46 117	98.29 166	99.27 269	94.90 440	97.83 380	98.37 367	94.90 331	99.84 178	93.85 436	99.54 300	99.51 165

ArgMatch-Sym		97.83 293	97.54 305	98.71 223	98.98 311	97.65 221	96.25 412	99.43 193	95.60 412	98.85 251	97.98 410	95.72 304	99.56 407	95.54 388	99.50 318	98.92 373

mvs_anonymous		97.83 293	98.16 242	96.87 430	98.18 439	91.89 481	97.31 326	98.90 353	97.37 297	98.83 256	99.46 80	96.28 274	99.79 247	98.90 94	98.16 466	98.95 367

ELoFTR		97.81 295	97.74 286	98.04 334	99.39 185	95.79 351	97.28 332	99.58 103	94.13 463	99.38 121	99.37 104	93.31 380	99.60 390	97.23 249	99.96 28	98.74 406

PMatch-Up-SfM		97.79 296	97.48 313	98.72 221	99.03 295	97.78 207	96.05 426	99.48 159	96.90 341	98.72 275	99.18 163	92.00 412	99.71 309	97.15 258	98.77 426	98.69 412

h-mvs33		97.77 297	97.33 322	99.10 130	99.21 241	97.84 196	98.35 161	98.57 402	99.11 100	98.58 304	99.02 213	88.65 449	99.96 13	98.11 158	96.34 514	99.49 177

test_vis1_rt		97.75 298	97.72 290	97.83 351	98.81 348	96.35 324	97.30 327	99.69 57	94.61 448	97.87 375	98.05 404	96.26 275	98.32 512	98.74 107	98.18 463	98.82 388

IterMVS		97.73 299	98.11 247	96.57 442	99.24 233	90.28 509	95.52 454	99.21 287	98.86 142	99.33 138	99.33 118	93.11 387	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		97.72 300	97.94 268	97.07 419	98.66 384	92.39 474	97.68 269	99.81 32	95.20 433	99.54 79	99.44 85	91.56 418	99.41 457	99.78 21	99.77 172	99.40 231

MSDG		97.71 301	97.52 308	98.28 302	98.91 326	96.82 297	94.42 493	99.37 217	97.65 260	98.37 333	98.29 381	97.40 195	99.33 469	94.09 428	99.22 376	98.68 416

dtuonlycased		97.70 302	98.19 236	96.24 455	99.75 34	89.51 516	94.69 483	99.64 79	98.23 201	99.46 101	98.57 340	98.25 107	99.85 158	95.65 381	99.44 334	99.36 252

CDS-MVSNet		97.69 303	97.35 320	98.69 227	98.73 359	97.02 283	96.92 359	98.75 385	95.89 397	98.59 302	98.67 317	92.08 411	99.74 290	96.72 303	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		97.68 304	97.75 285	97.45 399	98.23 436	93.78 446	97.29 328	98.84 368	96.10 386	98.64 290	98.65 324	96.04 285	99.36 463	96.84 291	99.14 390	99.20 313

Fast-Effi-MVS+		97.67 305	97.38 317	98.57 253	98.71 365	97.43 242	97.23 334	99.45 179	94.82 443	96.13 476	96.51 475	98.52 75	99.91 74	96.19 353	98.83 422	98.37 445

EU-MVSNet		97.66 306	98.50 175	95.13 496	99.63 83	85.84 531	98.35 161	98.21 424	98.23 201	99.54 79	99.46 80	95.02 329	99.68 340	98.24 147	99.87 100	99.87 22

pmmvs5		97.64 307	97.49 310	98.08 328	99.14 267	95.12 388	96.70 373	99.05 325	93.77 472	98.62 295	98.83 278	93.23 383	99.75 283	98.33 144	99.76 188	99.36 252

N_pmnet		97.63 308	97.17 331	98.99 156	99.27 221	97.86 194	95.98 429	93.41 526	95.25 430	99.47 100	98.90 257	95.63 307	99.85 158	96.91 280	99.73 199	99.27 290

mvsany_test1		97.60 309	97.54 305	97.77 356	97.72 469	95.35 374	95.36 460	97.13 463	94.13 463	99.71 49	99.33 118	97.93 141	99.30 474	97.60 217	98.94 417	98.67 418

YYNet1		97.60 309	97.67 294	97.39 403	99.04 292	93.04 462	95.27 463	98.38 417	97.25 311	98.92 235	98.95 246	95.48 315	99.73 297	96.99 273	98.74 429	99.41 222

MDA-MVSNet_test_wron		97.60 309	97.66 297	97.41 402	99.04 292	93.09 458	95.27 463	98.42 414	97.26 310	98.88 244	98.95 246	95.43 317	99.73 297	97.02 269	98.72 431	99.41 222

pmmvs4		97.58 312	97.28 323	98.51 270	98.84 340	96.93 290	95.40 459	98.52 408	93.60 474	98.61 297	98.65 324	95.10 327	99.60 390	96.97 277	99.79 159	98.99 358

mvsmamba		97.57 313	97.26 325	98.51 270	98.69 374	96.73 304	98.74 99	97.25 457	97.03 331	97.88 374	99.23 150	90.95 426	99.87 135	96.61 317	99.00 408	98.91 377

PVSNet_BlendedMVS		97.55 314	97.53 307	97.60 381	98.92 323	93.77 447	96.64 380	99.43 193	94.49 450	97.62 393	99.18 163	96.82 235	99.67 345	94.73 406	99.93 57	99.36 252

GDP-MVS		97.50 315	97.11 338	98.67 230	99.02 303	96.85 296	98.16 183	99.71 48	98.32 192	98.52 315	98.54 343	83.39 493	99.95 25	98.79 101	99.56 292	99.19 319

ppachtmachnet_test		97.50 315	97.74 286	96.78 436	98.70 369	91.23 497	94.55 489	99.05 325	96.36 372	99.21 174	98.79 287	96.39 265	99.78 259	96.74 300	99.82 133	99.34 262

FMVSNet3		97.50 315	97.24 327	98.29 301	98.08 450	95.83 348	97.86 241	98.91 352	97.89 240	98.95 224	98.95 246	87.06 458	99.81 225	97.77 197	99.69 233	99.23 303

CHOSEN 1792x2688		97.49 318	97.14 335	98.54 265	99.68 64	96.09 334	96.50 391	99.62 89	91.58 502	98.84 254	98.97 238	92.36 402	99.88 115	96.76 297	99.95 39	99.67 78

CLD-MVS		97.49 318	97.16 332	98.48 276	99.07 282	97.03 282	94.71 479	99.21 287	94.46 452	98.06 359	97.16 462	97.57 176	99.48 440	94.46 414	99.78 164	98.95 367
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		97.46 320	97.07 339	98.64 236	98.73 359	97.33 247	97.45 309	97.64 445	99.11 100	98.58 304	97.98 410	88.65 449	99.79 247	98.11 158	97.39 495	98.81 393

Vis-MVSNet (Re-imp)		97.46 320	97.16 332	98.34 295	99.55 117	96.10 331	98.94 81	98.44 411	98.32 192	98.16 348	98.62 333	88.76 445	99.73 297	93.88 434	99.79 159	99.18 323

jason		97.45 322	97.35 320	97.76 359	99.24 233	93.93 439	95.86 439	98.42 414	94.24 459	98.50 316	98.13 395	94.82 335	99.91 74	97.22 250	99.73 199	99.43 214
jason: jason.
CL-MVSNet_self_test		97.44 323	97.22 329	98.08 328	98.57 398	95.78 352	94.30 497	98.79 376	96.58 362	98.60 300	98.19 391	94.74 341	99.64 370	96.41 338	98.84 421	98.82 388

MGCNet		97.44 323	97.01 343	98.72 221	96.42 526	96.74 303	97.20 339	91.97 536	98.46 182	98.30 336	98.79 287	92.74 397	99.91 74	99.30 62	99.94 51	99.52 161

DSMNet-mixed		97.42 325	97.60 303	96.87 430	99.15 265	91.46 487	98.54 128	99.12 312	92.87 489	97.58 397	99.63 39	96.21 277	99.90 81	95.74 376	99.54 300	99.27 290

USDC		97.41 326	97.40 315	97.44 400	98.94 317	93.67 450	95.17 467	99.53 136	94.03 468	98.97 218	99.10 191	95.29 320	99.34 467	95.84 373	99.73 199	99.30 282

BP-MVS1		97.40 327	96.97 345	98.71 223	99.07 282	96.81 298	98.34 163	97.18 460	98.58 172	98.17 345	98.61 335	84.01 489	99.94 41	98.97 89	99.78 164	99.37 244

our_test_3		97.39 328	97.73 289	96.34 450	98.70 369	89.78 514	94.61 487	98.97 342	96.50 365	99.04 203	98.85 271	95.98 293	99.84 178	97.26 247	99.67 245	99.41 222

usedtu_dtu_shiyan1		97.37 329	97.13 336	98.11 321	99.03 295	95.40 371	94.47 491	98.99 339	96.87 344	97.97 367	97.81 424	92.12 408	99.75 283	97.49 232	99.43 336	99.16 333

FE-MVSNET3		97.37 329	97.13 336	98.11 321	99.03 295	95.40 371	94.47 491	98.99 339	96.87 344	97.97 367	97.81 424	92.12 408	99.75 283	97.49 232	99.43 336	99.16 333

c3_l		97.36 331	97.37 318	97.31 404	98.09 449	93.25 457	95.01 472	99.16 304	97.05 328	98.77 268	98.72 302	92.88 393	99.64 370	96.93 279	99.76 188	99.05 345

alignmvs		97.35 332	96.88 353	98.78 204	98.54 401	98.09 158	97.71 265	97.69 440	99.20 84	97.59 396	95.90 490	88.12 455	99.55 412	98.18 153	98.96 415	98.70 411

Patchmtry		97.35 332	96.97 345	98.50 274	97.31 497	96.47 319	98.18 179	98.92 350	98.95 131	98.78 265	99.37 104	85.44 476	99.85 158	95.96 365	99.83 126	99.17 327

DP-MVS Recon		97.33 334	96.92 349	98.57 253	99.09 278	97.99 174	96.79 364	99.35 227	93.18 480	97.71 387	98.07 403	95.00 330	99.31 472	93.97 430	99.13 392	98.42 440

SP-SuperGlue		97.31 335	97.23 328	97.57 388	96.96 508	97.24 260	96.26 411	98.76 381	97.68 257	96.88 446	97.85 421	94.32 356	98.01 517	97.76 201	98.57 447	97.45 491

QAPM		97.31 335	96.81 360	98.82 192	98.80 351	97.49 232	99.06 66	99.19 293	90.22 515	97.69 389	99.16 170	96.91 229	99.90 81	90.89 506	99.41 339	99.07 343

UnsupCasMVSNet_bld		97.30 337	96.92 349	98.45 279	99.28 218	96.78 302	96.20 414	99.27 269	95.42 423	98.28 340	98.30 378	93.16 385	99.71 309	94.99 399	97.37 496	98.87 383

F-COLMAP		97.30 337	96.68 368	99.14 124	99.19 249	98.39 123	97.27 333	99.30 254	92.93 486	96.62 459	98.00 408	95.73 303	99.68 340	92.62 473	98.46 451	99.35 258

1112_ss		97.29 339	96.86 354	98.58 250	99.34 204	96.32 325	96.75 369	99.58 103	93.14 481	96.89 444	97.48 447	92.11 410	99.86 144	96.91 280	99.54 300	99.57 124

CANet_DTU		97.26 340	97.06 340	97.84 350	97.57 480	94.65 409	96.19 415	98.79 376	97.23 317	95.14 501	98.24 386	93.22 384	99.84 178	97.34 239	99.84 114	99.04 349

Patchmatch-RL test		97.26 340	97.02 342	97.99 339	99.52 131	95.53 359	96.13 420	99.71 48	97.47 282	99.27 153	99.16 170	84.30 487	99.62 378	97.89 181	99.77 172	98.81 393

CDPH-MVS		97.26 340	96.66 372	99.07 138	99.00 307	98.15 149	96.03 427	99.01 336	91.21 508	97.79 383	97.85 421	96.89 230	99.69 328	92.75 470	99.38 345	99.39 232

PatchMatch-RL		97.24 343	96.78 361	98.61 246	99.03 295	97.83 197	96.36 402	99.06 321	93.49 477	97.36 419	97.78 426	95.75 302	99.49 436	93.44 450	98.77 426	98.52 428

eth_miper_zixun_eth		97.23 344	97.25 326	97.17 413	98.00 454	92.77 467	94.71 479	99.18 297	97.27 309	98.56 308	98.74 298	91.89 413	99.69 328	97.06 268	99.81 140	99.05 345

SP-LightGlue		97.22 345	97.01 343	97.88 347	97.33 496	97.19 268	96.38 400	99.08 319	97.28 307	96.53 464	97.50 445	92.36 402	98.70 507	97.84 189	98.76 428	97.74 479

sss		97.21 346	96.93 347	98.06 331	98.83 342	95.22 384	96.75 369	98.48 410	94.49 450	97.27 421	97.90 417	92.77 396	99.80 234	96.57 321	99.32 356	99.16 333

LFMVS		97.20 347	96.72 365	98.64 236	98.72 361	96.95 288	98.93 82	94.14 521	99.74 12	98.78 265	99.01 225	84.45 484	99.73 297	97.44 234	99.27 366	99.25 297

HyFIR lowres test		97.19 348	96.60 379	98.96 164	99.62 87	97.28 257	95.17 467	99.50 149	94.21 460	99.01 208	98.32 376	86.61 461	99.99 2	97.10 264	99.84 114	99.60 102

miper_lstm_enhance		97.18 349	97.16 332	97.25 409	98.16 442	92.85 465	95.15 469	99.31 246	97.25 311	98.74 274	98.78 289	90.07 434	99.78 259	97.19 252	99.80 152	99.11 340

CNLPA		97.17 350	96.71 366	98.55 260	98.56 399	98.05 169	96.33 404	98.93 346	96.91 340	97.06 431	97.39 453	94.38 353	99.45 450	91.66 489	99.18 386	98.14 455

xiu_mvs_v2_base		97.16 351	97.49 310	96.17 461	98.54 401	92.46 472	95.45 456	98.84 368	97.25 311	97.48 407	96.49 476	98.31 97	99.90 81	96.34 343	98.68 438	96.15 517

AdaColmap		97.14 352	96.71 366	98.46 278	98.34 422	97.80 206	96.95 354	98.93 346	95.58 414	96.92 438	97.66 433	95.87 299	99.53 421	90.97 503	99.14 390	98.04 460

ALIKED-LG		97.10 353	96.63 374	98.50 274	97.96 455	98.68 100	97.75 260	99.68 64	95.86 399	98.36 335	98.33 375	91.58 417	99.04 490	90.87 507	99.31 358	97.77 477

train_agg		97.10 353	96.45 388	99.07 138	98.71 365	98.08 162	95.96 432	99.03 330	91.64 500	95.85 484	97.53 441	96.47 260	99.76 271	93.67 440	99.16 387	99.36 252

OpenMVS	96.65 7	97.09 355	96.68 368	98.32 296	98.32 423	97.16 274	98.86 92	99.37 217	89.48 520	96.29 474	99.15 176	96.56 256	99.90 81	92.90 463	99.20 381	97.89 468

PS-MVSNAJ		97.08 356	97.39 316	96.16 463	98.56 399	92.46 472	95.24 465	98.85 367	97.25 311	97.49 406	95.99 487	98.07 128	99.90 81	96.37 340	98.67 439	96.12 518

MatchFormer		97.07 357	96.92 349	97.49 395	98.44 412	95.92 342	96.79 364	99.14 310	93.08 483	99.32 144	99.10 191	93.89 368	99.03 491	92.78 469	99.78 164	97.52 488

miper_ehance_all_eth		97.06 358	97.03 341	97.16 415	97.83 464	93.06 459	94.66 484	99.09 317	95.99 393	98.69 280	98.45 358	92.73 398	99.61 386	96.79 293	99.03 402	98.82 388

lupinMVS		97.06 358	96.86 354	97.65 374	98.88 333	93.89 443	95.48 455	97.97 433	93.53 475	98.16 348	97.58 438	93.81 371	99.91 74	96.77 296	99.57 288	99.17 327

API-MVS		97.04 360	96.91 352	97.42 401	97.88 460	98.23 144	98.18 179	98.50 409	97.57 269	97.39 417	96.75 471	96.77 240	99.15 487	90.16 511	99.02 405	94.88 524

cl____		97.02 361	96.83 357	97.58 383	97.82 465	94.04 429	94.66 484	99.16 304	97.04 329	98.63 291	98.71 304	88.68 448	99.69 328	97.00 271	99.81 140	99.00 357

DIV-MVS_self_test		97.02 361	96.84 356	97.58 383	97.82 465	94.03 430	94.66 484	99.16 304	97.04 329	98.63 291	98.71 304	88.69 446	99.69 328	97.00 271	99.81 140	99.01 354

RPMNet		97.02 361	96.93 347	97.30 405	97.71 472	94.22 418	98.11 191	99.30 254	99.37 60	96.91 440	99.34 115	86.72 460	99.87 135	97.53 224	97.36 498	97.81 473

HQP-MVS		97.00 364	96.49 384	98.55 260	98.67 379	96.79 299	96.29 407	99.04 328	96.05 387	95.55 491	96.84 468	93.84 369	99.54 418	92.82 466	99.26 370	99.32 273

FA-MVS(test-final)		96.99 365	96.82 358	97.50 394	98.70 369	94.78 402	99.34 23	96.99 466	95.07 435	98.48 319	99.33 118	88.41 452	99.65 365	96.13 359	98.92 419	98.07 459

new_pmnet		96.99 365	96.76 362	97.67 370	98.72 361	94.89 397	95.95 434	98.20 425	92.62 492	98.55 310	98.54 343	94.88 334	99.52 425	93.96 431	99.44 334	98.59 425

Test_1112_low_res		96.99 365	96.55 381	98.31 298	99.35 199	95.47 368	95.84 442	99.53 136	91.51 504	96.80 450	98.48 355	91.36 422	99.83 196	96.58 319	99.53 304	99.62 92

PVSNet_Blended		96.88 368	96.68 368	97.47 398	98.92 323	93.77 447	94.71 479	99.43 193	90.98 511	97.62 393	97.36 456	96.82 235	99.67 345	94.73 406	99.56 292	98.98 359

SP-DiffGlue		96.87 369	96.76 362	97.21 410	95.17 537	96.88 295	96.12 421	98.93 346	96.51 363	98.37 333	97.55 440	93.65 376	97.83 520	96.11 360	98.45 452	96.92 501

MVSTER		96.86 370	96.55 381	97.79 354	97.91 459	94.21 420	97.56 291	98.87 359	97.49 281	99.06 193	99.05 207	80.72 502	99.80 234	98.44 131	99.82 133	99.37 244

BH-untuned		96.83 371	96.75 364	97.08 417	98.74 358	93.33 456	96.71 372	98.26 421	96.72 354	98.44 324	97.37 455	95.20 322	99.47 443	91.89 485	97.43 493	98.44 436

BH-RMVSNet		96.83 371	96.58 380	97.58 383	98.47 407	94.05 427	96.67 377	97.36 450	96.70 357	97.87 375	97.98 410	95.14 326	99.44 452	90.47 510	98.58 446	99.25 297

PAPM_NR		96.82 373	96.32 392	98.30 300	99.07 282	96.69 306	97.48 303	98.76 381	95.81 404	96.61 460	96.47 478	94.12 365	99.17 485	90.82 508	97.78 481	99.06 344

MG-MVS		96.77 374	96.61 377	97.26 408	98.31 424	93.06 459	95.93 435	98.12 430	96.45 370	97.92 370	98.73 300	93.77 373	99.39 460	91.19 500	99.04 401	99.33 268

test_yl		96.69 375	96.29 394	97.90 344	98.28 428	95.24 380	97.29 328	97.36 450	98.21 205	98.17 345	97.86 419	86.27 463	99.55 412	94.87 403	98.32 455	98.89 379

DCV-MVSNet		96.69 375	96.29 394	97.90 344	98.28 428	95.24 380	97.29 328	97.36 450	98.21 205	98.17 345	97.86 419	86.27 463	99.55 412	94.87 403	98.32 455	98.89 379

WTY-MVS		96.67 377	96.27 396	97.87 349	98.81 348	94.61 410	96.77 367	97.92 435	94.94 439	97.12 426	97.74 429	91.11 425	99.82 208	93.89 433	98.15 467	99.18 323

PatchT		96.65 378	96.35 390	97.54 390	97.40 493	95.32 377	97.98 223	96.64 480	99.33 66	96.89 444	99.42 89	84.32 486	99.81 225	97.69 210	97.49 489	97.48 489

TAPA-MVS	96.21 11	96.63 379	95.95 402	98.65 234	98.93 319	98.09 158	96.93 357	99.28 266	83.58 538	98.13 352	97.78 426	96.13 281	99.40 458	93.52 446	99.29 364	98.45 433
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 380	96.25 397	97.71 366	99.04 292	94.66 408	99.16 55	96.92 473	97.23 317	97.87 375	99.10 191	86.11 467	99.65 365	91.65 490	99.21 379	98.82 388

SIFT-ConvMatch		96.57 381	96.62 375	96.43 446	98.20 437	98.27 137	93.88 510	96.88 474	95.29 428	98.88 244	98.25 384	95.18 324	97.43 527	93.22 456	99.83 126	93.59 528

SIFT-NCM-Cal		96.56 382	96.68 368	96.20 459	98.27 430	98.44 119	94.40 494	96.67 478	95.29 428	97.63 392	98.17 392	96.40 264	96.59 538	93.61 441	99.66 253	93.57 529

Patchmatch-test		96.55 383	96.34 391	97.17 413	98.35 421	93.06 459	98.40 156	97.79 436	97.33 300	98.41 327	98.67 317	83.68 492	99.69 328	95.16 397	99.31 358	98.77 401

PMMVS		96.51 384	95.98 400	98.09 325	97.53 485	95.84 347	94.92 474	98.84 368	91.58 502	96.05 481	95.58 496	95.68 306	99.66 358	95.59 385	98.09 470	98.76 403

PLC	94.65 16	96.51 384	95.73 408	98.85 182	98.75 357	97.91 188	96.42 398	99.06 321	90.94 512	95.59 488	97.38 454	94.41 350	99.59 395	90.93 504	98.04 476	99.05 345
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 386	95.77 406	98.69 227	99.48 158	97.43 242	97.84 244	99.55 126	81.42 541	96.51 468	98.58 339	95.53 311	99.67 345	93.41 451	99.58 284	98.98 359

dtuonly		96.49 387	97.28 323	94.10 508	98.80 351	83.27 543	93.66 515	99.48 159	95.10 434	97.87 375	98.30 378	95.61 308	99.68 340	96.98 276	99.75 192	99.33 268

SIFT-UM-Cal		96.49 387	96.62 375	96.12 466	98.13 447	97.89 191	93.35 521	98.44 411	95.48 420	98.63 291	98.34 371	95.45 316	97.45 526	92.22 481	99.50 318	93.02 536

test1111		96.49 387	96.82 358	95.52 487	99.42 178	87.08 528	99.22 46	87.14 546	99.11 100	99.46 101	99.58 47	88.69 446	99.86 144	98.80 100	99.95 39	99.62 92

MAR-MVS		96.47 390	95.70 409	98.79 201	97.92 458	99.12 62	98.28 167	98.60 398	92.16 497	95.54 494	96.17 484	94.77 340	99.52 425	89.62 514	98.23 460	97.72 481
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		96.46 391	96.24 398	97.10 416	96.71 516	95.98 339	96.00 428	97.33 454	95.82 403	94.93 505	97.10 467	93.70 375	98.01 517	96.30 346	98.30 458	97.30 495

SIFT-PointCN		96.45 392	96.47 385	96.39 448	98.13 447	97.54 230	93.31 522	97.23 459	94.67 447	98.68 283	98.32 376	94.64 343	97.81 521	93.50 448	99.77 172	93.83 526

ECVR-MVS		96.42 393	96.61 377	95.85 476	99.38 187	88.18 523	99.22 46	86.00 548	99.08 114	99.36 128	99.57 49	88.47 451	99.82 208	98.52 127	99.95 39	99.54 143

SCA		96.41 394	96.66 372	95.67 482	98.24 433	88.35 521	95.85 441	96.88 474	96.11 385	97.67 390	98.67 317	93.10 388	99.85 158	94.16 423	99.22 376	98.81 393

SIFT-PCN-Cal		96.34 395	96.46 387	96.01 470	98.17 441	96.89 293	93.48 519	97.35 453	94.84 442	99.35 130	98.30 378	94.70 342	97.92 519	92.03 482	99.88 95	93.21 535

SIFT-UMatch		96.33 396	96.47 385	95.89 474	98.29 426	97.95 182	93.84 511	97.24 458	95.78 406	98.72 275	98.04 405	93.45 379	96.81 534	93.14 458	99.73 199	92.91 538

DPM-MVS		96.32 397	95.59 416	98.51 270	98.76 355	97.21 266	94.54 490	98.26 421	91.94 499	96.37 472	97.25 460	93.06 390	99.43 454	91.42 495	98.74 429	98.89 379

SIFT-NCMNet		96.30 398	96.40 389	96.03 469	97.80 467	97.68 218	92.34 530	96.94 471	95.55 415	98.84 254	98.63 330	94.17 361	97.63 524	93.57 445	99.71 217	92.77 540

CMPMVS	75.91 23	96.29 399	95.44 423	98.84 188	96.25 529	98.69 99	97.02 348	99.12 312	88.90 524	97.83 380	98.86 268	89.51 441	98.90 501	91.92 484	99.51 310	98.92 373
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SIFT-CM-Cal		96.28 400	96.31 393	96.16 463	98.39 419	98.11 154	93.46 520	96.47 484	94.81 444	98.49 317	98.43 360	94.48 347	97.34 529	92.60 475	99.70 227	93.02 536

SD_0403		96.28 400	95.83 404	97.64 377	98.72 361	94.30 417	98.87 89	98.77 379	97.80 247	96.53 464	98.02 407	97.34 199	99.47 443	76.93 543	99.48 323	99.16 333

CR-MVSNet		96.28 400	95.95 402	97.28 406	97.71 472	94.22 418	98.11 191	98.92 350	92.31 495	96.91 440	99.37 104	85.44 476	99.81 225	97.39 237	97.36 498	97.81 473

MonoMVSNet		96.25 403	96.53 383	95.39 491	96.57 519	91.01 500	98.82 97	97.68 442	98.57 174	98.03 363	99.37 104	90.92 427	97.78 522	94.99 399	93.88 536	97.38 493

CVMVSNet		96.25 403	97.21 330	93.38 520	99.10 275	80.56 551	97.20 339	98.19 427	96.94 335	99.00 209	99.02 213	89.50 442	99.80 234	96.36 342	99.59 279	99.78 50

AUN-MVS		96.24 405	95.45 422	98.60 248	98.70 369	97.22 264	97.38 316	97.65 443	95.95 395	95.53 495	97.96 415	82.11 501	99.79 247	96.31 344	97.44 492	98.80 398

usedtu_blend_shiyan5		96.20 406	95.62 412	97.94 342	96.53 520	94.93 394	98.83 96	99.59 100	98.89 138	96.71 453	91.16 538	86.05 468	99.73 297	96.70 306	96.09 519	99.17 327

EPNet		96.14 407	95.44 423	98.25 305	90.76 551	95.50 364	97.92 232	94.65 510	98.97 127	92.98 527	98.85 271	89.12 444	99.87 135	95.99 363	99.68 239	99.39 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SIFT-NN-PointCN		96.06 408	96.11 399	95.91 473	97.88 460	97.73 214	93.49 518	97.51 447	93.22 479	96.57 461	98.26 383	96.23 276	96.60 537	92.54 476	99.27 366	93.40 531

wuyk23d		96.06 408	97.62 302	91.38 524	98.65 388	98.57 108	98.85 93	96.95 470	96.86 347	99.90 14	99.16 170	99.18 19	98.40 511	89.23 517	99.77 172	77.18 546

Syy-MVS		96.04 410	95.56 418	97.49 395	97.10 502	94.48 412	96.18 417	96.58 481	95.65 410	94.77 507	92.29 535	91.27 424	99.36 463	98.17 155	98.05 474	98.63 420

MASt3R-SfM		96.02 411	95.82 405	96.60 441	97.03 507	94.90 396	94.26 499	98.53 406	88.40 529	98.41 327	98.67 317	92.39 401	97.62 525	95.31 392	99.41 339	97.29 496

miper_enhance_ethall		96.01 412	95.74 407	96.81 434	96.41 527	92.27 478	93.69 514	98.89 356	91.14 509	98.30 336	97.35 457	90.58 431	99.58 402	96.31 344	99.03 402	98.60 422

FMVSNet5		96.01 412	95.20 438	98.41 285	97.53 485	96.10 331	98.74 99	99.50 149	97.22 320	98.03 363	99.04 209	69.80 528	99.88 115	97.27 246	99.71 217	99.25 297

blended_shiyan6		95.99 414	95.33 429	97.95 341	97.06 504	94.89 397	95.34 461	98.58 400	96.17 380	97.06 431	92.41 532	87.64 456	99.76 271	97.64 212	96.09 519	99.19 319

blended_shiyan8		95.98 415	95.33 429	97.94 342	97.05 506	94.87 399	95.34 461	98.59 399	96.17 380	97.09 429	92.39 533	87.62 457	99.76 271	97.65 211	96.05 525	99.20 313

dmvs_re		95.98 415	95.39 426	97.74 362	98.86 336	97.45 239	98.37 159	95.69 502	97.95 233	96.56 462	95.95 488	90.70 430	97.68 523	88.32 519	96.13 518	98.11 456

ALIKED-MNN		95.97 417	95.30 432	98.00 337	97.66 479	98.12 153	96.98 352	99.41 204	91.11 510	94.04 519	97.30 458	91.56 418	98.61 509	89.99 512	99.63 262	97.28 497

baseline1		95.96 418	95.44 423	97.52 392	98.51 405	93.99 437	98.39 157	96.09 492	98.21 205	98.40 332	97.76 428	86.88 459	99.63 373	95.42 390	89.27 541	98.95 367

SIFT-MNN		95.92 419	95.97 401	95.74 481	98.18 439	98.00 172	94.17 501	96.99 466	95.74 408	97.16 425	97.90 417	90.71 429	95.79 540	93.71 439	99.21 379	93.44 530

HY-MVS	95.94 13	95.90 420	95.35 428	97.55 389	97.95 456	94.79 401	98.81 98	96.94 471	92.28 496	95.17 500	98.57 340	89.90 436	99.75 283	91.20 499	97.33 500	98.10 457

MVStest1		95.86 421	95.60 414	96.63 440	95.87 535	91.70 483	97.93 229	98.94 343	98.03 227	99.56 74	99.66 32	71.83 525	98.26 513	99.35 58	99.24 372	99.91 13

GA-MVS		95.86 421	95.32 431	97.49 395	98.60 391	94.15 423	93.83 512	97.93 434	95.49 419	96.68 456	97.42 452	83.21 494	99.30 474	96.22 351	98.55 448	99.01 354

OpenMVS_ROB	95.38 14	95.84 423	95.18 439	97.81 353	98.41 418	97.15 275	97.37 320	98.62 397	83.86 537	98.65 287	98.37 367	94.29 358	99.68 340	88.41 518	98.62 444	96.60 509

cl22		95.79 424	95.39 426	96.98 423	96.77 515	92.79 466	94.40 494	98.53 406	94.59 449	97.89 373	98.17 392	82.82 498	99.24 480	96.37 340	99.03 402	98.92 373

1314		95.74 425	95.60 414	96.17 461	97.53 485	92.75 468	98.07 200	98.31 419	91.22 507	94.25 514	96.68 472	95.53 311	99.03 491	91.64 491	97.18 502	96.74 507

WB-MVSnew		95.73 426	95.57 417	96.23 457	96.70 517	90.70 507	96.07 425	93.86 523	95.60 412	97.04 433	95.45 506	96.00 288	99.55 412	91.04 501	98.31 457	98.43 438

PVSNet	93.40 17	95.67 427	95.70 409	95.57 485	98.83 342	88.57 519	92.50 528	97.72 438	92.69 491	96.49 471	96.44 479	93.72 374	99.43 454	93.61 441	99.28 365	98.71 408

FE-MVS		95.66 428	94.95 444	97.77 356	98.53 403	95.28 379	99.40 19	96.09 492	93.11 482	97.96 369	99.26 137	79.10 511	99.77 265	92.40 479	98.71 433	98.27 449

tttt0517		95.64 429	94.98 442	97.64 377	99.36 194	93.81 445	98.72 104	90.47 540	98.08 226	98.67 284	98.34 371	73.88 523	99.92 65	97.77 197	99.51 310	99.20 313

SIFT-NN-CMatch		95.63 430	95.48 419	96.08 467	98.24 433	98.00 172	92.71 526	94.29 516	94.20 461	95.85 484	97.26 459	95.72 304	97.01 531	91.99 483	99.02 405	93.23 533

PatchmatchNet		95.58 431	95.67 411	95.30 495	97.34 495	87.32 527	97.65 275	96.65 479	95.30 427	97.07 430	98.69 313	84.77 481	99.75 283	94.97 401	98.64 440	98.83 386
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 432	95.12 440	96.86 433	97.54 483	93.94 438	96.49 392	96.53 483	94.36 458	97.03 435	96.61 474	94.26 359	99.16 486	86.91 525	96.31 515	97.47 490

JIA-IIPM		95.52 433	95.03 441	97.00 421	96.85 512	94.03 430	96.93 357	95.82 497	99.20 84	94.63 511	99.71 22	83.09 495	99.60 390	94.42 417	94.64 532	97.36 494

CHOSEN 280x420		95.51 434	95.47 420	95.65 484	98.25 431	88.27 522	93.25 523	98.88 357	93.53 475	94.65 510	97.15 463	86.17 465	99.93 53	97.41 236	99.93 57	98.73 407

wanda-best-256-512		95.48 435	94.74 449	97.68 368	96.53 520	94.12 424	94.17 501	98.57 402	95.84 400	96.71 453	91.16 538	86.05 468	99.76 271	97.57 219	96.09 519	99.17 327

FE-blended-shiyan7		95.48 435	94.74 449	97.68 368	96.53 520	94.12 424	94.17 501	98.57 402	95.84 400	96.71 453	91.16 538	86.05 468	99.76 271	97.57 219	96.09 519	99.17 327

gbinet_0.2-2-1-0.02		95.44 437	94.55 452	98.14 319	95.99 534	95.34 376	94.71 479	98.29 420	96.00 392	96.05 481	90.50 542	84.99 478	99.79 247	97.33 241	97.07 505	99.28 287

ADS-MVSNet2		95.43 438	94.98 442	96.76 437	98.14 444	91.74 482	97.92 232	97.76 437	90.23 513	96.51 468	98.91 254	85.61 473	99.85 158	92.88 464	96.90 506	98.69 412

SIFT-NN-NCMNet		95.39 439	95.22 436	95.92 472	98.29 426	98.34 132	93.58 517	94.60 512	94.07 467	94.84 506	97.53 441	94.37 354	96.62 536	91.01 502	98.64 440	92.80 539

SIFT-NN-UMatch		95.38 440	95.26 433	95.75 479	98.25 431	97.78 207	93.24 524	95.66 504	94.01 469	95.10 502	97.47 449	93.12 386	96.78 535	92.42 478	98.04 476	92.69 541

PAPR		95.29 441	94.47 453	97.75 360	97.50 491	95.14 387	94.89 476	98.71 390	91.39 506	95.35 498	95.48 501	94.57 345	99.14 488	84.95 530	97.37 496	98.97 363

thisisatest0530		95.27 442	94.45 454	97.74 362	99.19 249	94.37 415	97.86 241	90.20 541	97.17 322	98.22 343	97.65 434	73.53 524	99.90 81	96.90 285	99.35 349	98.95 367

ADS-MVSNet		95.24 443	94.93 445	96.18 460	98.14 444	90.10 511	97.92 232	97.32 455	90.23 513	96.51 468	98.91 254	85.61 473	99.74 290	92.88 464	96.90 506	98.69 412

PDCNetPlus		95.22 444	94.73 451	96.70 439	97.85 462	91.14 499	93.94 509	99.97 1	93.06 484	98.95 224	98.89 263	74.32 522	99.14 488	95.63 382	99.93 57	99.82 36

WBMVS		95.18 445	94.78 447	96.37 449	97.68 477	89.74 515	95.80 443	98.73 388	97.54 276	98.30 336	98.44 359	70.06 527	99.82 208	96.62 316	99.87 100	99.54 143

BH-w/o		95.13 446	94.89 446	95.86 475	98.20 437	91.31 492	95.65 448	97.37 449	93.64 473	96.52 467	95.70 495	93.04 391	99.02 493	88.10 520	95.82 526	97.24 498

tpmrst		95.07 447	95.46 421	93.91 511	97.11 501	84.36 539	97.62 280	96.96 469	94.98 437	96.35 473	98.80 285	85.46 475	99.59 395	95.60 384	96.23 516	97.79 476

pmmvs3		95.03 448	94.40 456	96.93 426	97.70 474	92.53 471	95.08 470	97.71 439	88.57 527	97.71 387	98.08 402	79.39 509	99.82 208	96.19 353	99.11 396	98.43 438

tpmvs		95.02 449	95.25 434	94.33 504	96.39 528	85.87 530	98.08 196	96.83 476	95.46 421	95.51 496	98.69 313	85.91 471	99.53 421	94.16 423	96.23 516	97.58 486

reproduce_monomvs		95.00 450	95.25 434	94.22 506	97.51 490	83.34 542	97.86 241	98.44 411	98.51 179	99.29 149	99.30 125	67.68 533	99.56 407	98.89 96	99.81 140	99.77 53

EPNet_dtu		94.93 451	94.78 447	95.38 492	93.58 542	87.68 525	96.78 366	95.69 502	97.35 299	89.14 542	98.09 401	88.15 454	99.49 436	94.95 402	99.30 362	98.98 359
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		94.79 452	94.33 459	96.15 465	96.02 533	92.36 476	92.34 530	99.26 275	85.34 536	95.08 503	94.96 512	92.96 392	98.53 510	94.41 420	98.59 445	97.56 487

SP-NN		94.67 453	94.44 455	95.36 493	95.12 538	95.23 383	94.27 498	96.10 491	94.46 452	90.91 537	95.76 494	91.47 421	93.87 545	95.23 395	96.62 511	97.00 500

tpm		94.67 453	94.34 458	95.66 483	97.68 477	88.42 520	97.88 237	94.90 508	94.46 452	96.03 483	98.56 342	78.66 513	99.79 247	95.88 367	95.01 531	98.78 400

test0.0.03 1		94.51 455	93.69 465	96.99 422	96.05 531	93.61 454	94.97 473	93.49 525	96.17 380	97.57 399	94.88 513	82.30 499	99.01 495	93.60 443	94.17 535	98.37 445

thres600view7		94.45 456	93.83 463	96.29 452	99.06 287	91.53 486	97.99 222	94.24 519	98.34 188	97.44 413	95.01 509	79.84 505	99.67 345	84.33 531	98.23 460	97.66 483

PCF-MVS	92.86 18	94.36 457	93.00 476	98.42 283	98.70 369	97.56 228	93.16 525	99.11 314	79.59 542	97.55 400	97.43 451	92.19 406	99.73 297	79.85 540	99.45 327	97.97 465
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		94.32 458	92.59 480	99.53 38	99.46 164	99.21 32	98.65 114	99.34 233	98.62 166	97.54 401	45.85 547	97.50 186	99.83 196	96.79 293	99.53 304	99.56 130

MVS-HIRNet		94.32 458	95.62 412	90.42 527	98.46 409	75.36 552	96.29 407	89.13 543	95.25 430	95.38 497	99.75 16	92.88 393	99.19 484	94.07 429	99.39 342	96.72 508

ET-MVSNet_ETH3D		94.30 460	93.21 472	97.58 383	98.14 444	94.47 413	94.78 478	93.24 528	94.72 445	89.56 540	95.87 491	78.57 515	99.81 225	96.91 280	97.11 504	98.46 430

ALIKED-NN		94.29 461	93.41 470	96.94 425	96.18 530	97.66 219	94.90 475	98.68 391	88.85 525	90.43 538	96.81 470	89.82 437	96.59 538	86.67 526	98.33 454	96.58 510

thres100view900		94.19 462	93.67 466	95.75 479	99.06 287	91.35 491	98.03 207	94.24 519	98.33 190	97.40 415	94.98 511	79.84 505	99.62 378	83.05 534	98.08 471	96.29 513

E-PMN		94.17 463	94.37 457	93.58 515	96.86 511	85.71 533	90.11 537	97.07 464	98.17 213	97.82 382	97.19 461	84.62 483	98.94 497	89.77 513	97.68 484	96.09 519

thres400		94.14 464	93.44 468	96.24 455	98.93 319	91.44 489	97.60 286	94.29 516	97.94 235	97.10 427	94.31 519	79.67 507	99.62 378	83.05 534	98.08 471	97.66 483

thisisatest0515		94.12 465	93.16 473	96.97 424	98.60 391	92.90 464	93.77 513	90.61 539	94.10 465	96.91 440	95.87 491	74.99 521	99.80 234	94.52 412	99.12 395	98.20 451

tfpn200view9		94.03 466	93.44 468	95.78 478	98.93 319	91.44 489	97.60 286	94.29 516	97.94 235	97.10 427	94.31 519	79.67 507	99.62 378	83.05 534	98.08 471	96.29 513

CostFormer		93.97 467	93.78 464	94.51 503	97.53 485	85.83 532	97.98 223	95.96 494	89.29 522	94.99 504	98.63 330	78.63 514	99.62 378	94.54 411	96.50 512	98.09 458

test-LLR		93.90 468	93.85 462	94.04 509	96.53 520	84.62 537	94.05 506	92.39 530	96.17 380	94.12 516	95.07 507	82.30 499	99.67 345	95.87 370	98.18 463	97.82 471

EMVS		93.83 469	94.02 460	93.23 521	96.83 513	84.96 534	89.77 538	96.32 486	97.92 237	97.43 414	96.36 482	86.17 465	98.93 498	87.68 521	97.73 483	95.81 520

testing3-2		93.78 470	93.91 461	93.39 519	98.82 345	81.72 549	97.76 257	95.28 505	98.60 168	96.54 463	96.66 473	65.85 540	99.62 378	96.65 314	98.99 410	98.82 388

baseline2		93.73 471	92.83 478	96.42 447	97.70 474	91.28 494	96.84 363	89.77 542	93.96 471	92.44 532	95.93 489	79.14 510	99.77 265	92.94 461	96.76 510	98.21 450

thres200		93.72 472	93.14 474	95.46 490	98.66 384	91.29 493	96.61 383	94.63 511	97.39 295	96.83 448	93.71 522	79.88 504	99.56 407	82.40 537	98.13 468	95.54 522

EPMVS		93.72 472	93.27 471	95.09 498	96.04 532	87.76 524	98.13 186	85.01 549	94.69 446	96.92 438	98.64 328	78.47 517	99.31 472	95.04 398	96.46 513	98.20 451

testing3		93.51 474	92.09 487	97.75 360	98.60 391	94.40 414	97.32 324	95.26 506	97.56 271	96.79 451	95.50 499	53.57 552	99.77 265	95.26 394	98.97 414	99.08 341

dp		93.47 475	93.59 467	93.13 522	96.64 518	81.62 550	97.66 273	96.42 485	92.80 490	96.11 477	98.64 328	78.55 516	99.59 395	93.31 452	92.18 540	98.16 454

FPMVS		93.44 476	92.23 485	97.08 417	99.25 232	97.86 194	95.61 449	97.16 462	92.90 488	93.76 524	98.65 324	75.94 520	95.66 541	79.30 541	97.49 489	97.73 480

XFeat-MNN		93.41 477	92.98 477	94.68 501	92.63 544	92.92 463	89.72 539	95.81 498	92.10 498	97.23 424	96.29 483	84.95 479	97.31 530	89.60 515	98.54 449	93.81 527

testing91		93.32 478	92.27 484	96.47 445	97.54 483	91.25 495	96.17 419	96.76 477	97.18 321	93.65 525	93.50 524	65.11 542	99.63 373	93.04 459	97.45 491	98.53 427

tpm cat1		93.29 479	93.13 475	93.75 513	97.39 494	84.74 535	97.39 314	97.65 443	83.39 539	94.16 515	98.41 362	82.86 497	99.39 460	91.56 493	95.35 530	97.14 499

UBG		93.25 480	92.32 482	96.04 468	97.72 469	90.16 510	95.92 437	95.91 496	96.03 390	93.95 522	93.04 529	69.60 529	99.52 425	90.72 509	97.98 478	98.45 433

MVS		93.19 481	92.09 487	96.50 444	96.91 510	94.03 430	98.07 200	98.06 432	68.01 545	94.56 512	96.48 477	95.96 295	99.30 474	83.84 532	96.89 508	96.17 515

tpm2		93.09 482	92.58 481	94.62 502	97.56 481	86.53 529	97.66 273	95.79 499	86.15 534	94.07 518	98.23 388	75.95 519	99.53 421	90.91 505	96.86 509	97.81 473

testing11		93.08 483	92.02 489	96.26 454	97.56 481	90.83 504	96.32 405	95.70 500	96.47 368	92.66 530	93.73 521	64.36 543	99.59 395	93.77 438	97.57 485	98.37 445

testing99		93.04 484	91.98 492	96.23 457	97.53 485	90.70 507	96.35 403	95.94 495	96.87 344	93.41 526	93.43 526	63.84 544	99.59 395	93.24 455	97.19 501	98.40 441

SIFT-NN		92.96 485	92.79 479	93.46 516	96.92 509	96.45 320	91.89 532	94.39 514	92.91 487	92.54 531	95.46 502	88.26 453	90.71 548	85.22 529	97.52 487	93.22 534

dmvs_testset		92.94 486	92.21 486	95.13 496	98.59 394	90.99 501	97.65 275	92.09 532	96.95 334	94.00 520	93.55 523	92.34 404	96.97 533	72.20 544	92.52 538	97.43 492

myMVS_eth3d28		92.92 487	92.31 483	94.77 499	97.84 463	87.59 526	96.19 415	96.11 490	97.08 327	94.27 513	93.49 525	66.07 539	98.78 504	91.78 487	97.93 480	97.92 467

KD-MVS_2432*1600		92.87 488	91.99 490	95.51 488	91.37 547	89.27 517	94.07 504	98.14 428	95.42 423	97.25 422	96.44 479	67.86 531	99.24 480	91.28 497	96.08 523	98.02 461

miper_refine_blended		92.87 488	91.99 490	95.51 488	91.37 547	89.27 517	94.07 504	98.14 428	95.42 423	97.25 422	96.44 479	67.86 531	99.24 480	91.28 497	96.08 523	98.02 461

ETVMVS		92.60 490	91.08 499	97.18 411	97.70 474	93.65 452	96.54 387	95.70 500	96.51 363	94.68 509	92.39 533	61.80 548	99.50 432	86.97 523	97.41 494	98.40 441

MVE	83.40 22	92.50 491	91.92 493	94.25 505	98.83 342	91.64 484	92.71 526	83.52 550	95.92 396	86.46 545	95.46 502	95.20 322	95.40 542	80.51 539	98.64 440	95.73 521
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		92.39 492	91.89 494	93.89 512	99.38 187	82.28 547	99.32 26	66.03 554	99.08 114	98.77 268	99.57 49	66.26 537	99.84 178	98.71 110	99.95 39	99.54 143

UWE-MVS		92.38 493	91.76 496	94.21 507	97.16 500	84.65 536	95.42 458	88.45 544	95.96 394	96.17 475	95.84 493	66.36 536	99.71 309	91.87 486	98.64 440	98.28 448

gg-mvs-nofinetune		92.37 494	91.20 498	95.85 476	95.80 536	92.38 475	99.31 30	81.84 551	99.75 10	91.83 535	99.74 18	68.29 530	99.02 493	87.15 522	97.12 503	96.16 516

test-mter		92.33 495	91.76 496	94.04 509	96.53 520	84.62 537	94.05 506	92.39 530	94.00 470	94.12 516	95.07 507	65.63 541	99.67 345	95.87 370	98.18 463	97.82 471

IB-MVS	91.63 19	92.24 496	90.90 500	96.27 453	97.22 499	91.24 496	94.36 496	93.33 527	92.37 494	92.24 534	94.58 518	66.20 538	99.89 97	93.16 457	94.63 533	97.66 483
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		92.19 497	91.77 495	93.46 516	96.48 525	82.80 546	94.05 506	91.52 538	94.45 455	94.00 520	94.88 513	66.65 535	99.56 407	95.78 375	98.11 469	98.02 461

blend_shiyan4		92.09 498	90.16 505	97.88 347	96.78 514	94.93 394	95.24 465	98.58 400	96.22 378	96.07 479	91.42 537	63.46 547	99.73 297	96.70 306	76.98 547	98.98 359

testing222		91.96 499	90.37 502	96.72 438	97.47 492	92.59 469	96.11 422	94.76 509	96.83 348	92.90 528	92.87 530	57.92 550	99.55 412	86.93 524	97.52 487	98.00 464

myMVS_eth3d		91.92 500	90.45 501	96.30 451	97.10 502	90.90 502	96.18 417	96.58 481	95.65 410	94.77 507	92.29 535	53.88 551	99.36 463	89.59 516	98.05 474	98.63 420

PAPM		91.88 501	90.34 503	96.51 443	98.06 452	92.56 470	92.44 529	97.17 461	86.35 533	90.38 539	96.01 486	86.61 461	99.21 483	70.65 546	95.43 529	97.75 478

PVSNet_0	89.98 21	91.15 502	90.30 504	93.70 514	97.72 469	84.34 540	90.24 535	97.42 448	90.20 516	93.79 523	93.09 528	90.90 428	98.89 502	86.57 527	72.76 548	97.87 470

UWE-MVS-28		90.22 503	89.28 506	93.02 523	94.50 541	82.87 545	96.52 390	87.51 545	95.21 432	92.36 533	96.04 485	71.57 526	98.25 514	72.04 545	97.77 482	97.94 466

XFeat-NN		89.63 504	89.13 507	91.14 525	90.93 550	90.02 513	84.90 542	94.05 522	88.10 530	92.89 529	93.33 527	78.74 512	90.89 547	83.46 533	95.72 527	92.52 542

0.4-1-1-0.1		88.42 505	85.91 508	95.94 471	93.08 543	91.54 485	90.99 534	92.04 534	89.96 519	84.83 546	83.25 544	63.75 545	99.52 425	93.25 454	82.07 542	96.75 506

0.4-1-1-0.2		87.49 506	84.89 509	95.31 494	91.33 549	90.08 512	88.47 541	92.07 533	88.70 526	84.06 547	81.08 546	63.62 546	99.49 436	92.93 462	81.71 543	96.37 512

0.3-1-1-0.015		87.27 507	84.50 511	95.57 485	91.70 546	90.77 505	89.41 540	92.04 534	88.98 523	82.46 548	81.35 545	60.36 549	99.50 432	92.96 460	81.23 544	96.45 511

GLUNet-SfM		86.26 508	84.68 510	91.01 526	80.58 553	83.56 541	78.04 543	93.59 524	76.70 543	95.29 499	94.72 516	77.51 518	94.26 544	66.39 547	99.33 353	95.20 523

EGC-MVSNET		85.24 509	80.54 512	99.34 83	99.77 27	99.20 38	99.08 62	99.29 262	12.08 549	20.84 551	99.42 89	97.55 178	99.85 158	97.08 265	99.72 208	98.96 366

test_method		79.78 510	79.50 513	80.62 528	80.21 554	45.76 557	70.82 544	98.41 416	31.08 548	80.89 549	97.71 430	84.85 480	97.37 528	91.51 494	80.03 545	98.75 404

tmp_tt		78.77 511	78.73 514	78.90 529	58.45 555	74.76 554	94.20 500	78.26 553	39.16 547	86.71 544	92.82 531	80.50 503	75.19 550	86.16 528	92.29 539	86.74 543

dongtai		76.24 512	75.95 515	77.12 530	92.39 545	67.91 555	90.16 536	59.44 556	82.04 540	89.42 541	94.67 517	49.68 553	81.74 549	48.06 548	77.66 546	81.72 544

kuosan		69.30 513	68.95 516	70.34 531	87.68 552	65.00 556	91.11 533	59.90 555	69.02 544	74.46 550	88.89 543	48.58 554	68.03 551	28.61 549	72.33 549	77.99 545

cdsmvs_eth3d_5k		24.66 514	32.88 517	0.00 534	0.00 558	0.00 560	0.00 545	99.10 315	0.00 552	0.00 554	97.58 438	99.21 18	0.00 554	0.00 552	0.00 552	0.00 549

testmvs		17.12 515	20.53 518	6.87 533	12.05 556	4.20 559	93.62 516	6.73 557	4.62 551	10.41 552	24.33 548	8.28 556	3.56 553	9.69 551	15.07 550	12.86 548

test123		17.04 516	20.11 519	7.82 532	10.25 557	4.91 558	94.80 477	4.47 558	4.93 550	10.00 553	24.28 549	9.69 555	3.64 552	10.14 550	12.43 551	14.92 547

pcd_1.5k_mvsjas		8.17 517	10.90 520	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	98.07 128	0.00 554	0.00 552	0.00 552	0.00 549

ab-mvs-re		8.12 518	10.83 521	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	97.48 447	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

mmdepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test-260524					99.33 205	99.02 71		99.25 277		99.23 169		96.59 255	99.85 158	98.10 160	99.62 266

MED-MVS test				99.45 64	99.58 94	98.93 80	98.68 109	99.60 94	96.46 369	99.53 83	98.77 291		99.83 196	96.67 310	99.64 257	99.58 117

TestfortrainingZip				98.97 162	98.30 425	98.43 120	98.68 109	98.26 421	97.76 251	98.86 250	98.16 394	95.15 325	99.47 443		97.55 486	99.02 352

WAC-MVS						90.90 502								91.37 496

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		98.86 364					99.89 97	97.14 259	99.60 275	99.71 65

PC_three_1452									93.27 478	99.40 117	98.54 343	98.22 113	97.00 532	95.17 396	99.45 327	99.49 177

No_MVS				99.32 91	98.43 414	98.37 126		98.86 364					99.89 97	97.14 259	99.60 275	99.71 65

test_one_0601					99.39 185	99.20 38		99.31 246	98.49 180	98.66 286	99.02 213	97.64 168

eth-test2					0.00 558

eth-test					0.00 558

ZD-MVS					99.01 306	98.84 86		99.07 320	94.10 465	98.05 361	98.12 397	96.36 270	99.86 144	92.70 472	99.19 384

RE-MVS-def			98.58 162		99.20 245	99.38 12	98.48 143	99.30 254	98.64 161	98.95 224	98.96 242	97.75 159		96.56 325	99.39 342	99.45 206

IU-MVS					99.49 150	99.15 52		98.87 359	92.97 485	99.41 114				96.76 297	99.62 266	99.66 80

OPU-MVS				98.82 192	98.59 394	98.30 135	98.10 193				98.52 347	98.18 118	98.75 505	94.62 409	99.48 323	99.41 222

test_241102_TWO								99.30 254	98.03 227	99.26 157	99.02 213	97.51 185	99.88 115	96.91 280	99.60 275	99.66 80

test_241102_ONE					99.49 150	99.17 43		99.31 246	97.98 230	99.66 60	98.90 257	98.36 90	99.48 440

9.14			97.78 283		99.07 282		97.53 296	99.32 241	95.53 418	98.54 312	98.70 311	97.58 175	99.76 271	94.32 422	99.46 325

save fliter					99.11 273	97.97 178	96.53 389	99.02 333	98.24 200

test_0728_THIRD									98.17 213	99.08 191	99.02 213	97.89 147	99.88 115	97.07 266	99.71 217	99.70 70

test_0728_SECOND				99.60 16	99.50 141	99.23 30	98.02 210	99.32 241					99.88 115	96.99 273	99.63 262	99.68 73

test0726					99.50 141	99.21 32	98.17 182	99.35 227	97.97 231	99.26 157	99.06 200	97.61 172

GSMVS																98.81 393

test_part2					99.36 194	99.10 65				99.05 201

sam_mvs1												84.74 482				98.81 393

sam_mvs												84.29 488

ambc				98.24 307	98.82 345	95.97 341	98.62 118	99.00 338		99.27 153	99.21 154	96.99 224	99.50 432	96.55 328	99.50 318	99.26 296

MTGPA								99.20 289

test_post1							97.59 288				20.48 551	83.07 496	99.66 358	94.16 423

test_post											21.25 550	83.86 491	99.70 318

patchmatchnet-post											98.77 291	84.37 485	99.85 158

GG-mvs-BLEND				94.76 500	94.54 540	92.13 480	99.31 30	80.47 552		88.73 543	91.01 541	67.59 534	98.16 516	82.30 538	94.53 534	93.98 525

MTMP							97.93 229	91.91 537

gm-plane-assit					94.83 539	81.97 548			88.07 531		94.99 510		99.60 390	91.76 488

test9_res														93.28 453	99.15 389	99.38 241

TEST9					98.71 365	98.08 162	95.96 432	99.03 330	91.40 505	95.85 484	97.53 441	96.52 258	99.76 271

test_8					98.67 379	98.01 171	95.91 438	99.02 333	91.64 500	95.79 487	97.50 445	96.47 260	99.76 271

agg_prior2														92.50 477	99.16 387	99.37 244

agg_prior					98.68 378	97.99 174		99.01 336		95.59 488			99.77 265

TestCases				99.16 118	99.50 141	98.55 109		99.58 103	96.80 349	98.88 244	99.06 200	97.65 165	99.57 404	94.45 415	99.61 273	99.37 244

test_prior4						97.97 178	95.86 439

test_prior2							95.74 446		96.48 367	96.11 477	97.63 436	95.92 298		94.16 423	99.20 381

test_prior				98.95 166	98.69 374	97.95 182		99.03 330					99.59 395			99.30 282

旧先验2							95.76 445		88.56 528	97.52 403			99.66 358	94.48 413

新几何2							95.93 435

新几何1				98.91 175	98.94 317	97.76 210		98.76 381	87.58 532	96.75 452	98.10 399	94.80 338	99.78 259	92.73 471	99.00 408	99.20 313

旧先验1					98.82 345	97.45 239		98.76 381			98.34 371	95.50 314			99.01 407	99.23 303

无先验							95.74 446	98.74 387	89.38 521				99.73 297	92.38 480		99.22 308

原ACMM2							95.53 452

原ACMM1				98.35 294	98.90 327	96.25 327		98.83 372	92.48 493	96.07 479	98.10 399	95.39 318	99.71 309	92.61 474	98.99 410	99.08 341

test222					98.92 323	96.93 290	95.54 451	98.78 378	85.72 535	96.86 447	98.11 398	94.43 349			99.10 397	99.23 303

testdata2													99.79 247	92.80 468

segment_acmp												97.02 221

testdata				98.09 325	98.93 319	95.40 371		98.80 375	90.08 517	97.45 411	98.37 367	95.26 321	99.70 318	93.58 444	98.95 416	99.17 327

testdata1							95.44 457		96.32 374

test12				98.93 170	98.58 396	97.83 197		98.66 393		96.53 464		95.51 313	99.69 328		99.13 392	99.27 290

plane_prior7					99.19 249	97.87 193

plane_prior6					98.99 310	97.70 217						94.90 331

plane_prior5								99.27 269					99.70 318	94.42 417	99.51 310	99.45 206

plane_prior4											97.98 410

plane_prior3						97.78 207			97.41 292	97.79 383

plane_prior2							97.77 254		98.20 209

plane_prior1					99.05 290

plane_prior						97.65 221	97.07 347		96.72 354						99.36 346

n2								0.00 559

nn								0.00 559

door-mid								99.57 111

lessismore_v0				98.97 162	99.73 38	97.53 231		86.71 547		99.37 125	99.52 67	89.93 435	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	96.60 360	99.10 189	99.06 200	98.71 51	99.83 196	95.58 386	99.78 164	99.62 92

test11								98.87 359

door								99.41 204

HQP5-MVS						96.79 299

HQP-NCC					98.67 379		96.29 407		96.05 387	95.55 491

ACMP_Plane					98.67 379		96.29 407		96.05 387	95.55 491

BP-MVS														92.82 466

HQP4-MVS										95.56 490			99.54 418			99.32 273

HQP3-MVS								99.04 328							99.26 370

HQP2-MVS												93.84 369

NP-MVS					98.84 340	97.39 244					96.84 468

MDTV_nov1_ep13_2view						74.92 553	97.69 268		90.06 518	97.75 386		85.78 472		93.52 446		98.69 412

MDTV_nov1_ep13			95.22 436		97.06 504	83.20 544	97.74 262	96.16 488	94.37 457	96.99 436	98.83 278	83.95 490	99.53 421	93.90 432	97.95 479

ACMMP++_ref															99.77 172

ACMMP++															99.68 239

Test By Simon												96.52 258

ITE_SJBPF				98.87 179	99.22 239	98.48 116		99.35 227	97.50 279	98.28 340	98.60 337	97.64 168	99.35 466	93.86 435	99.27 366	98.79 399

DeepMVS_CX				93.44 518	98.24 433	94.21 420		94.34 515	64.28 546	91.34 536	94.87 515	89.45 443	92.77 546	77.54 542	93.14 537	93.35 532

High-resolution multi-view benchmark