High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

test_fmvsmconf0.01_n		99.57 7	99.63 7	99.36 64	99.87 12	98.13 131	98.08 160	99.95 1	99.45 36	99.98 2	99.75 11	99.80 1	99.97 4	99.82 8	99.99 5	99.99 1

test_vis3_rt		99.14 46	99.17 43	99.07 120	99.78 25	98.38 109	98.92 76	99.94 2	97.80 173	99.91 11	99.67 25	97.15 155	98.91 389	99.76 16	99.56 210	99.92 9

test_fmvs3		99.12 51	99.41 19	98.25 230	99.76 31	95.07 283	99.05 64	99.94 2	97.78 175	99.82 21	99.84 2	98.56 53	99.71 246	99.96 1	99.96 25	99.97 3

test_fmvs1_n		98.09 185	98.28 154	97.52 290	99.68 58	93.47 332	98.63 98	99.93 4	95.41 311	99.68 39	99.64 32	91.88 308	99.48 338	99.82 8	99.87 77	99.62 67

ANet_high		99.57 7	99.67 5	99.28 85	99.89 6	98.09 135	99.14 54	99.93 4	99.82 3	99.93 6	99.81 5	99.17 18	99.94 35	99.31 41	100.00 1	99.82 25

test_fmvs2		98.70 103	98.97 65	97.89 257	99.54 98	94.05 310	98.55 107	99.92 6	96.78 257	99.72 31	99.78 8	96.60 188	99.67 267	99.91 2	99.90 69	99.94 7

test_vis1_n_1920		98.40 150	98.92 68	96.81 327	99.74 37	90.76 376	98.15 152	99.91 7	98.33 130	99.89 15	99.55 48	95.07 245	99.88 83	99.76 16	99.93 44	99.79 30

test_vis1_n		98.31 162	98.50 120	97.73 273	99.76 31	94.17 308	98.68 95	99.91 7	96.31 279	99.79 25	99.57 42	92.85 295	99.42 350	99.79 13	99.84 85	99.60 74

test_fmvsmconf0.1_n		99.49 12	99.54 10	99.34 73	99.78 25	98.11 132	97.77 204	99.90 9	99.33 50	99.97 3	99.66 27	99.71 3	99.96 11	99.79 13	99.99 5	99.96 5

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 10	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 11	100.00 1	99.85 19

CS-MVS		99.13 49	99.10 54	99.24 95	99.06 212	99.15 47	99.36 19	99.88 11	99.36 48	98.21 245	98.46 262	98.68 42	99.93 40	99.03 59	99.85 81	98.64 315

CS-MVS-test		99.13 49	99.09 55	99.26 90	99.13 197	98.97 66	99.31 27	99.88 11	99.44 38	98.16 248	98.51 254	98.64 43	99.93 40	98.91 65	99.85 81	98.88 283

fmvsm_s_conf0.1_n_a		99.17 42	99.30 32	98.80 160	99.75 35	96.59 233	97.97 180	99.86 13	98.22 141	99.88 17	99.71 17	98.59 49	99.84 137	99.73 19	99.98 12	99.98 2

dcpmvs_2		98.78 90	99.11 52	97.78 264	99.56 89	93.67 328	99.06 62	99.86 13	99.50 30	99.66 42	99.26 100	97.21 153	99.99 2	98.00 122	99.91 63	99.68 54

fmvsm_s_conf0.1_n		99.16 45	99.33 26	98.64 181	99.71 47	96.10 246	97.87 192	99.85 15	98.56 122	99.90 12	99.68 20	98.69 41	99.85 120	99.72 21	99.98 12	99.97 3

test_fmvsmvis_n_1920		99.26 32	99.49 12	98.54 204	99.66 64	96.97 219	98.00 174	99.85 15	99.24 59	99.92 8	99.50 59	99.39 11	99.95 22	99.89 3	99.98 12	98.71 306

test_cas_vis1_n_1920		98.33 159	98.68 95	97.27 304	99.69 56	92.29 353	98.03 168	99.85 15	97.62 185	99.96 4	99.62 34	93.98 276	99.74 233	99.52 31	99.86 80	99.79 30

test_fmvsmconf_n		99.44 15	99.48 14	99.31 83	99.64 70	98.10 134	97.68 215	99.84 18	99.29 55	99.92 8	99.57 42	99.60 5	99.96 11	99.74 18	99.98 12	99.89 11

EC-MVSNet		99.09 54	99.05 59	99.20 99	99.28 159	98.93 71	99.24 41	99.84 18	99.08 84	98.12 253	98.37 270	98.72 38	99.90 64	99.05 57	99.77 124	98.77 300

test_fmvsm_n_1920		99.33 26	99.45 18	98.99 135	99.57 81	97.73 178	97.93 181	99.83 20	99.22 60	99.93 6	99.30 94	99.42 10	99.96 11	99.85 5	99.99 5	99.29 212

LCM-MVSNet-Re		98.64 118	98.48 125	99.11 112	98.85 251	98.51 102	98.49 119	99.83 20	98.37 127	99.69 37	99.46 66	98.21 77	99.92 50	94.13 315	99.30 258	98.91 279

fmvsm_s_conf0.5_n_a		99.10 53	99.20 41	98.78 166	99.55 93	96.59 233	97.79 201	99.82 22	98.21 142	99.81 23	99.53 54	98.46 59	99.84 137	99.70 22	99.97 19	99.90 10

bld_raw_dy_0_64		97.62 222	97.51 223	97.96 253	97.97 345	96.28 242	98.20 146	99.82 22	96.46 272	99.37 89	97.12 347	92.42 300	99.70 250	96.27 242	99.97 19	97.90 357

fmvsm_s_conf0.5_n		99.09 54	99.26 37	98.61 189	99.55 93	96.09 249	97.74 209	99.81 24	98.55 123	99.85 19	99.55 48	98.60 48	99.84 137	99.69 24	99.98 12	99.89 11

test_fmvs1		97.72 214	97.94 191	97.07 314	98.66 291	92.39 350	97.68 215	99.81 24	95.20 315	99.54 56	99.44 71	91.56 310	99.41 351	99.78 15	99.77 124	99.40 173

test_f		98.67 114	98.87 71	98.05 247	99.72 44	95.59 261	98.51 116	99.81 24	96.30 281	99.78 26	99.82 4	96.14 205	98.63 394	99.82 8	99.93 44	99.95 6

Vis-MVSNet		99.34 25	99.36 22	99.27 88	99.73 38	98.26 118	99.17 50	99.78 27	99.11 72	99.27 108	99.48 64	98.82 31	99.95 22	98.94 64	99.93 44	99.59 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 21	99.85 17	99.11 59	99.90 1	99.78 27	99.63 17	99.78 26	99.67 25	99.48 9	99.81 177	99.30 42	99.97 19	99.77 35
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
fmvsm_l_conf0.5_n_a		99.19 41	99.27 35	98.94 142	99.65 65	97.05 215	97.80 200	99.76 29	98.70 110	99.78 26	99.11 133	98.79 34	99.95 22	99.85 5	99.96 25	99.83 22

fmvsm_l_conf0.5_n		99.21 39	99.28 34	99.02 132	99.64 70	97.28 201	97.82 197	99.76 29	98.73 107	99.82 21	99.09 139	98.81 32	99.95 22	99.86 4	99.96 25	99.83 22

pmmvs6		99.67 3	99.70 3	99.60 11	99.90 4	99.27 22	99.53 7	99.76 29	99.64 15	99.84 20	99.83 3	99.50 8	99.87 100	99.36 38	99.92 55	99.64 63

Gipuma		99.03 59	99.16 45	98.64 181	99.94 2	98.51 102	99.32 23	99.75 32	99.58 25	98.60 211	99.62 34	98.22 75	99.51 332	97.70 141	99.73 142	97.89 359
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UA-Net		99.47 13	99.40 20	99.70 2	99.49 115	99.29 19	99.80 3	99.72 33	99.82 3	99.04 143	99.81 5	98.05 90	99.96 11	98.85 69	99.99 5	99.86 18

Patchmatch-RL test		97.26 247	97.02 250	97.99 251	99.52 103	95.53 265	96.13 320	99.71 34	97.47 201	99.27 108	99.16 122	84.30 362	99.62 292	97.89 127	99.77 124	98.81 292

mvs_tets		99.63 5	99.67 5	99.49 48	99.88 9	98.61 92	99.34 20	99.71 34	99.27 57	99.90 12	99.74 13	99.68 4	99.97 4	99.55 29	99.99 5	99.88 14

TDRefinement		99.42 19	99.38 21	99.55 23	99.76 31	99.33 16	99.68 5	99.71 34	99.38 44	99.53 60	99.61 37	98.64 43	99.80 184	98.24 105	99.84 85	99.52 118

test_vis1_rt		97.75 212	97.72 208	97.83 260	98.81 260	96.35 239	97.30 253	99.69 37	94.61 326	97.87 269	98.05 297	96.26 203	98.32 397	98.74 76	98.18 341	98.82 288

testf1		99.25 33	99.16 45	99.51 43	99.89 6	99.63 3	98.71 92	99.69 37	98.90 99	99.43 76	99.35 83	98.86 28	99.67 267	97.81 133	99.81 99	99.24 222

APD_test2		99.25 33	99.16 45	99.51 43	99.89 6	99.63 3	98.71 92	99.69 37	98.90 99	99.43 76	99.35 83	98.86 28	99.67 267	97.81 133	99.81 99	99.24 222

patch_mono-2		98.51 140	98.63 102	98.17 236	99.38 140	94.78 288	97.36 248	99.69 37	98.16 152	98.49 226	99.29 95	97.06 159	99.97 4	98.29 104	99.91 63	99.76 39

anonymousdsp		99.51 11	99.47 16	99.62 6	99.88 9	99.08 63	99.34 20	99.69 37	98.93 97	99.65 45	99.72 16	98.93 26	99.95 22	99.11 52	100.00 1	99.82 25

Effi-MVS+		98.02 189	97.82 201	98.62 186	98.53 309	97.19 209	97.33 250	99.68 42	97.30 221	96.68 337	97.46 332	98.56 53	99.80 184	96.63 214	98.20 340	98.86 285

PM-MVS		98.82 84	98.72 87	99.12 110	99.64 70	98.54 100	97.98 177	99.68 42	97.62 185	99.34 96	99.18 116	97.54 128	99.77 215	97.79 135	99.74 139	99.04 255

PVSNet_Blended_VisFu		98.17 180	98.15 171	98.22 233	99.73 38	95.15 279	97.36 248	99.68 42	94.45 332	98.99 149	99.27 98	96.87 170	99.94 35	97.13 170	99.91 63	99.57 91

jajsoiax		99.58 6	99.61 8	99.48 51	99.87 12	98.61 92	99.28 37	99.66 45	99.09 82	99.89 15	99.68 20	99.53 7	99.97 4	99.50 32	99.99 5	99.87 16

PS-MVSNAJss		99.46 14	99.49 12	99.35 70	99.90 4	98.15 128	99.20 45	99.65 46	99.48 32	99.92 8	99.71 17	98.07 87	99.96 11	99.53 30	100.00 1	99.93 8

pm-mvs1		99.44 15	99.48 14	99.33 78	99.80 22	98.63 89	99.29 33	99.63 47	99.30 54	99.65 45	99.60 39	99.16 20	99.82 164	99.07 55	99.83 92	99.56 97

casdiffmvs_mvg		99.12 51	99.16 45	98.99 135	99.43 134	97.73 178	98.00 174	99.62 48	99.22 60	99.55 55	99.22 109	98.93 26	99.75 228	98.66 82	99.81 99	99.50 123
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CHOSEN 1792x2688		97.49 230	97.14 246	98.54 204	99.68 58	96.09 249	96.50 297	99.62 48	91.58 370	98.84 181	98.97 171	92.36 301	99.88 83	96.76 203	99.95 32	99.67 57

XXY-MVS		99.14 46	99.15 50	99.10 114	99.76 31	97.74 176	98.85 82	99.62 48	98.48 125	99.37 89	99.49 63	98.75 36	99.86 108	98.20 108	99.80 109	99.71 46

v7n		99.53 9	99.57 9	99.41 60	99.88 9	98.54 100	99.45 10	99.61 51	99.66 13	99.68 39	99.66 27	98.44 60	99.95 22	99.73 19	99.96 25	99.75 43

EIA-MVS		98.00 191	97.74 205	98.80 160	98.72 271	98.09 135	98.05 165	99.60 52	97.39 212	96.63 339	95.55 373	97.68 113	99.80 184	96.73 207	99.27 262	98.52 322

EG-PatchMatch MVS		98.99 62	99.01 61	98.94 142	99.50 108	97.47 190	98.04 167	99.59 53	98.15 153	99.40 83	99.36 82	98.58 52	99.76 221	98.78 72	99.68 167	99.59 80

MIMVSNet1		99.38 23	99.32 28	99.55 23	99.86 15	99.19 37	99.41 13	99.59 53	99.59 23	99.71 33	99.57 42	97.12 156	99.90 64	99.21 48	99.87 77	99.54 108

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 18	99.34 15	99.69 4	99.58 55	99.90 2	99.86 18	99.78 8	99.58 6	99.95 22	99.00 61	99.95 32	99.78 33

AllTest		98.44 146	98.20 163	99.16 105	99.50 108	98.55 97	98.25 141	99.58 55	96.80 255	98.88 174	99.06 140	97.65 116	99.57 311	94.45 303	99.61 192	99.37 184

TestCases				99.16 105	99.50 108	98.55 97		99.58 55	96.80 255	98.88 174	99.06 140	97.65 116	99.57 311	94.45 303	99.61 192	99.37 184

diffmvs		98.22 173	98.24 160	98.17 236	99.00 221	95.44 269	96.38 304	99.58 55	97.79 174	98.53 223	98.50 258	96.76 180	99.74 233	97.95 126	99.64 181	99.34 196
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
OurMVSNet-221017-0		99.37 24	99.31 30	99.53 34	99.91 3	98.98 65	99.63 6	99.58 55	99.44 38	99.78 26	99.76 10	96.39 196	99.92 50	99.44 36	99.92 55	99.68 54

1112_ss		97.29 246	96.86 258	98.58 193	99.34 153	96.32 240	96.75 286	99.58 55	93.14 353	96.89 328	97.48 330	92.11 305	99.86 108	96.91 186	99.54 215	99.57 91

ACMH+	96.62 9	99.08 57	99.00 62	99.33 78	99.71 47	98.83 76	98.60 102	99.58 55	99.11 72	99.53 60	99.18 116	98.81 32	99.67 267	96.71 210	99.77 124	99.50 123

FC-MVSNet-test		99.27 30	99.25 38	99.34 73	99.77 28	98.37 111	99.30 32	99.57 62	99.61 22	99.40 83	99.50 59	97.12 156	99.85 120	99.02 60	99.94 40	99.80 29

casdiffmvs		98.95 69	99.00 62	98.81 158	99.38 140	97.33 198	97.82 197	99.57 62	99.17 70	99.35 94	99.17 120	98.35 67	99.69 255	98.46 95	99.73 142	99.41 164
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TransMVSNet (Re)		99.44 15	99.47 16	99.36 64	99.80 22	98.58 95	99.27 39	99.57 62	99.39 43	99.75 30	99.62 34	99.17 18	99.83 154	99.06 56	99.62 187	99.66 58

Baseline_NR-MVSNet		98.98 65	98.86 74	99.36 64	99.82 21	98.55 97	97.47 242	99.57 62	99.37 45	99.21 120	99.61 37	96.76 180	99.83 154	98.06 117	99.83 92	99.71 46

door-mid								99.57 62

RPSCF		98.62 122	98.36 144	99.42 58	99.65 65	99.42 7	98.55 107	99.57 62	97.72 179	98.90 168	99.26 100	96.12 207	99.52 328	95.72 271	99.71 154	99.32 203

CSCG		98.68 111	98.50 120	99.20 99	99.45 128	98.63 89	98.56 106	99.57 62	97.87 168	98.85 179	98.04 298	97.66 115	99.84 137	96.72 208	99.81 99	99.13 244

GeoE		99.05 58	98.99 64	99.25 93	99.44 129	98.35 115	98.73 89	99.56 69	98.42 126	98.91 167	98.81 208	98.94 25	99.91 59	98.35 100	99.73 142	99.49 127

MVSFormer		98.26 169	98.43 133	97.77 265	98.88 246	93.89 322	99.39 17	99.56 69	99.11 72	98.16 248	98.13 288	93.81 279	99.97 4	99.26 43	99.57 207	99.43 158

test_djsdf		99.52 10	99.51 11	99.53 34	99.86 15	98.74 82	99.39 17	99.56 69	99.11 72	99.70 35	99.73 15	99.00 22	99.97 4	99.26 43	99.98 12	99.89 11

COLMAP_ROB	96.50 10	98.99 62	98.85 75	99.41 60	99.58 77	99.10 60	98.74 86	99.56 69	99.09 82	99.33 97	99.19 113	98.40 62	99.72 245	95.98 258	99.76 135	99.42 161
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v10		98.97 66	99.11 52	98.55 201	99.44 129	96.21 245	98.90 77	99.55 73	98.73 107	99.48 68	99.60 39	96.63 187	99.83 154	99.70 22	99.99 5	99.61 73

WR-MVS_H		99.33 26	99.22 40	99.65 5	99.71 47	99.24 25	99.32 23	99.55 73	99.46 35	99.50 67	99.34 87	97.30 145	99.93 40	98.90 66	99.93 44	99.77 35

mvsmamba		99.24 37	99.15 50	99.49 48	99.83 19	98.85 74	99.41 13	99.55 73	99.54 27	99.40 83	99.52 57	95.86 224	99.91 59	99.32 40	99.95 32	99.70 51

114514_t		96.50 289	95.77 296	98.69 178	99.48 122	97.43 194	97.84 196	99.55 73	81.42 401	96.51 345	98.58 247	95.53 232	99.67 267	93.41 335	99.58 203	98.98 264

ACMH	96.65 7	99.25 33	99.24 39	99.26 90	99.72 44	98.38 109	99.07 61	99.55 73	98.30 133	99.65 45	99.45 70	99.22 15	99.76 221	98.44 96	99.77 124	99.64 63
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FOURS1					99.73 38	99.67 2	99.43 11	99.54 78	99.43 40	99.26 112

KD-MVS_self_test		99.25 33	99.18 42	99.44 57	99.63 74	99.06 64	98.69 94	99.54 78	99.31 52	99.62 51	99.53 54	97.36 143	99.86 108	99.24 47	99.71 154	99.39 175

PEN-MVS		99.41 20	99.34 25	99.62 6	99.73 38	99.14 52	99.29 33	99.54 78	99.62 20	99.56 53	99.42 73	98.16 83	99.96 11	98.78 72	99.93 44	99.77 35

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 47	99.10 60	99.29 33	99.53 81	99.53 29	99.46 71	99.41 76	98.23 72	99.95 22	98.89 68	99.95 32	99.81 28

Test_1112_low_res		96.99 269	96.55 280	98.31 226	99.35 151	95.47 268	95.84 337	99.53 81	91.51 372	96.80 333	98.48 261	91.36 312	99.83 154	96.58 216	99.53 219	99.62 67

USDC		97.41 237	97.40 229	97.44 297	98.94 230	93.67 328	95.17 357	99.53 81	94.03 342	98.97 154	99.10 136	95.29 239	99.34 361	95.84 267	99.73 142	99.30 210

FIs		99.14 46	99.09 55	99.29 84	99.70 54	98.28 117	99.13 55	99.52 84	99.48 32	99.24 117	99.41 76	96.79 177	99.82 164	98.69 81	99.88 74	99.76 39

Anonymous20231211		99.27 30	99.27 35	99.26 90	99.29 158	98.18 126	99.49 8	99.51 85	99.70 8	99.80 24	99.68 20	96.84 171	99.83 154	99.21 48	99.91 63	99.77 35

DTE-MVSNet		99.43 18	99.35 23	99.66 4	99.71 47	99.30 17	99.31 27	99.51 85	99.64 15	99.56 53	99.46 66	98.23 72	99.97 4	98.78 72	99.93 44	99.72 45

ETV-MVS		98.03 188	97.86 199	98.56 200	98.69 283	98.07 141	97.51 238	99.50 87	98.10 154	97.50 297	95.51 374	98.41 61	99.88 83	96.27 242	99.24 267	97.71 371

Fast-Effi-MVS+-dtu		98.27 167	98.09 176	98.81 158	98.43 318	98.11 132	97.61 226	99.50 87	98.64 111	97.39 306	97.52 328	98.12 86	99.95 22	96.90 191	98.71 319	98.38 335

HPM-MVS_fast		99.01 60	98.82 77	99.57 16	99.71 47	99.35 12	99.00 68	99.50 87	97.33 217	98.94 164	98.86 197	98.75 36	99.82 164	97.53 148	99.71 154	99.56 97

XVG-OURS		98.53 136	98.34 147	99.11 112	99.50 108	98.82 78	95.97 326	99.50 87	97.30 221	99.05 141	98.98 169	99.35 12	99.32 364	95.72 271	99.68 167	99.18 236

baseline		98.96 68	99.02 60	98.76 170	99.38 140	97.26 203	98.49 119	99.50 87	98.86 102	99.19 122	99.06 140	98.23 72	99.69 255	98.71 79	99.76 135	99.33 201

FMVSNet5		96.01 302	95.20 319	98.41 217	97.53 370	96.10 246	98.74 86	99.50 87	97.22 235	98.03 262	99.04 149	69.80 396	99.88 83	97.27 159	99.71 154	99.25 219

HyFIR lowres test		97.19 254	96.60 278	98.96 139	99.62 76	97.28 201	95.17 357	99.50 87	94.21 337	99.01 147	98.32 277	86.61 341	99.99 2	97.10 172	99.84 85	99.60 74

testgi		98.32 160	98.39 140	98.13 239	99.57 81	95.54 264	97.78 202	99.49 94	97.37 214	99.19 122	97.65 320	98.96 24	99.49 335	96.50 229	98.99 300	99.34 196

PGM-MVS		98.66 115	98.37 143	99.55 23	99.53 101	99.18 38	98.23 142	99.49 94	97.01 245	98.69 198	98.88 194	98.00 93	99.89 74	95.87 264	99.59 198	99.58 86

SDMVSNet		99.23 38	99.32 28	98.96 139	99.68 58	97.35 197	98.84 84	99.48 96	99.69 9	99.63 48	99.68 20	99.03 21	99.96 11	97.97 124	99.92 55	99.57 91

new-patchmatchnet		98.35 156	98.74 83	97.18 307	99.24 166	92.23 355	96.42 302	99.48 96	98.30 133	99.69 37	99.53 54	97.44 139	99.82 164	98.84 70	99.77 124	99.49 127

nrg030		99.40 21	99.35 23	99.54 27	99.58 77	99.13 55	98.98 71	99.48 96	99.68 11	99.46 71	99.26 100	98.62 46	99.73 238	99.17 51	99.92 55	99.76 39

APDe-MVS		98.99 62	98.79 80	99.60 11	99.21 173	99.15 47	98.87 79	99.48 96	97.57 191	99.35 94	99.24 105	97.83 103	99.89 74	97.88 130	99.70 159	99.75 43
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
XVG-OURS-SEG-HR		98.49 141	98.28 154	99.14 108	99.49 115	98.83 76	96.54 294	99.48 96	97.32 219	99.11 129	98.61 244	99.33 13	99.30 367	96.23 245	98.38 333	99.28 214

LPG-MVS_test		98.71 99	98.46 129	99.47 54	99.57 81	98.97 66	98.23 142	99.48 96	96.60 264	99.10 132	99.06 140	98.71 39	99.83 154	95.58 278	99.78 119	99.62 67

LGP-MVS_train				99.47 54	99.57 81	98.97 66		99.48 96	96.60 264	99.10 132	99.06 140	98.71 39	99.83 154	95.58 278	99.78 119	99.62 67

v8		99.01 60	99.16 45	98.57 196	99.47 124	96.31 241	98.90 77	99.47 103	99.03 88	99.52 62	99.57 42	96.93 167	99.81 177	99.60 25	99.98 12	99.60 74

LF4IMVS		97.90 196	97.69 209	98.52 206	99.17 188	97.66 181	97.19 264	99.47 103	96.31 279	97.85 272	98.20 285	96.71 184	99.52 328	94.62 297	99.72 149	98.38 335

sasdasda		98.34 157	98.26 157	98.58 193	98.46 314	97.82 168	98.96 72	99.46 105	99.19 68	97.46 300	95.46 377	98.59 49	99.46 343	98.08 115	98.71 319	98.46 324

canonicalmvs		98.34 157	98.26 157	98.58 193	98.46 314	97.82 168	98.96 72	99.46 105	99.19 68	97.46 300	95.46 377	98.59 49	99.46 343	98.08 115	98.71 319	98.46 324

XVG-ACMP-BASELINE		98.56 128	98.34 147	99.22 98	99.54 98	98.59 94	97.71 212	99.46 105	97.25 226	98.98 150	98.99 165	97.54 128	99.84 137	95.88 261	99.74 139	99.23 224

DeepC-MVS	97.60 4	98.97 66	98.93 67	99.10 114	99.35 151	97.98 151	98.01 173	99.46 105	97.56 193	99.54 56	99.50 59	98.97 23	99.84 137	98.06 117	99.92 55	99.49 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD_test1		98.83 83	98.66 98	99.34 73	99.78 25	99.47 6	98.42 129	99.45 109	98.28 138	98.98 150	99.19 113	97.76 109	99.58 309	96.57 218	99.55 213	98.97 267

Fast-Effi-MVS+		97.67 218	97.38 231	98.57 196	98.71 274	97.43 194	97.23 258	99.45 109	94.82 323	96.13 353	96.51 354	98.52 55	99.91 59	96.19 248	98.83 311	98.37 337

v1240		98.55 132	98.62 104	98.32 224	99.22 171	95.58 263	97.51 238	99.45 109	97.16 238	99.45 74	99.24 105	96.12 207	99.85 120	99.60 25	99.88 74	99.55 104

VPA-MVSNet		99.30 28	99.30 32	99.28 85	99.49 115	98.36 114	99.00 68	99.45 109	99.63 17	99.52 62	99.44 71	98.25 70	99.88 83	99.09 54	99.84 85	99.62 67

Anonymous20240521		98.69 106	98.87 71	98.16 238	99.77 28	95.11 282	99.08 58	99.44 113	99.34 49	99.33 97	99.55 48	94.10 275	99.94 35	99.25 45	99.96 25	99.42 161

tfpnnormal		98.90 75	98.90 70	98.91 147	99.67 62	97.82 168	99.00 68	99.44 113	99.45 36	99.51 66	99.24 105	98.20 78	99.86 108	95.92 260	99.69 162	99.04 255

GBi-Net		98.65 116	98.47 127	99.17 102	98.90 240	98.24 120	99.20 45	99.44 113	98.59 117	98.95 157	99.55 48	94.14 271	99.86 108	97.77 136	99.69 162	99.41 164

test1		98.65 116	98.47 127	99.17 102	98.90 240	98.24 120	99.20 45	99.44 113	98.59 117	98.95 157	99.55 48	94.14 271	99.86 108	97.77 136	99.69 162	99.41 164

FMVSNet1		99.17 42	99.17 43	99.17 102	99.55 93	98.24 120	99.20 45	99.44 113	99.21 62	99.43 76	99.55 48	97.82 106	99.86 108	98.42 98	99.89 73	99.41 164

TinyColmap		97.89 198	97.98 187	97.60 281	98.86 248	94.35 303	96.21 314	99.44 113	97.45 208	99.06 136	98.88 194	97.99 96	99.28 371	94.38 309	99.58 203	99.18 236

HPM-MVS		98.79 88	98.53 116	99.59 15	99.65 65	99.29 19	99.16 51	99.43 119	96.74 259	98.61 209	98.38 269	98.62 46	99.87 100	96.47 230	99.67 173	99.59 80
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PVSNet_BlendedMVS		97.55 227	97.53 221	97.60 281	98.92 236	93.77 326	96.64 291	99.43 119	94.49 328	97.62 285	99.18 116	96.82 174	99.67 267	94.73 294	99.93 44	99.36 190

PVSNet_Blended		96.88 272	96.68 271	97.47 295	98.92 236	93.77 326	94.71 369	99.43 119	90.98 378	97.62 285	97.36 338	96.82 174	99.67 267	94.73 294	99.56 210	98.98 264

TranMVSNet+NR-MVSNet		99.17 42	99.07 58	99.46 56	99.37 146	98.87 73	98.39 131	99.42 122	99.42 41	99.36 92	99.06 140	98.38 63	99.95 22	98.34 101	99.90 69	99.57 91

SF-MVS		98.53 136	98.27 156	99.32 80	99.31 154	98.75 81	98.19 147	99.41 123	96.77 258	98.83 182	98.90 187	97.80 107	99.82 164	95.68 274	99.52 222	99.38 182

door								99.41 123

PMMVS2		98.07 187	98.08 179	98.04 248	99.41 137	94.59 297	94.59 376	99.40 125	97.50 198	98.82 185	98.83 203	96.83 173	99.84 137	97.50 150	99.81 99	99.71 46

UniMVSNet_NR-MVSNet		98.86 81	98.68 95	99.40 62	99.17 188	98.74 82	97.68 215	99.40 125	99.14 71	99.06 136	98.59 246	96.71 184	99.93 40	98.57 88	99.77 124	99.53 115

DPE-MVS		98.59 126	98.26 157	99.57 16	99.27 161	99.15 47	97.01 270	99.39 127	97.67 181	99.44 75	98.99 165	97.53 130	99.89 74	95.40 282	99.68 167	99.66 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
IterMVS-LS		98.55 132	98.70 92	98.09 240	99.48 122	94.73 291	97.22 261	99.39 127	98.97 93	99.38 87	99.31 93	96.00 213	99.93 40	98.58 86	99.97 19	99.60 74
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MP-MVS-pluss		98.57 127	98.23 161	99.60 11	99.69 56	99.35 12	97.16 265	99.38 129	94.87 322	98.97 154	98.99 165	98.01 92	99.88 83	97.29 158	99.70 159	99.58 86
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
UniMVSNet (Re)		98.87 78	98.71 89	99.35 70	99.24 166	98.73 85	97.73 211	99.38 129	98.93 97	99.12 128	98.73 219	96.77 178	99.86 108	98.63 85	99.80 109	99.46 146

PHI-MVS		98.29 166	97.95 189	99.34 73	98.44 317	99.16 43	98.12 155	99.38 129	96.01 290	98.06 258	98.43 264	97.80 107	99.67 267	95.69 273	99.58 203	99.20 229

ACMP	95.32 15	98.41 148	98.09 176	99.36 64	99.51 105	98.79 80	97.68 215	99.38 129	95.76 298	98.81 187	98.82 206	98.36 64	99.82 164	94.75 293	99.77 124	99.48 137
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMMP		98.75 95	98.50 120	99.52 39	99.56 89	99.16 43	98.87 79	99.37 133	97.16 238	98.82 185	99.01 161	97.71 112	99.87 100	96.29 241	99.69 162	99.54 108
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
OpenMVS	96.65 7	97.09 260	96.68 271	98.32 224	98.32 326	97.16 212	98.86 81	99.37 133	89.48 386	96.29 351	99.15 126	96.56 189	99.90 64	92.90 342	99.20 273	97.89 359

MSDG		97.71 215	97.52 222	98.28 229	98.91 239	96.82 226	94.42 379	99.37 133	97.65 183	98.37 238	98.29 279	97.40 141	99.33 363	94.09 316	99.22 270	98.68 313

ACMM	96.08 12	98.91 73	98.73 85	99.48 51	99.55 93	99.14 52	98.07 162	99.37 133	97.62 185	99.04 143	98.96 174	98.84 30	99.79 197	97.43 152	99.65 179	99.49 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v144192		98.54 134	98.57 112	98.45 213	99.21 173	95.98 252	97.63 223	99.36 137	97.15 240	99.32 103	99.18 116	95.84 225	99.84 137	99.50 32	99.91 63	99.54 108

v1921920		98.54 134	98.60 109	98.38 220	99.20 177	95.76 260	97.56 232	99.36 137	97.23 232	99.38 87	99.17 120	96.02 211	99.84 137	99.57 27	99.90 69	99.54 108

v1192		98.60 124	98.66 98	98.41 217	99.27 161	95.88 255	97.52 236	99.36 137	97.41 210	99.33 97	99.20 112	96.37 199	99.82 164	99.57 27	99.92 55	99.55 104

SD-MVS		98.40 150	98.68 95	97.54 288	98.96 228	97.99 148	97.88 189	99.36 137	98.20 146	99.63 48	99.04 149	98.76 35	95.33 406	96.56 222	99.74 139	99.31 207
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
CP-MVS		98.70 103	98.42 135	99.52 39	99.36 147	99.12 57	98.72 90	99.36 137	97.54 196	98.30 240	98.40 266	97.86 102	99.89 74	96.53 227	99.72 149	99.56 97

test0726					99.50 108	99.21 28	98.17 151	99.35 142	97.97 159	99.26 112	99.06 140	97.61 122

MSP-MVS		98.40 150	98.00 185	99.61 9	99.57 81	99.25 24	98.57 105	99.35 142	97.55 195	99.31 105	97.71 316	94.61 260	99.88 83	96.14 252	99.19 276	99.70 51
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
VPNet		98.87 78	98.83 76	99.01 133	99.70 54	97.62 185	98.43 127	99.35 142	99.47 34	99.28 106	99.05 147	96.72 183	99.82 164	98.09 114	99.36 247	99.59 80

UnsupCasMVSNet_eth		97.89 198	97.60 218	98.75 173	99.31 154	97.17 211	97.62 224	99.35 142	98.72 109	98.76 193	98.68 228	92.57 299	99.74 233	97.76 140	95.60 392	99.34 196

DP-MVS Recon		97.33 242	96.92 254	98.57 196	99.09 204	97.99 148	96.79 282	99.35 142	93.18 352	97.71 280	98.07 296	95.00 247	99.31 365	93.97 318	99.13 284	98.42 332

ITE_SJBPF				98.87 151	99.22 171	98.48 104		99.35 142	97.50 198	98.28 242	98.60 245	97.64 119	99.35 360	93.86 323	99.27 262	98.79 298

v1144		98.60 124	98.66 98	98.41 217	99.36 147	95.90 254	97.58 230	99.34 148	97.51 197	99.27 108	99.15 126	96.34 201	99.80 184	99.47 34	99.93 44	99.51 120

XVS		98.72 98	98.45 130	99.53 34	99.46 125	99.21 28	98.65 96	99.34 148	98.62 115	97.54 293	98.63 240	97.50 134	99.83 154	96.79 199	99.53 219	99.56 97

X-MVStestdata		94.32 333	92.59 351	99.53 34	99.46 125	99.21 28	98.65 96	99.34 148	98.62 115	97.54 293	45.85 405	97.50 134	99.83 154	96.79 199	99.53 219	99.56 97

CP-MVSNet		99.21 39	99.09 55	99.56 21	99.65 65	98.96 70	99.13 55	99.34 148	99.42 41	99.33 97	99.26 100	97.01 164	99.94 35	98.74 76	99.93 44	99.79 30

test_0402		98.76 94	98.71 89	98.93 144	99.56 89	98.14 130	98.45 126	99.34 148	99.28 56	98.95 157	98.91 184	98.34 68	99.79 197	95.63 275	99.91 63	98.86 285

APD-MVS_3200maxsize		98.84 82	98.61 108	99.53 34	99.19 180	99.27 22	98.49 119	99.33 153	98.64 111	99.03 146	98.98 169	97.89 100	99.85 120	96.54 226	99.42 240	99.46 146

DP-MVS		98.93 71	98.81 79	99.28 85	99.21 173	98.45 106	98.46 124	99.33 153	99.63 17	99.48 68	99.15 126	97.23 151	99.75 228	97.17 164	99.66 178	99.63 66

DVP-MVS++		98.90 75	98.70 92	99.51 43	98.43 318	99.15 47	99.43 11	99.32 155	98.17 149	99.26 112	99.02 152	98.18 79	99.88 83	97.07 174	99.45 236	99.49 127

9.14			97.78 202		99.07 208		97.53 235	99.32 155	95.53 305	98.54 222	98.70 225	97.58 124	99.76 221	94.32 310	99.46 234

test_0728_SECOND				99.60 11	99.50 108	99.23 26	98.02 170	99.32 155					99.88 83	96.99 180	99.63 184	99.68 54

Anonymous20231206		98.21 175	98.21 162	98.20 234	99.51 105	95.43 270	98.13 153	99.32 155	96.16 284	98.93 165	98.82 206	96.00 213	99.83 154	97.32 157	99.73 142	99.36 190

LS3D		98.63 120	98.38 142	99.36 64	97.25 381	99.38 8	99.12 57	99.32 155	99.21 62	98.44 230	98.88 194	97.31 144	99.80 184	96.58 216	99.34 251	98.92 276

test_one_0601					99.39 139	99.20 34		99.31 160	98.49 124	98.66 202	99.02 152	97.64 119

SED-MVS		98.91 73	98.72 87	99.49 48	99.49 115	99.17 39	98.10 158	99.31 160	98.03 156	99.66 42	99.02 152	98.36 64	99.88 83	96.91 186	99.62 187	99.41 164

test_241102_ONE					99.49 115	99.17 39		99.31 160	97.98 158	99.66 42	98.90 187	98.36 64	99.48 338

miper_lstm_enhance		97.18 255	97.16 243	97.25 306	98.16 336	92.85 341	95.15 359	99.31 160	97.25 226	98.74 196	98.78 212	90.07 320	99.78 208	97.19 163	99.80 109	99.11 246

HFP-MVS		98.71 99	98.44 132	99.51 43	99.49 115	99.16 43	98.52 111	99.31 160	97.47 201	98.58 215	98.50 258	97.97 97	99.85 120	96.57 218	99.59 198	99.53 115

region2R		98.69 106	98.40 137	99.54 27	99.53 101	99.17 39	98.52 111	99.31 160	97.46 206	98.44 230	98.51 254	97.83 103	99.88 83	96.46 231	99.58 203	99.58 86

ACMMPR		98.70 103	98.42 135	99.54 27	99.52 103	99.14 52	98.52 111	99.31 160	97.47 201	98.56 218	98.54 250	97.75 110	99.88 83	96.57 218	99.59 198	99.58 86

SteuartSystems-ACMMP		98.79 88	98.54 115	99.54 27	99.73 38	99.16 43	98.23 142	99.31 160	97.92 164	98.90 168	98.90 187	98.00 93	99.88 83	96.15 251	99.72 149	99.58 86
Skip Steuart: Steuart Systems R&D Blog.
sd_testset		99.28 29	99.31 30	99.19 101	99.68 58	98.06 144	99.41 13	99.30 168	99.69 9	99.63 48	99.68 20	99.25 14	99.96 11	97.25 161	99.92 55	99.57 91

SR-MVS-dyc-post		98.81 86	98.55 113	99.57 16	99.20 177	99.38 8	98.48 122	99.30 168	98.64 111	98.95 157	98.96 174	97.49 137	99.86 108	96.56 222	99.39 243	99.45 150

RE-MVS-def			98.58 111		99.20 177	99.38 8	98.48 122	99.30 168	98.64 111	98.95 157	98.96 174	97.75 110		96.56 222	99.39 243	99.45 150

test_241102_TWO								99.30 168	98.03 156	99.26 112	99.02 152	97.51 133	99.88 83	96.91 186	99.60 194	99.66 58

RPMNet		97.02 265	96.93 252	97.30 302	97.71 359	94.22 304	98.11 156	99.30 168	99.37 45	96.91 324	99.34 87	86.72 340	99.87 100	97.53 148	97.36 370	97.81 364

MVS_111021_LR		98.30 163	98.12 174	98.83 155	99.16 190	98.03 146	96.09 322	99.30 168	97.58 190	98.10 255	98.24 281	98.25 70	99.34 361	96.69 211	99.65 179	99.12 245

F-COLMAP		97.30 244	96.68 271	99.14 108	99.19 180	98.39 108	97.27 257	99.30 168	92.93 356	96.62 340	98.00 299	95.73 227	99.68 264	92.62 351	98.46 332	99.35 194

3Dnovator	98.27 2	98.81 86	98.73 85	99.05 127	98.76 265	97.81 171	99.25 40	99.30 168	98.57 120	98.55 220	99.33 89	97.95 98	99.90 64	97.16 165	99.67 173	99.44 154

EGC-MVSNET		85.24 371	80.54 374	99.34 73	99.77 28	99.20 34	99.08 58	99.29 176	12.08 407	20.84 408	99.42 73	97.55 127	99.85 120	97.08 173	99.72 149	98.96 269

ZNCC-MVS		98.68 111	98.40 137	99.54 27	99.57 81	99.21 28	98.46 124	99.29 176	97.28 223	98.11 254	98.39 267	98.00 93	99.87 100	96.86 196	99.64 181	99.55 104

SR-MVS		98.71 99	98.43 133	99.57 16	99.18 187	99.35 12	98.36 134	99.29 176	98.29 136	98.88 174	98.85 200	97.53 130	99.87 100	96.14 252	99.31 255	99.48 137

pmmvs-eth3d		98.47 143	98.34 147	98.86 152	99.30 157	97.76 174	97.16 265	99.28 179	95.54 304	99.42 79	99.19 113	97.27 148	99.63 289	97.89 127	99.97 19	99.20 229

APD-MVS		98.10 182	97.67 210	99.42 58	99.11 199	98.93 71	97.76 207	99.28 179	94.97 319	98.72 197	98.77 214	97.04 160	99.85 120	93.79 325	99.54 215	99.49 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAPA-MVS	96.21 11	96.63 283	95.95 294	98.65 180	98.93 232	98.09 135	96.93 276	99.28 179	83.58 399	98.13 252	97.78 312	96.13 206	99.40 352	93.52 331	99.29 260	98.45 327
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HQP_MVS		97.99 194	97.67 210	98.93 144	99.19 180	97.65 182	97.77 204	99.27 182	98.20 146	97.79 276	97.98 301	94.90 248	99.70 250	94.42 305	99.51 224	99.45 150

plane_prior5								99.27 182					99.70 250	94.42 305	99.51 224	99.45 150

CPTT-MVS		97.84 208	97.36 233	99.27 88	99.31 154	98.46 105	98.29 137	99.27 182	94.90 321	97.83 273	98.37 270	94.90 248	99.84 137	93.85 324	99.54 215	99.51 120

UnsupCasMVSNet_bld		97.30 244	96.92 254	98.45 213	99.28 159	96.78 230	96.20 315	99.27 182	95.42 308	98.28 242	98.30 278	93.16 286	99.71 246	94.99 288	97.37 368	98.87 284

MVS_111021_HR		98.25 171	98.08 179	98.75 173	99.09 204	97.46 191	95.97 326	99.27 182	97.60 189	97.99 263	98.25 280	98.15 85	99.38 356	96.87 194	99.57 207	99.42 161

cascas		94.79 328	94.33 334	96.15 349	96.02 403	92.36 352	92.34 398	99.26 187	85.34 397	95.08 375	94.96 386	92.96 292	98.53 395	94.41 308	98.59 329	97.56 376

GST-MVS		98.61 123	98.30 152	99.52 39	99.51 105	99.20 34	98.26 140	99.25 188	97.44 209	98.67 200	98.39 267	97.68 113	99.85 120	96.00 256	99.51 224	99.52 118

IterMVS-SCA-FT		97.85 207	98.18 166	96.87 323	99.27 161	91.16 371	95.53 345	99.25 188	99.10 79	99.41 80	99.35 83	93.10 288	99.96 11	98.65 83	99.94 40	99.49 127

ACMMP_NAP		98.75 95	98.48 125	99.57 16	99.58 77	99.29 19	97.82 197	99.25 188	96.94 248	98.78 188	99.12 132	98.02 91	99.84 137	97.13 170	99.67 173	99.59 80

DU-MVS		98.82 84	98.63 102	99.39 63	99.16 190	98.74 82	97.54 234	99.25 188	98.84 105	99.06 136	98.76 216	96.76 180	99.93 40	98.57 88	99.77 124	99.50 123

OMC-MVS		97.88 200	97.49 225	99.04 129	98.89 245	98.63 89	96.94 274	99.25 188	95.02 317	98.53 223	98.51 254	97.27 148	99.47 341	93.50 333	99.51 224	99.01 259

test20.03		98.78 90	98.77 82	98.78 166	99.46 125	97.20 208	97.78 202	99.24 193	99.04 87	99.41 80	98.90 187	97.65 116	99.76 221	97.70 141	99.79 114	99.39 175

mPP-MVS		98.64 118	98.34 147	99.54 27	99.54 98	99.17 39	98.63 98	99.24 193	97.47 201	98.09 256	98.68 228	97.62 121	99.89 74	96.22 246	99.62 187	99.57 91

MSLP-MVS++		98.02 189	98.14 173	97.64 279	98.58 302	95.19 278	97.48 240	99.23 195	97.47 201	97.90 267	98.62 242	97.04 160	98.81 392	97.55 145	99.41 241	98.94 274

SMA-MVS		98.40 150	98.03 183	99.51 43	99.16 190	99.21 28	98.05 165	99.22 196	94.16 338	98.98 150	99.10 136	97.52 132	99.79 197	96.45 232	99.64 181	99.53 115
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
IterMVS		97.73 213	98.11 175	96.57 332	99.24 166	90.28 379	95.52 347	99.21 197	98.86 102	99.33 97	99.33 89	93.11 287	99.94 35	98.49 94	99.94 40	99.48 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		97.49 230	97.16 243	98.48 210	99.07 208	97.03 217	94.71 369	99.21 197	94.46 330	98.06 258	97.16 342	97.57 125	99.48 338	94.46 302	99.78 119	98.95 270
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MTGPA								99.20 199

MTAPA		98.88 77	98.64 101	99.61 9	99.67 62	99.36 11	98.43 127	99.20 199	98.83 106	98.89 170	98.90 187	96.98 166	99.92 50	97.16 165	99.70 159	99.56 97

NR-MVSNet		98.95 69	98.82 77	99.36 64	99.16 190	98.72 87	99.22 42	99.20 199	99.10 79	99.72 31	98.76 216	96.38 198	99.86 108	98.00 122	99.82 95	99.50 123

DELS-MVS		98.27 167	98.20 163	98.48 210	98.86 248	96.70 231	95.60 343	99.20 199	97.73 177	98.45 229	98.71 222	97.50 134	99.82 164	98.21 107	99.59 198	98.93 275
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
V42		98.78 90	98.78 81	98.76 170	99.44 129	97.04 216	98.27 139	99.19 203	97.87 168	99.25 116	99.16 122	96.84 171	99.78 208	99.21 48	99.84 85	99.46 146

MP-MVS		98.46 144	98.09 176	99.54 27	99.57 81	99.22 27	98.50 118	99.19 203	97.61 188	97.58 289	98.66 233	97.40 141	99.88 83	94.72 296	99.60 194	99.54 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
QAPM		97.31 243	96.81 264	98.82 156	98.80 263	97.49 189	99.06 62	99.19 203	90.22 382	97.69 282	99.16 122	96.91 168	99.90 64	90.89 376	99.41 241	99.07 249

3Dnovator+	97.89 3	98.69 106	98.51 118	99.24 95	98.81 260	98.40 107	99.02 65	99.19 203	98.99 91	98.07 257	99.28 96	97.11 158	99.84 137	96.84 197	99.32 253	99.47 144

eth_miper_zixun_eth		97.23 251	97.25 238	97.17 309	98.00 344	92.77 343	94.71 369	99.18 207	97.27 224	98.56 218	98.74 218	91.89 307	99.69 255	97.06 176	99.81 99	99.05 251

OPM-MVS		98.56 128	98.32 151	99.25 93	99.41 137	98.73 85	97.13 267	99.18 207	97.10 241	98.75 194	98.92 183	98.18 79	99.65 283	96.68 212	99.56 210	99.37 184
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVP-Stereo		98.08 186	97.92 193	98.57 196	98.96 228	96.79 227	97.90 186	99.18 207	96.41 275	98.46 228	98.95 178	95.93 221	99.60 299	96.51 228	98.98 302	99.31 207
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DeepPCF-MVS	96.93 5	98.32 160	98.01 184	99.23 97	98.39 323	98.97 66	95.03 361	99.18 207	96.88 251	99.33 97	98.78 212	98.16 83	99.28 371	96.74 205	99.62 187	99.44 154

xiu_mvs_v1_base_debu		97.86 202	98.17 167	96.92 320	98.98 225	93.91 319	96.45 299	99.17 211	97.85 170	98.41 233	97.14 344	98.47 56	99.92 50	98.02 119	99.05 290	96.92 383

xiu_mvs_v1_base		97.86 202	98.17 167	96.92 320	98.98 225	93.91 319	96.45 299	99.17 211	97.85 170	98.41 233	97.14 344	98.47 56	99.92 50	98.02 119	99.05 290	96.92 383

xiu_mvs_v1_base_debi		97.86 202	98.17 167	96.92 320	98.98 225	93.91 319	96.45 299	99.17 211	97.85 170	98.41 233	97.14 344	98.47 56	99.92 50	98.02 119	99.05 290	96.92 383

cl____		97.02 265	96.83 261	97.58 283	97.82 354	94.04 312	94.66 372	99.16 214	97.04 243	98.63 205	98.71 222	88.68 331	99.69 255	97.00 178	99.81 99	99.00 262

DIV-MVS_self_test		97.02 265	96.84 260	97.58 283	97.82 354	94.03 313	94.66 372	99.16 214	97.04 243	98.63 205	98.71 222	88.69 329	99.69 255	97.00 178	99.81 99	99.01 259

c3_l		97.36 239	97.37 232	97.31 301	98.09 340	93.25 334	95.01 362	99.16 214	97.05 242	98.77 191	98.72 221	92.88 293	99.64 286	96.93 185	99.76 135	99.05 251

Effi-MVS+-dtu		98.26 169	97.90 195	99.35 70	98.02 343	99.49 5	98.02 170	99.16 214	98.29 136	97.64 284	97.99 300	96.44 195	99.95 22	96.66 213	98.93 307	98.60 318

v2v482		98.56 128	98.62 104	98.37 221	99.42 135	95.81 258	97.58 230	99.16 214	97.90 166	99.28 106	99.01 161	95.98 218	99.79 197	99.33 39	99.90 69	99.51 120

MDA-MVSNet-bldmvs		97.94 195	97.91 194	98.06 245	99.44 129	94.96 285	96.63 292	99.15 219	98.35 128	98.83 182	99.11 133	94.31 268	99.85 120	96.60 215	98.72 317	99.37 184

iter_conf05		96.54 286	96.07 292	97.92 254	97.90 351	94.50 298	97.87 192	99.14 220	97.73 177	98.89 170	98.95 178	75.75 392	99.87 100	98.50 93	99.92 55	99.40 173

FMVSNet2		98.49 141	98.40 137	98.75 173	98.90 240	97.14 214	98.61 101	99.13 221	98.59 117	99.19 122	99.28 96	94.14 271	99.82 164	97.97 124	99.80 109	99.29 212

DSMNet-mixed		97.42 236	97.60 218	96.87 323	99.15 194	91.46 361	98.54 109	99.12 222	92.87 358	97.58 289	99.63 33	96.21 204	99.90 64	95.74 270	99.54 215	99.27 215

CMPMVS	75.91 23	96.29 295	95.44 310	98.84 154	96.25 400	98.69 88	97.02 269	99.12 222	88.90 389	97.83 273	98.86 197	89.51 324	98.90 390	91.92 356	99.51 224	98.92 276
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PCF-MVS	92.86 18	94.36 332	93.00 349	98.42 216	98.70 278	97.56 186	93.16 394	99.11 224	79.59 402	97.55 292	97.43 333	92.19 303	99.73 238	79.85 403	99.45 236	97.97 356
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvsany_test3		98.87 78	98.92 68	98.74 177	99.38 140	96.94 223	98.58 104	99.10 225	96.49 269	99.96 4	99.81 5	98.18 79	99.45 345	98.97 63	99.79 114	99.83 22

cdsmvs_eth3d_5k		24.66 374	32.88 377	0.00 392	0.00 415	0.00 417	0.00 403	99.10 225	0.00 410	0.00 411	97.58 324	99.21 16	0.00 411	0.00 410	0.00 409	0.00 407

miper_ehance_all_eth		97.06 262	97.03 249	97.16 311	97.83 353	93.06 336	94.66 372	99.09 227	95.99 291	98.69 198	98.45 263	92.73 297	99.61 298	96.79 199	99.03 294	98.82 288

DeepC-MVS_fast	96.85 6	98.30 163	98.15 171	98.75 173	98.61 295	97.23 204	97.76 207	99.09 227	97.31 220	98.75 194	98.66 233	97.56 126	99.64 286	96.10 255	99.55 213	99.39 175
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZD-MVS					99.01 220	98.84 75		99.07 229	94.10 340	98.05 260	98.12 290	96.36 200	99.86 108	92.70 350	99.19 276

v148		98.45 145	98.60 109	98.00 250	99.44 129	94.98 284	97.44 244	99.06 230	98.30 133	99.32 103	98.97 171	96.65 186	99.62 292	98.37 99	99.85 81	99.39 175

PatchMatch-RL		97.24 250	96.78 265	98.61 189	99.03 219	97.83 165	96.36 305	99.06 230	93.49 350	97.36 308	97.78 312	95.75 226	99.49 335	93.44 334	98.77 314	98.52 322

PLC	94.65 16	96.51 287	95.73 298	98.85 153	98.75 267	97.91 158	96.42 302	99.06 230	90.94 379	95.59 362	97.38 336	94.41 264	99.59 303	90.93 374	98.04 354	99.05 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ppachtmachnet_test		97.50 228	97.74 205	96.78 329	98.70 278	91.23 370	94.55 377	99.05 233	96.36 276	99.21 120	98.79 211	96.39 196	99.78 208	96.74 205	99.82 95	99.34 196

CANet		97.87 201	97.76 203	98.19 235	97.75 356	95.51 266	96.76 285	99.05 233	97.74 176	96.93 321	98.21 284	95.59 231	99.89 74	97.86 132	99.93 44	99.19 234

pmmvs5		97.64 220	97.49 225	98.08 243	99.14 195	95.12 281	96.70 289	99.05 233	93.77 345	98.62 207	98.83 203	93.23 284	99.75 228	98.33 103	99.76 135	99.36 190

HQP3-MVS								99.04 236							99.26 265

HQP-MVS		97.00 268	96.49 282	98.55 201	98.67 286	96.79 227	96.29 310	99.04 236	96.05 287	95.55 365	96.84 348	93.84 277	99.54 322	92.82 345	99.26 265	99.32 203

TEST9					98.71 274	98.08 139	95.96 328	99.03 238	91.40 373	95.85 359	97.53 326	96.52 191	99.76 221

train_agg		97.10 259	96.45 283	99.07 120	98.71 274	98.08 139	95.96 328	99.03 238	91.64 368	95.85 359	97.53 326	96.47 193	99.76 221	93.67 327	99.16 279	99.36 190

test_prior				98.95 141	98.69 283	97.95 156		99.03 238					99.59 303			99.30 210

save fliter					99.11 199	97.97 152	96.53 296	99.02 241	98.24 139

test_8					98.67 286	98.01 147	95.91 333	99.02 241	91.64 368	95.79 361	97.50 329	96.47 193	99.76 221

MVS_Test		98.18 178	98.36 144	97.67 275	98.48 312	94.73 291	98.18 148	99.02 241	97.69 180	98.04 261	99.11 133	97.22 152	99.56 314	98.57 88	98.90 309	98.71 306

agg_prior					98.68 285	97.99 148		99.01 244		95.59 362			99.77 215

CDPH-MVS		97.26 247	96.66 274	99.07 120	99.00 221	98.15 128	96.03 324	99.01 244	91.21 376	97.79 276	97.85 310	96.89 169	99.69 255	92.75 348	99.38 246	99.39 175

ambc				98.24 232	98.82 257	95.97 253	98.62 100	99.00 246		99.27 108	99.21 110	96.99 165	99.50 333	96.55 225	99.50 231	99.26 218

Anonymous20240529		98.93 71	98.87 71	99.12 110	99.19 180	98.22 125	99.01 66	98.99 247	99.25 58	99.54 56	99.37 79	97.04 160	99.80 184	97.89 127	99.52 222	99.35 194

our_test_3		97.39 238	97.73 207	96.34 337	98.70 278	89.78 381	94.61 375	98.97 248	96.50 268	99.04 143	98.85 200	95.98 218	99.84 137	97.26 160	99.67 173	99.41 164

TSAR-MVS + MP.		98.63 120	98.49 124	99.06 126	99.64 70	97.90 159	98.51 116	98.94 249	96.96 246	99.24 117	98.89 193	97.83 103	99.81 177	96.88 193	99.49 232	99.48 137
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
WR-MVS		98.40 150	98.19 165	99.03 130	99.00 221	97.65 182	96.85 280	98.94 249	98.57 120	98.89 170	98.50 258	95.60 230	99.85 120	97.54 147	99.85 81	99.59 80

CNVR-MVS		98.17 180	97.87 198	99.07 120	98.67 286	98.24 120	97.01 270	98.93 251	97.25 226	97.62 285	98.34 274	97.27 148	99.57 311	96.42 233	99.33 252	99.39 175

CNLPA		97.17 256	96.71 269	98.55 201	98.56 305	98.05 145	96.33 307	98.93 251	96.91 250	97.06 316	97.39 335	94.38 266	99.45 345	91.66 360	99.18 278	98.14 346

AdaColmap		97.14 258	96.71 269	98.46 212	98.34 325	97.80 172	96.95 273	98.93 251	95.58 303	96.92 322	97.66 319	95.87 223	99.53 324	90.97 373	99.14 282	98.04 351

CR-MVSNet		96.28 296	95.95 294	97.28 303	97.71 359	94.22 304	98.11 156	98.92 254	92.31 364	96.91 324	99.37 79	85.44 353	99.81 177	97.39 154	97.36 370	97.81 364

Patchmtry		97.35 240	96.97 251	98.50 209	97.31 380	96.47 236	98.18 148	98.92 254	98.95 96	98.78 188	99.37 79	85.44 353	99.85 120	95.96 259	99.83 92	99.17 240

FMVSNet3		97.50 228	97.24 239	98.29 228	98.08 341	95.83 257	97.86 194	98.91 256	97.89 167	98.95 157	98.95 178	87.06 338	99.81 177	97.77 136	99.69 162	99.23 224

mvs_anonymous		97.83 210	98.16 170	96.87 323	98.18 335	91.89 357	97.31 252	98.90 257	97.37 214	98.83 182	99.46 66	96.28 202	99.79 197	98.90 66	98.16 344	98.95 270

NCCC		97.86 202	97.47 228	99.05 127	98.61 295	98.07 141	96.98 272	98.90 257	97.63 184	97.04 317	97.93 306	95.99 217	99.66 278	95.31 283	98.82 313	99.43 158

miper_enhance_ethall		96.01 302	95.74 297	96.81 327	96.41 398	92.27 354	93.69 391	98.89 259	91.14 377	98.30 240	97.35 339	90.58 317	99.58 309	96.31 239	99.03 294	98.60 318

D2MVS		97.84 208	97.84 200	97.83 260	99.14 195	94.74 290	96.94 274	98.88 260	95.84 296	98.89 170	98.96 174	94.40 265	99.69 255	97.55 145	99.95 32	99.05 251

CHOSEN 280x420		95.51 318	95.47 307	95.65 357	98.25 330	88.27 387	93.25 393	98.88 260	93.53 348	94.65 380	97.15 343	86.17 345	99.93 40	97.41 153	99.93 44	98.73 305

IU-MVS					99.49 115	99.15 47		98.87 262	92.97 355	99.41 80				96.76 203	99.62 187	99.66 58

EI-MVSNet-UG-set		98.69 106	98.71 89	98.62 186	99.10 201	96.37 238	97.23 258	98.87 262	99.20 64	99.19 122	98.99 165	97.30 145	99.85 120	98.77 75	99.79 114	99.65 62

EI-MVSNet		98.40 150	98.51 118	98.04 248	99.10 201	94.73 291	97.20 262	98.87 262	98.97 93	99.06 136	99.02 152	96.00 213	99.80 184	98.58 86	99.82 95	99.60 74

test11								98.87 262

MVSTER		96.86 273	96.55 280	97.79 263	97.91 350	94.21 306	97.56 232	98.87 262	97.49 200	99.06 136	99.05 147	80.72 375	99.80 184	98.44 96	99.82 95	99.37 184

MSC_two_6792asdad				99.32 80	98.43 318	98.37 111		98.86 267					99.89 74	97.14 168	99.60 194	99.71 46

No_MVS				99.32 80	98.43 318	98.37 111		98.86 267					99.89 74	97.14 168	99.60 194	99.71 46

EI-MVSNet-Vis-set		98.68 111	98.70 92	98.63 185	99.09 204	96.40 237	97.23 258	98.86 267	99.20 64	99.18 126	98.97 171	97.29 147	99.85 120	98.72 78	99.78 119	99.64 63

PS-MVSNAJ		97.08 261	97.39 230	96.16 348	98.56 305	92.46 348	95.24 356	98.85 270	97.25 226	97.49 298	95.99 364	98.07 87	99.90 64	96.37 235	98.67 324	96.12 395

DVP-MVS		98.77 93	98.52 117	99.52 39	99.50 108	99.21 28	98.02 170	98.84 271	97.97 159	99.08 134	99.02 152	97.61 122	99.88 83	96.99 180	99.63 184	99.48 137
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
xiu_mvs_v2_base		97.16 257	97.49 225	96.17 346	98.54 307	92.46 348	95.45 349	98.84 271	97.25 226	97.48 299	96.49 355	98.31 69	99.90 64	96.34 238	98.68 323	96.15 394

MS-PatchMatch		97.68 217	97.75 204	97.45 296	98.23 333	93.78 325	97.29 254	98.84 271	96.10 286	98.64 204	98.65 235	96.04 210	99.36 357	96.84 197	99.14 282	99.20 229

PMMVS		96.51 287	95.98 293	98.09 240	97.53 370	95.84 256	94.92 365	98.84 271	91.58 370	96.05 357	95.58 372	95.68 228	99.66 278	95.59 277	98.09 348	98.76 302

原ACMM1				98.35 222	98.90 240	96.25 243		98.83 275	92.48 362	96.07 356	98.10 292	95.39 238	99.71 246	92.61 352	98.99 300	99.08 247

ab-mvs		98.41 148	98.36 144	98.59 192	99.19 180	97.23 204	99.32 23	98.81 276	97.66 182	98.62 207	99.40 78	96.82 174	99.80 184	95.88 261	99.51 224	98.75 303

TAMVS		98.24 172	98.05 181	98.80 160	99.07 208	97.18 210	97.88 189	98.81 276	96.66 263	99.17 127	99.21 110	94.81 254	99.77 215	96.96 184	99.88 74	99.44 154

testdata				98.09 240	98.93 232	95.40 271		98.80 278	90.08 384	97.45 302	98.37 270	95.26 240	99.70 250	93.58 330	98.95 305	99.17 240

CL-MVSNet_self_test		97.44 235	97.22 240	98.08 243	98.57 304	95.78 259	94.30 382	98.79 279	96.58 266	98.60 211	98.19 286	94.74 259	99.64 286	96.41 234	98.84 310	98.82 288

CANet_DTU		97.26 247	97.06 248	97.84 259	97.57 365	94.65 295	96.19 316	98.79 279	97.23 232	95.14 374	98.24 281	93.22 285	99.84 137	97.34 156	99.84 85	99.04 255

test222					98.92 236	96.93 224	95.54 344	98.78 281	85.72 396	96.86 330	98.11 291	94.43 263			99.10 289	99.23 224

WB-MVS		98.52 139	98.55 113	98.43 215	99.65 65	95.59 261	98.52 111	98.77 282	99.65 14	99.52 62	99.00 164	94.34 267	99.93 40	98.65 83	98.83 311	99.76 39

新几何1				98.91 147	98.94 230	97.76 174		98.76 283	87.58 393	96.75 336	98.10 292	94.80 255	99.78 208	92.73 349	99.00 299	99.20 229

旧先验1					98.82 257	97.45 192		98.76 283			98.34 274	95.50 235			99.01 298	99.23 224

PAPM_NR		96.82 276	96.32 286	98.30 227	99.07 208	96.69 232	97.48 240	98.76 283	95.81 297	96.61 341	96.47 357	94.12 274	99.17 378	90.82 377	97.78 356	99.06 250

HPM-MVS++		98.10 182	97.64 215	99.48 51	99.09 204	99.13 55	97.52 236	98.75 286	97.46 206	96.90 327	97.83 311	96.01 212	99.84 137	95.82 268	99.35 249	99.46 146

CDS-MVSNet		97.69 216	97.35 234	98.69 178	98.73 269	97.02 218	96.92 278	98.75 286	95.89 295	98.59 213	98.67 230	92.08 306	99.74 233	96.72 208	99.81 99	99.32 203
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
无先验							95.74 339	98.74 288	89.38 387				99.73 238	92.38 355		99.22 228

MCST-MVS		98.00 191	97.63 216	99.10 114	99.24 166	98.17 127	96.89 279	98.73 289	95.66 299	97.92 265	97.70 318	97.17 154	99.66 278	96.18 250	99.23 269	99.47 144

PAPR		95.29 320	94.47 329	97.75 269	97.50 375	95.14 280	94.89 366	98.71 290	91.39 374	95.35 372	95.48 376	94.57 261	99.14 381	84.95 394	97.37 368	98.97 267

PMVS	91.26 20	97.86 202	97.94 191	97.65 277	99.71 47	97.94 157	98.52 111	98.68 291	98.99 91	97.52 295	99.35 83	97.41 140	98.18 398	91.59 363	99.67 173	96.82 386
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
VNet		98.42 147	98.30 152	98.79 163	98.79 264	97.29 200	98.23 142	98.66 292	99.31 52	98.85 179	98.80 209	94.80 255	99.78 208	98.13 111	99.13 284	99.31 207

test12				98.93 144	98.58 302	97.83 165		98.66 292		96.53 343		95.51 234	99.69 255		99.13 284	99.27 215

TSAR-MVS + GP.		98.18 178	97.98 187	98.77 169	98.71 274	97.88 160	96.32 308	98.66 292	96.33 277	99.23 119	98.51 254	97.48 138	99.40 352	97.16 165	99.46 234	99.02 258

SSC-MVS		98.71 99	98.74 83	98.62 186	99.72 44	96.08 251	98.74 86	98.64 295	99.74 6	99.67 41	99.24 105	94.57 261	99.95 22	99.11 52	99.24 267	99.82 25

OpenMVS_ROB	95.38 14	95.84 308	95.18 320	97.81 262	98.41 322	97.15 213	97.37 247	98.62 296	83.86 398	98.65 203	98.37 270	94.29 269	99.68 264	88.41 384	98.62 328	96.60 389

MAR-MVS		96.47 291	95.70 299	98.79 163	97.92 349	99.12 57	98.28 138	98.60 297	92.16 366	95.54 368	96.17 362	94.77 258	99.52 328	89.62 381	98.23 338	97.72 370
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
h-mvs33		97.77 211	97.33 236	99.10 114	99.21 173	97.84 164	98.35 135	98.57 298	99.11 72	98.58 215	99.02 152	88.65 332	99.96 11	98.11 112	96.34 384	99.49 127

UGNet		98.53 136	98.45 130	98.79 163	97.94 348	96.96 221	99.08 58	98.54 299	99.10 79	96.82 332	99.47 65	96.55 190	99.84 137	98.56 91	99.94 40	99.55 104
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
cl22		95.79 309	95.39 313	96.98 317	96.77 392	92.79 342	94.40 380	98.53 300	94.59 327	97.89 268	98.17 287	82.82 371	99.24 373	96.37 235	99.03 294	98.92 276

pmmvs4		97.58 226	97.28 237	98.51 207	98.84 252	96.93 224	95.40 352	98.52 301	93.60 347	98.61 209	98.65 235	95.10 244	99.60 299	96.97 183	99.79 114	98.99 263

API-MVS		97.04 264	96.91 256	97.42 298	97.88 352	98.23 124	98.18 148	98.50 302	97.57 191	97.39 306	96.75 351	96.77 178	99.15 380	90.16 379	99.02 297	94.88 400

sss		97.21 252	96.93 252	98.06 245	98.83 254	95.22 277	96.75 286	98.48 303	94.49 328	97.27 309	97.90 307	92.77 296	99.80 184	96.57 218	99.32 253	99.16 243

Vis-MVSNet (Re-imp)		97.46 232	97.16 243	98.34 223	99.55 93	96.10 246	98.94 74	98.44 304	98.32 132	98.16 248	98.62 242	88.76 328	99.73 238	93.88 322	99.79 114	99.18 236

MDA-MVSNet_test_wron		97.60 223	97.66 213	97.41 299	99.04 216	93.09 335	95.27 354	98.42 305	97.26 225	98.88 174	98.95 178	95.43 237	99.73 238	97.02 177	98.72 317	99.41 164

jason		97.45 234	97.35 234	97.76 268	99.24 166	93.93 318	95.86 334	98.42 305	94.24 336	98.50 225	98.13 288	94.82 252	99.91 59	97.22 162	99.73 142	99.43 158
jason: jason.
test_method		79.78 372	79.50 375	80.62 388	80.21 411	45.76 414	70.82 402	98.41 307	31.08 406	80.89 407	97.71 316	84.85 355	97.37 401	91.51 365	80.03 405	98.75 303

YYNet1		97.60 223	97.67 210	97.39 300	99.04 216	93.04 339	95.27 354	98.38 308	97.25 226	98.92 166	98.95 178	95.48 236	99.73 238	96.99 180	98.74 315	99.41 164

IS-MVSNet		98.19 177	97.90 195	99.08 118	99.57 81	97.97 152	99.31 27	98.32 309	99.01 90	98.98 150	99.03 151	91.59 309	99.79 197	95.49 280	99.80 109	99.48 137

1314		95.74 310	95.60 303	96.17 346	97.53 370	92.75 344	98.07 162	98.31 310	91.22 375	94.25 383	96.68 352	95.53 232	99.03 382	91.64 362	97.18 374	96.74 387

DPM-MVS		96.32 294	95.59 304	98.51 207	98.76 265	97.21 207	94.54 378	98.26 311	91.94 367	96.37 349	97.25 340	93.06 290	99.43 348	91.42 366	98.74 315	98.89 280

BH-untuned		96.83 274	96.75 267	97.08 312	98.74 268	93.33 333	96.71 288	98.26 311	96.72 260	98.44 230	97.37 337	95.20 241	99.47 341	91.89 357	97.43 365	98.44 329

EU-MVSNet		97.66 219	98.50 120	95.13 365	99.63 74	85.84 395	98.35 135	98.21 313	98.23 140	99.54 56	99.46 66	95.02 246	99.68 264	98.24 105	99.87 77	99.87 16

SixPastTwentyTwo		98.75 95	98.62 104	99.16 105	99.83 19	97.96 155	99.28 37	98.20 314	99.37 45	99.70 35	99.65 30	92.65 298	99.93 40	99.04 58	99.84 85	99.60 74

new_pmnet		96.99 269	96.76 266	97.67 275	98.72 271	94.89 286	95.95 330	98.20 314	92.62 361	98.55 220	98.54 250	94.88 251	99.52 328	93.96 319	99.44 239	98.59 320

CVMVSNet		96.25 297	97.21 241	93.38 383	99.10 201	80.56 410	97.20 262	98.19 316	96.94 248	99.00 148	99.02 152	89.50 325	99.80 184	96.36 237	99.59 198	99.78 33

RRT_MVS		99.09 54	98.94 66	99.55 23	99.87 12	98.82 78	99.48 9	98.16 317	99.49 31	99.59 52	99.65 30	94.79 257	99.95 22	99.45 35	99.96 25	99.88 14

KD-MVS_2432*1600		92.87 357	91.99 359	95.51 360	91.37 408	89.27 382	94.07 384	98.14 318	95.42 308	97.25 310	96.44 358	67.86 398	99.24 373	91.28 368	96.08 389	98.02 352

miper_refine_blended		92.87 357	91.99 359	95.51 360	91.37 408	89.27 382	94.07 384	98.14 318	95.42 308	97.25 310	96.44 358	67.86 398	99.24 373	91.28 368	96.08 389	98.02 352

MG-MVS		96.77 277	96.61 276	97.26 305	98.31 327	93.06 336	95.93 331	98.12 320	96.45 273	97.92 265	98.73 219	93.77 281	99.39 354	91.19 371	99.04 293	99.33 201

EPP-MVSNet		98.30 163	98.04 182	99.07 120	99.56 89	97.83 165	99.29 33	98.07 321	99.03 88	98.59 213	99.13 130	92.16 304	99.90 64	96.87 194	99.68 167	99.49 127

MVS		93.19 352	92.09 356	96.50 334	96.91 388	94.03 313	98.07 162	98.06 322	68.01 403	94.56 382	96.48 356	95.96 220	99.30 367	83.84 396	96.89 379	96.17 392

lupinMVS		97.06 262	96.86 258	97.65 277	98.88 246	93.89 322	95.48 348	97.97 323	93.53 348	98.16 248	97.58 324	93.81 279	99.91 59	96.77 202	99.57 207	99.17 240

GA-MVS		95.86 307	95.32 316	97.49 293	98.60 297	94.15 309	93.83 389	97.93 324	95.49 306	96.68 337	97.42 334	83.21 367	99.30 367	96.22 246	98.55 331	99.01 259

WTY-MVS		96.67 281	96.27 290	97.87 258	98.81 260	94.61 296	96.77 284	97.92 325	94.94 320	97.12 312	97.74 315	91.11 314	99.82 164	93.89 321	98.15 345	99.18 236

Patchmatch-test		96.55 285	96.34 285	97.17 309	98.35 324	93.06 336	98.40 130	97.79 326	97.33 217	98.41 233	98.67 230	83.68 366	99.69 255	95.16 286	99.31 255	98.77 300

ADS-MVSNet2		95.43 319	94.98 323	96.76 330	98.14 337	91.74 358	97.92 183	97.76 327	90.23 380	96.51 345	98.91 184	85.61 350	99.85 120	92.88 343	96.90 377	98.69 310

PVSNet	93.40 17	95.67 312	95.70 299	95.57 358	98.83 254	88.57 384	92.50 396	97.72 328	92.69 360	96.49 348	96.44 358	93.72 282	99.43 348	93.61 328	99.28 261	98.71 306

pmmvs3		95.03 325	94.40 331	96.93 319	97.70 361	92.53 347	95.08 360	97.71 329	88.57 390	97.71 280	98.08 295	79.39 382	99.82 164	96.19 248	99.11 288	98.43 330

alignmvs		97.35 240	96.88 257	98.78 166	98.54 307	98.09 135	97.71 212	97.69 330	99.20 64	97.59 288	95.90 367	88.12 337	99.55 317	98.18 109	98.96 304	98.70 309

AUN-MVS		96.24 298	95.45 309	98.60 191	98.70 278	97.22 206	97.38 246	97.65 331	95.95 293	95.53 369	97.96 305	82.11 374	99.79 197	96.31 239	97.44 364	98.80 297

tpm cat1		93.29 351	93.13 348	93.75 378	97.39 378	84.74 399	97.39 245	97.65 331	83.39 400	94.16 384	98.41 265	82.86 370	99.39 354	91.56 364	95.35 394	97.14 382

hse-mvs2		97.46 232	97.07 247	98.64 181	98.73 269	97.33 198	97.45 243	97.64 333	99.11 72	98.58 215	97.98 301	88.65 332	99.79 197	98.11 112	97.39 367	98.81 292

PVSNet_0	89.98 21	91.15 370	90.30 373	93.70 379	97.72 357	84.34 404	90.24 399	97.42 334	90.20 383	93.79 390	93.09 398	90.90 315	98.89 391	86.57 392	72.76 406	97.87 361

BH-w/o		95.13 323	94.89 327	95.86 350	98.20 334	91.31 365	95.65 341	97.37 335	93.64 346	96.52 344	95.70 371	93.04 291	99.02 383	88.10 386	95.82 391	97.24 381

test_yl		96.69 279	96.29 288	97.90 255	98.28 328	95.24 275	97.29 254	97.36 336	98.21 142	98.17 246	97.86 308	86.27 343	99.55 317	94.87 291	98.32 334	98.89 280

DCV-MVSNet		96.69 279	96.29 288	97.90 255	98.28 328	95.24 275	97.29 254	97.36 336	98.21 142	98.17 246	97.86 308	86.27 343	99.55 317	94.87 291	98.32 334	98.89 280

BH-RMVSNet		96.83 274	96.58 279	97.58 283	98.47 313	94.05 310	96.67 290	97.36 336	96.70 262	97.87 269	97.98 301	95.14 243	99.44 347	90.47 378	98.58 330	99.25 219

ADS-MVSNet		95.24 322	94.93 326	96.18 345	98.14 337	90.10 380	97.92 183	97.32 339	90.23 380	96.51 345	98.91 184	85.61 350	99.74 233	92.88 343	96.90 377	98.69 310

VDDNet		98.21 175	97.95 189	99.01 133	99.58 77	97.74 176	99.01 66	97.29 340	99.67 12	98.97 154	99.50 59	90.45 318	99.80 184	97.88 130	99.20 273	99.48 137

PAPM		91.88 369	90.34 372	96.51 333	98.06 342	92.56 346	92.44 397	97.17 341	86.35 394	90.38 401	96.01 363	86.61 341	99.21 376	70.65 407	95.43 393	97.75 368

FPMVS		93.44 349	92.23 354	97.08 312	99.25 165	97.86 162	95.61 342	97.16 342	92.90 357	93.76 391	98.65 235	75.94 391	95.66 404	79.30 404	97.49 361	97.73 369

mvsany_test1		97.60 223	97.54 220	97.77 265	97.72 357	95.35 272	95.36 353	97.13 343	94.13 339	99.71 33	99.33 89	97.93 99	99.30 367	97.60 144	98.94 306	98.67 314

E-PMN		94.17 337	94.37 332	93.58 380	96.86 389	85.71 397	90.11 400	97.07 344	98.17 149	97.82 275	97.19 341	84.62 358	98.94 387	89.77 380	97.68 358	96.09 396

VDD-MVS		98.56 128	98.39 140	99.07 120	99.13 197	98.07 141	98.59 103	97.01 345	99.59 23	99.11 129	99.27 98	94.82 252	99.79 197	98.34 101	99.63 184	99.34 196

FA-MVS(test-final)		96.99 269	96.82 262	97.50 292	98.70 278	94.78 288	99.34 20	96.99 346	95.07 316	98.48 227	99.33 89	88.41 335	99.65 283	96.13 254	98.92 308	98.07 350

tt0805		98.69 106	98.62 104	98.90 150	99.75 35	99.30 17	99.15 53	96.97 347	98.86 102	98.87 178	97.62 323	98.63 45	98.96 386	99.41 37	98.29 337	98.45 327

tpmrst		95.07 324	95.46 308	93.91 376	97.11 384	84.36 403	97.62 224	96.96 348	94.98 318	96.35 350	98.80 209	85.46 352	99.59 303	95.60 276	96.23 386	97.79 367

wuyk23d		96.06 300	97.62 217	91.38 386	98.65 294	98.57 96	98.85 82	96.95 349	96.86 253	99.90 12	99.16 122	99.18 17	98.40 396	89.23 383	99.77 124	77.18 404

HY-MVS	95.94 13	95.90 306	95.35 315	97.55 287	97.95 347	94.79 287	98.81 85	96.94 350	92.28 365	95.17 373	98.57 248	89.90 322	99.75 228	91.20 370	97.33 372	98.10 348

MIMVSNet		96.62 284	96.25 291	97.71 274	99.04 216	94.66 294	99.16 51	96.92 351	97.23 232	97.87 269	99.10 136	86.11 347	99.65 283	91.65 361	99.21 272	98.82 288

SCA		96.41 293	96.66 274	95.67 355	98.24 331	88.35 386	95.85 336	96.88 352	96.11 285	97.67 283	98.67 230	93.10 288	99.85 120	94.16 311	99.22 270	98.81 292

tpmvs		95.02 326	95.25 317	94.33 371	96.39 399	85.87 394	98.08 160	96.83 353	95.46 307	95.51 370	98.69 226	85.91 348	99.53 324	94.16 311	96.23 386	97.58 375

testing91		93.32 350	92.27 353	96.47 335	97.54 368	91.25 368	96.17 319	96.76 354	97.18 236	93.65 392	93.50 396	65.11 406	99.63 289	93.04 340	97.45 363	98.53 321

PatchmatchNet		95.58 315	95.67 301	95.30 364	97.34 379	87.32 391	97.65 221	96.65 355	95.30 312	97.07 315	98.69 226	84.77 356	99.75 228	94.97 289	98.64 325	98.83 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchT		96.65 282	96.35 284	97.54 288	97.40 377	95.32 273	97.98 177	96.64 356	99.33 50	96.89 328	99.42 73	84.32 361	99.81 177	97.69 143	97.49 361	97.48 377

Syy-MVS		96.04 301	95.56 306	97.49 293	97.10 385	94.48 299	96.18 317	96.58 357	95.65 300	94.77 377	92.29 402	91.27 313	99.36 357	98.17 110	98.05 352	98.63 316

myMVS_eth3d		91.92 368	90.45 370	96.30 338	97.10 385	90.90 373	96.18 317	96.58 357	95.65 300	94.77 377	92.29 402	53.88 411	99.36 357	89.59 382	98.05 352	98.63 316

TR-MVS		95.55 316	95.12 321	96.86 326	97.54 368	93.94 317	96.49 298	96.53 359	94.36 335	97.03 319	96.61 353	94.26 270	99.16 379	86.91 391	96.31 385	97.47 378

dp		93.47 348	93.59 341	93.13 385	96.64 394	81.62 409	97.66 219	96.42 360	92.80 359	96.11 354	98.64 238	78.55 388	99.59 303	93.31 336	92.18 403	98.16 345

EMVS		93.83 343	94.02 335	93.23 384	96.83 391	84.96 398	89.77 401	96.32 361	97.92 164	97.43 304	96.36 361	86.17 345	98.93 388	87.68 387	97.73 357	95.81 397

iter_conf05_11		96.72 278	96.30 287	97.97 252	97.97 345	96.24 244	94.99 363	96.19 362	96.45 273	96.77 335	96.84 348	91.46 311	99.78 208	96.27 242	99.78 119	97.90 357

Anonymous202405211		97.90 196	97.50 224	99.08 118	98.90 240	98.25 119	98.53 110	96.16 363	98.87 101	99.11 129	98.86 197	90.40 319	99.78 208	97.36 155	99.31 255	99.19 234

MDTV_nov1_ep13			95.22 318		97.06 387	83.20 405	97.74 209	96.16 363	94.37 334	96.99 320	98.83 203	83.95 364	99.53 324	93.90 320	97.95 355

FE-MVS		95.66 313	94.95 325	97.77 265	98.53 309	95.28 274	99.40 16	96.09 365	93.11 354	97.96 264	99.26 100	79.10 384	99.77 215	92.40 354	98.71 319	98.27 341

baseline1		95.96 305	95.44 310	97.52 290	98.51 311	93.99 316	98.39 131	96.09 365	98.21 142	98.40 237	97.76 314	86.88 339	99.63 289	95.42 281	89.27 404	98.95 270

CostFormer		93.97 341	93.78 338	94.51 370	97.53 370	85.83 396	97.98 177	95.96 367	89.29 388	94.99 376	98.63 240	78.63 386	99.62 292	94.54 299	96.50 382	98.09 349

testing99		93.04 355	91.98 361	96.23 343	97.53 370	90.70 377	96.35 306	95.94 368	96.87 252	93.41 393	93.43 397	63.84 408	99.59 303	93.24 338	97.19 373	98.40 333

JIA-IIPM		95.52 317	95.03 322	97.00 315	96.85 390	94.03 313	96.93 276	95.82 369	99.20 64	94.63 381	99.71 17	83.09 368	99.60 299	94.42 305	94.64 396	97.36 380

tpm2		93.09 353	92.58 352	94.62 369	97.56 366	86.53 393	97.66 219	95.79 370	86.15 395	94.07 387	98.23 283	75.95 390	99.53 324	90.91 375	96.86 380	97.81 364

testing11		93.08 354	92.02 358	96.26 341	97.56 366	90.83 375	96.32 308	95.70 371	96.47 271	92.66 396	93.73 393	64.36 407	99.59 303	93.77 326	97.57 359	98.37 337

ETVMVS		92.60 359	91.08 368	97.18 307	97.70 361	93.65 330	96.54 294	95.70 371	96.51 267	94.68 379	92.39 401	61.80 409	99.50 333	86.97 389	97.41 366	98.40 333

dmvs_re		95.98 304	95.39 313	97.74 271	98.86 248	97.45 192	98.37 133	95.69 373	97.95 161	96.56 342	95.95 365	90.70 316	97.68 400	88.32 385	96.13 388	98.11 347

EPNet_dtu		94.93 327	94.78 328	95.38 363	93.58 407	87.68 390	96.78 283	95.69 373	97.35 216	89.14 403	98.09 294	88.15 336	99.49 335	94.95 290	99.30 258	98.98 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing3		93.51 347	92.09 356	97.75 269	98.60 297	94.40 301	97.32 251	95.26 375	97.56 193	96.79 334	95.50 375	53.57 412	99.77 215	95.26 284	98.97 303	99.08 247

tpm		94.67 329	94.34 333	95.66 356	97.68 364	88.42 385	97.88 189	94.90 376	94.46 330	96.03 358	98.56 249	78.66 385	99.79 197	95.88 261	95.01 395	98.78 299

testing222		91.96 367	90.37 371	96.72 331	97.47 376	92.59 345	96.11 321	94.76 377	96.83 254	92.90 395	92.87 399	57.92 410	99.55 317	86.93 390	97.52 360	98.00 355

EPNet		96.14 299	95.44 310	98.25 230	90.76 410	95.50 267	97.92 183	94.65 378	98.97 93	92.98 394	98.85 200	89.12 327	99.87 100	95.99 257	99.68 167	99.39 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		93.72 345	93.14 347	95.46 362	98.66 291	91.29 366	96.61 293	94.63 379	97.39 212	96.83 331	93.71 394	79.88 377	99.56 314	82.40 400	98.13 346	95.54 399

MM		98.22 173	97.99 186	98.91 147	98.66 291	96.97 219	97.89 188	94.44 380	99.54 27	98.95 157	99.14 129	93.50 283	99.92 50	99.80 12	99.96 25	99.85 19

DeepMVS_CX				93.44 382	98.24 331	94.21 306		94.34 381	64.28 404	91.34 400	94.87 389	89.45 326	92.77 407	77.54 405	93.14 400	93.35 402

tfpn200view9		94.03 340	93.44 342	95.78 353	98.93 232	91.44 362	97.60 227	94.29 382	97.94 162	97.10 313	94.31 391	79.67 380	99.62 292	83.05 397	98.08 349	96.29 390

thres400		94.14 338	93.44 342	96.24 342	98.93 232	91.44 362	97.60 227	94.29 382	97.94 162	97.10 313	94.31 391	79.67 380	99.62 292	83.05 397	98.08 349	97.66 372

thres100view900		94.19 336	93.67 340	95.75 354	99.06 212	91.35 364	98.03 168	94.24 384	98.33 130	97.40 305	94.98 385	79.84 378	99.62 292	83.05 397	98.08 349	96.29 390

thres600view7		94.45 331	93.83 337	96.29 339	99.06 212	91.53 360	97.99 176	94.24 384	98.34 129	97.44 303	95.01 383	79.84 378	99.67 267	84.33 395	98.23 338	97.66 372

LFMVS		97.20 253	96.72 268	98.64 181	98.72 271	96.95 222	98.93 75	94.14 386	99.74 6	98.78 188	99.01 161	84.45 359	99.73 238	97.44 151	99.27 262	99.25 219

WB-MVSnew		95.73 311	95.57 305	96.23 343	96.70 393	90.70 377	96.07 323	93.86 387	95.60 302	97.04 317	95.45 380	96.00 213	99.55 317	91.04 372	98.31 336	98.43 330

test0.0.03 1		94.51 330	93.69 339	96.99 316	96.05 401	93.61 331	94.97 364	93.49 388	96.17 282	97.57 291	94.88 387	82.30 372	99.01 385	93.60 329	94.17 399	98.37 337

N_pmnet		97.63 221	97.17 242	98.99 135	99.27 161	97.86 162	95.98 325	93.41 389	95.25 313	99.47 70	98.90 187	95.63 229	99.85 120	96.91 186	99.73 142	99.27 215

IB-MVS	91.63 19	92.24 365	90.90 369	96.27 340	97.22 382	91.24 369	94.36 381	93.33 390	92.37 363	92.24 398	94.58 390	66.20 404	99.89 74	93.16 339	94.63 397	97.66 372
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
ET-MVSNet_ETH3D		94.30 335	93.21 345	97.58 283	98.14 337	94.47 300	94.78 368	93.24 391	94.72 324	89.56 402	95.87 368	78.57 387	99.81 177	96.91 186	97.11 376	98.46 324

K. test v3		98.00 191	97.66 213	99.03 130	99.79 24	97.56 186	99.19 49	92.47 392	99.62 20	99.52 62	99.66 27	89.61 323	99.96 11	99.25 45	99.81 99	99.56 97

test-LLR		93.90 342	93.85 336	94.04 374	96.53 395	84.62 401	94.05 386	92.39 393	96.17 282	94.12 385	95.07 381	82.30 372	99.67 267	95.87 264	98.18 341	97.82 362

test-mter		92.33 364	91.76 365	94.04 374	96.53 395	84.62 401	94.05 386	92.39 393	94.00 343	94.12 385	95.07 381	65.63 405	99.67 267	95.87 264	98.18 341	97.82 362

dmvs_testset		92.94 356	92.21 355	95.13 365	98.59 300	90.99 372	97.65 221	92.09 395	96.95 247	94.00 388	93.55 395	92.34 302	96.97 403	72.20 406	92.52 401	97.43 379

MTMP							97.93 181	91.91 396

TESTMET0.1,1		92.19 366	91.77 364	93.46 381	96.48 397	82.80 406	94.05 386	91.52 397	94.45 332	94.00 388	94.88 387	66.65 401	99.56 314	95.78 269	98.11 347	98.02 352

MVS_0304		98.10 182	97.88 197	98.76 170	98.82 257	96.50 235	97.90 186	91.35 398	99.56 26	98.32 239	99.13 130	96.06 209	99.93 40	99.84 7	99.97 19	99.85 19

thisisatest0515		94.12 339	93.16 346	96.97 318	98.60 297	92.90 340	93.77 390	90.61 399	94.10 340	96.91 324	95.87 368	74.99 393	99.80 184	94.52 300	99.12 287	98.20 343

tttt0517		95.64 314	94.98 323	97.64 279	99.36 147	93.81 324	98.72 90	90.47 400	98.08 155	98.67 200	98.34 274	73.88 394	99.92 50	97.77 136	99.51 224	99.20 229

thisisatest0530		95.27 321	94.45 330	97.74 271	99.19 180	94.37 302	97.86 194	90.20 401	97.17 237	98.22 244	97.65 320	73.53 395	99.90 64	96.90 191	99.35 249	98.95 270

baseline2		93.73 344	92.83 350	96.42 336	97.70 361	91.28 367	96.84 281	89.77 402	93.96 344	92.44 397	95.93 366	79.14 383	99.77 215	92.94 341	96.76 381	98.21 342

MVS-HIRNet		94.32 333	95.62 302	90.42 387	98.46 314	75.36 411	96.29 310	89.13 403	95.25 313	95.38 371	99.75 11	92.88 293	99.19 377	94.07 317	99.39 243	96.72 388

UWE-MVS		92.38 362	91.76 365	94.21 373	97.16 383	84.65 400	95.42 351	88.45 404	95.96 292	96.17 352	95.84 370	66.36 402	99.71 246	91.87 358	98.64 325	98.28 340

test1111		96.49 290	96.82 262	95.52 359	99.42 135	87.08 392	99.22 42	87.14 405	99.11 72	99.46 71	99.58 41	88.69 329	99.86 108	98.80 71	99.95 32	99.62 67

lessismore_v0				98.97 138	99.73 38	97.53 188		86.71 406		99.37 89	99.52 57	89.93 321	99.92 50	98.99 62	99.72 149	99.44 154

ECVR-MVS		96.42 292	96.61 276	95.85 351	99.38 140	88.18 388	99.22 42	86.00 407	99.08 84	99.36 92	99.57 42	88.47 334	99.82 164	98.52 92	99.95 32	99.54 108

EPMVS		93.72 345	93.27 344	95.09 367	96.04 402	87.76 389	98.13 153	85.01 408	94.69 325	96.92 322	98.64 238	78.47 389	99.31 365	95.04 287	96.46 383	98.20 343

MVE	83.40 22	92.50 360	91.92 362	94.25 372	98.83 254	91.64 359	92.71 395	83.52 409	95.92 294	86.46 406	95.46 377	95.20 241	95.40 405	80.51 402	98.64 325	95.73 398
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
gg-mvs-nofinetune		92.37 363	91.20 367	95.85 351	95.80 404	92.38 351	99.31 27	81.84 410	99.75 5	91.83 399	99.74 13	68.29 397	99.02 383	87.15 388	97.12 375	96.16 393

GG-mvs-BLEND				94.76 368	94.54 406	92.13 356	99.31 27	80.47 411		88.73 404	91.01 404	67.59 400	98.16 399	82.30 401	94.53 398	93.98 401

tmp_tt		78.77 373	78.73 376	78.90 389	58.45 412	74.76 413	94.20 383	78.26 412	39.16 405	86.71 405	92.82 400	80.50 376	75.19 408	86.16 393	92.29 402	86.74 403

test2506		92.39 361	91.89 363	93.89 377	99.38 140	82.28 407	99.32 23	66.03 413	99.08 84	98.77 191	99.57 42	66.26 403	99.84 137	98.71 79	99.95 32	99.54 108

testmvs		17.12 375	20.53 378	6.87 391	12.05 413	4.20 416	93.62 392	6.73 414	4.62 409	10.41 409	24.33 406	8.28 414	3.56 410	9.69 409	15.07 407	12.86 406

test123		17.04 376	20.11 379	7.82 390	10.25 414	4.91 415	94.80 367	4.47 415	4.93 408	10.00 410	24.28 407	9.69 413	3.64 409	10.14 408	12.43 408	14.92 405

test_blank		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet_test		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

DCPMVS		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

pcd_1.5k_mvsjas		8.17 377	10.90 380	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	98.07 87	0.00 411	0.00 410	0.00 409	0.00 407

sosnet-low-res		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uncertanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

Regformer		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

n2								0.00 416

nn								0.00 416

ab-mvs-re		8.12 378	10.83 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	97.48 330	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

WAC-MVS						90.90 373								91.37 367

PC_three_1452									93.27 351	99.40 83	98.54 250	98.22 75	97.00 402	95.17 285	99.45 236	99.49 127

eth-test2					0.00 415

eth-test					0.00 415

OPU-MVS				98.82 156	98.59 300	98.30 116	98.10 158				98.52 253	98.18 79	98.75 393	94.62 297	99.48 233	99.41 164

test_0728_THIRD									98.17 149	99.08 134	99.02 152	97.89 100	99.88 83	97.07 174	99.71 154	99.70 51

GSMVS																98.81 292

test_part2					99.36 147	99.10 60				99.05 141

sam_mvs1												84.74 357				98.81 292

sam_mvs												84.29 363

test_post1							97.59 229				20.48 409	83.07 369	99.66 278	94.16 311

test_post											21.25 408	83.86 365	99.70 250

patchmatchnet-post											98.77 214	84.37 360	99.85 120

gm-plane-assit					94.83 405	81.97 408			88.07 392		94.99 384		99.60 299	91.76 359

test9_res														93.28 337	99.15 281	99.38 182

agg_prior2														92.50 353	99.16 279	99.37 184

test_prior4						97.97 152	95.86 334

test_prior2							95.74 339		96.48 270	96.11 354	97.63 322	95.92 222		94.16 311	99.20 273

旧先验2							95.76 338		88.56 391	97.52 295			99.66 278	94.48 301

新几何2							95.93 331

原ACMM2							95.53 345

testdata2													99.79 197	92.80 347

segment_acmp												97.02 163

testdata1							95.44 350		96.32 278

plane_prior7					99.19 180	97.87 161

plane_prior6					98.99 224	97.70 180						94.90 248

plane_prior4											97.98 301

plane_prior3						97.78 173			97.41 210	97.79 276

plane_prior2							97.77 204		98.20 146

plane_prior1					99.05 215

plane_prior						97.65 182	97.07 268		96.72 260						99.36 247

HQP5-MVS						96.79 227

HQP-NCC					98.67 286		96.29 310		96.05 287	95.55 365

ACMP_Plane					98.67 286		96.29 310		96.05 287	95.55 365

BP-MVS														92.82 345

HQP4-MVS										95.56 364			99.54 322			99.32 203

HQP2-MVS												93.84 277

NP-MVS					98.84 252	97.39 196					96.84 348

MDTV_nov1_ep13_2view						74.92 412	97.69 214		90.06 385	97.75 279		85.78 349		93.52 331		98.69 310

ACMMP++_ref															99.77 124

ACMMP++															99.68 167

Test By Simon												96.52 191

High-resolution multi-view benchmark