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.

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 70	99.34 23	99.69 58	98.93 133	99.65 64	99.72 22	98.93 33	99.95 26	99.11 78	100.00 1	99.82 37

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 150	99.20 49	99.65 78	99.48 45	99.92 8	99.71 23	98.07 129	99.96 13	99.53 49	100.00 1	99.93 12

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 17	100.00 1	99.85 31

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 159	99.14 58	99.93 6	99.82 8	99.93 6	99.81 8	99.17 20	99.94 42	99.31 62	100.00 1	99.82 37

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 155	97.77 257	99.90 12	99.33 67	99.97 3	99.66 33	99.71 3	99.96 13	99.79 20	99.99 5	99.96 9

test_fmvsmconf0.01_n		99.56 11	99.63 10	99.36 74	99.87 12	98.13 153	98.08 198	99.95 2	99.45 51	99.98 2	99.75 17	99.80 1	99.97 6	99.82 13	99.99 5	99.99 2

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 157	99.57 105	97.73 216	97.93 232	99.83 27	99.22 81	99.93 6	99.30 126	99.42 11	99.96 13	99.85 7	99.99 5	99.29 286

UA-Net		99.47 16	99.40 27	99.70 2	99.49 152	99.29 24	99.80 4	99.72 47	99.82 8	99.04 205	99.81 8	98.05 132	99.96 13	98.85 99	99.99 5	99.86 29

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 105	99.28 40	99.66 72	99.09 111	99.89 18	99.68 26	99.53 7	99.97 6	99.50 51	99.99 5	99.87 23

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 105	99.34 23	99.71 49	99.27 75	99.90 14	99.74 19	99.68 4	99.97 6	99.55 44	99.99 5	99.88 21

v10		98.97 100	99.11 75	98.55 261	99.44 173	96.21 332	98.90 84	99.55 127	98.73 152	99.48 97	99.60 46	96.63 255	99.83 199	99.70 34	99.99 5	99.61 101

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 161	99.59 94	97.18 273	97.44 314	99.83 27	99.56 40	99.91 12	99.34 116	99.36 13	99.93 54	99.83 11	99.98 12	99.85 31

fmvsm_s_conf0.1_n_2		99.20 51	99.38 28	98.65 235	99.69 62	96.08 338	97.49 305	99.90 12	99.53 42	99.88 21	99.64 38	98.51 77	99.90 82	99.83 11	99.98 12	99.97 4

fmvsm_s_conf0.1_n_a		99.17 53	99.30 45	98.80 198	99.75 34	96.59 310	97.97 230	99.86 17	98.22 205	99.88 21	99.71 23	98.59 68	99.84 181	99.73 29	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 57	99.33 38	98.64 237	99.71 50	96.10 333	97.87 243	99.85 19	98.56 178	99.90 14	99.68 26	98.69 58	99.85 160	99.72 31	99.98 12	99.97 4

fmvsm_s_conf0.5_n		99.09 74	99.26 51	98.61 247	99.55 119	96.09 336	97.74 265	99.81 33	98.55 179	99.85 28	99.55 57	98.60 67	99.84 181	99.69 36	99.98 12	99.89 17

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 78	98.10 158	97.68 272	99.84 23	99.29 73	99.92 8	99.57 50	99.60 5	99.96 13	99.74 28	99.98 12	99.89 17

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 266	99.66 71	96.97 287	98.00 217	99.85 19	99.24 78	99.92 8	99.50 69	99.39 12	99.95 26	99.89 3	99.98 12	98.71 411

v8		99.01 91	99.16 63	98.57 254	99.47 163	96.30 328	98.90 84	99.47 172	99.02 123	99.52 88	99.57 50	96.93 230	99.81 228	99.60 38	99.98 12	99.60 103

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 93	99.39 20	99.56 122	99.11 101	99.70 52	99.73 21	99.00 27	99.97 6	99.26 66	99.98 12	99.89 17

fmvsm_l_mol_unc0.5_1		99.35 29	99.38 28	99.25 104	99.72 45	97.83 198	96.88 366	99.84 23	99.64 26	99.86 24	99.81 8	98.84 37	99.96 13	99.86 4	99.97 21	99.97 4

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 94	98.21 147	97.82 248	99.84 23	99.41 58	99.92 8	99.41 94	99.51 8	99.95 26	99.84 10	99.97 21	99.87 23

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 205	99.46 166	96.58 313	97.65 278	99.72 47	99.47 48	99.86 24	99.50 69	98.94 31	99.89 98	99.75 27	99.97 21	99.86 29

fmvsm_s_conf0.5_n_2		99.14 63	99.31 42	98.63 241	99.49 152	96.08 338	97.38 319	99.81 33	99.48 45	99.84 31	99.57 50	98.46 83	99.89 98	99.82 13	99.97 21	99.91 14

fmvsm_s_conf0.5_n_a		99.10 73	99.20 59	98.78 205	99.55 119	96.59 310	97.79 253	99.82 32	98.21 207	99.81 37	99.53 65	98.46 83	99.84 181	99.70 34	99.97 21	99.90 16

pmmvs-eth3d		98.47 199	98.34 210	98.86 182	99.30 216	97.76 212	97.16 347	99.28 269	95.54 423	99.42 113	99.19 160	97.27 206	99.63 379	97.89 182	99.97 21	99.20 316

IterMVS-LS		98.55 185	98.70 140	98.09 325	99.48 160	94.73 409	97.22 341	99.39 214	98.97 128	99.38 122	99.31 125	96.00 291	99.93 54	98.58 120	99.97 21	99.60 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 65	99.90 1	99.78 37	99.63 29	99.78 40	99.67 31	99.48 10	99.81 228	99.30 63	99.97 21	99.77 54
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
ELoFTR		97.81 297	97.74 289	98.04 336	99.39 187	95.79 353	97.28 335	99.58 104	94.13 470	99.38 122	99.37 105	93.31 383	99.60 396	97.23 251	99.96 29	98.74 409

Elysia		99.15 58	99.14 69	99.18 114	99.63 85	97.92 187	98.50 139	99.43 196	99.67 20	99.70 52	99.13 183	96.66 251	99.98 4	99.54 45	99.96 29	99.64 87

StellarMVS		99.15 58	99.14 69	99.18 114	99.63 85	97.92 187	98.50 139	99.43 196	99.67 20	99.70 52	99.13 183	96.66 251	99.98 4	99.54 45	99.96 29	99.64 87

fmvsm_s_conf0.5_n_7		98.83 123	99.04 88	98.20 313	99.30 216	94.83 404	97.23 337	99.36 224	98.64 162	99.84 31	99.43 89	98.10 128	99.91 75	99.56 42	99.96 29	99.87 23

mvs5depth		99.30 34	99.59 12	98.44 282	99.65 72	95.35 376	99.82 3	99.94 3	99.83 7	99.42 113	99.94 2	98.13 126	99.96 13	99.63 37	99.96 29	100.00 1

fmvsm_l_conf0.5_n_a		99.19 52	99.27 48	98.94 168	99.65 72	97.05 282	97.80 252	99.76 40	98.70 160	99.78 40	99.11 189	98.79 44	99.95 26	99.85 7	99.96 29	99.83 34

fmvsm_l_conf0.5_n		99.21 48	99.28 47	99.02 152	99.64 78	97.28 259	97.82 248	99.76 40	98.73 152	99.82 35	99.09 198	98.81 40	99.95 26	99.86 4	99.96 29	99.83 34

MM		98.22 241	97.99 262	98.91 176	98.66 387	96.97 287	97.89 239	94.44 520	99.54 41	98.95 226	99.14 181	93.50 381	99.92 66	99.80 18	99.96 29	99.85 31

test_fmvs3		99.12 70	99.41 26	98.25 306	99.76 30	95.07 393	99.05 68	99.94 3	97.78 253	99.82 35	99.84 3	98.56 74	99.71 314	99.96 1	99.96 29	99.97 4

Anonymous20240521		98.69 152	98.87 112	98.16 319	99.77 27	95.11 392	99.08 62	99.44 190	99.34 66	99.33 139	99.55 57	94.10 369	99.94 42	99.25 68	99.96 29	99.42 220

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 113	99.45 14	99.61 93	99.66 23	99.68 58	99.66 33	98.44 85	99.95 26	99.73 29	99.96 29	99.75 63

fmvsm_s_conf0.5_n_11		99.21 48	99.34 36	98.80 198	99.48 160	96.56 315	97.97 230	99.69 58	99.63 29	99.84 31	99.54 63	98.21 116	99.94 42	99.76 24	99.95 40	99.88 21

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 45	98.98 73	99.51 10	99.85 19	99.86 6	99.88 21	99.82 5	99.02 26	99.90 82	99.54 45	99.95 40	99.61 101

tt0320		99.61 8	99.65 9	99.48 57	99.71 50	98.94 80	99.54 8	99.83 27	99.87 5	99.89 18	99.82 5	98.75 48	99.90 82	99.54 45	99.95 40	99.59 110

fmvsm_s_conf0.5_n_8		99.13 67	99.26 51	98.74 218	99.51 136	96.44 323	97.65 278	99.65 78	99.66 23	99.78 40	99.48 76	97.92 143	99.93 54	99.72 31	99.95 40	99.87 23

mmtdpeth		99.30 34	99.42 25	98.92 174	99.58 96	96.89 295	99.48 13	99.92 8	99.92 2	98.26 344	99.80 12	98.33 97	99.91 75	99.56 42	99.95 40	99.97 4

test2506		92.39 497	91.89 500	93.89 517	99.38 189	82.28 554	99.32 26	66.03 562	99.08 115	98.77 270	99.57 50	66.26 542	99.84 181	98.71 111	99.95 40	99.54 144

test1111		96.49 390	96.82 361	95.52 492	99.42 180	87.08 535	99.22 46	87.14 553	99.11 101	99.46 102	99.58 48	88.69 449	99.86 145	98.80 101	99.95 40	99.62 93

ECVR-MVS		96.42 396	96.61 380	95.85 481	99.38 189	88.18 530	99.22 46	86.00 555	99.08 115	99.36 129	99.57 50	88.47 454	99.82 211	98.52 128	99.95 40	99.54 144

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 20	99.69 5	99.58 104	99.90 3	99.86 24	99.78 14	99.58 6	99.95 26	99.00 88	99.95 40	99.78 51

D2MVS		97.84 293	97.84 282	97.83 353	99.14 270	94.74 408	96.94 359	98.88 360	95.84 405	98.89 242	98.96 243	94.40 355	99.69 333	97.55 223	99.95 40	99.05 348

PS-CasMVS		99.40 25	99.33 38	99.62 9	99.71 50	99.10 66	99.29 36	99.53 137	99.53 42	99.46 102	99.41 94	98.23 111	99.95 26	98.89 97	99.95 40	99.81 42

CHOSEN 1792x2688		97.49 320	97.14 338	98.54 266	99.68 65	96.09 336	96.50 397	99.62 90	91.58 509	98.84 256	98.97 239	92.36 405	99.88 116	96.76 300	99.95 40	99.67 79

fmvsm_s_conf0.5_n_10		99.15 58	99.27 48	98.78 205	99.47 163	96.56 315	97.75 263	99.71 49	99.60 36	99.74 47	99.44 86	97.96 140	99.95 26	99.86 4	99.94 52	99.82 37

MGCNet		97.44 325	97.01 346	98.72 222	96.42 532	96.74 305	97.20 342	91.97 543	98.46 183	98.30 338	98.79 289	92.74 400	99.91 75	99.30 63	99.94 52	99.52 162

IterMVS-SCA-FT		97.85 292	98.18 240	96.87 434	99.27 224	91.16 504	95.53 458	99.25 280	99.10 108	99.41 115	99.35 112	93.10 391	99.96 13	98.65 115	99.94 52	99.49 178

FC-MVSNet-test		99.27 38	99.25 53	99.34 83	99.77 27	98.37 127	99.30 35	99.57 112	99.61 35	99.40 118	99.50 69	97.12 216	99.85 160	99.02 87	99.94 52	99.80 46

UGNet		98.53 190	98.45 188	98.79 202	97.94 462	96.96 289	99.08 62	98.54 408	99.10 108	96.82 453	99.47 79	96.55 259	99.84 181	98.56 125	99.94 52	99.55 138
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
IterMVS		97.73 301	98.11 249	96.57 447	99.24 236	90.28 515	95.52 460	99.21 290	98.86 143	99.33 139	99.33 119	93.11 390	99.94 42	98.49 129	99.94 52	99.48 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PDCNetPlus		95.22 447	94.73 455	96.70 444	97.85 467	91.14 505	93.94 516	99.97 1	93.06 491	98.95 226	98.89 265	74.32 526	99.14 495	95.63 387	99.93 58	99.82 37

fmvsm_s_conf0.5_n_9		99.17 53	99.38 28	98.53 268	99.51 136	95.82 351	97.62 283	99.78 37	99.72 14	99.90 14	99.48 76	98.66 60	99.89 98	99.85 7	99.93 58	99.89 17

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 69	99.50 11	99.63 83	99.88 4	99.86 24	99.80 12	99.03 24	99.89 98	99.48 53	99.93 58	99.60 103

test_vis1_n_1920		98.40 208	98.92 103	96.81 438	99.74 37	90.76 512	98.15 186	99.91 10	98.33 192	99.89 18	99.55 57	95.07 331	99.88 116	99.76 24	99.93 58	99.79 48

test_f		98.67 162	98.87 112	98.05 335	99.72 45	95.59 357	98.51 137	99.81 33	96.30 381	99.78 40	99.82 5	96.14 282	98.63 515	99.82 13	99.93 58	99.95 10

CHOSEN 280x420		95.51 437	95.47 424	95.65 489	98.25 434	88.27 529	93.25 530	98.88 360	93.53 482	94.65 515	97.15 467	86.17 468	99.93 54	97.41 238	99.93 58	98.73 410

CANet		97.87 285	97.76 287	98.19 315	97.75 474	95.51 362	96.76 373	99.05 328	97.74 255	96.93 441	98.21 391	95.59 313	99.89 98	97.86 189	99.93 58	99.19 322

v1144		98.60 174	98.66 148	98.41 286	99.36 196	95.90 345	97.58 292	99.34 236	97.51 282	99.27 155	99.15 177	96.34 273	99.80 237	99.47 54	99.93 58	99.51 166

PEN-MVS		99.41 24	99.34 36	99.62 9	99.73 38	99.14 58	99.29 36	99.54 133	99.62 33	99.56 75	99.41 94	98.16 123	99.96 13	98.78 103	99.93 58	99.77 54

DTE-MVSNet		99.43 22	99.35 34	99.66 7	99.71 50	99.30 22	99.31 30	99.51 145	99.64 26	99.56 75	99.46 81	98.23 111	99.97 6	98.78 103	99.93 58	99.72 65

CP-MVSNet		99.21 48	99.09 83	99.56 26	99.65 72	98.96 79	99.13 59	99.34 236	99.42 56	99.33 139	99.26 138	97.01 225	99.94 42	98.74 108	99.93 58	99.79 48

WR-MVS_H		99.33 31	99.22 55	99.65 8	99.71 50	99.24 30	99.32 26	99.55 127	99.46 50	99.50 94	99.34 116	97.30 203	99.93 54	98.90 95	99.93 58	99.77 54

PVSNet_BlendedMVS		97.55 316	97.53 310	97.60 384	98.92 326	93.77 451	96.64 386	99.43 196	94.49 456	97.62 396	99.18 164	96.82 237	99.67 351	94.73 411	99.93 58	99.36 254

Vis-MVSNet		99.34 30	99.36 33	99.27 99	99.73 38	98.26 139	99.17 54	99.78 37	99.11 101	99.27 155	99.48 76	98.82 39	99.95 26	98.94 92	99.93 58	99.59 110
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_6		99.08 80	99.21 58	98.69 228	99.36 196	96.51 317	97.62 283	99.68 65	98.43 184	99.85 28	99.10 192	99.12 23	99.88 116	99.77 23	99.92 72	99.67 79

SSC-MVS3.2		98.53 190	98.79 125	97.74 365	99.46 166	93.62 457	96.45 400	99.34 236	99.33 67	98.93 235	98.70 313	97.90 144	99.90 82	99.12 77	99.92 72	99.69 73

SDMVSNet		99.23 46	99.32 40	98.96 165	99.68 65	97.35 247	98.84 95	99.48 160	99.69 17	99.63 67	99.68 26	99.03 24	99.96 13	97.97 178	99.92 72	99.57 125

sd_testset		99.28 37	99.31 42	99.19 113	99.68 65	98.06 169	99.41 17	99.30 257	99.69 17	99.63 67	99.68 26	99.25 16	99.96 13	97.25 250	99.92 72	99.57 125

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 27	99.53 9	99.76 40	99.64 26	99.84 31	99.83 4	99.50 9	99.87 136	99.36 58	99.92 72	99.64 87

nrg030		99.40 25	99.35 34	99.54 31	99.58 96	99.13 61	98.98 76	99.48 160	99.68 19	99.46 102	99.26 138	98.62 65	99.73 301	99.17 75	99.92 72	99.76 59

v1192		98.60 174	98.66 148	98.41 286	99.27 224	95.88 347	97.52 299	99.36 224	97.41 296	99.33 139	99.20 157	96.37 271	99.82 211	99.57 40	99.92 72	99.55 138

OurMVSNet-221017-0		99.37 28	99.31 42	99.53 38	99.91 3	98.98 73	99.63 7	99.58 104	99.44 53	99.78 40	99.76 16	96.39 267	99.92 66	99.44 55	99.92 72	99.68 74

DeepC-MVS	97.60 4	98.97 100	98.93 102	99.10 131	99.35 201	97.98 178	98.01 215	99.46 177	97.56 275	99.54 80	99.50 69	98.97 29	99.84 181	98.06 165	99.92 72	99.49 178
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FE-MVSNET2		99.15 58	99.22 55	98.94 168	99.70 58	97.49 234	98.62 119	99.67 71	98.85 146	99.34 136	99.54 63	98.47 78	99.81 228	98.93 93	99.91 81	99.51 166

VortexMVS		97.98 273	98.31 217	97.02 424	98.88 336	91.45 494	98.03 209	99.47 172	98.65 161	99.55 78	99.47 79	91.49 423	99.81 228	99.32 61	99.91 81	99.80 46

patch_mono-2		98.51 195	98.63 154	98.17 316	99.38 189	94.78 406	97.36 324	99.69 58	98.16 218	98.49 319	99.29 129	97.06 219	99.97 6	98.29 146	99.91 81	99.76 59

dcpmvs_2		98.78 134	99.11 75	97.78 358	99.56 113	93.67 454	99.06 66	99.86 17	99.50 44	99.66 61	99.26 138	97.21 211	99.99 2	98.00 173	99.91 81	99.68 74

Anonymous20231211		99.27 38	99.27 48	99.26 101	99.29 218	98.18 148	99.49 12	99.51 145	99.70 15	99.80 38	99.68 26	96.84 234	99.83 199	99.21 71	99.91 81	99.77 54

v144192		98.54 188	98.57 165	98.45 280	99.21 244	95.98 341	97.63 282	99.36 224	97.15 329	99.32 145	99.18 164	95.84 303	99.84 181	99.50 51	99.91 81	99.54 144

PVSNet_Blended_VisFu		98.17 252	98.15 245	98.22 312	99.73 38	95.15 389	97.36 324	99.68 65	94.45 461	98.99 215	99.27 132	96.87 233	99.94 42	97.13 264	99.91 81	99.57 125

test_0402		98.76 138	98.71 137	98.93 171	99.56 113	98.14 152	98.45 149	99.34 236	99.28 74	98.95 226	98.91 255	98.34 96	99.79 251	95.63 387	99.91 81	98.86 387

fmvsm_s_conf0.5_n_5		99.07 83	99.10 81	98.99 157	99.47 163	97.22 266	97.40 316	99.83 27	97.61 269	99.85 28	99.30 126	98.80 42	99.95 26	99.71 33	99.90 89	99.78 51

fmvsm_s_conf0.5_n_4		99.01 91	99.22 55	98.38 290	99.31 212	95.48 367	97.56 294	99.73 46	98.87 141	99.75 45	99.27 132	98.80 42	99.86 145	99.80 18	99.90 89	99.81 42

test_fmvs2		98.70 148	98.97 99	97.89 348	99.54 125	94.05 431	98.55 128	99.92 8	96.78 355	99.72 48	99.78 14	96.60 256	99.67 351	99.91 2	99.90 89	99.94 11

v1921920		98.54 188	98.60 161	98.38 290	99.20 248	95.76 355	97.56 294	99.36 224	97.23 321	99.38 122	99.17 169	96.02 289	99.84 181	99.57 40	99.90 89	99.54 144

v2v482		98.56 181	98.62 156	98.37 293	99.42 180	95.81 352	97.58 292	99.16 307	97.90 241	99.28 152	99.01 226	95.98 296	99.79 251	99.33 60	99.90 89	99.51 166

TranMVSNet+NR-MVSNet		99.17 53	99.07 86	99.46 63	99.37 195	98.87 86	98.39 159	99.42 203	99.42 56	99.36 129	99.06 201	98.38 90	99.95 26	98.34 143	99.90 89	99.57 125

FMVSNet1		99.17 53	99.17 61	99.17 116	99.55 119	98.24 141	99.20 49	99.44 190	99.21 83	99.43 109	99.55 57	97.82 155	99.86 145	98.42 138	99.89 95	99.41 224

SIFT-PCN-Cal		96.34 398	96.46 390	96.01 475	98.17 444	96.89 295	93.48 526	97.35 457	94.84 448	99.35 131	98.30 380	94.70 345	97.92 526	92.03 488	99.88 96	93.21 541

LuminaMVS		98.39 215	98.20 234	98.98 161	99.50 143	97.49 234	97.78 254	97.69 444	98.75 151	99.49 95	99.25 143	92.30 408	99.94 42	99.14 76	99.88 96	99.50 170

FIs		99.14 63	99.09 83	99.29 95	99.70 58	98.28 137	99.13 59	99.52 143	99.48 45	99.24 169	99.41 94	96.79 241	99.82 211	98.69 113	99.88 96	99.76 59

v1240		98.55 185	98.62 156	98.32 297	99.22 242	95.58 359	97.51 302	99.45 182	97.16 327	99.45 107	99.24 145	96.12 286	99.85 160	99.60 38	99.88 96	99.55 138

TAMVS		98.24 240	98.05 256	98.80 198	99.07 285	97.18 273	97.88 240	98.81 376	96.66 363	99.17 186	99.21 155	94.81 340	99.77 269	96.96 280	99.88 96	99.44 211

viewmacassd2359aftdt		98.86 117	98.87 112	98.83 191	99.53 129	97.32 252	97.70 270	99.64 80	98.22 205	99.25 167	99.27 132	98.40 87	99.61 392	97.98 177	99.87 101	99.55 138

KinetiMVS		99.03 89	99.02 91	99.03 149	99.70 58	97.48 237	98.43 150	99.29 265	99.70 15	99.60 72	99.07 200	96.13 284	99.94 42	99.42 56	99.87 101	99.68 74

AstraMVS		98.16 254	98.07 255	98.41 286	99.51 136	95.86 348	98.00 217	95.14 514	98.97 128	99.43 109	99.24 145	93.25 385	99.84 181	99.21 71	99.87 101	99.54 144

WBMVS		95.18 448	94.78 451	96.37 454	97.68 483	89.74 522	95.80 449	98.73 391	97.54 280	98.30 338	98.44 361	70.06 531	99.82 211	96.62 320	99.87 101	99.54 144

test_fmvs1_n		98.09 260	98.28 221	97.52 395	99.68 65	93.47 459	98.63 117	99.93 6	95.41 432	99.68 58	99.64 38	91.88 417	99.48 446	99.82 13	99.87 101	99.62 93

EU-MVSNet		97.66 308	98.50 177	95.13 501	99.63 85	85.84 538	98.35 163	98.21 427	98.23 203	99.54 80	99.46 81	95.02 332	99.68 346	98.24 148	99.87 101	99.87 23

MIMVSNet1		99.38 27	99.32 40	99.55 28	99.86 14	99.19 42	99.41 17	99.59 101	99.59 37	99.71 50	99.57 50	97.12 216	99.90 82	99.21 71	99.87 101	99.54 144

FE-MVSNET		98.59 176	98.50 177	98.87 180	99.58 96	97.30 253	98.08 198	99.74 45	96.94 339	98.97 220	99.10 192	96.94 229	99.74 293	97.33 243	99.86 108	99.55 138

test_cas_vis1_n_1920		98.33 223	98.68 143	97.27 410	99.69 62	92.29 483	98.03 209	99.85 19	97.62 266	99.96 4	99.62 41	93.98 370	99.74 293	99.52 50	99.86 108	99.79 48

diffmvs_AUTHOR		98.50 196	98.59 163	98.23 311	99.35 201	95.48 367	96.60 391	99.60 95	98.37 186	98.90 239	99.00 230	97.37 199	99.76 275	98.22 151	99.85 110	99.46 201

CS-MVS		99.13 67	99.10 81	99.24 107	99.06 290	99.15 53	99.36 22	99.88 15	99.36 64	98.21 346	98.46 359	98.68 59	99.93 54	99.03 86	99.85 110	98.64 423

SPE-MVS-test		99.13 67	99.09 83	99.26 101	99.13 273	98.97 75	99.31 30	99.88 15	99.44 53	98.16 350	98.51 350	98.64 62	99.93 54	98.91 94	99.85 110	98.88 385

v148		98.45 202	98.60 161	98.00 339	99.44 173	94.98 395	97.44 314	99.06 324	98.30 196	99.32 145	98.97 239	96.65 253	99.62 384	98.37 141	99.85 110	99.39 234

WR-MVS		98.40 208	98.19 238	99.03 149	99.00 310	97.65 223	96.85 367	98.94 346	98.57 175	98.89 242	98.50 354	95.60 312	99.85 160	97.54 225	99.85 110	99.59 110

DKM		98.18 249	97.95 267	98.85 183	99.35 201	98.31 135	96.68 380	99.69 58	96.90 345	98.61 299	98.77 293	94.41 353	98.93 505	97.32 245	99.84 115	99.32 275

E5new		99.05 84	99.11 75	98.85 183	99.60 90	97.30 253	98.42 153	99.63 83	98.73 152	99.26 159	99.39 101	98.71 52	99.70 323	98.43 134	99.84 115	99.54 144

E6new		99.05 84	99.11 75	98.85 183	99.60 90	97.30 253	98.42 153	99.63 83	98.73 152	99.26 159	99.39 101	98.71 52	99.70 323	98.43 134	99.84 115	99.54 144

E6		99.05 84	99.11 75	98.85 183	99.60 90	97.30 253	98.42 153	99.63 83	98.73 152	99.26 159	99.39 101	98.71 52	99.70 323	98.43 134	99.84 115	99.54 144

E5		99.05 84	99.11 75	98.85 183	99.60 90	97.30 253	98.42 153	99.63 83	98.73 152	99.26 159	99.39 101	98.71 52	99.70 323	98.43 134	99.84 115	99.54 144

test_vis1_n		98.31 228	98.50 177	97.73 368	99.76 30	94.17 426	98.68 109	99.91 10	96.31 379	99.79 39	99.57 50	92.85 398	99.42 462	99.79 20	99.84 115	99.60 103

CANet_DTU		97.26 342	97.06 343	97.84 352	97.57 486	94.65 413	96.19 421	98.79 379	97.23 321	95.14 505	98.24 388	93.22 387	99.84 181	97.34 241	99.84 115	99.04 352

V42		98.78 134	98.78 127	98.76 212	99.44 173	97.04 283	98.27 172	99.19 296	97.87 243	99.25 167	99.16 171	96.84 234	99.78 263	99.21 71	99.84 115	99.46 201

VPA-MVSNet		99.30 34	99.30 45	99.28 96	99.49 152	98.36 130	99.00 73	99.45 182	99.63 29	99.52 88	99.44 86	98.25 108	99.88 116	99.09 80	99.84 115	99.62 93

SixPastTwentyTwo		98.75 139	98.62 156	99.16 119	99.83 18	97.96 182	99.28 40	98.20 428	99.37 61	99.70 52	99.65 37	92.65 402	99.93 54	99.04 85	99.84 115	99.60 103

HyFIR lowres test		97.19 350	96.60 382	98.96 165	99.62 89	97.28 259	95.17 473	99.50 150	94.21 467	99.01 210	98.32 378	86.61 464	99.99 2	97.10 266	99.84 115	99.60 103

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 21	99.68 6	99.71 49	99.38 60	99.53 84	99.61 44	98.64 62	99.80 237	98.24 148	99.84 115	99.52 162

SIFT-ConvMatch		96.57 384	96.62 378	96.43 451	98.20 440	98.27 138	93.88 517	96.88 479	95.29 434	98.88 246	98.25 386	95.18 327	97.43 534	93.22 462	99.83 127	93.59 534

casdiffseed414692147		99.09 74	99.12 72	99.01 154	99.55 119	97.91 189	98.30 167	99.68 65	99.04 120	99.19 178	99.37 105	98.98 28	99.61 392	98.13 157	99.83 127	99.50 170

E4		98.87 113	98.88 109	98.81 195	99.52 133	97.23 263	97.62 283	99.61 93	98.58 173	99.18 183	99.33 119	98.29 100	99.69 333	97.99 176	99.83 127	99.52 162

guyue		98.01 268	97.93 272	98.26 304	99.45 171	95.48 367	98.08 198	96.24 492	98.89 139	99.34 136	99.14 181	91.32 426	99.82 211	99.07 81	99.83 127	99.48 189

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 102	99.29 36	99.63 83	99.30 72	99.65 64	99.60 46	99.16 22	99.82 211	99.07 81	99.83 127	99.56 131

Baseline_NR-MVSNet		98.98 99	98.86 116	99.36 74	99.82 19	98.55 110	97.47 310	99.57 112	99.37 61	99.21 176	99.61 44	96.76 244	99.83 199	98.06 165	99.83 127	99.71 66

Patchmtry		97.35 334	96.97 348	98.50 275	97.31 503	96.47 321	98.18 181	98.92 353	98.95 132	98.78 267	99.37 105	85.44 479	99.85 160	95.96 370	99.83 127	99.17 330

LoFTR		97.97 274	97.79 285	98.53 268	98.80 354	97.47 238	97.01 353	99.55 127	95.55 421	99.46 102	99.22 153	94.22 363	99.44 458	96.45 339	99.82 134	98.68 420

viewdifsd2359ckpt11		98.84 120	99.04 88	98.24 308	99.56 113	95.51 362	97.38 319	99.70 55	99.16 95	99.57 73	99.40 98	98.26 106	99.71 314	98.55 126	99.82 134	99.50 170

viewmsd2359difaftdt		98.84 120	99.04 88	98.24 308	99.56 113	95.51 362	97.38 319	99.70 55	99.16 95	99.57 73	99.40 98	98.26 106	99.71 314	98.55 126	99.82 134	99.50 170

ppachtmachnet_test		97.50 317	97.74 289	96.78 441	98.70 372	91.23 503	94.55 495	99.05 328	96.36 376	99.21 176	98.79 289	96.39 267	99.78 263	96.74 303	99.82 134	99.34 264

EI-MVSNet		98.40 208	98.51 174	98.04 336	99.10 278	94.73 409	97.20 342	98.87 362	98.97 128	99.06 195	99.02 214	96.00 291	99.80 237	98.58 120	99.82 134	99.60 103

NR-MVSNet		98.95 103	98.82 122	99.36 74	99.16 264	98.72 98	99.22 46	99.20 292	99.10 108	99.72 48	98.76 298	96.38 269	99.86 145	98.00 173	99.82 134	99.50 170

MVSTER		96.86 372	96.55 384	97.79 357	97.91 464	94.21 424	97.56 294	98.87 362	97.49 285	99.06 195	99.05 208	80.72 506	99.80 237	98.44 132	99.82 134	99.37 246

Casviewmamba		99.12 70	99.12 72	99.09 135	99.53 129	98.08 163	98.34 165	99.66 72	99.35 65	99.35 131	99.23 151	98.39 89	99.72 312	98.46 130	99.81 141	99.47 198

reproduce_monomvs		95.00 453	95.25 438	94.22 511	97.51 496	83.34 549	97.86 244	98.44 414	98.51 180	99.29 150	99.30 126	67.68 538	99.56 413	98.89 97	99.81 141	99.77 54

testf1		99.25 41	99.16 63	99.51 49	99.89 6	99.63 3	98.71 106	99.69 58	98.90 137	99.43 109	99.35 112	98.86 35	99.67 351	97.81 192	99.81 141	99.24 304

APD_test2		99.25 41	99.16 63	99.51 49	99.89 6	99.63 3	98.71 106	99.69 58	98.90 137	99.43 109	99.35 112	98.86 35	99.67 351	97.81 192	99.81 141	99.24 304

cl____		97.02 363	96.83 360	97.58 386	97.82 470	94.04 433	94.66 490	99.16 307	97.04 333	98.63 293	98.71 306	88.68 451	99.69 333	97.00 273	99.81 141	99.00 360

DIV-MVS_self_test		97.02 363	96.84 359	97.58 386	97.82 470	94.03 434	94.66 490	99.16 307	97.04 333	98.63 293	98.71 306	88.69 449	99.69 333	97.00 273	99.81 141	99.01 357

eth_miper_zixun_eth		97.23 346	97.25 329	97.17 416	98.00 458	92.77 473	94.71 485	99.18 300	97.27 313	98.56 310	98.74 300	91.89 416	99.69 333	97.06 270	99.81 141	99.05 348

PMMVS2		98.07 262	98.08 253	98.04 336	99.41 183	94.59 415	94.59 494	99.40 212	97.50 283	98.82 261	98.83 280	96.83 236	99.84 181	97.50 229	99.81 141	99.71 66

K. test v3		98.00 269	97.66 300	99.03 149	99.79 23	97.56 230	99.19 53	92.47 536	99.62 33	99.52 88	99.66 33	89.61 443	99.96 13	99.25 68	99.81 141	99.56 131

casdiffmvs_mvg		99.12 70	99.16 63	98.99 157	99.43 178	97.73 216	98.00 217	99.62 90	99.22 81	99.55 78	99.22 153	98.93 33	99.75 287	98.66 114	99.81 141	99.50 170
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CDS-MVSNet		97.69 305	97.35 323	98.69 228	98.73 362	97.02 285	96.92 363	98.75 388	95.89 402	98.59 304	98.67 319	92.08 414	99.74 293	96.72 306	99.81 141	99.32 275
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CSCG		98.68 158	98.50 177	99.20 111	99.45 171	98.63 102	98.56 127	99.57 112	97.87 243	98.85 253	98.04 407	97.66 165	99.84 181	96.72 306	99.81 141	99.13 341

hybridcas		99.08 80	99.13 71	98.92 174	99.54 125	97.61 228	98.22 179	99.66 72	99.27 75	99.40 118	99.24 145	98.47 78	99.70 323	98.59 119	99.80 153	99.46 201

SSM_0404		98.90 109	99.01 93	98.57 254	99.42 180	96.59 310	98.13 188	99.66 72	99.09 111	99.30 149	99.02 214	98.79 44	99.89 98	97.87 187	99.80 153	99.23 306

miper_lstm_enhance		97.18 351	97.16 335	97.25 412	98.16 445	92.85 471	95.15 475	99.31 249	97.25 315	98.74 276	98.78 291	90.07 437	99.78 263	97.19 254	99.80 153	99.11 343

UniMVSNet (Re)		98.87 113	98.71 137	99.35 80	99.24 236	98.73 96	97.73 267	99.38 216	98.93 133	99.12 187	98.73 302	96.77 242	99.86 145	98.63 117	99.80 153	99.46 201

FMVSNet2		98.49 197	98.40 195	98.75 214	98.90 330	97.14 278	98.61 121	99.13 314	98.59 170	99.19 178	99.28 130	94.14 365	99.82 211	97.97 178	99.80 153	99.29 286

XXY-MVS		99.14 63	99.15 68	99.10 131	99.76 30	97.74 214	98.85 93	99.62 90	98.48 182	99.37 126	99.49 75	98.75 48	99.86 145	98.20 153	99.80 153	99.71 66

IS-MVSNet		98.19 247	97.90 276	99.08 137	99.57 105	97.97 179	99.31 30	98.32 421	99.01 124	98.98 216	99.03 213	91.59 419	99.79 251	95.49 394	99.80 153	99.48 189

mvsany_test3		98.87 113	98.92 103	98.74 218	99.38 189	96.94 291	98.58 125	99.10 318	96.49 370	99.96 4	99.81 8	98.18 119	99.45 456	98.97 90	99.79 160	99.83 34

EI-MVSNet-UG-set		98.69 152	98.71 137	98.62 243	99.10 278	96.37 325	97.23 337	98.87 362	99.20 85	99.19 178	98.99 232	97.30 203	99.85 160	98.77 106	99.79 160	99.65 86

pmmvs4		97.58 314	97.28 326	98.51 271	98.84 343	96.93 292	95.40 465	98.52 411	93.60 481	98.61 299	98.65 326	95.10 330	99.60 396	96.97 279	99.79 160	98.99 361

test20.03		98.78 134	98.77 128	98.78 205	99.46 166	97.20 269	97.78 254	99.24 286	99.04 120	99.41 115	98.90 259	97.65 166	99.76 275	97.70 209	99.79 160	99.39 234

Vis-MVSNet (Re-imp)		97.46 322	97.16 335	98.34 296	99.55 119	96.10 333	98.94 81	98.44 414	98.32 194	98.16 350	98.62 335	88.76 448	99.73 301	93.88 440	99.79 160	99.18 326

hybridnocas07		98.32 224	98.37 204	98.17 316	99.14 270	95.51 362	96.67 382	99.56 122	97.85 245	98.75 273	98.95 247	96.65 253	99.63 379	98.00 173	99.78 165	99.37 246

MatchFormer		97.07 359	96.92 352	97.49 398	98.44 415	95.92 344	96.79 369	99.14 313	93.08 490	99.32 145	99.10 192	93.89 371	99.03 498	92.78 475	99.78 165	97.52 494

BP-MVS1		97.40 329	96.97 348	98.71 224	99.07 285	96.81 300	98.34 165	97.18 465	98.58 173	98.17 347	98.61 337	84.01 492	99.94 42	98.97 90	99.78 165	99.37 246

MVSMamba_PlusPlus		98.83 123	98.98 98	98.36 294	99.32 210	96.58 313	98.90 84	99.41 207	99.75 10	98.72 277	99.50 69	96.17 281	99.94 42	99.27 65	99.78 165	98.57 431

EI-MVSNet-Vis-set		98.68 158	98.70 140	98.63 241	99.09 281	96.40 324	97.23 337	98.86 367	99.20 85	99.18 183	98.97 239	97.29 205	99.85 160	98.72 110	99.78 165	99.64 87

LPG-MVS_test		98.71 143	98.46 187	99.47 61	99.57 105	98.97 75	98.23 175	99.48 160	96.60 364	99.10 191	99.06 201	98.71 52	99.83 199	95.58 391	99.78 165	99.62 93

LGP-MVS_train				99.47 61	99.57 105	98.97 75		99.48 160	96.60 364	99.10 191	99.06 201	98.71 52	99.83 199	95.58 391	99.78 165	99.62 93

CLD-MVS		97.49 320	97.16 335	98.48 277	99.07 285	97.03 284	94.71 485	99.21 290	94.46 458	98.06 361	97.16 466	97.57 178	99.48 446	94.46 419	99.78 165	98.95 370
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SIFT-PointCN		96.45 395	96.47 388	96.39 453	98.13 450	97.54 232	93.31 529	97.23 463	94.67 453	98.68 285	98.32 378	94.64 346	97.81 528	93.50 454	99.77 173	93.83 532

E2		98.70 148	98.68 143	98.73 220	99.40 185	97.10 280	97.48 306	99.57 112	98.09 226	99.00 211	99.20 157	97.90 144	99.67 351	97.73 206	99.77 173	99.43 215

E3		98.69 152	98.68 143	98.73 220	99.40 185	97.10 280	97.48 306	99.57 112	98.09 226	99.00 211	99.20 157	97.90 144	99.67 351	97.73 206	99.77 173	99.43 215

viewmanbaseed2359cas		98.58 178	98.54 169	98.70 226	99.28 221	97.13 279	97.47 310	99.55 127	97.55 277	98.96 225	98.92 252	97.77 158	99.59 401	97.59 220	99.77 173	99.39 234

test_fmvs1		97.72 302	97.94 270	97.07 422	98.66 387	92.39 480	97.68 272	99.81 33	95.20 439	99.54 80	99.44 86	91.56 421	99.41 463	99.78 22	99.77 173	99.40 233

BridgeMVS		98.63 168	98.72 134	98.38 290	98.66 387	96.68 309	98.90 84	99.42 203	98.99 125	98.97 220	99.19 160	95.81 304	99.85 160	98.77 106	99.77 173	98.60 427

new-patchmatchnet		98.35 218	98.74 129	97.18 414	99.24 236	92.23 485	96.42 404	99.48 160	98.30 196	99.69 56	99.53 65	97.44 195	99.82 211	98.84 100	99.77 173	99.49 178

Patchmatch-RL test		97.26 342	97.02 345	97.99 341	99.52 133	95.53 361	96.13 426	99.71 49	97.47 286	99.27 155	99.16 171	84.30 490	99.62 384	97.89 182	99.77 173	98.81 396

UniMVSNet_NR-MVSNet		98.86 117	98.68 143	99.40 71	99.17 262	98.74 93	97.68 272	99.40 212	99.14 99	99.06 195	98.59 340	96.71 249	99.93 54	98.57 122	99.77 173	99.53 158

DU-MVS		98.82 126	98.63 154	99.39 72	99.16 264	98.74 93	97.54 298	99.25 280	98.84 149	99.06 195	98.76 298	96.76 244	99.93 54	98.57 122	99.77 173	99.50 170

EC-MVSNet		99.09 74	99.05 87	99.20 111	99.28 221	98.93 81	99.24 44	99.84 23	99.08 115	98.12 355	98.37 369	98.72 51	99.90 82	99.05 84	99.77 173	98.77 404

ACMMP++_ref															99.77 173

wuyk23d		96.06 411	97.62 305	91.38 529	98.65 391	98.57 109	98.85 93	96.95 475	96.86 351	99.90 14	99.16 171	99.18 19	98.40 518	89.23 524	99.77 173	77.18 552

ACMP	95.32 15	98.41 205	98.09 250	99.36 74	99.51 136	98.79 91	97.68 272	99.38 216	95.76 413	98.81 263	98.82 283	98.36 91	99.82 211	94.75 410	99.77 173	99.48 189
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	96.62 9	99.08 80	99.00 95	99.33 89	99.71 50	98.83 88	98.60 122	99.58 104	99.11 101	99.53 84	99.18 164	98.81 40	99.67 351	96.71 308	99.77 173	99.50 170

ACMH	96.65 7	99.25 41	99.24 54	99.26 101	99.72 45	98.38 125	99.07 65	99.55 127	98.30 196	99.65 64	99.45 85	99.22 17	99.76 275	98.44 132	99.77 173	99.64 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
c3_l		97.36 333	97.37 321	97.31 407	98.09 452	93.25 461	95.01 478	99.16 307	97.05 332	98.77 270	98.72 304	92.88 396	99.64 376	96.93 282	99.76 189	99.05 348

pmmvs5		97.64 309	97.49 313	98.08 330	99.14 270	95.12 391	96.70 378	99.05 328	93.77 479	98.62 297	98.83 280	93.23 386	99.75 287	98.33 145	99.76 189	99.36 254

baseline		98.96 102	99.02 91	98.76 212	99.38 189	97.26 261	98.49 142	99.50 150	98.86 143	99.19 178	99.06 201	98.23 111	99.69 333	98.71 111	99.76 189	99.33 270

COLMAP_ROB	96.50 10	98.99 95	98.85 119	99.41 69	99.58 96	99.10 66	98.74 99	99.56 122	99.09 111	99.33 139	99.19 160	98.40 87	99.72 312	95.98 369	99.76 189	99.42 220
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DenseAffine		98.10 257	97.86 280	98.84 189	99.32 210	97.93 186	96.62 388	99.76 40	96.68 362	98.65 289	98.72 304	94.46 351	99.33 475	96.76 300	99.75 193	99.25 300

dtuonly		96.49 390	97.28 326	94.10 513	98.80 354	83.27 550	93.66 522	99.48 160	95.10 440	97.87 377	98.30 380	95.61 311	99.68 346	96.98 278	99.75 193	99.33 270

viewdifsd2359ckpt07		98.71 143	98.86 116	98.26 304	99.43 178	95.65 356	97.20 342	99.66 72	99.20 85	99.29 150	99.01 226	98.29 100	99.73 301	97.92 181	99.75 193	99.39 234

dtuplus		98.32 224	98.39 198	98.10 324	99.15 268	95.29 380	96.68 380	99.51 145	97.32 306	99.18 183	99.15 177	97.61 174	99.62 384	97.19 254	99.74 196	99.38 243

SD-MVS		98.40 208	98.68 143	97.54 393	98.96 318	97.99 175	97.88 240	99.36 224	98.20 211	99.63 67	99.04 210	98.76 47	95.33 550	96.56 329	99.74 196	99.31 280
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
PM-MVS		98.82 126	98.72 134	99.12 127	99.64 78	98.54 113	97.98 226	99.68 65	97.62 266	99.34 136	99.18 164	97.54 182	99.77 269	97.79 194	99.74 196	99.04 352

XVG-ACMP-BASELINE		98.56 181	98.34 210	99.22 110	99.54 125	98.59 107	97.71 268	99.46 177	97.25 315	98.98 216	98.99 232	97.54 182	99.84 181	95.88 372	99.74 196	99.23 306

SIFT-UMatch		96.33 399	96.47 388	95.89 479	98.29 429	97.95 183	93.84 518	97.24 462	95.78 412	98.72 277	98.04 407	93.45 382	96.81 541	93.14 464	99.73 200	92.91 544

reproduce_model		99.15 58	98.97 99	99.67 4	99.33 208	99.44 9	98.15 186	99.47 172	99.12 100	99.52 88	99.32 124	98.31 98	99.90 82	97.78 195	99.73 200	99.66 81

GeoE		99.05 84	98.99 97	99.25 104	99.44 173	98.35 131	98.73 103	99.56 122	98.42 185	98.91 238	98.81 286	98.94 31	99.91 75	98.35 142	99.73 200	99.49 178

Anonymous20231206		98.21 244	98.21 233	98.20 313	99.51 136	95.43 372	98.13 188	99.32 244	96.16 388	98.93 235	98.82 283	96.00 291	99.83 199	97.32 245	99.73 200	99.36 254

casdiffmvs		98.95 103	99.00 95	98.81 195	99.38 189	97.33 249	97.82 248	99.57 112	99.17 94	99.35 131	99.17 169	98.35 95	99.69 333	98.46 130	99.73 200	99.41 224
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
jason		97.45 324	97.35 323	97.76 362	99.24 236	93.93 443	95.86 445	98.42 417	94.24 466	98.50 318	98.13 397	94.82 338	99.91 75	97.22 252	99.73 200	99.43 215
jason: jason.
N_pmnet		97.63 310	97.17 334	98.99 157	99.27 224	97.86 195	95.98 435	93.41 533	95.25 436	99.47 101	98.90 259	95.63 310	99.85 160	96.91 283	99.73 200	99.27 293

USDC		97.41 328	97.40 318	97.44 403	98.94 320	93.67 454	95.17 473	99.53 137	94.03 475	98.97 220	99.10 192	95.29 323	99.34 473	95.84 378	99.73 200	99.30 284

Gipuma		99.03 89	99.16 63	98.64 237	99.94 2	98.51 115	99.32 26	99.75 44	99.58 39	98.60 302	99.62 41	98.22 114	99.51 437	97.70 209	99.73 200	97.89 474
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
mamba_0408		98.80 130	98.88 109	98.55 261	99.27 224	96.50 318	98.00 217	99.60 95	98.93 133	99.22 173	98.84 278	98.59 68	99.89 98	97.74 204	99.72 209	99.27 293

SSM_04072		98.80 130	98.88 109	98.56 259	99.27 224	96.50 318	98.00 217	99.60 95	98.93 133	99.22 173	98.84 278	98.59 68	99.90 82	97.74 204	99.72 209	99.27 293

SSM_0407		98.86 117	98.96 101	98.55 261	99.27 224	96.50 318	98.04 207	99.66 72	99.09 111	99.22 173	99.02 214	98.79 44	99.87 136	97.87 187	99.72 209	99.27 293

viewmambaseed2359dif		98.19 247	98.26 227	97.99 341	99.02 306	95.03 394	96.59 392	99.53 137	96.21 383	99.00 211	98.99 232	97.62 172	99.61 392	97.62 216	99.72 209	99.33 270

EGC-MVSNET		85.24 514	80.54 518	99.34 83	99.77 27	99.20 39	99.08 62	99.29 265	12.08 558	20.84 561	99.42 90	97.55 180	99.85 160	97.08 267	99.72 209	98.96 369

lessismore_v0				98.97 163	99.73 38	97.53 233		86.71 554		99.37 126	99.52 68	89.93 438	99.92 66	98.99 89	99.72 209	99.44 211

CP-MVS		98.70 148	98.42 193	99.52 44	99.36 196	99.12 63	98.72 104	99.36 224	97.54 280	98.30 338	98.40 365	97.86 151	99.89 98	96.53 334	99.72 209	99.56 131

SteuartSystems-ACMMP		98.79 132	98.54 169	99.54 31	99.73 38	99.16 49	98.23 175	99.31 249	97.92 239	98.90 239	98.90 259	98.00 135	99.88 116	96.15 361	99.72 209	99.58 118
Skip Steuart: Steuart Systems R&D Blog.
LF4IMVS		97.90 278	97.69 296	98.52 270	99.17 262	97.66 221	97.19 346	99.47 172	96.31 379	97.85 381	98.20 392	96.71 249	99.52 431	94.62 414	99.72 209	98.38 448

PatchmatchNet1														96.95 281	99.71 218	99.28 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
RoMa-HiRes		98.68 158	98.52 172	99.16 119	99.50 143	98.35 131	98.01 215	99.71 49	96.94 339	99.35 131	98.66 323	96.38 269	99.63 379	98.39 139	99.71 218	99.48 189

RoMa-SfM		98.46 200	98.27 224	99.02 152	99.35 201	98.32 134	97.56 294	99.70 55	95.88 403	99.38 122	98.65 326	96.41 265	99.46 453	97.78 195	99.71 218	99.28 289

SIFT-NCMNet		96.30 401	96.40 393	96.03 474	97.80 472	97.68 220	92.34 537	96.94 476	95.55 421	98.84 256	98.63 332	94.17 364	97.63 531	93.57 451	99.71 218	92.77 546

reproduce-ours		99.09 74	98.90 106	99.67 4	99.27 224	99.49 5	98.00 217	99.42 203	99.05 118	99.48 97	99.27 132	98.29 100	99.89 98	97.61 217	99.71 218	99.62 93

our_new_method		99.09 74	98.90 106	99.67 4	99.27 224	99.49 5	98.00 217	99.42 203	99.05 118	99.48 97	99.27 132	98.29 100	99.89 98	97.61 217	99.71 218	99.62 93

KD-MVS_self_test		99.25 41	99.18 60	99.44 65	99.63 85	99.06 71	98.69 108	99.54 133	99.31 70	99.62 70	99.53 65	97.36 200	99.86 145	99.24 70	99.71 218	99.39 234

test_0728_THIRD									98.17 215	99.08 193	99.02 214	97.89 148	99.88 116	97.07 268	99.71 218	99.70 71

HPM-MVS_fast		99.01 91	98.82 122	99.57 21	99.71 50	99.35 17	99.00 73	99.50 150	97.33 304	98.94 234	98.86 270	98.75 48	99.82 211	97.53 226	99.71 218	99.56 131

FMVSNet5		96.01 415	95.20 442	98.41 286	97.53 491	96.10 333	98.74 99	99.50 150	97.22 324	98.03 365	99.04 210	69.80 532	99.88 116	97.27 248	99.71 218	99.25 300

RPSCF		98.62 171	98.36 206	99.42 67	99.65 72	99.42 10	98.55 128	99.57 112	97.72 258	98.90 239	99.26 138	96.12 286	99.52 431	95.72 382	99.71 218	99.32 275

onestephybrid01		98.40 208	98.39 198	98.42 284	99.05 293	96.23 330	96.73 376	99.41 207	98.18 214	98.65 289	99.02 214	97.02 223	99.69 333	97.73 206	99.70 229	99.33 270

viewmamba		98.57 179	98.66 148	98.31 299	99.20 248	95.89 346	96.92 363	99.57 112	98.71 159	99.02 209	99.04 210	97.48 192	99.71 314	98.28 147	99.70 229	99.35 260

SIFT-CM-Cal		96.27 405	96.31 397	96.16 468	98.39 422	98.11 155	93.46 527	96.47 489	94.81 450	98.49 319	98.43 362	94.48 350	97.34 536	92.60 481	99.70 229	93.02 542

MP-MVS-pluss		98.57 179	98.23 232	99.60 16	99.69 62	99.35 17	97.16 347	99.38 216	94.87 447	98.97 220	98.99 232	98.01 134	99.88 116	97.29 247	99.70 229	99.58 118
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MTAPA		98.88 112	98.64 152	99.61 13	99.67 69	99.36 16	98.43 150	99.20 292	98.83 150	98.89 242	98.90 259	96.98 227	99.92 66	97.16 257	99.70 229	99.56 131

APDe-MVS		98.99 95	98.79 125	99.60 16	99.21 244	99.15 53	98.87 89	99.48 160	97.57 273	99.35 131	99.24 145	97.83 152	99.89 98	97.88 185	99.70 229	99.75 63
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
hybrid		98.22 241	98.27 224	98.08 330	99.13 273	95.24 382	96.61 389	99.53 137	97.43 295	98.46 323	98.97 239	96.75 247	99.65 371	97.84 190	99.69 235	99.35 260

tfpnnormal		98.90 109	98.90 106	98.91 176	99.67 69	97.82 204	99.00 73	99.44 190	99.45 51	99.51 93	99.24 145	98.20 118	99.86 145	95.92 371	99.69 235	99.04 352

GBi-Net		98.65 164	98.47 185	99.17 116	98.90 330	98.24 141	99.20 49	99.44 190	98.59 170	98.95 226	99.55 57	94.14 365	99.86 145	97.77 198	99.69 235	99.41 224

test1		98.65 164	98.47 185	99.17 116	98.90 330	98.24 141	99.20 49	99.44 190	98.59 170	98.95 226	99.55 57	94.14 365	99.86 145	97.77 198	99.69 235	99.41 224

FMVSNet3		97.50 317	97.24 330	98.29 302	98.08 453	95.83 350	97.86 244	98.91 355	97.89 242	98.95 226	98.95 247	87.06 461	99.81 228	97.77 198	99.69 235	99.23 306

ACMMP		98.75 139	98.50 177	99.52 44	99.56 113	99.16 49	98.87 89	99.37 220	97.16 327	98.82 261	99.01 226	97.71 162	99.87 136	96.29 353	99.69 235	99.54 144
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
DPE-MVS		98.59 176	98.26 227	99.57 21	99.27 224	99.15 53	97.01 353	99.39 214	97.67 261	99.44 108	98.99 232	97.53 184	99.89 98	95.40 396	99.68 241	99.66 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVG-OURS		98.53 190	98.34 210	99.11 129	99.50 143	98.82 90	95.97 436	99.50 150	97.30 309	99.05 203	98.98 237	99.35 14	99.32 477	95.72 382	99.68 241	99.18 326

EPNet		96.14 410	95.44 427	98.25 306	90.76 557	95.50 366	97.92 235	94.65 517	98.97 128	92.98 533	98.85 273	89.12 447	99.87 136	95.99 368	99.68 241	99.39 234
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		98.99 95	99.01 93	98.94 168	99.50 143	97.47 238	98.04 207	99.59 101	98.15 223	99.40 118	99.36 111	98.58 73	99.76 275	98.78 103	99.68 241	99.59 110

ACMMP++															99.68 241

EPP-MVSNet		98.30 229	98.04 257	99.07 139	99.56 113	97.83 198	99.29 36	98.07 435	99.03 122	98.59 304	99.13 183	92.16 410	99.90 82	96.87 291	99.68 241	99.49 178

viewcassd2359sk11		98.55 185	98.51 174	98.67 231	99.29 218	96.99 286	97.39 317	99.54 133	97.73 256	98.81 263	99.08 199	97.55 180	99.66 364	97.52 228	99.67 247	99.36 254

NormalMVS		98.26 236	97.97 266	99.15 124	99.64 78	97.83 198	98.28 169	99.43 196	99.24 78	98.80 265	98.85 273	89.76 441	99.94 42	98.04 168	99.67 247	99.68 74

lecture		99.25 41	99.12 72	99.62 9	99.64 78	99.40 11	98.89 88	99.51 145	99.19 90	99.37 126	99.25 143	98.36 91	99.88 116	98.23 150	99.67 247	99.59 110

our_test_3		97.39 330	97.73 292	96.34 455	98.70 372	89.78 521	94.61 493	98.97 345	96.50 369	99.04 205	98.85 273	95.98 296	99.84 181	97.26 249	99.67 247	99.41 224

ACMMP_NAP		98.75 139	98.48 183	99.57 21	99.58 96	99.29 24	97.82 248	99.25 280	96.94 339	98.78 267	99.12 187	98.02 133	99.84 181	97.13 264	99.67 247	99.59 110

HPM-MVS		98.79 132	98.53 171	99.59 20	99.65 72	99.29 24	99.16 55	99.43 196	96.74 357	98.61 299	98.38 368	98.62 65	99.87 136	96.47 337	99.67 247	99.59 110
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
3Dnovator	98.27 2	98.81 128	98.73 131	99.05 146	98.76 358	97.81 207	99.25 43	99.30 257	98.57 175	98.55 312	99.33 119	97.95 141	99.90 82	97.16 257	99.67 247	99.44 211

PMVS	91.26 20	97.86 286	97.94 270	97.65 377	99.71 50	97.94 185	98.52 132	98.68 394	98.99 125	97.52 406	99.35 112	97.41 196	98.18 522	91.59 498	99.67 247	96.82 511
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SIFT-NCM-Cal		96.56 385	96.68 371	96.20 464	98.27 433	98.44 120	94.40 500	96.67 483	95.29 434	97.63 395	98.17 394	96.40 266	96.59 545	93.61 447	99.66 255	93.57 535

DP-MVS		98.93 105	98.81 124	99.28 96	99.21 244	98.45 119	98.46 147	99.33 242	99.63 29	99.48 97	99.15 177	97.23 209	99.75 287	97.17 256	99.66 255	99.63 92

MVS_111021_LR		98.30 229	98.12 248	98.83 191	99.16 264	98.03 171	96.09 429	99.30 257	97.58 272	98.10 357	98.24 388	98.25 108	99.34 473	96.69 311	99.65 257	99.12 342

ACMM	96.08 12	98.91 107	98.73 131	99.48 57	99.55 119	99.14 58	98.07 202	99.37 220	97.62 266	99.04 205	98.96 243	98.84 37	99.79 251	97.43 237	99.65 257	99.49 178
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RA-MVS			98.73 131		99.64 78	99.38 12	98.68 109	99.46 177	97.63 264	99.28 152	98.91 255	97.64 169	99.86 145	97.65 212	99.64 259

aaatest				99.45 64	99.58 96	98.93 81	98.68 109	99.60 95	96.46 373	99.53 84	98.77 293		99.83 199	96.67 314	99.64 259	99.58 118

aaEdge-Enhanced		98.61 172	98.33 215	99.44 65	99.24 236	98.93 81	97.45 312	99.06 324	98.14 224	99.06 195	98.77 293	96.97 228	99.82 211	96.67 314	99.64 259	99.58 118

ZNCC-MVS		98.68 158	98.40 195	99.54 31	99.57 105	99.21 33	98.46 147	99.29 265	97.28 311	98.11 356	98.39 366	98.00 135	99.87 136	96.86 293	99.64 259	99.55 138

SMA-MVS		98.40 208	98.03 258	99.51 49	99.16 264	99.21 33	98.05 205	99.22 289	94.16 469	98.98 216	99.10 192	97.52 186	99.79 251	96.45 339	99.64 259	99.53 158
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
diffmvs		98.22 241	98.24 231	98.17 316	99.00 310	95.44 371	96.38 406	99.58 104	97.79 252	98.53 315	98.50 354	96.76 244	99.74 293	97.95 180	99.64 259	99.34 264
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ALIKED-MNN		95.97 420	95.30 436	98.00 339	97.66 485	98.12 154	96.98 356	99.41 207	91.11 517	94.04 525	97.30 462	91.56 421	98.61 516	89.99 519	99.63 265	97.28 503

DVP-MVS		98.77 137	98.52 172	99.52 44	99.50 143	99.21 33	98.02 212	98.84 371	97.97 233	99.08 193	99.02 214	97.61 174	99.88 116	96.99 275	99.63 265	99.48 189
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.60 16	99.50 143	99.23 31	98.02 212	99.32 244					99.88 116	96.99 275	99.63 265	99.68 74

VDD-MVS		98.56 181	98.39 198	99.07 139	99.13 273	98.07 166	98.59 124	97.01 470	99.59 37	99.11 188	99.27 132	94.82 338	99.79 251	98.34 143	99.63 265	99.34 264

test-260524					99.33 208	99.02 72		99.25 280		99.23 171		96.59 257	99.85 160	98.10 161	99.62 269

MED-MVS		99.01 91	98.84 120	99.52 44	99.58 96	98.93 81	98.68 109	99.60 95	98.85 146	99.53 84	99.16 171	97.87 150	99.83 199	96.67 314	99.62 269	99.81 42

SED-MVS		98.91 107	98.72 134	99.49 55	99.49 152	99.17 44	98.10 195	99.31 249	98.03 229	99.66 61	99.02 214	98.36 91	99.88 116	96.91 283	99.62 269	99.41 224

IU-MVS					99.49 152	99.15 53		98.87 362	92.97 492	99.41 115				96.76 300	99.62 269	99.66 81

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 108	99.27 42	99.57 112	99.39 59	99.75 45	99.62 41	99.17 20	99.83 199	99.06 83	99.62 269	99.66 81

mPP-MVS		98.64 166	98.34 210	99.54 31	99.54 125	99.17 44	98.63 117	99.24 286	97.47 286	98.09 358	98.68 317	97.62 172	99.89 98	96.22 356	99.62 269	99.57 125

DeepPCF-MVS	96.93 5	98.32 224	98.01 260	99.23 109	98.39 422	98.97 75	95.03 477	99.18 300	96.88 347	99.33 139	98.78 291	98.16 123	99.28 484	96.74 303	99.62 269	99.44 211

AllTest		98.44 203	98.20 234	99.16 119	99.50 143	98.55 110	98.25 174	99.58 104	96.80 353	98.88 246	99.06 201	97.65 166	99.57 410	94.45 420	99.61 276	99.37 246

TestCases				99.16 119	99.50 143	98.55 110		99.58 104	96.80 353	98.88 246	99.06 201	97.65 166	99.57 410	94.45 420	99.61 276	99.37 246

MSC_two_6792asdad				99.32 91	98.43 417	98.37 127		98.86 367					99.89 98	97.14 261	99.60 278	99.71 66

No_MVS				99.32 91	98.43 417	98.37 127		98.86 367					99.89 98	97.14 261	99.60 278	99.71 66

test_241102_TWO								99.30 257	98.03 229	99.26 159	99.02 214	97.51 187	99.88 116	96.91 283	99.60 278	99.66 81

MP-MVS		98.46 200	98.09 250	99.54 31	99.57 105	99.22 32	98.50 139	99.19 296	97.61 269	97.58 400	98.66 323	97.40 197	99.88 116	94.72 413	99.60 278	99.54 144
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HFP-MVS		98.71 143	98.44 190	99.51 49	99.49 152	99.16 49	98.52 132	99.31 249	97.47 286	98.58 306	98.50 354	97.97 139	99.85 160	96.57 325	99.59 282	99.53 158

CVMVSNet		96.25 406	97.21 333	93.38 525	99.10 278	80.56 558	97.20 342	98.19 430	96.94 339	99.00 211	99.02 214	89.50 445	99.80 237	96.36 346	99.59 282	99.78 51

ACMMPR		98.70 148	98.42 193	99.54 31	99.52 133	99.14 58	98.52 132	99.31 249	97.47 286	98.56 310	98.54 345	97.75 160	99.88 116	96.57 325	99.59 282	99.58 118

PGM-MVS		98.66 163	98.37 204	99.55 28	99.53 129	99.18 43	98.23 175	99.49 158	97.01 336	98.69 282	98.88 267	98.00 135	99.89 98	95.87 375	99.59 282	99.58 118

DELS-MVS		98.27 234	98.20 234	98.48 277	98.86 339	96.70 307	95.60 456	99.20 292	97.73 256	98.45 325	98.71 306	97.50 188	99.82 211	98.21 152	99.59 282	98.93 375
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
region2R		98.69 152	98.40 195	99.54 31	99.53 129	99.17 44	98.52 132	99.31 249	97.46 291	98.44 326	98.51 350	97.83 152	99.88 116	96.46 338	99.58 287	99.58 118

114514_t		96.50 389	95.77 410	98.69 228	99.48 160	97.43 244	97.84 247	99.55 127	81.42 548	96.51 472	98.58 341	95.53 314	99.67 351	93.41 457	99.58 287	98.98 362

PHI-MVS		98.29 232	97.95 267	99.34 83	98.44 415	99.16 49	98.12 192	99.38 216	96.01 396	98.06 361	98.43 362	97.80 156	99.67 351	95.69 384	99.58 287	99.20 316

TinyColmap		97.89 280	97.98 263	97.60 384	98.86 339	94.35 420	96.21 419	99.44 190	97.45 293	99.06 195	98.88 267	97.99 138	99.28 484	94.38 427	99.58 287	99.18 326

TestfortrainingZip a		99.09 74	98.92 103	99.61 13	99.58 96	99.17 44	98.68 109	99.27 272	98.85 146	99.61 71	99.16 171	97.14 215	99.86 145	98.39 139	99.57 291	99.81 42

MVSFormer		98.26 236	98.43 191	97.77 359	98.88 336	93.89 447	99.39 20	99.56 122	99.11 101	98.16 350	98.13 397	93.81 374	99.97 6	99.26 66	99.57 291	99.43 215

lupinMVS		97.06 360	96.86 357	97.65 377	98.88 336	93.89 447	95.48 461	97.97 437	93.53 482	98.16 350	97.58 442	93.81 374	99.91 75	96.77 299	99.57 291	99.17 330

MVS_111021_HR		98.25 239	98.08 253	98.75 214	99.09 281	97.46 240	95.97 436	99.27 272	97.60 271	97.99 368	98.25 386	98.15 125	99.38 468	96.87 291	99.57 291	99.42 220

ArgMatch-SfM		97.96 275	97.72 293	98.66 233	99.02 306	97.33 249	96.49 398	99.52 143	95.46 427	98.71 281	98.29 383	96.14 282	99.69 333	96.30 350	99.56 295	98.97 366

GDP-MVS		97.50 317	97.11 341	98.67 231	99.02 306	96.85 298	98.16 185	99.71 49	98.32 194	98.52 317	98.54 345	83.39 496	99.95 26	98.79 102	99.56 295	99.19 322

test_vis3_rt		99.14 63	99.17 61	99.07 139	99.78 24	98.38 125	98.92 83	99.94 3	97.80 249	99.91 12	99.67 31	97.15 214	98.91 507	99.76 24	99.56 295	99.92 13

OPM-MVS		98.56 181	98.32 216	99.25 104	99.41 183	98.73 96	97.13 349	99.18 300	97.10 330	98.75 273	98.92 252	98.18 119	99.65 371	96.68 312	99.56 295	99.37 246
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PVSNet_Blended		96.88 370	96.68 371	97.47 401	98.92 326	93.77 451	94.71 485	99.43 196	90.98 518	97.62 396	97.36 460	96.82 237	99.67 351	94.73 411	99.56 295	98.98 362

DKM-HiRes		98.14 255	97.80 284	99.16 119	99.51 136	98.40 122	96.70 378	99.63 83	97.55 277	97.45 414	98.74 300	93.27 384	99.54 424	97.78 195	99.55 300	99.53 158

APD_test1		98.83 123	98.66 148	99.34 83	99.78 24	99.47 8	98.42 153	99.45 182	98.28 201	98.98 216	99.19 160	97.76 159	99.58 408	96.57 325	99.55 300	98.97 366

DeepC-MVS_fast	96.85 6	98.30 229	98.15 245	98.75 214	98.61 392	97.23 263	97.76 260	99.09 320	97.31 308	98.75 273	98.66 323	97.56 179	99.64 376	96.10 366	99.55 300	99.39 234
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS		98.10 257	97.67 297	99.42 67	99.11 276	98.93 81	97.76 260	99.28 269	94.97 444	98.72 277	98.77 293	97.04 220	99.85 160	93.79 443	99.54 303	99.49 178
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DSMNet-mixed		97.42 327	97.60 306	96.87 434	99.15 268	91.46 493	98.54 130	99.12 315	92.87 496	97.58 400	99.63 40	96.21 280	99.90 82	95.74 381	99.54 303	99.27 293

CPTT-MVS		97.84 293	97.36 322	99.27 99	99.31 212	98.46 118	98.29 168	99.27 272	94.90 446	97.83 382	98.37 369	94.90 334	99.84 181	93.85 442	99.54 303	99.51 166

1112_ss		97.29 341	96.86 357	98.58 251	99.34 207	96.32 327	96.75 374	99.58 104	93.14 488	96.89 448	97.48 451	92.11 413	99.86 145	96.91 283	99.54 303	99.57 125

XVS		98.72 142	98.45 188	99.53 38	99.46 166	99.21 33	98.65 115	99.34 236	98.62 167	97.54 404	98.63 332	97.50 188	99.83 199	96.79 296	99.53 307	99.56 131

X-MVStestdata		94.32 461	92.59 485	99.53 38	99.46 166	99.21 33	98.65 115	99.34 236	98.62 167	97.54 404	45.85 557	97.50 188	99.83 199	96.79 296	99.53 307	99.56 131

Test_1112_low_res		96.99 367	96.55 384	98.31 299	99.35 201	95.47 370	95.84 448	99.53 137	91.51 511	96.80 454	98.48 357	91.36 425	99.83 199	96.58 323	99.53 307	99.62 93

E3new		98.41 205	98.34 210	98.62 243	99.19 252	96.90 294	97.32 327	99.50 150	97.40 298	98.63 293	98.92 252	97.21 211	99.65 371	97.34 241	99.52 310	99.31 280

SF-MVS		98.53 190	98.27 224	99.32 91	99.31 212	98.75 92	98.19 180	99.41 207	96.77 356	98.83 258	98.90 259	97.80 156	99.82 211	95.68 385	99.52 310	99.38 243

Anonymous20240529		98.93 105	98.87 112	99.12 127	99.19 252	98.22 146	99.01 71	98.99 342	99.25 77	99.54 80	99.37 105	97.04 220	99.80 237	97.89 182	99.52 310	99.35 260

viewdifsd2359ckpt09		98.13 256	97.92 273	98.77 210	99.18 260	97.35 247	97.29 331	99.53 137	95.81 410	98.09 358	98.47 358	96.34 273	99.66 364	97.02 271	99.51 313	99.29 286

GST-MVS		98.61 172	98.30 218	99.52 44	99.51 136	99.20 39	98.26 173	99.25 280	97.44 294	98.67 286	98.39 366	97.68 163	99.85 160	96.00 367	99.51 313	99.52 162

tttt0517		95.64 432	94.98 446	97.64 380	99.36 196	93.81 449	98.72 104	90.47 547	98.08 228	98.67 286	98.34 373	73.88 527	99.92 66	97.77 198	99.51 313	99.20 316

HQP_MVS		97.99 272	97.67 297	98.93 171	99.19 252	97.65 223	97.77 257	99.27 272	98.20 211	97.79 386	97.98 412	94.90 334	99.70 323	94.42 423	99.51 313	99.45 207

plane_prior5								99.27 272					99.70 323	94.42 423	99.51 313	99.45 207

ab-mvs		98.41 205	98.36 206	98.59 250	99.19 252	97.23 263	99.32 26	98.81 376	97.66 262	98.62 297	99.40 98	96.82 237	99.80 237	95.88 372	99.51 313	98.75 407

OMC-MVS		97.88 283	97.49 313	99.04 148	98.89 335	98.63 102	96.94 359	99.25 280	95.02 442	98.53 315	98.51 350	97.27 206	99.47 449	93.50 454	99.51 313	99.01 357

CMPMVS	75.91 23	96.29 402	95.44 427	98.84 189	96.25 535	98.69 100	97.02 352	99.12 315	88.90 531	97.83 382	98.86 270	89.51 444	98.90 508	91.92 490	99.51 313	98.92 376
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ArgMatch-Sym		97.83 295	97.54 308	98.71 224	98.98 314	97.65 223	96.25 418	99.43 196	95.60 418	98.85 253	97.98 412	95.72 307	99.56 413	95.54 393	99.50 321	98.92 376

SIFT-UM-Cal		96.49 390	96.62 378	96.12 471	98.13 450	97.89 192	93.35 528	98.44 414	95.48 426	98.63 293	98.34 373	95.45 319	97.45 533	92.22 487	99.50 321	93.02 542

SymmetryMVS		98.05 264	97.71 295	99.09 135	99.29 218	97.83 198	98.28 169	97.64 449	99.24 78	98.80 265	98.85 273	89.76 441	99.94 42	98.04 168	99.50 321	99.49 178

ambc				98.24 308	98.82 348	95.97 343	98.62 119	99.00 341		99.27 155	99.21 155	96.99 226	99.50 438	96.55 332	99.50 321	99.26 299

TSAR-MVS + MP.		98.63 168	98.49 182	99.06 145	99.64 78	97.90 191	98.51 137	98.94 346	96.96 337	99.24 169	98.89 265	97.83 152	99.81 228	96.88 290	99.49 325	99.48 189
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD_0403		96.28 403	95.83 408	97.64 380	98.72 364	94.30 421	98.87 89	98.77 382	97.80 249	96.53 468	98.02 409	97.34 201	99.47 449	76.93 550	99.48 326	99.16 336

OPU-MVS				98.82 193	98.59 397	98.30 136	98.10 195				98.52 349	98.18 119	98.75 512	94.62 414	99.48 326	99.41 224

9.14			97.78 286		99.07 285		97.52 299	99.32 244	95.53 424	98.54 314	98.70 313	97.58 177	99.76 275	94.32 428	99.46 328

TSAR-MVS + GP.		98.18 249	97.98 263	98.77 210	98.71 368	97.88 193	96.32 411	98.66 396	96.33 377	99.23 171	98.51 350	97.48 192	99.40 464	97.16 257	99.46 328	99.02 355

icg_test_0407_2		98.20 246	98.38 202	97.65 377	99.03 298	94.03 434	95.78 450	99.45 182	98.16 218	99.06 195	98.71 306	98.27 104	99.68 346	97.50 229	99.45 330	99.22 311

IMVS_0407		98.39 215	98.64 152	97.66 375	99.03 298	94.03 434	98.10 195	99.45 182	98.16 218	99.06 195	98.71 306	98.27 104	99.71 314	97.50 229	99.45 330	99.22 311

IMVS_0404		98.07 262	98.20 234	97.69 370	99.03 298	94.03 434	96.67 382	99.45 182	98.16 218	98.03 365	98.71 306	96.80 240	99.82 211	97.50 229	99.45 330	99.22 311

IMVS_0403		98.34 219	98.56 166	97.66 375	99.03 298	94.03 434	97.98 226	99.45 182	98.16 218	98.89 242	98.71 306	97.90 144	99.74 293	97.50 229	99.45 330	99.22 311

DVP-MVS++		98.90 109	98.70 140	99.51 49	98.43 417	99.15 53	99.43 15	99.32 244	98.17 215	99.26 159	99.02 214	98.18 119	99.88 116	97.07 268	99.45 330	99.49 178

PC_three_1452									93.27 485	99.40 118	98.54 345	98.22 114	97.00 539	95.17 401	99.45 330	99.49 178

PCF-MVS	92.86 18	94.36 460	93.00 481	98.42 284	98.70 372	97.56 230	93.16 532	99.11 317	79.59 549	97.55 403	97.43 455	92.19 409	99.73 301	79.85 547	99.45 330	97.97 471
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
dtuonlycased		97.70 304	98.19 238	96.24 460	99.75 34	89.51 523	94.69 489	99.64 80	98.23 203	99.46 102	98.57 342	98.25 108	99.85 160	95.65 386	99.44 337	99.36 254

new_pmnet		96.99 367	96.76 365	97.67 373	98.72 364	94.89 401	95.95 440	98.20 428	92.62 499	98.55 312	98.54 345	94.88 337	99.52 431	93.96 437	99.44 337	98.59 430

usedtu_dtu_shiyan1		97.37 331	97.13 339	98.11 322	99.03 298	95.40 373	94.47 497	98.99 342	96.87 348	97.97 369	97.81 427	92.12 411	99.75 287	97.49 234	99.43 339	99.16 336

FE-MVSNET3		97.37 331	97.13 339	98.11 322	99.03 298	95.40 373	94.47 497	98.99 342	96.87 348	97.97 369	97.81 427	92.12 411	99.75 287	97.49 234	99.43 339	99.16 336

APD-MVS_3200maxsize		98.84 120	98.61 160	99.53 38	99.19 252	99.27 27	98.49 142	99.33 242	98.64 162	99.03 208	98.98 237	97.89 148	99.85 160	96.54 333	99.42 341	99.46 201

MASt3R-SfM		96.02 414	95.82 409	96.60 446	97.03 513	94.90 400	94.26 506	98.53 409	88.40 536	98.41 329	98.67 319	92.39 404	97.62 532	95.31 397	99.41 342	97.29 502

MSLP-MVS++		98.02 266	98.14 247	97.64 380	98.58 399	95.19 387	97.48 306	99.23 288	97.47 286	97.90 374	98.62 335	97.04 220	98.81 510	97.55 223	99.41 342	98.94 374

QAPM		97.31 337	96.81 363	98.82 193	98.80 354	97.49 234	99.06 66	99.19 296	90.22 522	97.69 392	99.16 171	96.91 231	99.90 82	90.89 513	99.41 342	99.07 346

SR-MVS-dyc-post		98.81 128	98.55 167	99.57 21	99.20 248	99.38 12	98.48 145	99.30 257	98.64 162	98.95 226	98.96 243	97.49 191	99.86 145	96.56 329	99.39 345	99.45 207

RE-MVS-def			98.58 164		99.20 248	99.38 12	98.48 145	99.30 257	98.64 162	98.95 226	98.96 243	97.75 160		96.56 329	99.39 345	99.45 207

MVS-HIRNet		94.32 461	95.62 416	90.42 532	98.46 412	75.36 559	96.29 413	89.13 550	95.25 436	95.38 501	99.75 17	92.88 396	99.19 490	94.07 435	99.39 345	96.72 514

CDPH-MVS		97.26 342	96.66 375	99.07 139	99.00 310	98.15 150	96.03 433	99.01 339	91.21 515	97.79 386	97.85 424	96.89 232	99.69 333	92.75 476	99.38 348	99.39 234

usedtu_dtu_shiyan2		98.99 95	98.86 116	99.39 72	99.73 38	98.71 99	99.05 68	99.47 172	99.16 95	99.49 95	99.12 187	96.34 273	99.93 54	98.05 167	99.36 349	99.54 144

VPNet		98.87 113	98.83 121	99.01 154	99.70 58	97.62 227	98.43 150	99.35 230	99.47 48	99.28 152	99.05 208	96.72 248	99.82 211	98.09 162	99.36 349	99.59 110

plane_prior						97.65 223	97.07 351		96.72 358						99.36 349

thisisatest0530		95.27 445	94.45 458	97.74 365	99.19 252	94.37 419	97.86 244	90.20 548	97.17 326	98.22 345	97.65 438	73.53 528	99.90 82	96.90 288	99.35 352	98.95 370

HPM-MVS++		98.10 257	97.64 302	99.48 57	99.09 281	99.13 61	97.52 299	98.75 388	97.46 291	96.90 447	97.83 426	96.01 290	99.84 181	95.82 379	99.35 352	99.46 201

balanced_ft_v1		98.28 233	98.35 209	98.09 325	98.08 453	96.23 330	99.23 45	99.26 278	98.34 190	97.46 411	99.42 90	95.38 322	99.88 116	98.60 118	99.34 354	98.17 459

LS3D		98.63 168	98.38 202	99.36 74	97.25 504	99.38 12	99.12 61	99.32 244	99.21 83	98.44 326	98.88 267	97.31 202	99.80 237	96.58 323	99.34 354	98.91 379

GLUNet-SfM		86.26 513	84.68 516	91.01 531	80.58 559	83.56 548	78.04 550	93.59 531	76.70 550	95.29 503	94.72 520	77.51 522	94.26 551	66.39 554	99.33 356	95.20 529

viewdifsd2359ckpt13		98.39 215	98.29 220	98.70 226	99.26 233	97.19 270	97.51 302	99.48 160	96.94 339	98.58 306	98.82 283	97.47 194	99.55 418	97.21 253	99.33 356	99.34 264

CNVR-MVS		98.17 252	97.87 279	99.07 139	98.67 382	98.24 141	97.01 353	98.93 349	97.25 315	97.62 396	98.34 373	97.27 206	99.57 410	96.42 341	99.33 356	99.39 234

sss		97.21 348	96.93 350	98.06 333	98.83 345	95.22 386	96.75 374	98.48 413	94.49 456	97.27 425	97.90 420	92.77 399	99.80 237	96.57 325	99.32 359	99.16 336

3Dnovator+	97.89 3	98.69 152	98.51 174	99.24 107	98.81 351	98.40 122	99.02 70	99.19 296	98.99 125	98.07 360	99.28 130	97.11 218	99.84 181	96.84 294	99.32 359	99.47 198

ALIKED-LG		97.10 355	96.63 377	98.50 275	97.96 459	98.68 101	97.75 263	99.68 65	95.86 404	98.36 337	98.33 377	91.58 420	99.04 497	90.87 514	99.31 361	97.77 483

SR-MVS		98.71 143	98.43 191	99.57 21	99.18 260	99.35 17	98.36 162	99.29 265	98.29 199	98.88 246	98.85 273	97.53 184	99.87 136	96.14 362	99.31 361	99.48 189

Anonymous202405211		97.90 278	97.50 312	99.08 137	98.90 330	98.25 140	98.53 131	96.16 493	98.87 141	99.11 188	98.86 270	90.40 436	99.78 263	97.36 240	99.31 361	99.19 322

Patchmatch-test		96.55 386	96.34 395	97.17 416	98.35 424	93.06 463	98.40 158	97.79 440	97.33 304	98.41 329	98.67 319	83.68 495	99.69 333	95.16 402	99.31 361	98.77 404

LCM-MVSNet-Re		98.64 166	98.48 183	99.11 129	98.85 342	98.51 115	98.49 142	99.83 27	98.37 186	99.69 56	99.46 81	98.21 116	99.92 66	94.13 433	99.30 365	98.91 379

EPNet_dtu		94.93 454	94.78 451	95.38 497	93.58 548	87.68 532	96.78 371	95.69 508	97.35 303	89.14 548	98.09 403	88.15 457	99.49 442	94.95 407	99.30 365	98.98 362
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing915		96.85 373	96.43 392	98.09 325	99.35 201	95.18 388	98.60 122	97.22 464	98.37 186	97.41 418	97.98 412	83.29 497	99.69 333	96.35 347	99.29 367	99.42 220

TAPA-MVS	96.21 11	96.63 382	95.95 406	98.65 235	98.93 322	98.09 159	96.93 361	99.28 269	83.58 545	98.13 354	97.78 429	96.13 284	99.40 464	93.52 452	99.29 367	98.45 438
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet	93.40 17	95.67 430	95.70 413	95.57 490	98.83 345	88.57 526	92.50 535	97.72 442	92.69 498	96.49 475	96.44 483	93.72 377	99.43 460	93.61 447	99.28 369	98.71 411

SIFT-NN-PointCN		96.06 411	96.11 403	95.91 478	97.88 465	97.73 216	93.49 525	97.51 451	93.22 486	96.57 465	98.26 385	96.23 279	96.60 544	92.54 482	99.27 370	93.40 537

EIA-MVS		98.00 269	97.74 289	98.80 198	98.72 364	98.09 159	98.05 205	99.60 95	97.39 299	96.63 462	95.55 501	97.68 163	99.80 237	96.73 305	99.27 370	98.52 433

LFMVS		97.20 349	96.72 368	98.64 237	98.72 364	96.95 290	98.93 82	94.14 528	99.74 12	98.78 267	99.01 226	84.45 487	99.73 301	97.44 236	99.27 370	99.25 300

ITE_SJBPF				98.87 180	99.22 242	98.48 117		99.35 230	97.50 283	98.28 342	98.60 339	97.64 169	99.35 472	93.86 441	99.27 370	98.79 402

HQP3-MVS								99.04 331							99.26 374

HQP-MVS		97.00 366	96.49 387	98.55 261	98.67 382	96.79 301	96.29 413	99.04 331	96.05 392	95.55 495	96.84 472	93.84 372	99.54 424	92.82 472	99.26 374	99.32 275

MVStest1		95.86 424	95.60 418	96.63 445	95.87 541	91.70 489	97.93 232	98.94 346	98.03 229	99.56 75	99.66 33	71.83 529	98.26 520	99.35 59	99.24 376	99.91 14

SSC-MVS		98.71 143	98.74 129	98.62 243	99.72 45	96.08 338	98.74 99	98.64 399	99.74 12	99.67 60	99.24 145	94.57 348	99.95 26	99.11 78	99.24 376	99.82 37

ETV-MVS		98.03 265	97.86 280	98.56 259	98.69 377	98.07 166	97.51 302	99.50 150	98.10 225	97.50 408	95.51 502	98.41 86	99.88 116	96.27 354	99.24 376	97.71 488

MCST-MVS		98.00 269	97.63 304	99.10 131	99.24 236	98.17 149	96.89 365	98.73 391	95.66 415	97.92 372	97.70 436	97.17 213	99.66 364	96.18 360	99.23 379	99.47 198

ttmdpeth		97.91 277	98.02 259	97.58 386	98.69 377	94.10 430	98.13 188	98.90 356	97.95 235	97.32 424	99.58 48	95.95 299	98.75 512	96.41 342	99.22 380	99.87 23

SCA		96.41 397	96.66 375	95.67 487	98.24 436	88.35 528	95.85 447	96.88 479	96.11 390	97.67 393	98.67 319	93.10 391	99.85 160	94.16 429	99.22 380	98.81 396

MSDG		97.71 303	97.52 311	98.28 303	98.91 329	96.82 299	94.42 499	99.37 220	97.65 263	98.37 335	98.29 383	97.40 197	99.33 475	94.09 434	99.22 380	98.68 420

SIFT-MNN		95.92 422	95.97 405	95.74 486	98.18 442	98.00 173	94.17 508	96.99 471	95.74 414	97.16 429	97.90 420	90.71 432	95.79 547	93.71 445	99.21 383	93.44 536

MIMVSNet		96.62 383	96.25 401	97.71 369	99.04 295	94.66 412	99.16 55	96.92 478	97.23 321	97.87 377	99.10 192	86.11 470	99.65 371	91.65 496	99.21 383	98.82 391

test_prior2							95.74 452		96.48 371	96.11 481	97.63 440	95.92 301		94.16 429	99.20 385

VDDNet		98.21 244	97.95 267	99.01 154	99.58 96	97.74 214	99.01 71	97.29 460	99.67 20	98.97 220	99.50 69	90.45 435	99.80 237	97.88 185	99.20 385	99.48 189

OpenMVS	96.65 7	97.09 357	96.68 371	98.32 297	98.32 426	97.16 276	98.86 92	99.37 220	89.48 527	96.29 478	99.15 177	96.56 258	99.90 82	92.90 469	99.20 385	97.89 474

ZD-MVS					99.01 309	98.84 87		99.07 323	94.10 472	98.05 363	98.12 399	96.36 272	99.86 145	92.70 478	99.19 388

MSP-MVS		98.40 208	98.00 261	99.61 13	99.57 105	99.25 29	98.57 126	99.35 230	97.55 277	99.31 148	97.71 434	94.61 347	99.88 116	96.14 362	99.19 388	99.70 71
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
CNLPA		97.17 352	96.71 369	98.55 261	98.56 402	98.05 170	96.33 410	98.93 349	96.91 344	97.06 435	97.39 457	94.38 356	99.45 456	91.66 495	99.18 390	98.14 461

train_agg		97.10 355	96.45 391	99.07 139	98.71 368	98.08 163	95.96 438	99.03 333	91.64 507	95.85 488	97.53 445	96.47 262	99.76 275	93.67 446	99.16 391	99.36 254

agg_prior2														92.50 483	99.16 391	99.37 246

test9_res														93.28 459	99.15 393	99.38 243

MS-PatchMatch		97.68 306	97.75 288	97.45 402	98.23 439	93.78 450	97.29 331	98.84 371	96.10 391	98.64 292	98.65 326	96.04 288	99.36 469	96.84 294	99.14 394	99.20 316

AdaColmap		97.14 354	96.71 369	98.46 279	98.34 425	97.80 208	96.95 358	98.93 349	95.58 420	96.92 442	97.66 437	95.87 302	99.53 427	90.97 509	99.14 394	98.04 466

VNet		98.42 204	98.30 218	98.79 202	98.79 357	97.29 258	98.23 175	98.66 396	99.31 70	98.85 253	98.80 287	94.80 341	99.78 263	98.13 157	99.13 396	99.31 280

test12				98.93 171	98.58 399	97.83 198		98.66 396		96.53 468		95.51 316	99.69 333		99.13 396	99.27 293

DP-MVS Recon		97.33 336	96.92 352	98.57 254	99.09 281	97.99 175	96.79 369	99.35 230	93.18 487	97.71 390	98.07 405	95.00 333	99.31 478	93.97 436	99.13 396	98.42 445

thisisatest0515		94.12 468	93.16 477	96.97 428	98.60 394	92.90 469	93.77 520	90.61 546	94.10 472	96.91 444	95.87 495	74.99 525	99.80 237	94.52 417	99.12 399	98.20 457

pmmvs3		95.03 451	94.40 460	96.93 430	97.70 480	92.53 477	95.08 476	97.71 443	88.57 534	97.71 390	98.08 404	79.39 513	99.82 211	96.19 358	99.11 400	98.43 443

test222					98.92 326	96.93 292	95.54 457	98.78 381	85.72 542	96.86 451	98.11 400	94.43 352			99.10 401	99.23 306

PRO-TEST		97.86 286	97.88 278	97.81 355	98.01 457	94.96 396	97.99 224	99.48 160	97.80 249	97.83 382	97.76 431	96.27 277	99.80 237	96.68 312	99.07 402	98.69 415

xiu_mvs_v1_base_debu		97.86 286	98.17 241	96.92 431	98.98 314	93.91 444	96.45 400	99.17 304	97.85 245	98.41 329	97.14 468	98.47 78	99.92 66	98.02 170	99.05 403	96.92 507

xiu_mvs_v1_base		97.86 286	98.17 241	96.92 431	98.98 314	93.91 444	96.45 400	99.17 304	97.85 245	98.41 329	97.14 468	98.47 78	99.92 66	98.02 170	99.05 403	96.92 507

xiu_mvs_v1_base_debi		97.86 286	98.17 241	96.92 431	98.98 314	93.91 444	96.45 400	99.17 304	97.85 245	98.41 329	97.14 468	98.47 78	99.92 66	98.02 170	99.05 403	96.92 507

MG-MVS		96.77 377	96.61 380	97.26 411	98.31 427	93.06 463	95.93 441	98.12 433	96.45 374	97.92 372	98.73 302	93.77 376	99.39 466	91.19 506	99.04 406	99.33 270

cl22		95.79 427	95.39 430	96.98 427	96.77 521	92.79 472	94.40 500	98.53 409	94.59 455	97.89 375	98.17 394	82.82 502	99.24 486	96.37 344	99.03 407	98.92 376

miper_ehance_all_eth		97.06 360	97.03 344	97.16 418	97.83 469	93.06 463	94.66 490	99.09 320	95.99 398	98.69 282	98.45 360	92.73 401	99.61 392	96.79 296	99.03 407	98.82 391

miper_enhance_ethall		96.01 415	95.74 411	96.81 438	96.41 533	92.27 484	93.69 521	98.89 359	91.14 516	98.30 338	97.35 461	90.58 434	99.58 408	96.30 350	99.03 407	98.60 427

SIFT-NN-CMatch		95.63 433	95.48 423	96.08 472	98.24 436	98.00 173	92.71 533	94.29 523	94.20 468	95.85 488	97.26 463	95.72 307	97.01 538	91.99 489	99.02 410	93.23 539

API-MVS		97.04 362	96.91 355	97.42 404	97.88 465	98.23 145	98.18 181	98.50 412	97.57 273	97.39 421	96.75 475	96.77 242	99.15 494	90.16 518	99.02 410	94.88 530

旧先验1					98.82 348	97.45 241		98.76 384			98.34 373	95.50 317			99.01 412	99.23 306

新几何1				98.91 176	98.94 320	97.76 212		98.76 384	87.58 539	96.75 456	98.10 401	94.80 341	99.78 263	92.73 477	99.00 413	99.20 316

mvsmamba		97.57 315	97.26 328	98.51 271	98.69 377	96.73 306	98.74 99	97.25 461	97.03 335	97.88 376	99.23 151	90.95 429	99.87 136	96.61 321	99.00 413	98.91 379

testing3-2		93.78 474	93.91 465	93.39 524	98.82 348	81.72 556	97.76 260	95.28 512	98.60 169	96.54 467	96.66 477	65.85 545	99.62 384	96.65 318	98.99 415	98.82 391

原ACMM1				98.35 295	98.90 330	96.25 329		98.83 375	92.48 500	96.07 483	98.10 401	95.39 321	99.71 314	92.61 480	98.99 415	99.08 344

testgi		98.32 224	98.39 198	98.13 321	99.57 105	95.54 360	97.78 254	99.49 158	97.37 301	99.19 178	97.65 438	98.96 30	99.49 442	96.50 336	98.99 415	99.34 264

MVP-Stereo		98.08 261	97.92 273	98.57 254	98.96 318	96.79 301	97.90 238	99.18 300	96.41 375	98.46 323	98.95 247	95.93 300	99.60 396	96.51 335	98.98 418	99.31 280
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
testing3		93.51 478	92.09 492	97.75 363	98.60 394	94.40 418	97.32 327	95.26 513	97.56 275	96.79 455	95.50 503	53.57 558	99.77 269	95.26 399	98.97 419	99.08 344

alignmvs		97.35 334	96.88 356	98.78 205	98.54 404	98.09 159	97.71 268	97.69 444	99.20 85	97.59 399	95.90 494	88.12 458	99.55 418	98.18 154	98.96 420	98.70 414

testdata				98.09 325	98.93 322	95.40 373		98.80 378	90.08 524	97.45 414	98.37 369	95.26 324	99.70 323	93.58 450	98.95 421	99.17 330

mvsany_test1		97.60 311	97.54 308	97.77 359	97.72 475	95.35 376	95.36 466	97.13 468	94.13 470	99.71 50	99.33 119	97.93 142	99.30 480	97.60 219	98.94 422	98.67 422

Effi-MVS+-dtu		98.26 236	97.90 276	99.35 80	98.02 456	99.49 5	98.02 212	99.16 307	98.29 199	97.64 394	97.99 411	96.44 264	99.95 26	96.66 317	98.93 423	98.60 427

FA-MVS(test-final)		96.99 367	96.82 361	97.50 397	98.70 372	94.78 406	99.34 23	96.99 471	95.07 441	98.48 321	99.33 119	88.41 455	99.65 371	96.13 364	98.92 424	98.07 465

MVS_Test		98.18 249	98.36 206	97.67 373	98.48 409	94.73 409	98.18 181	99.02 336	97.69 259	98.04 364	99.11 189	97.22 210	99.56 413	98.57 122	98.90 425	98.71 411

CL-MVSNet_self_test		97.44 325	97.22 332	98.08 330	98.57 401	95.78 354	94.30 503	98.79 379	96.58 366	98.60 302	98.19 393	94.74 344	99.64 376	96.41 342	98.84 426	98.82 391

WB-MVS		98.52 194	98.55 167	98.43 283	99.65 72	95.59 357	98.52 132	98.77 382	99.65 25	99.52 88	99.00 230	94.34 358	99.93 54	98.65 115	98.83 427	99.76 59

Fast-Effi-MVS+		97.67 307	97.38 320	98.57 254	98.71 368	97.43 244	97.23 337	99.45 182	94.82 449	96.13 480	96.51 479	98.52 76	99.91 75	96.19 358	98.83 427	98.37 450

NCCC		97.86 286	97.47 317	99.05 146	98.61 392	98.07 166	96.98 356	98.90 356	97.63 264	97.04 437	97.93 419	95.99 295	99.66 364	95.31 397	98.82 429	99.43 215

PMatch-SfM		97.89 280	97.64 302	98.66 233	99.26 233	97.44 243	96.08 430	99.51 145	96.72 358	98.47 322	99.13 183	93.62 380	99.70 323	97.14 261	98.80 430	98.83 389

PMatch-Up-SfM		97.79 298	97.48 316	98.72 222	99.03 298	97.78 209	96.05 432	99.48 160	96.90 345	98.72 277	99.18 164	92.00 415	99.71 314	97.15 260	98.77 431	98.69 415

PatchMatch-RL		97.24 345	96.78 364	98.61 247	99.03 298	97.83 198	96.36 408	99.06 324	93.49 484	97.36 423	97.78 429	95.75 305	99.49 442	93.44 456	98.77 431	98.52 433

SP-LightGlue		97.22 347	97.01 346	97.88 349	97.33 502	97.19 270	96.38 406	99.08 322	97.28 311	96.53 468	97.50 449	92.36 405	98.70 514	97.84 190	98.76 433	97.74 485

DPM-MVS		96.32 400	95.59 420	98.51 271	98.76 358	97.21 268	94.54 496	98.26 424	91.94 506	96.37 476	97.25 464	93.06 393	99.43 460	91.42 501	98.74 434	98.89 382

YYNet1		97.60 311	97.67 297	97.39 406	99.04 295	93.04 466	95.27 469	98.38 420	97.25 315	98.92 237	98.95 247	95.48 318	99.73 301	96.99 275	98.74 434	99.41 224

MDA-MVSNet-bldmvs		97.94 276	97.91 275	98.06 333	99.44 173	94.96 396	96.63 387	99.15 312	98.35 189	98.83 258	99.11 189	94.31 360	99.85 160	96.60 322	98.72 436	99.37 246

MDA-MVSNet_test_wron		97.60 311	97.66 300	97.41 405	99.04 295	93.09 462	95.27 469	98.42 417	97.26 314	98.88 246	98.95 247	95.43 320	99.73 301	97.02 271	98.72 436	99.41 224

MGCFI-Net		98.34 219	98.28 221	98.51 271	98.47 410	97.59 229	98.96 78	99.48 160	99.18 93	97.40 419	95.50 503	98.66 60	99.50 438	98.18 154	98.71 438	98.44 441

sasdasda		98.34 219	98.26 227	98.58 251	98.46 412	97.82 204	98.96 78	99.46 177	99.19 90	97.46 411	95.46 506	98.59 68	99.46 453	98.08 163	98.71 438	98.46 435

FE-MVS		95.66 431	94.95 448	97.77 359	98.53 406	95.28 381	99.40 19	96.09 497	93.11 489	97.96 371	99.26 138	79.10 515	99.77 269	92.40 485	98.71 438	98.27 455

Fast-Effi-MVS+-dtu		98.27 234	98.09 250	98.81 195	98.43 417	98.11 155	97.61 288	99.50 150	98.64 162	97.39 421	97.52 448	98.12 127	99.95 26	96.90 288	98.71 438	98.38 448

canonicalmvs		98.34 219	98.26 227	98.58 251	98.46 412	97.82 204	98.96 78	99.46 177	99.19 90	97.46 411	95.46 506	98.59 68	99.46 453	98.08 163	98.71 438	98.46 435

xiu_mvs_v2_base		97.16 353	97.49 313	96.17 466	98.54 404	92.46 478	95.45 462	98.84 371	97.25 315	97.48 410	96.49 480	98.31 98	99.90 82	96.34 348	98.68 443	96.15 523

PS-MVSNAJ		97.08 358	97.39 319	96.16 468	98.56 402	92.46 478	95.24 471	98.85 370	97.25 315	97.49 409	95.99 491	98.07 129	99.90 82	96.37 344	98.67 444	96.12 524

SIFT-NN-NCMNet		95.39 442	95.22 440	95.92 477	98.29 429	98.34 133	93.58 524	94.60 519	94.07 474	94.84 511	97.53 445	94.37 357	96.62 543	91.01 508	98.64 445	92.80 545

UWE-MVS		92.38 498	91.76 502	94.21 512	97.16 506	84.65 543	95.42 464	88.45 551	95.96 399	96.17 479	95.84 497	66.36 541	99.71 314	91.87 492	98.64 445	98.28 453

PatchmatchNet		95.58 434	95.67 415	95.30 500	97.34 501	87.32 534	97.65 278	96.65 484	95.30 433	97.07 434	98.69 315	84.77 484	99.74 293	94.97 406	98.64 445	98.83 389
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVE	83.40 22	92.50 496	91.92 498	94.25 510	98.83 345	91.64 490	92.71 533	83.52 557	95.92 401	86.46 551	95.46 506	95.20 325	95.40 549	80.51 546	98.64 445	95.73 527
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
OpenMVS_ROB	95.38 14	95.84 426	95.18 443	97.81 355	98.41 421	97.15 277	97.37 323	98.62 400	83.86 544	98.65 289	98.37 369	94.29 361	99.68 346	88.41 525	98.62 449	96.60 515

cascas		94.79 455	94.33 463	96.15 470	96.02 539	92.36 482	92.34 537	99.26 278	85.34 543	95.08 507	94.96 516	92.96 395	98.53 517	94.41 426	98.59 450	97.56 493

BH-RMVSNet		96.83 374	96.58 383	97.58 386	98.47 410	94.05 431	96.67 382	97.36 454	96.70 361	97.87 377	97.98 412	95.14 329	99.44 458	90.47 517	98.58 451	99.25 300

SP-SuperGlue		97.31 337	97.23 331	97.57 391	96.96 514	97.24 262	96.26 417	98.76 384	97.68 260	96.88 450	97.85 424	94.32 359	98.01 524	97.76 202	98.57 452	97.45 497

GA-MVS		95.86 424	95.32 435	97.49 398	98.60 394	94.15 427	93.83 519	97.93 438	95.49 425	96.68 460	97.42 456	83.21 498	99.30 480	96.22 356	98.55 453	99.01 357

XFeat-MNN		93.41 481	92.98 482	94.68 506	92.63 550	92.92 468	89.72 546	95.81 504	92.10 505	97.23 428	96.29 487	84.95 482	97.31 537	89.60 522	98.54 454	93.81 533

RRT-MVS		97.88 283	97.98 263	97.61 383	98.15 446	93.77 451	98.97 77	99.64 80	99.16 95	98.69 282	99.42 90	91.60 418	99.89 98	97.63 215	98.52 455	99.16 336

F-COLMAP		97.30 339	96.68 371	99.14 125	99.19 252	98.39 124	97.27 336	99.30 257	92.93 493	96.62 463	98.00 410	95.73 306	99.68 346	92.62 479	98.46 456	99.35 260

SP-DiffGlue		96.87 371	96.76 365	97.21 413	95.17 543	96.88 297	96.12 427	98.93 349	96.51 367	98.37 335	97.55 444	93.65 379	97.83 527	96.11 365	98.45 457	96.92 507

XVG-OURS-SEG-HR		98.49 197	98.28 221	99.14 125	99.49 152	98.83 88	96.54 393	99.48 160	97.32 306	99.11 188	98.61 337	99.33 15	99.30 480	96.23 355	98.38 458	99.28 289

ALIKED-NN		94.29 464	93.41 474	96.94 429	96.18 536	97.66 221	94.90 481	98.68 394	88.85 532	90.43 544	96.81 474	89.82 440	96.59 545	86.67 533	98.33 459	96.58 516

test_yl		96.69 378	96.29 398	97.90 346	98.28 431	95.24 382	97.29 331	97.36 454	98.21 207	98.17 347	97.86 422	86.27 466	99.55 418	94.87 408	98.32 460	98.89 382

DCV-MVSNet		96.69 378	96.29 398	97.90 346	98.28 431	95.24 382	97.29 331	97.36 454	98.21 207	98.17 347	97.86 422	86.27 466	99.55 418	94.87 408	98.32 460	98.89 382

WB-MVSnew		95.73 429	95.57 421	96.23 462	96.70 523	90.70 513	96.07 431	93.86 530	95.60 418	97.04 437	95.45 510	96.00 291	99.55 418	91.04 507	98.31 462	98.43 443

SP-MNN		96.46 394	96.24 402	97.10 419	96.71 522	95.98 341	96.00 434	97.33 458	95.82 409	94.93 509	97.10 471	93.70 378	98.01 524	96.30 350	98.30 463	97.30 501

tt0805		98.69 152	98.62 156	98.90 179	99.75 34	99.30 22	99.15 57	96.97 473	98.86 143	98.87 251	97.62 441	98.63 64	98.96 503	99.41 57	98.29 464	98.45 438

thres600view7		94.45 459	93.83 467	96.29 457	99.06 290	91.53 492	97.99 224	94.24 526	98.34 190	97.44 416	95.01 513	79.84 509	99.67 351	84.33 538	98.23 465	97.66 489

MAR-MVS		96.47 393	95.70 413	98.79 202	97.92 463	99.12 63	98.28 169	98.60 401	92.16 504	95.54 498	96.17 488	94.77 343	99.52 431	89.62 521	98.23 465	97.72 487
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
Effi-MVS+		98.02 266	97.82 283	98.62 243	98.53 406	97.19 270	97.33 326	99.68 65	97.30 309	96.68 460	97.46 454	98.56 74	99.80 237	96.63 319	98.20 467	98.86 387

test_vis1_rt		97.75 300	97.72 293	97.83 353	98.81 351	96.35 326	97.30 330	99.69 58	94.61 454	97.87 377	98.05 406	96.26 278	98.32 519	98.74 108	98.18 468	98.82 391

test-LLR		93.90 472	93.85 466	94.04 514	96.53 526	84.62 544	94.05 513	92.39 537	96.17 384	94.12 521	95.07 511	82.30 503	99.67 351	95.87 375	98.18 468	97.82 477

test-mter		92.33 500	91.76 502	94.04 514	96.53 526	84.62 544	94.05 513	92.39 537	94.00 477	94.12 521	95.07 511	65.63 546	99.67 351	95.87 375	98.18 468	97.82 477

mvs_anonymous		97.83 295	98.16 244	96.87 434	98.18 442	91.89 487	97.31 329	98.90 356	97.37 301	98.83 258	99.46 81	96.28 276	99.79 251	98.90 95	98.16 471	98.95 370

WTY-MVS		96.67 380	96.27 400	97.87 351	98.81 351	94.61 414	96.77 372	97.92 439	94.94 445	97.12 430	97.74 433	91.11 428	99.82 211	93.89 439	98.15 472	99.18 326

thres200		93.72 476	93.14 478	95.46 495	98.66 387	91.29 499	96.61 389	94.63 518	97.39 299	96.83 452	93.71 527	79.88 508	99.56 413	82.40 544	98.13 473	95.54 528

TESTMET0.1,1		92.19 502	91.77 501	93.46 521	96.48 531	82.80 553	94.05 513	91.52 545	94.45 461	94.00 526	94.88 517	66.65 540	99.56 413	95.78 380	98.11 474	98.02 467

PMMVS		96.51 387	95.98 404	98.09 325	97.53 491	95.84 349	94.92 480	98.84 371	91.58 509	96.05 485	95.58 500	95.68 309	99.66 364	95.59 390	98.09 475	98.76 406

thres100view900		94.19 465	93.67 470	95.75 484	99.06 290	91.35 497	98.03 209	94.24 526	98.33 192	97.40 419	94.98 515	79.84 509	99.62 384	83.05 541	98.08 476	96.29 519

tfpn200view9		94.03 469	93.44 472	95.78 483	98.93 322	91.44 495	97.60 289	94.29 523	97.94 237	97.10 431	94.31 524	79.67 511	99.62 384	83.05 541	98.08 476	96.29 519

thres400		94.14 467	93.44 472	96.24 460	98.93 322	91.44 495	97.60 289	94.29 523	97.94 237	97.10 431	94.31 524	79.67 511	99.62 384	83.05 541	98.08 476	97.66 489

Syy-MVS		96.04 413	95.56 422	97.49 398	97.10 508	94.48 416	96.18 423	96.58 486	95.65 416	94.77 512	92.29 542	91.27 427	99.36 469	98.17 156	98.05 479	98.63 424

myMVS_eth3d		91.92 505	90.45 507	96.30 456	97.10 508	90.90 508	96.18 423	96.58 486	95.65 416	94.77 512	92.29 542	53.88 557	99.36 469	89.59 523	98.05 479	98.63 424

SIFT-NN-UMatch		95.38 443	95.26 437	95.75 484	98.25 434	97.78 209	93.24 531	95.66 510	94.01 476	95.10 506	97.47 453	93.12 389	96.78 542	92.42 484	98.04 481	92.69 547

PLC	94.65 16	96.51 387	95.73 412	98.85 183	98.75 360	97.91 189	96.42 404	99.06 324	90.94 519	95.59 492	97.38 458	94.41 353	99.59 401	90.93 511	98.04 481	99.05 348
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UBG		93.25 484	92.32 487	96.04 473	97.72 475	90.16 516	95.92 443	95.91 502	96.03 395	93.95 528	93.04 534	69.60 533	99.52 431	90.72 516	97.98 483	98.45 438

MDTV_nov1_ep13			95.22 440		97.06 510	83.20 551	97.74 265	96.16 493	94.37 464	96.99 440	98.83 280	83.95 493	99.53 427	93.90 438	97.95 484

myMVS_eth3d28		92.92 492	92.31 488	94.77 504	97.84 468	87.59 533	96.19 421	96.11 495	97.08 331	94.27 518	93.49 530	66.07 544	98.78 511	91.78 493	97.93 485	97.92 473

nomal-1		94.03 469	93.02 480	97.07 422	97.95 460	92.86 470	96.66 385	95.37 511	96.16 388	94.89 510	94.68 521	69.16 534	99.73 301	94.43 422	97.86 486	98.62 426

PAPM_NR		96.82 376	96.32 396	98.30 301	99.07 285	96.69 308	97.48 306	98.76 384	95.81 410	96.61 464	96.47 482	94.12 368	99.17 492	90.82 515	97.78 487	99.06 347

UWE-MVS-28		90.22 508	89.28 512	93.02 528	94.50 547	82.87 552	96.52 396	87.51 552	95.21 438	92.36 539	96.04 489	71.57 530	98.25 521	72.04 552	97.77 488	97.94 472

EMVS		93.83 473	94.02 464	93.23 526	96.83 519	84.96 541	89.77 545	96.32 491	97.92 239	97.43 417	96.36 486	86.17 468	98.93 505	87.68 528	97.73 489	95.81 526

E-PMN		94.17 466	94.37 461	93.58 520	96.86 517	85.71 540	90.11 544	97.07 469	98.17 215	97.82 385	97.19 465	84.62 486	98.94 504	89.77 520	97.68 490	96.09 525

testing11		93.08 488	92.02 494	96.26 459	97.56 487	90.83 510	96.32 411	95.70 506	96.47 372	92.66 536	93.73 526	64.36 549	99.59 401	93.77 444	97.57 491	98.37 450

TestfortrainingZip				98.97 163	98.30 428	98.43 121	98.68 109	98.26 424	97.76 254	98.86 252	98.16 396	95.15 328	99.47 449		97.55 492	99.02 355

SIFT-NN		92.96 490	92.79 484	93.46 521	96.92 515	96.45 322	91.89 539	94.39 521	92.91 494	92.54 537	95.46 506	88.26 456	90.71 555	85.22 536	97.52 493	93.22 540

testing222		91.96 504	90.37 508	96.72 443	97.47 498	92.59 475	96.11 428	94.76 516	96.83 352	92.90 534	92.87 536	57.92 556	99.55 418	86.93 531	97.52 493	98.00 470

PatchT		96.65 381	96.35 394	97.54 393	97.40 499	95.32 379	97.98 226	96.64 485	99.33 67	96.89 448	99.42 90	84.32 489	99.81 228	97.69 211	97.49 495	97.48 495

FPMVS		93.44 480	92.23 490	97.08 420	99.25 235	97.86 195	95.61 455	97.16 467	92.90 495	93.76 530	98.65 326	75.94 524	95.66 548	79.30 548	97.49 495	97.73 486

testing91		93.32 482	92.27 489	96.47 450	97.54 489	91.25 501	96.17 425	96.76 482	97.18 325	93.65 531	93.50 529	65.11 548	99.63 379	93.04 465	97.45 497	98.53 432

AUN-MVS		96.24 408	95.45 426	98.60 249	98.70 372	97.22 266	97.38 319	97.65 447	95.95 400	95.53 499	97.96 418	82.11 505	99.79 251	96.31 349	97.44 498	98.80 401

BH-untuned		96.83 374	96.75 367	97.08 420	98.74 361	93.33 460	96.71 377	98.26 424	96.72 358	98.44 326	97.37 459	95.20 325	99.47 449	91.89 491	97.43 499	98.44 441

ETVMVS		92.60 495	91.08 505	97.18 414	97.70 480	93.65 456	96.54 393	95.70 506	96.51 367	94.68 514	92.39 540	61.80 554	99.50 438	86.97 530	97.41 500	98.40 446

hse-mvs2		97.46 322	97.07 342	98.64 237	98.73 362	97.33 249	97.45 312	97.64 449	99.11 101	98.58 306	97.98 412	88.65 452	99.79 251	98.11 159	97.39 501	98.81 396

UnsupCasMVSNet_bld		97.30 339	96.92 352	98.45 280	99.28 221	96.78 304	96.20 420	99.27 272	95.42 429	98.28 342	98.30 380	93.16 388	99.71 314	94.99 404	97.37 502	98.87 386

PAPR		95.29 444	94.47 457	97.75 363	97.50 497	95.14 390	94.89 482	98.71 393	91.39 513	95.35 502	95.48 505	94.57 348	99.14 495	84.95 537	97.37 502	98.97 366

CR-MVSNet		96.28 403	95.95 406	97.28 409	97.71 478	94.22 422	98.11 193	98.92 353	92.31 502	96.91 444	99.37 105	85.44 479	99.81 228	97.39 239	97.36 504	97.81 479

RPMNet		97.02 363	96.93 350	97.30 408	97.71 478	94.22 422	98.11 193	99.30 257	99.37 61	96.91 444	99.34 116	86.72 463	99.87 136	97.53 226	97.36 504	97.81 479

HY-MVS	95.94 13	95.90 423	95.35 432	97.55 392	97.95 460	94.79 405	98.81 98	96.94 476	92.28 503	95.17 504	98.57 342	89.90 439	99.75 287	91.20 505	97.33 506	98.10 463

testing99		93.04 489	91.98 497	96.23 462	97.53 491	90.70 513	96.35 409	95.94 500	96.87 348	93.41 532	93.43 531	63.84 550	99.59 401	93.24 461	97.19 507	98.40 446

1314		95.74 428	95.60 418	96.17 466	97.53 491	92.75 474	98.07 202	98.31 422	91.22 514	94.25 519	96.68 476	95.53 314	99.03 498	91.64 497	97.18 508	96.74 513

FBQ-MVS		93.12 486	91.90 499	96.81 438	97.80 472	92.96 467	97.12 350	95.93 501	95.83 408	94.07 523	93.03 535	65.21 547	99.18 491	90.94 510	97.13 509	98.28 453

gg-mvs-nofinetune		92.37 499	91.20 504	95.85 481	95.80 542	92.38 481	99.31 30	81.84 558	99.75 10	91.83 541	99.74 19	68.29 535	99.02 500	87.15 529	97.12 510	96.16 522

ET-MVSNet_ETH3D		94.30 463	93.21 476	97.58 386	98.14 447	94.47 417	94.78 484	93.24 535	94.72 451	89.56 546	95.87 495	78.57 519	99.81 228	96.91 283	97.11 511	98.46 435

gbinet_0.2-2-1-0.02		95.44 440	94.55 456	98.14 320	95.99 540	95.34 378	94.71 485	98.29 423	96.00 397	96.05 485	90.50 549	84.99 481	99.79 251	97.33 243	97.07 512	99.28 289

ADS-MVSNet2		95.43 441	94.98 446	96.76 442	98.14 447	91.74 488	97.92 235	97.76 441	90.23 520	96.51 472	98.91 255	85.61 476	99.85 160	92.88 470	96.90 513	98.69 415

ADS-MVSNet		95.24 446	94.93 449	96.18 465	98.14 447	90.10 518	97.92 235	97.32 459	90.23 520	96.51 472	98.91 255	85.61 476	99.74 293	92.88 470	96.90 513	98.69 415

MVS		93.19 485	92.09 492	96.50 449	96.91 516	94.03 434	98.07 202	98.06 436	68.01 552	94.56 517	96.48 481	95.96 298	99.30 480	83.84 539	96.89 515	96.17 521

tpm2		93.09 487	92.58 486	94.62 507	97.56 487	86.53 536	97.66 276	95.79 505	86.15 541	94.07 523	98.23 390	75.95 523	99.53 427	90.91 512	96.86 516	97.81 479

baseline2		93.73 475	92.83 483	96.42 452	97.70 480	91.28 500	96.84 368	89.77 549	93.96 478	92.44 538	95.93 493	79.14 514	99.77 269	92.94 467	96.76 517	98.21 456

SP-NN		94.67 456	94.44 459	95.36 498	95.12 544	95.23 385	94.27 505	96.10 496	94.46 458	90.91 543	95.76 498	91.47 424	93.87 552	95.23 400	96.62 518	97.00 506

CostFormer		93.97 471	93.78 468	94.51 508	97.53 491	85.83 539	97.98 226	95.96 499	89.29 529	94.99 508	98.63 332	78.63 518	99.62 384	94.54 416	96.50 519	98.09 464

EPMVS		93.72 476	93.27 475	95.09 503	96.04 538	87.76 531	98.13 188	85.01 556	94.69 452	96.92 442	98.64 330	78.47 521	99.31 478	95.04 403	96.46 520	98.20 457

h-mvs33		97.77 299	97.33 325	99.10 131	99.21 244	97.84 197	98.35 163	98.57 405	99.11 101	98.58 306	99.02 214	88.65 452	99.96 13	98.11 159	96.34 521	99.49 178

TR-MVS		95.55 435	95.12 444	96.86 437	97.54 489	93.94 442	96.49 398	96.53 488	94.36 465	97.03 439	96.61 478	94.26 362	99.16 493	86.91 532	96.31 522	97.47 496

tpmvs		95.02 452	95.25 438	94.33 509	96.39 534	85.87 537	98.08 198	96.83 481	95.46 427	95.51 500	98.69 315	85.91 474	99.53 427	94.16 429	96.23 523	97.58 492

tpmrst		95.07 450	95.46 425	93.91 516	97.11 507	84.36 546	97.62 283	96.96 474	94.98 443	96.35 477	98.80 287	85.46 478	99.59 401	95.60 389	96.23 523	97.79 482

dmvs_re		95.98 418	95.39 430	97.74 365	98.86 339	97.45 241	98.37 161	95.69 508	97.95 235	96.56 466	95.95 492	90.70 433	97.68 530	88.32 526	96.13 525	98.11 462

wanda-best-256-512		95.48 438	94.74 453	97.68 371	96.53 526	94.12 428	94.17 508	98.57 405	95.84 405	96.71 457	91.16 545	86.05 471	99.76 275	97.57 221	96.09 526	99.17 330

FE-blended-shiyan7		95.48 438	94.74 453	97.68 371	96.53 526	94.12 428	94.17 508	98.57 405	95.84 405	96.71 457	91.16 545	86.05 471	99.76 275	97.57 221	96.09 526	99.17 330

blended_shiyan6		95.99 417	95.33 433	97.95 343	97.06 510	94.89 401	95.34 467	98.58 403	96.17 384	97.06 435	92.41 539	87.64 459	99.76 275	97.64 214	96.09 526	99.19 322

usedtu_blend_shiyan5		96.20 409	95.62 416	97.94 344	96.53 526	94.93 398	98.83 96	99.59 101	98.89 139	96.71 457	91.16 545	86.05 471	99.73 301	96.70 309	96.09 526	99.17 330

KD-MVS_2432*1600		92.87 493	91.99 495	95.51 493	91.37 553	89.27 524	94.07 511	98.14 431	95.42 429	97.25 426	96.44 483	67.86 536	99.24 486	91.28 503	96.08 530	98.02 467

miper_refine_blended		92.87 493	91.99 495	95.51 493	91.37 553	89.27 524	94.07 511	98.14 431	95.42 429	97.25 426	96.44 483	67.86 536	99.24 486	91.28 503	96.08 530	98.02 467

blended_shiyan8		95.98 418	95.33 433	97.94 344	97.05 512	94.87 403	95.34 467	98.59 402	96.17 384	97.09 433	92.39 540	87.62 460	99.76 275	97.65 212	96.05 532	99.20 316

BH-w/o		95.13 449	94.89 450	95.86 480	98.20 440	91.31 498	95.65 454	97.37 453	93.64 480	96.52 471	95.70 499	93.04 394	99.02 500	88.10 527	95.82 533	97.24 504

XFeat-NN		89.63 509	89.13 513	91.14 530	90.93 556	90.02 520	84.90 549	94.05 529	88.10 537	92.89 535	93.33 532	78.74 516	90.89 554	83.46 540	95.72 534	92.52 548

UnsupCasMVSNet_eth		97.89 280	97.60 306	98.75 214	99.31 212	97.17 275	97.62 283	99.35 230	98.72 158	98.76 272	98.68 317	92.57 403	99.74 293	97.76 202	95.60 535	99.34 264

PAPM		91.88 506	90.34 509	96.51 448	98.06 455	92.56 476	92.44 536	97.17 466	86.35 540	90.38 545	96.01 490	86.61 464	99.21 489	70.65 553	95.43 536	97.75 484

tpm cat1		93.29 483	93.13 479	93.75 518	97.39 500	84.74 542	97.39 317	97.65 447	83.39 546	94.16 520	98.41 364	82.86 501	99.39 466	91.56 499	95.35 537	97.14 505

tpm		94.67 456	94.34 462	95.66 488	97.68 483	88.42 527	97.88 240	94.90 515	94.46 458	96.03 487	98.56 344	78.66 517	99.79 251	95.88 372	95.01 538	98.78 403

JIA-IIPM		95.52 436	95.03 445	97.00 425	96.85 518	94.03 434	96.93 361	95.82 503	99.20 85	94.63 516	99.71 23	83.09 499	99.60 396	94.42 423	94.64 539	97.36 500

IB-MVS	91.63 19	92.24 501	90.90 506	96.27 458	97.22 505	91.24 502	94.36 502	93.33 534	92.37 501	92.24 540	94.58 523	66.20 543	99.89 98	93.16 463	94.63 540	97.66 489
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
GG-mvs-BLEND				94.76 505	94.54 546	92.13 486	99.31 30	80.47 559		88.73 549	91.01 548	67.59 539	98.16 523	82.30 545	94.53 541	93.98 531

test0.0.03 1		94.51 458	93.69 469	96.99 426	96.05 537	93.61 458	94.97 479	93.49 532	96.17 384	97.57 402	94.88 517	82.30 503	99.01 502	93.60 449	94.17 542	98.37 450

MonoMVSNet		96.25 406	96.53 386	95.39 496	96.57 525	91.01 506	98.82 97	97.68 446	98.57 175	98.03 365	99.37 105	90.92 430	97.78 529	94.99 404	93.88 543	97.38 499

DeepMVS_CX				93.44 523	98.24 436	94.21 424		94.34 522	64.28 553	91.34 542	94.87 519	89.45 446	92.77 553	77.54 549	93.14 544	93.35 538

dmvs_testset		92.94 491	92.21 491	95.13 501	98.59 397	90.99 507	97.65 278	92.09 539	96.95 338	94.00 526	93.55 528	92.34 407	96.97 540	72.20 551	92.52 545	97.43 498

tmp_tt		78.77 516	78.73 520	78.90 534	58.45 562	74.76 561	94.20 507	78.26 560	39.16 555	86.71 550	92.82 537	80.50 507	75.19 557	86.16 535	92.29 546	86.74 549

dp		93.47 479	93.59 471	93.13 527	96.64 524	81.62 557	97.66 276	96.42 490	92.80 497	96.11 481	98.64 330	78.55 520	99.59 401	93.31 458	92.18 547	98.16 460

baseline1		95.96 421	95.44 427	97.52 395	98.51 408	93.99 441	98.39 159	96.09 497	98.21 207	98.40 334	97.76 431	86.88 462	99.63 379	95.42 395	89.27 548	98.95 370

0.4-1-1-0.1		88.42 510	85.91 514	95.94 476	93.08 549	91.54 491	90.99 541	92.04 541	89.96 526	84.83 553	83.25 551	63.75 551	99.52 431	93.25 460	82.07 549	96.75 512

0.4-1-1-0.2		87.49 511	84.89 515	95.31 499	91.33 555	90.08 519	88.47 548	92.07 540	88.70 533	84.06 554	81.08 553	63.62 552	99.49 442	92.93 468	81.71 550	96.37 518

0.3-1-1-0.015		87.27 512	84.50 517	95.57 490	91.70 552	90.77 511	89.41 547	92.04 541	88.98 530	82.46 555	81.35 552	60.36 555	99.50 438	92.96 466	81.23 551	96.45 517

test_method		79.78 515	79.50 519	80.62 533	80.21 560	45.76 565	70.82 551	98.41 419	31.08 556	80.89 556	97.71 434	84.85 483	97.37 535	91.51 500	80.03 552	98.75 407

dongtai		76.24 517	75.95 521	77.12 535	92.39 551	67.91 562	90.16 543	59.44 564	82.04 547	89.42 547	94.67 522	49.68 559	81.74 556	48.06 557	77.66 553	81.72 550

blend_shiyan4		92.09 503	90.16 511	97.88 349	96.78 520	94.93 398	95.24 471	98.58 403	96.22 382	96.07 483	91.42 544	63.46 553	99.73 301	96.70 309	76.98 554	98.98 362

MVS_clip		56.94 520	60.93 523	44.97 538	71.47 561	51.70 564	61.73 552	21.77 565	28.88 557	86.09 552	92.75 538	48.89 560	27.00 560	61.70 555	75.08 555	56.23 553

PVSNet_0	89.98 21	91.15 507	90.30 510	93.70 519	97.72 475	84.34 547	90.24 542	97.42 452	90.20 523	93.79 529	93.09 533	90.90 431	98.89 509	86.57 534	72.76 556	97.87 476

kuosan		69.30 518	68.95 522	70.34 536	87.68 558	65.00 563	91.11 540	59.90 563	69.02 551	74.46 557	88.89 550	48.58 561	68.03 558	28.61 558	72.33 557	77.99 551

VLMVS_CLIP		57.57 519	58.80 524	53.85 537	47.22 563	42.89 566	60.06 553	76.87 561	39.44 554	65.76 558	80.47 554	36.24 562	64.75 559	58.06 556	65.11 558	53.91 554

MVS_baseline		25.61 522	31.27 527	8.63 540	32.09 565	3.00 570	22.13 555	5.43 568	1.36 562	58.03 559	69.99 555	18.40 564	0.00 564	18.79 560	55.18 559	22.88 556

VLMVS		32.15 521	34.06 525	26.43 539	35.38 564	29.60 567	32.69 554	19.27 566	3.29 561	44.01 560	60.07 556	35.02 563	20.44 561	22.64 559	54.15 560	29.25 555

testmvs		17.12 524	20.53 528	6.87 542	12.05 566	4.20 569	93.62 523	6.73 567	4.62 560	10.41 562	24.33 558	8.28 566	3.56 563	9.69 562	15.07 561	12.86 558

test123		17.04 525	20.11 529	7.82 541	10.25 567	4.91 568	94.80 483	4.47 569	4.93 559	10.00 563	24.28 559	9.69 565	3.64 562	10.14 561	12.43 562	14.92 557

mmdepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

monomultidepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

test_blank		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uanet_test		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

DCPMVS		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

cdsmvs_eth3d_5k		24.66 523	32.88 526	0.00 543	0.00 568	0.00 571	0.00 556	99.10 318	0.00 563	0.00 564	97.58 442	99.21 18	0.00 564	0.00 563	0.00 563	0.00 559

pcd_1.5k_mvsjas		8.17 526	10.90 530	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	98.07 129	0.00 564	0.00 563	0.00 563	0.00 559

sosnet-low-res		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

sosnet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uncertanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Regformer		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

ab-mvs-re		8.12 527	10.83 531	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	97.48 451	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Meshroom													0.00 564
: In preparation.
AliceVision / Meshro													0.00 564
: In preparation.
AliceVision_Meshroom													0.00 564
: In preparation.
PatchmatchNet2					0.00 568	90.12 517	94.29 504	98.12 433	94.40 463
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.85 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						90.90 508								91.37 502

FOURS1					99.73 38	99.67 2	99.43 15	99.54 133	99.43 55	99.26 159

test_one_0601					99.39 187	99.20 39		99.31 249	98.49 181	98.66 288	99.02 214	97.64 169

eth-test2					0.00 568

eth-test					0.00 568

test_241102_ONE					99.49 152	99.17 44		99.31 249	97.98 232	99.66 61	98.90 259	98.36 91	99.48 446

save fliter					99.11 276	97.97 179	96.53 395	99.02 336	98.24 202

test0726					99.50 143	99.21 33	98.17 184	99.35 230	97.97 233	99.26 159	99.06 201	97.61 174

GSMVS																98.81 396

test_part2					99.36 196	99.10 66				99.05 203

sam_mvs1												84.74 485				98.81 396

sam_mvs												84.29 491

MTGPA								99.20 292

test_post1							97.59 291				20.48 561	83.07 500	99.66 364	94.16 429

test_post											21.25 560	83.86 494	99.70 323

patchmatchnet-post											98.77 293	84.37 488	99.85 160

MTMP							97.93 232	91.91 544

gm-plane-assit					94.83 545	81.97 555			88.07 538		94.99 514		99.60 396	91.76 494

TEST9					98.71 368	98.08 163	95.96 438	99.03 333	91.40 512	95.85 488	97.53 445	96.52 260	99.76 275

test_8					98.67 382	98.01 172	95.91 444	99.02 336	91.64 507	95.79 491	97.50 449	96.47 262	99.76 275

agg_prior					98.68 381	97.99 175		99.01 339		95.59 492			99.77 269

test_prior4						97.97 179	95.86 445

test_prior				98.95 167	98.69 377	97.95 183		99.03 333					99.59 401			99.30 284

旧先验2							95.76 451		88.56 535	97.52 406			99.66 364	94.48 418

新几何2							95.93 441

无先验							95.74 452	98.74 390	89.38 528				99.73 301	92.38 486		99.22 311

原ACMM2							95.53 458

testdata2													99.79 251	92.80 474

segment_acmp												97.02 223

testdata1							95.44 463		96.32 378

plane_prior7					99.19 252	97.87 194

plane_prior6					98.99 313	97.70 219						94.90 334

plane_prior4											97.98 412

plane_prior3						97.78 209			97.41 296	97.79 386

plane_prior2							97.77 257		98.20 211

plane_prior1					99.05 293

n2								0.00 570

nn								0.00 570

door-mid								99.57 112

test11								98.87 362

door								99.41 207

HQP5-MVS						96.79 301

HQP-NCC					98.67 382		96.29 413		96.05 392	95.55 495

ACMP_Plane					98.67 382		96.29 413		96.05 392	95.55 495

BP-MVS														92.82 472

HQP4-MVS										95.56 494			99.54 424			99.32 275

HQP2-MVS												93.84 372

NP-MVS					98.84 343	97.39 246					96.84 472

MDTV_nov1_ep13_2view						74.92 560	97.69 271		90.06 525	97.75 389		85.78 475		93.52 452		98.69 415

Test By Simon												96.52 260

High-resolution multi-view benchmark