High-res multi-view results

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

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

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

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

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

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

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

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 4	99.98 3	99.76 71	99.12 245	100.00 1	100.00 1	99.99 7	99.91 32	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_fmvsm_n_1920		99.84 18	99.85 17	99.83 42	99.82 100	99.70 110	99.17 220	99.97 22	99.99 4	99.96 34	99.82 92	99.94 4	100.00 1	99.95 15	100.00 1	99.80 68

h-mvs33		98.61 352	98.34 377	99.44 264	99.60 271	98.67 365	99.27 181	99.44 364	99.68 137	99.32 353	99.49 351	92.50 466	100.00 1	99.24 140	96.51 541	99.65 159

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 22	99.99 4	100.00 1	99.98 13	99.78 24	100.00 1	99.92 31	100.00 1	99.87 46

DSMNet-mixed		99.48 136	99.65 75	98.95 386	99.71 208	97.27 467	99.50 102	99.82 123	99.59 180	99.41 328	99.85 69	99.62 41	100.00 1	99.53 91	99.89 193	99.59 216

HyFIR lowres test		98.91 316	98.64 333	99.73 114	99.85 76	99.47 190	98.07 445	99.83 116	98.64 357	99.89 73	99.60 296	92.57 462	100.00 1	99.33 127	99.97 78	99.72 100

fmvsm_l_mol_unc0.5_1		99.85 12	99.82 25	99.94 2	99.93 24	99.86 18	98.72 357	99.99 12	100.00 1	99.93 53	99.95 16	99.94 4	99.99 7	99.96 9	99.99 19	99.97 10

NormalMVS		99.09 275	98.91 305	99.62 185	99.78 147	99.11 296	99.36 144	99.77 171	99.82 86	99.68 209	99.53 335	93.30 451	99.99 7	99.24 140	99.76 297	99.74 92

SymmetryMVS		99.01 298	98.82 315	99.58 203	99.65 255	99.11 296	99.36 144	99.20 434	99.82 86	99.68 209	99.53 335	93.30 451	99.99 7	99.24 140	99.63 364	99.64 171

Elysia		99.69 60	99.65 75	99.81 55	99.86 61	99.72 96	99.34 149	99.77 171	99.94 37	99.91 63	99.76 156	98.55 221	99.99 7	99.70 62	99.98 55	99.72 100

StellarMVS		99.69 60	99.65 75	99.81 55	99.86 61	99.72 96	99.34 149	99.77 171	99.94 37	99.91 63	99.76 156	98.55 221	99.99 7	99.70 62	99.98 55	99.72 100

fmvsm_s_conf0.5_n_5		99.78 38	99.76 50	99.85 33	99.79 138	99.72 96	98.84 332	99.96 31	99.96 29	99.96 34	99.72 187	99.71 29	99.99 7	99.93 26	99.98 55	99.85 51

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 3	99.88 47	99.86 18	99.08 262	99.97 22	99.98 19	99.96 34	99.79 121	99.90 10	99.99 7	99.96 9	99.99 19	99.90 31

fmvsm_l_conf0.5_n_a		99.80 31	99.79 35	99.84 39	99.88 47	99.64 137	99.12 245	99.91 58	99.98 19	99.95 45	99.67 235	99.67 35	99.99 7	99.94 21	99.99 19	99.88 42

fmvsm_l_conf0.5_n		99.80 31	99.78 40	99.85 33	99.88 47	99.66 124	99.11 250	99.91 58	99.98 19	99.96 34	99.64 250	99.60 45	99.99 7	99.95 15	99.99 19	99.88 42

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 4	99.97 6	99.74 88	99.01 286	99.99 12	99.99 4	99.98 14	99.88 51	99.97 2	99.99 7	99.96 9	100.00 1	99.98 5

SSC-MVS		99.52 123	99.42 153	99.83 42	99.86 61	99.65 130	99.52 94	99.81 136	99.87 63	99.81 120	99.79 121	96.78 371	99.99 7	99.83 47	99.51 401	99.86 48

test_fmvsmconf_n		99.85 12	99.84 20	99.88 20	99.91 32	99.73 91	98.97 305	99.98 14	99.99 4	99.96 34	99.85 69	99.93 8	99.99 7	99.94 21	99.99 19	99.93 22

test_fmvsmvis_n_1920		99.84 18	99.86 13	99.81 55	99.88 47	99.55 174	99.17 220	99.98 14	99.99 4	99.96 34	99.84 77	99.96 3	99.99 7	99.96 9	99.99 19	99.88 42

SDMVSNet		99.77 45	99.77 46	99.76 88	99.80 124	99.65 130	99.63 64	99.86 90	99.97 26	99.89 73	99.89 42	99.52 61	99.99 7	99.42 112	99.96 92	99.65 159

sd_testset		99.78 38	99.78 40	99.80 65	99.80 124	99.76 71	99.80 14	99.79 153	99.97 26	99.89 73	99.89 42	99.53 59	99.99 7	99.36 120	99.96 92	99.65 159

test_vis1_n_1920		99.72 54	99.88 7	99.27 336	99.93 24	97.84 439	99.34 149	100.00 1	99.99 4	99.99 7	99.82 92	99.87 14	99.99 7	99.97 4	99.99 19	99.97 10

test_fmvs3		99.83 22	99.93 2	99.53 233	99.96 7	98.62 378	99.67 53	100.00 1	99.95 33	100.00 1	99.95 16	99.85 15	99.99 7	99.98 1	99.99 19	99.98 5

dcpmvs_2		99.61 99	99.64 80	99.53 233	99.79 138	98.82 350	99.58 82	99.97 22	99.95 33	99.96 34	99.76 156	98.44 246	99.99 7	99.34 124	99.96 92	99.78 78

IterMVS-SCA-FT		99.00 301	99.16 219	98.51 438	99.75 183	95.90 503	98.07 445	99.84 106	99.84 76	99.89 73	99.73 177	96.01 404	99.99 7	99.33 127	100.00 1	99.63 177

IterMVS		98.97 305	99.16 219	98.42 443	99.74 194	95.64 510	98.06 447	99.83 116	99.83 82	99.85 102	99.74 172	96.10 403	99.99 7	99.27 139	100.00 1	99.63 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PDCNetPlus		98.55 362	98.50 354	98.69 428	99.64 257	96.12 498	97.67 480	100.00 1	98.34 402	99.79 134	99.75 164	92.45 468	99.98 27	98.92 216	99.99 19	99.96 14

fmvsm_s_conf0.5_n_11		99.76 47	99.75 52	99.81 55	99.81 113	99.53 177	99.15 229	99.89 69	99.99 4	99.98 14	99.86 64	99.13 121	99.98 27	99.93 26	99.99 19	99.92 26

fmvsm_s_conf0.5_n_10		99.77 45	99.73 55	99.88 20	99.81 113	99.75 80	99.06 268	99.85 96	99.99 4	99.97 24	99.84 77	99.12 124	99.98 27	99.95 15	99.99 19	99.90 31

fmvsm_l_conf0.5_n_9		99.83 22	99.81 29	99.89 12	99.86 61	99.80 52	98.94 314	99.96 31	99.98 19	99.96 34	99.78 134	99.88 12	99.98 27	99.96 9	99.99 19	99.90 31

GDP-MVS		98.81 332	98.57 343	99.50 241	99.53 326	99.12 295	99.28 177	99.86 90	99.53 188	99.57 267	99.32 404	90.88 489	99.98 27	99.46 102	99.74 312	99.42 325

WB-MVS		99.44 156	99.32 182	99.80 65	99.81 113	99.61 155	99.47 112	99.81 136	99.82 86	99.71 194	99.72 187	96.60 377	99.98 27	99.75 57	99.23 447	99.82 67

test_fmvs1_n		99.68 65	99.81 29	99.28 330	99.95 15	97.93 435	99.49 107	100.00 1	99.82 86	99.99 7	99.89 42	99.21 106	99.98 27	99.97 4	99.98 55	99.93 22

test_fmvs2		99.72 54	99.85 17	99.34 310	99.91 32	98.08 426	99.48 109	100.00 1	99.90 50	99.99 7	99.91 32	99.50 63	99.98 27	99.98 1	99.99 19	99.96 14

patch_mono-2		99.51 125	99.46 139	99.64 168	99.70 224	99.11 296	99.04 274	99.87 81	99.71 123	99.47 308	99.79 121	98.24 272	99.98 27	99.38 116	99.96 92	99.83 60

CHOSEN 280x420		98.41 380	98.41 367	98.40 444	99.34 400	95.89 504	96.94 520	99.44 364	98.80 334	99.25 371	99.52 339	93.51 450	99.98 27	98.94 213	99.98 55	99.32 356

Fast-Effi-MVS+-dtu		99.20 240	99.12 231	99.43 268	99.25 423	99.69 115	99.05 269	99.82 123	99.50 193	98.97 414	99.05 459	98.98 156	99.98 27	98.20 302	99.24 445	98.62 479

Effi-MVS+-dtu		99.07 280	98.92 301	99.52 235	98.89 483	99.78 58	99.15 229	99.66 241	99.34 235	98.92 421	99.24 430	97.69 323	99.98 27	98.11 312	99.28 437	98.81 467

PS-MVSNAJss		99.84 18	99.82 25	99.89 12	99.96 7	99.77 64	99.68 48	99.85 96	99.95 33	99.98 14	99.92 28	99.28 94	99.98 27	99.75 57	100.00 1	99.94 19

jajsoiax		99.89 3	99.89 6	99.89 12	99.96 7	99.78 58	99.70 38	99.86 90	99.89 56	99.98 14	99.90 37	99.94 4	99.98 27	99.75 57	100.00 1	99.90 31

mvs_tets		99.90 2	99.90 4	99.90 9	99.96 7	99.79 55	99.72 33	99.88 75	99.92 46	99.98 14	99.93 23	99.94 4	99.98 27	99.77 56	100.00 1	99.92 26

MVSFormer		99.41 171	99.44 147	99.31 322	99.57 297	98.40 400	99.77 19	99.80 144	99.73 113	99.63 239	99.30 410	98.02 297	99.98 27	99.43 107	99.69 343	99.55 237

test_djsdf		99.84 18	99.81 29	99.91 4	99.94 18	99.84 27	99.77 19	99.80 144	99.73 113	99.97 24	99.92 28	99.77 26	99.98 27	99.43 107	100.00 1	99.90 31

Vis-MVSNet		99.75 50	99.74 54	99.79 73	99.88 47	99.66 124	99.69 45	99.92 48	99.67 145	99.77 152	99.75 164	99.61 42	99.98 27	99.35 123	99.98 55	99.72 100
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SSM_04072		99.55 112	99.55 113	99.55 222	99.71 208	99.24 265	99.27 181	99.79 153	99.72 117	99.78 140	99.64 250	99.36 82	99.97 45	98.74 241	99.90 177	99.45 298

KinetiMVS		99.66 78	99.63 83	99.76 88	99.89 41	99.57 169	99.37 140	99.82 123	99.95 33	99.90 68	99.63 266	98.57 217	99.97 45	99.65 71	99.94 136	99.74 92

LuminaMVS		99.39 177	99.28 198	99.73 114	99.83 91	99.49 185	99.00 292	99.05 450	99.81 92	99.89 73	99.79 121	96.54 381	99.97 45	99.64 74	99.98 55	99.73 96

fmvsm_s_conf0.5_n_8		99.76 47	99.72 56	99.88 20	99.82 100	99.75 80	99.02 281	99.87 81	99.98 19	99.98 14	99.81 99	99.07 135	99.97 45	99.91 34	99.99 19	99.92 26

mvs5depth		99.88 6	99.91 3	99.80 65	99.92 30	99.42 213	99.94 3	100.00 1	99.97 26	99.89 73	99.99 12	99.63 38	99.97 45	99.87 45	99.99 19	100.00 1

test_cas_vis1_n_1920		99.76 47	99.86 13	99.45 260	99.93 24	98.40 400	99.30 167	99.98 14	99.94 37	99.99 7	99.89 42	99.80 22	99.97 45	99.96 9	99.97 78	99.97 10

test_fmvs1		99.48 136	99.65 75	98.97 383	99.54 317	97.16 470	99.11 250	99.98 14	99.78 103	99.96 34	99.81 99	98.72 196	99.97 45	99.95 15	99.97 78	99.79 76

Anonymous20240521		99.44 156	99.42 153	99.49 245	99.89 41	98.96 324	99.62 67	99.76 179	99.85 72	99.82 113	99.88 51	96.39 388	99.97 45	99.59 79	99.98 55	99.55 237

xiu_mvs_v1_base_debu		99.23 224	99.34 176	98.91 397	99.59 277	98.23 409	98.47 401	99.66 241	99.61 171	99.68 209	98.94 478	99.39 72	99.97 45	99.18 156	99.55 390	98.51 489

xiu_mvs_v2_base		99.02 292	99.11 234	98.77 419	99.37 384	98.09 423	98.13 436	99.51 343	99.47 203	99.42 322	98.54 507	99.38 77	99.97 45	98.83 223	99.33 430	98.24 503

xiu_mvs_v1_base		99.23 224	99.34 176	98.91 397	99.59 277	98.23 409	98.47 401	99.66 241	99.61 171	99.68 209	98.94 478	99.39 72	99.97 45	99.18 156	99.55 390	98.51 489

xiu_mvs_v1_base_debi		99.23 224	99.34 176	98.91 397	99.59 277	98.23 409	98.47 401	99.66 241	99.61 171	99.68 209	98.94 478	99.39 72	99.97 45	99.18 156	99.55 390	98.51 489

anonymousdsp		99.80 31	99.77 46	99.90 9	99.96 7	99.88 12	99.73 30	99.85 96	99.70 130	99.92 60	99.93 23	99.45 64	99.97 45	99.36 120	100.00 1	99.85 51

UA-Net		99.78 38	99.76 50	99.86 31	99.72 203	99.71 102	99.91 4	99.95 39	99.96 29	99.71 194	99.91 32	99.15 116	99.97 45	99.50 96	100.00 1	99.90 31

PS-MVSNAJ		99.00 301	99.08 247	98.76 420	99.37 384	98.10 422	98.00 454	99.51 343	99.47 203	99.41 328	98.50 509	99.28 94	99.97 45	98.83 223	99.34 429	98.20 507

pmmvs3		98.08 410	97.80 423	98.91 397	99.41 376	97.69 447	97.87 467	99.66 241	95.87 510	99.50 301	99.51 343	90.35 498	99.97 45	98.55 270	99.47 409	99.08 421

DTE-MVSNet		99.68 65	99.61 90	99.88 20	99.80 124	99.87 15	99.67 53	99.71 209	99.72 117	99.84 105	99.78 134	98.67 203	99.97 45	99.30 132	99.95 117	99.80 68

jason		99.16 255	99.11 234	99.32 318	99.75 183	98.44 397	98.26 422	99.39 381	98.70 349	99.74 177	99.30 410	98.54 226	99.97 45	98.48 275	99.82 257	99.55 237
jason: jason.
lupinMVS		98.96 308	98.87 308	99.24 345	99.57 297	98.40 400	98.12 438	99.18 437	98.28 409	99.63 239	99.13 446	98.02 297	99.97 45	98.22 300	99.69 343	99.35 345

K. test v3		98.87 324	98.60 337	99.69 140	99.93 24	99.46 198	99.74 27	94.97 544	99.78 103	99.88 83	99.88 51	93.66 448	99.97 45	99.61 77	99.95 117	99.64 171

lessismore_v0				99.64 168	99.86 61	99.38 227		90.66 555		99.89 73	99.83 84	94.56 435	99.97 45	99.56 84	99.92 159	99.57 229

EPNet		98.13 407	97.77 427	99.18 354	94.57 557	97.99 429	99.24 193	97.96 509	99.74 112	97.29 521	99.62 276	93.13 455	99.97 45	98.59 265	99.83 247	99.58 222
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		99.40 173	99.38 162	99.44 264	99.90 38	98.66 368	98.94 314	99.91 58	97.97 433	99.79 134	99.73 177	99.05 144	99.97 45	99.15 165	99.99 19	99.68 127

IterMVS-LS		99.41 171	99.47 133	99.25 343	99.81 113	98.09 423	98.85 329	99.76 179	99.62 166	99.83 111	99.64 250	98.54 226	99.97 45	99.15 165	99.99 19	99.68 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ANet_high		99.88 6	99.87 11	99.91 4	99.99 1	99.91 4	99.65 62	100.00 1	99.90 50	100.00 1	99.97 14	99.61 42	99.97 45	99.75 57	100.00 1	99.84 56

usedtu_dtu_shiyan2		99.44 156	99.33 181	99.78 77	99.86 61	99.76 71	99.54 90	99.79 153	99.66 152	99.66 224	99.79 121	96.76 372	99.96 70	99.15 165	99.72 327	99.62 189

fmvsm_s_conf0.5_n_7		99.73 53	99.78 40	99.60 196	99.74 194	98.93 330	98.85 329	99.96 31	99.96 29	99.97 24	99.76 156	99.82 19	99.96 70	99.95 15	99.98 55	99.90 31

BP-MVS1		98.72 342	98.46 358	99.50 241	99.53 326	99.00 314	99.34 149	98.53 481	99.65 157	99.73 183	99.38 385	90.62 494	99.96 70	99.50 96	99.86 226	99.55 237

MVSMamba_PlusPlus		99.55 112	99.58 101	99.47 253	99.68 241	99.40 221	99.52 94	99.70 218	99.92 46	99.77 152	99.86 64	98.28 268	99.96 70	99.54 88	99.90 177	99.05 430

test_vis1_n		99.68 65	99.79 35	99.36 302	99.94 18	98.18 415	99.52 94	100.00 1	99.86 66	100.00 1	99.88 51	98.99 152	99.96 70	99.97 4	99.96 92	99.95 16

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 9	99.89 41	99.91 4	99.89 5	99.71 209	99.93 44	99.95 45	99.89 42	99.71 29	99.96 70	99.51 94	99.97 78	99.84 56

v7n		99.82 25	99.80 33	99.88 20	99.96 7	99.84 27	99.82 10	99.82 123	99.84 76	99.94 48	99.91 32	99.13 121	99.96 70	99.83 47	99.99 19	99.83 60

PS-CasMVS		99.66 78	99.58 101	99.89 12	99.80 124	99.85 22	99.66 57	99.73 196	99.62 166	99.84 105	99.71 197	98.62 209	99.96 70	99.30 132	99.96 92	99.86 48

PEN-MVS		99.66 78	99.59 97	99.89 12	99.83 91	99.87 15	99.66 57	99.73 196	99.70 130	99.84 105	99.73 177	98.56 220	99.96 70	99.29 135	99.94 136	99.83 60

TranMVSNet+NR-MVSNet		99.54 117	99.47 133	99.76 88	99.58 287	99.64 137	99.30 167	99.63 263	99.61 171	99.71 194	99.56 321	98.76 189	99.96 70	99.14 172	99.92 159	99.68 127

IB-MVS	95.41 20	95.30 505	94.46 511	97.84 474	98.76 502	95.33 516	97.33 499	96.07 533	96.02 509	95.37 542	97.41 533	76.17 547	99.96 70	97.54 379	95.44 548	98.22 504
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
OpenMVS	98.12 10	98.23 397	97.89 420	99.26 340	99.19 435	99.26 257	99.65 62	99.69 227	91.33 543	98.14 488	99.77 146	98.28 268	99.96 70	95.41 503	99.55 390	98.58 484

IMVS_0404		99.23 224	99.20 214	99.32 318	99.71 208	98.55 386	98.57 383	99.71 209	99.41 223	99.52 291	99.60 296	98.12 288	99.95 82	98.45 278	99.70 334	99.45 298

fmvsm_s_conf0.5_n_3		99.79 35	99.77 46	99.85 33	99.81 113	99.71 102	98.97 305	99.92 48	99.98 19	99.97 24	99.86 64	99.53 59	99.95 82	99.88 42	99.99 19	99.89 39

MM		99.18 247	99.05 260	99.55 222	99.35 391	98.81 351	99.05 269	97.79 516	99.99 4	99.48 306	99.59 306	96.29 395	99.95 82	99.94 21	99.98 55	99.88 42

GeoE		99.69 60	99.66 73	99.78 77	99.76 165	99.76 71	99.60 79	99.82 123	99.46 206	99.75 166	99.56 321	99.63 38	99.95 82	99.43 107	99.88 204	99.62 189

CS-MVS		99.67 77	99.70 58	99.58 203	99.53 326	99.84 27	99.79 15	99.96 31	99.90 50	99.61 256	99.41 371	99.51 62	99.95 82	99.66 70	99.89 193	98.96 446

CANet_DTU		98.91 316	98.85 310	99.09 367	98.79 497	98.13 418	98.18 427	99.31 407	99.48 198	98.86 429	99.51 343	96.56 378	99.95 82	99.05 189	99.95 117	99.19 388

MGCNet		98.61 352	98.30 382	99.52 235	97.88 536	98.95 325	98.76 349	94.11 549	99.84 76	99.32 353	99.57 317	95.57 414	99.95 82	99.68 67	99.98 55	99.68 127

SPE-MVS-test		99.68 65	99.70 58	99.64 168	99.57 297	99.83 34	99.78 17	99.97 22	99.92 46	99.50 301	99.38 385	99.57 53	99.95 82	99.69 65	99.90 177	99.15 397

Fast-Effi-MVS+		99.02 292	98.87 308	99.46 257	99.38 381	99.50 184	99.04 274	99.79 153	97.17 484	98.62 453	98.74 493	99.34 86	99.95 82	98.32 291	99.41 420	98.92 454

MTAPA		99.35 192	99.20 214	99.80 65	99.81 113	99.81 48	99.33 155	99.53 333	99.27 247	99.42 322	99.63 266	98.21 278	99.95 82	97.83 345	99.79 280	99.65 159

UniMVSNet_NR-MVSNet		99.37 185	99.25 207	99.72 123	99.47 357	99.56 170	98.97 305	99.61 274	99.43 218	99.67 217	99.28 416	97.85 311	99.95 82	99.17 160	99.81 267	99.65 159

DU-MVS		99.33 200	99.21 213	99.71 129	99.43 369	99.56 170	98.83 335	99.53 333	99.38 229	99.67 217	99.36 394	97.67 325	99.95 82	99.17 160	99.81 267	99.63 177

CP-MVSNet		99.54 117	99.43 150	99.87 27	99.76 165	99.82 42	99.57 85	99.61 274	99.54 186	99.80 127	99.64 250	97.79 315	99.95 82	99.21 147	99.94 136	99.84 56

Patchmtry		98.78 334	98.54 348	99.49 245	98.89 483	99.19 281	99.32 158	99.67 236	99.65 157	99.72 189	99.79 121	91.87 475	99.95 82	98.00 322	99.97 78	99.33 352

QAPM		98.40 382	97.99 407	99.65 161	99.39 378	99.47 190	99.67 53	99.52 338	91.70 542	98.78 440	99.80 109	98.55 221	99.95 82	94.71 515	99.75 305	99.53 258

3Dnovator	99.15 2	99.43 160	99.36 170	99.65 161	99.39 378	99.42 213	99.70 38	99.56 309	99.23 256	99.35 344	99.80 109	99.17 112	99.95 82	98.21 301	99.84 239	99.59 216

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 20	99.91 32	99.90 7	99.96 1	99.92 48	99.90 50	99.97 24	99.87 57	99.81 21	99.95 82	99.54 88	99.99 19	99.80 68
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
fmvsm_s_conf0.5_n_9		99.82 25	99.82 25	99.82 47	99.83 91	99.59 161	98.97 305	99.92 48	99.99 4	99.97 24	99.84 77	99.90 10	99.94 99	99.94 21	99.99 19	99.92 26

fmvsm_s_conf0.5_n_2		99.78 38	99.75 52	99.88 20	99.82 100	99.76 71	98.88 323	99.92 48	99.98 19	99.98 14	99.85 69	99.42 70	99.94 99	99.93 26	99.98 55	99.94 19

fmvsm_s_conf0.1_n_2		99.81 29	99.78 40	99.89 12	99.93 24	99.76 71	98.92 318	99.98 14	99.99 4	99.99 7	99.88 51	99.43 68	99.94 99	99.94 21	99.99 19	99.99 2

mmtdpeth		99.78 38	99.83 21	99.66 154	99.85 76	99.05 309	99.79 15	99.97 22	100.00 1	99.43 319	99.94 20	99.64 36	99.94 99	99.83 47	99.99 19	99.98 5

mvsany_test3		99.85 12	99.88 7	99.75 99	99.95 15	99.37 232	99.53 92	99.98 14	99.77 108	99.99 7	99.95 16	99.85 15	99.94 99	99.95 15	99.98 55	99.94 19

test_f		99.75 50	99.88 7	99.37 296	99.96 7	98.21 412	99.51 101	100.00 1	99.94 37	100.00 1	99.93 23	99.58 51	99.94 99	99.97 4	99.99 19	99.97 10

test_method		91.72 514	92.32 514	89.91 534	93.49 558	70.18 562	90.28 548	99.56 309	61.71 554	95.39 541	99.52 339	93.90 442	99.94 99	98.76 239	98.27 509	99.62 189

tttt0517		97.62 435	97.20 449	98.90 403	99.76 165	97.40 462	99.48 109	94.36 546	99.06 290	99.70 198	99.49 351	84.55 525	99.94 99	98.73 246	99.65 359	99.36 342

CANet		99.11 271	99.05 260	99.28 330	98.83 491	98.56 384	98.71 361	99.41 371	99.25 252	99.23 375	99.22 433	97.66 329	99.94 99	99.19 153	99.97 78	99.33 352

patchmatchnet-post											99.62 276	90.58 495	99.94 99

SCA		98.11 408	98.36 374	97.36 496	99.20 433	92.99 536	98.17 430	98.49 485	98.24 411	99.10 401	99.57 317	96.01 404	99.94 99	96.86 432	99.62 366	99.14 402

balanced_ft_v1		99.37 185	99.36 170	99.38 291	99.10 454	99.38 227	99.68 48	99.72 205	99.72 117	99.36 340	99.77 146	97.66 329	99.94 99	99.52 92	99.73 319	98.83 465

ADS-MVSNet2		97.78 428	97.66 433	98.12 462	99.14 443	95.36 515	99.22 202	98.75 468	96.97 492	98.25 475	99.64 250	90.90 487	99.94 99	96.51 456	99.56 386	99.08 421

WR-MVS_H		99.61 99	99.53 121	99.87 27	99.80 124	99.83 34	99.67 53	99.75 185	99.58 182	99.85 102	99.69 216	98.18 283	99.94 99	99.28 137	99.95 117	99.83 60

mvsmamba		99.08 276	98.95 295	99.45 260	99.36 387	99.18 287	99.39 129	98.81 465	99.37 230	99.35 344	99.70 207	96.36 390	99.94 99	98.66 258	99.59 381	99.22 377

SixPastTwentyTwo		99.42 164	99.30 189	99.76 88	99.92 30	99.67 121	99.70 38	99.14 443	99.65 157	99.89 73	99.90 37	96.20 399	99.94 99	99.42 112	99.92 159	99.67 136

CP-MVS		99.23 224	99.05 260	99.75 99	99.66 251	99.66 124	99.38 132	99.62 266	98.38 390	99.06 406	99.27 418	98.79 183	99.94 99	97.51 382	99.82 257	99.66 150

SteuartSystems-ACMMP		99.30 205	99.14 224	99.76 88	99.87 56	99.66 124	99.18 215	99.60 286	98.55 368	99.57 267	99.67 235	99.03 147	99.94 99	97.01 422	99.80 274	99.69 120
Skip Steuart: Steuart Systems R&D Blog.
PatchT		98.45 376	98.32 379	98.83 412	98.94 477	98.29 407	99.24 193	98.82 463	99.84 76	99.08 402	99.76 156	91.37 479	99.94 99	98.82 225	99.00 465	98.26 501

new_pmnet		98.88 323	98.89 306	98.84 410	99.70 224	97.62 449	98.15 433	99.50 347	97.98 432	99.62 249	99.54 332	98.15 284	99.94 99	97.55 378	99.84 239	98.95 448

wuyk23d		97.58 437	99.13 227	92.93 532	99.69 232	99.49 185	99.52 94	99.77 171	97.97 433	99.96 34	99.79 121	99.84 17	99.94 99	95.85 491	99.82 257	79.36 553

3Dnovator+	98.92 3	99.35 192	99.24 209	99.67 146	99.35 391	99.47 190	99.62 67	99.50 347	99.44 211	99.12 398	99.78 134	98.77 188	99.94 99	97.87 335	99.72 327	99.62 189

PatchmatchNet3													99.93 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
dtuonlycased		99.24 221	99.47 133	98.56 437	99.90 38	96.17 497	97.62 484	99.85 96	99.66 152	99.86 97	99.50 346	99.39 72	99.93 121	99.55 86	99.85 233	99.59 216

LoFTR		99.29 207	99.26 203	99.36 302	99.70 224	99.05 309	98.66 367	99.95 39	98.85 323	99.86 97	99.75 164	98.14 285	99.93 121	98.54 272	99.91 173	99.10 409

mamba_0408		99.54 117	99.55 113	99.54 228	99.71 208	99.24 265	99.27 181	99.79 153	99.72 117	99.78 140	99.64 250	99.36 82	99.93 121	98.74 241	99.90 177	99.45 298

SSM_0407		99.56 107	99.56 111	99.54 228	99.71 208	99.24 265	99.15 229	99.84 106	99.80 96	99.78 140	99.70 207	99.44 66	99.93 121	98.74 241	99.90 177	99.45 298

SSM_0404		99.57 103	99.58 101	99.54 228	99.76 165	99.28 251	99.19 211	99.84 106	99.80 96	99.78 140	99.70 207	99.44 66	99.93 121	98.74 241	99.95 117	99.41 326

AstraMVS		99.15 259	99.06 253	99.42 271	99.85 76	98.59 381	99.13 240	97.26 526	99.84 76	99.87 93	99.77 146	96.11 401	99.93 121	99.71 61	99.96 92	99.74 92

fmvsm_s_conf0.5_n_6		99.80 31	99.78 40	99.85 33	99.78 147	99.78 58	99.00 292	99.97 22	99.96 29	99.97 24	99.56 321	99.92 9	99.93 121	99.91 34	99.99 19	99.83 60

reproduce_model		99.50 128	99.40 158	99.83 42	99.60 271	99.83 34	99.12 245	99.68 231	99.49 195	99.80 127	99.79 121	99.01 149	99.93 121	98.24 298	99.82 257	99.73 96

reproduce-ours		99.46 148	99.35 174	99.82 47	99.56 311	99.83 34	99.05 269	99.65 251	99.45 209	99.78 140	99.78 134	98.93 162	99.93 121	98.11 312	99.81 267	99.70 108

our_new_method		99.46 148	99.35 174	99.82 47	99.56 311	99.83 34	99.05 269	99.65 251	99.45 209	99.78 140	99.78 134	98.93 162	99.93 121	98.11 312	99.81 267	99.70 108

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 4	99.95 15	99.82 42	99.10 254	99.98 14	99.99 4	99.98 14	99.91 32	99.68 34	99.93 121	99.93 26	99.99 19	99.99 2

fmvsm_s_conf0.5_n_a		99.82 25	99.79 35	99.89 12	99.85 76	99.82 42	99.03 277	99.96 31	99.99 4	99.97 24	99.84 77	99.58 51	99.93 121	99.92 31	99.98 55	99.93 22

mvsany_test1		99.44 156	99.45 142	99.40 284	99.37 384	98.64 375	97.90 466	99.59 292	99.27 247	99.92 60	99.82 92	99.74 27	99.93 121	99.55 86	99.87 218	99.63 177

ETV-MVS		99.18 247	99.18 217	99.16 355	99.34 400	99.28 251	99.12 245	99.79 153	99.48 198	98.93 418	98.55 506	99.40 71	99.93 121	98.51 274	99.52 400	98.28 499

thisisatest0530		97.45 445	96.95 459	98.94 387	99.68 241	97.73 445	99.09 259	94.19 548	98.61 363	99.56 275	99.30 410	84.30 527	99.93 121	98.27 295	99.54 395	99.16 395

our_test_3		98.85 328	99.09 245	98.13 461	99.66 251	94.90 524	97.72 475	99.58 301	99.07 288	99.64 234	99.62 276	98.19 281	99.93 121	98.41 283	99.95 117	99.55 237

MSP-MVS		99.04 288	98.79 321	99.81 55	99.78 147	99.73 91	99.35 148	99.57 304	98.54 371	99.54 284	98.99 468	96.81 370	99.93 121	96.97 425	99.53 397	99.77 82
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
region2R		99.23 224	99.05 260	99.77 81	99.76 165	99.70 110	99.31 164	99.59 292	98.41 385	99.32 353	99.36 394	98.73 195	99.93 121	97.29 398	99.74 312	99.67 136

RRT-MVS		99.08 276	99.00 279	99.33 313	99.27 419	98.65 372	99.62 67	99.93 44	99.66 152	99.67 217	99.82 92	95.27 423	99.93 121	98.64 262	99.09 457	99.41 326

APDe-MVS		99.48 136	99.36 170	99.85 33	99.55 315	99.81 48	99.50 102	99.69 227	98.99 298	99.75 166	99.71 197	98.79 183	99.93 121	98.46 277	99.85 233	99.80 68
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CVMVSNet		98.61 352	98.88 307	97.80 475	99.58 287	93.60 534	99.26 186	99.64 259	99.66 152	99.72 189	99.67 235	93.26 453	99.93 121	99.30 132	99.81 267	99.87 46

ACMMPR		99.23 224	99.06 253	99.76 88	99.74 194	99.69 115	99.31 164	99.59 292	98.36 392	99.35 344	99.38 385	98.61 211	99.93 121	97.43 387	99.75 305	99.67 136

PGM-MVS		99.20 240	99.01 275	99.77 81	99.75 183	99.71 102	99.16 226	99.72 205	97.99 431	99.42 322	99.60 296	98.81 178	99.93 121	96.91 429	99.74 312	99.66 150

LCM-MVSNet-Re		99.28 209	99.15 223	99.67 146	99.33 405	99.76 71	99.34 149	99.97 22	98.93 311	99.91 63	99.79 121	98.68 200	99.93 121	96.80 438	99.56 386	99.30 363

PMMVS2		99.48 136	99.45 142	99.57 211	99.76 165	98.99 316	98.09 442	99.90 65	98.95 305	99.78 140	99.58 309	99.57 53	99.93 121	99.48 98	99.95 117	99.79 76

mPP-MVS		99.19 243	99.00 279	99.76 88	99.76 165	99.68 118	99.38 132	99.54 322	98.34 402	99.01 411	99.50 346	98.53 231	99.93 121	97.18 415	99.78 288	99.66 150

OurMVSNet-221017-0		99.75 50	99.71 57	99.84 39	99.96 7	99.83 34	99.83 7	99.85 96	99.80 96	99.93 53	99.93 23	98.54 226	99.93 121	99.59 79	99.98 55	99.76 87

CHOSEN 1792x2688		99.39 177	99.30 189	99.65 161	99.88 47	99.25 260	98.78 347	99.88 75	98.66 354	99.96 34	99.79 121	97.45 338	99.93 121	99.34 124	99.99 19	99.78 78

N_pmnet		98.73 341	98.53 349	99.35 306	99.72 203	98.67 365	98.34 413	94.65 545	98.35 398	99.79 134	99.68 229	98.03 296	99.93 121	98.28 293	99.92 159	99.44 313

UGNet		99.38 180	99.34 176	99.49 245	98.90 479	98.90 336	99.70 38	99.35 392	99.86 66	98.57 459	99.81 99	98.50 238	99.93 121	99.38 116	99.98 55	99.66 150
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
EC-MVSNet		99.69 60	99.69 61	99.68 142	99.71 208	99.91 4	99.76 23	99.96 31	99.86 66	99.51 298	99.39 382	99.57 53	99.93 121	99.64 74	99.86 226	99.20 385

EPP-MVSNet		99.17 252	99.00 279	99.66 154	99.80 124	99.43 210	99.70 38	99.24 424	99.48 198	99.56 275	99.77 146	94.89 428	99.93 121	98.72 248	99.89 193	99.63 177

DeepC-MVS	98.90 4	99.62 95	99.61 90	99.67 146	99.72 203	99.44 206	99.24 193	99.71 209	99.27 247	99.93 53	99.90 37	99.70 32	99.93 121	98.99 198	99.99 19	99.64 171
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RoMa-HiRes		99.38 180	99.30 189	99.64 168	99.81 113	99.47 190	99.11 250	99.94 42	99.03 293	99.55 280	99.56 321	97.71 320	99.92 155	99.19 153	99.77 292	99.54 249

ELoFTR		99.25 217	99.26 203	99.21 348	99.86 61	98.66 368	99.00 292	99.93 44	98.56 366	99.83 111	99.83 84	97.34 344	99.92 155	99.03 192	100.00 1	99.04 433

icg_test_0407_2		99.30 205	99.29 195	99.31 322	99.71 208	98.55 386	98.17 430	99.71 209	99.41 223	99.73 183	99.60 296	99.17 112	99.92 155	98.45 278	99.70 334	99.45 298

lecture		99.56 107	99.48 131	99.81 55	99.78 147	99.86 18	99.50 102	99.70 218	99.59 180	99.75 166	99.71 197	98.94 161	99.92 155	98.59 265	99.76 297	99.66 150

guyue		99.12 266	99.02 269	99.41 281	99.84 82	98.56 384	99.19 211	98.30 498	99.82 86	99.84 105	99.75 164	94.84 429	99.92 155	99.68 67	99.94 136	99.74 92

sc_t1		99.81 29	99.80 33	99.82 47	99.88 47	99.88 12	99.83 7	99.79 153	99.94 37	99.93 53	99.92 28	99.35 85	99.92 155	99.64 74	99.94 136	99.68 127

tt0320-xc		99.82 25	99.82 25	99.82 47	99.82 100	99.84 27	99.82 10	99.92 48	99.94 37	99.94 48	99.93 23	99.34 86	99.92 155	99.70 62	99.96 92	99.70 108

tt0320		99.79 35	99.79 35	99.81 55	99.82 100	99.84 27	99.82 10	99.90 65	99.94 37	99.94 48	99.94 20	99.07 135	99.92 155	99.68 67	99.97 78	99.67 136

fmvsm_s_conf0.5_n_4		99.78 38	99.78 40	99.79 73	99.75 183	99.56 170	98.98 303	99.94 42	99.92 46	99.97 24	99.72 187	99.84 17	99.92 155	99.91 34	99.98 55	99.89 39

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 12	99.93 24	99.78 58	99.07 267	99.98 14	99.99 4	99.98 14	99.90 37	99.88 12	99.92 155	99.93 26	99.99 19	99.98 5

fmvsm_s_conf0.5_n		99.83 22	99.81 29	99.87 27	99.85 76	99.78 58	99.03 277	99.96 31	99.99 4	99.97 24	99.84 77	99.78 24	99.92 155	99.92 31	99.99 19	99.92 26

EGC-MVSNET		89.05 516	85.52 519	99.64 168	99.89 41	99.78 58	99.56 87	99.52 338	24.19 556	49.96 559	99.83 84	99.15 116	99.92 155	97.71 355	99.85 233	99.21 380

DVP-MVS++		99.38 180	99.25 207	99.77 81	99.03 467	99.77 64	99.74 27	99.61 274	99.18 264	99.76 161	99.61 286	99.00 150	99.92 155	97.72 353	99.60 377	99.62 189

MSC_two_6792asdad				99.74 104	99.03 467	99.53 177		99.23 425					99.92 155	97.77 346	99.69 343	99.78 78

No_MVS				99.74 104	99.03 467	99.53 177		99.23 425					99.92 155	97.77 346	99.69 343	99.78 78

ZD-MVS					99.43 369	99.61 155		99.43 368	96.38 504	99.11 399	99.07 457	97.86 309	99.92 155	94.04 524	99.49 406

SED-MVS		99.40 173	99.28 198	99.77 81	99.69 232	99.82 42	99.20 205	99.54 322	99.13 280	99.82 113	99.63 266	98.91 168	99.92 155	97.85 339	99.70 334	99.58 222

test_241102_TWO								99.54 322	99.13 280	99.76 161	99.63 266	98.32 264	99.92 155	97.85 339	99.69 343	99.75 90

ZNCC-MVS		99.22 233	99.04 266	99.77 81	99.76 165	99.73 91	99.28 177	99.56 309	98.19 415	99.14 394	99.29 414	98.84 177	99.92 155	97.53 381	99.80 274	99.64 171

test_0728_SECOND				99.83 42	99.70 224	99.79 55	99.14 233	99.61 274					99.92 155	97.88 332	99.72 327	99.77 82

SR-MVS		99.19 243	99.00 279	99.74 104	99.51 335	99.72 96	99.18 215	99.60 286	98.85 323	99.47 308	99.58 309	98.38 255	99.92 155	96.92 428	99.54 395	99.57 229

DPE-MVS		99.14 260	98.92 301	99.82 47	99.57 297	99.77 64	98.74 354	99.60 286	98.55 368	99.76 161	99.69 216	98.23 276	99.92 155	96.39 465	99.75 305	99.76 87
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS		99.06 281	98.83 314	99.76 88	99.76 165	99.71 102	99.32 158	99.50 347	98.35 398	98.97 414	99.48 355	98.37 256	99.92 155	95.95 487	99.75 305	99.63 177
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PM-MVS		99.36 190	99.29 195	99.58 203	99.83 91	99.66 124	98.95 312	99.86 90	98.85 323	99.81 120	99.73 177	98.40 254	99.92 155	98.36 287	99.83 247	99.17 393

HPM-MVS		99.25 217	99.07 251	99.78 77	99.81 113	99.75 80	99.61 73	99.67 236	97.72 455	99.35 344	99.25 424	99.23 104	99.92 155	97.21 410	99.82 257	99.67 136
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
tpm		97.15 457	96.95 459	97.75 477	98.91 478	94.24 528	99.32 158	97.96 509	97.71 456	98.29 473	99.32 404	86.72 519	99.92 155	98.10 316	96.24 544	99.09 415

RPMNet		98.60 355	98.53 349	98.83 412	99.05 462	98.12 419	99.30 167	99.62 266	99.86 66	99.16 389	99.74 172	92.53 464	99.92 155	98.75 240	98.77 481	98.44 494

CPTT-MVS		98.74 339	98.44 363	99.64 168	99.61 268	99.38 227	99.18 215	99.55 316	96.49 502	99.27 365	99.37 389	97.11 358	99.92 155	95.74 497	99.67 354	99.62 189

MIMVSNet1		99.66 78	99.62 86	99.80 65	99.94 18	99.87 15	99.69 45	99.77 171	99.78 103	99.93 53	99.89 42	97.94 304	99.92 155	99.65 71	99.98 55	99.62 189

CSCG		99.37 185	99.29 195	99.60 196	99.71 208	99.46 198	99.43 121	99.85 96	98.79 336	99.41 328	99.60 296	98.92 165	99.92 155	98.02 318	99.92 159	99.43 320

ACMMP		99.25 217	99.08 247	99.74 104	99.79 138	99.68 118	99.50 102	99.65 251	98.07 426	99.52 291	99.69 216	98.57 217	99.92 155	97.18 415	99.79 280	99.63 177
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
SSC-MVS3.2		99.64 86	99.67 66	99.56 215	99.75 183	98.98 318	98.96 309	99.87 81	99.88 61	99.84 105	99.64 250	99.32 89	99.91 186	99.78 55	99.96 92	99.80 68

SR-MVS-dyc-post		99.27 213	99.11 234	99.73 114	99.54 317	99.74 88	99.26 186	99.62 266	99.16 273	99.52 291	99.64 250	98.41 250	99.91 186	97.27 401	99.61 374	99.54 249

DVP-MVS		99.32 202	99.17 218	99.77 81	99.69 232	99.80 52	99.14 233	99.31 407	99.16 273	99.62 249	99.61 286	98.35 258	99.91 186	97.88 332	99.72 327	99.61 204
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_THIRD									99.18 264	99.62 249	99.61 286	98.58 216	99.91 186	97.72 353	99.80 274	99.77 82

GST-MVS		99.16 255	98.96 293	99.75 99	99.73 198	99.73 91	99.20 205	99.55 316	98.22 412	99.32 353	99.35 399	98.65 207	99.91 186	96.86 432	99.74 312	99.62 189

MP-MVS-pluss		99.14 260	98.92 301	99.80 65	99.83 91	99.83 34	98.61 372	99.63 263	96.84 497	99.44 315	99.58 309	98.81 178	99.91 186	97.70 358	99.82 257	99.67 136
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		99.25 217	99.08 247	99.76 88	99.73 198	99.70 110	99.31 164	99.59 292	98.36 392	99.36 340	99.37 389	98.80 182	99.91 186	97.43 387	99.75 305	99.68 127

HPM-MVS++		98.96 308	98.70 330	99.74 104	99.52 333	99.71 102	98.86 327	99.19 435	98.47 381	98.59 456	99.06 458	98.08 293	99.91 186	96.94 427	99.60 377	99.60 209

test-LLR		97.15 457	96.95 459	97.74 478	98.18 527	95.02 522	97.38 496	96.10 531	98.00 429	97.81 506	98.58 502	90.04 502	99.91 186	97.69 364	98.78 479	98.31 497

test-mter		96.23 485	95.73 488	97.74 478	98.18 527	95.02 522	97.38 496	96.10 531	97.90 441	97.81 506	98.58 502	79.12 540	99.91 186	97.69 364	98.78 479	98.31 497

VPA-MVSNet		99.66 78	99.62 86	99.79 73	99.68 241	99.75 80	99.62 67	99.69 227	99.85 72	99.80 127	99.81 99	98.81 178	99.91 186	99.47 101	99.88 204	99.70 108

XVG-ACMP-BASELINE		99.23 224	99.10 243	99.63 176	99.82 100	99.58 166	98.83 335	99.72 205	98.36 392	99.60 259	99.71 197	98.92 165	99.91 186	97.08 420	99.84 239	99.40 329

APD-MVS		98.87 324	98.59 339	99.71 129	99.50 341	99.62 145	99.01 286	99.57 304	96.80 499	99.54 284	99.63 266	98.29 267	99.91 186	95.24 506	99.71 331	99.61 204
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CR-MVSNet		98.35 387	98.20 391	98.83 412	99.05 462	98.12 419	99.30 167	99.67 236	97.39 473	99.16 389	99.79 121	91.87 475	99.91 186	98.78 238	98.77 481	98.44 494

FMVSNet5		97.80 427	97.25 447	99.42 271	98.83 491	98.97 321	99.38 132	99.80 144	98.87 320	99.25 371	99.69 216	80.60 532	99.91 186	98.96 205	99.90 177	99.38 335

XXY-MVS		99.71 57	99.67 66	99.81 55	99.89 41	99.72 96	99.59 80	99.82 123	99.39 228	99.82 113	99.84 77	99.38 77	99.91 186	99.38 116	99.93 150	99.80 68

sss		98.90 319	98.77 322	99.27 336	99.48 351	98.44 397	98.72 357	99.32 403	97.94 439	99.37 339	99.35 399	96.31 392	99.91 186	98.85 221	99.63 364	99.47 291

1112_ss		99.05 285	98.84 312	99.67 146	99.66 251	99.29 249	98.52 394	99.82 123	97.65 458	99.43 319	99.16 443	96.42 385	99.91 186	99.07 188	99.84 239	99.80 68

LS3D		99.24 221	99.11 234	99.61 192	98.38 519	99.79 55	99.57 85	99.68 231	99.61 171	99.15 392	99.71 197	98.70 198	99.91 186	97.54 379	99.68 348	99.13 405

RoMa-SfM		99.32 202	99.23 212	99.59 199	99.77 160	99.53 177	98.89 321	99.88 75	98.78 338	99.65 228	99.52 339	97.78 316	99.90 205	98.96 205	99.86 226	99.35 345

aaatest				99.74 104	99.76 165	99.65 130	99.38 132	99.78 166	99.58 182	99.81 120	99.66 241		99.90 205	97.69 364	99.79 280	99.67 136

MED-MVS		99.51 125	99.42 153	99.80 65	99.76 165	99.65 130	99.38 132	99.78 166	99.77 108	99.81 120	99.78 134	99.02 148	99.90 205	97.69 364	99.76 297	99.85 51

WB-MVSnew		98.34 389	98.14 398	98.96 384	98.14 530	97.90 437	98.27 420	97.26 526	98.63 358	98.80 436	98.00 521	97.77 317	99.90 205	97.37 391	98.98 466	99.09 415

testf1		99.63 87	99.60 94	99.72 123	99.94 18	99.95 2	99.47 112	99.89 69	99.43 218	99.88 83	99.80 109	99.26 98	99.90 205	98.81 229	99.88 204	99.32 356

APD_test2		99.63 87	99.60 94	99.72 123	99.94 18	99.95 2	99.47 112	99.89 69	99.43 218	99.88 83	99.80 109	99.26 98	99.90 205	98.81 229	99.88 204	99.32 356

BridgeMVS		99.50 128	99.50 126	99.50 241	99.42 374	99.49 185	99.52 94	99.75 185	99.86 66	99.78 140	99.71 197	98.20 280	99.90 205	99.39 115	99.88 204	99.10 409

test2506		94.73 509	94.59 509	95.15 529	99.59 277	85.90 559	99.75 25	74.01 562	99.89 56	99.71 194	99.86 64	79.00 541	99.90 205	99.52 92	99.99 19	99.65 159

test1111		97.74 429	98.16 396	96.49 521	99.60 271	89.86 557	99.71 37	91.21 554	99.89 56	99.88 83	99.87 57	93.73 447	99.90 205	99.56 84	99.99 19	99.70 108

KD-MVS_self_test		99.63 87	99.59 97	99.76 88	99.84 82	99.90 7	99.37 140	99.79 153	99.83 82	99.88 83	99.85 69	98.42 249	99.90 205	99.60 78	99.73 319	99.49 283

ET-MVSNet_ETH3D		96.78 465	96.07 479	98.91 397	99.26 422	97.92 436	97.70 478	96.05 534	97.96 436	92.37 550	98.43 510	87.06 513	99.90 205	98.27 295	97.56 527	98.91 456

tfpnnormal		99.43 160	99.38 162	99.60 196	99.87 56	99.75 80	99.59 80	99.78 166	99.71 123	99.90 68	99.69 216	98.85 176	99.90 205	97.25 407	99.78 288	99.15 397

pmmvs6		99.86 10	99.86 13	99.83 42	99.94 18	99.90 7	99.83 7	99.91 58	99.85 72	99.94 48	99.95 16	99.73 28	99.90 205	99.65 71	99.97 78	99.69 120

APD-MVS_3200maxsize		99.31 204	99.16 219	99.74 104	99.53 326	99.75 80	99.27 181	99.61 274	99.19 263	99.57 267	99.64 250	98.76 189	99.90 205	97.29 398	99.62 366	99.56 233

baseline2		96.83 464	96.28 472	98.46 442	99.09 458	96.91 478	98.83 335	93.87 551	97.23 481	96.23 538	98.36 512	88.12 510	99.90 205	96.68 444	98.14 516	98.57 486

XVG-OURS-SEG-HR		99.16 255	98.99 286	99.66 154	99.84 82	99.64 137	98.25 423	99.73 196	98.39 388	99.63 239	99.43 367	99.70 32	99.90 205	97.34 392	98.64 493	99.44 313

XVG-OURS		99.21 238	99.06 253	99.65 161	99.82 100	99.62 145	97.87 467	99.74 191	98.36 392	99.66 224	99.68 229	99.71 29	99.90 205	96.84 436	99.88 204	99.43 320

JIA-IIPM		98.06 412	97.92 417	98.50 439	98.59 511	97.02 474	98.80 343	98.51 483	99.88 61	97.89 499	99.87 57	91.89 474	99.90 205	98.16 309	97.68 526	98.59 482

GBi-Net		99.42 164	99.31 184	99.73 114	99.49 346	99.77 64	99.68 48	99.70 218	99.44 211	99.62 249	99.83 84	97.21 351	99.90 205	98.96 205	99.90 177	99.53 258

test1		99.42 164	99.31 184	99.73 114	99.49 346	99.77 64	99.68 48	99.70 218	99.44 211	99.62 249	99.83 84	97.21 351	99.90 205	98.96 205	99.90 177	99.53 258

FMVSNet1		99.66 78	99.63 83	99.73 114	99.78 147	99.77 64	99.68 48	99.70 218	99.67 145	99.82 113	99.83 84	98.98 156	99.90 205	99.24 140	99.97 78	99.53 258

WTY-MVS		98.59 358	98.37 372	99.26 340	99.43 369	98.40 400	98.74 354	99.13 445	98.10 421	99.21 381	99.24 430	94.82 430	99.90 205	97.86 337	98.77 481	99.49 283

VortexMVS		99.13 263	99.24 209	98.79 416	99.67 248	96.60 487	99.24 193	99.80 144	99.85 72	99.93 53	99.84 77	95.06 425	99.89 227	99.80 53	99.98 55	99.89 39

ECVR-MVS		97.73 430	98.04 404	96.78 513	99.59 277	90.81 551	99.72 33	90.43 556	99.89 56	99.86 97	99.86 64	93.60 449	99.89 227	99.46 102	99.99 19	99.65 159

EI-MVSNet-UG-set		99.48 136	99.50 126	99.42 271	99.57 297	98.65 372	99.24 193	99.46 358	99.68 137	99.80 127	99.66 241	98.99 152	99.89 227	99.19 153	99.90 177	99.72 100

EI-MVSNet-Vis-set		99.47 146	99.49 130	99.42 271	99.57 297	98.66 368	99.24 193	99.46 358	99.67 145	99.79 134	99.65 248	98.97 158	99.89 227	99.15 165	99.89 193	99.71 105

新几何1				99.52 235	99.50 341	99.22 271		99.26 417	95.66 516	98.60 455	99.28 416	97.67 325	99.89 227	95.95 487	99.32 432	99.45 298

testdata2													99.89 227	95.99 484

testdata				99.42 271	99.51 335	98.93 330		99.30 410	96.20 507	98.87 428	99.40 377	98.33 263	99.89 227	96.29 469	99.28 437	99.44 313

TESTMET0.1,1		96.24 484	95.84 485	97.41 492	98.24 524	93.84 531	97.38 496	95.84 539	98.43 382	97.81 506	98.56 505	79.77 536	99.89 227	97.77 346	98.77 481	98.52 488

test20.03		99.55 112	99.54 117	99.58 203	99.79 138	99.37 232	99.02 281	99.89 69	99.60 178	99.82 113	99.62 276	98.81 178	99.89 227	99.43 107	99.86 226	99.47 291

MDA-MVSNet-bldmvs		99.06 281	99.05 260	99.07 373	99.80 124	97.83 440	98.89 321	99.72 205	99.29 243	99.63 239	99.70 207	96.47 383	99.89 227	98.17 308	99.82 257	99.50 278

LPG-MVS_test		99.22 233	99.05 260	99.74 104	99.82 100	99.63 143	99.16 226	99.73 196	97.56 460	99.64 234	99.69 216	99.37 79	99.89 227	96.66 446	99.87 218	99.69 120

LGP-MVS_train				99.74 104	99.82 100	99.63 143		99.73 196	97.56 460	99.64 234	99.69 216	99.37 79	99.89 227	96.66 446	99.87 218	99.69 120

Test_1112_low_res		98.95 311	98.73 323	99.63 176	99.68 241	99.15 290	98.09 442	99.80 144	97.14 486	99.46 312	99.40 377	96.11 401	99.89 227	99.01 197	99.84 239	99.84 56

PatchmatchNet		97.65 434	97.80 423	97.18 503	98.82 494	92.49 539	99.17 220	98.39 493	98.12 420	98.79 438	99.58 309	90.71 493	99.89 227	97.23 408	99.41 420	99.16 395
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMP	97.51 14	99.05 285	98.84 312	99.67 146	99.78 147	99.55 174	98.88 323	99.66 241	97.11 488	99.47 308	99.60 296	99.07 135	99.89 227	96.18 476	99.85 233	99.58 222
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
nomal-1		96.75 467	96.26 473	98.21 458	99.06 460	95.71 508	98.65 370	97.76 517	98.51 375	97.96 494	97.91 524	79.57 537	99.88 242	98.11 312	98.84 477	99.05 430

TestfortrainingZip a		99.55 112	99.45 142	99.85 33	99.76 165	99.82 42	99.38 132	99.62 266	99.77 108	99.87 93	99.78 134	98.12 288	99.88 242	98.96 205	99.77 292	99.85 51

diffmvs_AUTHOR		99.48 136	99.48 131	99.47 253	99.80 124	98.89 339	98.71 361	99.82 123	99.79 100	99.66 224	99.63 266	98.87 174	99.88 242	99.13 175	99.95 117	99.62 189

test_vis3_rt		99.89 3	99.90 4	99.87 27	99.98 3	99.75 80	99.70 38	100.00 1	99.73 113	100.00 1	99.89 42	99.79 23	99.88 242	99.98 1	100.00 1	99.98 5

FE-MVS		97.85 423	97.42 440	99.15 357	99.44 366	98.75 359	99.77 19	98.20 501	95.85 511	99.33 350	99.80 109	88.86 507	99.88 242	96.40 464	99.12 453	98.81 467

ppachtmachnet_test		98.89 322	99.12 231	98.20 459	99.66 251	95.24 519	97.63 482	99.68 231	99.08 286	99.78 140	99.62 276	98.65 207	99.88 242	98.02 318	99.96 92	99.48 287

TSAR-MVS + MP.		99.34 197	99.24 209	99.63 176	99.82 100	99.37 232	99.26 186	99.35 392	98.77 341	99.57 267	99.70 207	99.27 97	99.88 242	97.71 355	99.75 305	99.65 159
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
new-patchmatchnet		99.35 192	99.57 106	98.71 427	99.82 100	96.62 485	98.55 387	99.75 185	99.50 193	99.88 83	99.87 57	99.31 90	99.88 242	99.43 107	100.00 1	99.62 189

Anonymous20231206		99.35 192	99.31 184	99.47 253	99.74 194	99.06 308	99.28 177	99.74 191	99.23 256	99.72 189	99.53 335	97.63 333	99.88 242	99.11 181	99.84 239	99.48 287

XVS		99.27 213	99.11 234	99.75 99	99.71 208	99.71 102	99.37 140	99.61 274	99.29 243	98.76 441	99.47 359	98.47 240	99.88 242	97.62 372	99.73 319	99.67 136

v1240		99.56 107	99.58 101	99.51 239	99.80 124	99.00 314	99.00 292	99.65 251	99.15 278	99.90 68	99.75 164	99.09 128	99.88 242	99.90 38	99.96 92	99.67 136

X-MVStestdata		96.09 489	94.87 505	99.75 99	99.71 208	99.71 102	99.37 140	99.61 274	99.29 243	98.76 441	61.30 566	98.47 240	99.88 242	97.62 372	99.73 319	99.67 136

旧先验2							97.94 461		95.33 520	98.94 417			99.88 242	96.75 440

UniMVSNet (Re)		99.37 185	99.26 203	99.68 142	99.51 335	99.58 166	98.98 303	99.60 286	99.43 218	99.70 198	99.36 394	97.70 321	99.88 242	99.20 151	99.87 218	99.59 216

HPM-MVS_fast		99.43 160	99.30 189	99.80 65	99.83 91	99.81 48	99.52 94	99.70 218	98.35 398	99.51 298	99.50 346	99.31 90	99.88 242	98.18 306	99.84 239	99.69 120

TDRefinement		99.72 54	99.70 58	99.77 81	99.90 38	99.85 22	99.86 6	99.92 48	99.69 133	99.78 140	99.92 28	99.37 79	99.88 242	98.93 214	99.95 117	99.60 209

PCF-MVS	96.03 18	96.73 468	95.86 484	99.33 313	99.44 366	99.16 288	96.87 523	99.44 364	86.58 547	98.95 416	99.40 377	94.38 438	99.88 242	87.93 543	99.80 274	98.95 448
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test-260524					99.64 257	99.70 110		99.58 301		99.69 202		97.64 332	99.87 259	98.68 255	99.76 297

UWE-MVS		96.21 487	95.78 486	97.49 485	98.53 513	93.83 532	98.04 448	93.94 550	98.96 302	98.46 466	98.17 517	79.86 534	99.87 259	96.99 423	99.06 458	98.78 470

SF-MVS		99.10 274	98.93 297	99.62 185	99.58 287	99.51 183	99.13 240	99.65 251	97.97 433	99.42 322	99.61 286	98.86 175	99.87 259	96.45 463	99.68 348	99.49 283

D2MVS		99.22 233	99.19 216	99.29 327	99.69 232	98.74 360	98.81 340	99.41 371	98.55 368	99.68 209	99.69 216	98.13 286	99.87 259	98.82 225	99.98 55	99.24 372

thisisatest0515		96.98 461	96.42 470	98.66 429	99.42 374	97.47 455	97.27 501	94.30 547	97.24 480	99.15 392	98.86 484	85.01 523	99.87 259	97.10 418	99.39 422	98.63 478

ACMMP_NAP		99.28 209	99.11 234	99.79 73	99.75 183	99.81 48	98.95 312	99.53 333	98.27 410	99.53 289	99.73 177	98.75 191	99.87 259	97.70 358	99.83 247	99.68 127

Patchmatch-test		98.10 409	97.98 409	98.48 440	99.27 419	96.48 488	99.40 127	99.07 447	98.81 332	99.23 375	99.57 317	90.11 501	99.87 259	96.69 443	99.64 361	99.09 415

v144192		99.55 112	99.54 117	99.58 203	99.78 147	99.20 278	99.11 250	99.62 266	99.18 264	99.89 73	99.72 187	98.66 205	99.87 259	99.88 42	99.97 78	99.66 150

v1921920		99.56 107	99.57 106	99.55 222	99.75 183	99.11 296	99.05 269	99.61 274	99.15 278	99.88 83	99.71 197	99.08 132	99.87 259	99.90 38	99.97 78	99.66 150

FC-MVSNet-test		99.70 58	99.65 75	99.86 31	99.88 47	99.86 18	99.72 33	99.78 166	99.90 50	99.82 113	99.83 84	98.45 245	99.87 259	99.51 94	99.97 78	99.86 48

pm-mvs1		99.79 35	99.79 35	99.78 77	99.91 32	99.83 34	99.76 23	99.87 81	99.73 113	99.89 73	99.87 57	99.63 38	99.87 259	99.54 88	99.92 159	99.63 177

TransMVSNet (Re)		99.78 38	99.77 46	99.81 55	99.91 32	99.85 22	99.75 25	99.86 90	99.70 130	99.91 63	99.89 42	99.60 45	99.87 259	99.59 79	99.74 312	99.71 105

NR-MVSNet		99.40 173	99.31 184	99.68 142	99.43 369	99.55 174	99.73 30	99.50 347	99.46 206	99.88 83	99.36 394	97.54 334	99.87 259	98.97 202	99.87 218	99.63 177

Baseline_NR-MVSNet		99.49 133	99.37 165	99.82 47	99.91 32	99.84 27	98.83 335	99.86 90	99.68 137	99.65 228	99.88 51	97.67 325	99.87 259	99.03 192	99.86 226	99.76 87

EG-PatchMatch MVS		99.57 103	99.56 111	99.62 185	99.77 160	99.33 242	99.26 186	99.76 179	99.32 239	99.80 127	99.78 134	99.29 92	99.87 259	99.15 165	99.91 173	99.66 150

DELS-MVS		99.34 197	99.30 189	99.48 251	99.51 335	99.36 236	98.12 438	99.53 333	99.36 234	99.41 328	99.61 286	99.22 105	99.87 259	99.21 147	99.68 348	99.20 385
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
FMVSNet2		99.35 192	99.28 198	99.55 222	99.49 346	99.35 239	99.45 117	99.57 304	99.44 211	99.70 198	99.74 172	97.21 351	99.87 259	99.03 192	99.94 136	99.44 313

ab-mvs		99.33 200	99.28 198	99.47 253	99.57 297	99.39 225	99.78 17	99.43 368	98.87 320	99.57 267	99.82 92	98.06 294	99.87 259	98.69 254	99.73 319	99.15 397

DP-MVS		99.48 136	99.39 159	99.74 104	99.57 297	99.62 145	99.29 175	99.61 274	99.87 63	99.74 177	99.76 156	98.69 199	99.87 259	98.20 302	99.80 274	99.75 90

F-COLMAP		98.74 339	98.45 361	99.62 185	99.57 297	99.47 190	98.84 332	99.65 251	96.31 506	98.93 418	99.19 441	97.68 324	99.87 259	96.52 455	99.37 425	99.53 258

aaEdge-Enhanced		99.26 215	99.10 243	99.73 114	99.60 271	99.65 130	98.75 353	99.45 363	99.31 241	99.65 228	99.66 241	98.00 302	99.86 279	97.69 364	99.79 280	99.67 136

WBMVS		97.50 444	97.18 450	98.48 440	98.85 488	95.89 504	98.44 407	99.52 338	99.53 188	99.52 291	99.42 369	80.10 533	99.86 279	99.24 140	99.95 117	99.68 127

Anonymous20240529		99.42 164	99.34 176	99.65 161	99.53 326	99.60 159	99.63 64	99.39 381	99.47 203	99.76 161	99.78 134	98.13 286	99.86 279	98.70 252	99.68 348	99.49 283

test_post											52.41 567	90.25 500	99.86 279

Anonymous20231211		99.62 95	99.57 106	99.76 88	99.61 268	99.60 159	99.81 13	99.73 196	99.82 86	99.90 68	99.90 37	97.97 303	99.86 279	99.42 112	99.96 92	99.80 68

v10		99.69 60	99.69 61	99.66 154	99.81 113	99.39 225	99.66 57	99.75 185	99.60 178	99.92 60	99.87 57	98.75 191	99.86 279	99.90 38	99.99 19	99.73 96

VPNet		99.46 148	99.37 165	99.71 129	99.82 100	99.59 161	99.48 109	99.70 218	99.81 92	99.69 202	99.58 309	97.66 329	99.86 279	99.17 160	99.44 414	99.67 136

testgi		99.29 207	99.26 203	99.37 296	99.75 183	98.81 351	98.84 332	99.89 69	98.38 390	99.75 166	99.04 461	99.36 82	99.86 279	99.08 185	99.25 443	99.45 298

mvs_anonymous		99.28 209	99.39 159	98.94 387	99.19 435	97.81 441	99.02 281	99.55 316	99.78 103	99.85 102	99.80 109	98.24 272	99.86 279	99.57 83	99.50 404	99.15 397

diffmvs		99.34 197	99.32 182	99.39 287	99.67 248	98.77 357	98.57 383	99.81 136	99.61 171	99.48 306	99.41 371	98.47 240	99.86 279	98.97 202	99.90 177	99.53 258
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WR-MVS		99.11 271	98.93 297	99.66 154	99.30 412	99.42 213	98.42 409	99.37 387	99.04 291	99.57 267	99.20 439	96.89 367	99.86 279	98.66 258	99.87 218	99.70 108

114514_t		98.49 371	98.11 400	99.64 168	99.73 198	99.58 166	99.24 193	99.76 179	89.94 545	99.42 322	99.56 321	97.76 319	99.86 279	97.74 351	99.82 257	99.47 291

UnsupCasMVSNet_eth		98.83 329	98.57 343	99.59 199	99.68 241	99.45 204	98.99 300	99.67 236	99.48 198	99.55 280	99.36 394	94.92 427	99.86 279	98.95 212	96.57 536	99.45 298

FMVSNet3		98.80 333	98.63 335	99.32 318	99.13 445	98.72 361	99.10 254	99.48 352	99.23 256	99.62 249	99.64 250	92.57 462	99.86 279	98.96 205	99.90 177	99.39 333

HY-MVS	98.23 9	98.21 401	97.95 411	98.99 380	99.03 467	98.24 408	99.61 73	98.72 469	96.81 498	98.73 443	99.51 343	94.06 441	99.86 279	96.91 429	98.20 511	98.86 462

TAMVS		99.49 133	99.45 142	99.63 176	99.48 351	99.42 213	99.45 117	99.57 304	99.66 152	99.78 140	99.83 84	97.85 311	99.86 279	99.44 105	99.96 92	99.61 204

ACMM	98.09 11	99.46 148	99.38 162	99.72 123	99.80 124	99.69 115	99.13 240	99.65 251	98.99 298	99.64 234	99.72 187	99.39 72	99.86 279	98.23 299	99.81 267	99.60 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS_ROB	97.31 17	97.36 451	96.84 464	98.89 404	99.29 414	99.45 204	98.87 326	99.48 352	86.54 548	99.44 315	99.74 172	97.34 344	99.86 279	91.61 533	99.28 437	97.37 527

COLMAP_ROB	98.06 12	99.45 152	99.37 165	99.70 134	99.83 91	99.70 110	99.38 132	99.78 166	99.53 188	99.67 217	99.78 134	99.19 109	99.86 279	97.32 394	99.87 218	99.55 237
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PRO-TEST		99.17 252	99.14 224	99.28 330	99.04 465	98.92 334	99.24 193	99.76 179	99.69 133	99.41 328	99.17 442	98.06 294	99.85 298	98.39 285	99.47 409	99.06 429

gbinet_0.2-2-1-0.02		97.52 443	97.07 454	98.88 406	97.35 546	97.35 464	97.17 506	99.25 420	97.86 448	98.41 469	96.54 554	90.74 492	99.85 298	98.80 231	97.51 528	99.43 320

FE-MVSNET2		99.68 65	99.67 66	99.72 123	99.86 61	99.68 118	99.46 116	99.88 75	99.62 166	99.87 93	99.85 69	99.06 142	99.85 298	99.44 105	99.98 55	99.63 177

testing3		96.48 477	95.63 490	99.01 379	99.23 427	97.81 441	98.90 320	99.10 446	98.72 346	97.84 504	97.92 523	72.44 554	99.85 298	97.21 410	99.33 430	99.35 345

hse-mvs2		98.52 366	98.30 382	99.16 355	99.29 414	98.60 379	98.77 348	99.02 452	99.68 137	99.32 353	99.04 461	92.50 466	99.85 298	99.24 140	97.87 524	99.03 436

AUN-MVS		97.82 424	97.38 441	99.14 360	99.27 419	98.53 390	98.72 357	99.02 452	98.10 421	97.18 524	99.03 465	89.26 506	99.85 298	97.94 327	97.91 522	99.03 436

miper_lstm_enhance		98.65 350	98.60 337	98.82 415	99.20 433	97.33 465	97.78 471	99.66 241	99.01 296	99.59 262	99.50 346	94.62 434	99.85 298	98.12 311	99.90 177	99.26 369

TEST9					99.35 391	99.35 239	98.11 440	99.41 371	94.83 529	97.92 496	98.99 468	98.02 297	99.85 298

train_agg		98.35 387	97.95 411	99.57 211	99.35 391	99.35 239	98.11 440	99.41 371	94.90 526	97.92 496	98.99 468	98.02 297	99.85 298	95.38 504	99.44 414	99.50 278

agg_prior					99.35 391	99.36 236		99.39 381		97.76 509			99.85 298

FIs		99.65 84	99.58 101	99.84 39	99.84 82	99.85 22	99.66 57	99.75 185	99.86 66	99.74 177	99.79 121	98.27 270	99.85 298	99.37 119	99.93 150	99.83 60

v1192		99.57 103	99.57 106	99.57 211	99.77 160	99.22 271	99.04 274	99.60 286	99.18 264	99.87 93	99.72 187	99.08 132	99.85 298	99.89 41	99.98 55	99.66 150

无先验							98.01 451	99.23 425	95.83 512				99.85 298	95.79 495		99.44 313

VDD-MVS		99.20 240	99.11 234	99.44 264	99.43 369	98.98 318	99.50 102	98.32 497	99.80 96	99.56 275	99.69 216	96.99 364	99.85 298	98.99 198	99.73 319	99.50 278

VDDNet		98.97 305	98.82 315	99.42 271	99.71 208	98.81 351	99.62 67	98.68 471	99.81 92	99.38 337	99.80 109	94.25 439	99.85 298	98.79 232	99.32 432	99.59 216

EI-MVSNet		99.38 180	99.44 147	99.21 348	99.58 287	98.09 423	99.26 186	99.46 358	99.62 166	99.75 166	99.67 235	98.54 226	99.85 298	99.15 165	99.92 159	99.68 127

MVSTER		98.47 373	98.22 389	99.24 345	99.06 460	98.35 406	99.08 262	99.46 358	99.27 247	99.75 166	99.66 241	88.61 508	99.85 298	99.14 172	99.92 159	99.52 269

ACMH	98.42 6	99.59 102	99.54 117	99.72 123	99.86 61	99.62 145	99.56 87	99.79 153	98.77 341	99.80 127	99.85 69	99.64 36	99.85 298	98.70 252	99.89 193	99.70 108
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
onestephybrid01		99.45 152	99.46 139	99.42 271	99.69 232	98.88 341	98.76 349	99.81 136	99.78 103	99.67 217	99.73 177	98.61 211	99.84 316	99.17 160	99.93 150	99.52 269

ArgMatch-SfM		99.14 260	99.06 253	99.36 302	99.59 277	99.14 292	98.45 406	99.81 136	98.67 353	99.50 301	99.42 369	98.55 221	99.84 316	97.85 339	99.73 319	99.11 406

MatchFormer		99.03 289	99.02 269	99.08 372	99.56 311	98.47 393	98.57 383	99.90 65	98.13 419	99.80 127	99.75 164	98.34 260	99.84 316	97.18 415	99.90 177	98.92 454

wanda-best-256-512		97.53 441	97.14 452	98.72 423	97.71 538	96.86 480	97.00 515	99.34 396	97.73 453	98.18 479	96.82 548	91.92 470	99.84 316	99.02 195	96.53 537	99.45 298

blended_shiyan8		97.82 424	97.45 438	98.92 392	98.06 532	97.45 458	97.73 473	99.35 392	97.96 436	98.35 471	97.34 535	92.76 461	99.84 316	99.04 190	96.49 543	99.47 291

FE-blended-shiyan7		97.53 441	97.14 452	98.72 423	97.71 538	96.86 480	97.00 515	99.34 396	97.73 453	98.18 479	96.82 548	91.92 470	99.84 316	99.02 195	96.53 537	99.45 298

blended_shiyan6		97.82 424	97.46 436	98.92 392	98.08 531	97.46 456	97.73 473	99.34 396	97.96 436	98.33 472	97.35 534	92.78 459	99.84 316	99.04 190	96.53 537	99.46 296

IMVS_0403		99.37 185	99.39 159	99.28 330	99.71 208	98.55 386	99.19 211	99.71 209	99.41 223	99.67 217	99.60 296	99.12 124	99.84 316	98.45 278	99.70 334	99.45 298

APD_test1		99.36 190	99.28 198	99.61 192	99.89 41	99.89 10	99.32 158	99.74 191	99.18 264	99.69 202	99.75 164	98.41 250	99.84 316	97.85 339	99.70 334	99.10 409

test_vis1_rt		99.45 152	99.46 139	99.41 281	99.71 208	98.63 377	98.99 300	99.96 31	99.03 293	99.95 45	99.12 450	98.75 191	99.84 316	99.82 51	99.82 257	99.77 82

FA-MVS(test-final)		98.52 366	98.32 379	99.10 366	99.48 351	98.67 365	99.77 19	98.60 479	97.35 475	99.63 239	99.80 109	93.07 456	99.84 316	97.92 328	99.30 434	98.78 470

EIA-MVS		99.12 266	99.01 275	99.45 260	99.36 387	99.62 145	99.34 149	99.79 153	98.41 385	98.84 431	98.89 482	98.75 191	99.84 316	98.15 310	99.51 401	98.89 459

Anonymous202405211		98.75 338	98.46 358	99.63 176	99.34 400	99.66 124	99.47 112	97.65 518	99.28 246	99.56 275	99.50 346	93.15 454	99.84 316	98.62 264	99.58 383	99.40 329

Effi-MVS+		99.06 281	98.97 291	99.34 310	99.31 408	98.98 318	98.31 418	99.91 58	98.81 332	98.79 438	98.94 478	99.14 119	99.84 316	98.79 232	98.74 486	99.20 385

gm-plane-assit					97.59 541	89.02 558			93.47 534		98.30 513		99.84 316	96.38 466

test_8					99.34 400	99.31 246	98.08 444	99.40 378	94.90 526	97.87 501	98.97 473	98.02 297	99.84 316

v1144		99.54 117	99.53 121	99.59 199	99.79 138	99.28 251	99.10 254	99.61 274	99.20 261	99.84 105	99.73 177	98.67 203	99.84 316	99.86 46	99.98 55	99.64 171

v8		99.68 65	99.69 61	99.65 161	99.80 124	99.40 221	99.66 57	99.76 179	99.64 161	99.93 53	99.85 69	98.66 205	99.84 316	99.88 42	99.99 19	99.71 105

v2v482		99.50 128	99.47 133	99.58 203	99.78 147	99.25 260	99.14 233	99.58 301	99.25 252	99.81 120	99.62 276	98.24 272	99.84 316	99.83 47	99.97 78	99.64 171

VNet		99.18 247	99.06 253	99.56 215	99.24 425	99.36 236	99.33 155	99.31 407	99.67 145	99.47 308	99.57 317	96.48 382	99.84 316	99.15 165	99.30 434	99.47 291

ADS-MVSNet		97.72 433	97.67 432	97.86 473	99.14 443	94.65 525	99.22 202	98.86 460	96.97 492	98.25 475	99.64 250	90.90 487	99.84 316	96.51 456	99.56 386	99.08 421

casdiffmvs_mvg		99.68 65	99.68 64	99.69 140	99.81 113	99.59 161	99.29 175	99.90 65	99.71 123	99.79 134	99.73 177	99.54 56	99.84 316	99.36 120	99.96 92	99.65 159
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LF4IMVS		99.01 298	98.92 301	99.27 336	99.71 208	99.28 251	98.59 377	99.77 171	98.32 406	99.39 336	99.41 371	98.62 209	99.84 316	96.62 452	99.84 239	98.69 477

DKM-HiRes		98.95 311	98.73 323	99.62 185	99.82 100	99.47 190	98.50 396	99.81 136	99.41 223	97.76 509	99.58 309	95.04 426	99.83 339	98.89 218	99.76 297	99.58 222

PMatch-Up-SfM		99.08 276	99.02 269	99.27 336	99.81 113	99.04 311	98.13 436	99.83 116	99.16 273	99.26 369	99.69 216	97.22 350	99.83 339	98.67 257	99.43 418	98.94 451

Casviewmamba		99.63 87	99.60 94	99.73 114	99.84 82	99.72 96	99.36 144	99.87 81	99.67 145	99.74 177	99.73 177	99.07 135	99.83 339	99.14 172	99.93 150	99.62 189

dtuonly		98.93 315	99.11 234	98.38 446	99.72 203	95.75 507	97.07 513	99.91 58	99.04 291	99.65 228	99.41 371	98.32 264	99.83 339	98.97 202	99.90 177	99.55 237

SIFT-PCN-Cal		98.24 395	98.51 351	97.43 491	99.65 255	98.64 375	97.09 510	99.35 392	98.16 417	99.69 202	99.52 339	95.59 412	99.83 339	97.57 377	100.00 1	93.81 545

SIFT-PointCN		98.28 390	98.47 356	97.71 481	99.70 224	98.91 335	96.98 517	99.70 218	97.90 441	99.36 340	99.35 399	95.51 417	99.83 339	97.84 344	99.89 193	94.39 537

usedtu_dtu_shiyan1		98.87 324	98.71 326	99.35 306	99.59 277	98.88 341	97.17 506	99.64 259	98.94 306	99.27 365	99.22 433	95.57 414	99.83 339	99.08 185	99.92 159	99.35 345

usedtu_blend_shiyan5		97.97 418	97.65 434	98.92 392	97.71 538	97.49 453	99.53 92	99.81 136	99.52 192	98.18 479	96.82 548	91.92 470	99.83 339	98.79 232	96.53 537	99.45 298

blend_shiyan4		95.04 507	93.76 513	98.88 406	97.92 534	97.49 453	97.72 475	99.34 396	97.93 440	97.65 514	97.11 541	77.69 544	99.83 339	98.79 232	79.72 556	99.33 352

FE-MVSNET3		98.87 324	98.71 326	99.35 306	99.59 277	98.88 341	97.17 506	99.64 259	98.94 306	99.27 365	99.22 433	95.57 414	99.83 339	99.08 185	99.92 159	99.35 345

viewdifsd2359ckpt11		99.62 95	99.64 80	99.56 215	99.86 61	99.19 281	99.02 281	99.93 44	99.83 82	99.88 83	99.81 99	98.99 152	99.83 339	99.48 98	99.96 92	99.65 159

viewmsd2359difaftdt		99.62 95	99.64 80	99.56 215	99.86 61	99.19 281	99.02 281	99.93 44	99.83 82	99.88 83	99.81 99	98.99 152	99.83 339	99.48 98	99.96 92	99.65 159

IMVS_0407		99.38 180	99.42 153	99.28 330	99.71 208	98.55 386	99.27 181	99.71 209	99.41 223	99.73 183	99.60 296	99.17 112	99.83 339	98.45 278	99.70 334	99.45 298

reproduce_monomvs		97.40 448	97.46 436	97.20 502	99.05 462	91.91 542	99.20 205	99.18 437	99.84 76	99.86 97	99.75 164	80.67 530	99.83 339	99.69 65	99.95 117	99.85 51

9.14			98.64 333		99.45 365		98.81 340	99.60 286	97.52 465	99.28 364	99.56 321	98.53 231	99.83 339	95.36 505	99.64 361

SMA-MVS		99.19 243	99.00 279	99.73 114	99.46 361	99.73 91	99.13 240	99.52 338	97.40 472	99.57 267	99.64 250	98.93 162	99.83 339	97.61 374	99.79 280	99.63 177
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
EU-MVSNet		99.39 177	99.62 86	98.72 423	99.88 47	96.44 489	99.56 87	99.85 96	99.90 50	99.90 68	99.85 69	98.09 291	99.83 339	99.58 82	99.95 117	99.90 31

YYNet1		98.95 311	98.99 286	98.84 410	99.64 257	97.14 472	98.22 425	99.32 403	98.92 314	99.59 262	99.66 241	97.40 340	99.83 339	98.27 295	99.90 177	99.55 237

MDA-MVSNet_test_wron		98.95 311	98.99 286	98.85 408	99.64 257	97.16 470	98.23 424	99.33 401	98.93 311	99.56 275	99.66 241	97.39 342	99.83 339	98.29 292	99.88 204	99.55 237

baseline		99.63 87	99.62 86	99.66 154	99.80 124	99.62 145	99.44 119	99.80 144	99.71 123	99.72 189	99.69 216	99.15 116	99.83 339	99.32 129	99.94 136	99.53 258

CDS-MVSNet		99.22 233	99.13 227	99.50 241	99.35 391	99.11 296	98.96 309	99.54 322	99.46 206	99.61 256	99.70 207	96.31 392	99.83 339	99.34 124	99.88 204	99.55 237
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DeepC-MVS_fast	98.47 5	99.23 224	99.12 231	99.56 215	99.28 417	99.22 271	98.99 300	99.40 378	99.08 286	99.58 264	99.64 250	98.90 171	99.83 339	97.44 386	99.75 305	99.63 177
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PLC	97.35 16	98.36 384	97.99 407	99.48 251	99.32 406	99.24 265	98.50 396	99.51 343	95.19 523	98.58 457	98.96 475	96.95 365	99.83 339	95.63 498	99.25 443	99.37 339
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
viewmamba		99.49 133	99.51 123	99.42 271	99.75 183	98.90 336	98.85 329	99.85 96	99.69 133	99.73 183	99.67 235	98.79 183	99.82 362	99.28 137	99.95 117	99.54 249

FE-MVSNET		99.45 152	99.36 170	99.71 129	99.84 82	99.64 137	99.16 226	99.91 58	98.65 355	99.73 183	99.73 177	98.54 226	99.82 362	98.71 250	99.96 92	99.67 136

pmmvs5		99.19 243	99.11 234	99.42 271	99.76 165	98.88 341	98.55 387	99.73 196	98.82 330	99.72 189	99.62 276	96.56 378	99.82 362	99.32 129	99.95 117	99.56 233

test_post1							99.14 233				51.63 568	89.54 505	99.82 362	96.86 432

原ACMM1				99.37 296	99.47 357	98.87 346		99.27 415	96.74 501	98.26 474	99.32 404	97.93 305	99.82 362	95.96 486	99.38 423	99.43 320

V42		99.56 107	99.54 117	99.63 176	99.79 138	99.46 198	99.39 129	99.59 292	99.24 254	99.86 97	99.70 207	98.55 221	99.82 362	99.79 54	99.95 117	99.60 209

CDPH-MVS		98.56 361	98.20 391	99.61 192	99.50 341	99.46 198	98.32 417	99.41 371	95.22 521	99.21 381	99.10 454	98.34 260	99.82 362	95.09 510	99.66 357	99.56 233

test12				99.54 228	99.29 414	99.33 242		99.16 440		98.43 467		97.54 334	99.82 362		99.47 409	99.48 287

casdiffmvs		99.63 87	99.61 90	99.67 146	99.79 138	99.59 161	99.13 240	99.85 96	99.79 100	99.76 161	99.72 187	99.33 88	99.82 362	99.21 147	99.94 136	99.59 216
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline1		97.73 430	97.33 443	98.96 384	99.30 412	97.73 445	99.40 127	98.42 489	99.33 238	99.46 312	99.21 437	91.18 482	99.82 362	98.35 288	91.26 549	99.32 356

HQP_MVS		98.90 319	98.68 331	99.55 222	99.58 287	99.24 265	98.80 343	99.54 322	98.94 306	99.14 394	99.25 424	97.24 348	99.82 362	95.84 492	99.78 288	99.60 209

plane_prior5								99.54 322					99.82 362	95.84 492	99.78 288	99.60 209

tpmrst		97.73 430	98.07 403	96.73 518	98.71 506	92.00 541	99.10 254	98.86 460	98.52 374	98.92 421	99.54 332	91.90 473	99.82 362	98.02 318	99.03 463	98.37 496

UnsupCasMVSNet_bld		98.55 362	98.27 385	99.40 284	99.56 311	99.37 232	97.97 459	99.68 231	97.49 467	99.08 402	99.35 399	95.41 421	99.82 362	97.70 358	98.19 513	99.01 442

dp		96.86 463	97.07 454	96.24 524	98.68 509	90.30 556	99.19 211	98.38 494	97.35 475	98.23 477	99.59 306	87.23 512	99.82 362	96.27 470	98.73 489	98.59 482

test_0402		99.22 233	99.14 224	99.45 260	99.79 138	99.43 210	99.28 177	99.68 231	99.54 186	99.40 334	99.56 321	99.07 135	99.82 362	96.01 481	99.96 92	99.11 406

PMMVS		98.49 371	98.29 384	99.11 364	98.96 476	98.42 399	97.54 487	99.32 403	97.53 464	98.47 465	98.15 518	97.88 308	99.82 362	97.46 385	99.24 445	99.09 415

dtuplus		99.52 123	99.55 113	99.43 268	99.76 165	98.90 336	98.71 361	99.89 69	99.67 145	99.79 134	99.77 146	99.25 102	99.81 379	99.18 156	99.96 92	99.57 229

hybridcas		99.65 84	99.63 83	99.70 134	99.85 76	99.67 121	99.30 167	99.87 81	99.67 145	99.81 120	99.77 146	99.21 106	99.81 379	99.24 140	99.94 136	99.61 204

viewdifsd2359ckpt07		99.51 125	99.50 126	99.52 235	99.80 124	99.19 281	98.92 318	99.88 75	99.72 117	99.64 234	99.62 276	99.06 142	99.81 379	98.96 205	99.94 136	99.56 233

testing222		95.60 504	94.59 509	98.61 431	98.66 510	97.45 458	98.54 390	97.90 513	98.53 372	96.54 534	96.47 556	70.62 557	99.81 379	95.91 490	98.15 515	98.56 487

tt0805		99.63 87	99.57 106	99.81 55	99.87 56	99.88 12	99.58 82	98.70 470	99.72 117	99.91 63	99.60 296	99.43 68	99.81 379	99.81 52	99.53 397	99.73 96

LFMVS		98.46 375	98.19 394	99.26 340	99.24 425	98.52 392	99.62 67	96.94 529	99.87 63	99.31 358	99.58 309	91.04 484	99.81 379	98.68 255	99.42 419	99.45 298

NCCC		98.82 330	98.57 343	99.58 203	99.21 430	99.31 246	98.61 372	99.25 420	98.65 355	98.43 467	99.26 422	97.86 309	99.81 379	96.55 453	99.27 440	99.61 204

MIMVSNet		98.43 378	98.20 391	99.11 364	99.53 326	98.38 404	99.58 82	98.61 476	98.96 302	99.33 350	99.76 156	90.92 486	99.81 379	97.38 390	99.76 297	99.15 397

IS-MVSNet		99.03 289	98.85 310	99.55 222	99.80 124	99.25 260	99.73 30	99.15 441	99.37 230	99.61 256	99.71 197	94.73 432	99.81 379	97.70 358	99.88 204	99.58 222

AdaColmap		98.60 355	98.35 376	99.38 291	99.12 447	99.22 271	98.67 365	99.42 370	97.84 450	98.81 434	99.27 418	97.32 346	99.81 379	95.14 508	99.53 397	99.10 409

PMatch-SfM		98.91 316	98.81 317	99.22 347	99.79 138	98.89 339	98.18 427	99.61 274	99.18 264	99.03 409	99.61 286	96.13 400	99.80 389	98.71 250	99.04 462	98.99 444

MCST-MVS		99.02 292	98.81 317	99.65 161	99.58 287	99.49 185	98.58 379	99.07 447	98.40 387	99.04 408	99.25 424	98.51 237	99.80 389	97.31 395	99.51 401	99.65 159

CostFormer		96.71 469	96.79 467	96.46 522	98.90 479	90.71 552	99.41 122	98.68 471	94.69 530	98.14 488	99.34 403	86.32 521	99.80 389	97.60 375	98.07 520	98.88 460

PHI-MVS		99.11 271	98.95 295	99.59 199	99.13 445	99.59 161	99.17 220	99.65 251	97.88 445	99.25 371	99.46 362	98.97 158	99.80 389	97.26 403	99.82 257	99.37 339

hybrid		99.42 164	99.43 150	99.37 296	99.75 183	98.77 357	98.72 357	99.84 106	99.61 171	99.65 228	99.68 229	98.53 231	99.79 393	99.16 164	99.94 136	99.54 249

Patchmatch-RL test		98.60 355	98.36 374	99.33 313	99.77 160	99.07 306	98.27 420	99.87 81	98.91 315	99.74 177	99.72 187	90.57 496	99.79 393	98.55 270	99.85 233	99.11 406

test0.0.03 1		97.37 450	96.91 462	98.74 421	97.72 537	97.57 450	97.60 485	97.36 524	98.00 429	99.21 381	98.02 519	90.04 502	99.79 393	98.37 286	95.89 546	98.86 462

MSDG		99.08 276	98.98 289	99.37 296	99.60 271	99.13 293	97.54 487	99.74 191	98.84 327	99.53 289	99.55 330	99.10 126	99.79 393	97.07 421	99.86 226	99.18 390

E5new		99.68 65	99.67 66	99.70 134	99.87 56	99.62 145	99.41 122	99.84 106	99.68 137	99.77 152	99.81 99	99.59 47	99.78 397	99.13 175	99.96 92	99.70 108

E6new		99.68 65	99.67 66	99.70 134	99.86 61	99.62 145	99.41 122	99.84 106	99.68 137	99.77 152	99.81 99	99.59 47	99.78 397	99.13 175	99.96 92	99.70 108

E6		99.68 65	99.67 66	99.70 134	99.86 61	99.62 145	99.41 122	99.84 106	99.68 137	99.77 152	99.81 99	99.59 47	99.78 397	99.13 175	99.96 92	99.70 108

E5		99.68 65	99.67 66	99.70 134	99.87 56	99.62 145	99.41 122	99.84 106	99.68 137	99.77 152	99.81 99	99.59 47	99.78 397	99.13 175	99.96 92	99.70 108

viewmambaseed2359dif		99.47 146	99.50 126	99.37 296	99.70 224	98.80 354	98.67 365	99.92 48	99.49 195	99.77 152	99.71 197	99.08 132	99.78 397	99.20 151	99.94 136	99.54 249

cl____		98.54 364	98.41 367	98.92 392	99.03 467	97.80 443	97.46 493	99.59 292	98.90 316	99.60 259	99.46 362	93.85 444	99.78 397	97.97 325	99.89 193	99.17 393

DIV-MVS_self_test		98.54 364	98.42 366	98.92 392	99.03 467	97.80 443	97.46 493	99.59 292	98.90 316	99.60 259	99.46 362	93.87 443	99.78 397	97.97 325	99.89 193	99.18 390

MVP-Stereo		99.16 255	99.08 247	99.43 268	99.48 351	99.07 306	99.08 262	99.55 316	98.63 358	99.31 358	99.68 229	98.19 281	99.78 397	98.18 306	99.58 383	99.45 298
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
nrg030		99.70 58	99.66 73	99.82 47	99.76 165	99.84 27	99.61 73	99.70 218	99.93 44	99.78 140	99.68 229	99.10 126	99.78 397	99.45 104	99.96 92	99.83 60

Vis-MVSNet (Re-imp)		98.77 336	98.58 342	99.34 310	99.78 147	98.88 341	99.61 73	99.56 309	99.11 284	99.24 374	99.56 321	93.00 458	99.78 397	97.43 387	99.89 193	99.35 345

CNLPA		98.57 360	98.34 377	99.28 330	99.18 438	99.10 303	98.34 413	99.41 371	98.48 380	98.52 462	98.98 471	97.05 360	99.78 397	95.59 499	99.50 404	98.96 446

ACMH+	98.40 8	99.50 128	99.43 150	99.71 129	99.86 61	99.76 71	99.32 158	99.77 171	99.53 188	99.77 152	99.76 156	99.26 98	99.78 397	97.77 346	99.88 204	99.60 209

CLD-MVS		98.76 337	98.57 343	99.33 313	99.57 297	98.97 321	97.53 489	99.55 316	96.41 503	99.27 365	99.13 446	99.07 135	99.78 397	96.73 442	99.89 193	99.23 375
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hybridnocas07		99.43 160	99.44 147	99.39 287	99.75 183	98.85 347	98.76 349	99.85 96	99.71 123	99.70 198	99.68 229	98.47 240	99.77 410	99.13 175	99.95 117	99.55 237

ALIKED-LG		98.78 334	98.66 332	99.14 360	99.02 473	99.40 221	98.74 354	99.79 153	98.62 362	99.18 387	99.38 385	97.54 334	99.77 410	95.94 489	99.74 312	98.25 502

0.4-1-1-0.1		93.18 511	91.66 515	97.73 480	95.83 550	95.29 517	95.30 541	95.90 537	93.59 533	90.58 552	94.40 560	77.87 542	99.77 410	97.31 395	84.20 551	98.15 509

E4		99.61 99	99.59 97	99.66 154	99.84 82	99.53 177	99.08 262	99.84 106	99.65 157	99.74 177	99.80 109	99.45 64	99.77 410	98.93 214	99.95 117	99.69 120

ttmdpeth		99.48 136	99.55 113	99.29 327	99.76 165	98.16 417	99.33 155	99.95 39	99.79 100	99.36 340	99.89 42	99.13 121	99.77 410	99.09 183	99.64 361	99.93 22

PVSNet_BlendedMVS		99.03 289	99.01 275	99.09 367	99.54 317	97.99 429	98.58 379	99.82 123	97.62 459	99.34 348	99.71 197	98.52 235	99.77 410	97.98 323	99.97 78	99.52 269

PVSNet_Blended		98.70 345	98.59 339	99.02 378	99.54 317	97.99 429	97.58 486	99.82 123	95.70 515	99.34 348	98.98 471	98.52 235	99.77 410	97.98 323	99.83 247	99.30 363

DenseAffine		99.17 252	99.06 253	99.49 245	99.76 165	99.33 242	98.43 408	99.97 22	99.11 284	99.17 388	99.61 286	97.05 360	99.76 417	98.56 269	99.88 204	99.38 335

0.3-1-1-0.015		92.36 513	90.68 517	97.39 493	94.94 554	94.41 527	94.21 545	95.89 538	92.87 536	88.87 554	93.49 563	75.30 548	99.76 417	97.19 413	83.41 553	98.02 514

E3new		99.42 164	99.37 165	99.56 215	99.68 241	99.38 227	98.93 317	99.79 153	99.30 242	99.55 280	99.69 216	98.88 172	99.76 417	98.63 263	99.89 193	99.53 258

E2		99.54 117	99.51 123	99.62 185	99.78 147	99.47 190	99.01 286	99.82 123	99.55 184	99.69 202	99.77 146	99.26 98	99.76 417	98.82 225	99.93 150	99.62 189

E3		99.54 117	99.51 123	99.62 185	99.78 147	99.47 190	99.01 286	99.82 123	99.55 184	99.69 202	99.77 146	99.25 102	99.76 417	98.82 225	99.93 150	99.62 189

viewdifsd2359ckpt09		99.24 221	99.16 219	99.49 245	99.70 224	99.22 271	98.88 323	99.81 136	98.70 349	99.38 337	99.37 389	98.22 277	99.76 417	98.48 275	99.88 204	99.51 272

viewcassd2359sk11		99.48 136	99.45 142	99.58 203	99.73 198	99.42 213	98.96 309	99.80 144	99.44 211	99.63 239	99.74 172	99.09 128	99.76 417	98.72 248	99.91 173	99.57 229

eth_miper_zixun_eth		98.68 347	98.71 326	98.60 432	99.10 454	96.84 482	97.52 491	99.54 322	98.94 306	99.58 264	99.48 355	96.25 396	99.76 417	98.01 321	99.93 150	99.21 380

OPM-MVS		99.26 215	99.13 227	99.63 176	99.70 224	99.61 155	98.58 379	99.48 352	98.50 377	99.52 291	99.63 266	99.14 119	99.76 417	97.89 331	99.77 292	99.51 272
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs-eth3d		99.48 136	99.47 133	99.51 239	99.77 160	99.41 220	98.81 340	99.66 241	99.42 222	99.75 166	99.66 241	99.20 108	99.76 417	98.98 200	99.99 19	99.36 342

pmmvs4		99.13 263	99.06 253	99.36 302	99.57 297	99.10 303	98.01 451	99.25 420	98.78 338	99.58 264	99.44 366	98.24 272	99.76 417	98.74 241	99.93 150	99.22 377

ArgMatch-Sym		99.06 281	98.96 293	99.35 306	99.62 266	99.22 271	98.34 413	99.79 153	98.80 334	99.50 301	99.29 414	98.30 266	99.75 428	97.30 397	99.71 331	99.08 421

MASt3R-SfM		98.45 376	98.51 351	98.26 457	99.32 406	97.43 461	97.43 495	99.69 227	94.97 525	99.75 166	99.41 371	98.49 239	99.75 428	97.73 352	99.79 280	97.61 523

DKM		99.12 266	98.98 289	99.54 228	99.71 208	99.48 189	98.53 392	99.88 75	99.18 264	98.99 413	99.64 250	96.25 396	99.75 428	98.66 258	99.93 150	99.40 329

0.4-1-1-0.2		92.59 512	91.07 516	97.15 506	94.73 556	93.68 533	93.50 546	95.91 535	92.68 537	90.48 553	93.52 562	77.77 543	99.75 428	97.19 413	83.88 552	98.01 515

ETVMVS		96.14 488	95.22 500	98.89 404	98.80 495	98.01 428	98.66 367	98.35 496	98.71 348	97.18 524	96.31 559	74.23 553	99.75 428	96.64 449	98.13 519	98.90 457

AllTest		99.21 238	99.07 251	99.63 176	99.78 147	99.64 137	99.12 245	99.83 116	98.63 358	99.63 239	99.72 187	98.68 200	99.75 428	96.38 466	99.83 247	99.51 272

TestCases				99.63 176	99.78 147	99.64 137		99.83 116	98.63 358	99.63 239	99.72 187	98.68 200	99.75 428	96.38 466	99.83 247	99.51 272

CL-MVSNet_self_test		98.71 344	98.56 347	99.15 357	99.22 428	98.66 368	97.14 509	99.51 343	98.09 423	99.54 284	99.27 418	96.87 368	99.74 435	98.43 282	98.96 467	99.03 436

MVS		95.72 500	94.63 508	98.99 380	98.56 512	97.98 434	99.30 167	98.86 460	72.71 552	97.30 520	99.08 456	98.34 260	99.74 435	89.21 537	98.33 506	99.26 369

MG-MVS		98.52 366	98.39 370	98.94 387	99.15 442	97.39 463	98.18 427	99.21 431	98.89 319	99.23 375	99.63 266	97.37 343	99.74 435	94.22 520	99.61 374	99.69 120

c3_l		98.72 342	98.71 326	98.72 423	99.12 447	97.22 469	97.68 479	99.56 309	98.90 316	99.54 284	99.48 355	96.37 389	99.73 438	97.88 332	99.88 204	99.21 380

tpmvs		97.39 449	97.69 430	96.52 520	98.41 518	91.76 543	99.30 167	98.94 458	97.74 452	97.85 503	99.55 330	92.40 469	99.73 438	96.25 471	98.73 489	98.06 511

viewmacassd2359aftdt		99.63 87	99.61 90	99.68 142	99.84 82	99.61 155	99.14 233	99.87 81	99.71 123	99.75 166	99.77 146	99.54 56	99.72 440	98.91 217	99.96 92	99.70 108

thres600view7		96.60 473	96.16 477	97.93 469	99.63 262	96.09 501	99.18 215	97.57 519	98.77 341	98.72 444	97.32 537	87.04 514	99.72 440	88.57 540	98.62 494	97.98 516

EPMVS		96.53 474	96.32 471	97.17 505	98.18 527	92.97 537	99.39 129	89.95 557	98.21 413	98.61 454	99.59 306	86.69 520	99.72 440	96.99 423	99.23 447	98.81 467

PVSNet	97.47 15	98.42 379	98.44 363	98.35 447	99.46 361	96.26 494	96.70 529	99.34 396	97.68 457	99.00 412	99.13 446	97.40 340	99.72 440	97.59 376	99.68 348	99.08 421

MAR-MVS		98.24 395	97.92 417	99.19 352	98.78 499	99.65 130	99.17 220	99.14 443	95.36 519	98.04 491	98.81 490	97.47 337	99.72 440	95.47 502	99.06 458	98.21 505
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
viewmanbaseed2359cas		99.50 128	99.47 133	99.61 192	99.73 198	99.52 182	99.03 277	99.83 116	99.49 195	99.65 228	99.64 250	99.18 110	99.71 445	98.73 246	99.92 159	99.58 222

testing91		96.00 493	95.32 498	98.02 463	98.76 502	95.39 514	98.38 411	98.65 475	98.82 330	96.84 528	96.71 552	75.06 550	99.71 445	96.46 462	98.23 510	98.98 445

miper_ehance_all_eth		98.59 358	98.59 339	98.59 433	98.98 474	97.07 473	97.49 492	99.52 338	98.50 377	99.52 291	99.37 389	96.41 387	99.71 445	97.86 337	99.62 366	99.00 443

Gipuma		99.57 103	99.59 97	99.49 245	99.98 3	99.71 102	99.72 33	99.84 106	99.81 92	99.94 48	99.78 134	98.91 168	99.71 445	98.41 283	99.95 117	99.05 430
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
viewdifsd2359ckpt13		99.42 164	99.37 165	99.57 211	99.72 203	99.46 198	99.01 286	99.80 144	99.20 261	99.51 298	99.60 296	98.92 165	99.70 449	98.65 261	99.90 177	99.55 237

ambc				99.20 351	99.35 391	98.53 390	99.17 220	99.46 358		99.67 217	99.80 109	98.46 244	99.70 449	97.92 328	99.70 334	99.38 335

HQP4-MVS										98.15 484			99.70 449			99.53 258

CNVR-MVS		98.99 304	98.80 320	99.56 215	99.25 423	99.43 210	98.54 390	99.27 415	98.58 365	98.80 436	99.43 367	98.53 231	99.70 449	97.22 409	99.59 381	99.54 249

tpm2		96.35 481	96.22 476	96.73 518	98.88 485	91.75 544	99.21 204	98.51 483	93.27 535	97.89 499	99.21 437	84.83 524	99.70 449	96.04 480	98.18 514	98.75 475

HQP-MVS		98.36 384	98.02 406	99.39 287	99.31 408	98.94 327	97.98 456	99.37 387	97.45 468	98.15 484	98.83 487	96.67 374	99.70 449	94.73 513	99.67 354	99.53 258

PatchMatch-RL		98.68 347	98.47 356	99.30 326	99.44 366	99.28 251	98.14 435	99.54 322	97.12 487	99.11 399	99.25 424	97.80 314	99.70 449	96.51 456	99.30 434	98.93 452

testing11		96.05 492	95.41 495	97.97 467	98.78 499	95.27 518	98.59 377	98.23 500	98.86 322	96.56 533	96.91 546	75.20 549	99.69 456	97.26 403	98.29 508	98.93 452

testing99		95.86 497	95.19 501	97.87 472	98.76 502	95.03 521	98.62 371	98.44 488	98.68 351	96.67 531	96.66 553	74.31 552	99.69 456	96.51 456	98.03 521	98.90 457

miper_enhance_ethall		98.03 413	97.94 415	98.32 450	98.27 523	96.43 490	96.95 519	99.41 371	96.37 505	99.43 319	98.96 475	94.74 431	99.69 456	97.71 355	99.62 366	98.83 465

test_yl		98.25 393	97.95 411	99.13 362	99.17 439	98.47 393	99.00 292	98.67 473	98.97 300	99.22 379	99.02 466	91.31 480	99.69 456	97.26 403	98.93 469	99.24 372

DCV-MVSNet		98.25 393	97.95 411	99.13 362	99.17 439	98.47 393	99.00 292	98.67 473	98.97 300	99.22 379	99.02 466	91.31 480	99.69 456	97.26 403	98.93 469	99.24 372

MS-PatchMatch		99.00 301	98.97 291	99.09 367	99.11 452	98.19 413	98.76 349	99.33 401	98.49 379	99.44 315	99.58 309	98.21 278	99.69 456	98.20 302	99.62 366	99.39 333

v148		99.40 173	99.41 157	99.39 287	99.76 165	98.94 327	99.09 259	99.59 292	99.17 271	99.81 120	99.61 286	98.41 250	99.69 456	99.32 129	99.94 136	99.53 258

test_prior				99.46 257	99.35 391	99.22 271		99.39 381					99.69 456			99.48 287

tpm cat1		96.78 465	96.98 458	96.16 525	98.85 488	90.59 553	99.08 262	99.32 403	92.37 538	97.73 511	99.46 362	91.15 483	99.69 456	96.07 479	98.80 478	98.21 505

PAPM_NR		98.36 384	98.04 404	99.33 313	99.48 351	98.93 330	98.79 346	99.28 414	97.54 463	98.56 461	98.57 504	97.12 357	99.69 456	94.09 523	98.90 475	99.38 335

PAPM		95.61 503	94.71 507	98.31 452	99.12 447	96.63 484	96.66 530	98.46 487	90.77 544	96.25 536	98.68 499	93.01 457	99.69 456	81.60 551	97.86 525	98.62 479

OMC-MVS		98.90 319	98.72 325	99.44 264	99.39 378	99.42 213	98.58 379	99.64 259	97.31 477	99.44 315	99.62 276	98.59 214	99.69 456	96.17 477	99.79 280	99.22 377

casdiffseed414692147		99.68 65	99.68 64	99.67 146	99.86 61	99.65 130	99.32 158	99.87 81	99.75 111	99.77 152	99.80 109	99.61 42	99.68 468	99.21 147	99.95 117	99.67 136

E-PMN		97.14 459	97.43 439	96.27 523	98.79 497	91.62 545	95.54 539	99.01 455	99.44 211	98.88 425	99.12 450	92.78 459	99.68 468	94.30 519	99.03 463	97.50 524

TSAR-MVS + GP.		99.12 266	99.04 266	99.38 291	99.34 400	99.16 288	98.15 433	99.29 411	98.18 416	99.63 239	99.62 276	99.18 110	99.68 468	98.20 302	99.74 312	99.30 363

MVS-HIRNet		97.86 422	98.22 389	96.76 515	99.28 417	91.53 546	98.38 411	92.60 552	99.13 280	99.31 358	99.96 15	97.18 355	99.68 468	98.34 289	99.83 247	99.07 427

PAPR		97.56 438	97.07 454	99.04 377	98.80 495	98.11 421	97.63 482	99.25 420	94.56 532	98.02 493	98.25 515	97.43 339	99.68 468	90.90 536	98.74 486	99.33 352

ITE_SJBPF				99.38 291	99.63 262	99.44 206		99.73 196	98.56 366	99.33 350	99.53 335	98.88 172	99.68 468	96.01 481	99.65 359	99.02 441

MVStest1		98.22 399	98.09 401	98.62 430	99.04 465	96.23 495	99.20 205	99.92 48	99.44 211	99.98 14	99.87 57	85.87 522	99.67 474	99.91 34	99.57 385	99.95 16

thres100view900		96.39 479	96.03 480	97.47 488	99.63 262	95.93 502	99.18 215	97.57 519	98.75 345	98.70 447	97.31 538	87.04 514	99.67 474	87.62 544	98.51 498	96.81 530

tfpn200view9		96.30 483	95.89 482	97.53 483	99.58 287	96.11 499	99.00 292	97.54 522	98.43 382	98.52 462	96.98 543	86.85 516	99.67 474	87.62 544	98.51 498	96.81 530

1314		98.00 416	97.90 419	98.27 456	98.90 479	97.45 458	99.30 167	99.06 449	94.98 524	97.21 523	99.12 450	98.43 247	99.67 474	95.58 500	98.56 496	97.71 521

thres400		96.40 478	95.89 482	97.92 470	99.58 287	96.11 499	99.00 292	97.54 522	98.43 382	98.52 462	96.98 543	86.85 516	99.67 474	87.62 544	98.51 498	97.98 516

testing3-2		96.51 476	96.43 469	96.74 517	99.36 387	91.38 548	99.10 254	97.87 514	99.48 198	98.57 459	98.71 495	76.65 546	99.66 479	98.87 220	99.26 441	99.18 390

EMVS		96.96 462	97.28 444	95.99 527	98.76 502	91.03 549	95.26 542	98.61 476	99.34 235	98.92 421	98.88 483	93.79 445	99.66 479	92.87 529	99.05 460	97.30 528

MVS_Test		99.28 209	99.31 184	99.19 352	99.35 391	98.79 355	99.36 144	99.49 351	99.17 271	99.21 381	99.67 235	98.78 186	99.66 479	99.09 183	99.66 357	99.10 409

EPNet_dtu		97.62 435	97.79 425	97.11 508	96.67 549	92.31 540	98.51 395	98.04 506	99.24 254	95.77 539	99.47 359	93.78 446	99.66 479	98.98 200	99.62 366	99.37 339
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-RMVSNet		98.41 380	98.14 398	99.21 348	99.21 430	98.47 393	98.60 374	98.26 499	98.35 398	98.93 418	99.31 407	97.20 354	99.66 479	94.32 518	99.10 455	99.51 272

MDTV_nov1_ep13			97.73 429		98.70 507	90.83 550	99.15 229	98.02 507	98.51 375	98.82 433	99.61 286	90.98 485	99.66 479	96.89 431	98.92 471

MVS_111021_LR		99.13 263	99.03 268	99.42 271	99.58 287	99.32 245	97.91 465	99.73 196	98.68 351	99.31 358	99.48 355	99.09 128	99.66 479	97.70 358	99.77 292	99.29 366

BH-untuned		98.22 399	98.09 401	98.58 436	99.38 381	97.24 468	98.55 387	98.98 457	97.81 451	99.20 386	98.76 492	97.01 362	99.65 486	94.83 512	98.33 506	98.86 462

RPSCF		99.18 247	99.02 269	99.64 168	99.83 91	99.85 22	99.44 119	99.82 123	98.33 405	99.50 301	99.78 134	97.90 306	99.65 486	96.78 439	99.83 247	99.44 313

SD_0403		97.42 447	96.90 463	98.98 382	99.54 317	97.90 437	99.52 94	99.54 322	99.34 235	97.87 501	98.85 485	98.72 196	99.64 488	78.93 554	99.83 247	99.40 329

USDC		98.96 308	98.93 297	99.05 376	99.54 317	97.99 429	97.07 513	99.80 144	98.21 413	99.75 166	99.77 146	98.43 247	99.64 488	97.90 330	99.88 204	99.51 272

DeepPCF-MVS	98.42 6	99.18 247	99.02 269	99.67 146	99.22 428	99.75 80	97.25 503	99.47 355	98.72 346	99.66 224	99.70 207	99.29 92	99.63 490	98.07 317	99.81 267	99.62 189

UBG		96.53 474	95.95 481	98.29 455	98.87 486	96.31 493	98.48 400	98.07 505	98.83 328	97.32 519	96.54 554	79.81 535	99.62 491	96.84 436	98.74 486	98.95 448

alignmvs		98.28 390	97.96 410	99.25 343	99.12 447	98.93 330	99.03 277	98.42 489	99.64 161	98.72 444	97.85 525	90.86 490	99.62 491	98.88 219	99.13 452	99.19 388

DeepMVS_CX				97.98 465	99.69 232	96.95 475		99.26 417	75.51 551	95.74 540	98.28 514	96.47 383	99.62 491	91.23 535	97.89 523	97.38 526

TinyColmap		98.97 305	98.93 297	99.07 373	99.46 361	98.19 413	97.75 472	99.75 185	98.79 336	99.54 284	99.70 207	98.97 158	99.62 491	96.63 450	99.83 247	99.41 326

TAPA-MVS	97.92 13	98.03 413	97.55 435	99.46 257	99.47 357	99.44 206	98.50 396	99.62 266	86.79 546	99.07 405	99.26 422	98.26 271	99.62 491	97.28 400	99.73 319	99.31 361
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DPM-MVS		98.28 390	97.94 415	99.32 318	99.36 387	99.11 296	97.31 500	98.78 467	96.88 495	98.84 431	99.11 453	97.77 317	99.61 496	94.03 525	99.36 426	99.23 375

thres200		96.09 489	95.68 489	97.33 499	99.48 351	96.22 496	98.53 392	97.57 519	98.06 427	98.37 470	96.73 551	86.84 518	99.61 496	86.99 547	98.57 495	96.16 534

DP-MVS Recon		98.50 369	98.23 388	99.31 322	99.49 346	99.46 198	98.56 386	99.63 263	94.86 528	98.85 430	99.37 389	97.81 313	99.59 498	96.08 478	99.44 414	98.88 460

PVSNet_0	95.53 19	95.85 498	95.31 499	97.47 488	98.78 499	93.48 535	95.72 538	99.40 378	96.18 508	97.37 518	97.73 527	95.73 409	99.58 499	95.49 501	81.40 555	99.36 342

MGCFI-Net		99.02 292	99.01 275	99.06 375	99.11 452	98.60 379	99.63 64	99.67 236	99.63 163	98.58 457	97.65 529	99.07 135	99.57 500	98.85 221	98.92 471	99.03 436

Syy-MVS		98.17 405	97.85 421	99.15 357	98.50 515	98.79 355	98.60 374	99.21 431	97.89 443	96.76 529	96.37 557	95.47 419	99.57 500	99.10 182	98.73 489	99.09 415

myMVS_eth3d		95.63 502	94.73 506	98.34 449	98.50 515	96.36 491	98.60 374	99.21 431	97.89 443	96.76 529	96.37 557	72.10 555	99.57 500	94.38 517	98.73 489	99.09 415

API-MVS		98.38 383	98.39 370	98.35 447	98.83 491	99.26 257	99.14 233	99.18 437	98.59 364	98.66 449	98.78 491	98.61 211	99.57 500	94.14 522	99.56 386	96.21 532

TestfortrainingZip				99.38 291	99.17 439	99.25 260	99.38 132	98.82 463	98.93 311	99.68 209	99.49 351	98.11 290	99.56 504		98.44 503	99.32 356

SP-LightGlue		98.62 351	98.51 351	98.94 387	98.69 508	99.01 313	98.34 413	99.54 322	99.27 247	97.72 512	99.15 445	95.88 408	99.54 505	98.53 273	99.47 409	98.27 500

sasdasda		99.02 292	99.00 279	99.09 367	99.10 454	98.70 363	99.61 73	99.66 241	99.63 163	98.64 450	97.65 529	99.04 145	99.54 505	98.79 232	98.92 471	99.04 433

KD-MVS_2432*1600		95.89 494	95.41 495	97.31 500	94.96 552	93.89 529	97.09 510	99.22 428	97.23 481	98.88 425	99.04 461	79.23 538	99.54 505	96.24 473	96.81 534	98.50 492

miper_refine_blended		95.89 494	95.41 495	97.31 500	94.96 552	93.89 529	97.09 510	99.22 428	97.23 481	98.88 425	99.04 461	79.23 538	99.54 505	96.24 473	96.81 534	98.50 492

canonicalmvs		99.02 292	99.00 279	99.09 367	99.10 454	98.70 363	99.61 73	99.66 241	99.63 163	98.64 450	97.65 529	99.04 145	99.54 505	98.79 232	98.92 471	99.04 433

MVS_111021_HR		99.12 266	99.02 269	99.40 284	99.50 341	99.11 296	97.92 463	99.71 209	98.76 344	99.08 402	99.47 359	99.17 112	99.54 505	97.85 339	99.76 297	99.54 249

test_241102_ONE					99.69 232	99.82 42		99.54 322	99.12 283	99.82 113	99.49 351	98.91 168	99.52 511

gg-mvs-nofinetune		95.87 496	95.17 502	97.97 467	98.19 526	96.95 475	99.69 45	89.23 558	99.89 56	96.24 537	99.94 20	81.19 529	99.51 512	93.99 526	98.20 511	97.44 525

TR-MVS		97.44 446	97.15 451	98.32 450	98.53 513	97.46 456	98.47 401	97.91 512	96.85 496	98.21 478	98.51 508	96.42 385	99.51 512	92.16 531	97.29 532	97.98 516

ALIKED-MNN		98.03 413	97.78 426	98.78 418	98.84 490	98.97 321	98.16 432	99.74 191	97.31 477	96.60 532	98.85 485	96.61 376	99.48 514	94.16 521	99.77 292	97.91 520

BH-w/o		97.20 455	97.01 457	97.76 476	99.08 459	95.69 509	98.03 450	98.52 482	95.76 514	97.96 494	98.02 519	95.62 411	99.47 515	92.82 530	97.25 533	98.12 510

PMVS	92.94 21	98.82 330	98.81 317	98.85 408	99.84 82	97.99 429	99.20 205	99.47 355	99.71 123	99.42 322	99.82 92	98.09 291	99.47 515	93.88 527	99.85 233	99.07 427
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CMPMVS	77.52 23	98.50 369	98.19 394	99.41 281	98.33 521	99.56 170	99.01 286	99.59 292	95.44 518	99.57 267	99.80 109	95.64 410	99.46 517	96.47 461	99.92 159	99.21 380
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SIFT-CM-Cal		97.96 420	98.15 397	97.39 493	99.61 268	99.15 290	96.75 526	98.41 492	98.04 428	99.03 409	99.54 332	95.24 424	99.41 518	96.97 425	99.80 274	93.61 548

GA-MVS		97.99 417	97.68 431	98.93 391	99.52 333	98.04 427	97.19 505	99.05 450	98.32 406	98.81 434	98.97 473	89.89 504	99.41 518	98.33 290	99.05 460	99.34 351

FBQ-MVS		96.06 491	95.42 493	97.98 465	98.90 479	95.77 506	98.71 361	98.20 501	98.34 402	97.83 505	97.34 535	74.90 551	99.39 520	96.20 475	98.40 505	98.78 470

cl22		97.56 438	97.28 444	98.40 444	98.37 520	96.75 483	97.24 504	99.37 387	97.31 477	99.41 328	99.22 433	87.30 511	99.37 521	97.70 358	99.62 366	99.08 421

SIFT-NCMNet		98.18 402	98.46 358	97.36 496	99.67 248	99.19 281	96.33 535	98.99 456	98.83 328	99.62 249	99.63 266	95.41 421	99.33 522	97.64 370	100.00 1	93.54 549

SIFT-UMatch		98.07 411	98.27 385	97.46 490	99.57 297	98.99 316	96.93 521	99.02 452	98.53 372	99.26 369	99.23 432	95.43 420	99.31 523	96.51 456	99.91 173	94.09 542

UWE-MVS-28		95.64 501	95.47 492	96.14 526	97.98 533	90.39 554	98.49 399	95.81 541	99.02 295	98.03 492	98.19 516	84.49 526	99.28 524	88.75 539	98.47 502	98.75 475

SP-DiffGlue		98.47 373	98.43 365	98.59 433	97.44 545	98.59 381	98.01 451	99.36 391	99.00 297	99.06 406	99.20 439	97.01 362	99.25 525	97.64 370	99.15 451	97.92 519

SIFT-UM-Cal		98.18 402	98.45 361	97.37 495	99.59 277	98.95 325	96.76 525	99.39 381	98.39 388	99.46 312	99.31 407	96.23 398	99.24 526	97.21 410	99.70 334	93.90 544

SP-MNN		97.94 421	97.82 422	98.31 452	98.30 522	97.67 448	97.81 470	97.93 511	98.14 418	97.16 526	98.64 501	96.31 392	99.21 527	97.34 392	98.75 485	98.05 513

dmvs_re		98.69 346	98.48 355	99.31 322	99.55 315	99.42 213	99.54 90	98.38 494	99.32 239	98.72 444	98.71 495	96.76 372	99.21 527	96.01 481	99.35 428	99.31 361

SP-SuperGlue		98.66 349	98.63 335	98.73 422	98.44 517	99.02 312	98.22 425	99.44 364	99.37 230	98.17 483	99.30 410	96.95 365	99.12 529	98.59 265	99.20 450	98.06 511

XFeat-MNN		96.67 470	96.56 468	96.98 511	96.73 548	95.62 512	94.54 544	98.93 459	97.42 471	98.18 479	98.67 500	91.60 478	99.12 529	93.88 527	99.10 455	96.21 532

GG-mvs-BLEND				97.36 496	97.59 541	96.87 479	99.70 38	88.49 559		94.64 545	97.26 539	80.66 531	99.12 529	91.50 534	96.50 542	96.08 535

MSLP-MVS++		99.05 285	99.09 245	98.91 397	99.21 430	98.36 405	98.82 339	99.47 355	98.85 323	98.90 424	99.56 321	98.78 186	99.09 532	98.57 268	99.68 348	99.26 369

FPMVS		96.32 482	95.50 491	98.79 416	99.60 271	98.17 416	98.46 405	98.80 466	97.16 485	96.28 535	99.63 266	82.19 528	99.09 532	88.45 541	98.89 476	99.10 409

SIFT-ConvMatch		98.16 406	98.37 372	97.52 484	99.54 317	99.20 278	96.97 518	98.47 486	98.09 423	99.14 394	99.40 377	95.93 407	99.05 534	97.87 335	99.92 159	94.31 538

SIFT-NCM-Cal		98.18 402	98.41 367	97.48 486	99.57 297	99.28 251	97.26 502	98.08 504	98.30 408	99.23 375	99.39 382	97.13 356	99.04 535	96.86 432	99.86 226	94.12 541

dmvs_testset		97.27 453	96.83 465	98.59 433	99.46 361	97.55 451	99.25 192	96.84 530	98.78 338	97.24 522	97.67 528	97.11 358	98.97 536	86.59 549	98.54 497	99.27 367

myMVS_eth3d28		96.23 485	95.74 487	97.70 482	98.86 487	95.59 513	98.66 367	98.14 503	98.96 302	97.67 513	97.06 542	76.78 545	98.92 537	97.10 418	98.41 504	98.58 484

SIFT-NN-UMatch		97.18 456	97.24 448	97.01 510	99.57 297	98.65 372	96.33 535	97.31 525	97.07 489	97.48 516	98.73 494	94.39 437	98.87 538	95.75 496	98.50 501	93.50 550

OPU-MVS				99.29 327	99.12 447	99.44 206	99.20 205				99.40 377	99.00 150	98.84 539	96.54 454	99.60 377	99.58 222

SIFT-NN-CMatch		97.30 452	97.34 442	97.18 503	99.54 317	98.85 347	96.02 537	95.77 542	97.05 490	97.55 515	98.70 497	96.35 391	98.75 540	95.82 494	99.26 441	93.95 543

cascas		96.99 460	96.82 466	97.48 486	97.57 543	95.64 510	96.43 533	99.56 309	91.75 541	97.13 527	97.61 532	95.58 413	98.63 541	96.68 444	99.11 454	98.18 508

MVE	92.54 22	96.66 471	96.11 478	98.31 452	99.68 241	97.55 451	97.94 461	95.60 543	99.37 230	90.68 551	98.70 497	96.56 378	98.61 542	86.94 548	99.55 390	98.77 473
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MonoMVSNet		98.23 397	98.32 379	97.99 464	98.97 475	96.62 485	99.49 107	98.42 489	99.62 166	99.40 334	99.79 121	95.51 417	98.58 543	97.68 369	95.98 545	98.76 474

SIFT-NN-PointCN		97.97 418	98.24 387	97.14 507	99.59 277	98.71 362	96.75 526	99.56 309	97.02 491	97.91 498	99.27 418	96.85 369	98.39 544	97.47 384	99.76 297	94.31 538

SIFT-NN-NCMNet		97.22 454	97.27 446	97.07 509	99.64 257	99.20 278	96.53 531	95.91 535	96.91 494	97.38 517	98.95 477	96.01 404	98.29 545	94.87 511	99.21 449	93.73 547

GLUNet-SfM		95.26 506	95.06 503	95.87 528	94.84 555	90.39 554	90.24 549	99.92 48	92.30 539	99.16 389	99.25 424	94.69 433	98.01 546	85.55 550	99.62 366	99.21 380

SIFT-MNN		97.55 440	97.74 428	96.98 511	99.38 381	98.85 347	96.92 522	98.61 476	98.36 392	98.63 452	99.10 454	92.51 465	97.85 547	96.63 450	99.48 408	94.25 540

ALIKED-NN		96.66 471	96.26 473	97.88 471	97.49 544	98.59 381	96.71 528	99.15 441	95.50 517	93.58 548	98.39 511	94.52 436	97.74 548	92.05 532	98.94 468	97.29 529

PC_three_1452									97.56 460	99.68 209	99.41 371	99.09 128	97.09 549	96.66 446	99.60 377	99.62 189

tmp_tt		95.75 499	95.42 493	96.76 515	89.90 559	94.42 526	98.86 327	97.87 514	78.01 550	99.30 363	99.69 216	97.70 321	95.89 550	99.29 135	98.14 516	99.95 16

SP-NN		96.37 480	96.23 475	96.77 514	96.83 547	96.95 475	96.47 532	97.07 528	96.75 500	93.41 549	97.75 526	94.13 440	95.69 551	96.25 471	97.43 529	97.68 522

dongtai		89.37 515	88.91 518	90.76 533	99.19 435	77.46 560	95.47 540	87.82 560	92.28 540	94.17 546	98.82 489	71.22 556	95.54 552	63.85 555	97.34 530	99.27 367

SD-MVS		99.01 298	99.30 189	98.15 460	99.50 341	99.40 221	98.94 314	99.61 274	99.22 260	99.75 166	99.82 92	99.54 56	95.51 553	97.48 383	99.87 218	99.54 249
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
XFeat-NN		93.89 510	93.91 512	93.83 531	95.49 551	92.69 538	90.85 547	97.98 508	94.69 530	95.08 543	96.98 543	88.36 509	94.23 554	88.42 542	97.34 530	94.57 536

SIFT-NN		94.78 508	94.89 504	94.45 530	98.23 525	97.29 466	94.93 543	95.84 539	95.82 513	94.78 544	97.12 540	90.26 499	92.28 555	88.91 538	98.14 516	93.77 546

kuosan		85.65 517	84.57 520	88.90 535	97.91 535	77.11 561	96.37 534	87.62 561	85.24 549	85.45 555	96.83 547	69.94 558	90.98 556	45.90 557	95.83 547	98.62 479

VLMVS_CLIP		76.68 518	76.70 522	76.61 536	60.81 561	61.63 564	78.48 551	91.77 553	64.66 553	83.93 556	93.59 561	55.35 560	75.94 557	79.82 553	81.86 554	92.28 551

VLMVS		62.60 520	63.55 523	59.72 538	60.35 562	58.44 565	68.37 552	54.75 563	23.35 557	80.04 557	90.18 565	54.59 561	52.33 558	63.04 556	77.30 557	68.41 554

MVS_clip		74.80 519	77.14 521	67.78 537	84.58 560	66.83 563	78.80 550	52.59 564	49.02 555	94.13 547	97.99 522	68.69 559	48.60 559	80.92 552	87.52 550	87.92 552

test123		29.31 522	33.05 527	18.08 540	25.93 565	12.24 566	97.53 489	10.93 567	11.78 558	24.21 560	50.08 570	21.04 563	8.60 560	23.51 559	32.43 560	33.39 556

testmvs		28.94 523	33.33 525	15.79 541	26.03 564	9.81 568	96.77 524	15.67 565	11.55 559	23.87 561	50.74 569	19.03 564	8.53 561	23.21 560	33.07 559	29.03 557

MVS_baseline		39.37 521	46.36 524	18.41 539	48.75 563	10.55 567	42.43 553	13.32 566	4.65 560	75.25 558	91.61 564	29.41 562	0.06 562	38.83 558	72.99 558	44.63 555

Meshroom													0.00 563
: In preparation.
AliceVision / Meshro													0.00 563
: In preparation.
AliceVision_Meshroom													0.00 563
: In preparation.
mmdepth		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

monomultidepth		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

test_blank		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

uanet_test		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

DCPMVS		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

cdsmvs_eth3d_5k		24.88 524	33.17 526	0.00 542	0.00 566	0.00 569	0.00 554	99.62 266	0.00 561	0.00 562	99.13 446	99.82 19	0.00 563	0.00 561	0.00 561	0.00 558

pcd_1.5k_mvsjas		16.61 525	22.14 528	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	99.28 94	0.00 563	0.00 561	0.00 561	0.00 558

sosnet-low-res		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

sosnet		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

uncertanet		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

Regformer		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

ab-mvs-re		8.26 536	11.02 539	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	99.16 443	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

uanet		8.33 526	11.11 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

PatchmatchNet2					0.00 566	95.19 520	97.64 481	99.19 435	98.09 423
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														98.28 293	99.92 159	99.44 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						96.36 491								95.20 507

FOURS1					99.83 91	99.89 10	99.74 27	99.71 209	99.69 133	99.63 239

test_one_0601					99.63 262	99.76 71		99.55 316	99.23 256	99.31 358	99.61 286	98.59 214

eth-test2					0.00 566

eth-test					0.00 566

RE-MVS-def			99.13 227		99.54 317	99.74 88	99.26 186	99.62 266	99.16 273	99.52 291	99.64 250	98.57 217		97.27 401	99.61 374	99.54 249

IU-MVS					99.69 232	99.77 64		99.22 428	97.50 466	99.69 202				97.75 350	99.70 334	99.77 82

save fliter					99.53 326	99.25 260	98.29 419	99.38 386	99.07 288

test0726					99.69 232	99.80 52	99.24 193	99.57 304	99.16 273	99.73 183	99.65 248	98.35 258

GSMVS																99.14 402

test_part2					99.62 266	99.67 121				99.55 280

sam_mvs1												90.81 491				99.14 402

sam_mvs												90.52 497

MTGPA								99.53 333

MTMP							99.09 259	98.59 480

test9_res														95.10 509	99.44 414	99.50 278

agg_prior2														94.58 516	99.46 413	99.50 278

test_prior4						99.19 281	98.00 454

test_prior2							97.95 460		97.87 446	98.05 490	99.05 459	97.90 306		95.99 484	99.49 406

新几何2							98.04 448

旧先验1					99.49 346	99.29 249		99.26 417			99.39 382	97.67 325			99.36 426	99.46 296

原ACMM2							97.92 463

test222					99.51 335	99.08 305	97.83 469	99.29 411	95.21 522	98.68 448	99.31 407	97.28 347			99.38 423	99.43 320

segment_acmp												98.37 256

testdata1							97.72 475		97.86 448

plane_prior7					99.58 287	99.38 227

plane_prior6					99.47 357	99.26 257						97.24 348

plane_prior4											99.25 424

plane_prior3						99.31 246			98.36 392	99.14 394

plane_prior2							98.80 343		98.94 306

plane_prior1					99.51 335

plane_prior						99.24 265	98.42 409		97.87 446						99.71 331

n2								0.00 568

nn								0.00 568

door-mid								99.83 116

test11								99.29 411

door								99.77 171

HQP5-MVS						98.94 327

HQP-NCC					99.31 408		97.98 456		97.45 468	98.15 484

ACMP_Plane					99.31 408		97.98 456		97.45 468	98.15 484

BP-MVS														94.73 513

HQP3-MVS								99.37 387							99.67 354

HQP2-MVS												96.67 374

NP-MVS					99.40 377	99.13 293					98.83 487

MDTV_nov1_ep13_2view						91.44 547	99.14 233		97.37 474	99.21 381		91.78 477		96.75 440		99.03 436

ACMMP++_ref															99.94 136

ACMMP++															99.79 280

Test By Simon												98.41 250

High-resolution multi-view benchmark