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.

CS-MVS		99.50 31	99.48 22	99.54 127	99.76 83	99.42 120	99.90 1	99.55 100	98.56 119	99.78 86	99.70 226	98.65 75	99.79 253	99.65 41	99.78 135	99.41 274

mmtdpeth		96.95 394	96.71 393	97.67 424	99.33 302	94.90 453	99.89 2	99.28 362	98.15 184	99.72 108	98.57 459	86.56 463	99.90 149	99.82 29	89.02 496	98.20 458

SPE-MVS-test		99.49 33	99.48 22	99.54 127	99.78 71	99.30 140	99.89 2	99.58 78	98.56 119	99.73 103	99.69 237	98.55 82	99.82 233	99.69 34	99.85 94	99.48 252

MVSFormer		99.17 109	99.12 96	99.29 216	99.51 238	98.94 203	99.88 4	99.46 248	97.55 290	99.80 78	99.65 259	97.39 126	99.28 385	99.03 144	99.85 94	99.65 184

test_djsdf		98.67 223	98.57 224	98.98 254	98.70 439	98.91 210	99.88 4	99.46 248	97.55 290	99.22 269	99.88 59	95.73 232	99.28 385	99.03 144	97.62 339	98.75 351

OurMVSNet-221017-0		97.88 308	97.77 299	98.19 374	98.71 438	96.53 394	99.88 4	99.00 415	97.79 259	98.78 355	99.94 6	91.68 389	99.35 375	97.21 366	96.99 375	98.69 368

EC-MVSNet		99.44 50	99.39 39	99.58 118	99.56 217	99.49 111	99.88 4	99.58 78	98.38 141	99.73 103	99.69 237	98.20 104	99.70 301	99.64 43	99.82 118	99.54 229

DVP-MVS++		99.59 15	99.50 19	99.88 16	99.51 238	99.88 10	99.87 8	99.51 162	98.99 69	99.88 42	99.81 143	99.27 6	99.96 41	98.85 176	99.80 126	99.81 79

FOURS1					99.91 1	99.93 1	99.87 8	99.56 90	99.10 48	99.81 72

K. test v3		97.10 390	96.79 391	98.01 388	98.72 435	96.33 402	99.87 8	97.05 508	97.59 284	96.16 470	99.80 161	88.71 436	99.04 438	96.69 399	96.55 383	98.65 392

FC-MVSNet-test		98.75 215	98.62 217	99.15 238	99.08 372	99.45 117	99.86 11	99.60 68	98.23 171	98.70 367	99.82 128	96.80 164	99.22 403	99.07 139	96.38 386	98.79 341

v7n		97.87 310	97.52 328	98.92 265	98.76 431	98.58 264	99.84 12	99.46 248	96.20 410	98.91 331	99.70 226	94.89 270	99.44 353	96.03 416	93.89 450	98.75 351

DTE-MVSNet		97.51 366	97.19 376	98.46 344	98.63 448	98.13 297	99.84 12	99.48 213	96.68 372	97.97 430	99.67 252	92.92 352	98.56 478	96.88 392	92.60 472	98.70 364

3Dnovator	97.25 9	99.24 96	99.05 113	99.81 60	99.12 361	99.66 72	99.84 12	99.74 13	99.09 55	98.92 329	99.90 36	95.94 218	99.98 20	98.95 156	99.92 38	99.79 92

FIs		98.78 210	98.63 212	99.23 228	99.18 345	99.54 100	99.83 15	99.59 73	98.28 156	98.79 354	99.81 143	96.75 167	99.37 368	99.08 138	96.38 386	98.78 343

MGCFI-Net		99.01 173	98.85 181	99.50 153	99.42 272	99.26 146	99.82 16	99.48 213	98.60 116	99.28 251	98.81 448	97.04 148	99.76 270	99.29 104	97.87 328	99.47 258

test_fmvs3		92.10 466	91.77 468	93.08 485	96.19 508	86.25 505	99.82 16	98.62 473	96.65 375	95.19 478	96.90 508	55.05 532	95.93 516	96.63 404	90.92 485	97.06 504

jajsoiax		98.43 237	98.28 244	98.88 280	98.60 453	98.43 283	99.82 16	99.53 125	98.19 179	98.63 379	99.80 161	93.22 347	99.44 353	99.22 114	97.50 351	98.77 347

OpenMVS	96.50 16	98.47 234	98.12 256	99.52 142	99.04 384	99.53 103	99.82 16	99.72 14	94.56 451	98.08 423	99.88 59	94.73 286	99.98 20	97.47 345	99.76 142	99.06 319

SDMVSNet		99.11 145	98.90 167	99.75 77	99.81 58	99.59 90	99.81 20	99.65 39	98.78 99	99.64 151	99.88 59	94.56 298	99.93 109	99.67 37	98.26 305	99.72 138

nrg030		98.64 227	98.42 234	99.28 220	99.05 382	99.69 64	99.81 20	99.46 248	98.04 225	99.01 312	99.82 128	96.69 169	99.38 365	99.34 88	94.59 434	98.78 343

HPM-MVS		99.42 55	99.28 68	99.83 56	99.90 4	99.72 57	99.81 20	99.54 109	97.59 284	99.68 125	99.63 271	98.91 38	99.94 91	98.58 221	99.91 45	99.84 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		99.13 129	98.99 143	99.53 135	99.65 163	99.06 175	99.81 20	99.33 336	97.43 307	99.60 166	99.88 59	97.14 139	99.84 202	99.13 129	98.94 253	99.69 157

3Dnovator+	97.12 13	99.18 104	98.97 148	99.82 57	99.17 353	99.68 65	99.81 20	99.51 162	99.20 34	98.72 360	99.89 45	95.68 234	99.97 29	98.86 174	99.86 87	99.81 79

sasdasda		99.02 168	98.86 178	99.51 147	99.42 272	99.32 133	99.80 25	99.48 213	98.63 111	99.31 243	98.81 448	97.09 144	99.75 273	99.27 109	97.90 324	99.47 258

FA-MVS(test-final)		98.75 215	98.53 228	99.41 187	99.55 221	99.05 177	99.80 25	99.01 414	96.59 385	99.58 171	99.59 285	95.39 244	99.90 149	97.78 308	99.49 179	99.28 293

GeoE		98.85 201	98.62 217	99.53 135	99.61 194	99.08 172	99.80 25	99.51 162	97.10 341	99.31 243	99.78 185	95.23 255	99.77 266	98.21 264	99.03 247	99.75 113

canonicalmvs		99.02 168	98.86 178	99.51 147	99.42 272	99.32 133	99.80 25	99.48 213	98.63 111	99.31 243	98.81 448	97.09 144	99.75 273	99.27 109	97.90 324	99.47 258

v8		97.95 299	97.63 318	98.93 263	98.95 399	98.81 240	99.80 25	99.41 284	96.03 424	99.10 295	99.42 347	94.92 267	99.30 383	96.94 387	94.08 447	98.66 390

Vis-MVSNet (Re-imp)		98.87 189	98.72 197	99.31 208	99.71 118	98.88 221	99.80 25	99.44 268	97.91 241	99.36 233	99.78 185	95.49 241	99.43 357	97.91 293	99.11 225	99.62 199

Anonymous20240521		96.20 411	95.89 414	97.13 444	97.72 485	94.96 452	99.79 31	99.29 360	93.01 471	97.20 454	99.03 424	89.69 426	98.36 482	91.16 487	96.13 393	98.07 466

balanced_ft_v1		99.02 168	98.98 146	99.15 238	99.39 285	98.12 299	99.79 31	99.51 162	98.20 176	99.66 136	99.87 75	94.84 272	99.93 109	99.69 34	99.84 102	99.41 274

PS-MVSNAJss		98.92 183	98.92 161	98.90 271	98.78 424	98.53 268	99.78 33	99.54 109	98.07 211	99.00 316	99.76 198	99.01 19	99.37 368	99.13 129	97.23 368	98.81 340

PEN-MVS		97.76 332	97.44 345	98.72 306	98.77 429	98.54 267	99.78 33	99.51 162	97.06 345	98.29 412	99.64 265	92.63 365	98.89 468	98.09 277	93.16 461	98.72 357

anonymousdsp		98.44 236	98.28 244	98.94 261	98.50 460	98.96 193	99.77 35	99.50 187	97.07 343	98.87 339	99.77 194	94.76 282	99.28 385	98.66 207	97.60 340	98.57 425

SixPastTwentyTwo		97.50 367	97.33 363	98.03 385	98.65 446	96.23 407	99.77 35	98.68 467	97.14 334	97.90 433	99.93 10	90.45 413	99.18 412	97.00 381	96.43 385	98.67 381

QAPM		98.67 223	98.30 243	99.80 64	99.20 339	99.67 69	99.77 35	99.72 14	94.74 448	98.73 359	99.90 36	95.78 229	99.98 20	96.96 385	99.88 73	99.76 107

SSC-MVS		92.73 463	93.73 453	89.72 505	95.02 524	81.38 521	99.76 38	99.23 378	94.87 445	92.80 497	98.93 438	94.71 288	91.37 534	74.49 533	93.80 451	96.42 513

test_vis3_rt		87.04 484	85.81 488	90.73 497	93.99 531	81.96 517	99.76 38	90.23 541	92.81 475	81.35 532	91.56 532	40.06 550	99.07 433	94.27 451	88.23 499	91.15 528

dcpmvs_2		99.23 97	99.58 9	98.16 376	99.83 47	94.68 459	99.76 38	99.52 134	99.07 58	99.98 13	99.88 59	98.56 81	99.93 109	99.67 37	99.98 4	99.87 41

RRT-MVS		98.91 184	98.75 193	99.39 194	99.46 262	98.61 262	99.76 38	99.50 187	98.06 215	99.81 72	99.88 59	93.91 330	99.94 91	99.11 132	99.27 196	99.61 201

HPM-MVS_fast		99.51 29	99.40 37	99.85 43	99.91 1	99.79 42	99.76 38	99.56 90	97.72 269	99.76 96	99.75 203	99.13 13	99.92 124	99.07 139	99.92 38	99.85 47

lecture		99.60 14	99.50 19	99.89 12	99.89 8	99.90 2	99.75 43	99.59 73	99.06 61	99.88 42	99.85 93	98.41 94	99.96 41	99.28 106	99.84 102	99.83 64

MVSMamba_PlusPlus		99.46 42	99.41 36	99.64 102	99.68 136	99.50 110	99.75 43	99.50 187	98.27 158	99.87 48	99.92 18	98.09 109	99.94 91	99.65 41	99.95 22	99.47 258

v10		97.85 313	97.52 328	98.86 287	98.99 392	98.67 252	99.75 43	99.41 284	95.70 428	98.98 319	99.41 351	94.75 283	99.23 396	96.01 418	94.63 433	98.67 381

APDe-MVS		99.66 7	99.57 10	99.92 1	99.77 79	99.89 6	99.75 43	99.56 90	99.02 62	99.88 42	99.85 93	99.18 11	99.96 41	99.22 114	99.92 38	99.90 27
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
IS-MVSNet		99.05 163	98.87 175	99.57 122	99.73 108	99.32 133	99.75 43	99.20 385	98.02 230	99.56 176	99.86 86	96.54 179	99.67 310	98.09 277	99.13 218	99.73 128

test_vis1_n		97.92 303	97.44 345	99.34 200	99.53 229	98.08 301	99.74 48	99.49 201	99.15 38	100.00 1	99.94 6	79.51 499	99.98 20	99.88 26	99.76 142	99.97 4

test_fmvs1_n		98.41 240	98.14 253	99.21 229	99.82 53	97.71 326	99.74 48	99.49 201	99.32 30	99.99 2	99.95 3	85.32 474	99.97 29	99.82 29	99.84 102	99.96 7

BridgeMVS		99.46 42	99.39 39	99.67 91	99.55 221	99.58 95	99.74 48	99.51 162	98.42 136	99.87 48	99.84 108	98.05 112	99.91 136	99.58 47	99.94 30	99.52 235

tttt0517		98.42 238	98.14 253	99.28 220	99.66 151	98.38 286	99.74 48	96.85 511	97.68 275	99.79 81	99.74 209	91.39 399	99.89 165	98.83 182	99.56 172	99.57 222

WB-MVS		93.10 461	94.10 446	90.12 502	95.51 520	81.88 518	99.73 52	99.27 369	95.05 440	93.09 496	98.91 442	94.70 289	91.89 532	76.62 528	94.02 449	96.58 512

test_fmvs2		97.25 384	97.30 367	97.09 446	99.43 270	93.31 481	99.73 52	98.87 439	98.83 89	99.28 251	99.80 161	84.45 479	99.66 313	97.88 295	97.45 356	98.30 451

SD_0403		97.55 361	97.53 327	97.62 426	99.61 194	93.64 478	99.72 54	99.44 268	98.03 227	98.62 382	99.39 360	96.06 208	99.57 334	87.88 504	99.01 250	99.66 177

MonoMVSNet		98.38 244	98.47 232	98.12 381	98.59 455	96.19 409	99.72 54	98.79 451	97.89 243	99.44 204	99.52 315	96.13 203	98.90 467	98.64 209	97.54 346	99.28 293

baseline		99.15 117	99.02 129	99.53 135	99.66 151	99.14 164	99.72 54	99.48 213	98.35 146	99.42 210	99.84 108	96.07 207	99.79 253	99.51 56	99.14 215	99.67 170

RPSCF		98.22 256	98.62 217	96.99 448	99.82 53	91.58 491	99.72 54	99.44 268	96.61 380	99.66 136	99.89 45	95.92 219	99.82 233	97.46 346	99.10 234	99.57 222

CSCG		99.32 78	99.32 53	99.32 206	99.85 31	98.29 288	99.71 58	99.66 32	98.11 201	99.41 215	99.80 161	98.37 97	99.96 41	98.99 148	99.96 17	99.72 138

dmvs_re		98.08 275	98.16 250	97.85 406	99.55 221	94.67 460	99.70 59	98.92 426	98.15 184	99.06 306	99.35 372	93.67 338	99.25 393	97.77 311	97.25 367	99.64 191

WR-MVS_H		98.13 267	97.87 287	98.90 271	99.02 386	98.84 232	99.70 59	99.59 73	97.27 322	98.40 400	99.19 404	95.53 239	99.23 396	98.34 254	93.78 452	98.61 412

mvsmamba		99.06 159	98.96 152	99.36 196	99.47 260	98.64 256	99.70 59	99.05 407	97.61 283	99.65 146	99.83 117	96.54 179	99.92 124	99.19 118	99.62 167	99.51 244

LTVRE_ROB	97.16 12	98.02 287	97.90 282	98.40 354	99.23 332	96.80 382	99.70 59	99.60 68	97.12 337	98.18 419	99.70 226	91.73 388	99.72 286	98.39 247	97.45 356	98.68 373
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
aaatest				99.87 22	99.88 13	99.81 34	99.69 63	99.87 6	99.34 28	99.90 34	99.83 117		99.95 76	98.83 182	99.89 67	99.83 64

MED-MVS		99.70 3	99.63 5	99.90 8	99.88 13	99.81 34	99.69 63	99.87 6	99.48 3	99.90 34	99.89 45	99.30 4	99.95 76	98.83 182	99.88 73	99.93 22

TestfortrainingZip a		99.70 3	99.63 5	99.92 1	99.88 13	99.90 2	99.69 63	99.79 11	99.48 3	99.93 29	99.89 45	98.78 53	99.93 109	99.32 92	99.88 73	99.93 22

TestfortrainingZip				99.69 89	99.58 207	99.62 84	99.69 63	99.38 303	98.98 72	99.84 56	99.75 203	98.84 45	99.78 261		99.21 203	99.66 177

test_f		91.90 468	91.26 471	93.84 479	95.52 519	85.92 506	99.69 63	98.53 479	95.31 434	93.87 491	96.37 514	55.33 531	98.27 483	95.70 425	90.98 484	97.32 498

XVS		99.53 27	99.42 32	99.87 22	99.85 31	99.83 23	99.69 63	99.68 24	98.98 72	99.37 227	99.74 209	98.81 49	99.94 91	98.79 190	99.86 87	99.84 54

X-MVStestdata		96.55 403	95.45 423	99.87 22	99.85 31	99.83 23	99.69 63	99.68 24	98.98 72	99.37 227	64.01 556	98.81 49	99.94 91	98.79 190	99.86 87	99.84 54

V42		98.06 277	97.79 294	98.86 287	98.98 395	98.84 232	99.69 63	99.34 327	96.53 387	99.30 247	99.37 366	94.67 291	99.32 380	97.57 333	94.66 432	98.42 443

mPP-MVS		99.44 50	99.30 61	99.86 34	99.88 13	99.79 42	99.69 63	99.48 213	98.12 199	99.50 191	99.75 203	98.78 53	99.97 29	98.57 224	99.89 67	99.83 64

CP-MVS		99.45 46	99.32 53	99.85 43	99.83 47	99.75 52	99.69 63	99.52 134	98.07 211	99.53 185	99.63 271	98.93 37	99.97 29	98.74 195	99.91 45	99.83 64

Casviewmamba		99.16 112	99.02 129	99.59 114	99.66 151	99.21 152	99.68 73	99.52 134	98.31 153	99.60 166	99.87 75	95.96 214	99.85 192	99.40 74	99.16 208	99.72 138

FE-MVS		98.48 233	98.17 249	99.40 189	99.54 228	98.96 193	99.68 73	98.81 447	95.54 430	99.62 158	99.70 226	93.82 333	99.93 109	97.35 356	99.46 180	99.32 289

PS-CasMVS		97.93 300	97.59 322	98.95 259	98.99 392	99.06 175	99.68 73	99.52 134	97.13 335	98.31 409	99.68 245	92.44 374	99.05 437	98.51 232	94.08 447	98.75 351

Vis-MVSNet		99.12 139	98.97 148	99.56 124	99.78 71	99.10 168	99.68 73	99.66 32	98.49 127	99.86 52	99.87 75	94.77 281	99.84 202	99.19 118	99.41 184	99.74 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		99.12 139	98.92 161	99.70 87	99.67 139	99.40 123	99.67 77	99.63 46	98.73 103	99.94 28	99.81 143	94.54 301	99.96 41	98.40 246	99.93 32	99.74 118

BP-MVS1		99.12 139	98.94 158	99.65 96	99.51 238	99.30 140	99.67 77	98.92 426	98.48 128	99.84 56	99.69 237	94.96 262	99.92 124	99.62 44	99.79 133	99.71 150

test_vis1_n_1920		98.63 228	98.40 236	99.31 208	99.86 25	97.94 314	99.67 77	99.62 52	99.43 19	99.99 2	99.91 26	87.29 455	100.00 1	99.92 24	99.92 38	99.98 2

EIA-MVS		99.18 104	99.09 104	99.45 175	99.49 252	99.18 155	99.67 77	99.53 125	97.66 278	99.40 220	99.44 343	98.10 108	99.81 238	98.94 157	99.62 167	99.35 284

MSP-MVS		99.42 55	99.27 72	99.88 16	99.89 8	99.80 39	99.67 77	99.50 187	98.70 107	99.77 90	99.49 325	98.21 103	99.95 76	98.46 239	99.77 139	99.88 36
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
MVS_Test		99.10 151	98.97 148	99.48 165	99.49 252	99.14 164	99.67 77	99.34 327	97.31 319	99.58 171	99.76 198	97.65 122	99.82 233	98.87 169	99.07 242	99.46 263

CP-MVSNet		98.09 271	97.78 297	99.01 250	98.97 397	99.24 149	99.67 77	99.46 248	97.25 324	98.48 394	99.64 265	93.79 334	99.06 436	98.63 211	94.10 446	98.74 355

MTAPA		99.52 28	99.39 39	99.89 12	99.90 4	99.86 18	99.66 84	99.47 235	98.79 96	99.68 125	99.81 143	98.43 91	99.97 29	98.88 166	99.90 56	99.83 64

HFP-MVS		99.49 33	99.37 43	99.86 34	99.87 20	99.80 39	99.66 84	99.67 27	98.15 184	99.68 125	99.69 237	99.06 17	99.96 41	98.69 203	99.87 79	99.84 54

mvs_tets		98.40 243	98.23 247	98.91 269	98.67 444	98.51 274	99.66 84	99.53 125	98.19 179	98.65 376	99.81 143	92.75 356	99.44 353	99.31 95	97.48 355	98.77 347

EU-MVSNet		97.98 294	98.03 268	97.81 415	98.72 435	96.65 390	99.66 84	99.66 32	98.09 206	98.35 406	99.82 128	95.25 253	98.01 489	97.41 352	95.30 418	98.78 343

ACMMPR		99.49 33	99.36 45	99.86 34	99.87 20	99.79 42	99.66 84	99.67 27	98.15 184	99.67 131	99.69 237	98.95 31	99.96 41	98.69 203	99.87 79	99.84 54

MP-MVS		99.33 77	99.15 92	99.87 22	99.88 13	99.82 29	99.66 84	99.46 248	98.09 206	99.48 195	99.74 209	98.29 100	99.96 41	97.93 292	99.87 79	99.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
hybridcas		99.13 129	99.00 141	99.51 147	99.70 123	99.04 178	99.65 90	99.52 134	98.20 176	99.75 100	99.88 59	95.78 229	99.78 261	99.41 72	99.16 208	99.71 150

NormalMVS		99.27 88	99.19 87	99.52 142	99.89 8	98.83 235	99.65 90	99.52 134	99.10 48	99.84 56	99.76 198	95.80 227	99.99 4	99.30 98	99.84 102	99.74 118

SymmetryMVS		99.15 117	99.02 129	99.52 142	99.72 112	98.83 235	99.65 90	99.34 327	99.10 48	99.84 56	99.76 198	95.80 227	99.99 4	99.30 98	98.72 274	99.73 128

Elysia		98.88 186	98.65 209	99.58 118	99.58 207	99.34 129	99.65 90	99.52 134	98.26 161	99.83 66	99.87 75	93.37 341	99.90 149	97.81 305	99.91 45	99.49 249

StellarMVS		98.88 186	98.65 209	99.58 118	99.58 207	99.34 129	99.65 90	99.52 134	98.26 161	99.83 66	99.87 75	93.37 341	99.90 149	97.81 305	99.91 45	99.49 249

test_cas_vis1_n_1920		99.16 112	99.01 137	99.61 110	99.81 58	98.86 229	99.65 90	99.64 42	99.39 24	99.97 25	99.94 6	93.20 348	99.98 20	99.55 50	99.91 45	99.99 1

region2R		99.48 37	99.35 47	99.87 22	99.88 13	99.80 39	99.65 90	99.66 32	98.13 191	99.66 136	99.68 245	98.96 26	99.96 41	98.62 212	99.87 79	99.84 54

TranMVSNet+NR-MVSNet		97.93 300	97.66 313	98.76 303	98.78 424	98.62 259	99.65 90	99.49 201	97.76 264	98.49 393	99.60 283	94.23 314	98.97 459	98.00 288	92.90 466	98.70 364

PRO-TEST		98.69 220	98.70 201	98.65 316	99.39 285	96.74 383	99.64 98	99.34 327	98.20 176	99.53 185	99.89 45	93.26 344	99.90 149	99.32 92	99.78 135	99.32 289

GDP-MVS		99.08 154	98.89 171	99.64 102	99.53 229	99.34 129	99.64 98	99.48 213	98.32 151	99.77 90	99.66 257	95.14 258	99.93 109	98.97 154	99.50 178	99.64 191

ttmdpeth		97.80 327	97.63 318	98.29 364	98.77 429	97.38 337	99.64 98	99.36 315	98.78 99	96.30 468	99.58 289	92.34 377	99.39 363	98.36 252	95.58 411	98.10 463

mvsany_test3		93.77 456	93.45 459	94.74 474	95.78 514	88.01 503	99.64 98	98.25 486	98.28 156	94.31 486	97.97 484	68.89 518	98.51 480	97.50 341	90.37 486	97.71 486

ZNCC-MVS		99.47 40	99.33 51	99.87 22	99.87 20	99.81 34	99.64 98	99.67 27	98.08 210	99.55 182	99.64 265	98.91 38	99.96 41	98.72 198	99.90 56	99.82 72

tfpnnormal		97.84 317	97.47 337	98.98 254	99.20 339	99.22 151	99.64 98	99.61 61	96.32 401	98.27 413	99.70 226	93.35 343	99.44 353	95.69 426	95.40 416	98.27 453

casdiffmvs_mvg		99.15 117	99.02 129	99.55 126	99.66 151	99.09 169	99.64 98	99.56 90	98.26 161	99.45 199	99.87 75	96.03 211	99.81 238	99.54 51	99.15 214	99.73 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS-dyc-post		99.45 46	99.31 59	99.85 43	99.76 83	99.82 29	99.63 105	99.52 134	98.38 141	99.76 96	99.82 128	98.53 84	99.95 76	98.61 215	99.81 121	99.77 100

RE-MVS-def			99.34 49		99.76 83	99.82 29	99.63 105	99.52 134	98.38 141	99.76 96	99.82 128	98.75 61		98.61 215	99.81 121	99.77 100

TSAR-MVS + MP.		99.58 16	99.50 19	99.81 60	99.91 1	99.66 72	99.63 105	99.39 294	98.91 83	99.78 86	99.85 93	99.36 2	99.94 91	98.84 179	99.88 73	99.82 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous20231206		96.22 409	96.03 410	96.79 456	97.31 492	94.14 470	99.63 105	99.08 401	96.17 413	97.04 458	99.06 418	93.94 327	97.76 495	86.96 511	95.06 423	98.47 437

APD-MVS_3200maxsize		99.48 37	99.35 47	99.85 43	99.76 83	99.83 23	99.63 105	99.54 109	98.36 145	99.79 81	99.82 128	98.86 42	99.95 76	98.62 212	99.81 121	99.78 98

test0726					99.85 31	99.89 6	99.62 110	99.50 187	99.10 48	99.86 52	99.82 128	98.94 33

EPNet		98.86 192	98.71 199	99.30 213	97.20 494	98.18 293	99.62 110	98.91 431	99.28 32	98.63 379	99.81 143	95.96 214	99.99 4	99.24 113	99.72 150	99.73 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 182	98.67 204	99.72 86	99.85 31	99.53 103	99.62 110	99.59 73	92.65 477	99.71 118	99.78 185	98.06 111	99.90 149	98.84 179	99.91 45	99.74 118

HY-MVS	97.30 7	98.85 201	98.64 211	99.47 171	99.42 272	99.08 172	99.62 110	99.36 315	97.39 313	99.28 251	99.68 245	96.44 185	99.92 124	98.37 250	98.22 308	99.40 277

ACMMP		99.45 46	99.32 53	99.82 57	99.89 8	99.67 69	99.62 110	99.69 22	98.12 199	99.63 154	99.84 108	98.73 67	99.96 41	98.55 230	99.83 114	99.81 79
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
DeepC-MVS	98.35 2	99.30 82	99.19 87	99.64 102	99.82 53	99.23 150	99.62 110	99.55 100	98.94 79	99.63 154	99.95 3	95.82 225	99.94 91	99.37 81	99.97 9	99.73 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffseed414692147		98.97 179	98.78 190	99.53 135	99.66 151	99.16 158	99.61 116	99.52 134	98.01 231	99.21 272	99.88 59	94.82 273	99.70 301	99.29 104	99.04 246	99.74 118

EI-MVSNet-Vis-set		99.58 16	99.56 12	99.64 102	99.78 71	99.15 163	99.61 116	99.45 259	99.01 64	99.89 39	99.82 128	99.01 19	99.92 124	99.56 49	99.95 22	99.85 47

E5new		99.14 125	99.02 129	99.50 153	99.69 129	98.91 210	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.26 198	99.84 202	99.30 98	99.10 234	99.76 107

E6new		99.15 117	99.03 118	99.50 153	99.66 151	98.90 215	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.31 192	99.84 202	99.30 98	99.10 234	99.76 107

E6		99.15 117	99.03 118	99.50 153	99.66 151	98.90 215	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.31 192	99.84 202	99.30 98	99.10 234	99.76 107

E5		99.14 125	99.02 129	99.50 153	99.69 129	98.91 210	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.26 198	99.84 202	99.30 98	99.10 234	99.76 107

reproduce_monomvs		97.89 307	97.87 287	97.96 395	99.51 238	95.45 436	99.60 118	99.25 374	99.17 36	98.85 346	99.49 325	89.29 430	99.64 322	99.35 83	96.31 389	98.78 343

test2506		96.81 398	96.65 394	97.29 441	99.74 101	92.21 489	99.60 118	85.06 547	99.13 41	99.77 90	99.93 10	87.82 453	99.85 192	99.38 80	99.38 185	99.80 88

SED-MVS		99.61 10	99.52 14	99.88 16	99.84 38	99.90 2	99.60 118	99.48 213	99.08 56	99.91 31	99.81 143	99.20 8	99.96 41	98.91 163	99.85 94	99.79 92

OPU-MVS				99.64 102	99.56 217	99.72 57	99.60 118				99.70 226	99.27 6	99.42 360	98.24 263	99.80 126	99.79 92

GST-MVS		99.40 64	99.24 77	99.85 43	99.86 25	99.79 42	99.60 118	99.67 27	97.97 236	99.63 154	99.68 245	98.52 85	99.95 76	98.38 248	99.86 87	99.81 79

EI-MVSNet-UG-set		99.58 16	99.57 10	99.64 102	99.78 71	99.14 164	99.60 118	99.45 259	99.01 64	99.90 34	99.83 117	98.98 25	99.93 109	99.59 45	99.95 22	99.86 43

ACMH	97.28 8	98.10 270	97.99 272	98.44 349	99.41 277	96.96 369	99.60 118	99.56 90	98.09 206	98.15 421	99.91 26	90.87 410	99.70 301	98.88 166	97.45 356	98.67 381
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VortexMVS		98.67 223	98.66 207	98.68 313	99.62 183	97.96 309	99.59 129	99.41 284	98.13 191	99.31 243	99.70 226	95.48 242	99.27 388	99.40 74	97.32 365	98.79 341

guyue		99.16 112	99.04 115	99.52 142	99.69 129	98.92 209	99.59 129	98.81 447	98.73 103	99.90 34	99.87 75	95.34 247	99.88 170	99.66 40	99.81 121	99.74 118

ECVR-MVS		98.04 283	98.05 266	98.00 390	99.74 101	94.37 467	99.59 129	94.98 524	99.13 41	99.66 136	99.93 10	90.67 412	99.84 202	99.40 74	99.38 185	99.80 88

SR-MVS		99.43 53	99.29 65	99.86 34	99.75 93	99.83 23	99.59 129	99.62 52	98.21 174	99.73 103	99.79 178	98.68 71	99.96 41	98.44 241	99.77 139	99.79 92

thres100view900		97.76 332	97.45 340	98.69 311	99.72 112	97.86 318	99.59 129	98.74 458	97.93 239	99.26 262	98.62 456	91.75 386	99.83 224	93.22 468	98.18 313	98.37 449

thres600view7		97.86 312	97.51 331	98.92 265	99.72 112	97.95 312	99.59 129	98.74 458	97.94 238	99.27 257	98.62 456	91.75 386	99.86 184	93.73 460	98.19 312	98.96 333

LCM-MVSNet-Re		97.83 320	98.15 252	96.87 454	99.30 311	92.25 488	99.59 129	98.26 485	97.43 307	96.20 469	99.13 410	96.27 195	98.73 475	98.17 269	98.99 251	99.64 191

baseline1		98.31 250	97.95 277	99.38 195	99.50 250	98.74 246	99.59 129	98.93 423	98.41 138	99.14 287	99.60 283	94.59 296	99.79 253	98.48 234	93.29 457	99.61 201

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 22	99.82 53	99.81 34	99.59 129	99.51 162	98.62 113	99.79 81	99.83 117	99.28 5	99.97 29	98.48 234	99.90 56	99.84 54
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CPTT-MVS		99.11 145	98.90 167	99.74 80	99.80 64	99.46 116	99.59 129	99.49 201	97.03 349	99.63 154	99.69 237	97.27 134	99.96 41	97.82 303	99.84 102	99.81 79

IMVS_0403		98.86 192	98.89 171	98.78 301	99.55 221	96.93 370	99.58 139	99.44 268	98.05 218	99.68 125	99.80 161	96.81 163	99.80 246	98.15 272	98.92 256	99.60 204

test_fmvsmvis_n_1920		99.65 8	99.61 8	99.77 74	99.38 289	99.37 125	99.58 139	99.62 52	99.41 23	99.87 48	99.92 18	98.81 49	100.00 1	99.97 2	99.93 32	99.94 17

dmvs_testset		95.02 439	96.12 407	91.72 490	99.10 366	80.43 526	99.58 139	97.87 495	97.47 299	95.22 476	98.82 447	93.99 325	95.18 520	88.09 502	94.91 428	99.56 226

test_fmvsm_n_1920		99.69 6	99.66 3	99.78 71	99.84 38	99.44 118	99.58 139	99.69 22	99.43 19	99.98 13	99.91 26	98.62 77	100.00 1	99.97 2	99.95 22	99.90 27

test1111		98.04 283	98.11 257	97.83 412	99.74 101	93.82 472	99.58 139	95.40 523	99.12 46	99.65 146	99.93 10	90.73 411	99.84 202	99.43 71	99.38 185	99.82 72

PGM-MVS		99.45 46	99.31 59	99.86 34	99.87 20	99.78 48	99.58 139	99.65 39	97.84 252	99.71 118	99.80 161	99.12 14	99.97 29	98.33 255	99.87 79	99.83 64

LPG-MVS_test		98.22 256	98.13 255	98.49 336	99.33 302	97.05 356	99.58 139	99.55 100	97.46 300	99.24 264	99.83 117	92.58 366	99.72 286	98.09 277	97.51 349	98.68 373

PHI-MVS		99.30 82	99.17 90	99.70 87	99.56 217	99.52 107	99.58 139	99.80 10	97.12 337	99.62 158	99.73 215	98.58 79	99.90 149	98.61 215	99.91 45	99.68 163

fmvsm_s_conf0.5_n_11		99.32 78	99.16 91	99.80 64	99.83 47	99.70 61	99.57 147	99.56 90	99.45 13	99.99 2	99.93 10	94.18 318	99.99 4	99.96 13	99.98 4	99.73 128

AstraMVS		99.09 152	99.03 118	99.25 223	99.66 151	98.13 297	99.57 147	98.24 487	98.82 90	99.91 31	99.88 59	95.81 226	99.90 149	99.72 32	99.67 160	99.74 118

SF-MVS		99.38 67	99.24 77	99.79 68	99.79 69	99.68 65	99.57 147	99.54 109	97.82 258	99.71 118	99.80 161	98.95 31	99.93 109	98.19 266	99.84 102	99.74 118

DVP-MVS		99.57 20	99.47 24	99.88 16	99.85 31	99.89 6	99.57 147	99.37 313	99.10 48	99.81 72	99.80 161	98.94 33	99.96 41	98.93 160	99.86 87	99.81 79
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.91 6	99.84 38	99.89 6	99.57 147	99.51 162					99.96 41	98.93 160	99.86 87	99.88 36

Effi-MVS+-dtu		98.78 210	98.89 171	98.47 343	99.33 302	96.91 375	99.57 147	99.30 356	98.47 129	99.41 215	98.99 431	96.78 165	99.74 276	98.73 197	99.38 185	98.74 355

v2v482		98.06 277	97.77 299	98.92 265	98.90 405	98.82 238	99.57 147	99.36 315	96.65 375	99.19 279	99.35 372	94.20 315	99.25 393	97.72 318	94.97 425	98.69 368

DSMNet-mixed		97.25 384	97.35 357	96.95 451	97.84 479	93.61 479	99.57 147	96.63 515	96.13 418	98.87 339	98.61 458	94.59 296	97.70 497	95.08 440	98.86 264	99.55 227

FE-MVSNET		94.07 455	93.36 460	96.22 463	94.05 530	94.71 458	99.56 155	98.36 482	93.15 469	93.76 492	97.55 497	86.47 464	96.49 511	87.48 506	89.83 492	97.48 496

reproduce_model		99.63 9	99.54 13	99.90 8	99.78 71	99.88 10	99.56 155	99.55 100	99.15 38	99.90 34	99.90 36	99.00 23	99.97 29	99.11 132	99.91 45	99.86 43

MVStest1		96.08 416	95.48 421	97.89 401	98.93 400	96.70 385	99.56 155	99.35 322	92.69 476	91.81 504	99.46 340	89.90 423	98.96 461	95.00 442	92.61 471	98.00 475

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 43	99.86 25	99.61 87	99.56 155	99.63 46	99.48 3	99.98 13	99.83 117	98.75 61	99.99 4	99.97 2	99.96 17	99.94 17

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 43	99.84 38	99.63 83	99.56 155	99.63 46	99.47 6	99.98 13	99.82 128	98.75 61	99.99 4	99.97 2	99.97 9	99.94 17

sd_testset		98.75 215	98.57 224	99.29 216	99.81 58	98.26 290	99.56 155	99.62 52	98.78 99	99.64 151	99.88 59	92.02 380	99.88 170	99.54 51	98.26 305	99.72 138

KD-MVS_self_test		95.00 440	94.34 444	96.96 450	97.07 498	95.39 439	99.56 155	99.44 268	95.11 437	97.13 456	97.32 504	91.86 384	97.27 503	90.35 492	81.23 521	98.23 457

ETV-MVS		99.26 91	99.21 83	99.40 189	99.46 262	99.30 140	99.56 155	99.52 134	98.52 123	99.44 204	99.27 394	98.41 94	99.86 184	99.10 135	99.59 170	99.04 321

SMA-MVS		99.44 50	99.30 61	99.85 43	99.73 108	99.83 23	99.56 155	99.47 235	97.45 303	99.78 86	99.82 128	99.18 11	99.91 136	98.79 190	99.89 67	99.81 79
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
AllTest		98.87 189	98.72 197	99.31 208	99.86 25	98.48 279	99.56 155	99.61 61	97.85 249	99.36 233	99.85 93	95.95 216	99.85 192	96.66 401	99.83 114	99.59 215

casdiffmvs		99.13 129	98.98 146	99.56 124	99.65 163	99.16 158	99.56 155	99.50 187	98.33 149	99.41 215	99.86 86	95.92 219	99.83 224	99.45 70	99.16 208	99.70 154
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XXY-MVS		98.38 244	98.09 261	99.24 226	99.26 324	99.32 133	99.56 155	99.55 100	97.45 303	98.71 361	99.83 117	93.23 345	99.63 328	98.88 166	96.32 388	98.76 349

ACMH+	97.24 10	97.92 303	97.78 297	98.32 361	99.46 262	96.68 389	99.56 155	99.54 109	98.41 138	97.79 439	99.87 75	90.18 421	99.66 313	98.05 285	97.18 371	98.62 403

ACMM	97.58 5	98.37 246	98.34 239	98.48 338	99.41 277	97.10 350	99.56 155	99.45 259	98.53 122	99.04 309	99.85 93	93.00 350	99.71 293	98.74 195	97.45 356	98.64 394
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 88	99.12 96	99.74 80	99.18 345	99.75 52	99.56 155	99.57 85	98.45 132	99.49 194	99.85 93	97.77 119	99.94 91	98.33 255	99.84 102	99.52 235

testing3-2		97.84 317	97.70 309	98.24 371	99.53 229	95.37 440	99.55 170	98.67 470	98.46 130	99.27 257	99.34 376	86.58 462	99.83 224	99.32 92	98.63 277	99.52 235

test_fmvsmconf0.01_n		99.22 99	99.03 118	99.79 68	98.42 464	99.48 113	99.55 170	99.51 162	99.39 24	99.78 86	99.93 10	94.80 276	99.95 76	99.93 23	99.95 22	99.94 17

test_fmvs1		98.88 186	98.79 189	99.16 234	99.69 129	97.61 330	99.55 170	99.49 201	99.32 30	99.98 13	99.91 26	91.41 398	99.96 41	99.82 29	99.92 38	99.90 27

v144192		97.92 303	97.60 321	98.87 284	98.83 418	98.65 254	99.55 170	99.34 327	96.20 410	99.32 242	99.40 356	94.36 308	99.26 391	96.37 412	95.03 424	98.70 364

API-MVS		99.04 164	99.03 118	99.06 244	99.40 282	99.31 137	99.55 170	99.56 90	98.54 121	99.33 241	99.39 360	98.76 58	99.78 261	96.98 383	99.78 135	98.07 466

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 29	99.54 175	99.66 32	99.46 9	99.98 13	99.89 45	97.27 134	99.99 4	99.97 2	99.95 22	99.95 11

fmvsm_s_conf0.1_n_a		99.26 91	99.06 110	99.85 43	99.52 235	99.62 84	99.54 175	99.62 52	98.69 108	99.99 2	99.96 1	94.47 305	99.94 91	99.88 26	99.92 38	99.98 2

APD_test1		95.87 418	96.49 398	94.00 477	99.53 229	84.01 512	99.54 175	99.32 347	95.91 426	97.99 428	99.85 93	85.49 472	99.88 170	91.96 481	98.84 266	98.12 462

thisisatest0530		98.35 248	98.03 268	99.31 208	99.63 173	98.56 265	99.54 175	96.75 513	97.53 294	99.73 103	99.65 259	91.25 403	99.89 165	98.62 212	99.56 172	99.48 252

MTMP							99.54 175	98.88 437

v1144		97.98 294	97.69 310	98.85 290	98.87 411	98.66 253	99.54 175	99.35 322	96.27 405	99.23 268	99.35 372	94.67 291	99.23 396	96.73 396	95.16 421	98.68 373

v148		97.79 329	97.55 323	98.50 335	98.74 432	97.72 323	99.54 175	99.33 336	96.26 406	98.90 333	99.51 319	94.68 290	99.14 417	97.83 302	93.15 462	98.63 401

CostFormer		97.72 342	97.73 306	97.71 422	99.15 359	94.02 471	99.54 175	99.02 412	94.67 449	99.04 309	99.35 372	92.35 376	99.77 266	98.50 233	97.94 323	99.34 287

MVSTER		98.49 232	98.32 241	99.00 252	99.35 296	99.02 180	99.54 175	99.38 303	97.41 311	99.20 276	99.73 215	93.86 332	99.36 372	98.87 169	97.56 344	98.62 403

fmvsm_s_conf0.5_n_10		99.41 59	99.24 77	99.92 1	99.83 47	99.84 20	99.53 184	99.56 90	99.45 13	99.99 2	99.92 18	94.92 267	99.99 4	99.97 2	99.97 9	99.95 11

fmvsm_s_conf0.1_n		99.29 84	99.10 99	99.86 34	99.70 123	99.65 76	99.53 184	99.62 52	98.74 102	99.99 2	99.95 3	94.53 303	99.94 91	99.89 25	99.96 17	99.97 4

E4		99.13 129	99.01 137	99.49 160	99.68 136	98.90 215	99.52 186	99.52 134	98.13 191	99.71 118	99.90 36	96.32 190	99.84 202	99.21 116	99.11 225	99.75 113

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 83	99.88 10	99.52 186	99.54 109	99.13 41	99.89 39	99.89 45	98.96 26	99.96 41	99.04 142	99.90 56	99.85 47

our_new_method		99.61 10	99.52 14	99.90 8	99.76 83	99.88 10	99.52 186	99.54 109	99.13 41	99.89 39	99.89 45	98.96 26	99.96 41	99.04 142	99.90 56	99.85 47

fmvsm_s_conf0.5_n_a		99.56 21	99.47 24	99.85 43	99.83 47	99.64 82	99.52 186	99.65 39	99.10 48	99.98 13	99.92 18	97.35 130	99.96 41	99.94 21	99.92 38	99.95 11

MM		99.40 64	99.28 68	99.74 80	99.67 139	99.31 137	99.52 186	98.87 439	99.55 1	99.74 101	99.80 161	96.47 182	99.98 20	99.97 2	99.97 9	99.94 17

patch_mono-2		99.26 91	99.62 7	98.16 376	99.81 58	94.59 463	99.52 186	99.64 42	99.33 29	99.73 103	99.90 36	99.00 23	99.99 4	99.69 34	99.98 4	99.89 30

Fast-Effi-MVS+-dtu		98.77 214	98.83 185	98.60 319	99.41 277	96.99 365	99.52 186	99.49 201	98.11 201	99.24 264	99.34 376	96.96 153	99.79 253	97.95 291	99.45 181	99.02 324

Fast-Effi-MVS+		98.70 219	98.43 233	99.51 147	99.51 238	99.28 143	99.52 186	99.47 235	96.11 419	99.01 312	99.34 376	96.20 200	99.84 202	97.88 295	98.82 268	99.39 278

v1921920		97.80 327	97.45 340	98.84 291	98.80 420	98.53 268	99.52 186	99.34 327	96.15 416	99.24 264	99.47 336	93.98 326	99.29 384	95.40 434	95.13 422	98.69 368

MIMVSNet1		95.51 426	95.04 430	96.92 453	97.38 489	95.60 428	99.52 186	99.50 187	93.65 461	96.97 460	99.17 405	85.28 475	96.56 510	88.36 501	95.55 413	98.60 415

FE-MVSNET2		95.10 437	94.44 442	97.08 447	95.08 522	95.97 413	99.51 196	99.37 313	95.02 441	94.10 488	97.57 496	86.18 466	97.66 499	93.28 467	89.86 491	97.61 491

viewmacassd2359aftdt		99.08 154	98.94 158	99.50 153	99.66 151	98.96 193	99.51 196	99.54 109	98.27 158	99.42 210	99.89 45	95.88 223	99.80 246	99.20 117	99.11 225	99.76 107

SSM_0407		99.13 129	99.03 118	99.43 184	99.62 183	98.88 221	99.51 196	99.50 187	98.14 188	99.37 227	99.85 93	96.85 156	99.83 224	99.19 118	99.25 199	99.60 204

fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 12	99.83 47	99.74 55	99.51 196	99.62 52	99.46 9	99.99 2	99.90 36	96.60 174	99.98 20	99.95 16	99.95 22	99.96 7

fmvsm_s_conf0.5_n		99.51 29	99.40 37	99.85 43	99.84 38	99.65 76	99.51 196	99.67 27	99.13 41	99.98 13	99.92 18	96.60 174	99.96 41	99.95 16	99.96 17	99.95 11

UniMVSNet_ETH3D		97.32 381	96.81 390	98.87 284	99.40 282	97.46 334	99.51 196	99.53 125	95.86 427	98.54 389	99.77 194	82.44 490	99.66 313	98.68 205	97.52 348	99.50 248

alignmvs		98.81 205	98.56 226	99.58 118	99.43 270	99.42 120	99.51 196	98.96 421	98.61 114	99.35 236	98.92 441	94.78 278	99.77 266	99.35 83	98.11 318	99.54 229

v1192		97.81 325	97.44 345	98.91 269	98.88 408	98.68 251	99.51 196	99.34 327	96.18 412	99.20 276	99.34 376	94.03 324	99.36 372	95.32 436	95.18 420	98.69 368

test20.03		96.12 414	95.96 412	96.63 457	97.44 487	95.45 436	99.51 196	99.38 303	96.55 386	96.16 470	99.25 397	93.76 336	96.17 513	87.35 508	94.22 442	98.27 453

mvs_anonymous		99.03 166	98.99 143	99.16 234	99.38 289	98.52 272	99.51 196	99.38 303	97.79 259	99.38 224	99.81 143	97.30 132	99.45 348	99.35 83	98.99 251	99.51 244

TAMVS		99.12 139	99.08 105	99.24 226	99.46 262	98.55 266	99.51 196	99.46 248	98.09 206	99.45 199	99.82 128	98.34 98	99.51 341	98.70 200	98.93 254	99.67 170

viewdifsd2359ckpt13		99.06 159	98.93 160	99.45 175	99.63 173	98.96 193	99.50 207	99.51 162	97.83 253	99.28 251	99.80 161	96.68 171	99.71 293	99.05 141	99.12 223	99.68 163

viewdifsd2359ckpt11		98.78 210	98.74 195	98.89 275	99.67 139	97.04 359	99.50 207	99.58 78	98.26 161	99.56 176	99.90 36	94.36 308	99.87 177	99.49 61	98.32 301	99.77 100

viewmsd2359difaftdt		98.78 210	98.74 195	98.90 271	99.67 139	97.04 359	99.50 207	99.58 78	98.26 161	99.56 176	99.90 36	94.36 308	99.87 177	99.49 61	98.32 301	99.77 100

IMVS_0407		98.86 192	98.91 165	98.72 306	99.55 221	96.93 370	99.50 207	99.44 268	98.05 218	99.66 136	99.80 161	97.13 140	99.65 318	98.15 272	98.92 256	99.60 204

viewmanbaseed2359cas		99.18 104	99.07 109	99.50 153	99.62 183	99.01 182	99.50 207	99.52 134	98.25 166	99.68 125	99.82 128	96.93 154	99.80 246	99.15 128	99.11 225	99.70 154

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 238	99.67 69	99.50 207	99.64 42	99.43 19	99.98 13	99.78 185	97.26 137	99.95 76	99.95 16	99.93 32	99.92 25

test_fmvsmconf0.1_n		99.55 23	99.45 30	99.86 34	99.44 269	99.65 76	99.50 207	99.61 61	99.45 13	99.87 48	99.92 18	97.31 131	99.97 29	99.95 16	99.99 1	99.97 4

test_yl		98.86 192	98.63 212	99.54 127	99.49 252	99.18 155	99.50 207	99.07 404	98.22 172	99.61 163	99.51 319	95.37 245	99.84 202	98.60 218	98.33 297	99.59 215

DCV-MVSNet		98.86 192	98.63 212	99.54 127	99.49 252	99.18 155	99.50 207	99.07 404	98.22 172	99.61 163	99.51 319	95.37 245	99.84 202	98.60 218	98.33 297	99.59 215

tfpn200view9		97.72 342	97.38 353	98.72 306	99.69 129	97.96 309	99.50 207	98.73 464	97.83 253	99.17 284	98.45 464	91.67 390	99.83 224	93.22 468	98.18 313	98.37 449

UA-Net		99.42 55	99.29 65	99.80 64	99.62 183	99.55 98	99.50 207	99.70 18	98.79 96	99.77 90	99.96 1	97.45 125	99.96 41	98.92 162	99.90 56	99.89 30

pm-mvs1		97.68 350	97.28 370	98.88 280	99.06 378	98.62 259	99.50 207	99.45 259	96.32 401	97.87 435	99.79 178	92.47 370	99.35 375	97.54 336	93.54 454	98.67 381

EI-MVSNet		98.67 223	98.67 204	98.68 313	99.35 296	97.97 307	99.50 207	99.38 303	96.93 358	99.20 276	99.83 117	97.87 115	99.36 372	98.38 248	97.56 344	98.71 359

CVMVSNet		98.57 230	98.67 204	98.30 363	99.35 296	95.59 429	99.50 207	99.55 100	98.60 116	99.39 222	99.83 117	94.48 304	99.45 348	98.75 193	98.56 284	99.85 47

VPA-MVSNet		98.29 253	97.95 277	99.30 213	99.16 355	99.54 100	99.50 207	99.58 78	98.27 158	99.35 236	99.37 366	92.53 368	99.65 318	99.35 83	94.46 435	98.72 357

thres400		97.77 331	97.38 353	98.92 265	99.69 129	97.96 309	99.50 207	98.73 464	97.83 253	99.17 284	98.45 464	91.67 390	99.83 224	93.22 468	98.18 313	98.96 333

APD-MVS		99.27 88	99.08 105	99.84 55	99.75 93	99.79 42	99.50 207	99.50 187	97.16 333	99.77 90	99.82 128	98.78 53	99.94 91	97.56 334	99.86 87	99.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
E2		99.15 117	99.03 118	99.49 160	99.65 163	98.93 208	99.49 224	99.52 134	98.14 188	99.72 108	99.88 59	96.57 178	99.84 202	99.17 124	99.13 218	99.72 138

E3		99.15 117	99.03 118	99.49 160	99.62 183	98.91 210	99.49 224	99.52 134	98.13 191	99.72 108	99.88 59	96.61 173	99.84 202	99.17 124	99.13 218	99.72 138

SSM_0404		99.16 112	99.06 110	99.44 180	99.65 163	98.96 193	99.49 224	99.50 187	98.14 188	99.62 158	99.85 93	96.85 156	99.85 192	99.19 118	99.26 198	99.52 235

fmvsm_s_conf0.5_n_4		99.36 72	99.24 77	99.73 83	99.78 71	99.53 103	99.49 224	99.60 68	99.42 22	99.99 2	99.86 86	95.15 257	99.95 76	99.95 16	99.89 67	99.73 128

test_vis1_rt		95.81 420	95.65 419	96.32 462	99.67 139	91.35 492	99.49 224	96.74 514	98.25 166	95.24 475	98.10 481	74.96 501	99.90 149	99.53 53	98.85 265	97.70 489

TransMVSNet (Re)		97.15 388	96.58 395	98.86 287	99.12 361	98.85 230	99.49 224	98.91 431	95.48 431	97.16 455	99.80 161	93.38 340	99.11 427	94.16 454	91.73 476	98.62 403

UniMVSNet (Re)		98.29 253	98.00 271	99.13 240	99.00 389	99.36 128	99.49 224	99.51 162	97.95 237	98.97 321	99.13 410	96.30 194	99.38 365	98.36 252	93.34 456	98.66 390

EPMVS		97.82 323	97.65 314	98.35 358	98.88 408	95.98 412	99.49 224	94.71 529	97.57 287	99.26 262	99.48 333	92.46 373	99.71 293	97.87 297	99.08 241	99.35 284

viewcassd2359sk11		99.18 104	99.08 105	99.49 160	99.65 163	98.95 199	99.48 232	99.51 162	98.10 205	99.72 108	99.87 75	97.13 140	99.84 202	99.13 129	99.14 215	99.69 157

fmvsm_s_conf0.5_n_9		99.41 59	99.28 68	99.81 60	99.84 38	99.52 107	99.48 232	99.62 52	99.46 9	99.99 2	99.92 18	95.24 254	99.96 41	99.97 2	99.97 9	99.96 7

SSC-MVS3.2		97.34 379	97.15 377	97.93 397	99.02 386	95.76 424	99.48 232	99.58 78	97.62 282	99.09 298	99.53 310	87.95 449	99.27 388	96.42 408	95.66 409	98.75 351

fmvsm_s_conf0.5_n_3		99.37 68	99.20 85	99.87 22	99.75 93	99.70 61	99.48 232	99.66 32	99.45 13	99.99 2	99.93 10	94.64 295	99.97 29	99.94 21	99.97 9	99.95 11

test_fmvsmconf_n		99.70 3	99.64 4	99.87 22	99.80 64	99.66 72	99.48 232	99.64 42	99.45 13	99.92 30	99.92 18	98.62 77	99.99 4	99.96 13	99.99 1	99.96 7

Anonymous20231211		97.88 308	97.54 326	98.90 271	99.71 118	98.53 268	99.48 232	99.57 85	94.16 454	98.81 350	99.68 245	93.23 345	99.42 360	98.84 179	94.42 438	98.76 349

v1240		97.69 347	97.32 365	98.79 299	98.85 415	98.43 283	99.48 232	99.36 315	96.11 419	99.27 257	99.36 369	93.76 336	99.24 395	94.46 448	95.23 419	98.70 364

VPNet		97.84 317	97.44 345	99.01 250	99.21 337	98.94 203	99.48 232	99.57 85	98.38 141	99.28 251	99.73 215	88.89 433	99.39 363	99.19 118	93.27 458	98.71 359

UniMVSNet_NR-MVSNet		98.22 256	97.97 274	98.96 257	98.92 402	98.98 185	99.48 232	99.53 125	97.76 264	98.71 361	99.46 340	96.43 186	99.22 403	98.57 224	92.87 468	98.69 368

TDRefinement		95.42 430	94.57 440	97.97 393	89.83 547	96.11 411	99.48 232	98.75 454	96.74 368	96.68 464	99.88 59	88.65 439	99.71 293	98.37 250	82.74 516	98.09 464

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 29	99.47 242	99.63 46	99.45 13	99.98 13	99.89 45	97.02 149	99.99 4	99.98 1	99.96 17	99.95 11

ACMMP_NAP		99.47 40	99.34 49	99.88 16	99.87 20	99.86 18	99.47 242	99.48 213	98.05 218	99.76 96	99.86 86	98.82 48	99.93 109	98.82 189	99.91 45	99.84 54

NR-MVSNet		97.97 297	97.61 320	99.02 249	98.87 411	99.26 146	99.47 242	99.42 281	97.63 280	97.08 457	99.50 322	95.07 260	99.13 420	97.86 298	93.59 453	98.68 373

PVSNet_Blended_VisFu		99.36 72	99.28 68	99.61 110	99.86 25	99.07 174	99.47 242	99.93 2	97.66 278	99.71 118	99.86 86	97.73 120	99.96 41	99.47 66	99.82 118	99.79 92

E3new		99.18 104	99.08 105	99.48 165	99.63 173	98.94 203	99.46 246	99.50 187	98.06 215	99.72 108	99.84 108	97.27 134	99.84 202	99.10 135	99.13 218	99.67 170

LuminaMVS		99.23 97	99.10 99	99.61 110	99.35 296	99.31 137	99.46 246	99.13 395	98.61 114	99.86 52	99.89 45	96.41 188	99.91 136	99.67 37	99.51 176	99.63 196

fmvsm_s_conf0.1_n_2		99.37 68	99.22 82	99.81 60	99.77 79	99.75 52	99.46 246	99.60 68	99.47 6	99.98 13	99.94 6	94.98 261	99.95 76	99.97 2	99.79 133	99.73 128

SD-MVS		99.41 59	99.52 14	99.05 246	99.74 101	99.68 65	99.46 246	99.52 134	99.11 47	99.88 42	99.91 26	99.43 1	97.70 497	98.72 198	99.93 32	99.77 100
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
viewdifsd2359ckpt07		99.11 145	99.00 141	99.43 184	99.63 173	98.73 247	99.45 250	99.54 109	98.33 149	99.62 158	99.81 143	96.17 201	99.87 177	99.27 109	99.14 215	99.69 157

testing3		97.28 382	96.76 392	98.82 293	99.37 292	98.07 302	99.45 250	99.36 315	97.56 289	97.89 434	98.95 436	83.70 483	98.82 469	96.03 416	98.56 284	99.58 219

tt0805		97.97 297	97.77 299	98.57 324	99.59 205	96.61 392	99.45 250	99.08 401	98.21 174	98.88 336	99.80 161	88.66 438	99.70 301	98.58 221	97.72 334	99.39 278

tpm2		97.44 374	97.34 360	97.74 421	99.15 359	94.36 468	99.45 250	98.94 422	93.45 466	98.90 333	99.44 343	91.35 400	99.59 332	97.31 357	98.07 319	99.29 292

FMVSNet2		97.72 342	97.36 355	98.80 298	99.51 238	98.84 232	99.45 250	99.42 281	96.49 389	98.86 345	99.29 389	90.26 415	98.98 452	96.44 407	96.56 382	98.58 423

CDS-MVSNet		99.09 152	99.03 118	99.25 223	99.42 272	98.73 247	99.45 250	99.46 248	98.11 201	99.46 198	99.77 194	98.01 113	99.37 368	98.70 200	98.92 256	99.66 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 192	98.63 212	99.54 127	99.37 292	99.66 72	99.45 250	99.54 109	96.61 380	99.01 312	99.40 356	97.09 144	99.86 184	97.68 324	99.53 175	99.10 308
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
viewdifsd2359ckpt09		99.01 173	98.87 175	99.40 189	99.62 183	98.79 241	99.44 257	99.51 162	97.76 264	99.35 236	99.69 237	96.42 187	99.75 273	98.97 154	99.11 225	99.66 177

fmvsm_s_conf0.5_n_2		99.32 78	99.13 94	99.89 12	99.80 64	99.77 49	99.44 257	99.58 78	99.47 6	99.99 2	99.93 10	94.04 323	99.96 41	99.96 13	99.93 32	99.93 22

UGNet		98.87 189	98.69 202	99.40 189	99.22 336	98.72 249	99.44 257	99.68 24	99.24 33	99.18 283	99.42 347	92.74 358	99.96 41	99.34 88	99.94 30	99.53 234
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
ab-mvs		98.86 192	98.63 212	99.54 127	99.64 168	99.19 153	99.44 257	99.54 109	97.77 262	99.30 247	99.81 143	94.20 315	99.93 109	99.17 124	98.82 268	99.49 249

test_0402		96.64 401	96.24 404	97.85 406	98.85 415	96.43 399	99.44 257	99.26 371	93.52 463	96.98 459	99.52 315	88.52 442	99.20 410	92.58 479	97.50 351	97.93 480

ACMP	97.20 11	98.06 277	97.94 279	98.45 346	99.37 292	97.01 363	99.44 257	99.49 201	97.54 293	98.45 397	99.79 178	91.95 382	99.72 286	97.91 293	97.49 354	98.62 403
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 346	98.55 457	98.16 294	99.43 263	93.68 532		97.23 451	98.46 463	89.30 429	99.22 403	95.43 433	98.22 308	97.98 477

HPM-MVS++		99.39 66	99.23 81	99.87 22	99.75 93	99.84 20	99.43 263	99.51 162	98.68 110	99.27 257	99.53 310	98.64 76	99.96 41	98.44 241	99.80 126	99.79 92

tpm cat1		97.39 376	97.36 355	97.50 433	99.17 353	93.73 474	99.43 263	99.31 351	91.27 490	98.71 361	99.08 415	94.31 313	99.77 266	96.41 410	98.50 288	99.00 325

tpm		97.67 353	97.55 323	98.03 385	99.02 386	95.01 450	99.43 263	98.54 478	96.44 395	99.12 290	99.34 376	91.83 385	99.60 331	97.75 314	96.46 384	99.48 252

GBi-Net		97.68 350	97.48 334	98.29 364	99.51 238	97.26 343	99.43 263	99.48 213	96.49 389	99.07 301	99.32 384	90.26 415	98.98 452	97.10 374	96.65 379	98.62 403

test1		97.68 350	97.48 334	98.29 364	99.51 238	97.26 343	99.43 263	99.48 213	96.49 389	99.07 301	99.32 384	90.26 415	98.98 452	97.10 374	96.65 379	98.62 403

FMVSNet1		96.84 397	96.36 401	98.29 364	99.32 309	97.26 343	99.43 263	99.48 213	95.11 437	98.55 388	99.32 384	83.95 482	98.98 452	95.81 421	96.26 390	98.62 403

viewmamba		99.20 100	99.12 96	99.44 180	99.61 194	98.87 225	99.42 270	99.52 134	98.42 136	99.84 56	99.84 108	96.85 156	99.78 261	99.46 68	99.11 225	99.67 170

fmvsm_s_conf0.5_n_7		99.34 75	99.29 65	99.48 165	99.70 123	98.63 257	99.42 270	99.63 46	99.46 9	99.98 13	99.88 59	95.59 237	99.96 41	99.97 2	99.98 4	99.85 47

fmvsm_s_conf0.5_n_5		99.37 68	99.21 83	99.86 34	99.80 64	99.68 65	99.42 270	99.61 61	99.37 26	99.97 25	99.86 86	94.96 262	99.99 4	99.97 2	99.93 32	99.92 25

testgi		97.65 355	97.50 332	98.13 380	99.36 295	96.45 398	99.42 270	99.48 213	97.76 264	97.87 435	99.45 342	91.09 407	98.81 470	94.53 447	98.52 287	99.13 307

F-COLMAP		99.19 101	99.04 115	99.64 102	99.78 71	99.27 145	99.42 270	99.54 109	97.29 321	99.41 215	99.59 285	98.42 93	99.93 109	98.19 266	99.69 155	99.73 128

Anonymous202405211		98.30 252	97.98 273	99.26 222	99.57 213	98.16 294	99.41 275	98.55 477	96.03 424	99.19 279	99.74 209	91.87 383	99.92 124	99.16 127	98.29 304	99.70 154

MSLP-MVS++		99.46 42	99.47 24	99.44 180	99.60 201	99.16 158	99.41 275	99.71 16	98.98 72	99.45 199	99.78 185	99.19 10	99.54 339	99.28 106	99.84 102	99.63 196

VNet		99.11 145	98.90 167	99.73 83	99.52 235	99.56 96	99.41 275	99.39 294	99.01 64	99.74 101	99.78 185	95.56 238	99.92 124	99.52 55	98.18 313	99.72 138

baseline2		97.87 310	97.55 323	98.82 293	99.18 345	98.02 304	99.41 275	96.58 517	96.97 352	96.51 465	99.17 405	93.43 339	99.57 334	97.71 319	99.03 247	98.86 337

DU-MVS		98.08 275	97.79 294	98.96 257	98.87 411	98.98 185	99.41 275	99.45 259	97.87 245	98.71 361	99.50 322	94.82 273	99.22 403	98.57 224	92.87 468	98.68 373

Baseline_NR-MVSNet		97.76 332	97.45 340	98.68 313	99.09 369	98.29 288	99.41 275	98.85 442	95.65 429	98.63 379	99.67 252	94.82 273	99.10 430	98.07 284	92.89 467	98.64 394

XVG-ACMP-BASELINE		97.83 320	97.71 308	98.20 373	99.11 363	96.33 402	99.41 275	99.52 134	98.06 215	99.05 308	99.50 322	89.64 427	99.73 282	97.73 316	97.38 363	98.53 429

DP-MVS		99.16 112	98.95 156	99.78 71	99.77 79	99.53 103	99.41 275	99.50 187	97.03 349	99.04 309	99.88 59	97.39 126	99.92 124	98.66 207	99.90 56	99.87 41

9.14			99.10 99		99.72 112		99.40 283	99.51 162	97.53 294	99.64 151	99.78 185	98.84 45	99.91 136	97.63 325	99.82 118

D2MVS		98.41 240	98.50 230	98.15 379	99.26 324	96.62 391	99.40 283	99.61 61	97.71 270	98.98 319	99.36 369	96.04 209	99.67 310	98.70 200	97.41 361	98.15 461

Anonymous20240529		98.09 271	97.68 311	99.34 200	99.66 151	98.44 282	99.40 283	99.43 279	93.67 460	99.22 269	99.89 45	90.23 418	99.93 109	99.26 112	98.33 297	99.66 177

FMVSNet3		98.03 285	97.76 303	98.84 291	99.39 285	98.98 185	99.40 283	99.38 303	96.67 373	99.07 301	99.28 391	92.93 351	98.98 452	97.10 374	96.65 379	98.56 426

LFMVS		97.90 306	97.35 357	99.54 127	99.52 235	99.01 182	99.39 287	98.24 487	97.10 341	99.65 146	99.79 178	84.79 477	99.91 136	99.28 106	98.38 294	99.69 157

HQP_MVS		98.27 255	98.22 248	98.44 349	99.29 315	96.97 367	99.39 287	99.47 235	98.97 76	99.11 292	99.61 280	92.71 361	99.69 307	97.78 308	97.63 337	98.67 381

plane_prior2							99.39 287		98.97 76

CHOSEN 1792x2688		99.19 101	99.10 99	99.45 175	99.89 8	98.52 272	99.39 287	99.94 1	98.73 103	99.11 292	99.89 45	95.50 240	99.94 91	99.50 57	99.97 9	99.89 30

PAPM_NR		99.04 164	98.84 183	99.66 92	99.74 101	99.44 118	99.39 287	99.38 303	97.70 273	99.28 251	99.28 391	98.34 98	99.85 192	96.96 385	99.45 181	99.69 157

onestephybrid01		99.17 109	99.06 110	99.49 160	99.60 201	98.98 185	99.38 292	99.50 187	98.52 123	99.81 72	99.87 75	96.27 195	99.81 238	99.47 66	99.10 234	99.67 170

hybridnocas07		99.13 129	99.03 118	99.46 173	99.63 173	98.90 215	99.38 292	99.52 134	98.41 138	99.82 70	99.84 108	96.09 206	99.80 246	99.40 74	99.16 208	99.68 163

gg-mvs-nofinetune		96.17 413	95.32 425	98.73 304	98.79 421	98.14 296	99.38 292	94.09 531	91.07 493	98.07 426	91.04 535	89.62 428	99.35 375	96.75 395	99.09 240	98.68 373

VDDNet		97.55 361	97.02 384	99.16 234	99.49 252	98.12 299	99.38 292	99.30 356	95.35 432	99.68 125	99.90 36	82.62 489	99.93 109	99.31 95	98.13 317	99.42 271

aaEdge-Enhanced		99.56 21	99.46 28	99.86 34	99.80 64	99.81 34	99.37 296	99.70 18	99.18 35	99.83 66	99.83 117	98.74 66	99.93 109	98.83 182	99.89 67	99.83 64

MGCNet		99.15 117	98.96 152	99.73 83	98.92 402	99.37 125	99.37 296	96.92 510	99.51 2	99.66 136	99.78 185	96.69 169	99.97 29	99.84 28	99.97 9	99.84 54

pmmvs6		96.53 404	96.09 409	97.82 414	98.69 442	95.47 434	99.37 296	99.47 235	93.46 465	97.41 445	99.78 185	87.06 460	99.33 378	96.92 390	92.70 470	98.65 392

PM-MVS		92.96 462	92.23 466	95.14 473	95.61 516	89.98 500	99.37 296	98.21 489	94.80 447	95.04 481	97.69 492	65.06 522	97.90 492	94.30 449	89.98 490	97.54 495

WTY-MVS		99.06 159	98.88 174	99.61 110	99.62 183	99.16 158	99.37 296	99.56 90	98.04 225	99.53 185	99.62 276	96.84 161	99.94 91	98.85 176	98.49 289	99.72 138

IterMVS-LS		98.46 235	98.42 234	98.58 323	99.59 205	98.00 305	99.37 296	99.43 279	96.94 357	99.07 301	99.59 285	97.87 115	99.03 440	98.32 257	95.62 410	98.71 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 346	97.28 370	98.97 256	99.70 123	97.27 341	99.36 302	99.45 259	98.94 79	99.66 136	99.64 265	94.93 265	99.99 4	99.48 64	84.36 506	99.65 184

DPE-MVS		99.46 42	99.32 53	99.91 6	99.78 71	99.88 10	99.36 302	99.51 162	98.73 103	99.88 42	99.84 108	98.72 68	99.96 41	98.16 270	99.87 79	99.88 36
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UnsupCasMVSNet_eth		96.44 406	96.12 407	97.40 437	98.65 446	95.65 427	99.36 302	99.51 162	97.13 335	96.04 472	98.99 431	88.40 443	98.17 485	96.71 397	90.27 488	98.40 446

sss		99.17 109	99.05 113	99.53 135	99.62 183	98.97 189	99.36 302	99.62 52	97.83 253	99.67 131	99.65 259	97.37 129	99.95 76	99.19 118	99.19 206	99.68 163

DeepC-MVS_fast	98.69 1	99.49 33	99.39 39	99.77 74	99.63 173	99.59 90	99.36 302	99.46 248	99.07 58	99.79 81	99.82 128	98.85 43	99.92 124	98.68 205	99.87 79	99.82 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
dtuplus		99.03 166	98.92 161	99.36 196	99.60 201	98.62 259	99.35 307	99.51 162	97.99 233	99.38 224	99.88 59	96.04 209	99.79 253	99.37 81	99.17 207	99.68 163

hybrid		99.11 145	99.01 137	99.41 187	99.64 168	98.76 245	99.35 307	99.52 134	98.31 153	99.80 78	99.84 108	96.16 202	99.79 253	99.40 74	99.06 243	99.68 163

CANet		99.25 95	99.14 93	99.59 114	99.41 277	99.16 158	99.35 307	99.57 85	98.82 90	99.51 190	99.61 280	96.46 183	99.95 76	99.59 45	99.98 4	99.65 184

pmmvs-eth3d		95.34 433	94.73 434	97.15 442	95.53 518	95.94 415	99.35 307	99.10 398	95.13 435	93.55 493	97.54 498	88.15 447	97.91 491	94.58 446	89.69 494	97.61 491

MDTV_nov1_ep13_2view						95.18 445	99.35 307		96.84 362	99.58 171		95.19 256		97.82 303		99.46 263

VDD-MVS		97.73 340	97.35 357	98.88 280	99.47 260	97.12 349	99.34 312	98.85 442	98.19 179	99.67 131	99.85 93	82.98 487	99.92 124	99.49 61	98.32 301	99.60 204

COLMAP_ROB	97.56 6	98.86 192	98.75 193	99.17 233	99.88 13	98.53 268	99.34 312	99.59 73	97.55 290	98.70 367	99.89 45	95.83 224	99.90 149	98.10 276	99.90 56	99.08 313
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
viewmambaseed2359dif		99.01 173	98.90 167	99.32 206	99.58 207	98.51 274	99.33 314	99.54 109	97.85 249	99.44 204	99.85 93	96.01 212	99.79 253	99.41 72	99.13 218	99.67 170

myMVS_eth3d28		97.69 347	97.34 360	98.73 304	99.27 320	97.52 332	99.33 314	98.78 452	98.03 227	98.82 349	98.49 462	86.64 461	99.46 346	98.44 241	98.24 307	99.23 300

EGC-MVSNET		82.80 492	77.86 499	97.62 426	97.91 475	96.12 410	99.33 314	99.28 362	8.40 557	25.05 559	99.27 394	84.11 481	99.33 378	89.20 496	98.22 308	97.42 497

nomal-1		97.78 330	97.52 328	98.54 334	99.27 320	96.47 397	99.32 317	98.56 474	97.43 307	98.92 329	98.91 442	88.14 448	99.72 286	98.75 193	98.39 292	99.44 268

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 165	99.64 168	98.85 230	99.32 317	99.48 213	98.50 126	99.81 72	99.81 143	96.82 162	99.88 170	99.40 74	99.12 223	99.71 150

ETVMVS		97.50 367	96.90 388	99.29 216	99.23 332	98.78 244	99.32 317	98.90 433	97.52 296	98.56 387	98.09 482	84.72 478	99.69 307	97.86 298	97.88 327	99.39 278

FMVSNet5		96.43 407	96.19 406	97.15 442	99.11 363	95.89 419	99.32 317	99.52 134	94.47 453	98.34 408	99.07 416	87.54 454	97.07 504	92.61 478	95.72 407	98.47 437

dp		97.75 336	97.80 293	97.59 430	99.10 366	93.71 475	99.32 317	98.88 437	96.48 392	99.08 300	99.55 300	92.67 364	99.82 233	96.52 405	98.58 281	99.24 299

tpmvs		97.98 294	98.02 270	97.84 409	99.04 384	94.73 456	99.31 322	99.20 385	96.10 423	98.76 357	99.42 347	94.94 264	99.81 238	96.97 384	98.45 290	98.97 331

tpmrst		98.33 249	98.48 231	97.90 400	99.16 355	94.78 455	99.31 322	99.11 397	97.27 322	99.45 199	99.59 285	95.33 248	99.84 202	98.48 234	98.61 278	99.09 312

testing99		97.36 377	96.94 387	98.63 317	99.18 345	96.70 385	99.30 324	98.93 423	97.71 270	98.23 414	98.26 473	84.92 476	99.84 202	98.04 286	97.85 330	99.35 284

MP-MVS-pluss		99.37 68	99.20 85	99.88 16	99.90 4	99.87 17	99.30 324	99.52 134	97.18 331	99.60 166	99.79 178	98.79 52	99.95 76	98.83 182	99.91 45	99.83 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 75	99.19 87	99.79 68	99.61 194	99.65 76	99.30 324	99.48 213	98.86 85	99.21 272	99.63 271	98.72 68	99.90 149	98.25 262	99.63 166	99.80 88

JIA-IIPM		97.50 367	97.02 384	98.93 263	98.73 433	97.80 320	99.30 324	98.97 419	91.73 487	98.91 331	94.86 520	95.10 259	99.71 293	97.58 329	97.98 321	99.28 293

BH-RMVSNet		98.41 240	98.08 262	99.40 189	99.41 277	98.83 235	99.30 324	98.77 453	97.70 273	98.94 327	99.65 259	92.91 354	99.74 276	96.52 405	99.55 174	99.64 191

usedtu_blend_shiyan5		95.04 438	94.10 446	97.86 405	96.45 505	95.92 416	99.29 329	99.22 380	86.17 511	98.36 403	97.68 493	91.20 404	99.07 433	97.53 337	80.97 523	98.60 415

testing11		97.50 367	97.10 381	98.71 309	99.20 339	96.91 375	99.29 329	98.82 445	97.89 243	98.21 417	98.40 466	85.63 470	99.83 224	98.45 240	98.04 320	99.37 282

Syy-MVS		97.09 391	97.14 378	96.95 451	99.00 389	92.73 485	99.29 329	99.39 294	97.06 345	97.41 445	98.15 477	93.92 329	98.68 476	91.71 483	98.34 295	99.45 266

myMVS_eth3d		96.89 395	96.37 400	98.43 351	99.00 389	97.16 347	99.29 329	99.39 294	97.06 345	97.41 445	98.15 477	83.46 485	98.68 476	95.27 437	98.34 295	99.45 266

MCST-MVS		99.43 53	99.30 61	99.82 57	99.79 69	99.74 55	99.29 329	99.40 291	98.79 96	99.52 188	99.62 276	98.91 38	99.90 149	98.64 209	99.75 144	99.82 72

LF4IMVS		97.52 364	97.46 339	97.70 423	98.98 395	95.55 430	99.29 329	98.82 445	98.07 211	98.66 370	99.64 265	89.97 422	99.61 330	97.01 380	96.68 378	97.94 479

hse-mvs2		97.50 367	97.14 378	98.59 320	99.49 252	97.05 356	99.28 335	99.22 380	98.94 79	99.66 136	99.42 347	94.93 265	99.65 318	99.48 64	83.80 510	99.08 313

OPM-MVS		98.19 260	98.10 258	98.45 346	98.88 408	97.07 354	99.28 335	99.38 303	98.57 118	99.22 269	99.81 143	92.12 378	99.66 313	98.08 281	97.54 346	98.61 412
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs		99.14 125	99.02 129	99.51 147	99.61 194	98.96 193	99.28 335	99.49 201	98.46 130	99.72 108	99.71 222	96.50 181	99.88 170	99.31 95	99.11 225	99.67 170
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 192	98.80 186	99.03 248	99.76 83	98.79 241	99.28 335	99.91 3	97.42 310	99.67 131	99.37 366	97.53 123	99.88 170	98.98 149	97.29 366	98.42 443

OMC-MVS		99.08 154	99.04 115	99.20 230	99.67 139	98.22 292	99.28 335	99.52 134	98.07 211	99.66 136	99.81 143	97.79 118	99.78 261	97.79 307	99.81 121	99.60 204

testing222		97.16 387	96.50 397	99.16 234	99.16 355	98.47 281	99.27 340	98.66 471	97.71 270	98.23 414	98.15 477	82.28 492	99.84 202	97.36 355	97.66 336	99.18 303

AUN-MVS		96.88 396	96.31 402	98.59 320	99.48 259	97.04 359	99.27 340	99.22 380	97.44 306	98.51 391	99.41 351	91.97 381	99.66 313	97.71 319	83.83 509	99.07 318

pmmvs5		97.52 364	97.30 367	98.16 376	98.57 456	96.73 384	99.27 340	98.90 433	96.14 417	98.37 402	99.53 310	91.54 395	99.14 417	97.51 340	95.87 402	98.63 401

1314		98.68 222	98.54 227	99.11 241	98.89 406	98.65 254	99.27 340	99.49 201	96.89 359	97.99 428	99.56 297	97.72 121	99.83 224	97.74 315	99.27 196	98.84 339

MVS		97.28 382	96.55 396	99.48 165	98.78 424	98.95 199	99.27 340	99.39 294	83.53 514	98.08 423	99.54 305	96.97 152	99.87 177	94.23 452	99.16 208	99.63 196

BH-untuned		98.42 238	98.36 237	98.59 320	99.49 252	96.70 385	99.27 340	99.13 395	97.24 326	98.80 352	99.38 363	95.75 231	99.74 276	97.07 378	99.16 208	99.33 288

MDTV_nov1_ep13			98.32 241		99.11 363	94.44 465	99.27 340	98.74 458	97.51 297	99.40 220	99.62 276	94.78 278	99.76 270	97.59 328	98.81 270

DP-MVS Recon		99.12 139	98.95 156	99.65 96	99.74 101	99.70 61	99.27 340	99.57 85	96.40 399	99.42 210	99.68 245	98.75 61	99.80 246	97.98 289	99.72 150	99.44 268

PatchmatchNet		98.31 250	98.36 237	98.19 374	99.16 355	95.32 441	99.27 340	98.92 426	97.37 314	99.37 227	99.58 289	94.90 269	99.70 301	97.43 351	99.21 203	99.54 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 358	97.28 370	98.62 318	99.64 168	98.03 303	99.26 349	98.74 458	97.68 275	99.09 298	98.32 470	91.66 392	99.81 238	92.88 473	98.22 308	98.03 470

CNVR-MVS		99.42 55	99.30 61	99.78 71	99.62 183	99.71 59	99.26 349	99.52 134	98.82 90	99.39 222	99.71 222	98.96 26	99.85 192	98.59 220	99.80 126	99.77 100

mamba_0408		99.08 154	98.96 152	99.44 180	99.62 183	98.88 221	99.25 351	99.47 235	98.05 218	99.37 227	99.81 143	96.85 156	99.85 192	98.98 149	99.25 199	99.60 204

SSM_04072		99.06 159	98.96 152	99.35 199	99.62 183	98.88 221	99.25 351	99.47 235	98.05 218	99.37 227	99.81 143	96.85 156	99.58 333	98.98 149	99.25 199	99.60 204

tt0320		95.71 423	95.07 428	97.62 426	99.05 382	95.02 449	99.25 351	99.52 134	86.81 506	97.97 430	99.72 219	83.58 484	99.15 415	96.38 411	93.35 455	98.68 373

1112_ss		98.98 177	98.77 191	99.59 114	99.68 136	99.02 180	99.25 351	99.48 213	97.23 327	99.13 288	99.58 289	96.93 154	99.90 149	98.87 169	98.78 271	99.84 54

TAPA-MVS	97.07 15	97.74 338	97.34 360	98.94 261	99.70 123	97.53 331	99.25 351	99.51 162	91.90 486	99.30 247	99.63 271	98.78 53	99.64 322	88.09 502	99.87 79	99.65 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UWE-MVS-28		97.36 377	97.24 374	97.75 419	98.84 417	94.44 465	99.24 356	97.58 503	97.98 235	99.00 316	99.00 429	91.35 400	99.53 340	93.75 459	98.39 292	99.27 297

UBG		97.85 313	97.48 334	98.95 259	99.25 328	97.64 328	99.24 356	98.74 458	97.90 242	98.64 377	98.20 475	88.65 439	99.81 238	98.27 260	98.40 291	99.42 271

PLC	97.94 4	99.02 168	98.85 181	99.53 135	99.66 151	99.01 182	99.24 356	99.52 134	96.85 361	99.27 257	99.48 333	98.25 102	99.91 136	97.76 312	99.62 167	99.65 184
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post1							99.23 359				65.14 555	94.18 318	99.71 293	97.58 329

ADS-MVSNet2		98.02 287	98.07 265	97.87 402	99.33 302	95.19 444	99.23 359	99.08 401	96.24 407	99.10 295	99.67 252	94.11 320	98.93 464	96.81 393	99.05 244	99.48 252

ADS-MVSNet		98.20 259	98.08 262	98.56 328	99.33 302	96.48 396	99.23 359	99.15 392	96.24 407	99.10 295	99.67 252	94.11 320	99.71 293	96.81 393	99.05 244	99.48 252

EPNet_dtu		98.03 285	97.96 275	98.23 372	98.27 467	95.54 432	99.23 359	98.75 454	99.02 62	97.82 437	99.71 222	96.11 205	99.48 342	93.04 471	99.65 163	99.69 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 263	97.93 280	98.87 284	99.18 345	98.49 277	99.22 363	99.33 336	96.96 353	99.56 176	99.38 363	94.33 311	99.00 449	94.83 445	98.58 281	99.14 304

RPMNet		96.72 399	95.90 413	99.19 231	99.18 345	98.49 277	99.22 363	99.52 134	88.72 503	99.56 176	97.38 501	94.08 322	99.95 76	86.87 512	98.58 281	99.14 304

sc_t1		95.75 421	95.05 429	97.87 402	98.83 418	94.61 462	99.21 365	99.45 259	87.45 505	97.97 430	99.85 93	81.19 495	99.43 357	98.27 260	93.20 460	99.57 222

WBMVS		97.74 338	97.50 332	98.46 344	99.24 330	97.43 335	99.21 365	99.42 281	97.45 303	98.96 323	99.41 351	88.83 434	99.23 396	98.94 157	96.02 395	98.71 359

plane_prior						96.97 367	99.21 365		98.45 132						97.60 340

ArgMatch-SfM		96.18 412	95.78 417	97.38 438	99.08 372	94.64 461	99.20 368	99.33 336	98.01 231	98.54 389	99.54 305	83.13 486	99.43 357	93.86 457	91.29 478	98.08 465

IMVS_0404		98.53 231	98.52 229	98.55 330	99.55 221	96.93 370	99.20 368	99.44 268	98.05 218	98.96 323	99.80 161	94.66 293	99.13 420	98.15 272	98.92 256	99.60 204

tt0320-xc		95.31 434	94.59 438	97.45 434	98.92 402	94.73 456	99.20 368	99.31 351	86.74 507	97.23 451	99.72 219	81.14 496	98.95 462	97.08 377	91.98 475	98.67 381

testing91		97.44 374	97.02 384	98.71 309	99.18 345	96.89 377	99.19 371	99.04 408	97.78 261	98.31 409	98.29 471	85.41 473	99.85 192	98.01 287	97.95 322	99.39 278

WR-MVS		98.06 277	97.73 306	99.06 244	98.86 414	99.25 148	99.19 371	99.35 322	97.30 320	98.66 370	99.43 345	93.94 327	99.21 408	98.58 221	94.28 441	98.71 359

new-patchmatchnet		94.48 449	94.08 448	95.67 469	95.08 522	92.41 486	99.18 373	99.28 362	94.55 452	93.49 494	97.37 502	87.86 452	97.01 506	91.57 484	88.36 498	97.61 491

AdaColmap		99.01 173	98.80 186	99.66 92	99.56 217	99.54 100	99.18 373	99.70 18	98.18 182	99.35 236	99.63 271	96.32 190	99.90 149	97.48 343	99.77 139	99.55 227

ArgMatch-Sym		96.59 402	96.31 402	97.42 435	98.89 406	94.84 454	99.16 375	99.39 294	98.11 201	98.35 406	99.53 310	84.38 480	99.40 362	94.16 454	94.85 431	98.03 470

EG-PatchMatch MVS		95.97 417	95.69 418	96.81 455	97.78 481	92.79 484	99.16 375	98.93 423	96.16 414	94.08 489	99.22 400	82.72 488	99.47 344	95.67 428	97.50 351	98.17 459

PatchT		97.03 393	96.44 399	98.79 299	98.99 392	98.34 287	99.16 375	99.07 404	92.13 484	99.52 188	97.31 505	94.54 301	98.98 452	88.54 500	98.73 273	99.03 322

CNLPA		99.14 125	98.99 143	99.59 114	99.58 207	99.41 122	99.16 375	99.44 268	98.45 132	99.19 279	99.49 325	98.08 110	99.89 165	97.73 316	99.75 144	99.48 252

usedtu_dtu_shiyan2		91.34 469	89.96 478	95.47 471	93.61 534	90.81 494	99.15 379	98.68 467	86.37 509	95.19 478	98.27 472	72.64 507	97.05 505	85.40 516	80.32 529	98.54 427

MDA-MVSNet-bldmvs		94.96 441	93.98 449	97.92 398	98.24 468	97.27 341	99.15 379	99.33 336	93.80 459	80.09 535	99.03 424	88.31 444	97.86 493	93.49 464	94.36 439	98.62 403

CDPH-MVS		99.13 129	98.91 165	99.80 64	99.75 93	99.71 59	99.15 379	99.41 284	96.60 383	99.60 166	99.55 300	98.83 47	99.90 149	97.48 343	99.83 114	99.78 98

save fliter					99.76 83	99.59 90	99.14 382	99.40 291	99.00 67

WB-MVSnew		97.65 355	97.65 314	97.63 425	98.78 424	97.62 329	99.13 383	98.33 483	97.36 315	99.07 301	98.94 437	95.64 236	99.15 415	92.95 472	98.68 276	96.12 517

testf1		90.42 474	90.68 474	89.65 506	97.78 481	73.97 537	99.13 383	98.81 447	89.62 497	91.80 505	98.93 438	62.23 526	98.80 471	86.61 513	91.17 479	96.19 515

APD_test2		90.42 474	90.68 474	89.65 506	97.78 481	73.97 537	99.13 383	98.81 447	89.62 497	91.80 505	98.93 438	62.23 526	98.80 471	86.61 513	91.17 479	96.19 515

xiu_mvs_v1_base_debu		99.29 84	99.27 72	99.34 200	99.63 173	98.97 189	99.12 386	99.51 162	98.86 85	99.84 56	99.47 336	98.18 105	99.99 4	99.50 57	99.31 193	99.08 313

xiu_mvs_v1_base		99.29 84	99.27 72	99.34 200	99.63 173	98.97 189	99.12 386	99.51 162	98.86 85	99.84 56	99.47 336	98.18 105	99.99 4	99.50 57	99.31 193	99.08 313

xiu_mvs_v1_base_debi		99.29 84	99.27 72	99.34 200	99.63 173	98.97 189	99.12 386	99.51 162	98.86 85	99.84 56	99.47 336	98.18 105	99.99 4	99.50 57	99.31 193	99.08 313

XVG-OURS-SEG-HR		98.69 220	98.62 217	98.89 275	99.71 118	97.74 321	99.12 386	99.54 109	98.44 135	99.42 210	99.71 222	94.20 315	99.92 124	98.54 231	98.90 262	99.00 325

jason		99.13 129	99.03 118	99.45 175	99.46 262	98.87 225	99.12 386	99.26 371	98.03 227	99.79 81	99.65 259	97.02 149	99.85 192	99.02 146	99.90 56	99.65 184
jason: jason.
N_pmnet		94.95 442	95.83 415	92.31 488	98.47 461	79.33 530	99.12 386	92.81 537	93.87 456	97.68 440	99.13 410	93.87 331	99.01 447	91.38 486	96.19 392	98.59 421

MDA-MVSNet_test_wron		95.45 427	94.60 437	98.01 388	98.16 472	97.21 346	99.11 392	99.24 377	93.49 464	80.73 534	98.98 433	93.02 349	98.18 484	94.22 453	94.45 437	98.64 394

Patchmtry		97.75 336	97.40 352	98.81 296	99.10 366	98.87 225	99.11 392	99.33 336	94.83 446	98.81 350	99.38 363	94.33 311	99.02 444	96.10 414	95.57 412	98.53 429

YYNet1		95.36 432	94.51 441	97.92 398	97.89 477	97.10 350	99.10 394	99.23 378	93.26 468	80.77 533	99.04 423	92.81 355	98.02 488	94.30 449	94.18 443	98.64 394

CANet_DTU		98.97 179	98.87 175	99.25 223	99.33 302	98.42 285	99.08 395	99.30 356	99.16 37	99.43 207	99.75 203	95.27 250	99.97 29	98.56 227	99.95 22	99.36 283

icg_test_0407_2		98.79 209	98.86 178	98.57 324	99.55 221	96.93 370	99.07 396	99.44 268	98.05 218	99.66 136	99.80 161	97.13 140	99.18 412	98.15 272	98.92 256	99.60 204

SCA		98.19 260	98.16 250	98.27 369	99.30 311	95.55 430	99.07 396	98.97 419	97.57 287	99.43 207	99.57 294	92.72 359	99.74 276	97.58 329	99.20 205	99.52 235

TSAR-MVS + GP.		99.36 72	99.36 45	99.36 196	99.67 139	98.61 262	99.07 396	99.33 336	99.00 67	99.82 70	99.81 143	99.06 17	99.84 202	99.09 137	99.42 183	99.65 184

MG-MVS		99.13 129	99.02 129	99.45 175	99.57 213	98.63 257	99.07 396	99.34 327	98.99 69	99.61 163	99.82 128	97.98 114	99.87 177	97.00 381	99.80 126	99.85 47

LoFTR		93.25 459	92.33 465	95.99 466	97.91 475	90.83 493	99.06 400	98.56 474	92.19 479	90.24 510	98.18 476	72.97 505	99.26 391	89.37 495	92.52 473	97.89 484

PatchMatch-RL		98.84 204	98.62 217	99.52 142	99.71 118	99.28 143	99.06 400	99.77 12	97.74 268	99.50 191	99.53 310	95.41 243	99.84 202	97.17 373	99.64 164	99.44 268

OpenMVS_ROB	92.34 20	94.38 450	93.70 456	96.41 461	97.38 489	93.17 482	99.06 400	98.75 454	86.58 508	94.84 484	98.26 473	81.53 493	99.32 380	89.01 498	97.87 328	96.76 508

TEST9					99.67 139	99.65 76	99.05 403	99.41 284	96.22 409	98.95 325	99.49 325	98.77 57	99.91 136

train_agg		99.02 168	98.77 191	99.77 74	99.67 139	99.65 76	99.05 403	99.41 284	96.28 403	98.95 325	99.49 325	98.76 58	99.91 136	97.63 325	99.72 150	99.75 113

lupinMVS		99.13 129	99.01 137	99.46 173	99.51 238	98.94 203	99.05 403	99.16 391	97.86 246	99.80 78	99.56 297	97.39 126	99.86 184	98.94 157	99.85 94	99.58 219

DELS-MVS		99.48 37	99.42 32	99.65 96	99.72 112	99.40 123	99.05 403	99.66 32	99.14 40	99.57 174	99.80 161	98.46 89	99.94 91	99.57 48	99.84 102	99.60 204
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
new_pmnet		96.38 408	96.03 410	97.41 436	98.13 473	95.16 446	99.05 403	99.20 385	93.94 455	97.39 448	98.79 451	91.61 394	99.04 438	90.43 491	95.77 404	98.05 468

Patchmatch-test		97.93 300	97.65 314	98.77 302	99.18 345	97.07 354	99.03 408	99.14 394	96.16 414	98.74 358	99.57 294	94.56 298	99.72 286	93.36 466	99.11 225	99.52 235

test_8					99.67 139	99.61 87	99.03 408	99.41 284	96.28 403	98.93 328	99.48 333	98.76 58	99.91 136

Test_1112_low_res		98.89 185	98.66 207	99.57 122	99.69 129	98.95 199	99.03 408	99.47 235	96.98 351	99.15 286	99.23 399	96.77 166	99.89 165	98.83 182	98.78 271	99.86 43

IterMVS-SCA-FT		97.82 323	97.75 304	98.06 384	99.57 213	96.36 401	99.02 411	99.49 201	97.18 331	98.71 361	99.72 219	92.72 359	99.14 417	97.44 350	95.86 403	98.67 381

xiu_mvs_v2_base		99.26 91	99.25 76	99.29 216	99.53 229	98.91 210	99.02 411	99.45 259	98.80 95	99.71 118	99.26 396	98.94 33	99.98 20	99.34 88	99.23 202	98.98 329

MIMVSNet		97.73 340	97.45 340	98.57 324	99.45 268	97.50 333	99.02 411	98.98 418	96.11 419	99.41 215	99.14 409	90.28 414	98.74 474	95.74 424	98.93 254	99.47 258

IterMVS		97.83 320	97.77 299	98.02 387	99.58 207	96.27 405	99.02 411	99.48 213	97.22 328	98.71 361	99.70 226	92.75 356	99.13 420	97.46 346	96.00 397	98.67 381
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 145	98.92 161	99.65 96	99.90 4	99.37 125	99.02 411	99.91 3	97.67 277	99.59 170	99.75 203	95.90 221	99.73 282	99.53 53	99.02 249	99.86 43

UWE-MVS		97.58 360	97.29 369	98.48 338	99.09 369	96.25 406	99.01 416	96.61 516	97.86 246	99.19 279	99.01 427	88.72 435	99.90 149	97.38 354	98.69 275	99.28 293

新几何2							99.01 416

BH-w/o		98.00 292	97.89 286	98.32 361	99.35 296	96.20 408	99.01 416	98.90 433	96.42 397	98.38 401	99.00 429	95.26 252	99.72 286	96.06 415	98.61 278	99.03 322

test_prior4						99.56 96	98.99 419

无先验							98.99 419	99.51 162	96.89 359				99.93 109	97.53 337		99.72 138

pmmvs4		98.13 267	97.90 282	98.81 296	98.61 451	98.87 225	98.99 419	99.21 384	96.44 395	99.06 306	99.58 289	95.90 221	99.11 427	97.18 372	96.11 394	98.46 440

HQP-NCC					99.19 342		98.98 422		98.24 168	98.66 370

ACMP_Plane					99.19 342		98.98 422		98.24 168	98.66 370

HQP-MVS		98.02 287	97.90 282	98.37 357	99.19 342	96.83 378	98.98 422	99.39 294	98.24 168	98.66 370	99.40 356	92.47 370	99.64 322	97.19 370	97.58 342	98.64 394

PS-MVSNAJ		99.32 78	99.32 53	99.30 213	99.57 213	98.94 203	98.97 425	99.46 248	98.92 82	99.71 118	99.24 398	99.01 19	99.98 20	99.35 83	99.66 161	98.97 331

MVP-Stereo		97.81 325	97.75 304	97.99 391	97.53 486	96.60 393	98.96 426	98.85 442	97.22 328	97.23 451	99.36 369	95.28 249	99.46 346	95.51 430	99.78 135	97.92 481
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							98.96 426		98.34 147	99.01 312	99.52 315	98.68 71		97.96 290	99.74 147

旧先验2							98.96 426		96.70 371	99.47 196			99.94 91	98.19 266

原ACMM2							98.95 429

MVS_111021_HR		99.41 59	99.32 53	99.66 92	99.72 112	99.47 115	98.95 429	99.85 8	98.82 90	99.54 183	99.73 215	98.51 86	99.74 276	98.91 163	99.88 73	99.77 100

mvsany_test1		99.50 31	99.46 28	99.62 109	99.61 194	99.09 169	98.94 431	99.48 213	99.10 48	99.96 27	99.91 26	98.85 43	99.96 41	99.72 32	99.58 171	99.82 72

MVS_111021_LR		99.41 59	99.33 51	99.65 96	99.77 79	99.51 109	98.94 431	99.85 8	98.82 90	99.65 146	99.74 209	98.51 86	99.80 246	98.83 182	99.89 67	99.64 191

MatchFormer		91.94 467	90.72 472	95.58 470	97.82 480	89.79 501	98.92 433	98.87 439	88.24 504	88.03 515	97.92 489	70.39 513	99.23 396	85.21 517	91.12 481	97.72 485

pmmvs3		94.09 454	93.25 461	96.60 458	94.76 526	94.49 464	98.92 433	98.18 491	89.66 496	96.48 466	98.06 483	86.28 465	97.33 501	89.68 494	87.20 502	97.97 478

XVG-OURS		98.73 218	98.68 203	98.88 280	99.70 123	97.73 322	98.92 433	99.55 100	98.52 123	99.45 199	99.84 108	95.27 250	99.91 136	98.08 281	98.84 266	99.00 325

test222					99.75 93	99.49 111	98.91 436	99.49 201	96.42 397	99.34 240	99.65 259	98.28 101			99.69 155	99.72 138

PMMVS2		86.87 486	85.37 491	91.35 492	90.21 544	83.80 514	98.89 437	97.45 505	83.13 516	91.67 507	95.03 518	48.49 542	94.70 525	85.86 515	77.62 533	95.54 518

miper_lstm_enhance		98.00 292	97.91 281	98.28 368	99.34 301	97.43 335	98.88 438	99.36 315	96.48 392	98.80 352	99.55 300	95.98 213	98.91 465	97.27 362	95.50 415	98.51 433

MVS-HIRNet		95.75 421	95.16 426	97.51 432	99.30 311	93.69 476	98.88 438	95.78 520	85.09 513	98.78 355	92.65 530	91.29 402	99.37 368	94.85 444	99.85 94	99.46 263

RoMa-HiRes		92.56 464	92.07 467	94.02 476	97.77 484	87.59 504	98.87 440	98.46 480	89.82 495	92.47 499	99.41 351	71.58 511	97.29 502	90.47 490	89.79 493	97.17 501

TR-MVS		97.76 332	97.41 351	98.82 293	99.06 378	97.87 316	98.87 440	98.56 474	96.63 379	98.68 369	99.22 400	92.49 369	99.65 318	95.40 434	97.79 332	98.95 335

RoMa-SfM		94.36 451	93.86 452	95.88 468	98.61 451	90.62 495	98.85 442	99.04 408	91.63 488	94.14 487	99.49 325	77.16 500	99.09 432	92.66 477	93.13 463	97.91 482

blended_shiyan8		95.56 424	94.79 432	97.87 402	96.60 503	95.90 418	98.85 442	99.27 369	92.19 479	98.47 395	97.94 488	91.43 397	99.11 427	97.26 363	81.09 522	98.60 415

blended_shiyan6		95.54 425	94.78 433	97.84 409	96.60 503	95.89 419	98.85 442	99.28 362	92.17 483	98.43 398	97.95 485	91.44 396	99.02 444	97.30 360	80.97 523	98.60 415

testdata1							98.85 442		98.32 151

blend_shiyan4		95.25 435	94.39 443	97.84 409	96.70 502	95.92 416	98.84 446	99.28 362	92.21 478	98.16 420	97.84 490	87.10 459	99.07 433	97.53 337	81.87 518	98.54 427

ET-MVSNet_ETH3D		96.49 405	95.64 420	99.05 246	99.53 229	98.82 238	98.84 446	97.51 504	97.63 280	84.77 521	99.21 403	92.09 379	98.91 465	98.98 149	92.21 474	99.41 274

our_test_3		97.65 355	97.68 311	97.55 431	98.62 449	94.97 451	98.84 446	99.30 356	96.83 364	98.19 418	99.34 376	97.01 151	99.02 444	95.00 442	96.01 396	98.64 394

MS-PatchMatch		97.24 386	97.32 365	96.99 448	98.45 463	93.51 480	98.82 449	99.32 347	97.41 311	98.13 422	99.30 387	88.99 432	99.56 336	95.68 427	99.80 126	97.90 483

c3_l		98.12 269	98.04 267	98.38 356	99.30 311	97.69 327	98.81 450	99.33 336	96.67 373	98.83 347	99.34 376	97.11 143	98.99 451	97.58 329	95.34 417	98.48 435

ppachtmachnet_test		97.49 372	97.45 340	97.61 429	98.62 449	95.24 442	98.80 451	99.46 248	96.11 419	98.22 416	99.62 276	96.45 184	98.97 459	93.77 458	95.97 401	98.61 412

PAPR		98.63 228	98.34 239	99.51 147	99.40 282	99.03 179	98.80 451	99.36 315	96.33 400	99.00 316	99.12 414	98.46 89	99.84 202	95.23 438	99.37 192	99.66 177

dtuonlycased		97.04 392	97.33 363	96.16 464	99.08 372	90.59 496	98.79 453	99.38 303	97.19 330	96.91 462	99.49 325	90.22 420	98.75 473	97.04 379	97.89 326	99.14 304

DenseAffine		94.28 452	93.53 458	96.52 460	98.72 435	92.31 487	98.78 454	99.02 412	93.14 470	94.45 485	99.01 427	74.73 504	99.20 410	90.98 488	92.94 465	98.04 469

test0.0.03 1		97.71 345	97.42 350	98.56 328	98.41 465	97.82 319	98.78 454	98.63 472	97.34 316	98.05 427	98.98 433	94.45 306	98.98 452	95.04 441	97.15 372	98.89 336

PVSNet_Blended		99.08 154	98.97 148	99.42 186	99.76 83	98.79 241	98.78 454	99.91 3	96.74 368	99.67 131	99.49 325	97.53 123	99.88 170	98.98 149	99.85 94	99.60 204

PatchmatchNet2					0.00 564	95.16 446	98.77 457	99.17 390	93.82 458
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PMMVS		98.80 208	98.62 217	99.34 200	99.27 320	98.70 250	98.76 458	99.31 351	97.34 316	99.21 272	99.07 416	97.20 138	99.82 233	98.56 227	98.87 263	99.52 235

test123		39.01 520	42.50 522	28.53 537	39.17 562	20.91 564	98.75 459	19.17 564	19.83 556	38.57 556	66.67 553	33.16 556	15.42 558	37.50 555	29.66 556	49.26 553

MSDG		98.98 177	98.80 186	99.53 135	99.76 83	99.19 153	98.75 459	99.55 100	97.25 324	99.47 196	99.77 194	97.82 117	99.87 177	96.93 388	99.90 56	99.54 229

CLD-MVS		98.16 264	98.10 258	98.33 359	99.29 315	96.82 380	98.75 459	99.44 268	97.83 253	99.13 288	99.55 300	92.92 352	99.67 310	98.32 257	97.69 335	98.48 435
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DKM		93.17 460	92.50 464	95.21 472	98.53 459	90.26 498	98.74 462	98.90 433	93.00 472	92.61 498	99.06 418	70.06 515	97.74 496	91.92 482	89.65 495	97.62 490

miper_ehance_all_eth		98.18 262	98.10 258	98.41 352	99.23 332	97.72 323	98.72 463	99.31 351	96.60 383	98.88 336	99.29 389	97.29 133	99.13 420	97.60 327	95.99 398	98.38 448

cl____		98.01 290	97.84 290	98.55 330	99.25 328	97.97 307	98.71 464	99.34 327	96.47 394	98.59 386	99.54 305	95.65 235	99.21 408	97.21 366	95.77 404	98.46 440

DIV-MVS_self_test		98.01 290	97.85 289	98.48 338	99.24 330	97.95 312	98.71 464	99.35 322	96.50 388	98.60 385	99.54 305	95.72 233	99.03 440	97.21 366	95.77 404	98.46 440

test-LLR		98.06 277	97.90 282	98.55 330	98.79 421	97.10 350	98.67 466	97.75 496	97.34 316	98.61 383	98.85 445	94.45 306	99.45 348	97.25 364	99.38 185	99.10 308

TESTMET0.1,1		97.55 361	97.27 373	98.40 354	98.93 400	96.53 394	98.67 466	97.61 501	96.96 353	98.64 377	99.28 391	88.63 441	99.45 348	97.30 360	99.38 185	99.21 302

test-mter		97.49 372	97.13 380	98.55 330	98.79 421	97.10 350	98.67 466	97.75 496	96.65 375	98.61 383	98.85 445	88.23 445	99.45 348	97.25 364	99.38 185	99.10 308

mvs5depth		96.66 400	96.22 405	97.97 393	97.00 499	96.28 404	98.66 469	99.03 411	96.61 380	96.93 461	99.79 178	87.20 456	99.47 344	96.65 403	94.13 444	98.16 460

IB-MVS	95.67 18	96.22 409	95.44 424	98.57 324	99.21 337	96.70 385	98.65 470	97.74 498	96.71 370	97.27 450	98.54 461	86.03 467	99.92 124	98.47 237	86.30 503	99.10 308
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
ELoFTR		89.95 476	88.65 481	93.85 478	95.93 511	85.85 507	98.64 471	98.31 484	90.34 494	85.03 520	97.76 491	60.28 529	99.01 447	87.27 509	84.26 507	96.71 511

DPM-MVS		98.95 181	98.71 199	99.66 92	99.63 173	99.55 98	98.64 471	99.10 398	97.93 239	99.42 210	99.55 300	98.67 73	99.80 246	95.80 422	99.68 158	99.61 201

DKM-HiRes		92.13 465	91.58 469	93.78 481	98.24 468	88.09 502	98.61 473	98.68 467	91.39 489	90.36 508	98.90 444	67.97 520	96.01 515	91.39 485	88.65 497	97.24 499

thisisatest0515		98.14 266	97.79 294	99.19 231	99.50 250	98.50 276	98.61 473	96.82 512	96.95 355	99.54 183	99.43 345	91.66 392	99.86 184	98.08 281	99.51 176	99.22 301

DeepPCF-MVS	98.18 3	98.81 205	99.37 43	97.12 445	99.60 201	91.75 490	98.61 473	99.44 268	99.35 27	99.83 66	99.85 93	98.70 70	99.81 238	99.02 146	99.91 45	99.81 79

cl22		97.85 313	97.64 317	98.48 338	99.09 369	97.87 316	98.60 476	99.33 336	97.11 340	98.87 339	99.22 400	92.38 375	99.17 414	98.21 264	95.99 398	98.42 443

usedtu_dtu_shiyan1		98.09 271	97.82 291	98.89 275	98.70 439	98.90 215	98.57 477	99.47 235	96.78 365	98.87 339	99.05 420	94.75 283	99.23 396	97.45 348	96.74 376	98.53 429

FE-MVSNET3		98.09 271	97.82 291	98.89 275	98.70 439	98.90 215	98.57 477	99.47 235	96.78 365	98.87 339	99.05 420	94.75 283	99.23 396	97.45 348	96.74 376	98.53 429

GA-MVS		97.85 313	97.47 337	99.00 252	99.38 289	97.99 306	98.57 477	99.15 392	97.04 348	98.90 333	99.30 387	89.83 424	99.38 365	96.70 398	98.33 297	99.62 199

TinyColmap		97.12 389	96.89 389	97.83 412	99.07 375	95.52 433	98.57 477	98.74 458	97.58 286	97.81 438	99.79 178	88.16 446	99.56 336	95.10 439	97.21 369	98.39 447

gbinet_0.2-2-1-0.02		95.40 431	94.58 439	97.85 406	96.11 510	95.97 413	98.56 481	99.26 371	92.12 485	98.47 395	97.49 499	90.23 418	99.00 449	97.71 319	81.25 520	98.58 423

eth_miper_zixun_eth		98.05 282	97.96 275	98.33 359	99.26 324	97.38 337	98.56 481	99.31 351	96.65 375	98.88 336	99.52 315	96.58 176	99.12 426	97.39 353	95.53 414	98.47 437

MASt3R-SfM		94.79 444	95.11 427	93.81 480	97.96 474	85.14 510	98.52 483	98.99 416	95.33 433	97.53 443	99.13 410	79.99 498	99.48 342	93.66 461	94.90 429	96.80 507

CMPMVS	69.68 23	94.13 453	94.90 431	91.84 489	97.24 493	80.01 527	98.52 483	99.48 213	89.01 500	91.99 503	99.67 252	85.67 469	99.13 420	95.44 432	97.03 374	96.39 514
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dtuonly		98.37 246	98.26 246	98.69 311	99.07 375	96.81 381	98.51 485	98.75 454	97.77 262	99.57 174	99.68 245	96.12 204	99.71 293	95.76 423	99.11 225	99.57 222

USDC		97.34 379	97.20 375	97.75 419	99.07 375	95.20 443	98.51 485	99.04 408	97.99 233	98.31 409	99.86 86	89.02 431	99.55 338	95.67 428	97.36 364	98.49 434

wanda-best-256-512		95.43 428	94.66 435	97.77 417	96.45 505	95.68 425	98.48 487	99.28 362	92.18 481	98.36 403	97.68 493	91.20 404	99.03 440	97.31 357	80.97 523	98.60 415

FE-blended-shiyan7		95.43 428	94.66 435	97.77 417	96.45 505	95.68 425	98.48 487	99.28 362	92.18 481	98.36 403	97.68 493	91.20 404	99.03 440	97.31 357	80.97 523	98.60 415

ambc				93.06 486	92.68 538	82.36 515	98.47 489	98.73 464		95.09 480	97.41 500	55.55 530	99.10 430	96.42 408	91.32 477	97.71 486

miper_enhance_ethall		98.16 264	98.08 262	98.41 352	98.96 398	97.72 323	98.45 490	99.32 347	96.95 355	98.97 321	99.17 405	97.06 147	99.22 403	97.86 298	95.99 398	98.29 452

CHOSEN 280x420		99.12 139	99.13 94	99.08 242	99.66 151	97.89 315	98.43 491	99.71 16	98.88 84	99.62 158	99.76 198	96.63 172	99.70 301	99.46 68	99.99 1	99.66 177

testmvs		39.17 519	43.78 521	25.37 538	36.04 563	16.84 565	98.36 492	26.56 562	20.06 555	38.51 557	67.32 552	29.64 557	15.30 559	37.59 554	39.90 553	43.98 554

FPMVS		84.93 489	85.65 489	82.75 515	86.77 551	63.39 544	98.35 493	98.92 426	74.11 521	83.39 526	98.98 433	50.85 535	92.40 531	84.54 518	94.97 425	92.46 524

KD-MVS_2432*1600		94.62 446	93.72 454	97.31 439	97.19 495	95.82 422	98.34 494	99.20 385	95.00 442	97.57 441	98.35 468	87.95 449	98.10 486	92.87 474	77.00 534	98.01 472

miper_refine_blended		94.62 446	93.72 454	97.31 439	97.19 495	95.82 422	98.34 494	99.20 385	95.00 442	97.57 441	98.35 468	87.95 449	98.10 486	92.87 474	77.00 534	98.01 472

CL-MVSNet_self_test		94.49 448	93.97 450	96.08 465	96.16 509	93.67 477	98.33 496	99.38 303	95.13 435	97.33 449	98.15 477	92.69 363	96.57 509	88.67 499	79.87 531	97.99 476

PVSNet	96.02 17	98.85 201	98.84 183	98.89 275	99.73 108	97.28 340	98.32 497	99.60 68	97.86 246	99.50 191	99.57 294	96.75 167	99.86 184	98.56 227	99.70 154	99.54 229

PAPM		97.59 359	97.09 382	99.07 243	99.06 378	98.26 290	98.30 498	99.10 398	94.88 444	98.08 423	99.34 376	96.27 195	99.64 322	89.87 493	98.92 256	99.31 291

Patchmatch-RL test		95.84 419	95.81 416	95.95 467	95.61 516	90.57 497	98.24 499	98.39 481	95.10 439	95.20 477	98.67 455	94.78 278	97.77 494	96.28 413	90.02 489	99.51 244

UnsupCasMVSNet_bld		93.53 457	92.51 463	96.58 459	97.38 489	93.82 472	98.24 499	99.48 213	91.10 492	93.10 495	96.66 510	74.89 503	98.37 481	94.03 456	87.71 501	97.56 494

LCM-MVSNet		86.80 487	85.22 492	91.53 491	87.81 550	80.96 523	98.23 501	98.99 416	71.05 526	90.13 511	96.51 513	48.45 543	96.88 507	90.51 489	85.30 505	96.76 508

SP-LightGlue		89.28 477	88.68 479	91.06 493	98.21 471	80.90 524	98.19 502	96.96 509	72.38 523	89.60 513	94.43 522	72.44 508	95.06 521	82.91 520	93.03 464	97.22 500

cascas		97.69 347	97.43 349	98.48 338	98.60 453	97.30 339	98.18 503	99.39 294	92.96 473	98.41 399	98.78 452	93.77 335	99.27 388	98.16 270	98.61 278	98.86 337

ALIKED-LG		88.17 483	87.32 485	90.75 496	98.67 444	81.68 519	98.16 504	94.72 528	78.63 518	86.08 519	97.07 506	70.16 514	96.62 508	71.97 536	90.37 486	93.95 522

SP-SuperGlue		89.23 478	88.68 479	90.88 495	98.23 470	80.60 525	98.16 504	97.30 506	73.08 522	89.64 512	94.62 521	71.80 510	94.91 522	82.11 522	93.22 459	97.14 503

kuosan		90.92 472	90.11 477	93.34 483	98.78 424	85.59 509	98.15 506	93.16 535	89.37 499	92.07 502	98.38 467	81.48 494	95.19 519	62.54 540	97.04 373	99.25 298

PDCNetPlus		84.77 490	83.24 493	89.36 508	94.33 529	83.93 513	98.13 507	76.80 552	83.26 515	86.31 517	97.33 503	62.90 524	92.65 529	87.20 510	62.90 540	91.50 527

Effi-MVS+		98.81 205	98.59 223	99.48 165	99.46 262	99.12 167	98.08 508	99.50 187	97.50 298	99.38 224	99.41 351	96.37 189	99.81 238	99.11 132	98.54 286	99.51 244

PCF-MVS	97.08 14	97.66 354	97.06 383	99.47 171	99.61 194	99.09 169	98.04 509	99.25 374	91.24 491	98.51 391	99.70 226	94.55 300	99.91 136	92.76 476	99.85 94	99.42 271
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SP-MNN		88.33 480	87.78 483	89.95 504	98.28 466	77.92 532	98.01 510	95.69 522	70.61 527	86.18 518	94.36 524	71.09 512	94.76 524	81.51 523	94.32 440	97.17 501

ALIKED-MNN		86.97 485	85.90 487	90.16 501	99.06 378	79.59 529	97.93 511	94.82 526	72.37 524	84.41 522	95.46 517	68.55 519	96.43 512	72.40 534	88.11 500	94.47 521

SP-DiffGlue		90.78 473	90.71 473	90.98 494	95.45 521	81.30 522	97.92 512	97.30 506	75.18 520	92.09 501	95.93 515	74.93 502	94.89 523	93.46 465	94.12 445	96.74 510

PMatch-SfM		88.28 481	86.92 486	92.38 487	95.93 511	84.56 511	97.84 513	96.01 519	88.80 502	84.11 523	97.95 485	49.73 538	95.66 518	89.15 497	82.72 517	96.91 505

PVSNet_0	94.43 19	96.09 415	95.47 422	97.94 396	99.31 310	94.34 469	97.81 514	99.70 18	97.12 337	97.46 444	98.75 453	89.71 425	99.79 253	97.69 323	81.69 519	99.68 163

E-PMN		80.61 495	79.88 497	82.81 514	90.75 542	76.38 535	97.69 515	95.76 521	66.44 531	83.52 525	92.25 531	62.54 525	87.16 543	68.53 538	61.40 541	84.89 536

dongtai		93.26 458	92.93 462	94.25 475	99.39 285	85.68 508	97.68 516	93.27 533	92.87 474	96.85 463	99.39 360	82.33 491	97.48 500	76.78 527	97.80 331	99.58 219

ANet_high		77.30 498	74.86 505	84.62 512	75.88 556	77.61 533	97.63 517	93.15 536	88.81 501	64.27 544	89.29 546	36.51 554	83.93 547	75.89 530	52.31 545	92.33 526

0.4-1-1-0.1		95.23 436	94.22 445	98.26 370	97.39 488	95.86 421	97.59 518	97.62 499	93.85 457	94.97 482	97.03 507	87.20 456	99.87 177	98.47 237	83.84 508	99.05 320

PMatch-Up-SfM		86.75 488	85.43 490	90.73 497	94.97 525	81.39 520	97.55 519	94.92 525	86.33 510	83.10 527	97.95 485	46.03 544	93.97 527	87.59 505	80.39 528	96.83 506

EMVS		80.02 496	79.22 498	82.43 516	91.19 541	76.40 534	97.55 519	92.49 538	66.36 533	83.01 528	91.27 533	64.63 523	85.79 546	65.82 539	60.65 542	85.08 535

SP-NN		88.62 479	88.17 482	89.96 503	97.89 477	78.51 531	97.19 521	96.09 518	71.28 525	88.29 514	94.00 526	71.98 509	93.65 528	82.37 521	94.46 435	97.71 486

ALIKED-NN		88.27 482	87.61 484	90.24 500	98.46 462	79.97 528	97.04 522	94.61 530	75.25 519	86.99 516	96.90 508	72.78 506	95.78 517	75.45 531	91.01 483	94.97 520

0.3-1-1-0.015		94.79 444	93.69 457	98.10 382	96.99 500	95.46 435	97.02 523	97.61 501	93.53 462	94.03 490	96.54 512	85.60 471	99.86 184	98.43 244	83.45 513	98.99 328

MVE	76.82 21	76.91 500	74.31 506	84.70 511	85.38 554	76.05 536	96.88 524	93.17 534	67.39 530	71.28 542	89.01 548	21.66 561	87.69 541	71.74 537	72.29 538	90.35 530
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
0.4-1-1-0.2		94.94 443	93.92 451	97.99 391	96.84 501	95.13 448	96.64 525	97.62 499	93.45 466	94.92 483	96.56 511	87.14 458	99.86 184	98.43 244	83.69 512	98.98 329

XFeat-MNN		82.40 494	82.10 495	83.31 513	93.04 536	68.49 541	95.39 526	90.86 539	60.29 536	81.56 531	94.09 525	66.79 521	91.70 533	76.62 528	80.26 530	89.74 531

test_method		91.10 470	91.36 470	90.31 499	95.85 513	73.72 539	94.89 527	99.25 374	68.39 529	95.82 473	99.02 426	80.50 497	98.95 462	93.64 462	94.89 430	98.25 455

SIFT-NN-NCMNet		75.53 503	75.57 503	75.42 521	93.93 532	61.35 546	94.41 528	86.44 546	58.51 539	76.23 538	90.44 539	50.56 536	89.34 536	46.60 543	83.04 515	75.58 541

SIFT-MNN		75.73 502	75.71 502	75.77 520	95.65 515	60.92 547	94.36 529	87.62 544	58.67 538	75.90 539	90.94 536	49.64 540	89.04 537	44.85 547	83.80 510	77.35 537

SIFT-NCM-Cal		71.65 506	70.76 511	74.34 523	94.61 527	60.18 550	94.16 530	81.72 549	57.21 543	55.36 551	89.56 545	42.48 545	88.45 539	41.31 553	80.41 527	74.39 543

SIFT-NN		76.99 499	77.37 500	75.84 519	97.10 497	62.39 545	94.15 531	87.21 545	59.41 537	79.90 537	90.73 537	54.60 533	88.56 538	47.22 542	86.03 504	76.57 539

Gipuma		90.99 471	90.15 476	93.51 482	98.73 433	90.12 499	93.98 532	99.45 259	79.32 517	92.28 500	94.91 519	69.61 516	97.98 490	87.42 507	95.67 408	92.45 525
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SIFT-NN-UMatch		71.65 506	70.86 510	74.00 524	90.69 543	60.53 548	93.59 533	81.89 548	58.42 540	60.99 548	89.71 544	50.18 537	87.89 540	45.77 545	66.55 539	73.57 545

SIFT-NN-PointCN		70.32 509	69.71 512	72.13 527	90.01 545	58.29 554	93.45 534	76.20 553	56.66 546	70.25 543	89.20 547	48.94 541	83.41 548	45.45 546	57.26 544	74.70 542

PMVS	70.75 22	75.98 501	74.97 504	79.01 518	70.98 557	55.18 556	93.37 535	98.21 489	65.08 534	61.78 547	93.83 527	21.74 560	92.53 530	78.59 526	91.12 481	89.34 533
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GLUNet-SfM		78.99 497	76.32 501	86.99 509	89.16 549	73.30 540	93.36 536	90.45 540	66.38 532	74.95 541	93.30 529	52.29 534	94.61 526	75.35 532	51.65 547	93.07 523

XFeat-NN		82.84 491	83.12 494	82.00 517	94.35 528	67.14 543	93.32 537	89.27 543	62.21 535	84.06 524	93.50 528	69.15 517	89.40 535	78.92 525	83.33 514	89.46 532

SIFT-NN-CMatch		72.61 504	71.92 509	74.68 522	92.79 537	60.24 549	93.28 538	81.57 550	58.24 541	75.18 540	90.26 541	49.66 539	87.35 542	46.02 544	60.26 543	76.45 540

SIFT-UMatch		68.14 511	66.40 515	73.38 526	92.20 540	59.42 552	92.84 539	76.01 554	56.87 544	58.37 549	90.35 540	41.97 548	87.16 543	42.64 549	46.35 549	73.55 546

tmp_tt		82.80 492	81.52 496	86.66 510	66.61 558	68.44 542	92.79 540	97.92 493	68.96 528	80.04 536	99.85 93	85.77 468	96.15 514	97.86 298	43.89 550	95.39 519

SIFT-ConvMatch		69.43 510	68.09 513	73.45 525	93.86 533	60.02 551	92.57 541	77.69 551	57.58 542	62.69 545	90.53 538	42.14 547	86.65 545	43.98 548	51.72 546	73.67 544

SIFT-UM-Cal		64.60 514	62.65 517	70.42 529	92.22 539	58.07 555	92.29 542	66.92 557	56.70 545	50.16 553	89.97 543	37.90 551	82.95 550	42.33 551	35.40 554	70.24 549

SIFT-PointCN		62.71 515	61.56 518	66.18 531	89.53 548	50.88 557	91.81 543	72.35 555	53.65 548	50.49 552	86.32 550	33.30 555	76.23 552	35.91 557	40.66 552	71.43 548

SIFT-CM-Cal		66.94 512	65.48 516	71.33 528	93.05 535	58.77 553	91.46 544	70.45 556	56.64 547	61.97 546	89.98 542	40.72 549	83.32 549	42.57 550	42.47 551	71.90 547

SIFT-PCN-Cal		61.29 516	60.21 519	64.54 532	89.88 546	50.56 558	91.21 545	65.73 559	53.15 549	48.59 554	87.20 549	36.60 553	76.52 551	37.37 556	32.17 555	66.54 550

SIFT-NCMNet		55.02 517	53.54 520	59.46 534	86.55 552	47.35 560	87.85 546	46.22 561	51.77 550	44.11 555	83.50 551	27.88 558	68.75 554	32.81 558	21.14 558	62.27 551

VLMVS_CLIP		71.76 505	73.17 508	67.54 530	63.66 560	40.57 563	82.57 547	89.67 542	44.24 551	82.97 529	95.88 516	37.85 552	71.58 553	83.87 519	77.80 532	90.48 529

MVS_clip		71.06 508	74.26 507	61.45 533	84.42 555	45.51 561	79.78 548	56.58 560	40.80 552	90.25 509	98.55 460	61.46 528	49.70 556	80.63 524	75.89 536	89.13 534

wuyk23d		40.18 518	41.29 523	36.84 536	86.18 553	49.12 559	79.73 549	22.81 563	27.64 554	25.46 558	28.45 557	21.98 559	48.89 557	55.80 541	23.56 557	12.51 555

VLMVS		64.83 513	67.01 514	58.30 535	65.95 559	42.53 562	76.90 550	66.20 558	29.52 553	82.93 530	94.37 523	42.34 546	55.19 555	72.39 535	72.45 537	77.18 538

MVS_baseline		35.35 521	39.65 524	22.45 539	47.29 561	11.23 566	38.03 551	9.90 565	5.09 558	58.24 550	91.18 534	16.48 562	0.13 560	42.28 552	48.39 548	55.99 552

mmdepth		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

monomultidepth		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

test_blank		0.13 525	0.17 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	1.57 558	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

uanet_test		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

DCPMVS		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

cdsmvs_eth3d_5k		24.64 522	32.85 525	0.00 540	0.00 564	0.00 567	0.00 552	99.51 162	0.00 559	0.00 560	99.56 297	96.58 176	0.00 561	0.00 559	0.00 559	0.00 556

pcd_1.5k_mvsjas		8.27 524	11.03 527	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	99.01 19	0.00 561	0.00 559	0.00 559	0.00 556

sosnet-low-res		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

sosnet		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

uncertanet		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

Regformer		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

ab-mvs-re		8.30 523	11.06 526	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	99.58 289	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

uanet		0.02 526	0.03 529	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.27 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

PatchmatchNet1														91.97 480	96.20 391	98.59 421
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.13 420
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					99.82 53	99.84 20		99.63 46		99.85 55		98.54 83	99.94 91	99.34 88	99.88 73

WAC-MVS						97.16 347								95.47 431

MSC_two_6792asdad				99.87 22	99.51 238	99.76 50		99.33 336					99.96 41	98.87 169	99.84 102	99.89 30

PC_three_1452									98.18 182	99.84 56	99.70 226	99.31 3	98.52 479	98.30 259	99.80 126	99.81 79

No_MVS				99.87 22	99.51 238	99.76 50		99.33 336					99.96 41	98.87 169	99.84 102	99.89 30

test_one_0601					99.81 58	99.88 10		99.49 201	98.97 76	99.65 146	99.81 143	99.09 15

eth-test2					0.00 564

eth-test					0.00 564

ZD-MVS					99.71 118	99.79 42		99.61 61	96.84 362	99.56 176	99.54 305	98.58 79	99.96 41	96.93 388	99.75 144

IU-MVS					99.84 38	99.88 10		99.32 347	98.30 155	99.84 56				98.86 174	99.85 94	99.89 30

test_241102_TWO								99.48 213	99.08 56	99.88 42	99.81 143	98.94 33	99.96 41	98.91 163	99.84 102	99.88 36

test_241102_ONE					99.84 38	99.90 2		99.48 213	99.07 58	99.91 31	99.74 209	99.20 8	99.76 270

test_0728_THIRD									98.99 69	99.81 72	99.80 161	99.09 15	99.96 41	98.85 176	99.90 56	99.88 36

GSMVS																99.52 235

test_part2					99.81 58	99.83 23				99.77 90

sam_mvs1												94.86 271				99.52 235

sam_mvs												94.72 287

MTGPA								99.47 235

test_post											65.99 554	94.65 294	99.73 282

patchmatchnet-post											98.70 454	94.79 277	99.74 276

gm-plane-assit					98.54 458	92.96 483			94.65 450		99.15 408		99.64 322	97.56 334

test9_res														97.49 342	99.72 150	99.75 113

agg_prior2														97.21 366	99.73 149	99.75 113

agg_prior					99.67 139	99.62 84		99.40 291		98.87 339			99.91 136

TestCases				99.31 208	99.86 25	98.48 279		99.61 61	97.85 249	99.36 233	99.85 93	95.95 216	99.85 192	96.66 401	99.83 114	99.59 215

test_prior				99.68 90	99.67 139	99.48 113		99.56 90					99.83 224			99.74 118

新几何1				99.75 77	99.75 93	99.59 90		99.54 109	96.76 367	99.29 250	99.64 265	98.43 91	99.94 91	96.92 390	99.66 161	99.72 138

旧先验1					99.74 101	99.59 90		99.54 109			99.69 237	98.47 88			99.68 158	99.73 128

原ACMM1				99.65 96	99.73 108	99.33 132		99.47 235	97.46 300	99.12 290	99.66 257	98.67 73	99.91 136	97.70 322	99.69 155	99.71 150

testdata2													99.95 76	96.67 400

segment_acmp												98.96 26

testdata				99.54 127	99.75 93	98.95 199		99.51 162	97.07 343	99.43 207	99.70 226	98.87 41	99.94 91	97.76 312	99.64 164	99.72 138

test12				99.75 77	99.64 168	99.61 87		99.29 360		99.21 272		98.38 96	99.89 165		99.74 147	99.74 118

plane_prior7					99.29 315	97.03 362

plane_prior6					99.27 320	96.98 366						92.71 361

plane_prior5								99.47 235					99.69 307	97.78 308	97.63 337	98.67 381

plane_prior4											99.61 280

plane_prior3						97.00 364			98.69 108	99.11 292

plane_prior1					99.26 324

n2								0.00 566

nn								0.00 566

door-mid								98.05 492

lessismore_v0				97.79 416	98.69 442	95.44 438		94.75 527		95.71 474	99.87 75	88.69 437	99.32 380	95.89 419	94.93 427	98.62 403

LGP-MVS_train				98.49 336	99.33 302	97.05 356		99.55 100	97.46 300	99.24 264	99.83 117	92.58 366	99.72 286	98.09 277	97.51 349	98.68 373

test11								99.35 322

door								97.92 493

HQP5-MVS						96.83 378

BP-MVS														97.19 370

HQP4-MVS										98.66 370			99.64 322			98.64 394

HQP3-MVS								99.39 294							97.58 342

HQP2-MVS												92.47 370

NP-MVS					99.23 332	96.92 374					99.40 356

ACMMP++_ref															97.19 370

ACMMP++															97.43 360

Test By Simon												98.75 61

ITE_SJBPF				98.08 383	99.29 315	96.37 400		98.92 426	98.34 147	98.83 347	99.75 203	91.09 407	99.62 329	95.82 420	97.40 362	98.25 455

DeepMVS_CX				93.34 483	99.29 315	82.27 516		99.22 380	85.15 512	96.33 467	99.05 420	90.97 409	99.73 282	93.57 463	97.77 333	98.01 472

High-resolution multi-view benchmark