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	99.54 125	99.76 82	99.42 118	99.90 1	99.55 100	98.56 118	99.78 81	99.70 216	98.65 74	99.79 245	99.65 41	99.78 134	99.41 264

mmtdpeth		96.95 381	96.71 380	97.67 406	99.33 291	94.90 433	99.89 2	99.28 347	98.15 175	99.72 102	98.57 437	86.56 444	99.90 148	99.82 29	89.02 464	98.20 437

SPE-MVS-test		99.49 33	99.48 22	99.54 125	99.78 70	99.30 138	99.89 2	99.58 78	98.56 118	99.73 97	99.69 227	98.55 81	99.82 227	99.69 35	99.85 94	99.48 243

MVSFormer		99.17 109	99.12 97	99.29 207	99.51 229	98.94 197	99.88 4	99.46 238	97.55 277	99.80 74	99.65 248	97.39 125	99.28 366	99.03 134	99.85 94	99.65 175

test_djsdf		98.67 214	98.57 215	98.98 245	98.70 421	98.91 204	99.88 4	99.46 238	97.55 277	99.22 258	99.88 57	95.73 223	99.28 366	99.03 134	97.62 325	98.75 335

OurMVSNet-221017-0		97.88 297	97.77 288	98.19 360	98.71 420	96.53 381	99.88 4	99.00 395	97.79 247	98.78 341	99.94 6	91.68 376	99.35 356	97.21 349	96.99 361	98.69 352

EC-MVSNet		99.44 50	99.39 40	99.58 116	99.56 208	99.49 109	99.88 4	99.58 78	98.38 137	99.73 97	99.69 227	98.20 103	99.70 286	99.64 43	99.82 117	99.54 219

DVP-MVS++		99.59 15	99.50 19	99.88 15	99.51 229	99.88 10	99.87 8	99.51 156	98.99 69	99.88 43	99.81 134	99.27 7	99.96 41	98.85 166	99.80 125	99.81 79

FOURS1					99.91 1	99.93 1	99.87 8	99.56 90	99.10 48	99.81 69

K. test v3		97.10 378	96.79 378	98.01 373	98.72 418	96.33 388	99.87 8	97.05 473	97.59 271	96.16 450	99.80 152	88.71 420	99.04 413	96.69 381	96.55 368	98.65 376

FC-MVSNet-test		98.75 207	98.62 208	99.15 229	99.08 360	99.45 115	99.86 11	99.60 67	98.23 165	98.70 353	99.82 119	96.80 162	99.22 381	99.07 129	96.38 371	98.79 325

v7n		97.87 299	97.52 317	98.92 256	98.76 414	98.58 253	99.84 12	99.46 238	96.20 394	98.91 318	99.70 216	94.89 261	99.44 336	96.03 398	93.89 429	98.75 335

DTE-MVSNet		97.51 354	97.19 363	98.46 331	98.63 428	98.13 286	99.84 12	99.48 204	96.68 356	97.97 412	99.67 241	92.92 339	98.56 449	96.88 374	92.60 447	98.70 348

3Dnovator	97.25 9	99.24 97	99.05 112	99.81 60	99.12 349	99.66 71	99.84 12	99.74 13	99.09 55	98.92 317	99.90 37	95.94 210	99.98 20	98.95 146	99.92 39	99.79 92

FIs		98.78 202	98.63 203	99.23 219	99.18 333	99.54 98	99.83 15	99.59 73	98.28 150	98.79 340	99.81 134	96.75 165	99.37 349	99.08 128	96.38 371	98.78 327

MGCFI-Net		99.01 166	98.85 174	99.50 149	99.42 263	99.26 144	99.82 16	99.48 204	98.60 115	99.28 240	98.81 426	97.04 147	99.76 257	99.29 95	97.87 314	99.47 249

test_fmvs3		92.10 439	91.77 442	93.08 454	96.19 471	86.25 474	99.82 16	98.62 447	96.65 359	95.19 458	96.90 474	55.05 488	95.93 481	96.63 386	90.92 456	97.06 470

jajsoiax		98.43 228	98.28 235	98.88 270	98.60 432	98.43 272	99.82 16	99.53 125	98.19 170	98.63 365	99.80 152	93.22 334	99.44 336	99.22 104	97.50 337	98.77 331

OpenMVS	96.50 16	98.47 225	98.12 246	99.52 139	99.04 368	99.53 101	99.82 16	99.72 14	94.56 434	98.08 405	99.88 57	94.73 274	99.98 20	97.47 330	99.76 140	99.06 306

SDMVSNet		99.11 141	98.90 160	99.75 77	99.81 57	99.59 88	99.81 20	99.65 39	98.78 98	99.64 144	99.88 57	94.56 286	99.93 110	99.67 37	98.26 292	99.72 137

nrg030		98.64 218	98.42 225	99.28 211	99.05 366	99.69 63	99.81 20	99.46 238	98.04 216	99.01 300	99.82 119	96.69 167	99.38 346	99.34 81	94.59 416	98.78 327

HPM-MVS		99.42 55	99.28 69	99.83 56	99.90 4	99.72 56	99.81 20	99.54 109	97.59 271	99.68 119	99.63 260	98.91 39	99.94 92	98.58 210	99.91 46	99.84 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		99.13 127	98.99 138	99.53 133	99.65 160	99.06 171	99.81 20	99.33 322	97.43 294	99.60 159	99.88 57	97.14 138	99.84 196	99.13 119	98.94 241	99.69 154

3Dnovator+	97.12 13	99.18 104	98.97 142	99.82 57	99.17 341	99.68 64	99.81 20	99.51 156	99.20 33	98.72 346	99.89 46	95.68 225	99.97 29	98.86 164	99.86 87	99.81 79

sasdasda		99.02 162	98.86 171	99.51 144	99.42 263	99.32 131	99.80 25	99.48 204	98.63 110	99.31 232	98.81 426	97.09 143	99.75 260	99.27 99	97.90 311	99.47 249

FA-MVS(test-final)		98.75 207	98.53 219	99.41 180	99.55 212	99.05 173	99.80 25	99.01 394	96.59 369	99.58 163	99.59 274	95.39 235	99.90 148	97.78 294	99.49 177	99.28 281

GeoE		98.85 193	98.62 208	99.53 133	99.61 189	99.08 168	99.80 25	99.51 156	97.10 326	99.31 232	99.78 176	95.23 246	99.77 253	98.21 250	99.03 235	99.75 113

canonicalmvs		99.02 162	98.86 171	99.51 144	99.42 263	99.32 131	99.80 25	99.48 204	98.63 110	99.31 232	98.81 426	97.09 143	99.75 260	99.27 99	97.90 311	99.47 249

v8		97.95 288	97.63 307	98.93 254	98.95 383	98.81 231	99.80 25	99.41 274	96.03 408	99.10 283	99.42 332	94.92 258	99.30 364	96.94 369	94.08 426	98.66 374

Vis-MVSNet (Re-imp)		98.87 181	98.72 189	99.31 199	99.71 117	98.88 213	99.80 25	99.44 258	97.91 229	99.36 222	99.78 176	95.49 232	99.43 340	97.91 279	99.11 218	99.62 190

Anonymous20240521		96.20 397	95.89 400	97.13 424	97.72 453	94.96 432	99.79 31	99.29 345	93.01 449	97.20 435	99.03 405	89.69 410	98.36 453	91.16 460	96.13 377	98.07 444

PS-MVSNAJss		98.92 175	98.92 155	98.90 262	98.78 407	98.53 257	99.78 32	99.54 109	98.07 202	99.00 304	99.76 189	99.01 20	99.37 349	99.13 119	97.23 354	98.81 324

PEN-MVS		97.76 320	97.44 333	98.72 296	98.77 412	98.54 256	99.78 32	99.51 156	97.06 330	98.29 394	99.64 254	92.63 352	98.89 440	98.09 263	93.16 439	98.72 341

anonymousdsp		98.44 227	98.28 235	98.94 252	98.50 438	98.96 187	99.77 34	99.50 179	97.07 328	98.87 326	99.77 185	94.76 271	99.28 366	98.66 196	97.60 326	98.57 406

SixPastTwentyTwo		97.50 355	97.33 351	98.03 370	98.65 426	96.23 393	99.77 34	98.68 443	97.14 319	97.90 415	99.93 10	90.45 399	99.18 389	97.00 363	96.43 370	98.67 365

QAPM		98.67 214	98.30 234	99.80 64	99.20 327	99.67 68	99.77 34	99.72 14	94.74 431	98.73 345	99.90 37	95.78 221	99.98 20	96.96 367	99.88 76	99.76 107

SSC-MVS		92.73 438	93.73 432	89.72 464	95.02 482	81.38 484	99.76 37	99.23 361	94.87 428	92.80 471	98.93 418	94.71 276	91.37 488	74.49 487	93.80 430	96.42 474

test_vis3_rt		87.04 446	85.81 449	90.73 461	93.99 485	81.96 482	99.76 37	90.23 496	92.81 452	81.35 484	91.56 484	40.06 492	99.07 408	94.27 432	88.23 466	91.15 484

dcpmvs_2		99.23 98	99.58 9	98.16 362	99.83 47	94.68 438	99.76 37	99.52 134	99.07 58	99.98 13	99.88 57	98.56 80	99.93 110	99.67 37	99.98 4	99.87 40

RRT-MVS		98.91 176	98.75 185	99.39 186	99.46 253	98.61 251	99.76 37	99.50 179	98.06 206	99.81 69	99.88 57	93.91 318	99.94 92	99.11 122	99.27 194	99.61 192

HPM-MVS_fast		99.51 29	99.40 38	99.85 43	99.91 1	99.79 41	99.76 37	99.56 90	97.72 256	99.76 91	99.75 194	99.13 14	99.92 123	99.07 129	99.92 39	99.85 46

lecture		99.60 14	99.50 19	99.89 11	99.89 8	99.90 3	99.75 42	99.59 73	99.06 61	99.88 43	99.85 85	98.41 93	99.96 41	99.28 96	99.84 102	99.83 63

MVSMamba_PlusPlus		99.46 42	99.41 37	99.64 101	99.68 134	99.50 108	99.75 42	99.50 179	98.27 152	99.87 49	99.92 18	98.09 108	99.94 92	99.65 41	99.95 23	99.47 249

v10		97.85 302	97.52 317	98.86 277	98.99 376	98.67 242	99.75 42	99.41 274	95.70 412	98.98 307	99.41 336	94.75 272	99.23 376	96.01 400	94.63 415	98.67 365

APDe-MVS		99.66 6	99.57 10	99.92 1	99.77 78	99.89 6	99.75 42	99.56 90	99.02 62	99.88 43	99.85 85	99.18 12	99.96 41	99.22 104	99.92 39	99.90 25
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
IS-MVSNet		99.05 158	98.87 168	99.57 120	99.73 107	99.32 131	99.75 42	99.20 368	98.02 221	99.56 167	99.86 78	96.54 177	99.67 294	98.09 263	99.13 211	99.73 127

test_vis1_n		97.92 292	97.44 333	99.34 191	99.53 220	98.08 289	99.74 47	99.49 192	99.15 38	100.00 1	99.94 6	79.51 476	99.98 20	99.88 26	99.76 140	99.97 4

test_fmvs1_n		98.41 231	98.14 243	99.21 220	99.82 53	97.71 315	99.74 47	99.49 192	99.32 29	99.99 2	99.95 3	85.32 454	99.97 29	99.82 29	99.84 102	99.96 7

balanced_conf03		99.46 42	99.39 40	99.67 90	99.55 212	99.58 93	99.74 47	99.51 156	98.42 134	99.87 49	99.84 100	98.05 111	99.91 135	99.58 47	99.94 31	99.52 226

tttt0517		98.42 229	98.14 243	99.28 211	99.66 150	98.38 275	99.74 47	96.85 475	97.68 262	99.79 76	99.74 199	91.39 386	99.89 163	98.83 172	99.56 170	99.57 213

WB-MVS		93.10 436	94.10 427	90.12 463	95.51 479	81.88 483	99.73 51	99.27 353	95.05 423	93.09 470	98.91 422	94.70 277	91.89 487	76.62 485	94.02 428	96.58 473

test_fmvs2		97.25 372	97.30 354	97.09 426	99.43 261	93.31 459	99.73 51	98.87 417	98.83 88	99.28 240	99.80 152	84.45 459	99.66 297	97.88 281	97.45 342	98.30 430

SD_0403		97.55 349	97.53 316	97.62 408	99.61 189	93.64 456	99.72 53	99.44 258	98.03 218	98.62 368	99.39 344	96.06 202	99.57 318	87.88 473	99.01 238	99.66 169

MonoMVSNet		98.38 235	98.47 223	98.12 367	98.59 434	96.19 395	99.72 53	98.79 428	97.89 231	99.44 194	99.52 302	96.13 199	98.90 439	98.64 198	97.54 332	99.28 281

baseline		99.15 115	99.02 127	99.53 133	99.66 150	99.14 160	99.72 53	99.48 204	98.35 142	99.42 200	99.84 100	96.07 201	99.79 245	99.51 56	99.14 208	99.67 164

RPSCF		98.22 246	98.62 208	96.99 428	99.82 53	91.58 468	99.72 53	99.44 258	96.61 364	99.66 130	99.89 46	95.92 211	99.82 227	97.46 331	99.10 225	99.57 213

CSCG		99.32 79	99.32 54	99.32 197	99.85 31	98.29 277	99.71 57	99.66 32	98.11 193	99.41 205	99.80 152	98.37 96	99.96 41	98.99 138	99.96 17	99.72 137

dmvs_re		98.08 264	98.16 240	97.85 390	99.55 212	94.67 439	99.70 58	98.92 405	98.15 175	99.06 294	99.35 356	93.67 326	99.25 373	97.77 297	97.25 353	99.64 182

WR-MVS_H		98.13 257	97.87 277	98.90 262	99.02 370	98.84 223	99.70 58	99.59 73	97.27 308	98.40 384	99.19 388	95.53 230	99.23 376	98.34 240	93.78 431	98.61 396

mvsmamba		99.06 154	98.96 146	99.36 188	99.47 251	98.64 246	99.70 58	99.05 389	97.61 270	99.65 139	99.83 106	96.54 177	99.92 123	99.19 108	99.62 165	99.51 235

LTVRE_ROB	97.16 12	98.02 276	97.90 272	98.40 341	99.23 320	96.80 370	99.70 58	99.60 67	97.12 322	98.18 401	99.70 216	91.73 375	99.72 273	98.39 233	97.45 342	98.68 357
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
MED-MVS test				99.87 21	99.88 13	99.81 33	99.69 62	99.87 6	99.34 26	99.90 34	99.83 106		99.95 76	98.83 172	99.89 68	99.83 63

MED-MVS		99.66 6	99.60 8	99.87 21	99.88 13	99.81 33	99.69 62	99.87 6	99.18 34	99.90 34	99.83 106	99.30 4	99.95 76	98.83 172	99.89 68	99.83 63

TestfortrainingZip a		99.73 1	99.67 1	99.92 1	99.88 13	99.91 2	99.69 62	99.87 6	99.34 26	99.90 34	99.83 106	99.30 4	99.95 76	99.32 84	99.89 68	99.90 25

TestfortrainingZip							99.69 62

test_f		91.90 440	91.26 444	93.84 450	95.52 478	85.92 475	99.69 62	98.53 451	95.31 417	93.87 465	96.37 477	55.33 487	98.27 454	95.70 406	90.98 455	97.32 469

XVS		99.53 27	99.42 32	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 216	99.74 199	98.81 49	99.94 92	98.79 180	99.86 87	99.84 53

X-MVStestdata		96.55 389	95.45 408	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 216	64.01 493	98.81 49	99.94 92	98.79 180	99.86 87	99.84 53

V42		98.06 266	97.79 283	98.86 277	98.98 379	98.84 223	99.69 62	99.34 314	96.53 371	99.30 236	99.37 350	94.67 279	99.32 361	97.57 318	94.66 414	98.42 422

mPP-MVS		99.44 50	99.30 62	99.86 34	99.88 13	99.79 41	99.69 62	99.48 204	98.12 191	99.50 181	99.75 194	98.78 53	99.97 29	98.57 213	99.89 68	99.83 63

CP-MVS		99.45 46	99.32 54	99.85 43	99.83 47	99.75 51	99.69 62	99.52 134	98.07 202	99.53 176	99.63 260	98.93 38	99.97 29	98.74 184	99.91 46	99.83 63

FE-MVS		98.48 224	98.17 239	99.40 181	99.54 219	98.96 187	99.68 72	98.81 424	95.54 414	99.62 151	99.70 216	93.82 321	99.93 110	97.35 340	99.46 178	99.32 278

PS-CasMVS		97.93 289	97.59 311	98.95 250	98.99 376	99.06 171	99.68 72	99.52 134	97.13 320	98.31 391	99.68 235	92.44 361	99.05 412	98.51 221	94.08 426	98.75 335

Vis-MVSNet		99.12 135	98.97 142	99.56 122	99.78 70	99.10 164	99.68 72	99.66 32	98.49 125	99.86 53	99.87 70	94.77 270	99.84 196	99.19 108	99.41 182	99.74 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		99.12 135	98.92 155	99.70 87	99.67 137	99.40 121	99.67 75	99.63 46	98.73 102	99.94 28	99.81 134	94.54 289	99.96 41	98.40 232	99.93 33	99.74 118

BP-MVS1		99.12 135	98.94 152	99.65 95	99.51 229	99.30 138	99.67 75	98.92 405	98.48 126	99.84 56	99.69 227	94.96 253	99.92 123	99.62 44	99.79 132	99.71 148

test_vis1_n_1920		98.63 219	98.40 227	99.31 199	99.86 25	97.94 302	99.67 75	99.62 51	99.43 17	99.99 2	99.91 26	87.29 438	100.00 1	99.92 24	99.92 39	99.98 2

EIA-MVS		99.18 104	99.09 104	99.45 169	99.49 243	99.18 152	99.67 75	99.53 125	97.66 265	99.40 210	99.44 328	98.10 107	99.81 232	98.94 147	99.62 165	99.35 273

MSP-MVS		99.42 55	99.27 73	99.88 15	99.89 8	99.80 38	99.67 75	99.50 179	98.70 106	99.77 85	99.49 312	98.21 102	99.95 76	98.46 227	99.77 137	99.88 35
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		99.10 146	98.97 142	99.48 160	99.49 243	99.14 160	99.67 75	99.34 314	97.31 305	99.58 163	99.76 189	97.65 121	99.82 227	98.87 159	99.07 232	99.46 254

CP-MVSNet		98.09 261	97.78 286	99.01 241	98.97 381	99.24 147	99.67 75	99.46 238	97.25 310	98.48 379	99.64 254	93.79 322	99.06 411	98.63 200	94.10 425	98.74 339

MTAPA		99.52 28	99.39 40	99.89 11	99.90 4	99.86 18	99.66 82	99.47 226	98.79 95	99.68 119	99.81 134	98.43 89	99.97 29	98.88 156	99.90 57	99.83 63

HFP-MVS		99.49 33	99.37 44	99.86 34	99.87 20	99.80 38	99.66 82	99.67 27	98.15 175	99.68 119	99.69 227	99.06 18	99.96 41	98.69 192	99.87 79	99.84 53

mvs_tets		98.40 234	98.23 237	98.91 260	98.67 425	98.51 263	99.66 82	99.53 125	98.19 170	98.65 362	99.81 134	92.75 343	99.44 336	99.31 86	97.48 341	98.77 331

EU-MVSNet		97.98 283	98.03 258	97.81 398	98.72 418	96.65 377	99.66 82	99.66 32	98.09 197	98.35 389	99.82 119	95.25 244	98.01 460	97.41 336	95.30 402	98.78 327

ACMMPR		99.49 33	99.36 46	99.86 34	99.87 20	99.79 41	99.66 82	99.67 27	98.15 175	99.67 125	99.69 227	98.95 32	99.96 41	98.69 192	99.87 79	99.84 53

MP-MVS		99.33 77	99.15 93	99.87 21	99.88 13	99.82 28	99.66 82	99.46 238	98.09 197	99.48 185	99.74 199	98.29 99	99.96 41	97.93 278	99.87 79	99.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NormalMVS		99.27 89	99.19 88	99.52 139	99.89 8	98.83 226	99.65 88	99.52 134	99.10 48	99.84 56	99.76 189	95.80 219	99.99 4	99.30 89	99.84 102	99.74 118

SymmetryMVS		99.15 115	99.02 127	99.52 139	99.72 111	98.83 226	99.65 88	99.34 314	99.10 48	99.84 56	99.76 189	95.80 219	99.99 4	99.30 89	98.72 262	99.73 127

Elysia		98.88 178	98.65 200	99.58 116	99.58 199	99.34 127	99.65 88	99.52 134	98.26 155	99.83 64	99.87 70	93.37 329	99.90 148	97.81 291	99.91 46	99.49 240

StellarMVS		98.88 178	98.65 200	99.58 116	99.58 199	99.34 127	99.65 88	99.52 134	98.26 155	99.83 64	99.87 70	93.37 329	99.90 148	97.81 291	99.91 46	99.49 240

test_cas_vis1_n_1920		99.16 111	99.01 134	99.61 109	99.81 57	98.86 220	99.65 88	99.64 42	99.39 22	99.97 25	99.94 6	93.20 335	99.98 20	99.55 50	99.91 46	99.99 1

region2R		99.48 37	99.35 48	99.87 21	99.88 13	99.80 38	99.65 88	99.66 32	98.13 183	99.66 130	99.68 235	98.96 27	99.96 41	98.62 201	99.87 79	99.84 53

TranMVSNet+NR-MVSNet		97.93 289	97.66 302	98.76 293	98.78 407	98.62 249	99.65 88	99.49 192	97.76 251	98.49 378	99.60 272	94.23 302	98.97 431	98.00 274	92.90 441	98.70 348

GDP-MVS		99.08 149	98.89 164	99.64 101	99.53 220	99.34 127	99.64 95	99.48 204	98.32 147	99.77 85	99.66 246	95.14 249	99.93 110	98.97 144	99.50 176	99.64 182

ttmdpeth		97.80 316	97.63 307	98.29 351	98.77 412	97.38 326	99.64 95	99.36 302	98.78 98	96.30 448	99.58 278	92.34 364	99.39 344	98.36 238	95.58 395	98.10 442

mvsany_test3		93.77 433	93.45 436	94.74 447	95.78 474	88.01 473	99.64 95	98.25 456	98.28 150	94.31 462	97.97 459	68.89 480	98.51 451	97.50 326	90.37 457	97.71 459

ZNCC-MVS		99.47 40	99.33 52	99.87 21	99.87 20	99.81 33	99.64 95	99.67 27	98.08 201	99.55 173	99.64 254	98.91 39	99.96 41	98.72 187	99.90 57	99.82 72

tfpnnormal		97.84 306	97.47 325	98.98 245	99.20 327	99.22 149	99.64 95	99.61 60	96.32 385	98.27 395	99.70 216	93.35 331	99.44 336	95.69 407	95.40 400	98.27 432

casdiffmvs_mvg		99.15 115	99.02 127	99.55 124	99.66 150	99.09 165	99.64 95	99.56 90	98.26 155	99.45 189	99.87 70	96.03 204	99.81 232	99.54 51	99.15 207	99.73 127
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 60	99.85 43	99.76 82	99.82 28	99.63 101	99.52 134	98.38 137	99.76 91	99.82 119	98.53 82	99.95 76	98.61 204	99.81 120	99.77 100

RE-MVS-def			99.34 50		99.76 82	99.82 28	99.63 101	99.52 134	98.38 137	99.76 91	99.82 119	98.75 60		98.61 204	99.81 120	99.77 100

TSAR-MVS + MP.		99.58 16	99.50 19	99.81 60	99.91 1	99.66 71	99.63 101	99.39 284	98.91 82	99.78 81	99.85 85	99.36 2	99.94 92	98.84 169	99.88 76	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		96.22 395	96.03 396	96.79 436	97.31 459	94.14 448	99.63 101	99.08 383	96.17 397	97.04 439	99.06 401	93.94 315	97.76 466	86.96 477	95.06 407	98.47 416

APD-MVS_3200maxsize		99.48 37	99.35 48	99.85 43	99.76 82	99.83 22	99.63 101	99.54 109	98.36 141	99.79 76	99.82 119	98.86 43	99.95 76	98.62 201	99.81 120	99.78 98

test0726					99.85 31	99.89 6	99.62 106	99.50 179	99.10 48	99.86 53	99.82 119	98.94 34

EPNet		98.86 184	98.71 191	99.30 204	97.20 461	98.18 282	99.62 106	98.91 410	99.28 31	98.63 365	99.81 134	95.96 207	99.99 4	99.24 103	99.72 148	99.73 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 174	98.67 195	99.72 86	99.85 31	99.53 101	99.62 106	99.59 73	92.65 454	99.71 112	99.78 176	98.06 110	99.90 148	98.84 169	99.91 46	99.74 118

HY-MVS	97.30 7	98.85 193	98.64 202	99.47 166	99.42 263	99.08 168	99.62 106	99.36 302	97.39 299	99.28 240	99.68 235	96.44 183	99.92 123	98.37 236	98.22 295	99.40 266

ACMMP		99.45 46	99.32 54	99.82 57	99.89 8	99.67 68	99.62 106	99.69 22	98.12 191	99.63 147	99.84 100	98.73 66	99.96 41	98.55 219	99.83 113	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 83	99.19 88	99.64 101	99.82 53	99.23 148	99.62 106	99.55 100	98.94 78	99.63 147	99.95 3	95.82 217	99.94 92	99.37 75	99.97 9	99.73 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		99.58 16	99.56 12	99.64 101	99.78 70	99.15 159	99.61 112	99.45 249	99.01 64	99.89 40	99.82 119	99.01 20	99.92 123	99.56 49	99.95 23	99.85 46

E5new		99.14 123	99.02 127	99.50 149	99.69 127	98.91 204	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.26 195	99.84 196	99.30 89	99.10 225	99.76 107

E6new		99.15 115	99.03 117	99.50 149	99.66 150	98.90 209	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.31 190	99.84 196	99.30 89	99.10 225	99.76 107

E6		99.15 115	99.03 117	99.50 149	99.66 150	98.90 209	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.31 190	99.84 196	99.30 89	99.10 225	99.76 107

E5		99.14 123	99.02 127	99.50 149	99.69 127	98.91 204	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.26 195	99.84 196	99.30 89	99.10 225	99.76 107

reproduce_monomvs		97.89 296	97.87 277	97.96 379	99.51 229	95.45 418	99.60 113	99.25 357	99.17 36	98.85 332	99.49 312	89.29 414	99.64 306	99.35 76	96.31 374	98.78 327

test2506		96.81 385	96.65 381	97.29 421	99.74 100	92.21 466	99.60 113	85.06 497	99.13 41	99.77 85	99.93 10	87.82 436	99.85 187	99.38 74	99.38 183	99.80 88

SED-MVS		99.61 10	99.52 14	99.88 15	99.84 38	99.90 3	99.60 113	99.48 204	99.08 56	99.91 31	99.81 134	99.20 9	99.96 41	98.91 153	99.85 94	99.79 92

OPU-MVS				99.64 101	99.56 208	99.72 56	99.60 113				99.70 216	99.27 7	99.42 342	98.24 249	99.80 125	99.79 92

GST-MVS		99.40 64	99.24 78	99.85 43	99.86 25	99.79 41	99.60 113	99.67 27	97.97 224	99.63 147	99.68 235	98.52 83	99.95 76	98.38 234	99.86 87	99.81 79

EI-MVSNet-UG-set		99.58 16	99.57 10	99.64 101	99.78 70	99.14 160	99.60 113	99.45 249	99.01 64	99.90 34	99.83 106	98.98 26	99.93 110	99.59 45	99.95 23	99.86 42

ACMH	97.28 8	98.10 260	97.99 262	98.44 336	99.41 268	96.96 358	99.60 113	99.56 90	98.09 197	98.15 403	99.91 26	90.87 396	99.70 286	98.88 156	97.45 342	98.67 365
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VortexMVS		98.67 214	98.66 198	98.68 302	99.62 178	97.96 297	99.59 124	99.41 274	98.13 183	99.31 232	99.70 216	95.48 233	99.27 369	99.40 71	97.32 351	98.79 325

guyue		99.16 111	99.04 114	99.52 139	99.69 127	98.92 203	99.59 124	98.81 424	98.73 102	99.90 34	99.87 70	95.34 238	99.88 168	99.66 40	99.81 120	99.74 118

ECVR-MVS		98.04 272	98.05 256	98.00 375	99.74 100	94.37 445	99.59 124	94.98 485	99.13 41	99.66 130	99.93 10	90.67 398	99.84 196	99.40 71	99.38 183	99.80 88

SR-MVS		99.43 53	99.29 66	99.86 34	99.75 92	99.83 22	99.59 124	99.62 51	98.21 168	99.73 97	99.79 169	98.68 70	99.96 41	98.44 229	99.77 137	99.79 92

thres100view900		97.76 320	97.45 328	98.69 301	99.72 111	97.86 306	99.59 124	98.74 434	97.93 227	99.26 251	98.62 434	91.75 373	99.83 218	93.22 445	98.18 300	98.37 428

thres600view7		97.86 301	97.51 319	98.92 256	99.72 111	97.95 300	99.59 124	98.74 434	97.94 226	99.27 246	98.62 434	91.75 373	99.86 181	93.73 439	98.19 299	98.96 317

LCM-MVSNet-Re		97.83 309	98.15 242	96.87 434	99.30 300	92.25 465	99.59 124	98.26 455	97.43 294	96.20 449	99.13 394	96.27 193	98.73 446	98.17 255	98.99 239	99.64 182

baseline1		98.31 240	97.95 267	99.38 187	99.50 241	98.74 236	99.59 124	98.93 402	98.41 135	99.14 275	99.60 272	94.59 284	99.79 245	98.48 223	93.29 436	99.61 192

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 21	99.82 53	99.81 33	99.59 124	99.51 156	98.62 112	99.79 76	99.83 106	99.28 6	99.97 29	98.48 223	99.90 57	99.84 53
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 141	98.90 160	99.74 80	99.80 63	99.46 114	99.59 124	99.49 192	97.03 334	99.63 147	99.69 227	97.27 133	99.96 41	97.82 289	99.84 102	99.81 79

IMVS_0403		98.86 184	98.89 164	98.78 291	99.55 212	96.93 359	99.58 134	99.44 258	98.05 209	99.68 119	99.80 152	96.81 161	99.80 239	98.15 258	98.92 244	99.60 195

test_fmvsmvis_n_1920		99.65 8	99.61 7	99.77 74	99.38 278	99.37 123	99.58 134	99.62 51	99.41 21	99.87 49	99.92 18	98.81 49	100.00 1	99.97 2	99.93 33	99.94 17

dmvs_testset		95.02 421	96.12 393	91.72 458	99.10 354	80.43 486	99.58 134	97.87 465	97.47 286	95.22 456	98.82 425	93.99 313	95.18 483	88.09 471	94.91 412	99.56 216

test_fmvsm_n_1920		99.69 5	99.66 4	99.78 71	99.84 38	99.44 116	99.58 134	99.69 22	99.43 17	99.98 13	99.91 26	98.62 76	100.00 1	99.97 2	99.95 23	99.90 25

test1111		98.04 272	98.11 247	97.83 395	99.74 100	93.82 450	99.58 134	95.40 484	99.12 46	99.65 139	99.93 10	90.73 397	99.84 196	99.43 69	99.38 183	99.82 72

PGM-MVS		99.45 46	99.31 60	99.86 34	99.87 20	99.78 47	99.58 134	99.65 39	97.84 240	99.71 112	99.80 152	99.12 15	99.97 29	98.33 241	99.87 79	99.83 63

LPG-MVS_test		98.22 246	98.13 245	98.49 323	99.33 291	97.05 345	99.58 134	99.55 100	97.46 287	99.24 253	99.83 106	92.58 353	99.72 273	98.09 263	97.51 335	98.68 357

PHI-MVS		99.30 83	99.17 91	99.70 87	99.56 208	99.52 105	99.58 134	99.80 11	97.12 322	99.62 151	99.73 205	98.58 78	99.90 148	98.61 204	99.91 46	99.68 160

fmvsm_s_conf0.5_n_11		99.32 79	99.16 92	99.80 64	99.83 47	99.70 60	99.57 142	99.56 90	99.45 11	99.99 2	99.93 10	94.18 306	99.99 4	99.96 13	99.98 4	99.73 127

AstraMVS		99.09 147	99.03 117	99.25 214	99.66 150	98.13 286	99.57 142	98.24 457	98.82 89	99.91 31	99.88 57	95.81 218	99.90 148	99.72 32	99.67 158	99.74 118

SF-MVS		99.38 67	99.24 78	99.79 68	99.79 68	99.68 64	99.57 142	99.54 109	97.82 246	99.71 112	99.80 152	98.95 32	99.93 110	98.19 252	99.84 102	99.74 118

DVP-MVS		99.57 20	99.47 24	99.88 15	99.85 31	99.89 6	99.57 142	99.37 300	99.10 48	99.81 69	99.80 152	98.94 34	99.96 41	98.93 150	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	99.57 142	99.51 156					99.96 41	98.93 150	99.86 87	99.88 35

Effi-MVS+-dtu		98.78 202	98.89 164	98.47 330	99.33 291	96.91 364	99.57 142	99.30 341	98.47 127	99.41 205	98.99 411	96.78 163	99.74 263	98.73 186	99.38 183	98.74 339

v2v482		98.06 266	97.77 288	98.92 256	98.90 389	98.82 229	99.57 142	99.36 302	96.65 359	99.19 267	99.35 356	94.20 303	99.25 373	97.72 304	94.97 409	98.69 352

DSMNet-mixed		97.25 372	97.35 345	96.95 431	97.84 449	93.61 457	99.57 142	96.63 479	96.13 402	98.87 326	98.61 436	94.59 284	97.70 467	95.08 421	98.86 252	99.55 217

FE-MVSNET		94.07 432	93.36 437	96.22 442	94.05 484	94.71 437	99.56 150	98.36 453	93.15 448	93.76 466	97.55 467	86.47 445	96.49 478	87.48 474	89.83 462	97.48 467

reproduce_model		99.63 9	99.54 13	99.90 8	99.78 70	99.88 10	99.56 150	99.55 100	99.15 38	99.90 34	99.90 37	99.00 24	99.97 29	99.11 122	99.91 46	99.86 42

MVStest1		96.08 401	95.48 406	97.89 385	98.93 384	96.70 372	99.56 150	99.35 309	92.69 453	91.81 475	99.46 325	89.90 407	98.96 433	95.00 423	92.61 446	98.00 451

fmvsm_l_conf0.5_n_a		99.71 2	99.67 1	99.85 43	99.86 25	99.61 85	99.56 150	99.63 46	99.48 3	99.98 13	99.83 106	98.75 60	99.99 4	99.97 2	99.96 17	99.94 17

fmvsm_l_conf0.5_n		99.71 2	99.67 1	99.85 43	99.84 38	99.63 82	99.56 150	99.63 46	99.47 4	99.98 13	99.82 119	98.75 60	99.99 4	99.97 2	99.97 9	99.94 17

sd_testset		98.75 207	98.57 215	99.29 207	99.81 57	98.26 279	99.56 150	99.62 51	98.78 98	99.64 144	99.88 57	92.02 367	99.88 168	99.54 51	98.26 292	99.72 137

KD-MVS_self_test		95.00 422	94.34 426	96.96 430	97.07 464	95.39 421	99.56 150	99.44 258	95.11 420	97.13 437	97.32 472	91.86 371	97.27 472	90.35 463	81.23 477	98.23 436

ETV-MVS		99.26 92	99.21 84	99.40 181	99.46 253	99.30 138	99.56 150	99.52 134	98.52 122	99.44 194	99.27 378	98.41 93	99.86 181	99.10 125	99.59 168	99.04 307

SMA-MVS		99.44 50	99.30 62	99.85 43	99.73 107	99.83 22	99.56 150	99.47 226	97.45 290	99.78 81	99.82 119	99.18 12	99.91 135	98.79 180	99.89 68	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		98.87 181	98.72 189	99.31 199	99.86 25	98.48 268	99.56 150	99.61 60	97.85 237	99.36 222	99.85 85	95.95 208	99.85 187	96.66 383	99.83 113	99.59 206

casdiffmvs		99.13 127	98.98 141	99.56 122	99.65 160	99.16 155	99.56 150	99.50 179	98.33 145	99.41 205	99.86 78	95.92 211	99.83 218	99.45 68	99.16 204	99.70 151
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		98.38 235	98.09 251	99.24 217	99.26 312	99.32 131	99.56 150	99.55 100	97.45 290	98.71 347	99.83 106	93.23 332	99.63 312	98.88 156	96.32 373	98.76 333

ACMH+	97.24 10	97.92 292	97.78 286	98.32 348	99.46 253	96.68 376	99.56 150	99.54 109	98.41 135	97.79 421	99.87 70	90.18 405	99.66 297	98.05 271	97.18 357	98.62 387

ACMM	97.58 5	98.37 237	98.34 230	98.48 325	99.41 268	97.10 339	99.56 150	99.45 249	98.53 121	99.04 297	99.85 85	93.00 337	99.71 279	98.74 184	97.45 342	98.64 378
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 89	99.12 97	99.74 80	99.18 333	99.75 51	99.56 150	99.57 85	98.45 130	99.49 184	99.85 85	97.77 118	99.94 92	98.33 241	99.84 102	99.52 226

testing3-2		97.84 306	97.70 298	98.24 357	99.53 220	95.37 422	99.55 165	98.67 444	98.46 128	99.27 246	99.34 360	86.58 443	99.83 218	99.32 84	98.63 265	99.52 226

test_fmvsmconf0.01_n		99.22 100	99.03 117	99.79 68	98.42 441	99.48 111	99.55 165	99.51 156	99.39 22	99.78 81	99.93 10	94.80 265	99.95 76	99.93 23	99.95 23	99.94 17

test_fmvs1		98.88 178	98.79 182	99.16 225	99.69 127	97.61 319	99.55 165	99.49 192	99.32 29	99.98 13	99.91 26	91.41 385	99.96 41	99.82 29	99.92 39	99.90 25

v144192		97.92 292	97.60 310	98.87 274	98.83 401	98.65 244	99.55 165	99.34 314	96.20 394	99.32 231	99.40 340	94.36 296	99.26 372	96.37 394	95.03 408	98.70 348

API-MVS		99.04 159	99.03 117	99.06 235	99.40 273	99.31 135	99.55 165	99.56 90	98.54 120	99.33 230	99.39 344	98.76 57	99.78 251	96.98 365	99.78 134	98.07 444

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 28	99.54 170	99.66 32	99.46 7	99.98 13	99.89 46	97.27 133	99.99 4	99.97 2	99.95 23	99.95 11

fmvsm_s_conf0.1_n_a		99.26 92	99.06 110	99.85 43	99.52 226	99.62 83	99.54 170	99.62 51	98.69 107	99.99 2	99.96 1	94.47 293	99.94 92	99.88 26	99.92 39	99.98 2

APD_test1		95.87 403	96.49 385	94.00 449	99.53 220	84.01 478	99.54 170	99.32 332	95.91 410	97.99 410	99.85 85	85.49 452	99.88 168	91.96 456	98.84 254	98.12 441

thisisatest0530		98.35 238	98.03 258	99.31 199	99.63 169	98.56 254	99.54 170	96.75 477	97.53 281	99.73 97	99.65 248	91.25 390	99.89 163	98.62 201	99.56 170	99.48 243

MTMP							99.54 170	98.88 415

v1144		97.98 283	97.69 299	98.85 280	98.87 394	98.66 243	99.54 170	99.35 309	96.27 389	99.23 257	99.35 356	94.67 279	99.23 376	96.73 378	95.16 405	98.68 357

v148		97.79 318	97.55 312	98.50 322	98.74 415	97.72 312	99.54 170	99.33 322	96.26 390	98.90 320	99.51 306	94.68 278	99.14 394	97.83 288	93.15 440	98.63 385

CostFormer		97.72 330	97.73 295	97.71 404	99.15 347	94.02 449	99.54 170	99.02 393	94.67 432	99.04 297	99.35 356	92.35 363	99.77 253	98.50 222	97.94 310	99.34 276

MVSTER		98.49 223	98.32 232	99.00 243	99.35 285	99.02 175	99.54 170	99.38 292	97.41 297	99.20 264	99.73 205	93.86 320	99.36 353	98.87 159	97.56 330	98.62 387

fmvsm_s_conf0.5_n_10		99.41 59	99.24 78	99.92 1	99.83 47	99.84 20	99.53 179	99.56 90	99.45 11	99.99 2	99.92 18	94.92 258	99.99 4	99.97 2	99.97 9	99.95 11

fmvsm_s_conf0.1_n		99.29 85	99.10 99	99.86 34	99.70 122	99.65 75	99.53 179	99.62 51	98.74 101	99.99 2	99.95 3	94.53 291	99.94 92	99.89 25	99.96 17	99.97 4

E4		99.13 127	99.01 134	99.49 156	99.68 134	98.90 209	99.52 181	99.52 134	98.13 183	99.71 112	99.90 37	96.32 188	99.84 196	99.21 106	99.11 218	99.75 113

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 181	99.54 109	99.13 41	99.89 40	99.89 46	98.96 27	99.96 41	99.04 132	99.90 57	99.85 46

our_new_method		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 181	99.54 109	99.13 41	99.89 40	99.89 46	98.96 27	99.96 41	99.04 132	99.90 57	99.85 46

fmvsm_s_conf0.5_n_a		99.56 21	99.47 24	99.85 43	99.83 47	99.64 81	99.52 181	99.65 39	99.10 48	99.98 13	99.92 18	97.35 129	99.96 41	99.94 21	99.92 39	99.95 11

MM		99.40 64	99.28 69	99.74 80	99.67 137	99.31 135	99.52 181	98.87 417	99.55 1	99.74 95	99.80 152	96.47 180	99.98 20	99.97 2	99.97 9	99.94 17

patch_mono-2		99.26 92	99.62 6	98.16 362	99.81 57	94.59 441	99.52 181	99.64 42	99.33 28	99.73 97	99.90 37	99.00 24	99.99 4	99.69 35	99.98 4	99.89 29

Fast-Effi-MVS+-dtu		98.77 206	98.83 178	98.60 307	99.41 268	96.99 354	99.52 181	99.49 192	98.11 193	99.24 253	99.34 360	96.96 152	99.79 245	97.95 277	99.45 179	99.02 310

Fast-Effi-MVS+		98.70 211	98.43 224	99.51 144	99.51 229	99.28 141	99.52 181	99.47 226	96.11 403	99.01 300	99.34 360	96.20 197	99.84 196	97.88 281	98.82 256	99.39 267

v1921920		97.80 316	97.45 328	98.84 281	98.80 403	98.53 257	99.52 181	99.34 314	96.15 400	99.24 253	99.47 321	93.98 314	99.29 365	95.40 415	95.13 406	98.69 352

MIMVSNet1		95.51 411	95.04 414	96.92 433	97.38 456	95.60 411	99.52 181	99.50 179	93.65 442	96.97 441	99.17 389	85.28 455	96.56 477	88.36 470	95.55 397	98.60 399

FE-MVSNET2		95.10 419	94.44 424	97.08 427	95.08 480	95.97 399	99.51 191	99.37 300	95.02 424	94.10 463	97.57 466	86.18 447	97.66 469	93.28 444	89.86 461	97.61 462

viewmacassd2359aftdt		99.08 149	98.94 152	99.50 149	99.66 150	98.96 187	99.51 191	99.54 109	98.27 152	99.42 200	99.89 46	95.88 215	99.80 239	99.20 107	99.11 218	99.76 107

SSM_0407		99.13 127	99.03 117	99.43 177	99.62 178	98.88 213	99.51 191	99.50 179	98.14 180	99.37 216	99.85 85	96.85 155	99.83 218	99.19 108	99.25 197	99.60 195

fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 11	99.83 47	99.74 54	99.51 191	99.62 51	99.46 7	99.99 2	99.90 37	96.60 172	99.98 20	99.95 16	99.95 23	99.96 7

fmvsm_s_conf0.5_n		99.51 29	99.40 38	99.85 43	99.84 38	99.65 75	99.51 191	99.67 27	99.13 41	99.98 13	99.92 18	96.60 172	99.96 41	99.95 16	99.96 17	99.95 11

UniMVSNet_ETH3D		97.32 369	96.81 377	98.87 274	99.40 273	97.46 323	99.51 191	99.53 125	95.86 411	98.54 375	99.77 185	82.44 468	99.66 297	98.68 194	97.52 334	99.50 239

alignmvs		98.81 197	98.56 217	99.58 116	99.43 261	99.42 118	99.51 191	98.96 400	98.61 113	99.35 225	98.92 421	94.78 267	99.77 253	99.35 76	98.11 305	99.54 219

v1192		97.81 314	97.44 333	98.91 260	98.88 391	98.68 241	99.51 191	99.34 314	96.18 396	99.20 264	99.34 360	94.03 312	99.36 353	95.32 417	95.18 404	98.69 352

test20.03		96.12 399	95.96 398	96.63 437	97.44 455	95.45 418	99.51 191	99.38 292	96.55 370	96.16 450	99.25 381	93.76 324	96.17 479	87.35 476	94.22 422	98.27 432

mvs_anonymous		99.03 161	98.99 138	99.16 225	99.38 278	98.52 261	99.51 191	99.38 292	97.79 247	99.38 214	99.81 134	97.30 131	99.45 331	99.35 76	98.99 239	99.51 235

TAMVS		99.12 135	99.08 105	99.24 217	99.46 253	98.55 255	99.51 191	99.46 238	98.09 197	99.45 189	99.82 119	98.34 97	99.51 325	98.70 189	98.93 242	99.67 164

viewdifsd2359ckpt13		99.06 154	98.93 154	99.45 169	99.63 169	98.96 187	99.50 202	99.51 156	97.83 241	99.28 240	99.80 152	96.68 169	99.71 279	99.05 131	99.12 216	99.68 160

viewdifsd2359ckpt11		98.78 202	98.74 187	98.89 266	99.67 137	97.04 348	99.50 202	99.58 78	98.26 155	99.56 167	99.90 37	94.36 296	99.87 175	99.49 61	98.32 288	99.77 100

viewmsd2359difaftdt		98.78 202	98.74 187	98.90 262	99.67 137	97.04 348	99.50 202	99.58 78	98.26 155	99.56 167	99.90 37	94.36 296	99.87 175	99.49 61	98.32 288	99.77 100

IMVS_0407		98.86 184	98.91 158	98.72 296	99.55 212	96.93 359	99.50 202	99.44 258	98.05 209	99.66 130	99.80 152	97.13 139	99.65 302	98.15 258	98.92 244	99.60 195

viewmanbaseed2359cas		99.18 104	99.07 109	99.50 149	99.62 178	99.01 177	99.50 202	99.52 134	98.25 160	99.68 119	99.82 119	96.93 153	99.80 239	99.15 118	99.11 218	99.70 151

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 229	99.67 68	99.50 202	99.64 42	99.43 17	99.98 13	99.78 176	97.26 136	99.95 76	99.95 16	99.93 33	99.92 23

test_fmvsmconf0.1_n		99.55 23	99.45 30	99.86 34	99.44 260	99.65 75	99.50 202	99.61 60	99.45 11	99.87 49	99.92 18	97.31 130	99.97 29	99.95 16	99.99 1	99.97 4

test_yl		98.86 184	98.63 203	99.54 125	99.49 243	99.18 152	99.50 202	99.07 386	98.22 166	99.61 156	99.51 306	95.37 236	99.84 196	98.60 207	98.33 284	99.59 206

DCV-MVSNet		98.86 184	98.63 203	99.54 125	99.49 243	99.18 152	99.50 202	99.07 386	98.22 166	99.61 156	99.51 306	95.37 236	99.84 196	98.60 207	98.33 284	99.59 206

tfpn200view9		97.72 330	97.38 341	98.72 296	99.69 127	97.96 297	99.50 202	98.73 440	97.83 241	99.17 272	98.45 441	91.67 377	99.83 218	93.22 445	98.18 300	98.37 428

UA-Net		99.42 55	99.29 66	99.80 64	99.62 178	99.55 96	99.50 202	99.70 18	98.79 95	99.77 85	99.96 1	97.45 124	99.96 41	98.92 152	99.90 57	99.89 29

pm-mvs1		97.68 338	97.28 357	98.88 270	99.06 363	98.62 249	99.50 202	99.45 249	96.32 385	97.87 417	99.79 169	92.47 357	99.35 356	97.54 321	93.54 433	98.67 365

EI-MVSNet		98.67 214	98.67 195	98.68 302	99.35 285	97.97 295	99.50 202	99.38 292	96.93 343	99.20 264	99.83 106	97.87 114	99.36 353	98.38 234	97.56 330	98.71 343

CVMVSNet		98.57 221	98.67 195	98.30 350	99.35 285	95.59 412	99.50 202	99.55 100	98.60 115	99.39 212	99.83 106	94.48 292	99.45 331	98.75 183	98.56 272	99.85 46

VPA-MVSNet		98.29 243	97.95 267	99.30 204	99.16 343	99.54 98	99.50 202	99.58 78	98.27 152	99.35 225	99.37 350	92.53 355	99.65 302	99.35 76	94.46 417	98.72 341

thres400		97.77 319	97.38 341	98.92 256	99.69 127	97.96 297	99.50 202	98.73 440	97.83 241	99.17 272	98.45 441	91.67 377	99.83 218	93.22 445	98.18 300	98.96 317

APD-MVS		99.27 89	99.08 105	99.84 55	99.75 92	99.79 41	99.50 202	99.50 179	97.16 318	99.77 85	99.82 119	98.78 53	99.94 92	97.56 319	99.86 87	99.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
E2		99.15 115	99.03 117	99.49 156	99.65 160	98.93 202	99.49 219	99.52 134	98.14 180	99.72 102	99.88 57	96.57 176	99.84 196	99.17 114	99.13 211	99.72 137

E3		99.15 115	99.03 117	99.49 156	99.62 178	98.91 204	99.49 219	99.52 134	98.13 183	99.72 102	99.88 57	96.61 171	99.84 196	99.17 114	99.13 211	99.72 137

SSM_0404		99.16 111	99.06 110	99.44 174	99.65 160	98.96 187	99.49 219	99.50 179	98.14 180	99.62 151	99.85 85	96.85 155	99.85 187	99.19 108	99.26 196	99.52 226

fmvsm_s_conf0.5_n_4		99.36 72	99.24 78	99.73 83	99.78 70	99.53 101	99.49 219	99.60 67	99.42 20	99.99 2	99.86 78	95.15 248	99.95 76	99.95 16	99.89 68	99.73 127

test_vis1_rt		95.81 405	95.65 404	96.32 441	99.67 137	91.35 469	99.49 219	96.74 478	98.25 160	95.24 455	98.10 456	74.96 477	99.90 148	99.53 53	98.85 253	97.70 461

TransMVSNet (Re)		97.15 376	96.58 382	98.86 277	99.12 349	98.85 221	99.49 219	98.91 410	95.48 415	97.16 436	99.80 152	93.38 328	99.11 403	94.16 435	91.73 450	98.62 387

UniMVSNet (Re)		98.29 243	98.00 261	99.13 230	99.00 373	99.36 126	99.49 219	99.51 156	97.95 225	98.97 309	99.13 394	96.30 192	99.38 346	98.36 238	93.34 435	98.66 374

EPMVS		97.82 312	97.65 303	98.35 345	98.88 391	95.98 398	99.49 219	94.71 487	97.57 274	99.26 251	99.48 318	92.46 360	99.71 279	97.87 283	99.08 231	99.35 273

viewcassd2359sk11		99.18 104	99.08 105	99.49 156	99.65 160	98.95 193	99.48 227	99.51 156	98.10 196	99.72 102	99.87 70	97.13 139	99.84 196	99.13 119	99.14 208	99.69 154

fmvsm_s_conf0.5_n_9		99.41 59	99.28 69	99.81 60	99.84 38	99.52 105	99.48 227	99.62 51	99.46 7	99.99 2	99.92 18	95.24 245	99.96 41	99.97 2	99.97 9	99.96 7

SSC-MVS3.2		97.34 367	97.15 364	97.93 381	99.02 370	95.76 408	99.48 227	99.58 78	97.62 269	99.09 286	99.53 298	87.95 432	99.27 369	96.42 390	95.66 393	98.75 335

fmvsm_s_conf0.5_n_3		99.37 68	99.20 86	99.87 21	99.75 92	99.70 60	99.48 227	99.66 32	99.45 11	99.99 2	99.93 10	94.64 283	99.97 29	99.94 21	99.97 9	99.95 11

test_fmvsmconf_n		99.70 4	99.64 5	99.87 21	99.80 63	99.66 71	99.48 227	99.64 42	99.45 11	99.92 30	99.92 18	98.62 76	99.99 4	99.96 13	99.99 1	99.96 7

Anonymous20231211		97.88 297	97.54 315	98.90 262	99.71 117	98.53 257	99.48 227	99.57 85	94.16 437	98.81 336	99.68 235	93.23 332	99.42 342	98.84 169	94.42 419	98.76 333

v1240		97.69 335	97.32 352	98.79 289	98.85 398	98.43 272	99.48 227	99.36 302	96.11 403	99.27 246	99.36 353	93.76 324	99.24 375	94.46 429	95.23 403	98.70 348

VPNet		97.84 306	97.44 333	99.01 241	99.21 325	98.94 197	99.48 227	99.57 85	98.38 137	99.28 240	99.73 205	88.89 417	99.39 344	99.19 108	93.27 437	98.71 343

UniMVSNet_NR-MVSNet		98.22 246	97.97 264	98.96 248	98.92 386	98.98 180	99.48 227	99.53 125	97.76 251	98.71 347	99.46 325	96.43 184	99.22 381	98.57 213	92.87 443	98.69 352

TDRefinement		95.42 414	94.57 422	97.97 377	89.83 490	96.11 397	99.48 227	98.75 431	96.74 352	96.68 444	99.88 57	88.65 423	99.71 279	98.37 236	82.74 474	98.09 443

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 28	99.47 237	99.63 46	99.45 11	99.98 13	99.89 46	97.02 148	99.99 4	99.98 1	99.96 17	99.95 11

ACMMP_NAP		99.47 40	99.34 50	99.88 15	99.87 20	99.86 18	99.47 237	99.48 204	98.05 209	99.76 91	99.86 78	98.82 48	99.93 110	98.82 179	99.91 46	99.84 53

NR-MVSNet		97.97 286	97.61 309	99.02 240	98.87 394	99.26 144	99.47 237	99.42 271	97.63 267	97.08 438	99.50 309	95.07 251	99.13 397	97.86 284	93.59 432	98.68 357

PVSNet_Blended_VisFu		99.36 72	99.28 69	99.61 109	99.86 25	99.07 170	99.47 237	99.93 2	97.66 265	99.71 112	99.86 78	97.73 119	99.96 41	99.47 66	99.82 117	99.79 92

E3new		99.18 104	99.08 105	99.48 160	99.63 169	98.94 197	99.46 241	99.50 179	98.06 206	99.72 102	99.84 100	97.27 133	99.84 196	99.10 125	99.13 211	99.67 164

LuminaMVS		99.23 98	99.10 99	99.61 109	99.35 285	99.31 135	99.46 241	99.13 377	98.61 113	99.86 53	99.89 46	96.41 186	99.91 135	99.67 37	99.51 174	99.63 187

fmvsm_s_conf0.1_n_2		99.37 68	99.22 83	99.81 60	99.77 78	99.75 51	99.46 241	99.60 67	99.47 4	99.98 13	99.94 6	94.98 252	99.95 76	99.97 2	99.79 132	99.73 127

SD-MVS		99.41 59	99.52 14	99.05 237	99.74 100	99.68 64	99.46 241	99.52 134	99.11 47	99.88 43	99.91 26	99.43 1	97.70 467	98.72 187	99.93 33	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		99.11 141	99.00 137	99.43 177	99.63 169	98.73 237	99.45 245	99.54 109	98.33 145	99.62 151	99.81 134	96.17 198	99.87 175	99.27 99	99.14 208	99.69 154

testing3		97.28 370	96.76 379	98.82 283	99.37 281	98.07 290	99.45 245	99.36 302	97.56 276	97.89 416	98.95 416	83.70 462	98.82 441	96.03 398	98.56 272	99.58 210

tt0805		97.97 286	97.77 288	98.57 312	99.59 197	96.61 379	99.45 245	99.08 383	98.21 168	98.88 323	99.80 152	88.66 422	99.70 286	98.58 210	97.72 320	99.39 267

tpm2		97.44 362	97.34 348	97.74 403	99.15 347	94.36 446	99.45 245	98.94 401	93.45 446	98.90 320	99.44 328	91.35 387	99.59 316	97.31 341	98.07 306	99.29 280

FMVSNet2		97.72 330	97.36 343	98.80 288	99.51 229	98.84 223	99.45 245	99.42 271	96.49 373	98.86 331	99.29 373	90.26 401	98.98 424	96.44 389	96.56 367	98.58 405

CDS-MVSNet		99.09 147	99.03 117	99.25 214	99.42 263	98.73 237	99.45 245	99.46 238	98.11 193	99.46 188	99.77 185	98.01 112	99.37 349	98.70 189	98.92 244	99.66 169
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 184	98.63 203	99.54 125	99.37 281	99.66 71	99.45 245	99.54 109	96.61 364	99.01 300	99.40 340	97.09 143	99.86 181	97.68 309	99.53 173	99.10 295
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		99.01 166	98.87 168	99.40 181	99.62 178	98.79 232	99.44 252	99.51 156	97.76 251	99.35 225	99.69 227	96.42 185	99.75 260	98.97 144	99.11 218	99.66 169

fmvsm_s_conf0.5_n_2		99.32 79	99.13 95	99.89 11	99.80 63	99.77 48	99.44 252	99.58 78	99.47 4	99.99 2	99.93 10	94.04 311	99.96 41	99.96 13	99.93 33	99.93 22

UGNet		98.87 181	98.69 193	99.40 181	99.22 324	98.72 239	99.44 252	99.68 24	99.24 32	99.18 271	99.42 332	92.74 345	99.96 41	99.34 81	99.94 31	99.53 225
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		98.86 184	98.63 203	99.54 125	99.64 165	99.19 150	99.44 252	99.54 109	97.77 250	99.30 236	99.81 134	94.20 303	99.93 110	99.17 114	98.82 256	99.49 240

test_0402		96.64 388	96.24 390	97.85 390	98.85 398	96.43 385	99.44 252	99.26 355	93.52 443	96.98 440	99.52 302	88.52 426	99.20 388	92.58 455	97.50 337	97.93 456

ACMP	97.20 11	98.06 266	97.94 269	98.45 333	99.37 281	97.01 352	99.44 252	99.49 192	97.54 280	98.45 381	99.79 169	91.95 369	99.72 273	97.91 279	97.49 340	98.62 387
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 333	98.55 436	98.16 283	99.43 258	93.68 489		97.23 432	98.46 440	89.30 413	99.22 381	95.43 414	98.22 295	97.98 453

HPM-MVS++		99.39 66	99.23 82	99.87 21	99.75 92	99.84 20	99.43 258	99.51 156	98.68 109	99.27 246	99.53 298	98.64 75	99.96 41	98.44 229	99.80 125	99.79 92

tpm cat1		97.39 364	97.36 343	97.50 415	99.17 341	93.73 452	99.43 258	99.31 336	91.27 462	98.71 347	99.08 398	94.31 301	99.77 253	96.41 392	98.50 276	99.00 311

tpm		97.67 341	97.55 312	98.03 370	99.02 370	95.01 430	99.43 258	98.54 450	96.44 379	99.12 278	99.34 360	91.83 372	99.60 315	97.75 300	96.46 369	99.48 243

GBi-Net		97.68 338	97.48 322	98.29 351	99.51 229	97.26 332	99.43 258	99.48 204	96.49 373	99.07 289	99.32 368	90.26 401	98.98 424	97.10 357	96.65 364	98.62 387

test1		97.68 338	97.48 322	98.29 351	99.51 229	97.26 332	99.43 258	99.48 204	96.49 373	99.07 289	99.32 368	90.26 401	98.98 424	97.10 357	96.65 364	98.62 387

FMVSNet1		96.84 384	96.36 388	98.29 351	99.32 298	97.26 332	99.43 258	99.48 204	95.11 420	98.55 374	99.32 368	83.95 461	98.98 424	95.81 403	96.26 375	98.62 387

fmvsm_s_conf0.5_n_7		99.34 75	99.29 66	99.48 160	99.70 122	98.63 247	99.42 265	99.63 46	99.46 7	99.98 13	99.88 57	95.59 228	99.96 41	99.97 2	99.98 4	99.85 46

fmvsm_s_conf0.5_n_5		99.37 68	99.21 84	99.86 34	99.80 63	99.68 64	99.42 265	99.61 60	99.37 24	99.97 25	99.86 78	94.96 253	99.99 4	99.97 2	99.93 33	99.92 23

mamv4		99.33 77	99.42 32	99.07 233	99.67 137	97.73 310	99.42 265	99.60 67	98.15 175	99.94 28	99.91 26	98.42 91	99.94 92	99.72 32	99.96 17	99.54 219

testgi		97.65 343	97.50 320	98.13 366	99.36 284	96.45 384	99.42 265	99.48 204	97.76 251	97.87 417	99.45 327	91.09 393	98.81 442	94.53 428	98.52 275	99.13 294

F-COLMAP		99.19 101	99.04 114	99.64 101	99.78 70	99.27 143	99.42 265	99.54 109	97.29 307	99.41 205	99.59 274	98.42 91	99.93 110	98.19 252	99.69 153	99.73 127

Anonymous202405211		98.30 242	97.98 263	99.26 213	99.57 204	98.16 283	99.41 270	98.55 449	96.03 408	99.19 267	99.74 199	91.87 370	99.92 123	99.16 117	98.29 291	99.70 151

MSLP-MVS++		99.46 42	99.47 24	99.44 174	99.60 195	99.16 155	99.41 270	99.71 16	98.98 72	99.45 189	99.78 176	99.19 11	99.54 323	99.28 96	99.84 102	99.63 187

VNet		99.11 141	98.90 160	99.73 83	99.52 226	99.56 94	99.41 270	99.39 284	99.01 64	99.74 95	99.78 176	95.56 229	99.92 123	99.52 55	98.18 300	99.72 137

baseline2		97.87 299	97.55 312	98.82 283	99.18 333	98.02 292	99.41 270	96.58 481	96.97 337	96.51 445	99.17 389	93.43 327	99.57 318	97.71 305	99.03 235	98.86 321

DU-MVS		98.08 264	97.79 283	98.96 248	98.87 394	98.98 180	99.41 270	99.45 249	97.87 233	98.71 347	99.50 309	94.82 263	99.22 381	98.57 213	92.87 443	98.68 357

Baseline_NR-MVSNet		97.76 320	97.45 328	98.68 302	99.09 357	98.29 277	99.41 270	98.85 419	95.65 413	98.63 365	99.67 241	94.82 263	99.10 406	98.07 270	92.89 442	98.64 378

XVG-ACMP-BASELINE		97.83 309	97.71 297	98.20 359	99.11 351	96.33 388	99.41 270	99.52 134	98.06 206	99.05 296	99.50 309	89.64 411	99.73 269	97.73 302	97.38 349	98.53 409

DP-MVS		99.16 111	98.95 150	99.78 71	99.77 78	99.53 101	99.41 270	99.50 179	97.03 334	99.04 297	99.88 57	97.39 125	99.92 123	98.66 196	99.90 57	99.87 40

9.14			99.10 99		99.72 111		99.40 278	99.51 156	97.53 281	99.64 144	99.78 176	98.84 46	99.91 135	97.63 310	99.82 117

D2MVS		98.41 231	98.50 221	98.15 365	99.26 312	96.62 378	99.40 278	99.61 60	97.71 257	98.98 307	99.36 353	96.04 203	99.67 294	98.70 189	97.41 347	98.15 440

Anonymous20240529		98.09 261	97.68 300	99.34 191	99.66 150	98.44 271	99.40 278	99.43 269	93.67 441	99.22 258	99.89 46	90.23 404	99.93 110	99.26 102	98.33 284	99.66 169

FMVSNet3		98.03 274	97.76 292	98.84 281	99.39 276	98.98 180	99.40 278	99.38 292	96.67 357	99.07 289	99.28 375	92.93 338	98.98 424	97.10 357	96.65 364	98.56 407

LFMVS		97.90 295	97.35 345	99.54 125	99.52 226	99.01 177	99.39 282	98.24 457	97.10 326	99.65 139	99.79 169	84.79 457	99.91 135	99.28 96	98.38 281	99.69 154

HQP_MVS		98.27 245	98.22 238	98.44 336	99.29 304	96.97 356	99.39 282	99.47 226	98.97 75	99.11 280	99.61 269	92.71 348	99.69 291	97.78 294	97.63 323	98.67 365

plane_prior2							99.39 282		98.97 75

CHOSEN 1792x2688		99.19 101	99.10 99	99.45 169	99.89 8	98.52 261	99.39 282	99.94 1	98.73 102	99.11 280	99.89 46	95.50 231	99.94 92	99.50 57	99.97 9	99.89 29

PAPM_NR		99.04 159	98.84 176	99.66 91	99.74 100	99.44 116	99.39 282	99.38 292	97.70 260	99.28 240	99.28 375	98.34 97	99.85 187	96.96 367	99.45 179	99.69 154

gg-mvs-nofinetune		96.17 398	95.32 410	98.73 294	98.79 404	98.14 285	99.38 287	94.09 488	91.07 465	98.07 408	91.04 486	89.62 412	99.35 356	96.75 377	99.09 230	98.68 357

VDDNet		97.55 349	97.02 371	99.16 225	99.49 243	98.12 288	99.38 287	99.30 341	95.35 416	99.68 119	99.90 37	82.62 467	99.93 110	99.31 86	98.13 304	99.42 261

ME-MVS		99.56 21	99.46 28	99.86 34	99.80 63	99.81 33	99.37 289	99.70 18	99.18 34	99.83 64	99.83 106	98.74 65	99.93 110	98.83 172	99.89 68	99.83 63

MGCNet		99.15 115	98.96 146	99.73 83	98.92 386	99.37 123	99.37 289	96.92 474	99.51 2	99.66 130	99.78 176	96.69 167	99.97 29	99.84 28	99.97 9	99.84 53

pmmvs6		96.53 390	96.09 395	97.82 397	98.69 423	95.47 417	99.37 289	99.47 226	93.46 445	97.41 426	99.78 176	87.06 441	99.33 359	96.92 372	92.70 445	98.65 376

PM-MVS		92.96 437	92.23 441	95.14 446	95.61 475	89.98 472	99.37 289	98.21 459	94.80 430	95.04 460	97.69 463	65.06 481	97.90 463	94.30 430	89.98 460	97.54 466

WTY-MVS		99.06 154	98.88 167	99.61 109	99.62 178	99.16 155	99.37 289	99.56 90	98.04 216	99.53 176	99.62 265	96.84 159	99.94 92	98.85 166	98.49 277	99.72 137

IterMVS-LS		98.46 226	98.42 225	98.58 311	99.59 197	98.00 293	99.37 289	99.43 269	96.94 342	99.07 289	99.59 274	97.87 114	99.03 415	98.32 243	95.62 394	98.71 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 334	97.28 357	98.97 247	99.70 122	97.27 330	99.36 295	99.45 249	98.94 78	99.66 130	99.64 254	94.93 256	99.99 4	99.48 64	84.36 471	99.65 175

DPE-MVS		99.46 42	99.32 54	99.91 6	99.78 70	99.88 10	99.36 295	99.51 156	98.73 102	99.88 43	99.84 100	98.72 67	99.96 41	98.16 256	99.87 79	99.88 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UnsupCasMVSNet_eth		96.44 392	96.12 393	97.40 418	98.65 426	95.65 410	99.36 295	99.51 156	97.13 320	96.04 452	98.99 411	88.40 427	98.17 456	96.71 379	90.27 458	98.40 425

sss		99.17 109	99.05 112	99.53 133	99.62 178	98.97 183	99.36 295	99.62 51	97.83 241	99.67 125	99.65 248	97.37 128	99.95 76	99.19 108	99.19 203	99.68 160

DeepC-MVS_fast	98.69 1	99.49 33	99.39 40	99.77 74	99.63 169	99.59 88	99.36 295	99.46 238	99.07 58	99.79 76	99.82 119	98.85 44	99.92 123	98.68 194	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
CANet		99.25 96	99.14 94	99.59 113	99.41 268	99.16 155	99.35 300	99.57 85	98.82 89	99.51 180	99.61 269	96.46 181	99.95 76	99.59 45	99.98 4	99.65 175

pmmvs-eth3d		95.34 416	94.73 418	97.15 422	95.53 477	95.94 400	99.35 300	99.10 380	95.13 418	93.55 467	97.54 468	88.15 431	97.91 462	94.58 427	89.69 463	97.61 462

MDTV_nov1_ep13_2view						95.18 427	99.35 300		96.84 347	99.58 163		95.19 247		97.82 289		99.46 254

VDD-MVS		97.73 328	97.35 345	98.88 270	99.47 251	97.12 338	99.34 303	98.85 419	98.19 170	99.67 125	99.85 85	82.98 465	99.92 123	99.49 61	98.32 288	99.60 195

COLMAP_ROB	97.56 6	98.86 184	98.75 185	99.17 224	99.88 13	98.53 257	99.34 303	99.59 73	97.55 277	98.70 353	99.89 46	95.83 216	99.90 148	98.10 262	99.90 57	99.08 300
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
viewmambaseed2359dif		99.01 166	98.90 160	99.32 197	99.58 199	98.51 263	99.33 305	99.54 109	97.85 237	99.44 194	99.85 85	96.01 205	99.79 245	99.41 70	99.13 211	99.67 164

myMVS_eth3d28		97.69 335	97.34 348	98.73 294	99.27 309	97.52 321	99.33 305	98.78 429	98.03 218	98.82 335	98.49 439	86.64 442	99.46 329	98.44 229	98.24 294	99.23 288

EGC-MVSNET		82.80 450	77.86 456	97.62 408	97.91 447	96.12 396	99.33 305	99.28 347	8.40 494	25.05 495	99.27 378	84.11 460	99.33 359	89.20 466	98.22 295	97.42 468

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 160	99.64 165	98.85 221	99.32 308	99.48 204	98.50 124	99.81 69	99.81 134	96.82 160	99.88 168	99.40 71	99.12 216	99.71 148

ETVMVS		97.50 355	96.90 375	99.29 207	99.23 320	98.78 235	99.32 308	98.90 412	97.52 283	98.56 373	98.09 457	84.72 458	99.69 291	97.86 284	97.88 313	99.39 267

FMVSNet5		96.43 393	96.19 392	97.15 422	99.11 351	95.89 404	99.32 308	99.52 134	94.47 436	98.34 390	99.07 399	87.54 437	97.07 473	92.61 454	95.72 391	98.47 416

dp		97.75 324	97.80 282	97.59 412	99.10 354	93.71 453	99.32 308	98.88 415	96.48 376	99.08 288	99.55 289	92.67 351	99.82 227	96.52 387	98.58 269	99.24 287

tpmvs		97.98 283	98.02 260	97.84 392	99.04 368	94.73 435	99.31 312	99.20 368	96.10 407	98.76 343	99.42 332	94.94 255	99.81 232	96.97 366	98.45 278	98.97 315

tpmrst		98.33 239	98.48 222	97.90 384	99.16 343	94.78 434	99.31 312	99.11 379	97.27 308	99.45 189	99.59 274	95.33 239	99.84 196	98.48 223	98.61 266	99.09 299

testing99		97.36 365	96.94 374	98.63 305	99.18 333	96.70 372	99.30 314	98.93 402	97.71 257	98.23 396	98.26 449	84.92 456	99.84 196	98.04 272	97.85 316	99.35 273

MP-MVS-pluss		99.37 68	99.20 86	99.88 15	99.90 4	99.87 17	99.30 314	99.52 134	97.18 316	99.60 159	99.79 169	98.79 52	99.95 76	98.83 172	99.91 46	99.83 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 75	99.19 88	99.79 68	99.61 189	99.65 75	99.30 314	99.48 204	98.86 84	99.21 261	99.63 260	98.72 67	99.90 148	98.25 248	99.63 164	99.80 88

JIA-IIPM		97.50 355	97.02 371	98.93 254	98.73 416	97.80 308	99.30 314	98.97 398	91.73 461	98.91 318	94.86 480	95.10 250	99.71 279	97.58 314	97.98 308	99.28 281

BH-RMVSNet		98.41 231	98.08 252	99.40 181	99.41 268	98.83 226	99.30 314	98.77 430	97.70 260	98.94 315	99.65 248	92.91 341	99.74 263	96.52 387	99.55 172	99.64 182

usedtu_blend_shiyan5		95.04 420	94.10 427	97.86 389	96.45 469	95.92 401	99.29 319	99.22 363	86.17 477	98.36 387	97.68 464	91.20 391	99.07 408	97.53 322	80.97 479	98.60 399

testing11		97.50 355	97.10 368	98.71 299	99.20 327	96.91 364	99.29 319	98.82 422	97.89 231	98.21 399	98.40 443	85.63 451	99.83 218	98.45 228	98.04 307	99.37 271

Syy-MVS		97.09 379	97.14 365	96.95 431	99.00 373	92.73 463	99.29 319	99.39 284	97.06 330	97.41 426	98.15 452	93.92 317	98.68 447	91.71 457	98.34 282	99.45 257

myMVS_eth3d		96.89 382	96.37 387	98.43 338	99.00 373	97.16 336	99.29 319	99.39 284	97.06 330	97.41 426	98.15 452	83.46 464	98.68 447	95.27 418	98.34 282	99.45 257

MCST-MVS		99.43 53	99.30 62	99.82 57	99.79 68	99.74 54	99.29 319	99.40 281	98.79 95	99.52 178	99.62 265	98.91 39	99.90 148	98.64 198	99.75 142	99.82 72

LF4IMVS		97.52 352	97.46 327	97.70 405	98.98 379	95.55 413	99.29 319	98.82 422	98.07 202	98.66 356	99.64 254	89.97 406	99.61 314	97.01 362	96.68 363	97.94 455

hse-mvs2		97.50 355	97.14 365	98.59 308	99.49 243	97.05 345	99.28 325	99.22 363	98.94 78	99.66 130	99.42 332	94.93 256	99.65 302	99.48 64	83.80 473	99.08 300

OPM-MVS		98.19 250	98.10 248	98.45 333	98.88 391	97.07 343	99.28 325	99.38 292	98.57 117	99.22 258	99.81 134	92.12 365	99.66 297	98.08 267	97.54 332	98.61 396
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs		99.14 123	99.02 127	99.51 144	99.61 189	98.96 187	99.28 325	99.49 192	98.46 128	99.72 102	99.71 212	96.50 179	99.88 168	99.31 86	99.11 218	99.67 164
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 184	98.80 179	99.03 239	99.76 82	98.79 232	99.28 325	99.91 3	97.42 296	99.67 125	99.37 350	97.53 122	99.88 168	98.98 139	97.29 352	98.42 422

OMC-MVS		99.08 149	99.04 114	99.20 221	99.67 137	98.22 281	99.28 325	99.52 134	98.07 202	99.66 130	99.81 134	97.79 117	99.78 251	97.79 293	99.81 120	99.60 195

testing222		97.16 375	96.50 384	99.16 225	99.16 343	98.47 270	99.27 330	98.66 445	97.71 257	98.23 396	98.15 452	82.28 470	99.84 196	97.36 339	97.66 322	99.18 291

AUN-MVS		96.88 383	96.31 389	98.59 308	99.48 250	97.04 348	99.27 330	99.22 363	97.44 293	98.51 376	99.41 336	91.97 368	99.66 297	97.71 305	83.83 472	99.07 305

pmmvs5		97.52 352	97.30 354	98.16 362	98.57 435	96.73 371	99.27 330	98.90 412	96.14 401	98.37 386	99.53 298	91.54 382	99.14 394	97.51 325	95.87 386	98.63 385

1314		98.68 213	98.54 218	99.11 231	98.89 390	98.65 244	99.27 330	99.49 192	96.89 344	97.99 410	99.56 286	97.72 120	99.83 218	97.74 301	99.27 194	98.84 323

MVS		97.28 370	96.55 383	99.48 160	98.78 407	98.95 193	99.27 330	99.39 284	83.53 480	98.08 405	99.54 294	96.97 151	99.87 175	94.23 433	99.16 204	99.63 187

BH-untuned		98.42 229	98.36 228	98.59 308	99.49 243	96.70 372	99.27 330	99.13 377	97.24 312	98.80 338	99.38 347	95.75 222	99.74 263	97.07 361	99.16 204	99.33 277

MDTV_nov1_ep13			98.32 232		99.11 351	94.44 443	99.27 330	98.74 434	97.51 284	99.40 210	99.62 265	94.78 267	99.76 257	97.59 313	98.81 258

DP-MVS Recon		99.12 135	98.95 150	99.65 95	99.74 100	99.70 60	99.27 330	99.57 85	96.40 383	99.42 200	99.68 235	98.75 60	99.80 239	97.98 275	99.72 148	99.44 259

PatchmatchNet		98.31 240	98.36 228	98.19 360	99.16 343	95.32 423	99.27 330	98.92 405	97.37 300	99.37 216	99.58 278	94.90 260	99.70 286	97.43 335	99.21 201	99.54 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 346	97.28 357	98.62 306	99.64 165	98.03 291	99.26 339	98.74 434	97.68 262	99.09 286	98.32 447	91.66 379	99.81 232	92.88 450	98.22 295	98.03 447

CNVR-MVS		99.42 55	99.30 62	99.78 71	99.62 178	99.71 58	99.26 339	99.52 134	98.82 89	99.39 212	99.71 212	98.96 27	99.85 187	98.59 209	99.80 125	99.77 100

mamba_0408		99.08 149	98.96 146	99.44 174	99.62 178	98.88 213	99.25 341	99.47 226	98.05 209	99.37 216	99.81 134	96.85 155	99.85 187	98.98 139	99.25 197	99.60 195

SSM_04072		99.06 154	98.96 146	99.35 190	99.62 178	98.88 213	99.25 341	99.47 226	98.05 209	99.37 216	99.81 134	96.85 155	99.58 317	98.98 139	99.25 197	99.60 195

tt0320		95.71 408	95.07 412	97.62 408	99.05 366	95.02 429	99.25 341	99.52 134	86.81 474	97.97 412	99.72 209	83.58 463	99.15 392	96.38 393	93.35 434	98.68 357

1112_ss		98.98 170	98.77 183	99.59 113	99.68 134	99.02 175	99.25 341	99.48 204	97.23 313	99.13 276	99.58 278	96.93 153	99.90 148	98.87 159	98.78 259	99.84 53

TAPA-MVS	97.07 15	97.74 326	97.34 348	98.94 252	99.70 122	97.53 320	99.25 341	99.51 156	91.90 460	99.30 236	99.63 260	98.78 53	99.64 306	88.09 471	99.87 79	99.65 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UWE-MVS-28		97.36 365	97.24 361	97.75 401	98.84 400	94.44 443	99.24 346	97.58 470	97.98 223	99.00 304	99.00 409	91.35 387	99.53 324	93.75 438	98.39 280	99.27 285

UBG		97.85 302	97.48 322	98.95 250	99.25 316	97.64 317	99.24 346	98.74 434	97.90 230	98.64 363	98.20 451	88.65 423	99.81 232	98.27 246	98.40 279	99.42 261

PLC	97.94 4	99.02 162	98.85 174	99.53 133	99.66 150	99.01 177	99.24 346	99.52 134	96.85 346	99.27 246	99.48 318	98.25 101	99.91 135	97.76 298	99.62 165	99.65 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post1							99.23 349				65.14 492	94.18 306	99.71 279	97.58 314

ADS-MVSNet2		98.02 276	98.07 255	97.87 386	99.33 291	95.19 426	99.23 349	99.08 383	96.24 391	99.10 283	99.67 241	94.11 308	98.93 436	96.81 375	99.05 233	99.48 243

ADS-MVSNet		98.20 249	98.08 252	98.56 316	99.33 291	96.48 383	99.23 349	99.15 374	96.24 391	99.10 283	99.67 241	94.11 308	99.71 279	96.81 375	99.05 233	99.48 243

EPNet_dtu		98.03 274	97.96 265	98.23 358	98.27 443	95.54 415	99.23 349	98.75 431	99.02 62	97.82 419	99.71 212	96.11 200	99.48 326	93.04 448	99.65 161	99.69 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 253	97.93 270	98.87 274	99.18 333	98.49 266	99.22 353	99.33 322	96.96 338	99.56 167	99.38 347	94.33 299	99.00 422	94.83 426	98.58 269	99.14 292

RPMNet		96.72 386	95.90 399	99.19 222	99.18 333	98.49 266	99.22 353	99.52 134	88.72 472	99.56 167	97.38 470	94.08 310	99.95 76	86.87 478	98.58 269	99.14 292

sc_t1		95.75 406	95.05 413	97.87 386	98.83 401	94.61 440	99.21 355	99.45 249	87.45 473	97.97 412	99.85 85	81.19 473	99.43 340	98.27 246	93.20 438	99.57 213

WBMVS		97.74 326	97.50 320	98.46 331	99.24 318	97.43 324	99.21 355	99.42 271	97.45 290	98.96 311	99.41 336	88.83 418	99.23 376	98.94 147	96.02 379	98.71 343

plane_prior						96.97 356	99.21 355		98.45 130						97.60 326

IMVS_0404		98.53 222	98.52 220	98.55 318	99.55 212	96.93 359	99.20 358	99.44 258	98.05 209	98.96 311	99.80 152	94.66 281	99.13 397	98.15 258	98.92 244	99.60 195

tt0320-xc		95.31 417	94.59 421	97.45 416	98.92 386	94.73 435	99.20 358	99.31 336	86.74 475	97.23 432	99.72 209	81.14 474	98.95 434	97.08 360	91.98 449	98.67 365

testing91		97.44 362	97.02 371	98.71 299	99.18 333	96.89 366	99.19 360	99.04 390	97.78 249	98.31 391	98.29 448	85.41 453	99.85 187	98.01 273	97.95 309	99.39 267

WR-MVS		98.06 266	97.73 295	99.06 235	98.86 397	99.25 146	99.19 360	99.35 309	97.30 306	98.66 356	99.43 330	93.94 315	99.21 386	98.58 210	94.28 421	98.71 343

new-patchmatchnet		94.48 428	94.08 429	95.67 445	95.08 480	92.41 464	99.18 362	99.28 347	94.55 435	93.49 468	97.37 471	87.86 435	97.01 474	91.57 458	88.36 465	97.61 462

AdaColmap		99.01 166	98.80 179	99.66 91	99.56 208	99.54 98	99.18 362	99.70 18	98.18 173	99.35 225	99.63 260	96.32 188	99.90 148	97.48 328	99.77 137	99.55 217

EG-PatchMatch MVS		95.97 402	95.69 403	96.81 435	97.78 450	92.79 462	99.16 364	98.93 402	96.16 398	94.08 464	99.22 384	82.72 466	99.47 327	95.67 409	97.50 337	98.17 438

PatchT		97.03 380	96.44 386	98.79 289	98.99 376	98.34 276	99.16 364	99.07 386	92.13 459	99.52 178	97.31 473	94.54 289	98.98 424	88.54 469	98.73 261	99.03 308

CNLPA		99.14 123	98.99 138	99.59 113	99.58 199	99.41 120	99.16 364	99.44 258	98.45 130	99.19 267	99.49 312	98.08 109	99.89 163	97.73 302	99.75 142	99.48 243

MDA-MVSNet-bldmvs		94.96 423	93.98 430	97.92 382	98.24 444	97.27 330	99.15 367	99.33 322	93.80 440	80.09 487	99.03 405	88.31 428	97.86 464	93.49 442	94.36 420	98.62 387

CDPH-MVS		99.13 127	98.91 158	99.80 64	99.75 92	99.71 58	99.15 367	99.41 274	96.60 367	99.60 159	99.55 289	98.83 47	99.90 148	97.48 328	99.83 113	99.78 98

save fliter					99.76 82	99.59 88	99.14 369	99.40 281	99.00 67

WB-MVSnew		97.65 343	97.65 303	97.63 407	98.78 407	97.62 318	99.13 370	98.33 454	97.36 301	99.07 289	98.94 417	95.64 227	99.15 392	92.95 449	98.68 264	96.12 478

testf1		90.42 444	90.68 445	89.65 465	97.78 450	73.97 493	99.13 370	98.81 424	89.62 467	91.80 476	98.93 418	62.23 484	98.80 443	86.61 479	91.17 452	96.19 476

APD_test2		90.42 444	90.68 445	89.65 465	97.78 450	73.97 493	99.13 370	98.81 424	89.62 467	91.80 476	98.93 418	62.23 484	98.80 443	86.61 479	91.17 452	96.19 476

xiu_mvs_v1_base_debu		99.29 85	99.27 73	99.34 191	99.63 169	98.97 183	99.12 373	99.51 156	98.86 84	99.84 56	99.47 321	98.18 104	99.99 4	99.50 57	99.31 191	99.08 300

xiu_mvs_v1_base		99.29 85	99.27 73	99.34 191	99.63 169	98.97 183	99.12 373	99.51 156	98.86 84	99.84 56	99.47 321	98.18 104	99.99 4	99.50 57	99.31 191	99.08 300

xiu_mvs_v1_base_debi		99.29 85	99.27 73	99.34 191	99.63 169	98.97 183	99.12 373	99.51 156	98.86 84	99.84 56	99.47 321	98.18 104	99.99 4	99.50 57	99.31 191	99.08 300

XVG-OURS-SEG-HR		98.69 212	98.62 208	98.89 266	99.71 117	97.74 309	99.12 373	99.54 109	98.44 133	99.42 200	99.71 212	94.20 303	99.92 123	98.54 220	98.90 250	99.00 311

jason		99.13 127	99.03 117	99.45 169	99.46 253	98.87 217	99.12 373	99.26 355	98.03 218	99.79 76	99.65 248	97.02 148	99.85 187	99.02 136	99.90 57	99.65 175
jason: jason.
N_pmnet		94.95 424	95.83 401	92.31 456	98.47 439	79.33 488	99.12 373	92.81 494	93.87 439	97.68 422	99.13 394	93.87 319	99.01 421	91.38 459	96.19 376	98.59 404

MDA-MVSNet_test_wron		95.45 412	94.60 420	98.01 373	98.16 445	97.21 335	99.11 379	99.24 360	93.49 444	80.73 486	98.98 413	93.02 336	98.18 455	94.22 434	94.45 418	98.64 378

Patchmtry		97.75 324	97.40 340	98.81 286	99.10 354	98.87 217	99.11 379	99.33 322	94.83 429	98.81 336	99.38 347	94.33 299	99.02 418	96.10 396	95.57 396	98.53 409

YYNet1		95.36 415	94.51 423	97.92 382	97.89 448	97.10 339	99.10 381	99.23 361	93.26 447	80.77 485	99.04 404	92.81 342	98.02 459	94.30 430	94.18 423	98.64 378

CANet_DTU		98.97 172	98.87 168	99.25 214	99.33 291	98.42 274	99.08 382	99.30 341	99.16 37	99.43 197	99.75 194	95.27 241	99.97 29	98.56 216	99.95 23	99.36 272

icg_test_0407_2		98.79 201	98.86 171	98.57 312	99.55 212	96.93 359	99.07 383	99.44 258	98.05 209	99.66 130	99.80 152	97.13 139	99.18 389	98.15 258	98.92 244	99.60 195

SCA		98.19 250	98.16 240	98.27 356	99.30 300	95.55 413	99.07 383	98.97 398	97.57 274	99.43 197	99.57 283	92.72 346	99.74 263	97.58 314	99.20 202	99.52 226

TSAR-MVS + GP.		99.36 72	99.36 46	99.36 188	99.67 137	98.61 251	99.07 383	99.33 322	99.00 67	99.82 68	99.81 134	99.06 18	99.84 196	99.09 127	99.42 181	99.65 175

MG-MVS		99.13 127	99.02 127	99.45 169	99.57 204	98.63 247	99.07 383	99.34 314	98.99 69	99.61 156	99.82 119	97.98 113	99.87 175	97.00 363	99.80 125	99.85 46

PatchMatch-RL		98.84 196	98.62 208	99.52 139	99.71 117	99.28 141	99.06 387	99.77 12	97.74 255	99.50 181	99.53 298	95.41 234	99.84 196	97.17 356	99.64 162	99.44 259

OpenMVS_ROB	92.34 20	94.38 429	93.70 435	96.41 440	97.38 456	93.17 460	99.06 387	98.75 431	86.58 476	94.84 461	98.26 449	81.53 471	99.32 361	89.01 467	97.87 314	96.76 471

TEST9					99.67 137	99.65 75	99.05 389	99.41 274	96.22 393	98.95 313	99.49 312	98.77 56	99.91 135

train_agg		99.02 162	98.77 183	99.77 74	99.67 137	99.65 75	99.05 389	99.41 274	96.28 387	98.95 313	99.49 312	98.76 57	99.91 135	97.63 310	99.72 148	99.75 113

lupinMVS		99.13 127	99.01 134	99.46 168	99.51 229	98.94 197	99.05 389	99.16 373	97.86 234	99.80 74	99.56 286	97.39 125	99.86 181	98.94 147	99.85 94	99.58 210

DELS-MVS		99.48 37	99.42 32	99.65 95	99.72 111	99.40 121	99.05 389	99.66 32	99.14 40	99.57 166	99.80 152	98.46 87	99.94 92	99.57 48	99.84 102	99.60 195
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		96.38 394	96.03 396	97.41 417	98.13 446	95.16 428	99.05 389	99.20 368	93.94 438	97.39 429	98.79 429	91.61 381	99.04 413	90.43 462	95.77 388	98.05 446

Patchmatch-test		97.93 289	97.65 303	98.77 292	99.18 333	97.07 343	99.03 394	99.14 376	96.16 398	98.74 344	99.57 283	94.56 286	99.72 273	93.36 443	99.11 218	99.52 226

test_8					99.67 137	99.61 85	99.03 394	99.41 274	96.28 387	98.93 316	99.48 318	98.76 57	99.91 135

Test_1112_low_res		98.89 177	98.66 198	99.57 120	99.69 127	98.95 193	99.03 394	99.47 226	96.98 336	99.15 274	99.23 383	96.77 164	99.89 163	98.83 172	98.78 259	99.86 42

IterMVS-SCA-FT		97.82 312	97.75 293	98.06 369	99.57 204	96.36 387	99.02 397	99.49 192	97.18 316	98.71 347	99.72 209	92.72 346	99.14 394	97.44 334	95.86 387	98.67 365

xiu_mvs_v2_base		99.26 92	99.25 77	99.29 207	99.53 220	98.91 204	99.02 397	99.45 249	98.80 94	99.71 112	99.26 380	98.94 34	99.98 20	99.34 81	99.23 200	98.98 314

MIMVSNet		97.73 328	97.45 328	98.57 312	99.45 259	97.50 322	99.02 397	98.98 397	96.11 403	99.41 205	99.14 393	90.28 400	98.74 445	95.74 405	98.93 242	99.47 249

IterMVS		97.83 309	97.77 288	98.02 372	99.58 199	96.27 391	99.02 397	99.48 204	97.22 314	98.71 347	99.70 216	92.75 343	99.13 397	97.46 331	96.00 381	98.67 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 141	98.92 155	99.65 95	99.90 4	99.37 123	99.02 397	99.91 3	97.67 264	99.59 162	99.75 194	95.90 213	99.73 269	99.53 53	99.02 237	99.86 42

UWE-MVS		97.58 348	97.29 356	98.48 325	99.09 357	96.25 392	99.01 402	96.61 480	97.86 234	99.19 267	99.01 408	88.72 419	99.90 148	97.38 338	98.69 263	99.28 281

新几何2							99.01 402

BH-w/o		98.00 281	97.89 276	98.32 348	99.35 285	96.20 394	99.01 402	98.90 412	96.42 381	98.38 385	99.00 409	95.26 243	99.72 273	96.06 397	98.61 266	99.03 308

test_prior4						99.56 94	98.99 405

无先验							98.99 405	99.51 156	96.89 344				99.93 110	97.53 322		99.72 137

pmmvs4		98.13 257	97.90 272	98.81 286	98.61 431	98.87 217	98.99 405	99.21 367	96.44 379	99.06 294	99.58 278	95.90 213	99.11 403	97.18 355	96.11 378	98.46 419

HQP-NCC					99.19 330		98.98 408		98.24 162	98.66 356

ACMP_Plane					99.19 330		98.98 408		98.24 162	98.66 356

HQP-MVS		98.02 276	97.90 272	98.37 344	99.19 330	96.83 367	98.98 408	99.39 284	98.24 162	98.66 356	99.40 340	92.47 357	99.64 306	97.19 353	97.58 328	98.64 378

PS-MVSNAJ		99.32 79	99.32 54	99.30 204	99.57 204	98.94 197	98.97 411	99.46 238	98.92 81	99.71 112	99.24 382	99.01 20	99.98 20	99.35 76	99.66 159	98.97 315

MVP-Stereo		97.81 314	97.75 293	97.99 376	97.53 454	96.60 380	98.96 412	98.85 419	97.22 314	97.23 432	99.36 353	95.28 240	99.46 329	95.51 411	99.78 134	97.92 457
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							98.96 412		98.34 143	99.01 300	99.52 302	98.68 70		97.96 276	99.74 145

旧先验2							98.96 412		96.70 355	99.47 186			99.94 92	98.19 252

原ACMM2							98.95 415

MVS_111021_HR		99.41 59	99.32 54	99.66 91	99.72 111	99.47 113	98.95 415	99.85 9	98.82 89	99.54 174	99.73 205	98.51 84	99.74 263	98.91 153	99.88 76	99.77 100

mvsany_test1		99.50 31	99.46 28	99.62 108	99.61 189	99.09 165	98.94 417	99.48 204	99.10 48	99.96 27	99.91 26	98.85 44	99.96 41	99.72 32	99.58 169	99.82 72

MVS_111021_LR		99.41 59	99.33 52	99.65 95	99.77 78	99.51 107	98.94 417	99.85 9	98.82 89	99.65 139	99.74 199	98.51 84	99.80 239	98.83 172	99.89 68	99.64 182

pmmvs3		94.09 431	93.25 438	96.60 438	94.76 483	94.49 442	98.92 419	98.18 461	89.66 466	96.48 446	98.06 458	86.28 446	97.33 471	89.68 465	87.20 468	97.97 454

XVG-OURS		98.73 210	98.68 194	98.88 270	99.70 122	97.73 310	98.92 419	99.55 100	98.52 122	99.45 189	99.84 100	95.27 241	99.91 135	98.08 267	98.84 254	99.00 311

test222					99.75 92	99.49 109	98.91 421	99.49 192	96.42 381	99.34 229	99.65 248	98.28 100			99.69 153	99.72 137

PMMVS2		86.87 447	85.37 451	91.35 460	90.21 489	83.80 479	98.89 422	97.45 472	83.13 481	91.67 478	95.03 478	48.49 490	94.70 484	85.86 481	77.62 483	95.54 479

miper_lstm_enhance		98.00 281	97.91 271	98.28 355	99.34 290	97.43 324	98.88 423	99.36 302	96.48 376	98.80 338	99.55 289	95.98 206	98.91 437	97.27 345	95.50 399	98.51 412

MVS-HIRNet		95.75 406	95.16 411	97.51 414	99.30 300	93.69 454	98.88 423	95.78 482	85.09 479	98.78 341	92.65 482	91.29 389	99.37 349	94.85 425	99.85 94	99.46 254

TR-MVS		97.76 320	97.41 339	98.82 283	99.06 363	97.87 304	98.87 425	98.56 448	96.63 363	98.68 355	99.22 384	92.49 356	99.65 302	95.40 415	97.79 318	98.95 319

blended_shiyan8		95.56 409	94.79 416	97.87 386	96.60 467	95.90 403	98.85 426	99.27 353	92.19 456	98.47 380	97.94 461	91.43 384	99.11 403	97.26 346	81.09 478	98.60 399

blended_shiyan6		95.54 410	94.78 417	97.84 392	96.60 467	95.89 404	98.85 426	99.28 347	92.17 458	98.43 382	97.95 460	91.44 383	99.02 418	97.30 343	80.97 479	98.60 399

testdata1							98.85 426		98.32 147

blend_shiyan4		95.25 418	94.39 425	97.84 392	96.70 466	95.92 401	98.84 429	99.28 347	92.21 455	98.16 402	97.84 462	87.10 440	99.07 408	97.53 322	81.87 475	98.54 408

ET-MVSNet_ETH3D		96.49 391	95.64 405	99.05 237	99.53 220	98.82 229	98.84 429	97.51 471	97.63 267	84.77 480	99.21 387	92.09 366	98.91 437	98.98 139	92.21 448	99.41 264

our_test_3		97.65 343	97.68 300	97.55 413	98.62 429	94.97 431	98.84 429	99.30 341	96.83 349	98.19 400	99.34 360	97.01 150	99.02 418	95.00 423	96.01 380	98.64 378

MS-PatchMatch		97.24 374	97.32 352	96.99 428	98.45 440	93.51 458	98.82 432	99.32 332	97.41 297	98.13 404	99.30 371	88.99 416	99.56 320	95.68 408	99.80 125	97.90 458

c3_l		98.12 259	98.04 257	98.38 343	99.30 300	97.69 316	98.81 433	99.33 322	96.67 357	98.83 333	99.34 360	97.11 142	98.99 423	97.58 314	95.34 401	98.48 414

ppachtmachnet_test		97.49 360	97.45 328	97.61 411	98.62 429	95.24 424	98.80 434	99.46 238	96.11 403	98.22 398	99.62 265	96.45 182	98.97 431	93.77 437	95.97 385	98.61 396

PAPR		98.63 219	98.34 230	99.51 144	99.40 273	99.03 174	98.80 434	99.36 302	96.33 384	99.00 304	99.12 397	98.46 87	99.84 196	95.23 419	99.37 190	99.66 169

test0.0.03 1		97.71 333	97.42 338	98.56 316	98.41 442	97.82 307	98.78 436	98.63 446	97.34 302	98.05 409	98.98 413	94.45 294	98.98 424	95.04 422	97.15 358	98.89 320

PVSNet_Blended		99.08 149	98.97 142	99.42 179	99.76 82	98.79 232	98.78 436	99.91 3	96.74 352	99.67 125	99.49 312	97.53 122	99.88 168	98.98 139	99.85 94	99.60 195

PMMVS		98.80 200	98.62 208	99.34 191	99.27 309	98.70 240	98.76 438	99.31 336	97.34 302	99.21 261	99.07 399	97.20 137	99.82 227	98.56 216	98.87 251	99.52 226

test123		39.01 459	42.50 461	28.53 475	39.17 498	20.91 500	98.75 439	19.17 500	19.83 493	38.57 492	66.67 490	33.16 494	15.42 494	37.50 494	29.66 492	49.26 489

MSDG		98.98 170	98.80 179	99.53 133	99.76 82	99.19 150	98.75 439	99.55 100	97.25 310	99.47 186	99.77 185	97.82 116	99.87 175	96.93 370	99.90 57	99.54 219

CLD-MVS		98.16 254	98.10 248	98.33 346	99.29 304	96.82 369	98.75 439	99.44 258	97.83 241	99.13 276	99.55 289	92.92 339	99.67 294	98.32 243	97.69 321	98.48 414
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_ehance_all_eth		98.18 252	98.10 248	98.41 339	99.23 320	97.72 312	98.72 442	99.31 336	96.60 367	98.88 323	99.29 373	97.29 132	99.13 397	97.60 312	95.99 382	98.38 427

cl____		98.01 279	97.84 280	98.55 318	99.25 316	97.97 295	98.71 443	99.34 314	96.47 378	98.59 372	99.54 294	95.65 226	99.21 386	97.21 349	95.77 388	98.46 419

DIV-MVS_self_test		98.01 279	97.85 279	98.48 325	99.24 318	97.95 300	98.71 443	99.35 309	96.50 372	98.60 371	99.54 294	95.72 224	99.03 415	97.21 349	95.77 388	98.46 419

test-LLR		98.06 266	97.90 272	98.55 318	98.79 404	97.10 339	98.67 445	97.75 466	97.34 302	98.61 369	98.85 423	94.45 294	99.45 331	97.25 347	99.38 183	99.10 295

TESTMET0.1,1		97.55 349	97.27 360	98.40 341	98.93 384	96.53 381	98.67 445	97.61 469	96.96 338	98.64 363	99.28 375	88.63 425	99.45 331	97.30 343	99.38 183	99.21 290

test-mter		97.49 360	97.13 367	98.55 318	98.79 404	97.10 339	98.67 445	97.75 466	96.65 359	98.61 369	98.85 423	88.23 429	99.45 331	97.25 347	99.38 183	99.10 295

mvs5depth		96.66 387	96.22 391	97.97 377	97.00 465	96.28 390	98.66 448	99.03 392	96.61 364	96.93 442	99.79 169	87.20 439	99.47 327	96.65 385	94.13 424	98.16 439

IB-MVS	95.67 18	96.22 395	95.44 409	98.57 312	99.21 325	96.70 372	98.65 449	97.74 468	96.71 354	97.27 431	98.54 438	86.03 448	99.92 123	98.47 226	86.30 469	99.10 295
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
DPM-MVS		98.95 173	98.71 191	99.66 91	99.63 169	99.55 96	98.64 450	99.10 380	97.93 227	99.42 200	99.55 289	98.67 72	99.80 239	95.80 404	99.68 156	99.61 192

thisisatest0515		98.14 256	97.79 283	99.19 222	99.50 241	98.50 265	98.61 451	96.82 476	96.95 340	99.54 174	99.43 330	91.66 379	99.86 181	98.08 267	99.51 174	99.22 289

DeepPCF-MVS	98.18 3	98.81 197	99.37 44	97.12 425	99.60 195	91.75 467	98.61 451	99.44 258	99.35 25	99.83 64	99.85 85	98.70 69	99.81 232	99.02 136	99.91 46	99.81 79

cl22		97.85 302	97.64 306	98.48 325	99.09 357	97.87 304	98.60 453	99.33 322	97.11 325	98.87 326	99.22 384	92.38 362	99.17 391	98.21 250	95.99 382	98.42 422

FE-MVSNET3		98.09 261	97.82 281	98.89 266	98.70 421	98.90 209	98.57 454	99.47 226	96.78 350	98.87 326	99.05 402	94.75 272	99.23 376	97.45 333	96.74 362	98.53 409

GA-MVS		97.85 302	97.47 325	99.00 243	99.38 278	97.99 294	98.57 454	99.15 374	97.04 333	98.90 320	99.30 371	89.83 408	99.38 346	96.70 380	98.33 284	99.62 190

TinyColmap		97.12 377	96.89 376	97.83 395	99.07 361	95.52 416	98.57 454	98.74 434	97.58 273	97.81 420	99.79 169	88.16 430	99.56 320	95.10 420	97.21 355	98.39 426

eth_miper_zixun_eth		98.05 271	97.96 265	98.33 346	99.26 312	97.38 326	98.56 457	99.31 336	96.65 359	98.88 323	99.52 302	96.58 174	99.12 402	97.39 337	95.53 398	98.47 416

CMPMVS	69.68 23	94.13 430	94.90 415	91.84 457	97.24 460	80.01 487	98.52 458	99.48 204	89.01 470	91.99 474	99.67 241	85.67 450	99.13 397	95.44 413	97.03 360	96.39 475
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 367	97.20 362	97.75 401	99.07 361	95.20 425	98.51 459	99.04 390	97.99 222	98.31 391	99.86 78	89.02 415	99.55 322	95.67 409	97.36 350	98.49 413

FE-blended-shiyan7		95.43 413	94.66 419	97.77 400	96.45 469	95.68 409	98.48 460	99.28 347	92.18 457	98.36 387	97.68 464	91.20 391	99.03 415	97.31 341	80.97 479	98.60 399

ambc				93.06 455	92.68 486	82.36 480	98.47 461	98.73 440		95.09 459	97.41 469	55.55 486	99.10 406	96.42 390	91.32 451	97.71 459

miper_enhance_ethall		98.16 254	98.08 252	98.41 339	98.96 382	97.72 312	98.45 462	99.32 332	96.95 340	98.97 309	99.17 389	97.06 146	99.22 381	97.86 284	95.99 382	98.29 431

CHOSEN 280x420		99.12 135	99.13 95	99.08 232	99.66 150	97.89 303	98.43 463	99.71 16	98.88 83	99.62 151	99.76 189	96.63 170	99.70 286	99.46 67	99.99 1	99.66 169

testmvs		39.17 458	43.78 460	25.37 476	36.04 499	16.84 501	98.36 464	26.56 498	20.06 492	38.51 493	67.32 489	29.64 495	15.30 495	37.59 493	39.90 491	43.98 490

FPMVS		84.93 449	85.65 450	82.75 471	86.77 492	63.39 497	98.35 465	98.92 405	74.11 483	83.39 482	98.98 413	50.85 489	92.40 486	84.54 482	94.97 409	92.46 481

KD-MVS_2432*1600		94.62 425	93.72 433	97.31 419	97.19 462	95.82 406	98.34 466	99.20 368	95.00 425	97.57 423	98.35 445	87.95 432	98.10 457	92.87 451	77.00 484	98.01 448

miper_refine_blended		94.62 425	93.72 433	97.31 419	97.19 462	95.82 406	98.34 466	99.20 368	95.00 425	97.57 423	98.35 445	87.95 432	98.10 457	92.87 451	77.00 484	98.01 448

CL-MVSNet_self_test		94.49 427	93.97 431	96.08 443	96.16 472	93.67 455	98.33 468	99.38 292	95.13 418	97.33 430	98.15 452	92.69 350	96.57 476	88.67 468	79.87 482	97.99 452

PVSNet	96.02 17	98.85 193	98.84 176	98.89 266	99.73 107	97.28 329	98.32 469	99.60 67	97.86 234	99.50 181	99.57 283	96.75 165	99.86 181	98.56 216	99.70 152	99.54 219

PAPM		97.59 347	97.09 369	99.07 233	99.06 363	98.26 279	98.30 470	99.10 380	94.88 427	98.08 405	99.34 360	96.27 193	99.64 306	89.87 464	98.92 244	99.31 279

Patchmatch-RL test		95.84 404	95.81 402	95.95 444	95.61 475	90.57 470	98.24 471	98.39 452	95.10 422	95.20 457	98.67 433	94.78 267	97.77 465	96.28 395	90.02 459	99.51 235

UnsupCasMVSNet_bld		93.53 434	92.51 440	96.58 439	97.38 456	93.82 450	98.24 471	99.48 204	91.10 464	93.10 469	96.66 475	74.89 478	98.37 452	94.03 436	87.71 467	97.56 465

LCM-MVSNet		86.80 448	85.22 452	91.53 459	87.81 491	80.96 485	98.23 473	98.99 396	71.05 484	90.13 479	96.51 476	48.45 491	96.88 475	90.51 461	85.30 470	96.76 471

cascas		97.69 335	97.43 337	98.48 325	98.60 432	97.30 328	98.18 474	99.39 284	92.96 450	98.41 383	98.78 430	93.77 323	99.27 369	98.16 256	98.61 266	98.86 321

kuosan		90.92 443	90.11 448	93.34 452	98.78 407	85.59 477	98.15 475	93.16 492	89.37 469	92.07 473	98.38 444	81.48 472	95.19 482	62.54 491	97.04 359	99.25 286

Effi-MVS+		98.81 197	98.59 214	99.48 160	99.46 253	99.12 163	98.08 476	99.50 179	97.50 285	99.38 214	99.41 336	96.37 187	99.81 232	99.11 122	98.54 274	99.51 235

PCF-MVS	97.08 14	97.66 342	97.06 370	99.47 166	99.61 189	99.09 165	98.04 477	99.25 357	91.24 463	98.51 376	99.70 216	94.55 288	99.91 135	92.76 453	99.85 94	99.42 261
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 400	95.47 407	97.94 380	99.31 299	94.34 447	97.81 478	99.70 18	97.12 322	97.46 425	98.75 431	89.71 409	99.79 245	97.69 308	81.69 476	99.68 160

E-PMN		80.61 452	79.88 454	82.81 470	90.75 488	76.38 491	97.69 479	95.76 483	66.44 488	83.52 481	92.25 483	62.54 483	87.16 490	68.53 489	61.40 487	84.89 488

dongtai		93.26 435	92.93 439	94.25 448	99.39 276	85.68 476	97.68 480	93.27 490	92.87 451	96.85 443	99.39 344	82.33 469	97.48 470	76.78 484	97.80 317	99.58 210

ANet_high		77.30 454	74.86 458	84.62 469	75.88 495	77.61 489	97.63 481	93.15 493	88.81 471	64.27 490	89.29 487	36.51 493	83.93 492	75.89 486	52.31 489	92.33 483

EMVS		80.02 453	79.22 455	82.43 472	91.19 487	76.40 490	97.55 482	92.49 495	66.36 489	83.01 483	91.27 485	64.63 482	85.79 491	65.82 490	60.65 488	85.08 487

MVE	76.82 21	76.91 455	74.31 459	84.70 468	85.38 494	76.05 492	96.88 483	93.17 491	67.39 487	71.28 489	89.01 488	21.66 498	87.69 489	71.74 488	72.29 486	90.35 485
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 441	91.36 443	90.31 462	95.85 473	73.72 495	94.89 484	99.25 357	68.39 486	95.82 453	99.02 407	80.50 475	98.95 434	93.64 440	94.89 413	98.25 434

Gipuma		90.99 442	90.15 447	93.51 451	98.73 416	90.12 471	93.98 485	99.45 249	79.32 482	92.28 472	94.91 479	69.61 479	97.98 461	87.42 475	95.67 392	92.45 482
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 456	74.97 457	79.01 473	70.98 496	55.18 498	93.37 486	98.21 459	65.08 490	61.78 491	93.83 481	21.74 497	92.53 485	78.59 483	91.12 454	89.34 486
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 450	81.52 453	86.66 467	66.61 497	68.44 496	92.79 487	97.92 463	68.96 485	80.04 488	99.85 85	85.77 449	96.15 480	97.86 284	43.89 490	95.39 480

wuyk23d		40.18 457	41.29 462	36.84 474	86.18 493	49.12 499	79.73 488	22.81 499	27.64 491	25.46 494	28.45 494	21.98 496	48.89 493	55.80 492	23.56 493	12.51 491

mmdepth		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

monomultidepth		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

test_blank		0.13 463	0.17 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	1.57 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet_test		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

DCPMVS		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

cdsmvs_eth3d_5k		24.64 460	32.85 463	0.00 477	0.00 500	0.00 502	0.00 489	99.51 156	0.00 495	0.00 496	99.56 286	96.58 174	0.00 496	0.00 495	0.00 494	0.00 492

pcd_1.5k_mvsjas		8.27 462	11.03 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	99.01 20	0.00 496	0.00 495	0.00 494	0.00 492

sosnet-low-res		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

sosnet		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uncertanet		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

Regformer		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

ab-mvs-re		8.30 461	11.06 464	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	99.58 278	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet		0.02 464	0.03 467	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.27 496	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

WAC-MVS						97.16 336								95.47 412

MSC_two_6792asdad				99.87 21	99.51 229	99.76 49		99.33 322					99.96 41	98.87 159	99.84 102	99.89 29

PC_three_1452									98.18 173	99.84 56	99.70 216	99.31 3	98.52 450	98.30 245	99.80 125	99.81 79

No_MVS				99.87 21	99.51 229	99.76 49		99.33 322					99.96 41	98.87 159	99.84 102	99.89 29

test_one_0601					99.81 57	99.88 10		99.49 192	98.97 75	99.65 139	99.81 134	99.09 16

eth-test2					0.00 500

eth-test					0.00 500

ZD-MVS					99.71 117	99.79 41		99.61 60	96.84 347	99.56 167	99.54 294	98.58 78	99.96 41	96.93 370	99.75 142

IU-MVS					99.84 38	99.88 10		99.32 332	98.30 149	99.84 56				98.86 164	99.85 94	99.89 29

test_241102_TWO								99.48 204	99.08 56	99.88 43	99.81 134	98.94 34	99.96 41	98.91 153	99.84 102	99.88 35

test_241102_ONE					99.84 38	99.90 3		99.48 204	99.07 58	99.91 31	99.74 199	99.20 9	99.76 257

test_0728_THIRD									98.99 69	99.81 69	99.80 152	99.09 16	99.96 41	98.85 166	99.90 57	99.88 35

GSMVS																99.52 226

test_part2					99.81 57	99.83 22				99.77 85

sam_mvs1												94.86 262				99.52 226

sam_mvs												94.72 275

MTGPA								99.47 226

test_post											65.99 491	94.65 282	99.73 269

patchmatchnet-post											98.70 432	94.79 266	99.74 263

gm-plane-assit					98.54 437	92.96 461			94.65 433		99.15 392		99.64 306	97.56 319

test9_res														97.49 327	99.72 148	99.75 113

agg_prior2														97.21 349	99.73 147	99.75 113

agg_prior					99.67 137	99.62 83		99.40 281		98.87 326			99.91 135

TestCases				99.31 199	99.86 25	98.48 268		99.61 60	97.85 237	99.36 222	99.85 85	95.95 208	99.85 187	96.66 383	99.83 113	99.59 206

test_prior				99.68 89	99.67 137	99.48 111		99.56 90					99.83 218			99.74 118

新几何1				99.75 77	99.75 92	99.59 88		99.54 109	96.76 351	99.29 239	99.64 254	98.43 89	99.94 92	96.92 372	99.66 159	99.72 137

旧先验1					99.74 100	99.59 88		99.54 109			99.69 227	98.47 86			99.68 156	99.73 127

原ACMM1				99.65 95	99.73 107	99.33 130		99.47 226	97.46 287	99.12 278	99.66 246	98.67 72	99.91 135	97.70 307	99.69 153	99.71 148

testdata2													99.95 76	96.67 382

segment_acmp												98.96 27

testdata				99.54 125	99.75 92	98.95 193		99.51 156	97.07 328	99.43 197	99.70 216	98.87 42	99.94 92	97.76 298	99.64 162	99.72 137

test12				99.75 77	99.64 165	99.61 85		99.29 345		99.21 261		98.38 95	99.89 163		99.74 145	99.74 118

plane_prior7					99.29 304	97.03 351

plane_prior6					99.27 309	96.98 355						92.71 348

plane_prior5								99.47 226					99.69 291	97.78 294	97.63 323	98.67 365

plane_prior4											99.61 269

plane_prior3						97.00 353			98.69 107	99.11 280

plane_prior1					99.26 312

n2								0.00 501

nn								0.00 501

door-mid								98.05 462

lessismore_v0				97.79 399	98.69 423	95.44 420		94.75 486		95.71 454	99.87 70	88.69 421	99.32 361	95.89 401	94.93 411	98.62 387

LGP-MVS_train				98.49 323	99.33 291	97.05 345		99.55 100	97.46 287	99.24 253	99.83 106	92.58 353	99.72 273	98.09 263	97.51 335	98.68 357

test11								99.35 309

door								97.92 463

HQP5-MVS						96.83 367

BP-MVS														97.19 353

HQP4-MVS										98.66 356			99.64 306			98.64 378

HQP3-MVS								99.39 284							97.58 328

HQP2-MVS												92.47 357

NP-MVS					99.23 320	96.92 363					99.40 340

ACMMP++_ref															97.19 356

ACMMP++															97.43 346

Test By Simon												98.75 60

ITE_SJBPF				98.08 368	99.29 304	96.37 386		98.92 405	98.34 143	98.83 333	99.75 194	91.09 393	99.62 313	95.82 402	97.40 348	98.25 434

DeepMVS_CX				93.34 452	99.29 304	82.27 481		99.22 363	85.15 478	96.33 447	99.05 402	90.97 395	99.73 269	93.57 441	97.77 319	98.01 448

High-resolution multi-view benchmark