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	99.70 212	98.65 74	99.79 241	99.65 41	99.78 134	99.41 260

mmtdpeth		96.95 376	96.71 375	97.67 396	99.33 287	94.90 423	99.89 2	99.28 343	98.15 175	99.72 102	98.57 432	86.56 434	99.90 148	99.82 29	89.02 460	98.20 427

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	99.69 223	98.55 81	99.82 223	99.69 35	99.85 94	99.48 239

MVSFormer		99.17 109	99.12 97	99.29 203	99.51 225	98.94 197	99.88 4	99.46 233	97.55 273	99.80 74	99.65 244	97.39 125	99.28 362	99.03 130	99.85 94	99.65 171

test_djsdf		98.67 210	98.57 211	98.98 241	98.70 417	98.91 204	99.88 4	99.46 233	97.55 273	99.22 254	99.88 53	95.73 219	99.28 362	99.03 130	97.62 321	98.75 331

OurMVSNet-221017-0		97.88 292	97.77 283	98.19 355	98.71 416	96.53 376	99.88 4	99.00 386	97.79 243	98.78 336	99.94 6	91.68 371	99.35 352	97.21 339	96.99 357	98.69 348

EC-MVSNet		99.44 50	99.39 40	99.58 116	99.56 204	99.49 109	99.88 4	99.58 78	98.38 137	99.73 97	99.69 223	98.20 103	99.70 282	99.64 43	99.82 117	99.54 215

DVP-MVS++		99.59 15	99.50 19	99.88 15	99.51 225	99.88 10	99.87 8	99.51 152	98.99 69	99.88 43	99.81 130	99.27 7	99.96 41	98.85 162	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		97.10 373	96.79 373	98.01 368	98.72 414	96.33 383	99.87 8	97.05 464	97.59 267	96.16 440	99.80 148	88.71 411	99.04 405	96.69 371	96.55 363	98.65 372

FC-MVSNet-test		98.75 203	98.62 204	99.15 225	99.08 356	99.45 115	99.86 11	99.60 67	98.23 165	98.70 348	99.82 115	96.80 162	99.22 376	99.07 125	96.38 366	98.79 321

v7n		97.87 294	97.52 312	98.92 252	98.76 410	98.58 248	99.84 12	99.46 233	96.20 389	98.91 314	99.70 212	94.89 257	99.44 332	96.03 388	93.89 424	98.75 331

DTE-MVSNet		97.51 349	97.19 358	98.46 326	98.63 423	98.13 281	99.84 12	99.48 200	96.68 351	97.97 402	99.67 237	92.92 334	98.56 439	96.88 364	92.60 442	98.70 344

3Dnovator	97.25 9	99.24 97	99.05 112	99.81 60	99.12 345	99.66 71	99.84 12	99.74 13	99.09 55	98.92 313	99.90 33	95.94 206	99.98 20	98.95 142	99.92 39	99.79 92

FIs		98.78 198	98.63 199	99.23 215	99.18 329	99.54 98	99.83 15	99.59 73	98.28 150	98.79 335	99.81 130	96.75 165	99.37 345	99.08 124	96.38 366	98.78 323

MGCFI-Net		99.01 162	98.85 170	99.50 149	99.42 259	99.26 144	99.82 16	99.48 200	98.60 115	99.28 236	98.81 421	97.04 147	99.76 253	99.29 91	97.87 310	99.47 245

test_fmvs3		92.10 430	91.77 433	93.08 445	96.19 461	86.25 465	99.82 16	98.62 438	96.65 354	95.19 448	96.90 465	55.05 479	95.93 472	96.63 376	90.92 451	97.06 461

jajsoiax		98.43 224	98.28 231	98.88 265	98.60 427	98.43 267	99.82 16	99.53 125	98.19 170	98.63 360	99.80 148	93.22 329	99.44 332	99.22 100	97.50 333	98.77 327

OpenMVS	96.50 16	98.47 221	98.12 242	99.52 139	99.04 364	99.53 101	99.82 16	99.72 14	94.56 429	98.08 395	99.88 53	94.73 269	99.98 20	97.47 324	99.76 140	99.06 302

SDMVSNet		99.11 137	98.90 156	99.75 77	99.81 57	99.59 88	99.81 20	99.65 39	98.78 98	99.64 140	99.88 53	94.56 281	99.93 110	99.67 37	98.26 288	99.72 133

nrg030		98.64 214	98.42 221	99.28 207	99.05 362	99.69 63	99.81 20	99.46 233	98.04 212	99.01 296	99.82 115	96.69 167	99.38 342	99.34 81	94.59 411	98.78 323

HPM-MVS		99.42 55	99.28 69	99.83 56	99.90 4	99.72 56	99.81 20	99.54 109	97.59 267	99.68 115	99.63 256	98.91 39	99.94 92	98.58 206	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		99.13 123	98.99 134	99.53 133	99.65 156	99.06 171	99.81 20	99.33 318	97.43 290	99.60 155	99.88 53	97.14 138	99.84 196	99.13 115	98.94 237	99.69 150

3Dnovator+	97.12 13	99.18 104	98.97 138	99.82 57	99.17 337	99.68 64	99.81 20	99.51 152	99.20 33	98.72 341	99.89 42	95.68 221	99.97 29	98.86 160	99.86 87	99.81 79

sasdasda		99.02 158	98.86 167	99.51 144	99.42 259	99.32 131	99.80 25	99.48 200	98.63 110	99.31 228	98.81 421	97.09 143	99.75 256	99.27 95	97.90 307	99.47 245

FA-MVS(test-final)		98.75 203	98.53 215	99.41 176	99.55 208	99.05 173	99.80 25	99.01 385	96.59 364	99.58 159	99.59 270	95.39 231	99.90 148	97.78 290	99.49 177	99.28 277

GeoE		98.85 189	98.62 204	99.53 133	99.61 185	99.08 168	99.80 25	99.51 152	97.10 322	99.31 228	99.78 172	95.23 242	99.77 249	98.21 246	99.03 231	99.75 109

canonicalmvs		99.02 158	98.86 167	99.51 144	99.42 259	99.32 131	99.80 25	99.48 200	98.63 110	99.31 228	98.81 421	97.09 143	99.75 256	99.27 95	97.90 307	99.47 245

v8		97.95 283	97.63 302	98.93 250	98.95 379	98.81 226	99.80 25	99.41 269	96.03 403	99.10 279	99.42 328	94.92 254	99.30 360	96.94 359	94.08 421	98.66 370

Vis-MVSNet (Re-imp)		98.87 177	98.72 185	99.31 195	99.71 117	98.88 208	99.80 25	99.44 253	97.91 225	99.36 218	99.78 172	95.49 228	99.43 336	97.91 275	99.11 218	99.62 186

Anonymous20240521		96.20 392	95.89 395	97.13 414	97.72 448	94.96 422	99.79 31	99.29 341	93.01 445	97.20 425	99.03 400	89.69 401	98.36 443	91.16 451	96.13 372	98.07 434

PS-MVSNAJss		98.92 171	98.92 151	98.90 258	98.78 403	98.53 252	99.78 32	99.54 109	98.07 198	99.00 300	99.76 185	99.01 20	99.37 345	99.13 115	97.23 350	98.81 320

PEN-MVS		97.76 315	97.44 328	98.72 291	98.77 408	98.54 251	99.78 32	99.51 152	97.06 326	98.29 385	99.64 250	92.63 347	98.89 430	98.09 259	93.16 434	98.72 337

anonymousdsp		98.44 223	98.28 231	98.94 248	98.50 433	98.96 187	99.77 34	99.50 175	97.07 324	98.87 322	99.77 181	94.76 267	99.28 362	98.66 192	97.60 322	98.57 398

SixPastTwentyTwo		97.50 350	97.33 346	98.03 365	98.65 421	96.23 388	99.77 34	98.68 434	97.14 315	97.90 405	99.93 10	90.45 390	99.18 384	97.00 353	96.43 365	98.67 361

QAPM		98.67 210	98.30 230	99.80 64	99.20 323	99.67 68	99.77 34	99.72 14	94.74 426	98.73 340	99.90 33	95.78 217	99.98 20	96.96 357	99.88 76	99.76 107

SSC-MVS		92.73 429	93.73 422	89.72 455	95.02 472	81.38 475	99.76 37	99.23 353	94.87 423	92.80 462	98.93 413	94.71 271	91.37 479	74.49 478	93.80 425	96.42 465

test_vis3_rt		87.04 437	85.81 440	90.73 452	93.99 476	81.96 473	99.76 37	90.23 487	92.81 448	81.35 475	91.56 475	40.06 483	99.07 402	94.27 422	88.23 462	91.15 475

dcpmvs_2		99.23 98	99.58 9	98.16 357	99.83 47	94.68 428	99.76 37	99.52 130	99.07 58	99.98 13	99.88 53	98.56 80	99.93 110	99.67 37	99.98 4	99.87 40

RRT-MVS		98.91 172	98.75 181	99.39 182	99.46 249	98.61 246	99.76 37	99.50 175	98.06 202	99.81 69	99.88 53	93.91 313	99.94 92	99.11 118	99.27 194	99.61 188

HPM-MVS_fast		99.51 29	99.40 38	99.85 43	99.91 1	99.79 41	99.76 37	99.56 90	97.72 252	99.76 91	99.75 190	99.13 14	99.92 123	99.07 125	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 81	98.41 93	99.96 41	99.28 92	99.84 102	99.83 63

MVSMamba_PlusPlus		99.46 42	99.41 37	99.64 101	99.68 132	99.50 108	99.75 42	99.50 175	98.27 152	99.87 49	99.92 18	98.09 108	99.94 92	99.65 41	99.95 23	99.47 245

v10		97.85 297	97.52 312	98.86 272	98.99 372	98.67 237	99.75 42	99.41 269	95.70 407	98.98 303	99.41 332	94.75 268	99.23 372	96.01 390	94.63 410	98.67 361

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 81	99.18 12	99.96 41	99.22 100	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		99.05 154	98.87 164	99.57 120	99.73 107	99.32 131	99.75 42	99.20 359	98.02 217	99.56 163	99.86 74	96.54 177	99.67 290	98.09 259	99.13 211	99.73 123

test_vis1_n		97.92 287	97.44 328	99.34 187	99.53 216	98.08 284	99.74 47	99.49 188	99.15 38	100.00 1	99.94 6	79.51 466	99.98 20	99.88 26	99.76 140	99.97 4

test_fmvs1_n		98.41 227	98.14 239	99.21 216	99.82 53	97.71 310	99.74 47	99.49 188	99.32 29	99.99 2	99.95 3	85.32 444	99.97 29	99.82 29	99.84 102	99.96 7

balanced_conf03		99.46 42	99.39 40	99.67 90	99.55 208	99.58 93	99.74 47	99.51 152	98.42 134	99.87 49	99.84 96	98.05 111	99.91 135	99.58 47	99.94 31	99.52 222

tttt0517		98.42 225	98.14 239	99.28 207	99.66 148	98.38 270	99.74 47	96.85 466	97.68 258	99.79 76	99.74 195	91.39 379	99.89 163	98.83 168	99.56 170	99.57 209

WB-MVS		93.10 427	94.10 418	90.12 454	95.51 469	81.88 474	99.73 51	99.27 346	95.05 418	93.09 461	98.91 417	94.70 272	91.89 478	76.62 476	94.02 423	96.58 464

test_fmvs2		97.25 367	97.30 349	97.09 416	99.43 257	93.31 450	99.73 51	98.87 408	98.83 88	99.28 236	99.80 148	84.45 449	99.66 293	97.88 277	97.45 338	98.30 420

SD_0403		97.55 344	97.53 311	97.62 398	99.61 185	93.64 447	99.72 53	99.44 253	98.03 214	98.62 363	99.39 340	96.06 198	99.57 314	87.88 464	99.01 234	99.66 165

MonoMVSNet		98.38 231	98.47 219	98.12 362	98.59 429	96.19 390	99.72 53	98.79 419	97.89 227	99.44 190	99.52 298	96.13 195	98.90 429	98.64 194	97.54 328	99.28 277

baseline		99.15 115	99.02 125	99.53 133	99.66 148	99.14 160	99.72 53	99.48 200	98.35 142	99.42 196	99.84 96	96.07 197	99.79 241	99.51 56	99.14 208	99.67 160

RPSCF		98.22 242	98.62 204	96.99 418	99.82 53	91.58 459	99.72 53	99.44 253	96.61 359	99.66 126	99.89 42	95.92 207	99.82 223	97.46 325	99.10 225	99.57 209

CSCG		99.32 79	99.32 54	99.32 193	99.85 31	98.29 272	99.71 57	99.66 32	98.11 189	99.41 201	99.80 148	98.37 96	99.96 41	98.99 134	99.96 17	99.72 133

dmvs_re		98.08 259	98.16 236	97.85 383	99.55 208	94.67 429	99.70 58	98.92 396	98.15 175	99.06 290	99.35 352	93.67 321	99.25 369	97.77 293	97.25 349	99.64 178

WR-MVS_H		98.13 253	97.87 273	98.90 258	99.02 366	98.84 218	99.70 58	99.59 73	97.27 304	98.40 377	99.19 384	95.53 226	99.23 372	98.34 236	93.78 426	98.61 392

mvsmamba		99.06 150	98.96 142	99.36 184	99.47 247	98.64 241	99.70 58	99.05 380	97.61 266	99.65 135	99.83 102	96.54 177	99.92 123	99.19 104	99.62 165	99.51 231

LTVRE_ROB	97.16 12	98.02 271	97.90 268	98.40 336	99.23 316	96.80 365	99.70 58	99.60 67	97.12 318	98.18 392	99.70 212	91.73 370	99.72 269	98.39 229	97.45 338	98.68 353
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	99.83 102		99.95 76	98.83 168	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	99.83 102	99.30 4	99.95 76	98.83 168	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	99.83 102	99.30 4	99.95 76	99.32 84	99.89 68	99.90 25

TestfortrainingZip							99.69 62

test_f		91.90 431	91.26 435	93.84 441	95.52 468	85.92 466	99.69 62	98.53 442	95.31 412	93.87 455	96.37 468	55.33 478	98.27 444	95.70 396	90.98 450	97.32 460

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	99.37 212	99.74 195	98.81 49	99.94 92	98.79 176	99.86 87	99.84 53

X-MVStestdata		96.55 384	95.45 403	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 212	64.01 484	98.81 49	99.94 92	98.79 176	99.86 87	99.84 53

V42		98.06 261	97.79 278	98.86 272	98.98 375	98.84 218	99.69 62	99.34 310	96.53 366	99.30 232	99.37 346	94.67 274	99.32 357	97.57 314	94.66 409	98.42 412

mPP-MVS		99.44 50	99.30 62	99.86 34	99.88 13	99.79 41	99.69 62	99.48 200	98.12 187	99.50 177	99.75 190	98.78 53	99.97 29	98.57 209	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	99.52 130	98.07 198	99.53 172	99.63 256	98.93 38	99.97 29	98.74 180	99.91 46	99.83 63

FE-MVS		98.48 220	98.17 235	99.40 177	99.54 215	98.96 187	99.68 72	98.81 415	95.54 409	99.62 147	99.70 212	93.82 316	99.93 110	97.35 333	99.46 178	99.32 274

PS-CasMVS		97.93 284	97.59 306	98.95 246	98.99 372	99.06 171	99.68 72	99.52 130	97.13 316	98.31 382	99.68 231	92.44 356	99.05 404	98.51 217	94.08 421	98.75 331

Vis-MVSNet		99.12 131	98.97 138	99.56 122	99.78 70	99.10 164	99.68 72	99.66 32	98.49 125	99.86 53	99.87 66	94.77 266	99.84 196	99.19 104	99.41 182	99.74 114
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		99.12 131	98.92 151	99.70 87	99.67 135	99.40 121	99.67 75	99.63 46	98.73 102	99.94 28	99.81 130	94.54 284	99.96 41	98.40 228	99.93 33	99.74 114

BP-MVS1		99.12 131	98.94 148	99.65 95	99.51 225	99.30 138	99.67 75	98.92 396	98.48 126	99.84 56	99.69 223	94.96 249	99.92 123	99.62 44	99.79 132	99.71 144

test_vis1_n_1920		98.63 215	98.40 223	99.31 195	99.86 25	97.94 297	99.67 75	99.62 51	99.43 17	99.99 2	99.91 26	87.29 429	100.00 1	99.92 24	99.92 39	99.98 2

EIA-MVS		99.18 104	99.09 104	99.45 165	99.49 239	99.18 152	99.67 75	99.53 125	97.66 261	99.40 206	99.44 324	98.10 107	99.81 228	98.94 143	99.62 165	99.35 269

MSP-MVS		99.42 55	99.27 73	99.88 15	99.89 8	99.80 38	99.67 75	99.50 175	98.70 106	99.77 85	99.49 308	98.21 102	99.95 76	98.46 223	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		99.10 142	98.97 138	99.48 156	99.49 239	99.14 160	99.67 75	99.34 310	97.31 301	99.58 159	99.76 185	97.65 121	99.82 223	98.87 155	99.07 228	99.46 250

CP-MVSNet		98.09 257	97.78 281	99.01 237	98.97 377	99.24 147	99.67 75	99.46 233	97.25 306	98.48 374	99.64 250	93.79 317	99.06 403	98.63 196	94.10 420	98.74 335

MTAPA		99.52 28	99.39 40	99.89 11	99.90 4	99.86 18	99.66 82	99.47 222	98.79 95	99.68 115	99.81 130	98.43 89	99.97 29	98.88 152	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	99.68 115	99.69 223	99.06 18	99.96 41	98.69 188	99.87 79	99.84 53

mvs_tets		98.40 230	98.23 233	98.91 256	98.67 420	98.51 258	99.66 82	99.53 125	98.19 170	98.65 357	99.81 130	92.75 338	99.44 332	99.31 86	97.48 337	98.77 327

EU-MVSNet		97.98 278	98.03 254	97.81 389	98.72 414	96.65 372	99.66 82	99.66 32	98.09 193	98.35 380	99.82 115	95.25 240	98.01 450	97.41 329	95.30 397	98.78 323

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	99.67 121	99.69 223	98.95 32	99.96 41	98.69 188	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	99.46 233	98.09 193	99.48 181	99.74 195	98.29 99	99.96 41	97.93 274	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	98.83 221	99.65 88	99.52 130	99.10 48	99.84 56	99.76 185	95.80 215	99.99 4	99.30 89	99.84 102	99.74 114

SymmetryMVS		99.15 115	99.02 125	99.52 139	99.72 111	98.83 221	99.65 88	99.34 310	99.10 48	99.84 56	99.76 185	95.80 215	99.99 4	99.30 89	98.72 258	99.73 123

Elysia		98.88 174	98.65 196	99.58 116	99.58 195	99.34 127	99.65 88	99.52 130	98.26 155	99.83 64	99.87 66	93.37 324	99.90 148	97.81 287	99.91 46	99.49 236

StellarMVS		98.88 174	98.65 196	99.58 116	99.58 195	99.34 127	99.65 88	99.52 130	98.26 155	99.83 64	99.87 66	93.37 324	99.90 148	97.81 287	99.91 46	99.49 236

test_cas_vis1_n_1920		99.16 111	99.01 130	99.61 109	99.81 57	98.86 215	99.65 88	99.64 42	99.39 22	99.97 25	99.94 6	93.20 330	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	99.66 126	99.68 231	98.96 27	99.96 41	98.62 197	99.87 79	99.84 53

TranMVSNet+NR-MVSNet		97.93 284	97.66 297	98.76 288	98.78 403	98.62 244	99.65 88	99.49 188	97.76 247	98.49 373	99.60 268	94.23 297	98.97 421	98.00 270	92.90 436	98.70 344

GDP-MVS		99.08 145	98.89 160	99.64 101	99.53 216	99.34 127	99.64 95	99.48 200	98.32 147	99.77 85	99.66 242	95.14 245	99.93 110	98.97 140	99.50 176	99.64 178

ttmdpeth		97.80 311	97.63 302	98.29 346	98.77 408	97.38 321	99.64 95	99.36 298	98.78 98	96.30 438	99.58 274	92.34 359	99.39 340	98.36 234	95.58 390	98.10 432

mvsany_test3		93.77 423	93.45 426	94.74 438	95.78 464	88.01 464	99.64 95	98.25 447	98.28 150	94.31 452	97.97 454	68.89 471	98.51 441	97.50 320	90.37 452	97.71 449

ZNCC-MVS		99.47 40	99.33 52	99.87 21	99.87 20	99.81 33	99.64 95	99.67 27	98.08 197	99.55 169	99.64 250	98.91 39	99.96 41	98.72 183	99.90 57	99.82 72

tfpnnormal		97.84 301	97.47 320	98.98 241	99.20 323	99.22 149	99.64 95	99.61 60	96.32 380	98.27 386	99.70 212	93.35 326	99.44 332	95.69 397	95.40 395	98.27 422

casdiffmvs_mvg		99.15 115	99.02 125	99.55 124	99.66 148	99.09 165	99.64 95	99.56 90	98.26 155	99.45 185	99.87 66	96.03 200	99.81 228	99.54 51	99.15 207	99.73 123
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS-dyc-post		99.45 46	99.31 60	99.85 43	99.76 82	99.82 28	99.63 101	99.52 130	98.38 137	99.76 91	99.82 115	98.53 82	99.95 76	98.61 200	99.81 120	99.77 100

RE-MVS-def			99.34 50		99.76 82	99.82 28	99.63 101	99.52 130	98.38 137	99.76 91	99.82 115	98.75 60		98.61 200	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	99.39 279	98.91 82	99.78 81	99.85 81	99.36 2	99.94 92	98.84 165	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		96.22 390	96.03 391	96.79 426	97.31 454	94.14 439	99.63 101	99.08 374	96.17 392	97.04 429	99.06 397	93.94 310	97.76 456	86.96 468	95.06 402	98.47 406

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	99.82 115	98.86 43	99.95 76	98.62 197	99.81 120	99.78 98

test0726					99.85 31	99.89 6	99.62 106	99.50 175	99.10 48	99.86 53	99.82 115	98.94 34

EPNet		98.86 180	98.71 187	99.30 200	97.20 456	98.18 277	99.62 106	98.91 401	99.28 31	98.63 360	99.81 130	95.96 203	99.99 4	99.24 99	99.72 148	99.73 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 170	98.67 191	99.72 86	99.85 31	99.53 101	99.62 106	99.59 73	92.65 450	99.71 108	99.78 172	98.06 110	99.90 148	98.84 165	99.91 46	99.74 114

HY-MVS	97.30 7	98.85 189	98.64 198	99.47 162	99.42 259	99.08 168	99.62 106	99.36 298	97.39 295	99.28 236	99.68 231	96.44 183	99.92 123	98.37 232	98.22 291	99.40 262

ACMMP		99.45 46	99.32 54	99.82 57	99.89 8	99.67 68	99.62 106	99.69 22	98.12 187	99.63 143	99.84 96	98.73 66	99.96 41	98.55 215	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	99.63 143	99.95 3	95.82 213	99.94 92	99.37 75	99.97 9	99.73 123
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	99.45 244	99.01 64	99.89 40	99.82 115	99.01 20	99.92 123	99.56 49	99.95 23	99.85 46

reproduce_monomvs		97.89 291	97.87 273	97.96 374	99.51 225	95.45 408	99.60 113	99.25 349	99.17 36	98.85 327	99.49 308	89.29 405	99.64 302	99.35 76	96.31 369	98.78 323

test2506		96.81 380	96.65 376	97.29 411	99.74 100	92.21 457	99.60 113	85.06 488	99.13 41	99.77 85	99.93 10	87.82 427	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	99.48 200	99.08 56	99.91 31	99.81 130	99.20 9	99.96 41	98.91 149	99.85 94	99.79 92

OPU-MVS				99.64 101	99.56 204	99.72 56	99.60 113				99.70 212	99.27 7	99.42 338	98.24 245	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	97.97 220	99.63 143	99.68 231	98.52 83	99.95 76	98.38 230	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	99.45 244	99.01 64	99.90 34	99.83 102	98.98 26	99.93 110	99.59 45	99.95 23	99.86 42

ACMH	97.28 8	98.10 256	97.99 258	98.44 331	99.41 264	96.96 353	99.60 113	99.56 90	98.09 193	98.15 393	99.91 26	90.87 387	99.70 282	98.88 152	97.45 338	98.67 361
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VortexMVS		98.67 210	98.66 194	98.68 297	99.62 174	97.96 292	99.59 120	99.41 269	98.13 183	99.31 228	99.70 212	95.48 229	99.27 365	99.40 71	97.32 347	98.79 321

guyue		99.16 111	99.04 114	99.52 139	99.69 127	98.92 203	99.59 120	98.81 415	98.73 102	99.90 34	99.87 66	95.34 234	99.88 168	99.66 40	99.81 120	99.74 114

ECVR-MVS		98.04 267	98.05 252	98.00 370	99.74 100	94.37 435	99.59 120	94.98 476	99.13 41	99.66 126	99.93 10	90.67 389	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	99.59 120	99.62 51	98.21 168	99.73 97	99.79 165	98.68 70	99.96 41	98.44 225	99.77 137	99.79 92

thres100view900		97.76 315	97.45 323	98.69 296	99.72 111	97.86 301	99.59 120	98.74 425	97.93 223	99.26 247	98.62 429	91.75 368	99.83 214	93.22 435	98.18 296	98.37 418

thres600view7		97.86 296	97.51 314	98.92 252	99.72 111	97.95 295	99.59 120	98.74 425	97.94 222	99.27 242	98.62 429	91.75 368	99.86 181	93.73 429	98.19 295	98.96 313

LCM-MVSNet-Re		97.83 304	98.15 238	96.87 424	99.30 296	92.25 456	99.59 120	98.26 446	97.43 290	96.20 439	99.13 390	96.27 191	98.73 436	98.17 251	98.99 235	99.64 178

baseline1		98.31 236	97.95 263	99.38 183	99.50 237	98.74 231	99.59 120	98.93 393	98.41 135	99.14 271	99.60 268	94.59 279	99.79 241	98.48 219	93.29 431	99.61 188

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 21	99.82 53	99.81 33	99.59 120	99.51 152	98.62 112	99.79 76	99.83 102	99.28 6	99.97 29	98.48 219	99.90 57	99.84 53
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 137	98.90 156	99.74 80	99.80 63	99.46 114	99.59 120	99.49 188	97.03 330	99.63 143	99.69 223	97.27 133	99.96 41	97.82 285	99.84 102	99.81 79

IMVS_0403		98.86 180	98.89 160	98.78 286	99.55 208	96.93 354	99.58 130	99.44 253	98.05 205	99.68 115	99.80 148	96.81 161	99.80 235	98.15 254	98.92 240	99.60 191

test_fmvsmvis_n_1920		99.65 8	99.61 7	99.77 74	99.38 274	99.37 123	99.58 130	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		95.02 411	96.12 388	91.72 449	99.10 350	80.43 477	99.58 130	97.87 456	97.47 282	95.22 446	98.82 420	93.99 308	95.18 474	88.09 462	94.91 407	99.56 212

test_fmvsm_n_1920		99.69 5	99.66 4	99.78 71	99.84 38	99.44 116	99.58 130	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		98.04 267	98.11 243	97.83 386	99.74 100	93.82 441	99.58 130	95.40 475	99.12 46	99.65 135	99.93 10	90.73 388	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	99.58 130	99.65 39	97.84 236	99.71 108	99.80 148	99.12 15	99.97 29	98.33 237	99.87 79	99.83 63

LPG-MVS_test		98.22 242	98.13 241	98.49 318	99.33 287	97.05 340	99.58 130	99.55 100	97.46 283	99.24 249	99.83 102	92.58 348	99.72 269	98.09 259	97.51 331	98.68 353

PHI-MVS		99.30 83	99.17 91	99.70 87	99.56 204	99.52 105	99.58 130	99.80 11	97.12 318	99.62 147	99.73 201	98.58 78	99.90 148	98.61 200	99.91 46	99.68 156

fmvsm_s_conf0.5_n_11		99.32 79	99.16 92	99.80 64	99.83 47	99.70 60	99.57 138	99.56 90	99.45 11	99.99 2	99.93 10	94.18 301	99.99 4	99.96 13	99.98 4	99.73 123

AstraMVS		99.09 143	99.03 117	99.25 210	99.66 148	98.13 281	99.57 138	98.24 448	98.82 89	99.91 31	99.88 53	95.81 214	99.90 148	99.72 32	99.67 158	99.74 114

SF-MVS		99.38 67	99.24 78	99.79 68	99.79 68	99.68 64	99.57 138	99.54 109	97.82 242	99.71 108	99.80 148	98.95 32	99.93 110	98.19 248	99.84 102	99.74 114

DVP-MVS		99.57 20	99.47 24	99.88 15	99.85 31	99.89 6	99.57 138	99.37 296	99.10 48	99.81 69	99.80 148	98.94 34	99.96 41	98.93 146	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	99.57 138	99.51 152					99.96 41	98.93 146	99.86 87	99.88 35

Effi-MVS+-dtu		98.78 198	98.89 160	98.47 325	99.33 287	96.91 359	99.57 138	99.30 337	98.47 127	99.41 201	98.99 406	96.78 163	99.74 259	98.73 182	99.38 183	98.74 335

v2v482		98.06 261	97.77 283	98.92 252	98.90 385	98.82 224	99.57 138	99.36 298	96.65 354	99.19 263	99.35 352	94.20 298	99.25 369	97.72 300	94.97 404	98.69 348

DSMNet-mixed		97.25 367	97.35 340	96.95 421	97.84 444	93.61 448	99.57 138	96.63 470	96.13 397	98.87 322	98.61 431	94.59 279	97.70 457	95.08 411	98.86 248	99.55 213

FE-MVSNET		94.07 422	93.36 427	96.22 433	94.05 475	94.71 427	99.56 146	98.36 444	93.15 444	93.76 456	97.55 458	86.47 435	96.49 469	87.48 465	89.83 457	97.48 458

reproduce_model		99.63 9	99.54 13	99.90 8	99.78 70	99.88 10	99.56 146	99.55 100	99.15 38	99.90 34	99.90 33	99.00 24	99.97 29	99.11 118	99.91 46	99.86 42

MVStest1		96.08 396	95.48 401	97.89 380	98.93 380	96.70 367	99.56 146	99.35 305	92.69 449	91.81 466	99.46 321	89.90 398	98.96 423	95.00 413	92.61 441	98.00 441

fmvsm_l_conf0.5_n_a		99.71 2	99.67 1	99.85 43	99.86 25	99.61 85	99.56 146	99.63 46	99.48 3	99.98 13	99.83 102	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	99.56 146	99.63 46	99.47 4	99.98 13	99.82 115	98.75 60	99.99 4	99.97 2	99.97 9	99.94 17

sd_testset		98.75 203	98.57 211	99.29 203	99.81 57	98.26 274	99.56 146	99.62 51	98.78 98	99.64 140	99.88 53	92.02 362	99.88 168	99.54 51	98.26 288	99.72 133

KD-MVS_self_test		95.00 412	94.34 417	96.96 420	97.07 459	95.39 411	99.56 146	99.44 253	95.11 415	97.13 427	97.32 463	91.86 366	97.27 463	90.35 454	81.23 472	98.23 426

ETV-MVS		99.26 92	99.21 84	99.40 177	99.46 249	99.30 138	99.56 146	99.52 130	98.52 122	99.44 190	99.27 374	98.41 93	99.86 181	99.10 121	99.59 168	99.04 303

SMA-MVS		99.44 50	99.30 62	99.85 43	99.73 107	99.83 22	99.56 146	99.47 222	97.45 286	99.78 81	99.82 115	99.18 12	99.91 135	98.79 176	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		98.87 177	98.72 185	99.31 195	99.86 25	98.48 263	99.56 146	99.61 60	97.85 233	99.36 218	99.85 81	95.95 204	99.85 187	96.66 373	99.83 113	99.59 202

casdiffmvs		99.13 123	98.98 137	99.56 122	99.65 156	99.16 155	99.56 146	99.50 175	98.33 145	99.41 201	99.86 74	95.92 207	99.83 214	99.45 68	99.16 204	99.70 147
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		98.38 231	98.09 247	99.24 213	99.26 308	99.32 131	99.56 146	99.55 100	97.45 286	98.71 342	99.83 102	93.23 327	99.63 308	98.88 152	96.32 368	98.76 329

ACMH+	97.24 10	97.92 287	97.78 281	98.32 343	99.46 249	96.68 371	99.56 146	99.54 109	98.41 135	97.79 411	99.87 66	90.18 396	99.66 293	98.05 267	97.18 353	98.62 383

ACMM	97.58 5	98.37 233	98.34 226	98.48 320	99.41 264	97.10 334	99.56 146	99.45 244	98.53 121	99.04 293	99.85 81	93.00 332	99.71 275	98.74 180	97.45 338	98.64 374
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	99.18 329	99.75 51	99.56 146	99.57 85	98.45 130	99.49 180	99.85 81	97.77 118	99.94 92	98.33 237	99.84 102	99.52 222

testing3-2		97.84 301	97.70 293	98.24 352	99.53 216	95.37 412	99.55 161	98.67 435	98.46 128	99.27 242	99.34 356	86.58 433	99.83 214	99.32 84	98.63 261	99.52 222

test_fmvsmconf0.01_n		99.22 100	99.03 117	99.79 68	98.42 436	99.48 111	99.55 161	99.51 152	99.39 22	99.78 81	99.93 10	94.80 261	99.95 76	99.93 23	99.95 23	99.94 17

test_fmvs1		98.88 174	98.79 178	99.16 221	99.69 127	97.61 314	99.55 161	99.49 188	99.32 29	99.98 13	99.91 26	91.41 378	99.96 41	99.82 29	99.92 39	99.90 25

v144192		97.92 287	97.60 305	98.87 269	98.83 397	98.65 239	99.55 161	99.34 310	96.20 389	99.32 227	99.40 336	94.36 291	99.26 368	96.37 384	95.03 403	98.70 344

API-MVS		99.04 155	99.03 117	99.06 231	99.40 269	99.31 135	99.55 161	99.56 90	98.54 120	99.33 226	99.39 340	98.76 57	99.78 247	96.98 355	99.78 134	98.07 434

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 28	99.54 166	99.66 32	99.46 7	99.98 13	99.89 42	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	99.52 222	99.62 83	99.54 166	99.62 51	98.69 107	99.99 2	99.96 1	94.47 288	99.94 92	99.88 26	99.92 39	99.98 2

APD_test1		95.87 398	96.49 380	94.00 440	99.53 216	84.01 469	99.54 166	99.32 328	95.91 405	97.99 400	99.85 81	85.49 442	99.88 168	91.96 447	98.84 250	98.12 431

thisisatest0530		98.35 234	98.03 254	99.31 195	99.63 165	98.56 249	99.54 166	96.75 468	97.53 277	99.73 97	99.65 244	91.25 383	99.89 163	98.62 197	99.56 170	99.48 239

MTMP							99.54 166	98.88 406

v1144		97.98 278	97.69 294	98.85 275	98.87 390	98.66 238	99.54 166	99.35 305	96.27 384	99.23 253	99.35 352	94.67 274	99.23 372	96.73 368	95.16 400	98.68 353

v148		97.79 313	97.55 307	98.50 317	98.74 411	97.72 307	99.54 166	99.33 318	96.26 385	98.90 316	99.51 302	94.68 273	99.14 389	97.83 284	93.15 435	98.63 381

CostFormer		97.72 325	97.73 290	97.71 394	99.15 343	94.02 440	99.54 166	99.02 384	94.67 427	99.04 293	99.35 352	92.35 358	99.77 249	98.50 218	97.94 306	99.34 272

MVSTER		98.49 219	98.32 228	99.00 239	99.35 281	99.02 175	99.54 166	99.38 287	97.41 293	99.20 260	99.73 201	93.86 315	99.36 349	98.87 155	97.56 326	98.62 383

fmvsm_s_conf0.5_n_10		99.41 59	99.24 78	99.92 1	99.83 47	99.84 20	99.53 175	99.56 90	99.45 11	99.99 2	99.92 18	94.92 254	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	99.53 175	99.62 51	98.74 101	99.99 2	99.95 3	94.53 286	99.94 92	99.89 25	99.96 17	99.97 4

E4		99.13 123	99.01 130	99.49 152	99.68 132	98.90 207	99.52 177	99.52 130	98.13 183	99.71 108	99.90 33	96.32 188	99.84 196	99.21 102	99.11 218	99.75 109

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 177	99.54 109	99.13 41	99.89 40	99.89 42	98.96 27	99.96 41	99.04 128	99.90 57	99.85 46

our_new_method		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 177	99.54 109	99.13 41	99.89 40	99.89 42	98.96 27	99.96 41	99.04 128	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	99.52 177	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	99.67 135	99.31 135	99.52 177	98.87 408	99.55 1	99.74 95	99.80 148	96.47 180	99.98 20	99.97 2	99.97 9	99.94 17

patch_mono-2		99.26 92	99.62 6	98.16 357	99.81 57	94.59 431	99.52 177	99.64 42	99.33 28	99.73 97	99.90 33	99.00 24	99.99 4	99.69 35	99.98 4	99.89 29

Fast-Effi-MVS+-dtu		98.77 202	98.83 174	98.60 302	99.41 264	96.99 349	99.52 177	99.49 188	98.11 189	99.24 249	99.34 356	96.96 152	99.79 241	97.95 273	99.45 179	99.02 306

Fast-Effi-MVS+		98.70 207	98.43 220	99.51 144	99.51 225	99.28 141	99.52 177	99.47 222	96.11 398	99.01 296	99.34 356	96.20 193	99.84 196	97.88 277	98.82 252	99.39 263

v1921920		97.80 311	97.45 323	98.84 276	98.80 399	98.53 252	99.52 177	99.34 310	96.15 395	99.24 249	99.47 317	93.98 309	99.29 361	95.40 405	95.13 401	98.69 348

MIMVSNet1		95.51 404	95.04 409	96.92 423	97.38 451	95.60 401	99.52 177	99.50 175	93.65 437	96.97 431	99.17 385	85.28 445	96.56 468	88.36 461	95.55 392	98.60 395

FE-MVSNET2		95.10 410	94.44 416	97.08 417	95.08 470	95.97 394	99.51 187	99.37 296	95.02 419	94.10 453	97.57 457	86.18 437	97.66 459	93.28 434	89.86 456	97.61 452

viewmacassd2359aftdt		99.08 145	98.94 148	99.50 149	99.66 148	98.96 187	99.51 187	99.54 109	98.27 152	99.42 196	99.89 42	95.88 211	99.80 235	99.20 103	99.11 218	99.76 107

SSM_0407		99.13 123	99.03 117	99.43 173	99.62 174	98.88 208	99.51 187	99.50 175	98.14 180	99.37 212	99.85 81	96.85 155	99.83 214	99.19 104	99.25 197	99.60 191

fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 11	99.83 47	99.74 54	99.51 187	99.62 51	99.46 7	99.99 2	99.90 33	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	99.51 187	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		97.32 364	96.81 372	98.87 269	99.40 269	97.46 318	99.51 187	99.53 125	95.86 406	98.54 370	99.77 181	82.44 458	99.66 293	98.68 190	97.52 330	99.50 235

alignmvs		98.81 193	98.56 213	99.58 116	99.43 257	99.42 118	99.51 187	98.96 391	98.61 113	99.35 221	98.92 416	94.78 263	99.77 249	99.35 76	98.11 301	99.54 215

v1192		97.81 309	97.44 328	98.91 256	98.88 387	98.68 236	99.51 187	99.34 310	96.18 391	99.20 260	99.34 356	94.03 307	99.36 349	95.32 407	95.18 399	98.69 348

test20.03		96.12 394	95.96 393	96.63 427	97.44 450	95.45 408	99.51 187	99.38 287	96.55 365	96.16 440	99.25 377	93.76 319	96.17 470	87.35 467	94.22 417	98.27 422

mvs_anonymous		99.03 157	98.99 134	99.16 221	99.38 274	98.52 256	99.51 187	99.38 287	97.79 243	99.38 210	99.81 130	97.30 131	99.45 327	99.35 76	98.99 235	99.51 231

TAMVS		99.12 131	99.08 105	99.24 213	99.46 249	98.55 250	99.51 187	99.46 233	98.09 193	99.45 185	99.82 115	98.34 97	99.51 321	98.70 185	98.93 238	99.67 160

viewdifsd2359ckpt13		99.06 150	98.93 150	99.45 165	99.63 165	98.96 187	99.50 198	99.51 152	97.83 237	99.28 236	99.80 148	96.68 169	99.71 275	99.05 127	99.12 216	99.68 156

viewdifsd2359ckpt11		98.78 198	98.74 183	98.89 262	99.67 135	97.04 343	99.50 198	99.58 78	98.26 155	99.56 163	99.90 33	94.36 291	99.87 175	99.49 61	98.32 284	99.77 100

viewmsd2359difaftdt		98.78 198	98.74 183	98.90 258	99.67 135	97.04 343	99.50 198	99.58 78	98.26 155	99.56 163	99.90 33	94.36 291	99.87 175	99.49 61	98.32 284	99.77 100

IMVS_0407		98.86 180	98.91 154	98.72 291	99.55 208	96.93 354	99.50 198	99.44 253	98.05 205	99.66 126	99.80 148	97.13 139	99.65 298	98.15 254	98.92 240	99.60 191

viewmanbaseed2359cas		99.18 104	99.07 109	99.50 149	99.62 174	99.01 177	99.50 198	99.52 130	98.25 160	99.68 115	99.82 115	96.93 153	99.80 235	99.15 114	99.11 218	99.70 147

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 225	99.67 68	99.50 198	99.64 42	99.43 17	99.98 13	99.78 172	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	99.44 256	99.65 75	99.50 198	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		98.86 180	98.63 199	99.54 125	99.49 239	99.18 152	99.50 198	99.07 377	98.22 166	99.61 152	99.51 302	95.37 232	99.84 196	98.60 203	98.33 280	99.59 202

DCV-MVSNet		98.86 180	98.63 199	99.54 125	99.49 239	99.18 152	99.50 198	99.07 377	98.22 166	99.61 152	99.51 302	95.37 232	99.84 196	98.60 203	98.33 280	99.59 202

tfpn200view9		97.72 325	97.38 336	98.72 291	99.69 127	97.96 292	99.50 198	98.73 431	97.83 237	99.17 268	98.45 436	91.67 372	99.83 214	93.22 435	98.18 296	98.37 418

UA-Net		99.42 55	99.29 66	99.80 64	99.62 174	99.55 96	99.50 198	99.70 18	98.79 95	99.77 85	99.96 1	97.45 124	99.96 41	98.92 148	99.90 57	99.89 29

pm-mvs1		97.68 333	97.28 352	98.88 265	99.06 359	98.62 244	99.50 198	99.45 244	96.32 380	97.87 407	99.79 165	92.47 352	99.35 352	97.54 317	93.54 428	98.67 361

EI-MVSNet		98.67 210	98.67 191	98.68 297	99.35 281	97.97 290	99.50 198	99.38 287	96.93 339	99.20 260	99.83 102	97.87 114	99.36 349	98.38 230	97.56 326	98.71 339

CVMVSNet		98.57 217	98.67 191	98.30 345	99.35 281	95.59 402	99.50 198	99.55 100	98.60 115	99.39 208	99.83 102	94.48 287	99.45 327	98.75 179	98.56 268	99.85 46

VPA-MVSNet		98.29 239	97.95 263	99.30 200	99.16 339	99.54 98	99.50 198	99.58 78	98.27 152	99.35 221	99.37 346	92.53 350	99.65 298	99.35 76	94.46 412	98.72 337

thres400		97.77 314	97.38 336	98.92 252	99.69 127	97.96 292	99.50 198	98.73 431	97.83 237	99.17 268	98.45 436	91.67 372	99.83 214	93.22 435	98.18 296	98.96 313

APD-MVS		99.27 89	99.08 105	99.84 55	99.75 92	99.79 41	99.50 198	99.50 175	97.16 314	99.77 85	99.82 115	98.78 53	99.94 92	97.56 315	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	99.49 152	99.65 156	98.93 202	99.49 215	99.52 130	98.14 180	99.72 102	99.88 53	96.57 176	99.84 196	99.17 110	99.13 211	99.72 133

E3		99.15 115	99.03 117	99.49 152	99.62 174	98.91 204	99.49 215	99.52 130	98.13 183	99.72 102	99.88 53	96.61 171	99.84 196	99.17 110	99.13 211	99.72 133

SSM_0404		99.16 111	99.06 110	99.44 170	99.65 156	98.96 187	99.49 215	99.50 175	98.14 180	99.62 147	99.85 81	96.85 155	99.85 187	99.19 104	99.26 196	99.52 222

fmvsm_s_conf0.5_n_4		99.36 72	99.24 78	99.73 83	99.78 70	99.53 101	99.49 215	99.60 67	99.42 20	99.99 2	99.86 74	95.15 244	99.95 76	99.95 16	99.89 68	99.73 123

test_vis1_rt		95.81 400	95.65 399	96.32 432	99.67 135	91.35 460	99.49 215	96.74 469	98.25 160	95.24 445	98.10 451	74.96 467	99.90 148	99.53 53	98.85 249	97.70 451

TransMVSNet (Re)		97.15 371	96.58 377	98.86 272	99.12 345	98.85 216	99.49 215	98.91 401	95.48 410	97.16 426	99.80 148	93.38 323	99.11 398	94.16 425	91.73 445	98.62 383

UniMVSNet (Re)		98.29 239	98.00 257	99.13 226	99.00 369	99.36 126	99.49 215	99.51 152	97.95 221	98.97 305	99.13 390	96.30 190	99.38 342	98.36 234	93.34 430	98.66 370

EPMVS		97.82 307	97.65 298	98.35 340	98.88 387	95.98 393	99.49 215	94.71 478	97.57 270	99.26 247	99.48 314	92.46 355	99.71 275	97.87 279	99.08 227	99.35 269

viewcassd2359sk11		99.18 104	99.08 105	99.49 152	99.65 156	98.95 193	99.48 223	99.51 152	98.10 192	99.72 102	99.87 66	97.13 139	99.84 196	99.13 115	99.14 208	99.69 150

fmvsm_s_conf0.5_n_9		99.41 59	99.28 69	99.81 60	99.84 38	99.52 105	99.48 223	99.62 51	99.46 7	99.99 2	99.92 18	95.24 241	99.96 41	99.97 2	99.97 9	99.96 7

SSC-MVS3.2		97.34 362	97.15 359	97.93 376	99.02 366	95.76 399	99.48 223	99.58 78	97.62 265	99.09 282	99.53 294	87.95 423	99.27 365	96.42 380	95.66 388	98.75 331

fmvsm_s_conf0.5_n_3		99.37 68	99.20 86	99.87 21	99.75 92	99.70 60	99.48 223	99.66 32	99.45 11	99.99 2	99.93 10	94.64 278	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	99.48 223	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		97.88 292	97.54 310	98.90 258	99.71 117	98.53 252	99.48 223	99.57 85	94.16 432	98.81 331	99.68 231	93.23 327	99.42 338	98.84 165	94.42 414	98.76 329

v1240		97.69 330	97.32 347	98.79 284	98.85 394	98.43 267	99.48 223	99.36 298	96.11 398	99.27 242	99.36 349	93.76 319	99.24 371	94.46 419	95.23 398	98.70 344

VPNet		97.84 301	97.44 328	99.01 237	99.21 321	98.94 197	99.48 223	99.57 85	98.38 137	99.28 236	99.73 201	88.89 408	99.39 340	99.19 104	93.27 432	98.71 339

UniMVSNet_NR-MVSNet		98.22 242	97.97 260	98.96 244	98.92 382	98.98 180	99.48 223	99.53 125	97.76 247	98.71 342	99.46 321	96.43 184	99.22 376	98.57 209	92.87 438	98.69 348

TDRefinement		95.42 406	94.57 414	97.97 372	89.83 481	96.11 392	99.48 223	98.75 422	96.74 347	96.68 434	99.88 53	88.65 414	99.71 275	98.37 232	82.74 470	98.09 433

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 28	99.47 233	99.63 46	99.45 11	99.98 13	99.89 42	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	99.47 233	99.48 200	98.05 205	99.76 91	99.86 74	98.82 48	99.93 110	98.82 175	99.91 46	99.84 53

NR-MVSNet		97.97 281	97.61 304	99.02 236	98.87 390	99.26 144	99.47 233	99.42 266	97.63 263	97.08 428	99.50 305	95.07 247	99.13 392	97.86 280	93.59 427	98.68 353

PVSNet_Blended_VisFu		99.36 72	99.28 69	99.61 109	99.86 25	99.07 170	99.47 233	99.93 2	97.66 261	99.71 108	99.86 74	97.73 119	99.96 41	99.47 66	99.82 117	99.79 92

E3new		99.18 104	99.08 105	99.48 156	99.63 165	98.94 197	99.46 237	99.50 175	98.06 202	99.72 102	99.84 96	97.27 133	99.84 196	99.10 121	99.13 211	99.67 160

LuminaMVS		99.23 98	99.10 99	99.61 109	99.35 281	99.31 135	99.46 237	99.13 368	98.61 113	99.86 53	99.89 42	96.41 186	99.91 135	99.67 37	99.51 174	99.63 183

fmvsm_s_conf0.1_n_2		99.37 68	99.22 83	99.81 60	99.77 78	99.75 51	99.46 237	99.60 67	99.47 4	99.98 13	99.94 6	94.98 248	99.95 76	99.97 2	99.79 132	99.73 123

SD-MVS		99.41 59	99.52 14	99.05 233	99.74 100	99.68 64	99.46 237	99.52 130	99.11 47	99.88 43	99.91 26	99.43 1	97.70 457	98.72 183	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		99.11 137	99.00 133	99.43 173	99.63 165	98.73 232	99.45 241	99.54 109	98.33 145	99.62 147	99.81 130	96.17 194	99.87 175	99.27 95	99.14 208	99.69 150

testing3		97.28 365	96.76 374	98.82 278	99.37 277	98.07 285	99.45 241	99.36 298	97.56 272	97.89 406	98.95 411	83.70 452	98.82 431	96.03 388	98.56 268	99.58 206

tt0805		97.97 281	97.77 283	98.57 307	99.59 193	96.61 374	99.45 241	99.08 374	98.21 168	98.88 319	99.80 148	88.66 413	99.70 282	98.58 206	97.72 316	99.39 263

tpm2		97.44 357	97.34 343	97.74 393	99.15 343	94.36 436	99.45 241	98.94 392	93.45 441	98.90 316	99.44 324	91.35 380	99.59 312	97.31 334	98.07 302	99.29 276

FMVSNet2		97.72 325	97.36 338	98.80 283	99.51 225	98.84 218	99.45 241	99.42 266	96.49 368	98.86 326	99.29 369	90.26 392	98.98 414	96.44 379	96.56 362	98.58 397

CDS-MVSNet		99.09 143	99.03 117	99.25 210	99.42 259	98.73 232	99.45 241	99.46 233	98.11 189	99.46 184	99.77 181	98.01 112	99.37 345	98.70 185	98.92 240	99.66 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 180	98.63 199	99.54 125	99.37 277	99.66 71	99.45 241	99.54 109	96.61 359	99.01 296	99.40 336	97.09 143	99.86 181	97.68 305	99.53 173	99.10 291
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		99.01 162	98.87 164	99.40 177	99.62 174	98.79 227	99.44 248	99.51 152	97.76 247	99.35 221	99.69 223	96.42 185	99.75 256	98.97 140	99.11 218	99.66 165

fmvsm_s_conf0.5_n_2		99.32 79	99.13 95	99.89 11	99.80 63	99.77 48	99.44 248	99.58 78	99.47 4	99.99 2	99.93 10	94.04 306	99.96 41	99.96 13	99.93 33	99.93 22

UGNet		98.87 177	98.69 189	99.40 177	99.22 320	98.72 234	99.44 248	99.68 24	99.24 32	99.18 267	99.42 328	92.74 340	99.96 41	99.34 81	99.94 31	99.53 221
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		98.86 180	98.63 199	99.54 125	99.64 161	99.19 150	99.44 248	99.54 109	97.77 246	99.30 232	99.81 130	94.20 298	99.93 110	99.17 110	98.82 252	99.49 236

test_0402		96.64 383	96.24 385	97.85 383	98.85 394	96.43 380	99.44 248	99.26 347	93.52 438	96.98 430	99.52 298	88.52 417	99.20 383	92.58 445	97.50 333	97.93 446

ACMP	97.20 11	98.06 261	97.94 265	98.45 328	99.37 277	97.01 347	99.44 248	99.49 188	97.54 276	98.45 375	99.79 165	91.95 364	99.72 269	97.91 275	97.49 336	98.62 383
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 328	98.55 431	98.16 278	99.43 254	93.68 480		97.23 422	98.46 435	89.30 404	99.22 376	95.43 404	98.22 291	97.98 443

HPM-MVS++		99.39 66	99.23 82	99.87 21	99.75 92	99.84 20	99.43 254	99.51 152	98.68 109	99.27 242	99.53 294	98.64 75	99.96 41	98.44 225	99.80 125	99.79 92

tpm cat1		97.39 359	97.36 338	97.50 405	99.17 337	93.73 443	99.43 254	99.31 332	91.27 454	98.71 342	99.08 394	94.31 296	99.77 249	96.41 382	98.50 272	99.00 307

tpm		97.67 336	97.55 307	98.03 365	99.02 366	95.01 420	99.43 254	98.54 441	96.44 374	99.12 274	99.34 356	91.83 367	99.60 311	97.75 296	96.46 364	99.48 239

GBi-Net		97.68 333	97.48 317	98.29 346	99.51 225	97.26 327	99.43 254	99.48 200	96.49 368	99.07 285	99.32 364	90.26 392	98.98 414	97.10 347	96.65 359	98.62 383

test1		97.68 333	97.48 317	98.29 346	99.51 225	97.26 327	99.43 254	99.48 200	96.49 368	99.07 285	99.32 364	90.26 392	98.98 414	97.10 347	96.65 359	98.62 383

FMVSNet1		96.84 379	96.36 383	98.29 346	99.32 294	97.26 327	99.43 254	99.48 200	95.11 415	98.55 369	99.32 364	83.95 451	98.98 414	95.81 393	96.26 370	98.62 383

fmvsm_s_conf0.5_n_7		99.34 75	99.29 66	99.48 156	99.70 122	98.63 242	99.42 261	99.63 46	99.46 7	99.98 13	99.88 53	95.59 224	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	99.42 261	99.61 60	99.37 24	99.97 25	99.86 74	94.96 249	99.99 4	99.97 2	99.93 33	99.92 23

mamv4		99.33 77	99.42 32	99.07 229	99.67 135	97.73 305	99.42 261	99.60 67	98.15 175	99.94 28	99.91 26	98.42 91	99.94 92	99.72 32	99.96 17	99.54 215

testgi		97.65 338	97.50 315	98.13 361	99.36 280	96.45 379	99.42 261	99.48 200	97.76 247	97.87 407	99.45 323	91.09 384	98.81 432	94.53 418	98.52 271	99.13 290

F-COLMAP		99.19 101	99.04 114	99.64 101	99.78 70	99.27 143	99.42 261	99.54 109	97.29 303	99.41 201	99.59 270	98.42 91	99.93 110	98.19 248	99.69 153	99.73 123

Anonymous202405211		98.30 238	97.98 259	99.26 209	99.57 200	98.16 278	99.41 266	98.55 440	96.03 403	99.19 263	99.74 195	91.87 365	99.92 123	99.16 113	98.29 287	99.70 147

MSLP-MVS++		99.46 42	99.47 24	99.44 170	99.60 191	99.16 155	99.41 266	99.71 16	98.98 72	99.45 185	99.78 172	99.19 11	99.54 319	99.28 92	99.84 102	99.63 183

VNet		99.11 137	98.90 156	99.73 83	99.52 222	99.56 94	99.41 266	99.39 279	99.01 64	99.74 95	99.78 172	95.56 225	99.92 123	99.52 55	98.18 296	99.72 133

baseline2		97.87 294	97.55 307	98.82 278	99.18 329	98.02 287	99.41 266	96.58 472	96.97 333	96.51 435	99.17 385	93.43 322	99.57 314	97.71 301	99.03 231	98.86 317

DU-MVS		98.08 259	97.79 278	98.96 244	98.87 390	98.98 180	99.41 266	99.45 244	97.87 229	98.71 342	99.50 305	94.82 259	99.22 376	98.57 209	92.87 438	98.68 353

Baseline_NR-MVSNet		97.76 315	97.45 323	98.68 297	99.09 353	98.29 272	99.41 266	98.85 410	95.65 408	98.63 360	99.67 237	94.82 259	99.10 400	98.07 266	92.89 437	98.64 374

XVG-ACMP-BASELINE		97.83 304	97.71 292	98.20 354	99.11 347	96.33 383	99.41 266	99.52 130	98.06 202	99.05 292	99.50 305	89.64 402	99.73 265	97.73 298	97.38 345	98.53 400

DP-MVS		99.16 111	98.95 146	99.78 71	99.77 78	99.53 101	99.41 266	99.50 175	97.03 330	99.04 293	99.88 53	97.39 125	99.92 123	98.66 192	99.90 57	99.87 40

9.14			99.10 99		99.72 111		99.40 274	99.51 152	97.53 277	99.64 140	99.78 172	98.84 46	99.91 135	97.63 306	99.82 117

D2MVS		98.41 227	98.50 217	98.15 360	99.26 308	96.62 373	99.40 274	99.61 60	97.71 253	98.98 303	99.36 349	96.04 199	99.67 290	98.70 185	97.41 343	98.15 430

Anonymous20240529		98.09 257	97.68 295	99.34 187	99.66 148	98.44 266	99.40 274	99.43 264	93.67 436	99.22 254	99.89 42	90.23 395	99.93 110	99.26 98	98.33 280	99.66 165

FMVSNet3		98.03 269	97.76 287	98.84 276	99.39 272	98.98 180	99.40 274	99.38 287	96.67 352	99.07 285	99.28 371	92.93 333	98.98 414	97.10 347	96.65 359	98.56 399

LFMVS		97.90 290	97.35 340	99.54 125	99.52 222	99.01 177	99.39 278	98.24 448	97.10 322	99.65 135	99.79 165	84.79 447	99.91 135	99.28 92	98.38 277	99.69 150

HQP_MVS		98.27 241	98.22 234	98.44 331	99.29 300	96.97 351	99.39 278	99.47 222	98.97 75	99.11 276	99.61 265	92.71 343	99.69 287	97.78 290	97.63 319	98.67 361

plane_prior2							99.39 278		98.97 75

CHOSEN 1792x2688		99.19 101	99.10 99	99.45 165	99.89 8	98.52 256	99.39 278	99.94 1	98.73 102	99.11 276	99.89 42	95.50 227	99.94 92	99.50 57	99.97 9	99.89 29

PAPM_NR		99.04 155	98.84 172	99.66 91	99.74 100	99.44 116	99.39 278	99.38 287	97.70 256	99.28 236	99.28 371	98.34 97	99.85 187	96.96 357	99.45 179	99.69 150

gg-mvs-nofinetune		96.17 393	95.32 405	98.73 289	98.79 400	98.14 280	99.38 283	94.09 479	91.07 457	98.07 398	91.04 477	89.62 403	99.35 352	96.75 367	99.09 226	98.68 353

VDDNet		97.55 344	97.02 366	99.16 221	99.49 239	98.12 283	99.38 283	99.30 337	95.35 411	99.68 115	99.90 33	82.62 457	99.93 110	99.31 86	98.13 300	99.42 257

ME-MVS		99.56 21	99.46 28	99.86 34	99.80 63	99.81 33	99.37 285	99.70 18	99.18 34	99.83 64	99.83 102	98.74 65	99.93 110	98.83 168	99.89 68	99.83 63

MGCNet		99.15 115	98.96 142	99.73 83	98.92 382	99.37 123	99.37 285	96.92 465	99.51 2	99.66 126	99.78 172	96.69 167	99.97 29	99.84 28	99.97 9	99.84 53

pmmvs6		96.53 385	96.09 390	97.82 388	98.69 418	95.47 407	99.37 285	99.47 222	93.46 440	97.41 416	99.78 172	87.06 431	99.33 355	96.92 362	92.70 440	98.65 372

PM-MVS		92.96 428	92.23 432	95.14 437	95.61 465	89.98 463	99.37 285	98.21 450	94.80 425	95.04 450	97.69 455	65.06 472	97.90 453	94.30 420	89.98 455	97.54 457

WTY-MVS		99.06 150	98.88 163	99.61 109	99.62 174	99.16 155	99.37 285	99.56 90	98.04 212	99.53 172	99.62 261	96.84 159	99.94 92	98.85 162	98.49 273	99.72 133

IterMVS-LS		98.46 222	98.42 221	98.58 306	99.59 193	98.00 288	99.37 285	99.43 264	96.94 338	99.07 285	99.59 270	97.87 114	99.03 407	98.32 239	95.62 389	98.71 339
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 329	97.28 352	98.97 243	99.70 122	97.27 325	99.36 291	99.45 244	98.94 78	99.66 126	99.64 250	94.93 252	99.99 4	99.48 64	84.36 467	99.65 171

DPE-MVS		99.46 42	99.32 54	99.91 6	99.78 70	99.88 10	99.36 291	99.51 152	98.73 102	99.88 43	99.84 96	98.72 67	99.96 41	98.16 252	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		96.44 387	96.12 388	97.40 408	98.65 421	95.65 400	99.36 291	99.51 152	97.13 316	96.04 442	98.99 406	88.40 418	98.17 446	96.71 369	90.27 453	98.40 415

sss		99.17 109	99.05 112	99.53 133	99.62 174	98.97 183	99.36 291	99.62 51	97.83 237	99.67 121	99.65 244	97.37 128	99.95 76	99.19 104	99.19 203	99.68 156

DeepC-MVS_fast	98.69 1	99.49 33	99.39 40	99.77 74	99.63 165	99.59 88	99.36 291	99.46 233	99.07 58	99.79 76	99.82 115	98.85 44	99.92 123	98.68 190	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	99.41 264	99.16 155	99.35 296	99.57 85	98.82 89	99.51 176	99.61 265	96.46 181	99.95 76	99.59 45	99.98 4	99.65 171

pmmvs-eth3d		95.34 408	94.73 411	97.15 412	95.53 467	95.94 395	99.35 296	99.10 371	95.13 413	93.55 457	97.54 459	88.15 422	97.91 452	94.58 417	89.69 458	97.61 452

MDTV_nov1_ep13_2view						95.18 417	99.35 296		96.84 343	99.58 159		95.19 243		97.82 285		99.46 250

FE-MVSNET1		93.64 424	92.69 430	96.48 430	94.12 474	94.21 438	99.34 299	99.38 287	93.42 442	93.33 459	97.58 456	74.82 469	97.65 460	92.56 446	89.64 459	97.58 455

VDD-MVS		97.73 323	97.35 340	98.88 265	99.47 247	97.12 333	99.34 299	98.85 410	98.19 170	99.67 121	99.85 81	82.98 455	99.92 123	99.49 61	98.32 284	99.60 191

COLMAP_ROB	97.56 6	98.86 180	98.75 181	99.17 220	99.88 13	98.53 252	99.34 299	99.59 73	97.55 273	98.70 348	99.89 42	95.83 212	99.90 148	98.10 258	99.90 57	99.08 296
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
viewmambaseed2359dif		99.01 162	98.90 156	99.32 193	99.58 195	98.51 258	99.33 302	99.54 109	97.85 233	99.44 190	99.85 81	96.01 201	99.79 241	99.41 70	99.13 211	99.67 160

myMVS_eth3d28		97.69 330	97.34 343	98.73 289	99.27 305	97.52 316	99.33 302	98.78 420	98.03 214	98.82 330	98.49 434	86.64 432	99.46 325	98.44 225	98.24 290	99.23 284

EGC-MVSNET		82.80 441	77.86 447	97.62 398	97.91 442	96.12 391	99.33 302	99.28 343	8.40 485	25.05 486	99.27 374	84.11 450	99.33 355	89.20 457	98.22 291	97.42 459

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 156	99.64 161	98.85 216	99.32 305	99.48 200	98.50 124	99.81 69	99.81 130	96.82 160	99.88 168	99.40 71	99.12 216	99.71 144

ETVMVS		97.50 350	96.90 370	99.29 203	99.23 316	98.78 230	99.32 305	98.90 403	97.52 279	98.56 368	98.09 452	84.72 448	99.69 287	97.86 280	97.88 309	99.39 263

FMVSNet5		96.43 388	96.19 387	97.15 412	99.11 347	95.89 396	99.32 305	99.52 130	94.47 431	98.34 381	99.07 395	87.54 428	97.07 464	92.61 444	95.72 386	98.47 406

dp		97.75 319	97.80 277	97.59 402	99.10 350	93.71 444	99.32 305	98.88 406	96.48 371	99.08 284	99.55 285	92.67 346	99.82 223	96.52 377	98.58 265	99.24 283

tpmvs		97.98 278	98.02 256	97.84 385	99.04 364	94.73 425	99.31 309	99.20 359	96.10 402	98.76 338	99.42 328	94.94 251	99.81 228	96.97 356	98.45 274	98.97 311

tpmrst		98.33 235	98.48 218	97.90 379	99.16 339	94.78 424	99.31 309	99.11 370	97.27 304	99.45 185	99.59 270	95.33 235	99.84 196	98.48 219	98.61 262	99.09 295

testing99		97.36 360	96.94 369	98.63 300	99.18 329	96.70 367	99.30 311	98.93 393	97.71 253	98.23 387	98.26 444	84.92 446	99.84 196	98.04 268	97.85 312	99.35 269

MP-MVS-pluss		99.37 68	99.20 86	99.88 15	99.90 4	99.87 17	99.30 311	99.52 130	97.18 312	99.60 155	99.79 165	98.79 52	99.95 76	98.83 168	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	99.61 185	99.65 75	99.30 311	99.48 200	98.86 84	99.21 257	99.63 256	98.72 67	99.90 148	98.25 244	99.63 164	99.80 88

JIA-IIPM		97.50 350	97.02 366	98.93 250	98.73 412	97.80 303	99.30 311	98.97 389	91.73 453	98.91 314	94.86 471	95.10 246	99.71 275	97.58 310	97.98 304	99.28 277

BH-RMVSNet		98.41 227	98.08 248	99.40 177	99.41 264	98.83 221	99.30 311	98.77 421	97.70 256	98.94 311	99.65 244	92.91 336	99.74 259	96.52 377	99.55 172	99.64 178

testing11		97.50 350	97.10 363	98.71 294	99.20 323	96.91 359	99.29 316	98.82 413	97.89 227	98.21 390	98.40 438	85.63 441	99.83 214	98.45 224	98.04 303	99.37 267

Syy-MVS		97.09 374	97.14 360	96.95 421	99.00 369	92.73 454	99.29 316	99.39 279	97.06 326	97.41 416	98.15 447	93.92 312	98.68 437	91.71 448	98.34 278	99.45 253

myMVS_eth3d		96.89 377	96.37 382	98.43 333	99.00 369	97.16 331	99.29 316	99.39 279	97.06 326	97.41 416	98.15 447	83.46 454	98.68 437	95.27 408	98.34 278	99.45 253

MCST-MVS		99.43 53	99.30 62	99.82 57	99.79 68	99.74 54	99.29 316	99.40 276	98.79 95	99.52 174	99.62 261	98.91 39	99.90 148	98.64 194	99.75 142	99.82 72

LF4IMVS		97.52 347	97.46 322	97.70 395	98.98 375	95.55 403	99.29 316	98.82 413	98.07 198	98.66 351	99.64 250	89.97 397	99.61 310	97.01 352	96.68 358	97.94 445

hse-mvs2		97.50 350	97.14 360	98.59 303	99.49 239	97.05 340	99.28 321	99.22 355	98.94 78	99.66 126	99.42 328	94.93 252	99.65 298	99.48 64	83.80 469	99.08 296

OPM-MVS		98.19 246	98.10 244	98.45 328	98.88 387	97.07 338	99.28 321	99.38 287	98.57 117	99.22 254	99.81 130	92.12 360	99.66 293	98.08 263	97.54 328	98.61 392
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs		99.14 121	99.02 125	99.51 144	99.61 185	98.96 187	99.28 321	99.49 188	98.46 128	99.72 102	99.71 208	96.50 179	99.88 168	99.31 86	99.11 218	99.67 160
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		98.86 180	98.80 175	99.03 235	99.76 82	98.79 227	99.28 321	99.91 3	97.42 292	99.67 121	99.37 346	97.53 122	99.88 168	98.98 135	97.29 348	98.42 412

OMC-MVS		99.08 145	99.04 114	99.20 217	99.67 135	98.22 276	99.28 321	99.52 130	98.07 198	99.66 126	99.81 130	97.79 117	99.78 247	97.79 289	99.81 120	99.60 191

testing222		97.16 370	96.50 379	99.16 221	99.16 339	98.47 265	99.27 326	98.66 436	97.71 253	98.23 387	98.15 447	82.28 460	99.84 196	97.36 332	97.66 318	99.18 287

AUN-MVS		96.88 378	96.31 384	98.59 303	99.48 246	97.04 343	99.27 326	99.22 355	97.44 289	98.51 371	99.41 332	91.97 363	99.66 293	97.71 301	83.83 468	99.07 301

pmmvs5		97.52 347	97.30 349	98.16 357	98.57 430	96.73 366	99.27 326	98.90 403	96.14 396	98.37 379	99.53 294	91.54 377	99.14 389	97.51 319	95.87 381	98.63 381

1314		98.68 209	98.54 214	99.11 227	98.89 386	98.65 239	99.27 326	99.49 188	96.89 340	97.99 400	99.56 282	97.72 120	99.83 214	97.74 297	99.27 194	98.84 319

MVS		97.28 365	96.55 378	99.48 156	98.78 403	98.95 193	99.27 326	99.39 279	83.53 471	98.08 395	99.54 290	96.97 151	99.87 175	94.23 423	99.16 204	99.63 183

BH-untuned		98.42 225	98.36 224	98.59 303	99.49 239	96.70 367	99.27 326	99.13 368	97.24 308	98.80 333	99.38 343	95.75 218	99.74 259	97.07 351	99.16 204	99.33 273

MDTV_nov1_ep13			98.32 228		99.11 347	94.44 433	99.27 326	98.74 425	97.51 280	99.40 206	99.62 261	94.78 263	99.76 253	97.59 309	98.81 254

DP-MVS Recon		99.12 131	98.95 146	99.65 95	99.74 100	99.70 60	99.27 326	99.57 85	96.40 378	99.42 196	99.68 231	98.75 60	99.80 235	97.98 271	99.72 148	99.44 255

PatchmatchNet		98.31 236	98.36 224	98.19 355	99.16 339	95.32 413	99.27 326	98.92 396	97.37 296	99.37 212	99.58 274	94.90 256	99.70 282	97.43 328	99.21 201	99.54 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 341	97.28 352	98.62 301	99.64 161	98.03 286	99.26 335	98.74 425	97.68 258	99.09 282	98.32 442	91.66 374	99.81 228	92.88 440	98.22 291	98.03 437

CNVR-MVS		99.42 55	99.30 62	99.78 71	99.62 174	99.71 58	99.26 335	99.52 130	98.82 89	99.39 208	99.71 208	98.96 27	99.85 187	98.59 205	99.80 125	99.77 100

mamba_0408		99.08 145	98.96 142	99.44 170	99.62 174	98.88 208	99.25 337	99.47 222	98.05 205	99.37 212	99.81 130	96.85 155	99.85 187	98.98 135	99.25 197	99.60 191

SSM_04072		99.06 150	98.96 142	99.35 186	99.62 174	98.88 208	99.25 337	99.47 222	98.05 205	99.37 212	99.81 130	96.85 155	99.58 313	98.98 135	99.25 197	99.60 191

tt0320		95.71 403	95.07 407	97.62 398	99.05 362	95.02 419	99.25 337	99.52 130	86.81 466	97.97 402	99.72 205	83.58 453	99.15 387	96.38 383	93.35 429	98.68 353

1112_ss		98.98 166	98.77 179	99.59 113	99.68 132	99.02 175	99.25 337	99.48 200	97.23 309	99.13 272	99.58 274	96.93 153	99.90 148	98.87 155	98.78 255	99.84 53

TAPA-MVS	97.07 15	97.74 321	97.34 343	98.94 248	99.70 122	97.53 315	99.25 337	99.51 152	91.90 452	99.30 232	99.63 256	98.78 53	99.64 302	88.09 462	99.87 79	99.65 171
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UWE-MVS-28		97.36 360	97.24 356	97.75 391	98.84 396	94.44 433	99.24 342	97.58 461	97.98 219	99.00 300	99.00 404	91.35 380	99.53 320	93.75 428	98.39 276	99.27 281

UBG		97.85 297	97.48 317	98.95 246	99.25 312	97.64 312	99.24 342	98.74 425	97.90 226	98.64 358	98.20 446	88.65 414	99.81 228	98.27 242	98.40 275	99.42 257

PLC	97.94 4	99.02 158	98.85 170	99.53 133	99.66 148	99.01 177	99.24 342	99.52 130	96.85 342	99.27 242	99.48 314	98.25 101	99.91 135	97.76 294	99.62 165	99.65 171
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post1							99.23 345				65.14 483	94.18 301	99.71 275	97.58 310

ADS-MVSNet2		98.02 271	98.07 251	97.87 381	99.33 287	95.19 416	99.23 345	99.08 374	96.24 386	99.10 279	99.67 237	94.11 303	98.93 426	96.81 365	99.05 229	99.48 239

ADS-MVSNet		98.20 245	98.08 248	98.56 311	99.33 287	96.48 378	99.23 345	99.15 365	96.24 386	99.10 279	99.67 237	94.11 303	99.71 275	96.81 365	99.05 229	99.48 239

EPNet_dtu		98.03 269	97.96 261	98.23 353	98.27 438	95.54 405	99.23 345	98.75 422	99.02 62	97.82 409	99.71 208	96.11 196	99.48 322	93.04 438	99.65 161	99.69 150
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 249	97.93 266	98.87 269	99.18 329	98.49 261	99.22 349	99.33 318	96.96 334	99.56 163	99.38 343	94.33 294	99.00 412	94.83 416	98.58 265	99.14 288

RPMNet		96.72 381	95.90 394	99.19 218	99.18 329	98.49 261	99.22 349	99.52 130	88.72 464	99.56 163	97.38 461	94.08 305	99.95 76	86.87 469	98.58 265	99.14 288

sc_t1		95.75 401	95.05 408	97.87 381	98.83 397	94.61 430	99.21 351	99.45 244	87.45 465	97.97 402	99.85 81	81.19 463	99.43 336	98.27 242	93.20 433	99.57 209

WBMVS		97.74 321	97.50 315	98.46 326	99.24 314	97.43 319	99.21 351	99.42 266	97.45 286	98.96 307	99.41 332	88.83 409	99.23 372	98.94 143	96.02 374	98.71 339

plane_prior						96.97 351	99.21 351		98.45 130						97.60 322

IMVS_0404		98.53 218	98.52 216	98.55 313	99.55 208	96.93 354	99.20 354	99.44 253	98.05 205	98.96 307	99.80 148	94.66 276	99.13 392	98.15 254	98.92 240	99.60 191

tt0320-xc		95.31 409	94.59 413	97.45 406	98.92 382	94.73 425	99.20 354	99.31 332	86.74 467	97.23 422	99.72 205	81.14 464	98.95 424	97.08 350	91.98 444	98.67 361

testing91		97.44 357	97.02 366	98.71 294	99.18 329	96.89 361	99.19 356	99.04 381	97.78 245	98.31 382	98.29 443	85.41 443	99.85 187	98.01 269	97.95 305	99.39 263

WR-MVS		98.06 261	97.73 290	99.06 231	98.86 393	99.25 146	99.19 356	99.35 305	97.30 302	98.66 351	99.43 326	93.94 310	99.21 381	98.58 206	94.28 416	98.71 339

new-patchmatchnet		94.48 418	94.08 419	95.67 436	95.08 470	92.41 455	99.18 358	99.28 343	94.55 430	93.49 458	97.37 462	87.86 426	97.01 465	91.57 449	88.36 461	97.61 452

AdaColmap		99.01 162	98.80 175	99.66 91	99.56 204	99.54 98	99.18 358	99.70 18	98.18 173	99.35 221	99.63 256	96.32 188	99.90 148	97.48 322	99.77 137	99.55 213

EG-PatchMatch MVS		95.97 397	95.69 398	96.81 425	97.78 445	92.79 453	99.16 360	98.93 393	96.16 393	94.08 454	99.22 380	82.72 456	99.47 323	95.67 399	97.50 333	98.17 428

PatchT		97.03 375	96.44 381	98.79 284	98.99 372	98.34 271	99.16 360	99.07 377	92.13 451	99.52 174	97.31 464	94.54 284	98.98 414	88.54 460	98.73 257	99.03 304

CNLPA		99.14 121	98.99 134	99.59 113	99.58 195	99.41 120	99.16 360	99.44 253	98.45 130	99.19 263	99.49 308	98.08 109	99.89 163	97.73 298	99.75 142	99.48 239

MDA-MVSNet-bldmvs		94.96 413	93.98 420	97.92 377	98.24 439	97.27 325	99.15 363	99.33 318	93.80 435	80.09 478	99.03 400	88.31 419	97.86 454	93.49 432	94.36 415	98.62 383

CDPH-MVS		99.13 123	98.91 154	99.80 64	99.75 92	99.71 58	99.15 363	99.41 269	96.60 362	99.60 155	99.55 285	98.83 47	99.90 148	97.48 322	99.83 113	99.78 98

save fliter					99.76 82	99.59 88	99.14 365	99.40 276	99.00 67

WB-MVSnew		97.65 338	97.65 298	97.63 397	98.78 403	97.62 313	99.13 366	98.33 445	97.36 297	99.07 285	98.94 412	95.64 223	99.15 387	92.95 439	98.68 260	96.12 469

testf1		90.42 435	90.68 436	89.65 456	97.78 445	73.97 484	99.13 366	98.81 415	89.62 459	91.80 467	98.93 413	62.23 475	98.80 433	86.61 470	91.17 447	96.19 467

APD_test2		90.42 435	90.68 436	89.65 456	97.78 445	73.97 484	99.13 366	98.81 415	89.62 459	91.80 467	98.93 413	62.23 475	98.80 433	86.61 470	91.17 447	96.19 467

xiu_mvs_v1_base_debu		99.29 85	99.27 73	99.34 187	99.63 165	98.97 183	99.12 369	99.51 152	98.86 84	99.84 56	99.47 317	98.18 104	99.99 4	99.50 57	99.31 191	99.08 296

xiu_mvs_v1_base		99.29 85	99.27 73	99.34 187	99.63 165	98.97 183	99.12 369	99.51 152	98.86 84	99.84 56	99.47 317	98.18 104	99.99 4	99.50 57	99.31 191	99.08 296

xiu_mvs_v1_base_debi		99.29 85	99.27 73	99.34 187	99.63 165	98.97 183	99.12 369	99.51 152	98.86 84	99.84 56	99.47 317	98.18 104	99.99 4	99.50 57	99.31 191	99.08 296

XVG-OURS-SEG-HR		98.69 208	98.62 204	98.89 262	99.71 117	97.74 304	99.12 369	99.54 109	98.44 133	99.42 196	99.71 208	94.20 298	99.92 123	98.54 216	98.90 246	99.00 307

jason		99.13 123	99.03 117	99.45 165	99.46 249	98.87 212	99.12 369	99.26 347	98.03 214	99.79 76	99.65 244	97.02 148	99.85 187	99.02 132	99.90 57	99.65 171
jason: jason.
N_pmnet		94.95 414	95.83 396	92.31 447	98.47 434	79.33 479	99.12 369	92.81 485	93.87 434	97.68 412	99.13 390	93.87 314	99.01 411	91.38 450	96.19 371	98.59 396

MDA-MVSNet_test_wron		95.45 405	94.60 412	98.01 368	98.16 440	97.21 330	99.11 375	99.24 352	93.49 439	80.73 477	98.98 408	93.02 331	98.18 445	94.22 424	94.45 413	98.64 374

Patchmtry		97.75 319	97.40 335	98.81 281	99.10 350	98.87 212	99.11 375	99.33 318	94.83 424	98.81 331	99.38 343	94.33 294	99.02 409	96.10 386	95.57 391	98.53 400

YYNet1		95.36 407	94.51 415	97.92 377	97.89 443	97.10 334	99.10 377	99.23 353	93.26 443	80.77 476	99.04 399	92.81 337	98.02 449	94.30 420	94.18 418	98.64 374

CANet_DTU		98.97 168	98.87 164	99.25 210	99.33 287	98.42 269	99.08 378	99.30 337	99.16 37	99.43 193	99.75 190	95.27 237	99.97 29	98.56 212	99.95 23	99.36 268

icg_test_0407_2		98.79 197	98.86 167	98.57 307	99.55 208	96.93 354	99.07 379	99.44 253	98.05 205	99.66 126	99.80 148	97.13 139	99.18 384	98.15 254	98.92 240	99.60 191

SCA		98.19 246	98.16 236	98.27 351	99.30 296	95.55 403	99.07 379	98.97 389	97.57 270	99.43 193	99.57 279	92.72 341	99.74 259	97.58 310	99.20 202	99.52 222

TSAR-MVS + GP.		99.36 72	99.36 46	99.36 184	99.67 135	98.61 246	99.07 379	99.33 318	99.00 67	99.82 68	99.81 130	99.06 18	99.84 196	99.09 123	99.42 181	99.65 171

MG-MVS		99.13 123	99.02 125	99.45 165	99.57 200	98.63 242	99.07 379	99.34 310	98.99 69	99.61 152	99.82 115	97.98 113	99.87 175	97.00 353	99.80 125	99.85 46

PatchMatch-RL		98.84 192	98.62 204	99.52 139	99.71 117	99.28 141	99.06 383	99.77 12	97.74 251	99.50 177	99.53 294	95.41 230	99.84 196	97.17 346	99.64 162	99.44 255

OpenMVS_ROB	92.34 20	94.38 419	93.70 425	96.41 431	97.38 451	93.17 451	99.06 383	98.75 422	86.58 468	94.84 451	98.26 444	81.53 461	99.32 357	89.01 458	97.87 310	96.76 462

TEST9					99.67 135	99.65 75	99.05 385	99.41 269	96.22 388	98.95 309	99.49 308	98.77 56	99.91 135

train_agg		99.02 158	98.77 179	99.77 74	99.67 135	99.65 75	99.05 385	99.41 269	96.28 382	98.95 309	99.49 308	98.76 57	99.91 135	97.63 306	99.72 148	99.75 109

lupinMVS		99.13 123	99.01 130	99.46 164	99.51 225	98.94 197	99.05 385	99.16 364	97.86 230	99.80 74	99.56 282	97.39 125	99.86 181	98.94 143	99.85 94	99.58 206

DELS-MVS		99.48 37	99.42 32	99.65 95	99.72 111	99.40 121	99.05 385	99.66 32	99.14 40	99.57 162	99.80 148	98.46 87	99.94 92	99.57 48	99.84 102	99.60 191
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		96.38 389	96.03 391	97.41 407	98.13 441	95.16 418	99.05 385	99.20 359	93.94 433	97.39 419	98.79 424	91.61 376	99.04 405	90.43 453	95.77 383	98.05 436

Patchmatch-test		97.93 284	97.65 298	98.77 287	99.18 329	97.07 338	99.03 390	99.14 367	96.16 393	98.74 339	99.57 279	94.56 281	99.72 269	93.36 433	99.11 218	99.52 222

test_8					99.67 135	99.61 85	99.03 390	99.41 269	96.28 382	98.93 312	99.48 314	98.76 57	99.91 135

Test_1112_low_res		98.89 173	98.66 194	99.57 120	99.69 127	98.95 193	99.03 390	99.47 222	96.98 332	99.15 270	99.23 379	96.77 164	99.89 163	98.83 168	98.78 255	99.86 42

IterMVS-SCA-FT		97.82 307	97.75 288	98.06 364	99.57 200	96.36 382	99.02 393	99.49 188	97.18 312	98.71 342	99.72 205	92.72 341	99.14 389	97.44 327	95.86 382	98.67 361

xiu_mvs_v2_base		99.26 92	99.25 77	99.29 203	99.53 216	98.91 204	99.02 393	99.45 244	98.80 94	99.71 108	99.26 376	98.94 34	99.98 20	99.34 81	99.23 200	98.98 310

MIMVSNet		97.73 323	97.45 323	98.57 307	99.45 255	97.50 317	99.02 393	98.98 388	96.11 398	99.41 201	99.14 389	90.28 391	98.74 435	95.74 395	98.93 238	99.47 245

IterMVS		97.83 304	97.77 283	98.02 367	99.58 195	96.27 386	99.02 393	99.48 200	97.22 310	98.71 342	99.70 212	92.75 338	99.13 392	97.46 325	96.00 376	98.67 361
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 137	98.92 151	99.65 95	99.90 4	99.37 123	99.02 393	99.91 3	97.67 260	99.59 158	99.75 190	95.90 209	99.73 265	99.53 53	99.02 233	99.86 42

UWE-MVS		97.58 343	97.29 351	98.48 320	99.09 353	96.25 387	99.01 398	96.61 471	97.86 230	99.19 263	99.01 403	88.72 410	99.90 148	97.38 331	98.69 259	99.28 277

新几何2							99.01 398

BH-w/o		98.00 276	97.89 272	98.32 343	99.35 281	96.20 389	99.01 398	98.90 403	96.42 376	98.38 378	99.00 404	95.26 239	99.72 269	96.06 387	98.61 262	99.03 304

test_prior4						99.56 94	98.99 401

无先验							98.99 401	99.51 152	96.89 340				99.93 110	97.53 318		99.72 133

pmmvs4		98.13 253	97.90 268	98.81 281	98.61 426	98.87 212	98.99 401	99.21 358	96.44 374	99.06 290	99.58 274	95.90 209	99.11 398	97.18 345	96.11 373	98.46 409

HQP-NCC					99.19 326		98.98 404		98.24 162	98.66 351

ACMP_Plane					99.19 326		98.98 404		98.24 162	98.66 351

HQP-MVS		98.02 271	97.90 268	98.37 339	99.19 326	96.83 362	98.98 404	99.39 279	98.24 162	98.66 351	99.40 336	92.47 352	99.64 302	97.19 343	97.58 324	98.64 374

PS-MVSNAJ		99.32 79	99.32 54	99.30 200	99.57 200	98.94 197	98.97 407	99.46 233	98.92 81	99.71 108	99.24 378	99.01 20	99.98 20	99.35 76	99.66 159	98.97 311

MVP-Stereo		97.81 309	97.75 288	97.99 371	97.53 449	96.60 375	98.96 408	98.85 410	97.22 310	97.23 422	99.36 349	95.28 236	99.46 325	95.51 401	99.78 134	97.92 447
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							98.96 408		98.34 143	99.01 296	99.52 298	98.68 70		97.96 272	99.74 145

旧先验2							98.96 408		96.70 350	99.47 182			99.94 92	98.19 248

原ACMM2							98.95 411

MVS_111021_HR		99.41 59	99.32 54	99.66 91	99.72 111	99.47 113	98.95 411	99.85 9	98.82 89	99.54 170	99.73 201	98.51 84	99.74 259	98.91 149	99.88 76	99.77 100

mvsany_test1		99.50 31	99.46 28	99.62 108	99.61 185	99.09 165	98.94 413	99.48 200	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	98.94 413	99.85 9	98.82 89	99.65 135	99.74 195	98.51 84	99.80 235	98.83 168	99.89 68	99.64 178

pmmvs3		94.09 421	93.25 428	96.60 428	94.76 473	94.49 432	98.92 415	98.18 452	89.66 458	96.48 436	98.06 453	86.28 436	97.33 462	89.68 456	87.20 464	97.97 444

XVG-OURS		98.73 206	98.68 190	98.88 265	99.70 122	97.73 305	98.92 415	99.55 100	98.52 122	99.45 185	99.84 96	95.27 237	99.91 135	98.08 263	98.84 250	99.00 307

test222					99.75 92	99.49 109	98.91 417	99.49 188	96.42 376	99.34 225	99.65 244	98.28 100			99.69 153	99.72 133

PMMVS2		86.87 438	85.37 442	91.35 451	90.21 480	83.80 470	98.89 418	97.45 463	83.13 472	91.67 469	95.03 469	48.49 481	94.70 475	85.86 472	77.62 474	95.54 470

miper_lstm_enhance		98.00 276	97.91 267	98.28 350	99.34 286	97.43 319	98.88 419	99.36 298	96.48 371	98.80 333	99.55 285	95.98 202	98.91 427	97.27 336	95.50 394	98.51 402

MVS-HIRNet		95.75 401	95.16 406	97.51 404	99.30 296	93.69 445	98.88 419	95.78 473	85.09 470	98.78 336	92.65 473	91.29 382	99.37 345	94.85 415	99.85 94	99.46 250

TR-MVS		97.76 315	97.41 334	98.82 278	99.06 359	97.87 299	98.87 421	98.56 439	96.63 358	98.68 350	99.22 380	92.49 351	99.65 298	95.40 405	97.79 314	98.95 315

testdata1							98.85 422		98.32 147

ET-MVSNet_ETH3D		96.49 386	95.64 400	99.05 233	99.53 216	98.82 224	98.84 423	97.51 462	97.63 263	84.77 471	99.21 383	92.09 361	98.91 427	98.98 135	92.21 443	99.41 260

our_test_3		97.65 338	97.68 295	97.55 403	98.62 424	94.97 421	98.84 423	99.30 337	96.83 345	98.19 391	99.34 356	97.01 150	99.02 409	95.00 413	96.01 375	98.64 374

MS-PatchMatch		97.24 369	97.32 347	96.99 418	98.45 435	93.51 449	98.82 425	99.32 328	97.41 293	98.13 394	99.30 367	88.99 407	99.56 316	95.68 398	99.80 125	97.90 448

c3_l		98.12 255	98.04 253	98.38 338	99.30 296	97.69 311	98.81 426	99.33 318	96.67 352	98.83 328	99.34 356	97.11 142	98.99 413	97.58 310	95.34 396	98.48 404

ppachtmachnet_test		97.49 355	97.45 323	97.61 401	98.62 424	95.24 414	98.80 427	99.46 233	96.11 398	98.22 389	99.62 261	96.45 182	98.97 421	93.77 427	95.97 380	98.61 392

PAPR		98.63 215	98.34 226	99.51 144	99.40 269	99.03 174	98.80 427	99.36 298	96.33 379	99.00 300	99.12 393	98.46 87	99.84 196	95.23 409	99.37 190	99.66 165

test0.0.03 1		97.71 328	97.42 333	98.56 311	98.41 437	97.82 302	98.78 429	98.63 437	97.34 298	98.05 399	98.98 408	94.45 289	98.98 414	95.04 412	97.15 354	98.89 316

PVSNet_Blended		99.08 145	98.97 138	99.42 175	99.76 82	98.79 227	98.78 429	99.91 3	96.74 347	99.67 121	99.49 308	97.53 122	99.88 168	98.98 135	99.85 94	99.60 191

PMMVS		98.80 196	98.62 204	99.34 187	99.27 305	98.70 235	98.76 431	99.31 332	97.34 298	99.21 257	99.07 395	97.20 137	99.82 223	98.56 212	98.87 247	99.52 222

test123		39.01 450	42.50 452	28.53 466	39.17 489	20.91 491	98.75 432	19.17 491	19.83 484	38.57 483	66.67 481	33.16 485	15.42 485	37.50 485	29.66 483	49.26 480

MSDG		98.98 166	98.80 175	99.53 133	99.76 82	99.19 150	98.75 432	99.55 100	97.25 306	99.47 182	99.77 181	97.82 116	99.87 175	96.93 360	99.90 57	99.54 215

CLD-MVS		98.16 250	98.10 244	98.33 341	99.29 300	96.82 364	98.75 432	99.44 253	97.83 237	99.13 272	99.55 285	92.92 334	99.67 290	98.32 239	97.69 317	98.48 404
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		98.18 248	98.10 244	98.41 334	99.23 316	97.72 307	98.72 435	99.31 332	96.60 362	98.88 319	99.29 369	97.29 132	99.13 392	97.60 308	95.99 377	98.38 417

cl____		98.01 274	97.84 276	98.55 313	99.25 312	97.97 290	98.71 436	99.34 310	96.47 373	98.59 367	99.54 290	95.65 222	99.21 381	97.21 339	95.77 383	98.46 409

DIV-MVS_self_test		98.01 274	97.85 275	98.48 320	99.24 314	97.95 295	98.71 436	99.35 305	96.50 367	98.60 366	99.54 290	95.72 220	99.03 407	97.21 339	95.77 383	98.46 409

test-LLR		98.06 261	97.90 268	98.55 313	98.79 400	97.10 334	98.67 438	97.75 457	97.34 298	98.61 364	98.85 418	94.45 289	99.45 327	97.25 337	99.38 183	99.10 291

TESTMET0.1,1		97.55 344	97.27 355	98.40 336	98.93 380	96.53 376	98.67 438	97.61 460	96.96 334	98.64 358	99.28 371	88.63 416	99.45 327	97.30 335	99.38 183	99.21 286

test-mter		97.49 355	97.13 362	98.55 313	98.79 400	97.10 334	98.67 438	97.75 457	96.65 354	98.61 364	98.85 418	88.23 420	99.45 327	97.25 337	99.38 183	99.10 291

mvs5depth		96.66 382	96.22 386	97.97 372	97.00 460	96.28 385	98.66 441	99.03 383	96.61 359	96.93 432	99.79 165	87.20 430	99.47 323	96.65 375	94.13 419	98.16 429

IB-MVS	95.67 18	96.22 390	95.44 404	98.57 307	99.21 321	96.70 367	98.65 442	97.74 459	96.71 349	97.27 421	98.54 433	86.03 438	99.92 123	98.47 222	86.30 465	99.10 291
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		98.95 169	98.71 187	99.66 91	99.63 165	99.55 96	98.64 443	99.10 371	97.93 223	99.42 196	99.55 285	98.67 72	99.80 235	95.80 394	99.68 156	99.61 188

thisisatest0515		98.14 252	97.79 278	99.19 218	99.50 237	98.50 260	98.61 444	96.82 467	96.95 336	99.54 170	99.43 326	91.66 374	99.86 181	98.08 263	99.51 174	99.22 285

DeepPCF-MVS	98.18 3	98.81 193	99.37 44	97.12 415	99.60 191	91.75 458	98.61 444	99.44 253	99.35 25	99.83 64	99.85 81	98.70 69	99.81 228	99.02 132	99.91 46	99.81 79

cl22		97.85 297	97.64 301	98.48 320	99.09 353	97.87 299	98.60 446	99.33 318	97.11 321	98.87 322	99.22 380	92.38 357	99.17 386	98.21 246	95.99 377	98.42 412

GA-MVS		97.85 297	97.47 320	99.00 239	99.38 274	97.99 289	98.57 447	99.15 365	97.04 329	98.90 316	99.30 367	89.83 399	99.38 342	96.70 370	98.33 280	99.62 186

TinyColmap		97.12 372	96.89 371	97.83 386	99.07 357	95.52 406	98.57 447	98.74 425	97.58 269	97.81 410	99.79 165	88.16 421	99.56 316	95.10 410	97.21 351	98.39 416

eth_miper_zixun_eth		98.05 266	97.96 261	98.33 341	99.26 308	97.38 321	98.56 449	99.31 332	96.65 354	98.88 319	99.52 298	96.58 174	99.12 397	97.39 330	95.53 393	98.47 406

CMPMVS	69.68 23	94.13 420	94.90 410	91.84 448	97.24 455	80.01 478	98.52 450	99.48 200	89.01 462	91.99 465	99.67 237	85.67 440	99.13 392	95.44 403	97.03 356	96.39 466
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 362	97.20 357	97.75 391	99.07 357	95.20 415	98.51 451	99.04 381	97.99 218	98.31 382	99.86 74	89.02 406	99.55 318	95.67 399	97.36 346	98.49 403

ambc				93.06 446	92.68 477	82.36 471	98.47 452	98.73 431		95.09 449	97.41 460	55.55 477	99.10 400	96.42 380	91.32 446	97.71 449

miper_enhance_ethall		98.16 250	98.08 248	98.41 334	98.96 378	97.72 307	98.45 453	99.32 328	96.95 336	98.97 305	99.17 385	97.06 146	99.22 376	97.86 280	95.99 377	98.29 421

CHOSEN 280x420		99.12 131	99.13 95	99.08 228	99.66 148	97.89 298	98.43 454	99.71 16	98.88 83	99.62 147	99.76 185	96.63 170	99.70 282	99.46 67	99.99 1	99.66 165

testmvs		39.17 449	43.78 451	25.37 467	36.04 490	16.84 492	98.36 455	26.56 489	20.06 483	38.51 484	67.32 480	29.64 486	15.30 486	37.59 484	39.90 482	43.98 481

FPMVS		84.93 440	85.65 441	82.75 462	86.77 483	63.39 488	98.35 456	98.92 396	74.11 474	83.39 473	98.98 408	50.85 480	92.40 477	84.54 473	94.97 404	92.46 472

KD-MVS_2432*1600		94.62 415	93.72 423	97.31 409	97.19 457	95.82 397	98.34 457	99.20 359	95.00 420	97.57 413	98.35 440	87.95 423	98.10 447	92.87 441	77.00 475	98.01 438

miper_refine_blended		94.62 415	93.72 423	97.31 409	97.19 457	95.82 397	98.34 457	99.20 359	95.00 420	97.57 413	98.35 440	87.95 423	98.10 447	92.87 441	77.00 475	98.01 438

CL-MVSNet_self_test		94.49 417	93.97 421	96.08 434	96.16 462	93.67 446	98.33 459	99.38 287	95.13 413	97.33 420	98.15 447	92.69 345	96.57 467	88.67 459	79.87 473	97.99 442

PVSNet	96.02 17	98.85 189	98.84 172	98.89 262	99.73 107	97.28 324	98.32 460	99.60 67	97.86 230	99.50 177	99.57 279	96.75 165	99.86 181	98.56 212	99.70 152	99.54 215

PAPM		97.59 342	97.09 364	99.07 229	99.06 359	98.26 274	98.30 461	99.10 371	94.88 422	98.08 395	99.34 356	96.27 191	99.64 302	89.87 455	98.92 240	99.31 275

Patchmatch-RL test		95.84 399	95.81 397	95.95 435	95.61 465	90.57 461	98.24 462	98.39 443	95.10 417	95.20 447	98.67 428	94.78 263	97.77 455	96.28 385	90.02 454	99.51 231

UnsupCasMVSNet_bld		93.53 425	92.51 431	96.58 429	97.38 451	93.82 441	98.24 462	99.48 200	91.10 456	93.10 460	96.66 466	74.89 468	98.37 442	94.03 426	87.71 463	97.56 456

LCM-MVSNet		86.80 439	85.22 443	91.53 450	87.81 482	80.96 476	98.23 464	98.99 387	71.05 475	90.13 470	96.51 467	48.45 482	96.88 466	90.51 452	85.30 466	96.76 462

cascas		97.69 330	97.43 332	98.48 320	98.60 427	97.30 323	98.18 465	99.39 279	92.96 446	98.41 376	98.78 425	93.77 318	99.27 365	98.16 252	98.61 262	98.86 317

kuosan		90.92 434	90.11 439	93.34 443	98.78 403	85.59 468	98.15 466	93.16 483	89.37 461	92.07 464	98.38 439	81.48 462	95.19 473	62.54 482	97.04 355	99.25 282

Effi-MVS+		98.81 193	98.59 210	99.48 156	99.46 249	99.12 163	98.08 467	99.50 175	97.50 281	99.38 210	99.41 332	96.37 187	99.81 228	99.11 118	98.54 270	99.51 231

PCF-MVS	97.08 14	97.66 337	97.06 365	99.47 162	99.61 185	99.09 165	98.04 468	99.25 349	91.24 455	98.51 371	99.70 212	94.55 283	99.91 135	92.76 443	99.85 94	99.42 257
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 395	95.47 402	97.94 375	99.31 295	94.34 437	97.81 469	99.70 18	97.12 318	97.46 415	98.75 426	89.71 400	99.79 241	97.69 304	81.69 471	99.68 156

E-PMN		80.61 443	79.88 445	82.81 461	90.75 479	76.38 482	97.69 470	95.76 474	66.44 479	83.52 472	92.25 474	62.54 474	87.16 481	68.53 480	61.40 478	84.89 479

dongtai		93.26 426	92.93 429	94.25 439	99.39 272	85.68 467	97.68 471	93.27 481	92.87 447	96.85 433	99.39 340	82.33 459	97.48 461	76.78 475	97.80 313	99.58 206

ANet_high		77.30 445	74.86 449	84.62 460	75.88 486	77.61 480	97.63 472	93.15 484	88.81 463	64.27 481	89.29 478	36.51 484	83.93 483	75.89 477	52.31 480	92.33 474

EMVS		80.02 444	79.22 446	82.43 463	91.19 478	76.40 481	97.55 473	92.49 486	66.36 480	83.01 474	91.27 476	64.63 473	85.79 482	65.82 481	60.65 479	85.08 478

MVE	76.82 21	76.91 446	74.31 450	84.70 459	85.38 485	76.05 483	96.88 474	93.17 482	67.39 478	71.28 480	89.01 479	21.66 489	87.69 480	71.74 479	72.29 477	90.35 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 432	91.36 434	90.31 453	95.85 463	73.72 486	94.89 475	99.25 349	68.39 477	95.82 443	99.02 402	80.50 465	98.95 424	93.64 430	94.89 408	98.25 424

Gipuma		90.99 433	90.15 438	93.51 442	98.73 412	90.12 462	93.98 476	99.45 244	79.32 473	92.28 463	94.91 470	69.61 470	97.98 451	87.42 466	95.67 387	92.45 473
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 447	74.97 448	79.01 464	70.98 487	55.18 489	93.37 477	98.21 450	65.08 481	61.78 482	93.83 472	21.74 488	92.53 476	78.59 474	91.12 449	89.34 477
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 441	81.52 444	86.66 458	66.61 488	68.44 487	92.79 478	97.92 454	68.96 476	80.04 479	99.85 81	85.77 439	96.15 471	97.86 280	43.89 481	95.39 471

wuyk23d		40.18 448	41.29 453	36.84 465	86.18 484	49.12 490	79.73 479	22.81 490	27.64 482	25.46 485	28.45 485	21.98 487	48.89 484	55.80 483	23.56 484	12.51 482

mmdepth		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

monomultidepth		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

test_blank		0.13 454	0.17 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	1.57 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet_test		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

DCPMVS		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

cdsmvs_eth3d_5k		24.64 451	32.85 454	0.00 468	0.00 491	0.00 493	0.00 480	99.51 152	0.00 486	0.00 487	99.56 282	96.58 174	0.00 487	0.00 486	0.00 485	0.00 483

pcd_1.5k_mvsjas		8.27 453	11.03 456	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	99.01 20	0.00 487	0.00 486	0.00 485	0.00 483

sosnet-low-res		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uncertanet		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

Regformer		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

ab-mvs-re		8.30 452	11.06 455	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	99.58 274	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet		0.02 455	0.03 458	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.27 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

WAC-MVS						97.16 331								95.47 402

MSC_two_6792asdad				99.87 21	99.51 225	99.76 49		99.33 318					99.96 41	98.87 155	99.84 102	99.89 29

PC_three_1452									98.18 173	99.84 56	99.70 212	99.31 3	98.52 440	98.30 241	99.80 125	99.81 79

No_MVS				99.87 21	99.51 225	99.76 49		99.33 318					99.96 41	98.87 155	99.84 102	99.89 29

test_one_0601					99.81 57	99.88 10		99.49 188	98.97 75	99.65 135	99.81 130	99.09 16

eth-test2					0.00 491

eth-test					0.00 491

ZD-MVS					99.71 117	99.79 41		99.61 60	96.84 343	99.56 163	99.54 290	98.58 78	99.96 41	96.93 360	99.75 142

IU-MVS					99.84 38	99.88 10		99.32 328	98.30 149	99.84 56				98.86 160	99.85 94	99.89 29

test_241102_TWO								99.48 200	99.08 56	99.88 43	99.81 130	98.94 34	99.96 41	98.91 149	99.84 102	99.88 35

test_241102_ONE					99.84 38	99.90 3		99.48 200	99.07 58	99.91 31	99.74 195	99.20 9	99.76 253

test_0728_THIRD									98.99 69	99.81 69	99.80 148	99.09 16	99.96 41	98.85 162	99.90 57	99.88 35

GSMVS																99.52 222

test_part2					99.81 57	99.83 22				99.77 85

sam_mvs1												94.86 258				99.52 222

sam_mvs												94.72 270

MTGPA								99.47 222

test_post											65.99 482	94.65 277	99.73 265

patchmatchnet-post											98.70 427	94.79 262	99.74 259

gm-plane-assit					98.54 432	92.96 452			94.65 428		99.15 388		99.64 302	97.56 315

test9_res														97.49 321	99.72 148	99.75 109

agg_prior2														97.21 339	99.73 147	99.75 109

agg_prior					99.67 135	99.62 83		99.40 276		98.87 322			99.91 135

TestCases				99.31 195	99.86 25	98.48 263		99.61 60	97.85 233	99.36 218	99.85 81	95.95 204	99.85 187	96.66 373	99.83 113	99.59 202

test_prior				99.68 89	99.67 135	99.48 111		99.56 90					99.83 214			99.74 114

新几何1				99.75 77	99.75 92	99.59 88		99.54 109	96.76 346	99.29 235	99.64 250	98.43 89	99.94 92	96.92 362	99.66 159	99.72 133

旧先验1					99.74 100	99.59 88		99.54 109			99.69 223	98.47 86			99.68 156	99.73 123

原ACMM1				99.65 95	99.73 107	99.33 130		99.47 222	97.46 283	99.12 274	99.66 242	98.67 72	99.91 135	97.70 303	99.69 153	99.71 144

testdata2													99.95 76	96.67 372

segment_acmp												98.96 27

testdata				99.54 125	99.75 92	98.95 193		99.51 152	97.07 324	99.43 193	99.70 212	98.87 42	99.94 92	97.76 294	99.64 162	99.72 133

test12				99.75 77	99.64 161	99.61 85		99.29 341		99.21 257		98.38 95	99.89 163		99.74 145	99.74 114

plane_prior7					99.29 300	97.03 346

plane_prior6					99.27 305	96.98 350						92.71 343

plane_prior5								99.47 222					99.69 287	97.78 290	97.63 319	98.67 361

plane_prior4											99.61 265

plane_prior3						97.00 348			98.69 107	99.11 276

plane_prior1					99.26 308

n2								0.00 492

nn								0.00 492

door-mid								98.05 453

lessismore_v0				97.79 390	98.69 418	95.44 410		94.75 477		95.71 444	99.87 66	88.69 412	99.32 357	95.89 391	94.93 406	98.62 383

LGP-MVS_train				98.49 318	99.33 287	97.05 340		99.55 100	97.46 283	99.24 249	99.83 102	92.58 348	99.72 269	98.09 259	97.51 331	98.68 353

test11								99.35 305

door								97.92 454

HQP5-MVS						96.83 362

BP-MVS														97.19 343

HQP4-MVS										98.66 351			99.64 302			98.64 374

HQP3-MVS								99.39 279							97.58 324

HQP2-MVS												92.47 352

NP-MVS					99.23 316	96.92 358					99.40 336

ACMMP++_ref															97.19 352

ACMMP++															97.43 342

Test By Simon												98.75 60

ITE_SJBPF				98.08 363	99.29 300	96.37 381		98.92 396	98.34 143	98.83 328	99.75 190	91.09 384	99.62 309	95.82 392	97.40 344	98.25 424

DeepMVS_CX				93.34 443	99.29 300	82.27 472		99.22 355	85.15 469	96.33 437	99.05 398	90.97 386	99.73 265	93.57 431	97.77 315	98.01 438

High-resolution multi-view benchmark