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	99.70 210	98.65 74	99.79 239	99.65 41	99.78 134	99.41 258

mmtdpeth		96.95 374	96.71 373	97.67 394	99.33 285	94.90 420	99.89 2	99.28 339	98.15 175	99.72 102	98.57 430	86.56 432	99.90 148	99.82 29	89.02 456	98.20 425

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	99.69 221	98.55 81	99.82 221	99.69 35	99.85 94	99.48 237

MVSFormer		99.17 108	99.12 97	99.29 201	99.51 223	98.94 197	99.88 4	99.46 231	97.55 271	99.80 74	99.65 242	97.39 125	99.28 360	99.03 128	99.85 94	99.65 169

test_djsdf		98.67 208	98.57 209	98.98 239	98.70 415	98.91 203	99.88 4	99.46 231	97.55 271	99.22 252	99.88 52	95.73 217	99.28 360	99.03 128	97.62 319	98.75 329

OurMVSNet-221017-0		97.88 290	97.77 281	98.19 353	98.71 414	96.53 374	99.88 4	99.00 382	97.79 241	98.78 334	99.94 6	91.68 369	99.35 350	97.21 337	96.99 355	98.69 346

EC-MVSNet		99.44 50	99.39 40	99.58 116	99.56 202	99.49 109	99.88 4	99.58 78	98.38 137	99.73 97	99.69 221	98.20 103	99.70 280	99.64 43	99.82 117	99.54 213

DVP-MVS++		99.59 15	99.50 19	99.88 15	99.51 223	99.88 10	99.87 8	99.51 151	98.99 69	99.88 43	99.81 128	99.27 7	99.96 41	98.85 160	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		97.10 371	96.79 371	98.01 366	98.72 412	96.33 381	99.87 8	97.05 460	97.59 265	96.16 438	99.80 146	88.71 409	99.04 403	96.69 369	96.55 361	98.65 370

FC-MVSNet-test		98.75 201	98.62 202	99.15 223	99.08 354	99.45 115	99.86 11	99.60 67	98.23 165	98.70 346	99.82 113	96.80 161	99.22 374	99.07 123	96.38 364	98.79 319

v7n		97.87 292	97.52 310	98.92 250	98.76 408	98.58 246	99.84 12	99.46 231	96.20 387	98.91 312	99.70 210	94.89 255	99.44 330	96.03 386	93.89 422	98.75 329

DTE-MVSNet		97.51 347	97.19 356	98.46 324	98.63 421	98.13 279	99.84 12	99.48 198	96.68 349	97.97 400	99.67 235	92.92 332	98.56 437	96.88 362	92.60 440	98.70 342

3Dnovator	97.25 9	99.24 97	99.05 111	99.81 60	99.12 343	99.66 71	99.84 12	99.74 13	99.09 55	98.92 311	99.90 33	95.94 204	99.98 20	98.95 140	99.92 39	99.79 92

FIs		98.78 196	98.63 197	99.23 213	99.18 327	99.54 98	99.83 15	99.59 73	98.28 150	98.79 333	99.81 128	96.75 164	99.37 343	99.08 122	96.38 364	98.78 321

MGCFI-Net		99.01 160	98.85 168	99.50 149	99.42 257	99.26 144	99.82 16	99.48 198	98.60 115	99.28 234	98.81 419	97.04 146	99.76 251	99.29 91	97.87 308	99.47 243

test_fmvs3		92.10 426	91.77 429	93.08 441	96.19 459	86.25 461	99.82 16	98.62 434	96.65 352	95.19 446	96.90 461	55.05 475	95.93 468	96.63 374	90.92 449	97.06 457

jajsoiax		98.43 222	98.28 229	98.88 263	98.60 425	98.43 265	99.82 16	99.53 125	98.19 170	98.63 358	99.80 146	93.22 327	99.44 330	99.22 100	97.50 331	98.77 325

OpenMVS	96.50 16	98.47 219	98.12 240	99.52 139	99.04 362	99.53 101	99.82 16	99.72 14	94.56 426	98.08 393	99.88 52	94.73 267	99.98 20	97.47 322	99.76 140	99.06 300

SDMVSNet		99.11 135	98.90 154	99.75 77	99.81 57	99.59 88	99.81 20	99.65 39	98.78 98	99.64 138	99.88 52	94.56 279	99.93 110	99.67 37	98.26 286	99.72 132

nrg030		98.64 212	98.42 219	99.28 205	99.05 360	99.69 63	99.81 20	99.46 231	98.04 210	99.01 294	99.82 113	96.69 166	99.38 340	99.34 81	94.59 409	98.78 321

HPM-MVS		99.42 55	99.28 69	99.83 56	99.90 4	99.72 56	99.81 20	99.54 109	97.59 265	99.68 113	99.63 254	98.91 39	99.94 92	98.58 204	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		99.13 122	98.99 132	99.53 133	99.65 155	99.06 171	99.81 20	99.33 314	97.43 288	99.60 153	99.88 52	97.14 137	99.84 196	99.13 114	98.94 235	99.69 149

3Dnovator+	97.12 13	99.18 104	98.97 136	99.82 57	99.17 335	99.68 64	99.81 20	99.51 151	99.20 33	98.72 339	99.89 41	95.68 219	99.97 29	98.86 158	99.86 87	99.81 79

sasdasda		99.02 156	98.86 165	99.51 144	99.42 257	99.32 131	99.80 25	99.48 198	98.63 110	99.31 226	98.81 419	97.09 142	99.75 254	99.27 95	97.90 305	99.47 243

FA-MVS(test-final)		98.75 201	98.53 213	99.41 174	99.55 206	99.05 173	99.80 25	99.01 381	96.59 362	99.58 157	99.59 268	95.39 229	99.90 148	97.78 288	99.49 177	99.28 275

GeoE		98.85 187	98.62 202	99.53 133	99.61 183	99.08 168	99.80 25	99.51 151	97.10 320	99.31 226	99.78 170	95.23 240	99.77 247	98.21 244	99.03 229	99.75 109

canonicalmvs		99.02 156	98.86 165	99.51 144	99.42 257	99.32 131	99.80 25	99.48 198	98.63 110	99.31 226	98.81 419	97.09 142	99.75 254	99.27 95	97.90 305	99.47 243

v8		97.95 281	97.63 300	98.93 248	98.95 377	98.81 224	99.80 25	99.41 267	96.03 401	99.10 277	99.42 326	94.92 252	99.30 358	96.94 357	94.08 419	98.66 368

Vis-MVSNet (Re-imp)		98.87 175	98.72 183	99.31 193	99.71 117	98.88 206	99.80 25	99.44 251	97.91 223	99.36 216	99.78 170	95.49 226	99.43 334	97.91 273	99.11 217	99.62 184

Anonymous20240521		96.20 390	95.89 393	97.13 412	97.72 446	94.96 419	99.79 31	99.29 337	93.01 441	97.20 423	99.03 398	89.69 399	98.36 441	91.16 447	96.13 370	98.07 432

PS-MVSNAJss		98.92 169	98.92 149	98.90 256	98.78 401	98.53 250	99.78 32	99.54 109	98.07 197	99.00 298	99.76 183	99.01 20	99.37 343	99.13 114	97.23 348	98.81 318

PEN-MVS		97.76 313	97.44 326	98.72 289	98.77 406	98.54 249	99.78 32	99.51 151	97.06 324	98.29 383	99.64 248	92.63 345	98.89 428	98.09 257	93.16 432	98.72 335

anonymousdsp		98.44 221	98.28 229	98.94 246	98.50 431	98.96 187	99.77 34	99.50 174	97.07 322	98.87 320	99.77 179	94.76 265	99.28 360	98.66 190	97.60 320	98.57 396

SixPastTwentyTwo		97.50 348	97.33 344	98.03 363	98.65 419	96.23 386	99.77 34	98.68 430	97.14 313	97.90 403	99.93 10	90.45 388	99.18 382	97.00 351	96.43 363	98.67 359

QAPM		98.67 208	98.30 228	99.80 64	99.20 321	99.67 68	99.77 34	99.72 14	94.74 423	98.73 338	99.90 33	95.78 215	99.98 20	96.96 355	99.88 76	99.76 107

SSC-MVS		92.73 425	93.73 419	89.72 451	95.02 469	81.38 471	99.76 37	99.23 349	94.87 420	92.80 458	98.93 411	94.71 269	91.37 475	74.49 474	93.80 423	96.42 461

test_vis3_rt		87.04 433	85.81 436	90.73 448	93.99 472	81.96 469	99.76 37	90.23 483	92.81 444	81.35 471	91.56 471	40.06 479	99.07 400	94.27 420	88.23 458	91.15 471

dcpmvs_2		99.23 98	99.58 9	98.16 355	99.83 47	94.68 425	99.76 37	99.52 130	99.07 58	99.98 13	99.88 52	98.56 80	99.93 110	99.67 37	99.98 4	99.87 40

RRT-MVS		98.91 170	98.75 179	99.39 180	99.46 247	98.61 244	99.76 37	99.50 174	98.06 201	99.81 69	99.88 52	93.91 311	99.94 92	99.11 117	99.27 194	99.61 186

HPM-MVS_fast		99.51 29	99.40 38	99.85 43	99.91 1	99.79 41	99.76 37	99.56 90	97.72 250	99.76 91	99.75 188	99.13 14	99.92 123	99.07 123	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 80	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	99.50 174	98.27 152	99.87 49	99.92 18	98.09 108	99.94 92	99.65 41	99.95 23	99.47 243

v10		97.85 295	97.52 310	98.86 270	98.99 370	98.67 235	99.75 42	99.41 267	95.70 405	98.98 301	99.41 330	94.75 266	99.23 370	96.01 388	94.63 408	98.67 359

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 80	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		99.05 152	98.87 162	99.57 120	99.73 107	99.32 131	99.75 42	99.20 355	98.02 215	99.56 161	99.86 73	96.54 176	99.67 288	98.09 257	99.13 211	99.73 122

test_vis1_n		97.92 285	97.44 326	99.34 185	99.53 214	98.08 282	99.74 47	99.49 186	99.15 38	100.00 1	99.94 6	79.51 463	99.98 20	99.88 26	99.76 140	99.97 4

test_fmvs1_n		98.41 225	98.14 237	99.21 214	99.82 53	97.71 308	99.74 47	99.49 186	99.32 29	99.99 2	99.95 3	85.32 441	99.97 29	99.82 29	99.84 102	99.96 7

balanced_conf03		99.46 42	99.39 40	99.67 90	99.55 206	99.58 93	99.74 47	99.51 151	98.42 134	99.87 49	99.84 95	98.05 111	99.91 135	99.58 47	99.94 31	99.52 220

tttt0517		98.42 223	98.14 237	99.28 205	99.66 147	98.38 268	99.74 47	96.85 462	97.68 256	99.79 76	99.74 193	91.39 377	99.89 163	98.83 166	99.56 170	99.57 207

WB-MVS		93.10 423	94.10 415	90.12 450	95.51 467	81.88 470	99.73 51	99.27 342	95.05 416	93.09 457	98.91 415	94.70 270	91.89 474	76.62 472	94.02 421	96.58 460

test_fmvs2		97.25 365	97.30 347	97.09 414	99.43 255	93.31 446	99.73 51	98.87 404	98.83 88	99.28 234	99.80 146	84.45 446	99.66 291	97.88 275	97.45 336	98.30 418

SD_0403		97.55 342	97.53 309	97.62 396	99.61 183	93.64 443	99.72 53	99.44 251	98.03 212	98.62 361	99.39 338	96.06 196	99.57 312	87.88 460	99.01 232	99.66 163

MonoMVSNet		98.38 229	98.47 217	98.12 360	98.59 427	96.19 388	99.72 53	98.79 415	97.89 225	99.44 188	99.52 296	96.13 193	98.90 427	98.64 192	97.54 326	99.28 275

baseline		99.15 114	99.02 124	99.53 133	99.66 147	99.14 160	99.72 53	99.48 198	98.35 142	99.42 194	99.84 95	96.07 195	99.79 239	99.51 56	99.14 208	99.67 159

RPSCF		98.22 240	98.62 202	96.99 415	99.82 53	91.58 455	99.72 53	99.44 251	96.61 357	99.66 124	99.89 41	95.92 205	99.82 221	97.46 323	99.10 223	99.57 207

CSCG		99.32 79	99.32 54	99.32 191	99.85 31	98.29 270	99.71 57	99.66 32	98.11 188	99.41 199	99.80 146	98.37 96	99.96 41	98.99 132	99.96 17	99.72 132

dmvs_re		98.08 257	98.16 234	97.85 381	99.55 206	94.67 426	99.70 58	98.92 392	98.15 175	99.06 288	99.35 350	93.67 319	99.25 367	97.77 291	97.25 347	99.64 176

WR-MVS_H		98.13 251	97.87 271	98.90 256	99.02 364	98.84 216	99.70 58	99.59 73	97.27 302	98.40 375	99.19 382	95.53 224	99.23 370	98.34 234	93.78 424	98.61 390

mvsmamba		99.06 148	98.96 140	99.36 182	99.47 245	98.64 239	99.70 58	99.05 376	97.61 264	99.65 133	99.83 100	96.54 176	99.92 123	99.19 103	99.62 165	99.51 229

LTVRE_ROB	97.16 12	98.02 269	97.90 266	98.40 334	99.23 314	96.80 363	99.70 58	99.60 67	97.12 316	98.18 390	99.70 210	91.73 368	99.72 267	98.39 227	97.45 336	98.68 351
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	99.83 100		99.95 76	98.83 166	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	99.83 100	99.30 4	99.95 76	98.83 166	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	99.83 100	99.30 4	99.95 76	99.32 84	99.89 68	99.90 25

TestfortrainingZip							99.69 62

test_f		91.90 427	91.26 431	93.84 437	95.52 466	85.92 462	99.69 62	98.53 438	95.31 410	93.87 452	96.37 464	55.33 474	98.27 442	95.70 394	90.98 448	97.32 456

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	99.37 210	99.74 193	98.81 49	99.94 92	98.79 174	99.86 87	99.84 53

X-MVStestdata		96.55 382	95.45 401	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 210	64.01 480	98.81 49	99.94 92	98.79 174	99.86 87	99.84 53

V42		98.06 259	97.79 276	98.86 270	98.98 373	98.84 216	99.69 62	99.34 306	96.53 364	99.30 230	99.37 344	94.67 272	99.32 355	97.57 312	94.66 407	98.42 410

mPP-MVS		99.44 50	99.30 62	99.86 34	99.88 13	99.79 41	99.69 62	99.48 198	98.12 186	99.50 175	99.75 188	98.78 53	99.97 29	98.57 207	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	98.07 197	99.53 170	99.63 254	98.93 38	99.97 29	98.74 178	99.91 46	99.83 63

FE-MVS		98.48 218	98.17 233	99.40 175	99.54 213	98.96 187	99.68 72	98.81 411	95.54 407	99.62 145	99.70 210	93.82 314	99.93 110	97.35 331	99.46 178	99.32 272

PS-CasMVS		97.93 282	97.59 304	98.95 244	98.99 370	99.06 171	99.68 72	99.52 130	97.13 314	98.31 380	99.68 229	92.44 354	99.05 402	98.51 215	94.08 419	98.75 329

Vis-MVSNet		99.12 129	98.97 136	99.56 122	99.78 70	99.10 164	99.68 72	99.66 32	98.49 125	99.86 53	99.87 65	94.77 264	99.84 196	99.19 103	99.41 182	99.74 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		99.12 129	98.92 149	99.70 87	99.67 134	99.40 121	99.67 75	99.63 46	98.73 102	99.94 28	99.81 128	94.54 282	99.96 41	98.40 226	99.93 33	99.74 113

BP-MVS1		99.12 129	98.94 146	99.65 95	99.51 223	99.30 138	99.67 75	98.92 392	98.48 126	99.84 56	99.69 221	94.96 247	99.92 123	99.62 44	99.79 132	99.71 143

test_vis1_n_1920		98.63 213	98.40 221	99.31 193	99.86 25	97.94 295	99.67 75	99.62 51	99.43 17	99.99 2	99.91 26	87.29 427	100.00 1	99.92 24	99.92 39	99.98 2

EIA-MVS		99.18 104	99.09 104	99.45 163	99.49 237	99.18 152	99.67 75	99.53 125	97.66 259	99.40 204	99.44 322	98.10 107	99.81 226	98.94 141	99.62 165	99.35 267

MSP-MVS		99.42 55	99.27 73	99.88 15	99.89 8	99.80 38	99.67 75	99.50 174	98.70 106	99.77 85	99.49 306	98.21 102	99.95 76	98.46 221	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		99.10 140	98.97 136	99.48 155	99.49 237	99.14 160	99.67 75	99.34 306	97.31 299	99.58 157	99.76 183	97.65 121	99.82 221	98.87 153	99.07 226	99.46 248

CP-MVSNet		98.09 255	97.78 279	99.01 235	98.97 375	99.24 147	99.67 75	99.46 231	97.25 304	98.48 372	99.64 248	93.79 315	99.06 401	98.63 194	94.10 418	98.74 333

MTAPA		99.52 28	99.39 40	99.89 11	99.90 4	99.86 18	99.66 82	99.47 220	98.79 95	99.68 113	99.81 128	98.43 89	99.97 29	98.88 150	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	99.68 113	99.69 221	99.06 18	99.96 41	98.69 186	99.87 79	99.84 53

mvs_tets		98.40 228	98.23 231	98.91 254	98.67 418	98.51 256	99.66 82	99.53 125	98.19 170	98.65 355	99.81 128	92.75 336	99.44 330	99.31 86	97.48 335	98.77 325

EU-MVSNet		97.98 276	98.03 252	97.81 387	98.72 412	96.65 370	99.66 82	99.66 32	98.09 192	98.35 378	99.82 113	95.25 238	98.01 448	97.41 327	95.30 395	98.78 321

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	99.67 119	99.69 221	98.95 32	99.96 41	98.69 186	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	99.46 231	98.09 192	99.48 179	99.74 193	98.29 99	99.96 41	97.93 272	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	98.83 219	99.65 88	99.52 130	99.10 48	99.84 56	99.76 183	95.80 213	99.99 4	99.30 89	99.84 102	99.74 113

SymmetryMVS		99.15 114	99.02 124	99.52 139	99.72 111	98.83 219	99.65 88	99.34 306	99.10 48	99.84 56	99.76 183	95.80 213	99.99 4	99.30 89	98.72 256	99.73 122

Elysia		98.88 172	98.65 194	99.58 116	99.58 193	99.34 127	99.65 88	99.52 130	98.26 155	99.83 64	99.87 65	93.37 322	99.90 148	97.81 285	99.91 46	99.49 234

StellarMVS		98.88 172	98.65 194	99.58 116	99.58 193	99.34 127	99.65 88	99.52 130	98.26 155	99.83 64	99.87 65	93.37 322	99.90 148	97.81 285	99.91 46	99.49 234

test_cas_vis1_n_1920		99.16 110	99.01 129	99.61 109	99.81 57	98.86 213	99.65 88	99.64 42	99.39 22	99.97 25	99.94 6	93.20 328	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	99.66 124	99.68 229	98.96 27	99.96 41	98.62 195	99.87 79	99.84 53

TranMVSNet+NR-MVSNet		97.93 282	97.66 295	98.76 286	98.78 401	98.62 242	99.65 88	99.49 186	97.76 245	98.49 371	99.60 266	94.23 295	98.97 419	98.00 268	92.90 434	98.70 342

GDP-MVS		99.08 143	98.89 158	99.64 101	99.53 214	99.34 127	99.64 95	99.48 198	98.32 147	99.77 85	99.66 240	95.14 243	99.93 110	98.97 138	99.50 176	99.64 176

ttmdpeth		97.80 309	97.63 300	98.29 344	98.77 406	97.38 319	99.64 95	99.36 294	98.78 98	96.30 436	99.58 272	92.34 357	99.39 338	98.36 232	95.58 388	98.10 430

mvsany_test3		93.77 420	93.45 423	94.74 434	95.78 462	88.01 460	99.64 95	98.25 443	98.28 150	94.31 450	97.97 452	68.89 467	98.51 439	97.50 318	90.37 450	97.71 447

ZNCC-MVS		99.47 40	99.33 52	99.87 21	99.87 20	99.81 33	99.64 95	99.67 27	98.08 196	99.55 167	99.64 248	98.91 39	99.96 41	98.72 181	99.90 57	99.82 72

tfpnnormal		97.84 299	97.47 318	98.98 239	99.20 321	99.22 149	99.64 95	99.61 60	96.32 378	98.27 384	99.70 210	93.35 324	99.44 330	95.69 395	95.40 393	98.27 420

casdiffmvs_mvg		99.15 114	99.02 124	99.55 124	99.66 147	99.09 165	99.64 95	99.56 90	98.26 155	99.45 183	99.87 65	96.03 198	99.81 226	99.54 51	99.15 207	99.73 122
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	99.82 113	98.53 82	99.95 76	98.61 198	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	99.82 113	98.75 60		98.61 198	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	99.39 277	98.91 82	99.78 81	99.85 80	99.36 2	99.94 92	98.84 163	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		96.22 388	96.03 389	96.79 423	97.31 452	94.14 435	99.63 101	99.08 370	96.17 390	97.04 427	99.06 395	93.94 308	97.76 454	86.96 464	95.06 400	98.47 404

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	99.82 113	98.86 43	99.95 76	98.62 195	99.81 120	99.78 98

test0726					99.85 31	99.89 6	99.62 106	99.50 174	99.10 48	99.86 53	99.82 113	98.94 34

EPNet		98.86 178	98.71 185	99.30 198	97.20 454	98.18 275	99.62 106	98.91 397	99.28 31	98.63 358	99.81 128	95.96 201	99.99 4	99.24 99	99.72 148	99.73 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 168	98.67 189	99.72 86	99.85 31	99.53 101	99.62 106	99.59 73	92.65 446	99.71 107	99.78 170	98.06 110	99.90 148	98.84 163	99.91 46	99.74 113

HY-MVS	97.30 7	98.85 187	98.64 196	99.47 160	99.42 257	99.08 168	99.62 106	99.36 294	97.39 293	99.28 234	99.68 229	96.44 182	99.92 123	98.37 230	98.22 289	99.40 260

ACMMP		99.45 46	99.32 54	99.82 57	99.89 8	99.67 68	99.62 106	99.69 22	98.12 186	99.63 141	99.84 95	98.73 66	99.96 41	98.55 213	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	99.63 141	99.95 3	95.82 211	99.94 92	99.37 75	99.97 9	99.73 122
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	99.45 242	99.01 64	99.89 40	99.82 113	99.01 20	99.92 123	99.56 49	99.95 23	99.85 46

reproduce_monomvs		97.89 289	97.87 271	97.96 372	99.51 223	95.45 405	99.60 113	99.25 345	99.17 36	98.85 325	99.49 306	89.29 403	99.64 300	99.35 76	96.31 367	98.78 321

test2506		96.81 378	96.65 374	97.29 409	99.74 100	92.21 453	99.60 113	85.06 484	99.13 41	99.77 85	99.93 10	87.82 425	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	99.48 198	99.08 56	99.91 31	99.81 128	99.20 9	99.96 41	98.91 147	99.85 94	99.79 92

OPU-MVS				99.64 101	99.56 202	99.72 56	99.60 113				99.70 210	99.27 7	99.42 336	98.24 243	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	97.97 218	99.63 141	99.68 229	98.52 83	99.95 76	98.38 228	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	99.45 242	99.01 64	99.90 34	99.83 100	98.98 26	99.93 110	99.59 45	99.95 23	99.86 42

ACMH	97.28 8	98.10 254	97.99 256	98.44 329	99.41 262	96.96 351	99.60 113	99.56 90	98.09 192	98.15 391	99.91 26	90.87 385	99.70 280	98.88 150	97.45 336	98.67 359
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VortexMVS		98.67 208	98.66 192	98.68 295	99.62 172	97.96 290	99.59 120	99.41 267	98.13 183	99.31 226	99.70 210	95.48 227	99.27 363	99.40 71	97.32 345	98.79 319

guyue		99.16 110	99.04 113	99.52 139	99.69 127	98.92 202	99.59 120	98.81 411	98.73 102	99.90 34	99.87 65	95.34 232	99.88 168	99.66 40	99.81 120	99.74 113

ECVR-MVS		98.04 265	98.05 250	98.00 368	99.74 100	94.37 432	99.59 120	94.98 472	99.13 41	99.66 124	99.93 10	90.67 387	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	99.79 163	98.68 70	99.96 41	98.44 223	99.77 137	99.79 92

thres100view900		97.76 313	97.45 321	98.69 294	99.72 111	97.86 299	99.59 120	98.74 421	97.93 221	99.26 245	98.62 427	91.75 366	99.83 212	93.22 432	98.18 294	98.37 416

thres600view7		97.86 294	97.51 312	98.92 250	99.72 111	97.95 293	99.59 120	98.74 421	97.94 220	99.27 240	98.62 427	91.75 366	99.86 181	93.73 427	98.19 293	98.96 311

LCM-MVSNet-Re		97.83 302	98.15 236	96.87 421	99.30 294	92.25 452	99.59 120	98.26 442	97.43 288	96.20 437	99.13 388	96.27 189	98.73 434	98.17 249	98.99 233	99.64 176

baseline1		98.31 234	97.95 261	99.38 181	99.50 235	98.74 229	99.59 120	98.93 389	98.41 135	99.14 269	99.60 266	94.59 277	99.79 239	98.48 217	93.29 429	99.61 186

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 21	99.82 53	99.81 33	99.59 120	99.51 151	98.62 112	99.79 76	99.83 100	99.28 6	99.97 29	98.48 217	99.90 57	99.84 53
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 135	98.90 154	99.74 80	99.80 63	99.46 114	99.59 120	99.49 186	97.03 328	99.63 141	99.69 221	97.27 133	99.96 41	97.82 283	99.84 102	99.81 79

IMVS_0403		98.86 178	98.89 158	98.78 284	99.55 206	96.93 352	99.58 130	99.44 251	98.05 203	99.68 113	99.80 146	96.81 160	99.80 233	98.15 252	98.92 238	99.60 189

test_fmvsmvis_n_1920		99.65 8	99.61 7	99.77 74	99.38 272	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		95.02 408	96.12 386	91.72 445	99.10 348	80.43 473	99.58 130	97.87 452	97.47 280	95.22 444	98.82 418	93.99 306	95.18 470	88.09 458	94.91 405	99.56 210

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		98.04 265	98.11 241	97.83 384	99.74 100	93.82 437	99.58 130	95.40 471	99.12 46	99.65 133	99.93 10	90.73 386	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	97.84 234	99.71 107	99.80 146	99.12 15	99.97 29	98.33 235	99.87 79	99.83 63

LPG-MVS_test		98.22 240	98.13 239	98.49 316	99.33 285	97.05 338	99.58 130	99.55 100	97.46 281	99.24 247	99.83 100	92.58 346	99.72 267	98.09 257	97.51 329	98.68 351

PHI-MVS		99.30 83	99.17 91	99.70 87	99.56 202	99.52 105	99.58 130	99.80 11	97.12 316	99.62 145	99.73 199	98.58 78	99.90 148	98.61 198	99.91 46	99.68 155

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	94.18 299	99.99 4	99.96 13	99.98 4	99.73 122

AstraMVS		99.09 141	99.03 116	99.25 208	99.66 147	98.13 279	99.57 138	98.24 444	98.82 89	99.91 31	99.88 52	95.81 212	99.90 148	99.72 32	99.67 158	99.74 113

SF-MVS		99.38 67	99.24 78	99.79 68	99.79 68	99.68 64	99.57 138	99.54 109	97.82 240	99.71 107	99.80 146	98.95 32	99.93 110	98.19 246	99.84 102	99.74 113

DVP-MVS		99.57 20	99.47 24	99.88 15	99.85 31	99.89 6	99.57 138	99.37 293	99.10 48	99.81 69	99.80 146	98.94 34	99.96 41	98.93 144	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	99.51 151					99.96 41	98.93 144	99.86 87	99.88 35

Effi-MVS+-dtu		98.78 196	98.89 158	98.47 323	99.33 285	96.91 357	99.57 138	99.30 333	98.47 127	99.41 199	98.99 404	96.78 162	99.74 257	98.73 180	99.38 183	98.74 333

v2v482		98.06 259	97.77 281	98.92 250	98.90 383	98.82 222	99.57 138	99.36 294	96.65 352	99.19 261	99.35 350	94.20 296	99.25 367	97.72 298	94.97 402	98.69 346

DSMNet-mixed		97.25 365	97.35 338	96.95 418	97.84 442	93.61 444	99.57 138	96.63 466	96.13 395	98.87 320	98.61 429	94.59 277	97.70 455	95.08 409	98.86 246	99.55 211

FE-MVSNET		94.07 419	93.36 424	96.22 429	94.05 471	94.71 424	99.56 146	98.36 440	93.15 440	93.76 453	97.55 454	86.47 433	96.49 465	87.48 461	89.83 454	97.48 454

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	99.11 117	99.91 46	99.86 42

MVStest1		96.08 394	95.48 399	97.89 378	98.93 378	96.70 365	99.56 146	99.35 301	92.69 445	91.81 462	99.46 319	89.90 396	98.96 421	95.00 411	92.61 439	98.00 439

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	99.83 100	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	99.82 113	98.75 60	99.99 4	99.97 2	99.97 9	99.94 17

sd_testset		98.75 201	98.57 209	99.29 201	99.81 57	98.26 272	99.56 146	99.62 51	98.78 98	99.64 138	99.88 52	92.02 360	99.88 168	99.54 51	98.26 286	99.72 132

KD-MVS_self_test		95.00 409	94.34 414	96.96 417	97.07 457	95.39 408	99.56 146	99.44 251	95.11 413	97.13 425	97.32 459	91.86 364	97.27 459	90.35 450	81.23 468	98.23 424

ETV-MVS		99.26 92	99.21 84	99.40 175	99.46 247	99.30 138	99.56 146	99.52 130	98.52 122	99.44 188	99.27 372	98.41 93	99.86 181	99.10 120	99.59 168	99.04 301

SMA-MVS		99.44 50	99.30 62	99.85 43	99.73 107	99.83 22	99.56 146	99.47 220	97.45 284	99.78 81	99.82 113	99.18 12	99.91 135	98.79 174	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		98.87 175	98.72 183	99.31 193	99.86 25	98.48 261	99.56 146	99.61 60	97.85 231	99.36 216	99.85 80	95.95 202	99.85 187	96.66 371	99.83 113	99.59 200

casdiffmvs		99.13 122	98.98 135	99.56 122	99.65 155	99.16 155	99.56 146	99.50 174	98.33 145	99.41 199	99.86 73	95.92 205	99.83 212	99.45 68	99.16 204	99.70 146
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		98.38 229	98.09 245	99.24 211	99.26 306	99.32 131	99.56 146	99.55 100	97.45 284	98.71 340	99.83 100	93.23 325	99.63 306	98.88 150	96.32 366	98.76 327

ACMH+	97.24 10	97.92 285	97.78 279	98.32 341	99.46 247	96.68 369	99.56 146	99.54 109	98.41 135	97.79 409	99.87 65	90.18 394	99.66 291	98.05 265	97.18 351	98.62 381

ACMM	97.58 5	98.37 231	98.34 224	98.48 318	99.41 262	97.10 332	99.56 146	99.45 242	98.53 121	99.04 291	99.85 80	93.00 330	99.71 273	98.74 178	97.45 336	98.64 372
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	99.18 327	99.75 51	99.56 146	99.57 85	98.45 130	99.49 178	99.85 80	97.77 118	99.94 92	98.33 235	99.84 102	99.52 220

testing3-2		97.84 299	97.70 291	98.24 350	99.53 214	95.37 409	99.55 161	98.67 431	98.46 128	99.27 240	99.34 354	86.58 431	99.83 212	99.32 84	98.63 259	99.52 220

test_fmvsmconf0.01_n		99.22 100	99.03 116	99.79 68	98.42 434	99.48 111	99.55 161	99.51 151	99.39 22	99.78 81	99.93 10	94.80 259	99.95 76	99.93 23	99.95 23	99.94 17

test_fmvs1		98.88 172	98.79 176	99.16 219	99.69 127	97.61 312	99.55 161	99.49 186	99.32 29	99.98 13	99.91 26	91.41 376	99.96 41	99.82 29	99.92 39	99.90 25

v144192		97.92 285	97.60 303	98.87 267	98.83 395	98.65 237	99.55 161	99.34 306	96.20 387	99.32 225	99.40 334	94.36 289	99.26 366	96.37 382	95.03 401	98.70 342

API-MVS		99.04 153	99.03 116	99.06 229	99.40 267	99.31 135	99.55 161	99.56 90	98.54 120	99.33 224	99.39 338	98.76 57	99.78 245	96.98 353	99.78 134	98.07 432

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 41	97.27 133	99.99 4	99.97 2	99.95 23	99.95 11

fmvsm_s_conf0.1_n_a		99.26 92	99.06 109	99.85 43	99.52 220	99.62 83	99.54 166	99.62 51	98.69 107	99.99 2	99.96 1	94.47 286	99.94 92	99.88 26	99.92 39	99.98 2

APD_test1		95.87 396	96.49 378	94.00 436	99.53 214	84.01 465	99.54 166	99.32 324	95.91 403	97.99 398	99.85 80	85.49 439	99.88 168	91.96 443	98.84 248	98.12 429

thisisatest0530		98.35 232	98.03 252	99.31 193	99.63 164	98.56 247	99.54 166	96.75 464	97.53 275	99.73 97	99.65 242	91.25 381	99.89 163	98.62 195	99.56 170	99.48 237

MTMP							99.54 166	98.88 402

v1144		97.98 276	97.69 292	98.85 273	98.87 388	98.66 236	99.54 166	99.35 301	96.27 382	99.23 251	99.35 350	94.67 272	99.23 370	96.73 366	95.16 398	98.68 351

v148		97.79 311	97.55 305	98.50 315	98.74 409	97.72 305	99.54 166	99.33 314	96.26 383	98.90 314	99.51 300	94.68 271	99.14 387	97.83 282	93.15 433	98.63 379

CostFormer		97.72 323	97.73 288	97.71 392	99.15 341	94.02 436	99.54 166	99.02 380	94.67 424	99.04 291	99.35 350	92.35 356	99.77 247	98.50 216	97.94 304	99.34 270

MVSTER		98.49 217	98.32 226	99.00 237	99.35 279	99.02 175	99.54 166	99.38 285	97.41 291	99.20 258	99.73 199	93.86 313	99.36 347	98.87 153	97.56 324	98.62 381

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	94.92 252	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	94.53 284	99.94 92	99.89 25	99.96 17	99.97 4

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 41	98.96 27	99.96 41	99.04 126	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 41	98.96 27	99.96 41	99.04 126	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	99.67 134	99.31 135	99.52 177	98.87 404	99.55 1	99.74 95	99.80 146	96.47 179	99.98 20	99.97 2	99.97 9	99.94 17

patch_mono-2		99.26 92	99.62 6	98.16 355	99.81 57	94.59 428	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		98.77 200	98.83 172	98.60 300	99.41 262	96.99 347	99.52 177	99.49 186	98.11 188	99.24 247	99.34 354	96.96 151	99.79 239	97.95 271	99.45 179	99.02 304

Fast-Effi-MVS+		98.70 205	98.43 218	99.51 144	99.51 223	99.28 141	99.52 177	99.47 220	96.11 396	99.01 294	99.34 354	96.20 191	99.84 196	97.88 275	98.82 250	99.39 261

v1921920		97.80 309	97.45 321	98.84 274	98.80 397	98.53 250	99.52 177	99.34 306	96.15 393	99.24 247	99.47 315	93.98 307	99.29 359	95.40 403	95.13 399	98.69 346

MIMVSNet1		95.51 402	95.04 407	96.92 420	97.38 449	95.60 398	99.52 177	99.50 174	93.65 434	96.97 429	99.17 383	85.28 442	96.56 464	88.36 457	95.55 390	98.60 393

viewmacassd2359aftdt		99.08 143	98.94 146	99.50 149	99.66 147	98.96 187	99.51 186	99.54 109	98.27 152	99.42 194	99.89 41	95.88 209	99.80 233	99.20 102	99.11 217	99.76 107

SSM_0407		99.13 122	99.03 116	99.43 171	99.62 172	98.88 206	99.51 186	99.50 174	98.14 180	99.37 210	99.85 80	96.85 154	99.83 212	99.19 103	99.25 197	99.60 189

fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 11	99.83 47	99.74 54	99.51 186	99.62 51	99.46 7	99.99 2	99.90 33	96.60 171	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	99.51 186	99.67 27	99.13 41	99.98 13	99.92 18	96.60 171	99.96 41	99.95 16	99.96 17	99.95 11

UniMVSNet_ETH3D		97.32 362	96.81 370	98.87 267	99.40 267	97.46 316	99.51 186	99.53 125	95.86 404	98.54 368	99.77 179	82.44 455	99.66 291	98.68 188	97.52 328	99.50 233

alignmvs		98.81 191	98.56 211	99.58 116	99.43 255	99.42 118	99.51 186	98.96 387	98.61 113	99.35 219	98.92 414	94.78 261	99.77 247	99.35 76	98.11 299	99.54 213

v1192		97.81 307	97.44 326	98.91 254	98.88 385	98.68 234	99.51 186	99.34 306	96.18 389	99.20 258	99.34 354	94.03 305	99.36 347	95.32 405	95.18 397	98.69 346

test20.03		96.12 392	95.96 391	96.63 424	97.44 448	95.45 405	99.51 186	99.38 285	96.55 363	96.16 438	99.25 375	93.76 317	96.17 466	87.35 463	94.22 415	98.27 420

mvs_anonymous		99.03 155	98.99 132	99.16 219	99.38 272	98.52 254	99.51 186	99.38 285	97.79 241	99.38 208	99.81 128	97.30 131	99.45 325	99.35 76	98.99 233	99.51 229

TAMVS		99.12 129	99.08 105	99.24 211	99.46 247	98.55 248	99.51 186	99.46 231	98.09 192	99.45 183	99.82 113	98.34 97	99.51 319	98.70 183	98.93 236	99.67 159

viewdifsd2359ckpt13		99.06 148	98.93 148	99.45 163	99.63 164	98.96 187	99.50 196	99.51 151	97.83 235	99.28 234	99.80 146	96.68 168	99.71 273	99.05 125	99.12 215	99.68 155

viewdifsd2359ckpt11		98.78 196	98.74 181	98.89 260	99.67 134	97.04 341	99.50 196	99.58 78	98.26 155	99.56 161	99.90 33	94.36 289	99.87 175	99.49 61	98.32 282	99.77 100

viewmsd2359difaftdt		98.78 196	98.74 181	98.90 256	99.67 134	97.04 341	99.50 196	99.58 78	98.26 155	99.56 161	99.90 33	94.36 289	99.87 175	99.49 61	98.32 282	99.77 100

IMVS_0407		98.86 178	98.91 152	98.72 289	99.55 206	96.93 352	99.50 196	99.44 251	98.05 203	99.66 124	99.80 146	97.13 138	99.65 296	98.15 252	98.92 238	99.60 189

viewmanbaseed2359cas		99.18 104	99.07 108	99.50 149	99.62 172	99.01 177	99.50 196	99.52 130	98.25 160	99.68 113	99.82 113	96.93 152	99.80 233	99.15 113	99.11 217	99.70 146

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 223	99.67 68	99.50 196	99.64 42	99.43 17	99.98 13	99.78 170	97.26 135	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	99.44 254	99.65 75	99.50 196	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		98.86 178	98.63 197	99.54 125	99.49 237	99.18 152	99.50 196	99.07 373	98.22 166	99.61 150	99.51 300	95.37 230	99.84 196	98.60 201	98.33 278	99.59 200

DCV-MVSNet		98.86 178	98.63 197	99.54 125	99.49 237	99.18 152	99.50 196	99.07 373	98.22 166	99.61 150	99.51 300	95.37 230	99.84 196	98.60 201	98.33 278	99.59 200

tfpn200view9		97.72 323	97.38 334	98.72 289	99.69 127	97.96 290	99.50 196	98.73 427	97.83 235	99.17 266	98.45 434	91.67 370	99.83 212	93.22 432	98.18 294	98.37 416

UA-Net		99.42 55	99.29 66	99.80 64	99.62 172	99.55 96	99.50 196	99.70 18	98.79 95	99.77 85	99.96 1	97.45 124	99.96 41	98.92 146	99.90 57	99.89 29

pm-mvs1		97.68 331	97.28 350	98.88 263	99.06 357	98.62 242	99.50 196	99.45 242	96.32 378	97.87 405	99.79 163	92.47 350	99.35 350	97.54 315	93.54 426	98.67 359

EI-MVSNet		98.67 208	98.67 189	98.68 295	99.35 279	97.97 288	99.50 196	99.38 285	96.93 337	99.20 258	99.83 100	97.87 114	99.36 347	98.38 228	97.56 324	98.71 337

CVMVSNet		98.57 215	98.67 189	98.30 343	99.35 279	95.59 399	99.50 196	99.55 100	98.60 115	99.39 206	99.83 100	94.48 285	99.45 325	98.75 177	98.56 266	99.85 46

VPA-MVSNet		98.29 237	97.95 261	99.30 198	99.16 337	99.54 98	99.50 196	99.58 78	98.27 152	99.35 219	99.37 344	92.53 348	99.65 296	99.35 76	94.46 410	98.72 335

thres400		97.77 312	97.38 334	98.92 250	99.69 127	97.96 290	99.50 196	98.73 427	97.83 235	99.17 266	98.45 434	91.67 370	99.83 212	93.22 432	98.18 294	98.96 311

APD-MVS		99.27 89	99.08 105	99.84 55	99.75 92	99.79 41	99.50 196	99.50 174	97.16 312	99.77 85	99.82 113	98.78 53	99.94 92	97.56 313	99.86 87	99.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
E2		99.15 114	99.03 116	99.49 152	99.65 155	98.93 201	99.49 213	99.52 130	98.14 180	99.72 102	99.88 52	96.57 175	99.84 196	99.17 109	99.13 211	99.72 132

E3		99.15 114	99.03 116	99.49 152	99.62 172	98.91 203	99.49 213	99.52 130	98.13 183	99.72 102	99.88 52	96.61 170	99.84 196	99.17 109	99.13 211	99.72 132

SSM_0404		99.16 110	99.06 109	99.44 168	99.65 155	98.96 187	99.49 213	99.50 174	98.14 180	99.62 145	99.85 80	96.85 154	99.85 187	99.19 103	99.26 196	99.52 220

fmvsm_s_conf0.5_n_4		99.36 72	99.24 78	99.73 83	99.78 70	99.53 101	99.49 213	99.60 67	99.42 20	99.99 2	99.86 73	95.15 242	99.95 76	99.95 16	99.89 68	99.73 122

test_vis1_rt		95.81 398	95.65 397	96.32 428	99.67 134	91.35 456	99.49 213	96.74 465	98.25 160	95.24 443	98.10 449	74.96 464	99.90 148	99.53 53	98.85 247	97.70 449

TransMVSNet (Re)		97.15 369	96.58 375	98.86 270	99.12 343	98.85 214	99.49 213	98.91 397	95.48 408	97.16 424	99.80 146	93.38 321	99.11 396	94.16 423	91.73 443	98.62 381

UniMVSNet (Re)		98.29 237	98.00 255	99.13 224	99.00 367	99.36 126	99.49 213	99.51 151	97.95 219	98.97 303	99.13 388	96.30 188	99.38 340	98.36 232	93.34 428	98.66 368

EPMVS		97.82 305	97.65 296	98.35 338	98.88 385	95.98 391	99.49 213	94.71 474	97.57 268	99.26 245	99.48 312	92.46 353	99.71 273	97.87 277	99.08 225	99.35 267

viewcassd2359sk11		99.18 104	99.08 105	99.49 152	99.65 155	98.95 193	99.48 221	99.51 151	98.10 191	99.72 102	99.87 65	97.13 138	99.84 196	99.13 114	99.14 208	99.69 149

fmvsm_s_conf0.5_n_9		99.41 59	99.28 69	99.81 60	99.84 38	99.52 105	99.48 221	99.62 51	99.46 7	99.99 2	99.92 18	95.24 239	99.96 41	99.97 2	99.97 9	99.96 7

SSC-MVS3.2		97.34 360	97.15 357	97.93 374	99.02 364	95.76 396	99.48 221	99.58 78	97.62 263	99.09 280	99.53 292	87.95 421	99.27 363	96.42 378	95.66 386	98.75 329

fmvsm_s_conf0.5_n_3		99.37 68	99.20 86	99.87 21	99.75 92	99.70 60	99.48 221	99.66 32	99.45 11	99.99 2	99.93 10	94.64 276	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	99.48 221	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		97.88 290	97.54 308	98.90 256	99.71 117	98.53 250	99.48 221	99.57 85	94.16 429	98.81 329	99.68 229	93.23 325	99.42 336	98.84 163	94.42 412	98.76 327

v1240		97.69 328	97.32 345	98.79 282	98.85 392	98.43 265	99.48 221	99.36 294	96.11 396	99.27 240	99.36 347	93.76 317	99.24 369	94.46 417	95.23 396	98.70 342

VPNet		97.84 299	97.44 326	99.01 235	99.21 319	98.94 197	99.48 221	99.57 85	98.38 137	99.28 234	99.73 199	88.89 406	99.39 338	99.19 103	93.27 430	98.71 337

UniMVSNet_NR-MVSNet		98.22 240	97.97 258	98.96 242	98.92 380	98.98 180	99.48 221	99.53 125	97.76 245	98.71 340	99.46 319	96.43 183	99.22 374	98.57 207	92.87 436	98.69 346

TDRefinement		95.42 404	94.57 412	97.97 370	89.83 477	96.11 390	99.48 221	98.75 418	96.74 345	96.68 432	99.88 52	88.65 412	99.71 273	98.37 230	82.74 466	98.09 431

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 28	99.47 231	99.63 46	99.45 11	99.98 13	99.89 41	97.02 147	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	99.47 231	99.48 198	98.05 203	99.76 91	99.86 73	98.82 48	99.93 110	98.82 173	99.91 46	99.84 53

NR-MVSNet		97.97 279	97.61 302	99.02 234	98.87 388	99.26 144	99.47 231	99.42 264	97.63 261	97.08 426	99.50 303	95.07 245	99.13 390	97.86 278	93.59 425	98.68 351

PVSNet_Blended_VisFu		99.36 72	99.28 69	99.61 109	99.86 25	99.07 170	99.47 231	99.93 2	97.66 259	99.71 107	99.86 73	97.73 119	99.96 41	99.47 66	99.82 117	99.79 92

LuminaMVS		99.23 98	99.10 99	99.61 109	99.35 279	99.31 135	99.46 235	99.13 364	98.61 113	99.86 53	99.89 41	96.41 185	99.91 135	99.67 37	99.51 174	99.63 181

fmvsm_s_conf0.1_n_2		99.37 68	99.22 83	99.81 60	99.77 78	99.75 51	99.46 235	99.60 67	99.47 4	99.98 13	99.94 6	94.98 246	99.95 76	99.97 2	99.79 132	99.73 122

SD-MVS		99.41 59	99.52 14	99.05 231	99.74 100	99.68 64	99.46 235	99.52 130	99.11 47	99.88 43	99.91 26	99.43 1	97.70 455	98.72 181	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		99.11 135	99.00 131	99.43 171	99.63 164	98.73 230	99.45 238	99.54 109	98.33 145	99.62 145	99.81 128	96.17 192	99.87 175	99.27 95	99.14 208	99.69 149

testing3		97.28 363	96.76 372	98.82 276	99.37 275	98.07 283	99.45 238	99.36 294	97.56 270	97.89 404	98.95 409	83.70 449	98.82 429	96.03 386	98.56 266	99.58 204

tt0805		97.97 279	97.77 281	98.57 305	99.59 191	96.61 372	99.45 238	99.08 370	98.21 168	98.88 317	99.80 146	88.66 411	99.70 280	98.58 204	97.72 314	99.39 261

tpm2		97.44 355	97.34 341	97.74 391	99.15 341	94.36 433	99.45 238	98.94 388	93.45 438	98.90 314	99.44 322	91.35 378	99.59 310	97.31 332	98.07 300	99.29 274

FMVSNet2		97.72 323	97.36 336	98.80 281	99.51 223	98.84 216	99.45 238	99.42 264	96.49 366	98.86 324	99.29 367	90.26 390	98.98 412	96.44 377	96.56 360	98.58 395

CDS-MVSNet		99.09 141	99.03 116	99.25 208	99.42 257	98.73 230	99.45 238	99.46 231	98.11 188	99.46 182	99.77 179	98.01 112	99.37 343	98.70 183	98.92 238	99.66 163
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 178	98.63 197	99.54 125	99.37 275	99.66 71	99.45 238	99.54 109	96.61 357	99.01 294	99.40 334	97.09 142	99.86 181	97.68 303	99.53 173	99.10 289
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		99.01 160	98.87 162	99.40 175	99.62 172	98.79 225	99.44 245	99.51 151	97.76 245	99.35 219	99.69 221	96.42 184	99.75 254	98.97 138	99.11 217	99.66 163

fmvsm_s_conf0.5_n_2		99.32 79	99.13 95	99.89 11	99.80 63	99.77 48	99.44 245	99.58 78	99.47 4	99.99 2	99.93 10	94.04 304	99.96 41	99.96 13	99.93 33	99.93 22

UGNet		98.87 175	98.69 187	99.40 175	99.22 318	98.72 232	99.44 245	99.68 24	99.24 32	99.18 265	99.42 326	92.74 338	99.96 41	99.34 81	99.94 31	99.53 219
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		98.86 178	98.63 197	99.54 125	99.64 160	99.19 150	99.44 245	99.54 109	97.77 244	99.30 230	99.81 128	94.20 296	99.93 110	99.17 109	98.82 250	99.49 234

test_0402		96.64 381	96.24 383	97.85 381	98.85 392	96.43 378	99.44 245	99.26 343	93.52 435	96.98 428	99.52 296	88.52 415	99.20 381	92.58 442	97.50 331	97.93 444

ACMP	97.20 11	98.06 259	97.94 263	98.45 326	99.37 275	97.01 345	99.44 245	99.49 186	97.54 274	98.45 373	99.79 163	91.95 362	99.72 267	97.91 273	97.49 334	98.62 381
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 326	98.55 429	98.16 276	99.43 251	93.68 476		97.23 420	98.46 433	89.30 402	99.22 374	95.43 402	98.22 289	97.98 441

HPM-MVS++		99.39 66	99.23 82	99.87 21	99.75 92	99.84 20	99.43 251	99.51 151	98.68 109	99.27 240	99.53 292	98.64 75	99.96 41	98.44 223	99.80 125	99.79 92

tpm cat1		97.39 357	97.36 336	97.50 403	99.17 335	93.73 439	99.43 251	99.31 328	91.27 450	98.71 340	99.08 392	94.31 294	99.77 247	96.41 380	98.50 270	99.00 305

tpm		97.67 334	97.55 305	98.03 363	99.02 364	95.01 417	99.43 251	98.54 437	96.44 372	99.12 272	99.34 354	91.83 365	99.60 309	97.75 294	96.46 362	99.48 237

GBi-Net		97.68 331	97.48 315	98.29 344	99.51 223	97.26 325	99.43 251	99.48 198	96.49 366	99.07 283	99.32 362	90.26 390	98.98 412	97.10 345	96.65 357	98.62 381

test1		97.68 331	97.48 315	98.29 344	99.51 223	97.26 325	99.43 251	99.48 198	96.49 366	99.07 283	99.32 362	90.26 390	98.98 412	97.10 345	96.65 357	98.62 381

FMVSNet1		96.84 377	96.36 381	98.29 344	99.32 292	97.26 325	99.43 251	99.48 198	95.11 413	98.55 367	99.32 362	83.95 448	98.98 412	95.81 391	96.26 368	98.62 381

fmvsm_s_conf0.5_n_7		99.34 75	99.29 66	99.48 155	99.70 122	98.63 240	99.42 258	99.63 46	99.46 7	99.98 13	99.88 52	95.59 222	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	99.42 258	99.61 60	99.37 24	99.97 25	99.86 73	94.96 247	99.99 4	99.97 2	99.93 33	99.92 23

mamv4		99.33 77	99.42 32	99.07 227	99.67 134	97.73 303	99.42 258	99.60 67	98.15 175	99.94 28	99.91 26	98.42 91	99.94 92	99.72 32	99.96 17	99.54 213

testgi		97.65 336	97.50 313	98.13 359	99.36 278	96.45 377	99.42 258	99.48 198	97.76 245	97.87 405	99.45 321	91.09 382	98.81 430	94.53 416	98.52 269	99.13 288

F-COLMAP		99.19 101	99.04 113	99.64 101	99.78 70	99.27 143	99.42 258	99.54 109	97.29 301	99.41 199	99.59 268	98.42 91	99.93 110	98.19 246	99.69 153	99.73 122

Anonymous202405211		98.30 236	97.98 257	99.26 207	99.57 198	98.16 276	99.41 263	98.55 436	96.03 401	99.19 261	99.74 193	91.87 363	99.92 123	99.16 112	98.29 285	99.70 146

MSLP-MVS++		99.46 42	99.47 24	99.44 168	99.60 189	99.16 155	99.41 263	99.71 16	98.98 72	99.45 183	99.78 170	99.19 11	99.54 317	99.28 92	99.84 102	99.63 181

VNet		99.11 135	98.90 154	99.73 83	99.52 220	99.56 94	99.41 263	99.39 277	99.01 64	99.74 95	99.78 170	95.56 223	99.92 123	99.52 55	98.18 294	99.72 132

baseline2		97.87 292	97.55 305	98.82 276	99.18 327	98.02 285	99.41 263	96.58 468	96.97 331	96.51 433	99.17 383	93.43 320	99.57 312	97.71 299	99.03 229	98.86 315

DU-MVS		98.08 257	97.79 276	98.96 242	98.87 388	98.98 180	99.41 263	99.45 242	97.87 227	98.71 340	99.50 303	94.82 257	99.22 374	98.57 207	92.87 436	98.68 351

Baseline_NR-MVSNet		97.76 313	97.45 321	98.68 295	99.09 351	98.29 270	99.41 263	98.85 406	95.65 406	98.63 358	99.67 235	94.82 257	99.10 398	98.07 264	92.89 435	98.64 372

XVG-ACMP-BASELINE		97.83 302	97.71 290	98.20 352	99.11 345	96.33 381	99.41 263	99.52 130	98.06 201	99.05 290	99.50 303	89.64 400	99.73 263	97.73 296	97.38 343	98.53 398

DP-MVS		99.16 110	98.95 144	99.78 71	99.77 78	99.53 101	99.41 263	99.50 174	97.03 328	99.04 291	99.88 52	97.39 125	99.92 123	98.66 190	99.90 57	99.87 40

9.14			99.10 99		99.72 111		99.40 271	99.51 151	97.53 275	99.64 138	99.78 170	98.84 46	99.91 135	97.63 304	99.82 117

D2MVS		98.41 225	98.50 215	98.15 358	99.26 306	96.62 371	99.40 271	99.61 60	97.71 251	98.98 301	99.36 347	96.04 197	99.67 288	98.70 183	97.41 341	98.15 428

Anonymous20240529		98.09 255	97.68 293	99.34 185	99.66 147	98.44 264	99.40 271	99.43 262	93.67 433	99.22 252	99.89 41	90.23 393	99.93 110	99.26 98	98.33 278	99.66 163

FMVSNet3		98.03 267	97.76 285	98.84 274	99.39 270	98.98 180	99.40 271	99.38 285	96.67 350	99.07 283	99.28 369	92.93 331	98.98 412	97.10 345	96.65 357	98.56 397

LFMVS		97.90 288	97.35 338	99.54 125	99.52 220	99.01 177	99.39 275	98.24 444	97.10 320	99.65 133	99.79 163	84.79 444	99.91 135	99.28 92	98.38 275	99.69 149

HQP_MVS		98.27 239	98.22 232	98.44 329	99.29 298	96.97 349	99.39 275	99.47 220	98.97 75	99.11 274	99.61 263	92.71 341	99.69 285	97.78 288	97.63 317	98.67 359

plane_prior2							99.39 275		98.97 75

CHOSEN 1792x2688		99.19 101	99.10 99	99.45 163	99.89 8	98.52 254	99.39 275	99.94 1	98.73 102	99.11 274	99.89 41	95.50 225	99.94 92	99.50 57	99.97 9	99.89 29

PAPM_NR		99.04 153	98.84 170	99.66 91	99.74 100	99.44 116	99.39 275	99.38 285	97.70 254	99.28 234	99.28 369	98.34 97	99.85 187	96.96 355	99.45 179	99.69 149

gg-mvs-nofinetune		96.17 391	95.32 403	98.73 287	98.79 398	98.14 278	99.38 280	94.09 475	91.07 453	98.07 396	91.04 473	89.62 401	99.35 350	96.75 365	99.09 224	98.68 351

VDDNet		97.55 342	97.02 364	99.16 219	99.49 237	98.12 281	99.38 280	99.30 333	95.35 409	99.68 113	99.90 33	82.62 454	99.93 110	99.31 86	98.13 298	99.42 255

ME-MVS		99.56 21	99.46 28	99.86 34	99.80 63	99.81 33	99.37 282	99.70 18	99.18 34	99.83 64	99.83 100	98.74 65	99.93 110	98.83 166	99.89 68	99.83 63

MGCNet		99.15 114	98.96 140	99.73 83	98.92 380	99.37 123	99.37 282	96.92 461	99.51 2	99.66 124	99.78 170	96.69 166	99.97 29	99.84 28	99.97 9	99.84 53

pmmvs6		96.53 383	96.09 388	97.82 386	98.69 416	95.47 404	99.37 282	99.47 220	93.46 437	97.41 414	99.78 170	87.06 429	99.33 353	96.92 360	92.70 438	98.65 370

PM-MVS		92.96 424	92.23 428	95.14 433	95.61 463	89.98 459	99.37 282	98.21 446	94.80 422	95.04 448	97.69 453	65.06 468	97.90 451	94.30 418	89.98 453	97.54 453

WTY-MVS		99.06 148	98.88 161	99.61 109	99.62 172	99.16 155	99.37 282	99.56 90	98.04 210	99.53 170	99.62 259	96.84 158	99.94 92	98.85 160	98.49 271	99.72 132

IterMVS-LS		98.46 220	98.42 219	98.58 304	99.59 191	98.00 286	99.37 282	99.43 262	96.94 336	99.07 283	99.59 268	97.87 114	99.03 405	98.32 237	95.62 387	98.71 337
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 327	97.28 350	98.97 241	99.70 122	97.27 323	99.36 288	99.45 242	98.94 78	99.66 124	99.64 248	94.93 250	99.99 4	99.48 64	84.36 463	99.65 169

DPE-MVS		99.46 42	99.32 54	99.91 6	99.78 70	99.88 10	99.36 288	99.51 151	98.73 102	99.88 43	99.84 95	98.72 67	99.96 41	98.16 250	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		96.44 385	96.12 386	97.40 406	98.65 419	95.65 397	99.36 288	99.51 151	97.13 314	96.04 440	98.99 404	88.40 416	98.17 444	96.71 367	90.27 451	98.40 413

sss		99.17 108	99.05 111	99.53 133	99.62 172	98.97 183	99.36 288	99.62 51	97.83 235	99.67 119	99.65 242	97.37 128	99.95 76	99.19 103	99.19 203	99.68 155

DeepC-MVS_fast	98.69 1	99.49 33	99.39 40	99.77 74	99.63 164	99.59 88	99.36 288	99.46 231	99.07 58	99.79 76	99.82 113	98.85 44	99.92 123	98.68 188	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	99.41 262	99.16 155	99.35 293	99.57 85	98.82 89	99.51 174	99.61 263	96.46 180	99.95 76	99.59 45	99.98 4	99.65 169

pmmvs-eth3d		95.34 406	94.73 409	97.15 410	95.53 465	95.94 392	99.35 293	99.10 367	95.13 411	93.55 454	97.54 455	88.15 420	97.91 450	94.58 415	89.69 455	97.61 450

MDTV_nov1_ep13_2view						95.18 414	99.35 293		96.84 341	99.58 157		95.19 241		97.82 283		99.46 248

VDD-MVS		97.73 321	97.35 338	98.88 263	99.47 245	97.12 331	99.34 296	98.85 406	98.19 170	99.67 119	99.85 80	82.98 452	99.92 123	99.49 61	98.32 282	99.60 189

COLMAP_ROB	97.56 6	98.86 178	98.75 179	99.17 218	99.88 13	98.53 250	99.34 296	99.59 73	97.55 271	98.70 346	99.89 41	95.83 210	99.90 148	98.10 256	99.90 57	99.08 294
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
viewmambaseed2359dif		99.01 160	98.90 154	99.32 191	99.58 193	98.51 256	99.33 298	99.54 109	97.85 231	99.44 188	99.85 80	96.01 199	99.79 239	99.41 70	99.13 211	99.67 159

myMVS_eth3d28		97.69 328	97.34 341	98.73 287	99.27 303	97.52 314	99.33 298	98.78 416	98.03 212	98.82 328	98.49 432	86.64 430	99.46 323	98.44 223	98.24 288	99.23 282

EGC-MVSNET		82.80 437	77.86 443	97.62 396	97.91 440	96.12 389	99.33 298	99.28 339	8.40 481	25.05 482	99.27 372	84.11 447	99.33 353	89.20 453	98.22 289	97.42 455

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 155	99.64 160	98.85 214	99.32 301	99.48 198	98.50 124	99.81 69	99.81 128	96.82 159	99.88 168	99.40 71	99.12 215	99.71 143

ETVMVS		97.50 348	96.90 368	99.29 201	99.23 314	98.78 228	99.32 301	98.90 399	97.52 277	98.56 366	98.09 450	84.72 445	99.69 285	97.86 278	97.88 307	99.39 261

FMVSNet5		96.43 386	96.19 385	97.15 410	99.11 345	95.89 393	99.32 301	99.52 130	94.47 428	98.34 379	99.07 393	87.54 426	97.07 460	92.61 441	95.72 384	98.47 404

dp		97.75 317	97.80 275	97.59 400	99.10 348	93.71 440	99.32 301	98.88 402	96.48 369	99.08 282	99.55 283	92.67 344	99.82 221	96.52 375	98.58 263	99.24 281

tpmvs		97.98 276	98.02 254	97.84 383	99.04 362	94.73 422	99.31 305	99.20 355	96.10 400	98.76 336	99.42 326	94.94 249	99.81 226	96.97 354	98.45 272	98.97 309

tpmrst		98.33 233	98.48 216	97.90 377	99.16 337	94.78 421	99.31 305	99.11 366	97.27 302	99.45 183	99.59 268	95.33 233	99.84 196	98.48 217	98.61 260	99.09 293

testing99		97.36 358	96.94 367	98.63 298	99.18 327	96.70 365	99.30 307	98.93 389	97.71 251	98.23 385	98.26 442	84.92 443	99.84 196	98.04 266	97.85 310	99.35 267

MP-MVS-pluss		99.37 68	99.20 86	99.88 15	99.90 4	99.87 17	99.30 307	99.52 130	97.18 310	99.60 153	99.79 163	98.79 52	99.95 76	98.83 166	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	99.61 183	99.65 75	99.30 307	99.48 198	98.86 84	99.21 255	99.63 254	98.72 67	99.90 148	98.25 242	99.63 164	99.80 88

JIA-IIPM		97.50 348	97.02 364	98.93 248	98.73 410	97.80 301	99.30 307	98.97 385	91.73 449	98.91 312	94.86 467	95.10 244	99.71 273	97.58 308	97.98 302	99.28 275

BH-RMVSNet		98.41 225	98.08 246	99.40 175	99.41 262	98.83 219	99.30 307	98.77 417	97.70 254	98.94 309	99.65 242	92.91 334	99.74 257	96.52 375	99.55 172	99.64 176

testing11		97.50 348	97.10 361	98.71 292	99.20 321	96.91 357	99.29 312	98.82 409	97.89 225	98.21 388	98.40 436	85.63 438	99.83 212	98.45 222	98.04 301	99.37 265

Syy-MVS		97.09 372	97.14 358	96.95 418	99.00 367	92.73 450	99.29 312	99.39 277	97.06 324	97.41 414	98.15 445	93.92 310	98.68 435	91.71 444	98.34 276	99.45 251

myMVS_eth3d		96.89 375	96.37 380	98.43 331	99.00 367	97.16 329	99.29 312	99.39 277	97.06 324	97.41 414	98.15 445	83.46 451	98.68 435	95.27 406	98.34 276	99.45 251

MCST-MVS		99.43 53	99.30 62	99.82 57	99.79 68	99.74 54	99.29 312	99.40 274	98.79 95	99.52 172	99.62 259	98.91 39	99.90 148	98.64 192	99.75 142	99.82 72

LF4IMVS		97.52 345	97.46 320	97.70 393	98.98 373	95.55 400	99.29 312	98.82 409	98.07 197	98.66 349	99.64 248	89.97 395	99.61 308	97.01 350	96.68 356	97.94 443

hse-mvs2		97.50 348	97.14 358	98.59 301	99.49 237	97.05 338	99.28 317	99.22 351	98.94 78	99.66 124	99.42 326	94.93 250	99.65 296	99.48 64	83.80 465	99.08 294

OPM-MVS		98.19 244	98.10 242	98.45 326	98.88 385	97.07 336	99.28 317	99.38 285	98.57 117	99.22 252	99.81 128	92.12 358	99.66 291	98.08 261	97.54 326	98.61 390
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs		99.14 120	99.02 124	99.51 144	99.61 183	98.96 187	99.28 317	99.49 186	98.46 128	99.72 102	99.71 206	96.50 178	99.88 168	99.31 86	99.11 217	99.67 159
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 178	98.80 173	99.03 233	99.76 82	98.79 225	99.28 317	99.91 3	97.42 290	99.67 119	99.37 344	97.53 122	99.88 168	98.98 133	97.29 346	98.42 410

OMC-MVS		99.08 143	99.04 113	99.20 215	99.67 134	98.22 274	99.28 317	99.52 130	98.07 197	99.66 124	99.81 128	97.79 117	99.78 245	97.79 287	99.81 120	99.60 189

testing222		97.16 368	96.50 377	99.16 219	99.16 337	98.47 263	99.27 322	98.66 432	97.71 251	98.23 385	98.15 445	82.28 457	99.84 196	97.36 330	97.66 316	99.18 285

AUN-MVS		96.88 376	96.31 382	98.59 301	99.48 244	97.04 341	99.27 322	99.22 351	97.44 287	98.51 369	99.41 330	91.97 361	99.66 291	97.71 299	83.83 464	99.07 299

pmmvs5		97.52 345	97.30 347	98.16 355	98.57 428	96.73 364	99.27 322	98.90 399	96.14 394	98.37 377	99.53 292	91.54 375	99.14 387	97.51 317	95.87 379	98.63 379

1314		98.68 207	98.54 212	99.11 225	98.89 384	98.65 237	99.27 322	99.49 186	96.89 338	97.99 398	99.56 280	97.72 120	99.83 212	97.74 295	99.27 194	98.84 317

MVS		97.28 363	96.55 376	99.48 155	98.78 401	98.95 193	99.27 322	99.39 277	83.53 467	98.08 393	99.54 288	96.97 150	99.87 175	94.23 421	99.16 204	99.63 181

BH-untuned		98.42 223	98.36 222	98.59 301	99.49 237	96.70 365	99.27 322	99.13 364	97.24 306	98.80 331	99.38 341	95.75 216	99.74 257	97.07 349	99.16 204	99.33 271

MDTV_nov1_ep13			98.32 226		99.11 345	94.44 430	99.27 322	98.74 421	97.51 278	99.40 204	99.62 259	94.78 261	99.76 251	97.59 307	98.81 252

DP-MVS Recon		99.12 129	98.95 144	99.65 95	99.74 100	99.70 60	99.27 322	99.57 85	96.40 376	99.42 194	99.68 229	98.75 60	99.80 233	97.98 269	99.72 148	99.44 253

PatchmatchNet		98.31 234	98.36 222	98.19 353	99.16 337	95.32 410	99.27 322	98.92 392	97.37 294	99.37 210	99.58 272	94.90 254	99.70 280	97.43 326	99.21 201	99.54 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 339	97.28 350	98.62 299	99.64 160	98.03 284	99.26 331	98.74 421	97.68 256	99.09 280	98.32 440	91.66 372	99.81 226	92.88 437	98.22 289	98.03 435

CNVR-MVS		99.42 55	99.30 62	99.78 71	99.62 172	99.71 58	99.26 331	99.52 130	98.82 89	99.39 206	99.71 206	98.96 27	99.85 187	98.59 203	99.80 125	99.77 100

mamba_0408		99.08 143	98.96 140	99.44 168	99.62 172	98.88 206	99.25 333	99.47 220	98.05 203	99.37 210	99.81 128	96.85 154	99.85 187	98.98 133	99.25 197	99.60 189

SSM_04072		99.06 148	98.96 140	99.35 184	99.62 172	98.88 206	99.25 333	99.47 220	98.05 203	99.37 210	99.81 128	96.85 154	99.58 311	98.98 133	99.25 197	99.60 189

tt0320		95.71 401	95.07 405	97.62 396	99.05 360	95.02 416	99.25 333	99.52 130	86.81 462	97.97 400	99.72 203	83.58 450	99.15 385	96.38 381	93.35 427	98.68 351

1112_ss		98.98 164	98.77 177	99.59 113	99.68 132	99.02 175	99.25 333	99.48 198	97.23 307	99.13 270	99.58 272	96.93 152	99.90 148	98.87 153	98.78 253	99.84 53

TAPA-MVS	97.07 15	97.74 319	97.34 341	98.94 246	99.70 122	97.53 313	99.25 333	99.51 151	91.90 448	99.30 230	99.63 254	98.78 53	99.64 300	88.09 458	99.87 79	99.65 169
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UWE-MVS-28		97.36 358	97.24 354	97.75 389	98.84 394	94.44 430	99.24 338	97.58 457	97.98 217	99.00 298	99.00 402	91.35 378	99.53 318	93.75 426	98.39 274	99.27 279

UBG		97.85 295	97.48 315	98.95 244	99.25 310	97.64 310	99.24 338	98.74 421	97.90 224	98.64 356	98.20 444	88.65 412	99.81 226	98.27 240	98.40 273	99.42 255

PLC	97.94 4	99.02 156	98.85 168	99.53 133	99.66 147	99.01 177	99.24 338	99.52 130	96.85 340	99.27 240	99.48 312	98.25 101	99.91 135	97.76 292	99.62 165	99.65 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post1							99.23 341				65.14 479	94.18 299	99.71 273	97.58 308

ADS-MVSNet2		98.02 269	98.07 249	97.87 379	99.33 285	95.19 413	99.23 341	99.08 370	96.24 384	99.10 277	99.67 235	94.11 301	98.93 424	96.81 363	99.05 227	99.48 237

ADS-MVSNet		98.20 243	98.08 246	98.56 309	99.33 285	96.48 376	99.23 341	99.15 361	96.24 384	99.10 277	99.67 235	94.11 301	99.71 273	96.81 363	99.05 227	99.48 237

EPNet_dtu		98.03 267	97.96 259	98.23 351	98.27 436	95.54 402	99.23 341	98.75 418	99.02 62	97.82 407	99.71 206	96.11 194	99.48 320	93.04 435	99.65 161	99.69 149
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 247	97.93 264	98.87 267	99.18 327	98.49 259	99.22 345	99.33 314	96.96 332	99.56 161	99.38 341	94.33 292	99.00 410	94.83 414	98.58 263	99.14 286

RPMNet		96.72 379	95.90 392	99.19 216	99.18 327	98.49 259	99.22 345	99.52 130	88.72 460	99.56 161	97.38 457	94.08 303	99.95 76	86.87 465	98.58 263	99.14 286

sc_t1		95.75 399	95.05 406	97.87 379	98.83 395	94.61 427	99.21 347	99.45 242	87.45 461	97.97 400	99.85 80	81.19 460	99.43 334	98.27 240	93.20 431	99.57 207

WBMVS		97.74 319	97.50 313	98.46 324	99.24 312	97.43 317	99.21 347	99.42 264	97.45 284	98.96 305	99.41 330	88.83 407	99.23 370	98.94 141	96.02 372	98.71 337

plane_prior						96.97 349	99.21 347		98.45 130						97.60 320

IMVS_0404		98.53 216	98.52 214	98.55 311	99.55 206	96.93 352	99.20 350	99.44 251	98.05 203	98.96 305	99.80 146	94.66 274	99.13 390	98.15 252	98.92 238	99.60 189

tt0320-xc		95.31 407	94.59 411	97.45 404	98.92 380	94.73 422	99.20 350	99.31 328	86.74 463	97.23 420	99.72 203	81.14 461	98.95 422	97.08 348	91.98 442	98.67 359

testing91		97.44 355	97.02 364	98.71 292	99.18 327	96.89 359	99.19 352	99.04 377	97.78 243	98.31 380	98.29 441	85.41 440	99.85 187	98.01 267	97.95 303	99.39 261

WR-MVS		98.06 259	97.73 288	99.06 229	98.86 391	99.25 146	99.19 352	99.35 301	97.30 300	98.66 349	99.43 324	93.94 308	99.21 379	98.58 204	94.28 414	98.71 337

new-patchmatchnet		94.48 415	94.08 416	95.67 432	95.08 468	92.41 451	99.18 354	99.28 339	94.55 427	93.49 455	97.37 458	87.86 424	97.01 461	91.57 445	88.36 457	97.61 450

AdaColmap		99.01 160	98.80 173	99.66 91	99.56 202	99.54 98	99.18 354	99.70 18	98.18 173	99.35 219	99.63 254	96.32 187	99.90 148	97.48 320	99.77 137	99.55 211

EG-PatchMatch MVS		95.97 395	95.69 396	96.81 422	97.78 443	92.79 449	99.16 356	98.93 389	96.16 391	94.08 451	99.22 378	82.72 453	99.47 321	95.67 397	97.50 331	98.17 426

PatchT		97.03 373	96.44 379	98.79 282	98.99 370	98.34 269	99.16 356	99.07 373	92.13 447	99.52 172	97.31 460	94.54 282	98.98 412	88.54 456	98.73 255	99.03 302

CNLPA		99.14 120	98.99 132	99.59 113	99.58 193	99.41 120	99.16 356	99.44 251	98.45 130	99.19 261	99.49 306	98.08 109	99.89 163	97.73 296	99.75 142	99.48 237

MDA-MVSNet-bldmvs		94.96 410	93.98 417	97.92 375	98.24 437	97.27 323	99.15 359	99.33 314	93.80 432	80.09 474	99.03 398	88.31 417	97.86 452	93.49 430	94.36 413	98.62 381

CDPH-MVS		99.13 122	98.91 152	99.80 64	99.75 92	99.71 58	99.15 359	99.41 267	96.60 360	99.60 153	99.55 283	98.83 47	99.90 148	97.48 320	99.83 113	99.78 98

save fliter					99.76 82	99.59 88	99.14 361	99.40 274	99.00 67

WB-MVSnew		97.65 336	97.65 296	97.63 395	98.78 401	97.62 311	99.13 362	98.33 441	97.36 295	99.07 283	98.94 410	95.64 221	99.15 385	92.95 436	98.68 258	96.12 465

testf1		90.42 431	90.68 432	89.65 452	97.78 443	73.97 480	99.13 362	98.81 411	89.62 455	91.80 463	98.93 411	62.23 471	98.80 431	86.61 466	91.17 445	96.19 463

APD_test2		90.42 431	90.68 432	89.65 452	97.78 443	73.97 480	99.13 362	98.81 411	89.62 455	91.80 463	98.93 411	62.23 471	98.80 431	86.61 466	91.17 445	96.19 463

xiu_mvs_v1_base_debu		99.29 85	99.27 73	99.34 185	99.63 164	98.97 183	99.12 365	99.51 151	98.86 84	99.84 56	99.47 315	98.18 104	99.99 4	99.50 57	99.31 191	99.08 294

xiu_mvs_v1_base		99.29 85	99.27 73	99.34 185	99.63 164	98.97 183	99.12 365	99.51 151	98.86 84	99.84 56	99.47 315	98.18 104	99.99 4	99.50 57	99.31 191	99.08 294

xiu_mvs_v1_base_debi		99.29 85	99.27 73	99.34 185	99.63 164	98.97 183	99.12 365	99.51 151	98.86 84	99.84 56	99.47 315	98.18 104	99.99 4	99.50 57	99.31 191	99.08 294

XVG-OURS-SEG-HR		98.69 206	98.62 202	98.89 260	99.71 117	97.74 302	99.12 365	99.54 109	98.44 133	99.42 194	99.71 206	94.20 296	99.92 123	98.54 214	98.90 244	99.00 305

jason		99.13 122	99.03 116	99.45 163	99.46 247	98.87 210	99.12 365	99.26 343	98.03 212	99.79 76	99.65 242	97.02 147	99.85 187	99.02 130	99.90 57	99.65 169
jason: jason.
N_pmnet		94.95 411	95.83 394	92.31 443	98.47 432	79.33 475	99.12 365	92.81 481	93.87 431	97.68 410	99.13 388	93.87 312	99.01 409	91.38 446	96.19 369	98.59 394

MDA-MVSNet_test_wron		95.45 403	94.60 410	98.01 366	98.16 438	97.21 328	99.11 371	99.24 348	93.49 436	80.73 473	98.98 406	93.02 329	98.18 443	94.22 422	94.45 411	98.64 372

Patchmtry		97.75 317	97.40 333	98.81 279	99.10 348	98.87 210	99.11 371	99.33 314	94.83 421	98.81 329	99.38 341	94.33 292	99.02 407	96.10 384	95.57 389	98.53 398

YYNet1		95.36 405	94.51 413	97.92 375	97.89 441	97.10 332	99.10 373	99.23 349	93.26 439	80.77 472	99.04 397	92.81 335	98.02 447	94.30 418	94.18 416	98.64 372

CANet_DTU		98.97 166	98.87 162	99.25 208	99.33 285	98.42 267	99.08 374	99.30 333	99.16 37	99.43 191	99.75 188	95.27 235	99.97 29	98.56 210	99.95 23	99.36 266

icg_test_0407_2		98.79 195	98.86 165	98.57 305	99.55 206	96.93 352	99.07 375	99.44 251	98.05 203	99.66 124	99.80 146	97.13 138	99.18 382	98.15 252	98.92 238	99.60 189

SCA		98.19 244	98.16 234	98.27 349	99.30 294	95.55 400	99.07 375	98.97 385	97.57 268	99.43 191	99.57 277	92.72 339	99.74 257	97.58 308	99.20 202	99.52 220

TSAR-MVS + GP.		99.36 72	99.36 46	99.36 182	99.67 134	98.61 244	99.07 375	99.33 314	99.00 67	99.82 68	99.81 128	99.06 18	99.84 196	99.09 121	99.42 181	99.65 169

MG-MVS		99.13 122	99.02 124	99.45 163	99.57 198	98.63 240	99.07 375	99.34 306	98.99 69	99.61 150	99.82 113	97.98 113	99.87 175	97.00 351	99.80 125	99.85 46

PatchMatch-RL		98.84 190	98.62 202	99.52 139	99.71 117	99.28 141	99.06 379	99.77 12	97.74 249	99.50 175	99.53 292	95.41 228	99.84 196	97.17 344	99.64 162	99.44 253

OpenMVS_ROB	92.34 20	94.38 416	93.70 422	96.41 427	97.38 449	93.17 447	99.06 379	98.75 418	86.58 464	94.84 449	98.26 442	81.53 458	99.32 355	89.01 454	97.87 308	96.76 458

TEST9					99.67 134	99.65 75	99.05 381	99.41 267	96.22 386	98.95 307	99.49 306	98.77 56	99.91 135

train_agg		99.02 156	98.77 177	99.77 74	99.67 134	99.65 75	99.05 381	99.41 267	96.28 380	98.95 307	99.49 306	98.76 57	99.91 135	97.63 304	99.72 148	99.75 109

lupinMVS		99.13 122	99.01 129	99.46 162	99.51 223	98.94 197	99.05 381	99.16 360	97.86 228	99.80 74	99.56 280	97.39 125	99.86 181	98.94 141	99.85 94	99.58 204

DELS-MVS		99.48 37	99.42 32	99.65 95	99.72 111	99.40 121	99.05 381	99.66 32	99.14 40	99.57 160	99.80 146	98.46 87	99.94 92	99.57 48	99.84 102	99.60 189
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		96.38 387	96.03 389	97.41 405	98.13 439	95.16 415	99.05 381	99.20 355	93.94 430	97.39 417	98.79 422	91.61 374	99.04 403	90.43 449	95.77 381	98.05 434

Patchmatch-test		97.93 282	97.65 296	98.77 285	99.18 327	97.07 336	99.03 386	99.14 363	96.16 391	98.74 337	99.57 277	94.56 279	99.72 267	93.36 431	99.11 217	99.52 220

test_8					99.67 134	99.61 85	99.03 386	99.41 267	96.28 380	98.93 310	99.48 312	98.76 57	99.91 135

Test_1112_low_res		98.89 171	98.66 192	99.57 120	99.69 127	98.95 193	99.03 386	99.47 220	96.98 330	99.15 268	99.23 377	96.77 163	99.89 163	98.83 166	98.78 253	99.86 42

IterMVS-SCA-FT		97.82 305	97.75 286	98.06 362	99.57 198	96.36 380	99.02 389	99.49 186	97.18 310	98.71 340	99.72 203	92.72 339	99.14 387	97.44 325	95.86 380	98.67 359

xiu_mvs_v2_base		99.26 92	99.25 77	99.29 201	99.53 214	98.91 203	99.02 389	99.45 242	98.80 94	99.71 107	99.26 374	98.94 34	99.98 20	99.34 81	99.23 200	98.98 308

MIMVSNet		97.73 321	97.45 321	98.57 305	99.45 253	97.50 315	99.02 389	98.98 384	96.11 396	99.41 199	99.14 387	90.28 389	98.74 433	95.74 393	98.93 236	99.47 243

IterMVS		97.83 302	97.77 281	98.02 365	99.58 193	96.27 384	99.02 389	99.48 198	97.22 308	98.71 340	99.70 210	92.75 336	99.13 390	97.46 323	96.00 374	98.67 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 135	98.92 149	99.65 95	99.90 4	99.37 123	99.02 389	99.91 3	97.67 258	99.59 156	99.75 188	95.90 207	99.73 263	99.53 53	99.02 231	99.86 42

UWE-MVS		97.58 341	97.29 349	98.48 318	99.09 351	96.25 385	99.01 394	96.61 467	97.86 228	99.19 261	99.01 401	88.72 408	99.90 148	97.38 329	98.69 257	99.28 275

新几何2							99.01 394

BH-w/o		98.00 274	97.89 270	98.32 341	99.35 279	96.20 387	99.01 394	98.90 399	96.42 374	98.38 376	99.00 402	95.26 237	99.72 267	96.06 385	98.61 260	99.03 302

test_prior4						99.56 94	98.99 397

无先验							98.99 397	99.51 151	96.89 338				99.93 110	97.53 316		99.72 132

pmmvs4		98.13 251	97.90 266	98.81 279	98.61 424	98.87 210	98.99 397	99.21 354	96.44 372	99.06 288	99.58 272	95.90 207	99.11 396	97.18 343	96.11 371	98.46 407

HQP-NCC					99.19 324		98.98 400		98.24 162	98.66 349

ACMP_Plane					99.19 324		98.98 400		98.24 162	98.66 349

HQP-MVS		98.02 269	97.90 266	98.37 337	99.19 324	96.83 360	98.98 400	99.39 277	98.24 162	98.66 349	99.40 334	92.47 350	99.64 300	97.19 341	97.58 322	98.64 372

PS-MVSNAJ		99.32 79	99.32 54	99.30 198	99.57 198	98.94 197	98.97 403	99.46 231	98.92 81	99.71 107	99.24 376	99.01 20	99.98 20	99.35 76	99.66 159	98.97 309

MVP-Stereo		97.81 307	97.75 286	97.99 369	97.53 447	96.60 373	98.96 404	98.85 406	97.22 308	97.23 420	99.36 347	95.28 234	99.46 323	95.51 399	99.78 134	97.92 445
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							98.96 404		98.34 143	99.01 294	99.52 296	98.68 70		97.96 270	99.74 145

旧先验2							98.96 404		96.70 348	99.47 180			99.94 92	98.19 246

原ACMM2							98.95 407

MVS_111021_HR		99.41 59	99.32 54	99.66 91	99.72 111	99.47 113	98.95 407	99.85 9	98.82 89	99.54 168	99.73 199	98.51 84	99.74 257	98.91 147	99.88 76	99.77 100

mvsany_test1		99.50 31	99.46 28	99.62 108	99.61 183	99.09 165	98.94 409	99.48 198	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	98.94 409	99.85 9	98.82 89	99.65 133	99.74 193	98.51 84	99.80 233	98.83 166	99.89 68	99.64 176

pmmvs3		94.09 418	93.25 425	96.60 425	94.76 470	94.49 429	98.92 411	98.18 448	89.66 454	96.48 434	98.06 451	86.28 434	97.33 458	89.68 452	87.20 460	97.97 442

XVG-OURS		98.73 204	98.68 188	98.88 263	99.70 122	97.73 303	98.92 411	99.55 100	98.52 122	99.45 183	99.84 95	95.27 235	99.91 135	98.08 261	98.84 248	99.00 305

test222					99.75 92	99.49 109	98.91 413	99.49 186	96.42 374	99.34 223	99.65 242	98.28 100			99.69 153	99.72 132

PMMVS2		86.87 434	85.37 438	91.35 447	90.21 476	83.80 466	98.89 414	97.45 459	83.13 468	91.67 465	95.03 465	48.49 477	94.70 471	85.86 468	77.62 470	95.54 466

miper_lstm_enhance		98.00 274	97.91 265	98.28 348	99.34 284	97.43 317	98.88 415	99.36 294	96.48 369	98.80 331	99.55 283	95.98 200	98.91 425	97.27 334	95.50 392	98.51 400

MVS-HIRNet		95.75 399	95.16 404	97.51 402	99.30 294	93.69 441	98.88 415	95.78 469	85.09 466	98.78 334	92.65 469	91.29 380	99.37 343	94.85 413	99.85 94	99.46 248

TR-MVS		97.76 313	97.41 332	98.82 276	99.06 357	97.87 297	98.87 417	98.56 435	96.63 356	98.68 348	99.22 378	92.49 349	99.65 296	95.40 403	97.79 312	98.95 313

testdata1							98.85 418		98.32 147

ET-MVSNet_ETH3D		96.49 384	95.64 398	99.05 231	99.53 214	98.82 222	98.84 419	97.51 458	97.63 261	84.77 467	99.21 381	92.09 359	98.91 425	98.98 133	92.21 441	99.41 258

our_test_3		97.65 336	97.68 293	97.55 401	98.62 422	94.97 418	98.84 419	99.30 333	96.83 343	98.19 389	99.34 354	97.01 149	99.02 407	95.00 411	96.01 373	98.64 372

MS-PatchMatch		97.24 367	97.32 345	96.99 415	98.45 433	93.51 445	98.82 421	99.32 324	97.41 291	98.13 392	99.30 365	88.99 405	99.56 314	95.68 396	99.80 125	97.90 446

c3_l		98.12 253	98.04 251	98.38 336	99.30 294	97.69 309	98.81 422	99.33 314	96.67 350	98.83 326	99.34 354	97.11 141	98.99 411	97.58 308	95.34 394	98.48 402

ppachtmachnet_test		97.49 353	97.45 321	97.61 399	98.62 422	95.24 411	98.80 423	99.46 231	96.11 396	98.22 387	99.62 259	96.45 181	98.97 419	93.77 425	95.97 378	98.61 390

PAPR		98.63 213	98.34 224	99.51 144	99.40 267	99.03 174	98.80 423	99.36 294	96.33 377	99.00 298	99.12 391	98.46 87	99.84 196	95.23 407	99.37 190	99.66 163

test0.0.03 1		97.71 326	97.42 331	98.56 309	98.41 435	97.82 300	98.78 425	98.63 433	97.34 296	98.05 397	98.98 406	94.45 287	98.98 412	95.04 410	97.15 352	98.89 314

PVSNet_Blended		99.08 143	98.97 136	99.42 173	99.76 82	98.79 225	98.78 425	99.91 3	96.74 345	99.67 119	99.49 306	97.53 122	99.88 168	98.98 133	99.85 94	99.60 189

PMMVS		98.80 194	98.62 202	99.34 185	99.27 303	98.70 233	98.76 427	99.31 328	97.34 296	99.21 255	99.07 393	97.20 136	99.82 221	98.56 210	98.87 245	99.52 220

test123		39.01 446	42.50 448	28.53 462	39.17 485	20.91 487	98.75 428	19.17 487	19.83 480	38.57 479	66.67 477	33.16 481	15.42 481	37.50 481	29.66 479	49.26 476

MSDG		98.98 164	98.80 173	99.53 133	99.76 82	99.19 150	98.75 428	99.55 100	97.25 304	99.47 180	99.77 179	97.82 116	99.87 175	96.93 358	99.90 57	99.54 213

CLD-MVS		98.16 248	98.10 242	98.33 339	99.29 298	96.82 362	98.75 428	99.44 251	97.83 235	99.13 270	99.55 283	92.92 332	99.67 288	98.32 237	97.69 315	98.48 402
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		98.18 246	98.10 242	98.41 332	99.23 314	97.72 305	98.72 431	99.31 328	96.60 360	98.88 317	99.29 367	97.29 132	99.13 390	97.60 306	95.99 375	98.38 415

cl____		98.01 272	97.84 274	98.55 311	99.25 310	97.97 288	98.71 432	99.34 306	96.47 371	98.59 365	99.54 288	95.65 220	99.21 379	97.21 337	95.77 381	98.46 407

DIV-MVS_self_test		98.01 272	97.85 273	98.48 318	99.24 312	97.95 293	98.71 432	99.35 301	96.50 365	98.60 364	99.54 288	95.72 218	99.03 405	97.21 337	95.77 381	98.46 407

test-LLR		98.06 259	97.90 266	98.55 311	98.79 398	97.10 332	98.67 434	97.75 453	97.34 296	98.61 362	98.85 416	94.45 287	99.45 325	97.25 335	99.38 183	99.10 289

TESTMET0.1,1		97.55 342	97.27 353	98.40 334	98.93 378	96.53 374	98.67 434	97.61 456	96.96 332	98.64 356	99.28 369	88.63 414	99.45 325	97.30 333	99.38 183	99.21 284

test-mter		97.49 353	97.13 360	98.55 311	98.79 398	97.10 332	98.67 434	97.75 453	96.65 352	98.61 362	98.85 416	88.23 418	99.45 325	97.25 335	99.38 183	99.10 289

mvs5depth		96.66 380	96.22 384	97.97 370	97.00 458	96.28 383	98.66 437	99.03 379	96.61 357	96.93 430	99.79 163	87.20 428	99.47 321	96.65 373	94.13 417	98.16 427

IB-MVS	95.67 18	96.22 388	95.44 402	98.57 305	99.21 319	96.70 365	98.65 438	97.74 455	96.71 347	97.27 419	98.54 431	86.03 435	99.92 123	98.47 220	86.30 461	99.10 289
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		98.95 167	98.71 185	99.66 91	99.63 164	99.55 96	98.64 439	99.10 367	97.93 221	99.42 194	99.55 283	98.67 72	99.80 233	95.80 392	99.68 156	99.61 186

thisisatest0515		98.14 250	97.79 276	99.19 216	99.50 235	98.50 258	98.61 440	96.82 463	96.95 334	99.54 168	99.43 324	91.66 372	99.86 181	98.08 261	99.51 174	99.22 283

DeepPCF-MVS	98.18 3	98.81 191	99.37 44	97.12 413	99.60 189	91.75 454	98.61 440	99.44 251	99.35 25	99.83 64	99.85 80	98.70 69	99.81 226	99.02 130	99.91 46	99.81 79

cl22		97.85 295	97.64 299	98.48 318	99.09 351	97.87 297	98.60 442	99.33 314	97.11 319	98.87 320	99.22 378	92.38 355	99.17 384	98.21 244	95.99 375	98.42 410

GA-MVS		97.85 295	97.47 318	99.00 237	99.38 272	97.99 287	98.57 443	99.15 361	97.04 327	98.90 314	99.30 365	89.83 397	99.38 340	96.70 368	98.33 278	99.62 184

TinyColmap		97.12 370	96.89 369	97.83 384	99.07 355	95.52 403	98.57 443	98.74 421	97.58 267	97.81 408	99.79 163	88.16 419	99.56 314	95.10 408	97.21 349	98.39 414

eth_miper_zixun_eth		98.05 264	97.96 259	98.33 339	99.26 306	97.38 319	98.56 445	99.31 328	96.65 352	98.88 317	99.52 296	96.58 173	99.12 395	97.39 328	95.53 391	98.47 404

CMPMVS	69.68 23	94.13 417	94.90 408	91.84 444	97.24 453	80.01 474	98.52 446	99.48 198	89.01 458	91.99 461	99.67 235	85.67 437	99.13 390	95.44 401	97.03 354	96.39 462
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 360	97.20 355	97.75 389	99.07 355	95.20 412	98.51 447	99.04 377	97.99 216	98.31 380	99.86 73	89.02 404	99.55 316	95.67 397	97.36 344	98.49 401

ambc				93.06 442	92.68 473	82.36 467	98.47 448	98.73 427		95.09 447	97.41 456	55.55 473	99.10 398	96.42 378	91.32 444	97.71 447

miper_enhance_ethall		98.16 248	98.08 246	98.41 332	98.96 376	97.72 305	98.45 449	99.32 324	96.95 334	98.97 303	99.17 383	97.06 145	99.22 374	97.86 278	95.99 375	98.29 419

CHOSEN 280x420		99.12 129	99.13 95	99.08 226	99.66 147	97.89 296	98.43 450	99.71 16	98.88 83	99.62 145	99.76 183	96.63 169	99.70 280	99.46 67	99.99 1	99.66 163

testmvs		39.17 445	43.78 447	25.37 463	36.04 486	16.84 488	98.36 451	26.56 485	20.06 479	38.51 480	67.32 476	29.64 482	15.30 482	37.59 480	39.90 478	43.98 477

FPMVS		84.93 436	85.65 437	82.75 458	86.77 479	63.39 484	98.35 452	98.92 392	74.11 470	83.39 469	98.98 406	50.85 476	92.40 473	84.54 469	94.97 402	92.46 468

KD-MVS_2432*1600		94.62 412	93.72 420	97.31 407	97.19 455	95.82 394	98.34 453	99.20 355	95.00 417	97.57 411	98.35 438	87.95 421	98.10 445	92.87 438	77.00 471	98.01 436

miper_refine_blended		94.62 412	93.72 420	97.31 407	97.19 455	95.82 394	98.34 453	99.20 355	95.00 417	97.57 411	98.35 438	87.95 421	98.10 445	92.87 438	77.00 471	98.01 436

CL-MVSNet_self_test		94.49 414	93.97 418	96.08 430	96.16 460	93.67 442	98.33 455	99.38 285	95.13 411	97.33 418	98.15 445	92.69 343	96.57 463	88.67 455	79.87 469	97.99 440

PVSNet	96.02 17	98.85 187	98.84 170	98.89 260	99.73 107	97.28 322	98.32 456	99.60 67	97.86 228	99.50 175	99.57 277	96.75 164	99.86 181	98.56 210	99.70 152	99.54 213

PAPM		97.59 340	97.09 362	99.07 227	99.06 357	98.26 272	98.30 457	99.10 367	94.88 419	98.08 393	99.34 354	96.27 189	99.64 300	89.87 451	98.92 238	99.31 273

Patchmatch-RL test		95.84 397	95.81 395	95.95 431	95.61 463	90.57 457	98.24 458	98.39 439	95.10 415	95.20 445	98.67 426	94.78 261	97.77 453	96.28 383	90.02 452	99.51 229

UnsupCasMVSNet_bld		93.53 421	92.51 427	96.58 426	97.38 449	93.82 437	98.24 458	99.48 198	91.10 452	93.10 456	96.66 462	74.89 465	98.37 440	94.03 424	87.71 459	97.56 452

LCM-MVSNet		86.80 435	85.22 439	91.53 446	87.81 478	80.96 472	98.23 460	98.99 383	71.05 471	90.13 466	96.51 463	48.45 478	96.88 462	90.51 448	85.30 462	96.76 458

cascas		97.69 328	97.43 330	98.48 318	98.60 425	97.30 321	98.18 461	99.39 277	92.96 442	98.41 374	98.78 423	93.77 316	99.27 363	98.16 250	98.61 260	98.86 315

kuosan		90.92 430	90.11 435	93.34 439	98.78 401	85.59 464	98.15 462	93.16 479	89.37 457	92.07 460	98.38 437	81.48 459	95.19 469	62.54 478	97.04 353	99.25 280

Effi-MVS+		98.81 191	98.59 208	99.48 155	99.46 247	99.12 163	98.08 463	99.50 174	97.50 279	99.38 208	99.41 330	96.37 186	99.81 226	99.11 117	98.54 268	99.51 229

PCF-MVS	97.08 14	97.66 335	97.06 363	99.47 160	99.61 183	99.09 165	98.04 464	99.25 345	91.24 451	98.51 369	99.70 210	94.55 281	99.91 135	92.76 440	99.85 94	99.42 255
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 393	95.47 400	97.94 373	99.31 293	94.34 434	97.81 465	99.70 18	97.12 316	97.46 413	98.75 424	89.71 398	99.79 239	97.69 302	81.69 467	99.68 155

E-PMN		80.61 439	79.88 441	82.81 457	90.75 475	76.38 478	97.69 466	95.76 470	66.44 475	83.52 468	92.25 470	62.54 470	87.16 477	68.53 476	61.40 474	84.89 475

dongtai		93.26 422	92.93 426	94.25 435	99.39 270	85.68 463	97.68 467	93.27 477	92.87 443	96.85 431	99.39 338	82.33 456	97.48 457	76.78 471	97.80 311	99.58 204

ANet_high		77.30 441	74.86 445	84.62 456	75.88 482	77.61 476	97.63 468	93.15 480	88.81 459	64.27 477	89.29 474	36.51 480	83.93 479	75.89 473	52.31 476	92.33 470

EMVS		80.02 440	79.22 442	82.43 459	91.19 474	76.40 477	97.55 469	92.49 482	66.36 476	83.01 470	91.27 472	64.63 469	85.79 478	65.82 477	60.65 475	85.08 474

MVE	76.82 21	76.91 442	74.31 446	84.70 455	85.38 481	76.05 479	96.88 470	93.17 478	67.39 474	71.28 476	89.01 475	21.66 485	87.69 476	71.74 475	72.29 473	90.35 472
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 428	91.36 430	90.31 449	95.85 461	73.72 482	94.89 471	99.25 345	68.39 473	95.82 441	99.02 400	80.50 462	98.95 422	93.64 428	94.89 406	98.25 422

Gipuma		90.99 429	90.15 434	93.51 438	98.73 410	90.12 458	93.98 472	99.45 242	79.32 469	92.28 459	94.91 466	69.61 466	97.98 449	87.42 462	95.67 385	92.45 469
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 443	74.97 444	79.01 460	70.98 483	55.18 485	93.37 473	98.21 446	65.08 477	61.78 478	93.83 468	21.74 484	92.53 472	78.59 470	91.12 447	89.34 473
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 437	81.52 440	86.66 454	66.61 484	68.44 483	92.79 474	97.92 450	68.96 472	80.04 475	99.85 80	85.77 436	96.15 467	97.86 278	43.89 477	95.39 467

wuyk23d		40.18 444	41.29 449	36.84 461	86.18 480	49.12 486	79.73 475	22.81 486	27.64 478	25.46 481	28.45 481	21.98 483	48.89 480	55.80 479	23.56 480	12.51 478

mmdepth		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

monomultidepth		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

test_blank		0.13 450	0.17 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	1.57 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet_test		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

DCPMVS		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

cdsmvs_eth3d_5k		24.64 447	32.85 450	0.00 464	0.00 487	0.00 489	0.00 476	99.51 151	0.00 482	0.00 483	99.56 280	96.58 173	0.00 483	0.00 482	0.00 481	0.00 479

pcd_1.5k_mvsjas		8.27 449	11.03 452	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	99.01 20	0.00 483	0.00 482	0.00 481	0.00 479

sosnet-low-res		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

sosnet		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uncertanet		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

Regformer		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

ab-mvs-re		8.30 448	11.06 451	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	99.58 272	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet		0.02 451	0.03 454	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.27 483	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

WAC-MVS						97.16 329								95.47 400

MSC_two_6792asdad				99.87 21	99.51 223	99.76 49		99.33 314					99.96 41	98.87 153	99.84 102	99.89 29

PC_three_1452									98.18 173	99.84 56	99.70 210	99.31 3	98.52 438	98.30 239	99.80 125	99.81 79

No_MVS				99.87 21	99.51 223	99.76 49		99.33 314					99.96 41	98.87 153	99.84 102	99.89 29

test_one_0601					99.81 57	99.88 10		99.49 186	98.97 75	99.65 133	99.81 128	99.09 16

eth-test2					0.00 487

eth-test					0.00 487

ZD-MVS					99.71 117	99.79 41		99.61 60	96.84 341	99.56 161	99.54 288	98.58 78	99.96 41	96.93 358	99.75 142

IU-MVS					99.84 38	99.88 10		99.32 324	98.30 149	99.84 56				98.86 158	99.85 94	99.89 29

test_241102_TWO								99.48 198	99.08 56	99.88 43	99.81 128	98.94 34	99.96 41	98.91 147	99.84 102	99.88 35

test_241102_ONE					99.84 38	99.90 3		99.48 198	99.07 58	99.91 31	99.74 193	99.20 9	99.76 251

test_0728_THIRD									98.99 69	99.81 69	99.80 146	99.09 16	99.96 41	98.85 160	99.90 57	99.88 35

GSMVS																99.52 220

test_part2					99.81 57	99.83 22				99.77 85

sam_mvs1												94.86 256				99.52 220

sam_mvs												94.72 268

MTGPA								99.47 220

test_post											65.99 478	94.65 275	99.73 263

patchmatchnet-post											98.70 425	94.79 260	99.74 257

gm-plane-assit					98.54 430	92.96 448			94.65 425		99.15 386		99.64 300	97.56 313

test9_res														97.49 319	99.72 148	99.75 109

agg_prior2														97.21 337	99.73 147	99.75 109

agg_prior					99.67 134	99.62 83		99.40 274		98.87 320			99.91 135

TestCases				99.31 193	99.86 25	98.48 261		99.61 60	97.85 231	99.36 216	99.85 80	95.95 202	99.85 187	96.66 371	99.83 113	99.59 200

test_prior				99.68 89	99.67 134	99.48 111		99.56 90					99.83 212			99.74 113

新几何1				99.75 77	99.75 92	99.59 88		99.54 109	96.76 344	99.29 233	99.64 248	98.43 89	99.94 92	96.92 360	99.66 159	99.72 132

旧先验1					99.74 100	99.59 88		99.54 109			99.69 221	98.47 86			99.68 156	99.73 122

原ACMM1				99.65 95	99.73 107	99.33 130		99.47 220	97.46 281	99.12 272	99.66 240	98.67 72	99.91 135	97.70 301	99.69 153	99.71 143

testdata2													99.95 76	96.67 370

segment_acmp												98.96 27

testdata				99.54 125	99.75 92	98.95 193		99.51 151	97.07 322	99.43 191	99.70 210	98.87 42	99.94 92	97.76 292	99.64 162	99.72 132

test12				99.75 77	99.64 160	99.61 85		99.29 337		99.21 255		98.38 95	99.89 163		99.74 145	99.74 113

plane_prior7					99.29 298	97.03 344

plane_prior6					99.27 303	96.98 348						92.71 341

plane_prior5								99.47 220					99.69 285	97.78 288	97.63 317	98.67 359

plane_prior4											99.61 263

plane_prior3						97.00 346			98.69 107	99.11 274

plane_prior1					99.26 306

n2								0.00 488

nn								0.00 488

door-mid								98.05 449

lessismore_v0				97.79 388	98.69 416	95.44 407		94.75 473		95.71 442	99.87 65	88.69 410	99.32 355	95.89 389	94.93 404	98.62 381

LGP-MVS_train				98.49 316	99.33 285	97.05 338		99.55 100	97.46 281	99.24 247	99.83 100	92.58 346	99.72 267	98.09 257	97.51 329	98.68 351

test11								99.35 301

door								97.92 450

HQP5-MVS						96.83 360

BP-MVS														97.19 341

HQP4-MVS										98.66 349			99.64 300			98.64 372

HQP3-MVS								99.39 277							97.58 322

HQP2-MVS												92.47 350

NP-MVS					99.23 314	96.92 356					99.40 334

ACMMP++_ref															97.19 350

ACMMP++															97.43 340

Test By Simon												98.75 60

ITE_SJBPF				98.08 361	99.29 298	96.37 379		98.92 392	98.34 143	98.83 326	99.75 188	91.09 382	99.62 307	95.82 390	97.40 342	98.25 422

DeepMVS_CX				93.34 439	99.29 298	82.27 468		99.22 351	85.15 465	96.33 435	99.05 396	90.97 384	99.73 263	93.57 429	97.77 313	98.01 436

High-resolution multi-view benchmark