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 20	99.48 15	99.54 97	99.76 65	99.42 96	99.90 1	99.55 77	98.56 87	99.78 47	99.70 158	98.65 68	99.79 183	99.65 23	99.78 104	99.41 195

CS-MVS-test		99.49 22	99.48 15	99.54 97	99.78 56	99.30 109	99.89 2	99.58 61	98.56 87	99.73 62	99.69 168	98.55 75	99.82 169	99.69 19	99.85 69	99.48 178

RRT_MVS		98.70 151	98.66 139	98.83 225	98.90 311	98.45 218	99.89 2	99.28 281	97.76 180	98.94 247	99.92 14	96.98 134	99.25 299	99.28 63	97.00 288	98.80 248

mvsmamba		98.92 121	98.87 115	99.08 175	99.07 287	99.16 125	99.88 4	99.51 115	98.15 133	99.40 152	99.89 30	97.12 127	99.33 285	99.38 48	97.40 275	98.73 262

MVSFormer		99.17 81	99.12 74	99.29 151	99.51 170	98.94 165	99.88 4	99.46 182	97.55 203	99.80 40	99.65 186	97.39 116	99.28 294	99.03 85	99.85 69	99.65 129

test_djsdf		98.67 156	98.57 156	98.98 190	98.70 341	98.91 169	99.88 4	99.46 182	97.55 203	99.22 195	99.88 36	95.73 179	99.28 294	99.03 85	97.62 251	98.75 257

OurMVSNet-221017-0		97.88 235	97.77 226	98.19 290	98.71 340	96.53 312	99.88 4	99.00 319	97.79 177	98.78 271	99.94 6	91.68 313	99.35 282	97.21 270	96.99 289	98.69 274

EC-MVSNet		99.44 37	99.39 27	99.58 90	99.56 156	99.49 87	99.88 4	99.58 61	98.38 102	99.73 62	99.69 168	98.20 95	99.70 219	99.64 24	99.82 90	99.54 161

DVP-MVS++		99.59 8	99.50 13	99.88 5	99.51 170	99.88 8	99.87 9	99.51 115	98.99 45	99.88 20	99.81 90	99.27 5	99.96 30	98.85 112	99.80 97	99.81 61

FOURS1					99.91 1	99.93 1	99.87 9	99.56 69	99.10 27	99.81 37

K. test v3		97.10 304	96.79 304	98.01 302	98.72 338	96.33 319	99.87 9	97.05 387	97.59 197	96.16 364	99.80 103	88.71 348	99.04 332	96.69 301	96.55 295	98.65 296

FC-MVSNet-test		98.75 146	98.62 147	99.15 172	99.08 286	99.45 93	99.86 12	99.60 54	98.23 121	98.70 283	99.82 76	96.80 139	99.22 306	99.07 83	96.38 298	98.79 249

v7n		97.87 237	97.52 252	98.92 200	98.76 334	98.58 201	99.84 13	99.46 182	96.20 317	98.91 251	99.70 158	94.89 208	99.44 262	96.03 315	93.89 351	98.75 257

DTE-MVSNet		97.51 285	97.19 293	98.46 264	98.63 347	98.13 234	99.84 13	99.48 155	96.68 280	97.97 332	99.67 180	92.92 277	98.56 363	96.88 294	92.60 366	98.70 270

3Dnovator	97.25 9	99.24 74	99.05 83	99.81 44	99.12 276	99.66 53	99.84 13	99.74 10	99.09 32	98.92 250	99.90 26	95.94 170	99.98 13	98.95 93	99.92 24	99.79 74

FIs		98.78 143	98.63 142	99.23 162	99.18 262	99.54 79	99.83 16	99.59 57	98.28 113	98.79 270	99.81 90	96.75 142	99.37 275	99.08 82	96.38 298	98.78 250

test_fmvs3		92.10 351	91.77 354	93.08 364	96.19 383	86.25 386	99.82 17	98.62 363	96.65 283	95.19 372	96.90 384	55.05 399	95.93 392	96.63 305	90.92 374	97.06 380

jajsoiax		98.43 169	98.28 175	98.88 211	98.60 351	98.43 220	99.82 17	99.53 96	98.19 127	98.63 294	99.80 103	93.22 272	99.44 262	99.22 69	97.50 263	98.77 253

OpenMVS	96.50 16	98.47 166	98.12 186	99.52 111	99.04 294	99.53 82	99.82 17	99.72 11	94.56 356	98.08 325	99.88 36	94.73 221	99.98 13	97.47 256	99.76 110	99.06 229

SDMVSNet		99.11 99	98.90 110	99.75 58	99.81 46	99.59 70	99.81 20	99.65 33	98.78 73	99.64 93	99.88 36	94.56 231	99.93 84	99.67 21	98.26 225	99.72 103

nrg030		98.64 159	98.42 165	99.28 155	99.05 293	99.69 47	99.81 20	99.46 182	98.04 154	99.01 235	99.82 76	96.69 144	99.38 270	99.34 55	94.59 339	98.78 250

HPM-MVS		99.42 42	99.28 55	99.83 40	99.90 4	99.72 42	99.81 20	99.54 85	97.59 197	99.68 74	99.63 198	98.91 34	99.94 69	98.58 152	99.91 31	99.84 40
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		99.13 89	98.99 97	99.53 105	99.65 126	99.06 142	99.81 20	99.33 257	97.43 219	99.60 106	99.88 36	97.14 126	99.84 151	99.13 76	98.94 185	99.69 115

3Dnovator+	97.12 13	99.18 79	98.97 101	99.82 41	99.17 268	99.68 48	99.81 20	99.51 115	99.20 18	98.72 276	99.89 30	95.68 182	99.97 21	98.86 110	99.86 62	99.81 61

FA-MVS(test-final)		98.75 146	98.53 160	99.41 129	99.55 160	99.05 144	99.80 25	99.01 318	96.59 292	99.58 110	99.59 212	95.39 190	99.90 116	97.78 222	99.49 143	99.28 209

bld_raw_dy_0_64		98.69 153	98.58 155	98.99 188	98.88 314	98.96 157	99.80 25	99.41 212	97.91 164	99.32 172	99.87 44	95.70 181	99.31 291	99.09 80	97.27 280	98.71 265

GeoE		98.85 135	98.62 147	99.53 105	99.61 141	99.08 139	99.80 25	99.51 115	97.10 251	99.31 174	99.78 121	95.23 199	99.77 190	98.21 186	99.03 180	99.75 88

canonicalmvs		99.02 112	98.86 118	99.51 113	99.42 200	99.32 104	99.80 25	99.48 155	98.63 82	99.31 174	98.81 352	97.09 129	99.75 196	99.27 66	97.90 241	99.47 184

v8		97.95 227	97.63 244	98.93 198	98.95 308	98.81 183	99.80 25	99.41 212	96.03 331	99.10 219	99.42 265	94.92 206	99.30 292	96.94 289	94.08 348	98.66 294

Vis-MVSNet (Re-imp)		98.87 125	98.72 130	99.31 143	99.71 96	98.88 171	99.80 25	99.44 201	97.91 164	99.36 164	99.78 121	95.49 188	99.43 266	97.91 209	99.11 171	99.62 142

Anonymous20240521		96.20 321	95.89 324	97.13 338	97.72 371	94.96 350	99.79 31	99.29 279	93.01 370	97.20 352	99.03 333	89.69 341	98.36 367	91.16 374	96.13 303	98.07 357

PS-MVSNAJss		98.92 121	98.92 107	98.90 206	98.78 329	98.53 205	99.78 32	99.54 85	98.07 148	99.00 239	99.76 135	99.01 18	99.37 275	99.13 76	97.23 282	98.81 247

PEN-MVS		97.76 255	97.44 266	98.72 237	98.77 333	98.54 204	99.78 32	99.51 115	97.06 255	98.29 317	99.64 192	92.63 290	98.89 354	98.09 195	93.16 359	98.72 263

anonymousdsp		98.44 168	98.28 175	98.94 196	98.50 356	98.96 157	99.77 34	99.50 135	97.07 253	98.87 259	99.77 129	94.76 219	99.28 294	98.66 139	97.60 252	98.57 324

SixPastTwentyTwo		97.50 286	97.33 283	98.03 299	98.65 345	96.23 322	99.77 34	98.68 360	97.14 244	97.90 333	99.93 9	90.45 331	99.18 314	97.00 283	96.43 297	98.67 286

QAPM		98.67 156	98.30 174	99.80 46	99.20 257	99.67 51	99.77 34	99.72 11	94.74 353	98.73 275	99.90 26	95.78 177	99.98 13	96.96 287	99.88 51	99.76 87

SSC-MVS		92.73 350	93.73 346	89.72 374	95.02 393	81.38 394	99.76 37	99.23 290	94.87 350	92.80 383	98.93 344	94.71 223	91.37 398	74.49 398	93.80 352	96.42 384

test_vis3_rt		87.04 357	85.81 360	90.73 371	93.99 395	81.96 392	99.76 37	90.23 406	92.81 372	81.35 394	91.56 394	40.06 403	99.07 329	94.27 348	88.23 381	91.15 394

dcpmvs_2		99.23 75	99.58 7	98.16 292	99.83 39	94.68 353	99.76 37	99.52 101	99.07 35	99.98 6	99.88 36	98.56 74	99.93 84	99.67 21	99.98 4	99.87 31

HPM-MVS_fast		99.51 18	99.40 25	99.85 28	99.91 1	99.79 30	99.76 37	99.56 69	97.72 185	99.76 56	99.75 138	99.13 12	99.92 95	99.07 83	99.92 24	99.85 36

v10		97.85 240	97.52 252	98.86 219	98.99 301	98.67 192	99.75 41	99.41 212	95.70 335	98.98 241	99.41 269	94.75 220	99.23 303	96.01 317	94.63 338	98.67 286

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 62	99.89 4	99.75 41	99.56 69	99.02 38	99.88 20	99.85 54	99.18 10	99.96 30	99.22 69	99.92 24	99.90 17
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
IS-MVSNet		99.05 108	98.87 115	99.57 92	99.73 87	99.32 104	99.75 41	99.20 296	98.02 157	99.56 114	99.86 49	96.54 149	99.67 227	98.09 195	99.13 170	99.73 97

test_vis1_n		97.92 231	97.44 266	99.34 136	99.53 163	98.08 236	99.74 44	99.49 143	99.15 20	100.00 1	99.94 6	79.51 387	99.98 13	99.88 14	99.76 110	99.97 4

test_fmvs1_n		98.41 172	98.14 183	99.21 163	99.82 42	97.71 260	99.74 44	99.49 143	99.32 14	99.99 2	99.95 3	85.32 371	99.97 21	99.82 16	99.84 77	99.96 7

tttt0517		98.42 170	98.14 183	99.28 155	99.66 120	98.38 223	99.74 44	96.85 388	97.68 190	99.79 42	99.74 143	91.39 321	99.89 126	98.83 118	99.56 138	99.57 156

WB-MVS		93.10 348	94.10 342	90.12 373	95.51 391	81.88 393	99.73 47	99.27 284	95.05 346	93.09 382	98.91 348	94.70 224	91.89 397	76.62 396	94.02 350	96.58 383

test_fmvs2		97.25 298	97.30 286	97.09 340	99.43 198	93.31 371	99.73 47	98.87 339	98.83 64	99.28 180	99.80 103	84.45 375	99.66 230	97.88 211	97.45 269	98.30 346

baseline		99.15 85	99.02 91	99.53 105	99.66 120	99.14 131	99.72 49	99.48 155	98.35 107	99.42 143	99.84 64	96.07 163	99.79 183	99.51 35	99.14 169	99.67 122

RPSCF		98.22 186	98.62 147	96.99 341	99.82 42	91.58 380	99.72 49	99.44 201	96.61 288	99.66 83	99.89 30	95.92 171	99.82 169	97.46 257	99.10 174	99.57 156

CSCG		99.32 59	99.32 40	99.32 142	99.85 26	98.29 225	99.71 51	99.66 28	98.11 140	99.41 147	99.80 103	98.37 88	99.96 30	98.99 89	99.96 12	99.72 103

dmvs_re		98.08 203	98.16 180	97.85 312	99.55 160	94.67 354	99.70 52	98.92 328	98.15 133	99.06 229	99.35 286	93.67 265	99.25 299	97.77 225	97.25 281	99.64 136

WR-MVS_H		98.13 197	97.87 217	98.90 206	99.02 296	98.84 177	99.70 52	99.59 57	97.27 233	98.40 310	99.19 317	95.53 186	99.23 303	98.34 178	93.78 353	98.61 318

LTVRE_ROB	97.16 12	98.02 215	97.90 212	98.40 273	99.23 250	96.80 303	99.70 52	99.60 54	97.12 247	98.18 322	99.70 158	91.73 312	99.72 207	98.39 172	97.45 269	98.68 279
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
test_f		91.90 352	91.26 356	93.84 361	95.52 390	85.92 387	99.69 55	98.53 367	95.31 340	93.87 378	96.37 387	55.33 398	98.27 368	95.70 323	90.98 373	97.32 379

XVS		99.53 16	99.42 22	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	99.74 143	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

X-MVStestdata		96.55 313	95.45 331	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	64.01 403	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

V42		98.06 205	97.79 221	98.86 219	98.98 304	98.84 177	99.69 55	99.34 250	96.53 294	99.30 176	99.37 280	94.67 226	99.32 288	97.57 246	94.66 337	98.42 338

mPP-MVS		99.44 37	99.30 49	99.86 21	99.88 11	99.79 30	99.69 55	99.48 155	98.12 138	99.50 126	99.75 138	98.78 48	99.97 21	98.57 155	99.89 48	99.83 49

CP-MVS		99.45 33	99.32 40	99.85 28	99.83 39	99.75 39	99.69 55	99.52 101	98.07 148	99.53 121	99.63 198	98.93 33	99.97 21	98.74 127	99.91 31	99.83 49

FE-MVS		98.48 165	98.17 179	99.40 130	99.54 162	98.96 157	99.68 61	98.81 345	95.54 337	99.62 100	99.70 158	93.82 260	99.93 84	97.35 264	99.46 144	99.32 206

PS-CasMVS		97.93 228	97.59 247	98.95 195	98.99 301	99.06 142	99.68 61	99.52 101	97.13 245	98.31 315	99.68 174	92.44 299	99.05 331	98.51 163	94.08 348	98.75 257

Vis-MVSNet		99.12 95	98.97 101	99.56 94	99.78 56	99.10 135	99.68 61	99.66 28	98.49 93	99.86 27	99.87 44	94.77 218	99.84 151	99.19 71	99.41 148	99.74 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_vis1_n_1920		98.63 160	98.40 167	99.31 143	99.86 20	97.94 248	99.67 64	99.62 41	99.43 7	99.99 2	99.91 20	87.29 364	100.00 1	99.92 12	99.92 24	99.98 2

EIA-MVS		99.18 79	99.09 79	99.45 123	99.49 181	99.18 122	99.67 64	99.53 96	97.66 193	99.40 152	99.44 261	98.10 99	99.81 174	98.94 94	99.62 134	99.35 202

MSP-MVS		99.42 42	99.27 58	99.88 5	99.89 8	99.80 27	99.67 64	99.50 135	98.70 78	99.77 51	99.49 247	98.21 94	99.95 59	98.46 169	99.77 107	99.88 26
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		99.10 103	98.97 101	99.48 117	99.49 181	99.14 131	99.67 64	99.34 250	97.31 230	99.58 110	99.76 135	97.65 112	99.82 169	98.87 105	99.07 177	99.46 186

CP-MVSNet		98.09 201	97.78 224	99.01 184	98.97 306	99.24 117	99.67 64	99.46 182	97.25 235	98.48 307	99.64 192	93.79 261	99.06 330	98.63 142	94.10 347	98.74 260

MTAPA		99.52 17	99.39 27	99.89 4	99.90 4	99.86 13	99.66 69	99.47 173	98.79 70	99.68 74	99.81 90	98.43 83	99.97 21	98.88 102	99.90 39	99.83 49

HFP-MVS		99.49 22	99.37 30	99.86 21	99.87 15	99.80 27	99.66 69	99.67 23	98.15 133	99.68 74	99.69 168	99.06 16	99.96 30	98.69 135	99.87 54	99.84 40

mvs_tets		98.40 175	98.23 177	98.91 204	98.67 344	98.51 211	99.66 69	99.53 96	98.19 127	98.65 292	99.81 90	92.75 281	99.44 262	99.31 58	97.48 267	98.77 253

EU-MVSNet		97.98 222	98.03 198	97.81 318	98.72 338	96.65 308	99.66 69	99.66 28	98.09 143	98.35 313	99.82 76	95.25 198	98.01 374	97.41 261	95.30 325	98.78 250

ACMMPR		99.49 22	99.36 32	99.86 21	99.87 15	99.79 30	99.66 69	99.67 23	98.15 133	99.67 78	99.69 168	98.95 27	99.96 30	98.69 135	99.87 54	99.84 40

MP-MVS		99.33 58	99.15 71	99.87 11	99.88 11	99.82 22	99.66 69	99.46 182	98.09 143	99.48 130	99.74 143	98.29 91	99.96 30	97.93 208	99.87 54	99.82 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_cas_vis1_n_1920		99.16 83	99.01 95	99.61 84	99.81 46	98.86 175	99.65 75	99.64 36	99.39 10	99.97 13	99.94 6	93.20 273	99.98 13	99.55 29	99.91 31	99.99 1

region2R		99.48 26	99.35 34	99.87 11	99.88 11	99.80 27	99.65 75	99.66 28	98.13 137	99.66 83	99.68 174	98.96 24	99.96 30	98.62 143	99.87 54	99.84 40

TranMVSNet+NR-MVSNet		97.93 228	97.66 239	98.76 235	98.78 329	98.62 197	99.65 75	99.49 143	97.76 180	98.49 306	99.60 210	94.23 244	98.97 348	98.00 204	92.90 361	98.70 270

mvsany_test3		93.77 346	93.45 350	94.74 359	95.78 386	88.01 385	99.64 78	98.25 371	98.28 113	94.31 376	97.97 376	68.89 391	98.51 365	97.50 252	90.37 375	97.71 371

ZNCC-MVS		99.47 29	99.33 38	99.87 11	99.87 15	99.81 25	99.64 78	99.67 23	98.08 147	99.55 118	99.64 192	98.91 34	99.96 30	98.72 130	99.90 39	99.82 54

tfpnnormal		97.84 243	97.47 258	98.98 190	99.20 257	99.22 119	99.64 78	99.61 48	96.32 308	98.27 318	99.70 158	93.35 269	99.44 262	95.69 324	95.40 323	98.27 348

casdiffmvs_mvg		99.15 85	99.02 91	99.55 96	99.66 120	99.09 136	99.64 78	99.56 69	98.26 116	99.45 134	99.87 44	96.03 165	99.81 174	99.54 30	99.15 168	99.73 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
iter_conf_final		98.71 150	98.61 153	98.99 188	99.49 181	98.96 157	99.63 82	99.41 212	98.19 127	99.39 155	99.77 129	94.82 210	99.38 270	99.30 61	97.52 259	98.64 298

SR-MVS-dyc-post		99.45 33	99.31 47	99.85 28	99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.53 76	99.95 59	98.61 146	99.81 93	99.77 82

RE-MVS-def			99.34 36		99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.75 55		98.61 146	99.81 93	99.77 82

TSAR-MVS + MP.		99.58 9	99.50 13	99.81 44	99.91 1	99.66 53	99.63 82	99.39 223	98.91 58	99.78 47	99.85 54	99.36 2	99.94 69	98.84 115	99.88 51	99.82 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous20231206		96.22 319	96.03 320	96.79 349	97.31 377	94.14 361	99.63 82	99.08 310	96.17 320	97.04 356	99.06 330	93.94 255	97.76 380	86.96 389	95.06 330	98.47 332

APD-MVS_3200maxsize		99.48 26	99.35 34	99.85 28	99.76 65	99.83 16	99.63 82	99.54 85	98.36 106	99.79 42	99.82 76	98.86 38	99.95 59	98.62 143	99.81 93	99.78 80

test0726					99.85 26	99.89 4	99.62 88	99.50 135	99.10 27	99.86 27	99.82 76	98.94 29

EPNet		98.86 128	98.71 132	99.30 148	97.20 379	98.18 230	99.62 88	98.91 332	99.28 16	98.63 294	99.81 90	95.96 167	99.99 4	99.24 68	99.72 118	99.73 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 120	98.67 136	99.72 65	99.85 26	99.53 82	99.62 88	99.59 57	92.65 373	99.71 68	99.78 121	98.06 102	99.90 116	98.84 115	99.91 31	99.74 92

HY-MVS	97.30 7	98.85 135	98.64 141	99.47 120	99.42 200	99.08 139	99.62 88	99.36 240	97.39 224	99.28 180	99.68 174	96.44 154	99.92 95	98.37 175	98.22 227	99.40 197

ACMMP		99.45 33	99.32 40	99.82 41	99.89 8	99.67 51	99.62 88	99.69 18	98.12 138	99.63 96	99.84 64	98.73 60	99.96 30	98.55 161	99.83 86	99.81 61
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 61	99.19 68	99.64 78	99.82 42	99.23 118	99.62 88	99.55 77	98.94 54	99.63 96	99.95 3	95.82 176	99.94 69	99.37 50	99.97 7	99.73 97
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 9	99.56 10	99.64 78	99.78 56	99.15 130	99.61 94	99.45 193	99.01 40	99.89 19	99.82 76	99.01 18	99.92 95	99.56 28	99.95 16	99.85 36

test2506		96.81 310	96.65 306	97.29 335	99.74 80	92.21 378	99.60 95	85.06 407	99.13 22	99.77 51	99.93 9	87.82 362	99.85 145	99.38 48	99.38 149	99.80 70

SED-MVS		99.61 7	99.52 11	99.88 5	99.84 32	99.90 2	99.60 95	99.48 155	99.08 33	99.91 16	99.81 90	99.20 7	99.96 30	98.91 99	99.85 69	99.79 74

OPU-MVS				99.64 78	99.56 156	99.72 42	99.60 95				99.70 158	99.27 5	99.42 267	98.24 185	99.80 97	99.79 74

GST-MVS		99.40 50	99.24 63	99.85 28	99.86 20	99.79 30	99.60 95	99.67 23	97.97 159	99.63 96	99.68 174	98.52 77	99.95 59	98.38 173	99.86 62	99.81 61

EI-MVSNet-UG-set		99.58 9	99.57 8	99.64 78	99.78 56	99.14 131	99.60 95	99.45 193	99.01 40	99.90 18	99.83 68	98.98 23	99.93 84	99.59 25	99.95 16	99.86 33

ACMH	97.28 8	98.10 200	97.99 202	98.44 268	99.41 203	96.96 297	99.60 95	99.56 69	98.09 143	98.15 323	99.91 20	90.87 328	99.70 219	98.88 102	97.45 269	98.67 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ECVR-MVS		98.04 211	98.05 196	98.00 304	99.74 80	94.37 358	99.59 101	94.98 397	99.13 22	99.66 83	99.93 9	90.67 330	99.84 151	99.40 47	99.38 149	99.80 70

SR-MVS		99.43 40	99.29 53	99.86 21	99.75 73	99.83 16	99.59 101	99.62 41	98.21 124	99.73 62	99.79 115	98.68 64	99.96 30	98.44 170	99.77 107	99.79 74

thres100view900		97.76 255	97.45 261	98.69 239	99.72 91	97.86 252	99.59 101	98.74 352	97.93 162	99.26 188	98.62 358	91.75 310	99.83 163	93.22 359	98.18 232	98.37 344

thres600view7		97.86 239	97.51 254	98.92 200	99.72 91	97.95 246	99.59 101	98.74 352	97.94 161	99.27 184	98.62 358	91.75 310	99.86 139	93.73 354	98.19 231	98.96 240

LCM-MVSNet-Re		97.83 245	98.15 182	96.87 347	99.30 233	92.25 377	99.59 101	98.26 370	97.43 219	96.20 363	99.13 323	96.27 159	98.73 360	98.17 191	98.99 183	99.64 136

baseline1		98.31 180	97.95 207	99.38 134	99.50 179	98.74 187	99.59 101	98.93 326	98.41 100	99.14 211	99.60 210	94.59 229	99.79 183	98.48 165	93.29 357	99.61 144

SteuartSystems-ACMMP		99.54 15	99.42 22	99.87 11	99.82 42	99.81 25	99.59 101	99.51 115	98.62 83	99.79 42	99.83 68	99.28 4	99.97 21	98.48 165	99.90 39	99.84 40
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 99	98.90 110	99.74 61	99.80 52	99.46 92	99.59 101	99.49 143	97.03 259	99.63 96	99.69 168	97.27 124	99.96 30	97.82 219	99.84 77	99.81 61

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 55	99.38 212	99.37 100	99.58 109	99.62 41	99.41 9	99.87 25	99.92 14	98.81 44	100.00 1	99.97 1	99.93 22	99.94 11

dmvs_testset		95.02 335	96.12 317	91.72 368	99.10 281	80.43 396	99.58 109	97.87 379	97.47 212	95.22 370	98.82 351	93.99 253	95.18 393	88.09 385	94.91 335	99.56 158

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 52	99.84 32	99.44 94	99.58 109	99.69 18	99.43 7	99.98 6	99.91 20	98.62 70	100.00 1	99.97 1	99.95 16	99.90 17

test1111		98.04 211	98.11 187	97.83 315	99.74 80	93.82 363	99.58 109	95.40 396	99.12 25	99.65 89	99.93 9	90.73 329	99.84 151	99.43 46	99.38 149	99.82 54

PGM-MVS		99.45 33	99.31 47	99.86 21	99.87 15	99.78 36	99.58 109	99.65 33	97.84 171	99.71 68	99.80 103	99.12 13	99.97 21	98.33 179	99.87 54	99.83 49

LPG-MVS_test		98.22 186	98.13 185	98.49 257	99.33 225	97.05 286	99.58 109	99.55 77	97.46 213	99.24 190	99.83 68	92.58 291	99.72 207	98.09 195	97.51 261	98.68 279

PHI-MVS		99.30 61	99.17 70	99.70 67	99.56 156	99.52 85	99.58 109	99.80 8	97.12 247	99.62 100	99.73 149	98.58 72	99.90 116	98.61 146	99.91 31	99.68 119

SF-MVS		99.38 53	99.24 63	99.79 49	99.79 54	99.68 48	99.57 116	99.54 85	97.82 176	99.71 68	99.80 103	98.95 27	99.93 84	98.19 188	99.84 77	99.74 92

DVP-MVS		99.57 12	99.47 17	99.88 5	99.85 26	99.89 4	99.57 116	99.37 239	99.10 27	99.81 37	99.80 103	98.94 29	99.96 30	98.93 96	99.86 62	99.81 61
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 2	99.84 32	99.89 4	99.57 116	99.51 115					99.96 30	98.93 96	99.86 62	99.88 26

Effi-MVS+-dtu		98.78 143	98.89 113	98.47 263	99.33 225	96.91 299	99.57 116	99.30 275	98.47 94	99.41 147	98.99 337	96.78 140	99.74 197	98.73 129	99.38 149	98.74 260

v2v482		98.06 205	97.77 226	98.92 200	98.90 311	98.82 181	99.57 116	99.36 240	96.65 283	99.19 204	99.35 286	94.20 245	99.25 299	97.72 232	94.97 332	98.69 274

DSMNet-mixed		97.25 298	97.35 278	96.95 344	97.84 367	93.61 369	99.57 116	96.63 392	96.13 325	98.87 259	98.61 360	94.59 229	97.70 381	95.08 338	98.86 192	99.55 159

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 28	99.86 20	99.61 67	99.56 122	99.63 39	99.48 3	99.98 6	99.83 68	98.75 55	99.99 4	99.97 1	99.96 12	99.94 11

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 28	99.84 32	99.63 64	99.56 122	99.63 39	99.47 4	99.98 6	99.82 76	98.75 55	99.99 4	99.97 1	99.97 7	99.94 11

sd_testset		98.75 146	98.57 156	99.29 151	99.81 46	98.26 227	99.56 122	99.62 41	98.78 73	99.64 93	99.88 36	92.02 304	99.88 131	99.54 30	98.26 225	99.72 103

KD-MVS_self_test		95.00 336	94.34 341	96.96 343	97.07 382	95.39 341	99.56 122	99.44 201	95.11 343	97.13 354	97.32 382	91.86 308	97.27 384	90.35 377	81.23 391	98.23 352

ETV-MVS		99.26 69	99.21 66	99.40 130	99.46 191	99.30 109	99.56 122	99.52 101	98.52 91	99.44 139	99.27 307	98.41 86	99.86 139	99.10 79	99.59 136	99.04 230

SMA-MVS		99.44 37	99.30 49	99.85 28	99.73 87	99.83 16	99.56 122	99.47 173	97.45 216	99.78 47	99.82 76	99.18 10	99.91 105	98.79 123	99.89 48	99.81 61
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		98.87 125	98.72 130	99.31 143	99.86 20	98.48 215	99.56 122	99.61 48	97.85 169	99.36 164	99.85 54	95.95 168	99.85 145	96.66 303	99.83 86	99.59 150

casdiffmvs		99.13 89	98.98 100	99.56 94	99.65 126	99.16 125	99.56 122	99.50 135	98.33 110	99.41 147	99.86 49	95.92 171	99.83 163	99.45 45	99.16 165	99.70 113
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		98.38 176	98.09 191	99.24 160	99.26 244	99.32 104	99.56 122	99.55 77	97.45 216	98.71 277	99.83 68	93.23 270	99.63 243	98.88 102	96.32 300	98.76 255

ACMH+	97.24 10	97.92 231	97.78 224	98.32 280	99.46 191	96.68 307	99.56 122	99.54 85	98.41 100	97.79 339	99.87 44	90.18 337	99.66 230	98.05 203	97.18 285	98.62 309

ACMM	97.58 5	98.37 177	98.34 170	98.48 259	99.41 203	97.10 280	99.56 122	99.45 193	98.53 90	99.04 232	99.85 54	93.00 275	99.71 213	98.74 127	97.45 269	98.64 298
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 67	99.12 74	99.74 61	99.18 262	99.75 39	99.56 122	99.57 64	98.45 96	99.49 129	99.85 54	97.77 109	99.94 69	98.33 179	99.84 77	99.52 167

test_fmvsmconf0.01_n		99.22 76	99.03 87	99.79 49	98.42 359	99.48 89	99.55 134	99.51 115	99.39 10	99.78 47	99.93 9	94.80 213	99.95 59	99.93 11	99.95 16	99.94 11

test_fmvs1		98.88 124	98.79 126	99.16 168	99.69 106	97.61 263	99.55 134	99.49 143	99.32 14	99.98 6	99.91 20	91.41 320	99.96 30	99.82 16	99.92 24	99.90 17

v144192		97.92 231	97.60 246	98.87 215	98.83 324	98.65 194	99.55 134	99.34 250	96.20 317	99.32 172	99.40 272	94.36 240	99.26 298	96.37 311	95.03 331	98.70 270

iter_conf05		98.55 163	98.44 163	98.87 215	99.34 223	98.60 200	99.55 134	99.42 209	98.21 124	99.37 160	99.77 129	93.55 266	99.38 270	99.30 61	97.48 267	98.63 306

API-MVS		99.04 109	99.03 87	99.06 178	99.40 208	99.31 107	99.55 134	99.56 69	98.54 89	99.33 171	99.39 276	98.76 52	99.78 188	96.98 285	99.78 104	98.07 357

fmvsm_s_conf0.1_n_a		99.26 69	99.06 82	99.85 28	99.52 167	99.62 65	99.54 139	99.62 41	98.69 79	99.99 2	99.96 1	94.47 237	99.94 69	99.88 14	99.92 24	99.98 2

APD_test1		95.87 326	96.49 310	94.00 360	99.53 163	84.01 388	99.54 139	99.32 267	95.91 333	97.99 330	99.85 54	85.49 370	99.88 131	91.96 370	98.84 194	98.12 355

thisisatest0530		98.35 178	98.03 198	99.31 143	99.63 131	98.56 202	99.54 139	96.75 390	97.53 207	99.73 62	99.65 186	91.25 324	99.89 126	98.62 143	99.56 138	99.48 178

MTMP							99.54 139	98.88 337

v1144		97.98 222	97.69 236	98.85 222	98.87 318	98.66 193	99.54 139	99.35 246	96.27 312	99.23 194	99.35 286	94.67 226	99.23 303	96.73 298	95.16 328	98.68 279

v148		97.79 253	97.55 248	98.50 256	98.74 335	97.72 257	99.54 139	99.33 257	96.26 313	98.90 253	99.51 241	94.68 225	99.14 317	97.83 218	93.15 360	98.63 306

CostFormer		97.72 264	97.73 233	97.71 322	99.15 274	94.02 362	99.54 139	99.02 317	94.67 354	99.04 232	99.35 286	92.35 301	99.77 190	98.50 164	97.94 240	99.34 204

MVSTER		98.49 164	98.32 172	99.00 186	99.35 219	99.02 146	99.54 139	99.38 231	97.41 222	99.20 201	99.73 149	93.86 259	99.36 279	98.87 105	97.56 256	98.62 309

fmvsm_s_conf0.1_n		99.29 63	99.10 76	99.86 21	99.70 101	99.65 57	99.53 147	99.62 41	98.74 75	99.99 2	99.95 3	94.53 235	99.94 69	99.89 13	99.96 12	99.97 4

fmvsm_s_conf0.5_n_a		99.56 13	99.47 17	99.85 28	99.83 39	99.64 63	99.52 148	99.65 33	99.10 27	99.98 6	99.92 14	97.35 120	99.96 30	99.94 10	99.92 24	99.95 9

MM		99.40 50	99.28 55	99.74 61	99.67 111	99.31 107	99.52 148	98.87 339	99.55 1	99.74 60	99.80 103	96.47 151	99.98 13	99.97 1	99.97 7	99.94 11

patch_mono-2		99.26 69	99.62 5	98.16 292	99.81 46	94.59 355	99.52 148	99.64 36	99.33 13	99.73 62	99.90 26	99.00 22	99.99 4	99.69 19	99.98 4	99.89 20

Fast-Effi-MVS+-dtu		98.77 145	98.83 122	98.60 243	99.41 203	96.99 293	99.52 148	99.49 143	98.11 140	99.24 190	99.34 290	96.96 136	99.79 183	97.95 207	99.45 145	99.02 233

MVS_0304		99.42 42	99.32 40	99.72 65	99.70 101	99.27 113	99.52 148	97.57 384	99.51 2	99.82 35	99.78 121	98.09 100	99.96 30	99.97 1	99.97 7	99.94 11

Fast-Effi-MVS+		98.70 151	98.43 164	99.51 113	99.51 170	99.28 111	99.52 148	99.47 173	96.11 326	99.01 235	99.34 290	96.20 161	99.84 151	97.88 211	98.82 196	99.39 198

v1921920		97.80 252	97.45 261	98.84 223	98.80 325	98.53 205	99.52 148	99.34 250	96.15 323	99.24 190	99.47 255	93.98 254	99.29 293	95.40 332	95.13 329	98.69 274

MIMVSNet1		95.51 330	95.04 335	96.92 346	97.38 374	95.60 332	99.52 148	99.50 135	93.65 364	96.97 358	99.17 318	85.28 372	96.56 389	88.36 384	95.55 320	98.60 321

fmvsm_s_conf0.5_n		99.51 18	99.40 25	99.85 28	99.84 32	99.65 57	99.51 156	99.67 23	99.13 22	99.98 6	99.92 14	96.60 146	99.96 30	99.95 8	99.96 12	99.95 9

UniMVSNet_ETH3D		97.32 295	96.81 303	98.87 215	99.40 208	97.46 266	99.51 156	99.53 96	95.86 334	98.54 303	99.77 129	82.44 383	99.66 230	98.68 137	97.52 259	99.50 176

alignmvs		98.81 139	98.56 158	99.58 90	99.43 198	99.42 96	99.51 156	98.96 324	98.61 84	99.35 167	98.92 347	94.78 215	99.77 190	99.35 51	98.11 237	99.54 161

v1192		97.81 250	97.44 266	98.91 204	98.88 314	98.68 191	99.51 156	99.34 250	96.18 319	99.20 201	99.34 290	94.03 252	99.36 279	95.32 334	95.18 327	98.69 274

test20.03		96.12 323	95.96 322	96.63 350	97.44 373	95.45 339	99.51 156	99.38 231	96.55 293	96.16 364	99.25 310	93.76 263	96.17 390	87.35 388	94.22 345	98.27 348

mvs_anonymous		99.03 111	98.99 97	99.16 168	99.38 212	98.52 209	99.51 156	99.38 231	97.79 177	99.38 158	99.81 90	97.30 122	99.45 257	99.35 51	98.99 183	99.51 173

TAMVS		99.12 95	99.08 80	99.24 160	99.46 191	98.55 203	99.51 156	99.46 182	98.09 143	99.45 134	99.82 76	98.34 89	99.51 253	98.70 132	98.93 186	99.67 122

test_fmvsmconf0.1_n		99.55 14	99.45 21	99.86 21	99.44 197	99.65 57	99.50 163	99.61 48	99.45 5	99.87 25	99.92 14	97.31 121	99.97 21	99.95 8	99.99 1	99.97 4

test_yl		98.86 128	98.63 142	99.54 97	99.49 181	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 191	99.84 151	98.60 149	98.33 219	99.59 150

DCV-MVSNet		98.86 128	98.63 142	99.54 97	99.49 181	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 191	99.84 151	98.60 149	98.33 219	99.59 150

tfpn200view9		97.72 264	97.38 274	98.72 237	99.69 106	97.96 244	99.50 163	98.73 357	97.83 172	99.17 208	98.45 363	91.67 314	99.83 163	93.22 359	98.18 232	98.37 344

UA-Net		99.42 42	99.29 53	99.80 46	99.62 137	99.55 77	99.50 163	99.70 15	98.79 70	99.77 51	99.96 1	97.45 115	99.96 30	98.92 98	99.90 39	99.89 20

pm-mvs1		97.68 271	97.28 288	98.88 211	99.06 290	98.62 197	99.50 163	99.45 193	96.32 308	97.87 335	99.79 115	92.47 295	99.35 282	97.54 249	93.54 355	98.67 286

EI-MVSNet		98.67 156	98.67 136	98.68 240	99.35 219	97.97 242	99.50 163	99.38 231	96.93 268	99.20 201	99.83 68	97.87 105	99.36 279	98.38 173	97.56 256	98.71 265

CVMVSNet		98.57 162	98.67 136	98.30 282	99.35 219	95.59 333	99.50 163	99.55 77	98.60 85	99.39 155	99.83 68	94.48 236	99.45 257	98.75 126	98.56 209	99.85 36

VPA-MVSNet		98.29 183	97.95 207	99.30 148	99.16 270	99.54 79	99.50 163	99.58 61	98.27 115	99.35 167	99.37 280	92.53 293	99.65 235	99.35 51	94.46 340	98.72 263

thres400		97.77 254	97.38 274	98.92 200	99.69 106	97.96 244	99.50 163	98.73 357	97.83 172	99.17 208	98.45 363	91.67 314	99.83 163	93.22 359	98.18 232	98.96 240

APD-MVS		99.27 67	99.08 80	99.84 39	99.75 73	99.79 30	99.50 163	99.50 135	97.16 243	99.77 51	99.82 76	98.78 48	99.94 69	97.56 247	99.86 62	99.80 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_vis1_rt		95.81 328	95.65 328	96.32 354	99.67 111	91.35 381	99.49 174	96.74 391	98.25 117	95.24 369	98.10 373	74.96 388	99.90 116	99.53 32	98.85 193	97.70 373

TransMVSNet (Re)		97.15 302	96.58 307	98.86 219	99.12 276	98.85 176	99.49 174	98.91 332	95.48 338	97.16 353	99.80 103	93.38 268	99.11 325	94.16 351	91.73 368	98.62 309

UniMVSNet (Re)		98.29 183	98.00 201	99.13 173	99.00 298	99.36 102	99.49 174	99.51 115	97.95 160	98.97 243	99.13 323	96.30 158	99.38 270	98.36 177	93.34 356	98.66 294

EPMVS		97.82 248	97.65 240	98.35 277	98.88 314	95.98 326	99.49 174	94.71 399	97.57 200	99.26 188	99.48 252	92.46 298	99.71 213	97.87 213	99.08 176	99.35 202

test_fmvsmconf_n		99.70 3	99.64 4	99.87 11	99.80 52	99.66 53	99.48 178	99.64 36	99.45 5	99.92 15	99.92 14	98.62 70	99.99 4	99.96 7	99.99 1	99.96 7

Anonymous20231211		97.88 235	97.54 251	98.90 206	99.71 96	98.53 205	99.48 178	99.57 64	94.16 359	98.81 266	99.68 174	93.23 270	99.42 267	98.84 115	94.42 342	98.76 255

v1240		97.69 269	97.32 284	98.79 232	98.85 322	98.43 220	99.48 178	99.36 240	96.11 326	99.27 184	99.36 283	93.76 263	99.24 302	94.46 345	95.23 326	98.70 270

VPNet		97.84 243	97.44 266	99.01 184	99.21 255	98.94 165	99.48 178	99.57 64	98.38 102	99.28 180	99.73 149	88.89 347	99.39 269	99.19 71	93.27 358	98.71 265

UniMVSNet_NR-MVSNet		98.22 186	97.97 204	98.96 193	98.92 310	98.98 150	99.48 178	99.53 96	97.76 180	98.71 277	99.46 259	96.43 155	99.22 306	98.57 155	92.87 363	98.69 274

TDRefinement		95.42 332	94.57 339	97.97 306	89.83 400	96.11 325	99.48 178	98.75 349	96.74 276	96.68 359	99.88 36	88.65 351	99.71 213	98.37 175	82.74 389	98.09 356

ACMMP_NAP		99.47 29	99.34 36	99.88 5	99.87 15	99.86 13	99.47 184	99.48 155	98.05 153	99.76 56	99.86 49	98.82 43	99.93 84	98.82 122	99.91 31	99.84 40

NR-MVSNet		97.97 225	97.61 245	99.02 183	98.87 318	99.26 115	99.47 184	99.42 209	97.63 195	97.08 355	99.50 244	95.07 202	99.13 320	97.86 214	93.59 354	98.68 279

PVSNet_Blended_VisFu		99.36 55	99.28 55	99.61 84	99.86 20	99.07 141	99.47 184	99.93 2	97.66 193	99.71 68	99.86 49	97.73 110	99.96 30	99.47 43	99.82 90	99.79 74

SD-MVS		99.41 47	99.52 11	99.05 180	99.74 80	99.68 48	99.46 187	99.52 101	99.11 26	99.88 20	99.91 20	99.43 1	97.70 381	98.72 130	99.93 22	99.77 82
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
testing3		97.28 296	96.76 305	98.82 226	99.37 215	98.07 237	99.45 188	99.36 240	97.56 202	97.89 334	98.95 342	83.70 378	98.82 355	96.03 315	98.56 209	99.58 154

tt0805		97.97 225	97.77 226	98.57 248	99.59 148	96.61 310	99.45 188	99.08 310	98.21 124	98.88 256	99.80 103	88.66 350	99.70 219	98.58 152	97.72 246	99.39 198

tpm2		97.44 292	97.34 281	97.74 321	99.15 274	94.36 359	99.45 188	98.94 325	93.45 368	98.90 253	99.44 261	91.35 322	99.59 247	97.31 265	98.07 238	99.29 208

FMVSNet2		97.72 264	97.36 276	98.80 231	99.51 170	98.84 177	99.45 188	99.42 209	96.49 296	98.86 263	99.29 302	90.26 333	98.98 341	96.44 308	96.56 294	98.58 323

CDS-MVSNet		99.09 104	99.03 87	99.25 158	99.42 200	98.73 188	99.45 188	99.46 182	98.11 140	99.46 133	99.77 129	98.01 103	99.37 275	98.70 132	98.92 188	99.66 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 128	98.63 142	99.54 97	99.37 215	99.66 53	99.45 188	99.54 85	96.61 288	99.01 235	99.40 272	97.09 129	99.86 139	97.68 237	99.53 141	99.10 218
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
UGNet		98.87 125	98.69 134	99.40 130	99.22 254	98.72 189	99.44 194	99.68 20	99.24 17	99.18 207	99.42 265	92.74 283	99.96 30	99.34 55	99.94 21	99.53 166
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		98.86 128	98.63 142	99.54 97	99.64 128	99.19 120	99.44 194	99.54 85	97.77 179	99.30 176	99.81 90	94.20 245	99.93 84	99.17 74	98.82 196	99.49 177

test_0402		96.64 312	96.24 315	97.85 312	98.85 322	96.43 316	99.44 194	99.26 285	93.52 365	96.98 357	99.52 238	88.52 353	99.20 313	92.58 369	97.50 263	97.93 368

ACMP	97.20 11	98.06 205	97.94 209	98.45 265	99.37 215	97.01 291	99.44 194	99.49 143	97.54 206	98.45 308	99.79 115	91.95 306	99.72 207	97.91 209	97.49 266	98.62 309
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 265	98.55 354	98.16 231	99.43 198	93.68 401		97.23 350	98.46 362	89.30 344	99.22 306	95.43 331	98.22 227	97.98 365

HPM-MVS++		99.39 52	99.23 65	99.87 11	99.75 73	99.84 15	99.43 198	99.51 115	98.68 81	99.27 184	99.53 235	98.64 69	99.96 30	98.44 170	99.80 97	99.79 74

tpm cat1		97.39 293	97.36 276	97.50 330	99.17 268	93.73 365	99.43 198	99.31 271	91.27 377	98.71 277	99.08 327	94.31 243	99.77 190	96.41 310	98.50 213	99.00 234

tpm		97.67 274	97.55 248	98.03 299	99.02 296	95.01 348	99.43 198	98.54 366	96.44 302	99.12 214	99.34 290	91.83 309	99.60 246	97.75 228	96.46 296	99.48 178

GBi-Net		97.68 271	97.48 256	98.29 283	99.51 170	97.26 273	99.43 198	99.48 155	96.49 296	99.07 224	99.32 297	90.26 333	98.98 341	97.10 278	96.65 291	98.62 309

test1		97.68 271	97.48 256	98.29 283	99.51 170	97.26 273	99.43 198	99.48 155	96.49 296	99.07 224	99.32 297	90.26 333	98.98 341	97.10 278	96.65 291	98.62 309

FMVSNet1		96.84 309	96.36 313	98.29 283	99.32 231	97.26 273	99.43 198	99.48 155	95.11 343	98.55 302	99.32 297	83.95 377	98.98 341	95.81 320	96.26 301	98.62 309

testgi		97.65 276	97.50 255	98.13 296	99.36 218	96.45 315	99.42 205	99.48 155	97.76 180	97.87 335	99.45 260	91.09 325	98.81 356	94.53 344	98.52 212	99.13 217

F-COLMAP		99.19 77	99.04 85	99.64 78	99.78 56	99.27 113	99.42 205	99.54 85	97.29 232	99.41 147	99.59 212	98.42 85	99.93 84	98.19 188	99.69 123	99.73 97

Anonymous202405211		98.30 182	97.98 203	99.26 157	99.57 152	98.16 231	99.41 207	98.55 365	96.03 331	99.19 204	99.74 143	91.87 307	99.92 95	99.16 75	98.29 224	99.70 113

MSLP-MVS++		99.46 31	99.47 17	99.44 127	99.60 146	99.16 125	99.41 207	99.71 13	98.98 48	99.45 134	99.78 121	99.19 9	99.54 252	99.28 63	99.84 77	99.63 140

VNet		99.11 99	98.90 110	99.73 64	99.52 167	99.56 75	99.41 207	99.39 223	99.01 40	99.74 60	99.78 121	95.56 185	99.92 95	99.52 34	98.18 232	99.72 103

baseline2		97.87 237	97.55 248	98.82 226	99.18 262	98.02 239	99.41 207	96.58 393	96.97 262	96.51 360	99.17 318	93.43 267	99.57 248	97.71 233	99.03 180	98.86 244

DU-MVS		98.08 203	97.79 221	98.96 193	98.87 318	98.98 150	99.41 207	99.45 193	97.87 166	98.71 277	99.50 244	94.82 210	99.22 306	98.57 155	92.87 363	98.68 279

Baseline_NR-MVSNet		97.76 255	97.45 261	98.68 240	99.09 284	98.29 225	99.41 207	98.85 341	95.65 336	98.63 294	99.67 180	94.82 210	99.10 327	98.07 202	92.89 362	98.64 298

XVG-ACMP-BASELINE		97.83 245	97.71 235	98.20 289	99.11 278	96.33 319	99.41 207	99.52 101	98.06 152	99.05 231	99.50 244	89.64 342	99.73 203	97.73 230	97.38 277	98.53 326

DP-MVS		99.16 83	98.95 105	99.78 52	99.77 62	99.53 82	99.41 207	99.50 135	97.03 259	99.04 232	99.88 36	97.39 116	99.92 95	98.66 139	99.90 39	99.87 31

9.14			99.10 76		99.72 91		99.40 215	99.51 115	97.53 207	99.64 93	99.78 121	98.84 41	99.91 105	97.63 238	99.82 90

D2MVS		98.41 172	98.50 161	98.15 295	99.26 244	96.62 309	99.40 215	99.61 48	97.71 186	98.98 241	99.36 283	96.04 164	99.67 227	98.70 132	97.41 274	98.15 354

Anonymous20240529		98.09 201	97.68 237	99.34 136	99.66 120	98.44 219	99.40 215	99.43 207	93.67 363	99.22 195	99.89 30	90.23 336	99.93 84	99.26 67	98.33 219	99.66 125

FMVSNet3		98.03 213	97.76 230	98.84 223	99.39 211	98.98 150	99.40 215	99.38 231	96.67 281	99.07 224	99.28 304	92.93 276	98.98 341	97.10 278	96.65 291	98.56 325

LFMVS		97.90 234	97.35 278	99.54 97	99.52 167	99.01 148	99.39 219	98.24 372	97.10 251	99.65 89	99.79 115	84.79 373	99.91 105	99.28 63	98.38 216	99.69 115

HQP_MVS		98.27 185	98.22 178	98.44 268	99.29 237	96.97 295	99.39 219	99.47 173	98.97 51	99.11 216	99.61 207	92.71 286	99.69 224	97.78 222	97.63 249	98.67 286

plane_prior2							99.39 219		98.97 51

CHOSEN 1792x2688		99.19 77	99.10 76	99.45 123	99.89 8	98.52 209	99.39 219	99.94 1	98.73 76	99.11 216	99.89 30	95.50 187	99.94 69	99.50 36	99.97 7	99.89 20

PAPM_NR		99.04 109	98.84 120	99.66 69	99.74 80	99.44 94	99.39 219	99.38 231	97.70 188	99.28 180	99.28 304	98.34 89	99.85 145	96.96 287	99.45 145	99.69 115

gg-mvs-nofinetune		96.17 322	95.32 333	98.73 236	98.79 326	98.14 233	99.38 224	94.09 400	91.07 380	98.07 328	91.04 396	89.62 343	99.35 282	96.75 297	99.09 175	98.68 279

VDDNet		97.55 281	97.02 299	99.16 168	99.49 181	98.12 235	99.38 224	99.30 275	95.35 339	99.68 74	99.90 26	82.62 382	99.93 84	99.31 58	98.13 236	99.42 193

pmmvs6		96.53 314	96.09 319	97.82 317	98.69 342	95.47 338	99.37 226	99.47 173	93.46 367	97.41 344	99.78 121	87.06 365	99.33 285	96.92 292	92.70 365	98.65 296

PM-MVS		92.96 349	92.23 353	95.14 358	95.61 387	89.98 384	99.37 226	98.21 373	94.80 352	95.04 374	97.69 377	65.06 392	97.90 377	94.30 346	89.98 378	97.54 377

WTY-MVS		99.06 107	98.88 114	99.61 84	99.62 137	99.16 125	99.37 226	99.56 69	98.04 154	99.53 121	99.62 203	96.84 138	99.94 69	98.85 112	98.49 214	99.72 103

IterMVS-LS		98.46 167	98.42 165	98.58 247	99.59 148	98.00 240	99.37 226	99.43 207	96.94 267	99.07 224	99.59 212	97.87 105	99.03 334	98.32 181	95.62 318	98.71 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 268	97.28 288	98.97 192	99.70 101	97.27 271	99.36 230	99.45 193	98.94 54	99.66 83	99.64 192	94.93 204	99.99 4	99.48 41	84.36 386	99.65 129

DPE-MVS		99.46 31	99.32 40	99.91 2	99.78 56	99.88 8	99.36 230	99.51 115	98.73 76	99.88 20	99.84 64	98.72 61	99.96 30	98.16 192	99.87 54	99.88 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UnsupCasMVSNet_eth		96.44 316	96.12 317	97.40 332	98.65 345	95.65 331	99.36 230	99.51 115	97.13 245	96.04 366	98.99 337	88.40 354	98.17 370	96.71 299	90.27 376	98.40 341

sss		99.17 81	99.05 83	99.53 105	99.62 137	98.97 153	99.36 230	99.62 41	97.83 172	99.67 78	99.65 186	97.37 119	99.95 59	99.19 71	99.19 164	99.68 119

DeepC-MVS_fast	98.69 1	99.49 22	99.39 27	99.77 55	99.63 131	99.59 70	99.36 230	99.46 182	99.07 35	99.79 42	99.82 76	98.85 39	99.92 95	98.68 137	99.87 54	99.82 54
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 73	99.14 72	99.59 87	99.41 203	99.16 125	99.35 235	99.57 64	98.82 65	99.51 125	99.61 207	96.46 152	99.95 59	99.59 25	99.98 4	99.65 129

pmmvs-eth3d		95.34 334	94.73 337	97.15 336	95.53 389	95.94 327	99.35 235	99.10 307	95.13 341	93.55 379	97.54 378	88.15 358	97.91 376	94.58 343	89.69 379	97.61 374

MDTV_nov1_ep13_2view						95.18 346	99.35 235		96.84 272	99.58 110		95.19 200		97.82 219		99.46 186

VDD-MVS		97.73 262	97.35 278	98.88 211	99.47 190	97.12 279	99.34 238	98.85 341	98.19 127	99.67 78	99.85 54	82.98 380	99.92 95	99.49 40	98.32 223	99.60 146

COLMAP_ROB	97.56 6	98.86 128	98.75 129	99.17 167	99.88 11	98.53 205	99.34 238	99.59 57	97.55 203	98.70 283	99.89 30	95.83 175	99.90 116	98.10 194	99.90 39	99.08 223
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EGC-MVSNET		82.80 361	77.86 367	97.62 325	97.91 365	96.12 324	99.33 240	99.28 281	8.40 404	25.05 405	99.27 307	84.11 376	99.33 285	89.20 380	98.22 227	97.42 378

ETVMVS		97.50 286	96.90 301	99.29 151	99.23 250	98.78 186	99.32 241	98.90 334	97.52 209	98.56 301	98.09 374	84.72 374	99.69 224	97.86 214	97.88 242	99.39 198

FMVSNet5		96.43 317	96.19 316	97.15 336	99.11 278	95.89 328	99.32 241	99.52 101	94.47 358	98.34 314	99.07 328	87.54 363	97.07 385	92.61 368	95.72 316	98.47 332

dp		97.75 259	97.80 220	97.59 327	99.10 281	93.71 366	99.32 241	98.88 337	96.48 299	99.08 223	99.55 226	92.67 289	99.82 169	96.52 306	98.58 206	99.24 211

tpmvs		97.98 222	98.02 200	97.84 314	99.04 294	94.73 352	99.31 244	99.20 296	96.10 330	98.76 273	99.42 265	94.94 203	99.81 174	96.97 286	98.45 215	98.97 238

tpmrst		98.33 179	98.48 162	97.90 310	99.16 270	94.78 351	99.31 244	99.11 306	97.27 233	99.45 134	99.59 212	95.33 193	99.84 151	98.48 165	98.61 203	99.09 222

MP-MVS-pluss		99.37 54	99.20 67	99.88 5	99.90 4	99.87 12	99.30 246	99.52 101	97.18 241	99.60 106	99.79 115	98.79 47	99.95 59	98.83 118	99.91 31	99.83 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 57	99.19 68	99.79 49	99.61 141	99.65 57	99.30 246	99.48 155	98.86 60	99.21 198	99.63 198	98.72 61	99.90 116	98.25 184	99.63 133	99.80 70

JIA-IIPM		97.50 286	97.02 299	98.93 198	98.73 336	97.80 254	99.30 246	98.97 322	91.73 376	98.91 251	94.86 390	95.10 201	99.71 213	97.58 242	97.98 239	99.28 209

BH-RMVSNet		98.41 172	98.08 192	99.40 130	99.41 203	98.83 180	99.30 246	98.77 348	97.70 188	98.94 247	99.65 186	92.91 279	99.74 197	96.52 306	99.55 140	99.64 136

Syy-MVS		97.09 305	97.14 294	96.95 344	99.00 298	92.73 375	99.29 250	99.39 223	97.06 255	97.41 344	98.15 369	93.92 257	98.68 361	91.71 371	98.34 217	99.45 189

myMVS_eth3d		96.89 307	96.37 312	98.43 270	99.00 298	97.16 277	99.29 250	99.39 223	97.06 255	97.41 344	98.15 369	83.46 379	98.68 361	95.27 335	98.34 217	99.45 189

MCST-MVS		99.43 40	99.30 49	99.82 41	99.79 54	99.74 41	99.29 250	99.40 220	98.79 70	99.52 123	99.62 203	98.91 34	99.90 116	98.64 141	99.75 112	99.82 54

LF4IMVS		97.52 283	97.46 260	97.70 323	98.98 304	95.55 334	99.29 250	98.82 344	98.07 148	98.66 286	99.64 192	89.97 338	99.61 245	97.01 282	96.68 290	97.94 367

hse-mvs2		97.50 286	97.14 294	98.59 244	99.49 181	97.05 286	99.28 254	99.22 292	98.94 54	99.66 83	99.42 265	94.93 204	99.65 235	99.48 41	83.80 388	99.08 223

OPM-MVS		98.19 190	98.10 188	98.45 265	98.88 314	97.07 284	99.28 254	99.38 231	98.57 86	99.22 195	99.81 90	92.12 302	99.66 230	98.08 199	97.54 258	98.61 318
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs		99.14 87	99.02 91	99.51 113	99.61 141	98.96 157	99.28 254	99.49 143	98.46 95	99.72 67	99.71 154	96.50 150	99.88 131	99.31 58	99.11 171	99.67 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 128	98.80 123	99.03 182	99.76 65	98.79 184	99.28 254	99.91 3	97.42 221	99.67 78	99.37 280	97.53 113	99.88 131	98.98 90	97.29 279	98.42 338

OMC-MVS		99.08 105	99.04 85	99.20 164	99.67 111	98.22 229	99.28 254	99.52 101	98.07 148	99.66 83	99.81 90	97.79 108	99.78 188	97.79 221	99.81 93	99.60 146

testing222		97.16 301	96.50 309	99.16 168	99.16 270	98.47 217	99.27 259	98.66 361	97.71 186	98.23 319	98.15 369	82.28 384	99.84 151	97.36 263	97.66 248	99.18 214

AUN-MVS		96.88 308	96.31 314	98.59 244	99.48 189	97.04 289	99.27 259	99.22 292	97.44 218	98.51 304	99.41 269	91.97 305	99.66 230	97.71 233	83.83 387	99.07 228

pmmvs5		97.52 283	97.30 286	98.16 292	98.57 353	96.73 304	99.27 259	98.90 334	96.14 324	98.37 312	99.53 235	91.54 319	99.14 317	97.51 251	95.87 311	98.63 306

1314		98.68 155	98.54 159	99.11 174	98.89 313	98.65 194	99.27 259	99.49 143	96.89 269	97.99 330	99.56 223	97.72 111	99.83 163	97.74 229	99.27 160	98.84 246

MVS		97.28 296	96.55 308	99.48 117	98.78 329	98.95 162	99.27 259	99.39 223	83.53 390	98.08 325	99.54 231	96.97 135	99.87 136	94.23 349	99.16 165	99.63 140

BH-untuned		98.42 170	98.36 168	98.59 244	99.49 181	96.70 305	99.27 259	99.13 305	97.24 237	98.80 268	99.38 277	95.75 178	99.74 197	97.07 281	99.16 165	99.33 205

MDTV_nov1_ep13			98.32 172		99.11 278	94.44 357	99.27 259	98.74 352	97.51 210	99.40 152	99.62 203	94.78 215	99.76 194	97.59 241	98.81 198

DP-MVS Recon		99.12 95	98.95 105	99.65 73	99.74 80	99.70 46	99.27 259	99.57 64	96.40 306	99.42 143	99.68 174	98.75 55	99.80 180	97.98 205	99.72 118	99.44 191

PatchmatchNet		98.31 180	98.36 168	98.19 290	99.16 270	95.32 342	99.27 259	98.92 328	97.37 225	99.37 160	99.58 216	94.90 207	99.70 219	97.43 260	99.21 162	99.54 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 279	97.28 288	98.62 242	99.64 128	98.03 238	99.26 268	98.74 352	97.68 190	99.09 222	98.32 367	91.66 316	99.81 174	92.88 364	98.22 227	98.03 360

CNVR-MVS		99.42 42	99.30 49	99.78 52	99.62 137	99.71 44	99.26 268	99.52 101	98.82 65	99.39 155	99.71 154	98.96 24	99.85 145	98.59 151	99.80 97	99.77 82

1112_ss		98.98 116	98.77 127	99.59 87	99.68 110	99.02 146	99.25 270	99.48 155	97.23 238	99.13 212	99.58 216	96.93 137	99.90 116	98.87 105	98.78 199	99.84 40

TAPA-MVS	97.07 15	97.74 261	97.34 281	98.94 196	99.70 101	97.53 264	99.25 270	99.51 115	91.90 375	99.30 176	99.63 198	98.78 48	99.64 238	88.09 385	99.87 54	99.65 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	97.94 4	99.02 112	98.85 119	99.53 105	99.66 120	99.01 148	99.24 272	99.52 101	96.85 271	99.27 184	99.48 252	98.25 93	99.91 105	97.76 226	99.62 134	99.65 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post1							99.23 273				65.14 402	94.18 248	99.71 213	97.58 242

ADS-MVSNet2		98.02 215	98.07 195	97.87 311	99.33 225	95.19 345	99.23 273	99.08 310	96.24 314	99.10 219	99.67 180	94.11 249	98.93 351	96.81 295	99.05 178	99.48 178

ADS-MVSNet		98.20 189	98.08 192	98.56 251	99.33 225	96.48 314	99.23 273	99.15 302	96.24 314	99.10 219	99.67 180	94.11 249	99.71 213	96.81 295	99.05 178	99.48 178

EPNet_dtu		98.03 213	97.96 205	98.23 288	98.27 361	95.54 336	99.23 273	98.75 349	99.02 38	97.82 337	99.71 154	96.11 162	99.48 254	93.04 362	99.65 130	99.69 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 193	97.93 210	98.87 215	99.18 262	98.49 213	99.22 277	99.33 257	96.96 263	99.56 114	99.38 277	94.33 241	99.00 339	94.83 342	98.58 206	99.14 215

RPMNet		96.72 311	95.90 323	99.19 165	99.18 262	98.49 213	99.22 277	99.52 101	88.72 386	99.56 114	97.38 380	94.08 251	99.95 59	86.87 390	98.58 206	99.14 215

plane_prior						96.97 295	99.21 279		98.45 96						97.60 252

WR-MVS		98.06 205	97.73 233	99.06 178	98.86 321	99.25 116	99.19 280	99.35 246	97.30 231	98.66 286	99.43 263	93.94 255	99.21 311	98.58 152	94.28 344	98.71 265

new-patchmatchnet		94.48 342	94.08 343	95.67 357	95.08 392	92.41 376	99.18 281	99.28 281	94.55 357	93.49 380	97.37 381	87.86 361	97.01 386	91.57 372	88.36 380	97.61 374

AdaColmap		99.01 115	98.80 123	99.66 69	99.56 156	99.54 79	99.18 281	99.70 15	98.18 131	99.35 167	99.63 198	96.32 157	99.90 116	97.48 254	99.77 107	99.55 159

EG-PatchMatch MVS		95.97 325	95.69 327	96.81 348	97.78 368	92.79 374	99.16 283	98.93 326	96.16 321	94.08 377	99.22 313	82.72 381	99.47 255	95.67 326	97.50 263	98.17 353

PatchT		97.03 306	96.44 311	98.79 232	98.99 301	98.34 224	99.16 283	99.07 313	92.13 374	99.52 123	97.31 383	94.54 234	98.98 341	88.54 383	98.73 201	99.03 231

CNLPA		99.14 87	98.99 97	99.59 87	99.58 150	99.41 98	99.16 283	99.44 201	98.45 96	99.19 204	99.49 247	98.08 101	99.89 126	97.73 230	99.75 112	99.48 178

MDA-MVSNet-bldmvs		94.96 337	93.98 344	97.92 308	98.24 362	97.27 271	99.15 286	99.33 257	93.80 362	80.09 397	99.03 333	88.31 355	97.86 378	93.49 357	94.36 343	98.62 309

CDPH-MVS		99.13 89	98.91 109	99.80 46	99.75 73	99.71 44	99.15 286	99.41 212	96.60 290	99.60 106	99.55 226	98.83 42	99.90 116	97.48 254	99.83 86	99.78 80

save fliter					99.76 65	99.59 70	99.14 288	99.40 220	99.00 43

WB-MVSnew		97.65 276	97.65 240	97.63 324	98.78 329	97.62 262	99.13 289	98.33 369	97.36 226	99.07 224	98.94 343	95.64 184	99.15 316	92.95 363	98.68 202	96.12 388

testf1		90.42 355	90.68 357	89.65 375	97.78 368	73.97 403	99.13 289	98.81 345	89.62 382	91.80 386	98.93 344	62.23 395	98.80 357	86.61 391	91.17 370	96.19 386

APD_test2		90.42 355	90.68 357	89.65 375	97.78 368	73.97 403	99.13 289	98.81 345	89.62 382	91.80 386	98.93 344	62.23 395	98.80 357	86.61 391	91.17 370	96.19 386

xiu_mvs_v1_base_debu		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 292	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 223

xiu_mvs_v1_base		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 292	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 223

xiu_mvs_v1_base_debi		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 292	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 223

XVG-OURS-SEG-HR		98.69 153	98.62 147	98.89 209	99.71 96	97.74 255	99.12 292	99.54 85	98.44 99	99.42 143	99.71 154	94.20 245	99.92 95	98.54 162	98.90 190	99.00 234

jason		99.13 89	99.03 87	99.45 123	99.46 191	98.87 172	99.12 292	99.26 285	98.03 156	99.79 42	99.65 186	97.02 132	99.85 145	99.02 87	99.90 39	99.65 129
jason: jason.
N_pmnet		94.95 338	95.83 325	92.31 366	98.47 357	79.33 398	99.12 292	92.81 404	93.87 361	97.68 340	99.13 323	93.87 258	99.01 338	91.38 373	96.19 302	98.59 322

MDA-MVSNet_test_wron		95.45 331	94.60 338	98.01 302	98.16 363	97.21 276	99.11 298	99.24 289	93.49 366	80.73 396	98.98 339	93.02 274	98.18 369	94.22 350	94.45 341	98.64 298

Patchmtry		97.75 259	97.40 273	98.81 229	99.10 281	98.87 172	99.11 298	99.33 257	94.83 351	98.81 266	99.38 277	94.33 241	99.02 336	96.10 313	95.57 319	98.53 326

YYNet1		95.36 333	94.51 340	97.92 308	97.89 366	97.10 280	99.10 300	99.23 290	93.26 369	80.77 395	99.04 332	92.81 280	98.02 373	94.30 346	94.18 346	98.64 298

CANet_DTU		98.97 118	98.87 115	99.25 158	99.33 225	98.42 222	99.08 301	99.30 275	99.16 19	99.43 140	99.75 138	95.27 195	99.97 21	98.56 158	99.95 16	99.36 201

SCA		98.19 190	98.16 180	98.27 287	99.30 233	95.55 334	99.07 302	98.97 322	97.57 200	99.43 140	99.57 220	92.72 284	99.74 197	97.58 242	99.20 163	99.52 167

TSAR-MVS + GP.		99.36 55	99.36 32	99.36 135	99.67 111	98.61 199	99.07 302	99.33 257	99.00 43	99.82 35	99.81 90	99.06 16	99.84 151	99.09 80	99.42 147	99.65 129

MG-MVS		99.13 89	99.02 91	99.45 123	99.57 152	98.63 196	99.07 302	99.34 250	98.99 45	99.61 103	99.82 76	97.98 104	99.87 136	97.00 283	99.80 97	99.85 36

PatchMatch-RL		98.84 138	98.62 147	99.52 111	99.71 96	99.28 111	99.06 305	99.77 9	97.74 184	99.50 126	99.53 235	95.41 189	99.84 151	97.17 277	99.64 131	99.44 191

OpenMVS_ROB	92.34 20	94.38 343	93.70 349	96.41 353	97.38 374	93.17 372	99.06 305	98.75 349	86.58 387	94.84 375	98.26 368	81.53 385	99.32 288	89.01 381	97.87 243	96.76 381

TEST9					99.67 111	99.65 57	99.05 307	99.41 212	96.22 316	98.95 245	99.49 247	98.77 51	99.91 105

train_agg		99.02 112	98.77 127	99.77 55	99.67 111	99.65 57	99.05 307	99.41 212	96.28 310	98.95 245	99.49 247	98.76 52	99.91 105	97.63 238	99.72 118	99.75 88

lupinMVS		99.13 89	99.01 95	99.46 122	99.51 170	98.94 165	99.05 307	99.16 301	97.86 167	99.80 40	99.56 223	97.39 116	99.86 139	98.94 94	99.85 69	99.58 154

DELS-MVS		99.48 26	99.42 22	99.65 73	99.72 91	99.40 99	99.05 307	99.66 28	99.14 21	99.57 113	99.80 103	98.46 81	99.94 69	99.57 27	99.84 77	99.60 146
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		96.38 318	96.03 320	97.41 331	98.13 364	95.16 347	99.05 307	99.20 296	93.94 360	97.39 347	98.79 353	91.61 318	99.04 332	90.43 376	95.77 313	98.05 359

Patchmatch-test		97.93 228	97.65 240	98.77 234	99.18 262	97.07 284	99.03 312	99.14 304	96.16 321	98.74 274	99.57 220	94.56 231	99.72 207	93.36 358	99.11 171	99.52 167

test_8					99.67 111	99.61 67	99.03 312	99.41 212	96.28 310	98.93 249	99.48 252	98.76 52	99.91 105

Test_1112_low_res		98.89 123	98.66 139	99.57 92	99.69 106	98.95 162	99.03 312	99.47 173	96.98 261	99.15 210	99.23 312	96.77 141	99.89 126	98.83 118	98.78 199	99.86 33

IterMVS-SCA-FT		97.82 248	97.75 231	98.06 298	99.57 152	96.36 318	99.02 315	99.49 143	97.18 241	98.71 277	99.72 153	92.72 284	99.14 317	97.44 259	95.86 312	98.67 286

xiu_mvs_v2_base		99.26 69	99.25 62	99.29 151	99.53 163	98.91 169	99.02 315	99.45 193	98.80 69	99.71 68	99.26 309	98.94 29	99.98 13	99.34 55	99.23 161	98.98 237

MIMVSNet		97.73 262	97.45 261	98.57 248	99.45 196	97.50 265	99.02 315	98.98 321	96.11 326	99.41 147	99.14 322	90.28 332	98.74 359	95.74 322	98.93 186	99.47 184

IterMVS		97.83 245	97.77 226	98.02 301	99.58 150	96.27 321	99.02 315	99.48 155	97.22 239	98.71 277	99.70 158	92.75 281	99.13 320	97.46 257	96.00 306	98.67 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 99	98.92 107	99.65 73	99.90 4	99.37 100	99.02 315	99.91 3	97.67 192	99.59 109	99.75 138	95.90 173	99.73 203	99.53 32	99.02 182	99.86 33

新几何2							99.01 320

BH-w/o		98.00 220	97.89 216	98.32 280	99.35 219	96.20 323	99.01 320	98.90 334	96.42 304	98.38 311	99.00 336	95.26 197	99.72 207	96.06 314	98.61 203	99.03 231

test_prior4						99.56 75	98.99 322

无先验							98.99 322	99.51 115	96.89 269				99.93 84	97.53 250		99.72 103

pmmvs4		98.13 197	97.90 212	98.81 229	98.61 350	98.87 172	98.99 322	99.21 295	96.44 302	99.06 229	99.58 216	95.90 173	99.11 325	97.18 276	96.11 304	98.46 335

HQP-NCC					99.19 259		98.98 325		98.24 118	98.66 286

ACMP_Plane					99.19 259		98.98 325		98.24 118	98.66 286

HQP-MVS		98.02 215	97.90 212	98.37 276	99.19 259	96.83 300	98.98 325	99.39 223	98.24 118	98.66 286	99.40 272	92.47 295	99.64 238	97.19 274	97.58 254	98.64 298

PS-MVSNAJ		99.32 59	99.32 40	99.30 148	99.57 152	98.94 165	98.97 328	99.46 182	98.92 57	99.71 68	99.24 311	99.01 18	99.98 13	99.35 51	99.66 128	98.97 238

MVP-Stereo		97.81 250	97.75 231	97.99 305	97.53 372	96.60 311	98.96 329	98.85 341	97.22 239	97.23 350	99.36 283	95.28 194	99.46 256	95.51 328	99.78 104	97.92 369
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							98.96 329		98.34 108	99.01 235	99.52 238	98.68 64		97.96 206	99.74 115

旧先验2							98.96 329		96.70 279	99.47 131			99.94 69	98.19 188

原ACMM2							98.95 332

MVS_111021_HR		99.41 47	99.32 40	99.66 69	99.72 91	99.47 91	98.95 332	99.85 6	98.82 65	99.54 119	99.73 149	98.51 78	99.74 197	98.91 99	99.88 51	99.77 82

mvsany_test1		99.50 20	99.46 20	99.62 83	99.61 141	99.09 136	98.94 334	99.48 155	99.10 27	99.96 14	99.91 20	98.85 39	99.96 30	99.72 18	99.58 137	99.82 54

MVS_111021_LR		99.41 47	99.33 38	99.65 73	99.77 62	99.51 86	98.94 334	99.85 6	98.82 65	99.65 89	99.74 143	98.51 78	99.80 180	98.83 118	99.89 48	99.64 136

pmmvs3		94.09 345	93.25 351	96.60 351	94.76 394	94.49 356	98.92 336	98.18 375	89.66 381	96.48 361	98.06 375	86.28 366	97.33 383	89.68 379	87.20 383	97.97 366

XVG-OURS		98.73 149	98.68 135	98.88 211	99.70 101	97.73 256	98.92 336	99.55 77	98.52 91	99.45 134	99.84 64	95.27 195	99.91 105	98.08 199	98.84 194	99.00 234

test222					99.75 73	99.49 87	98.91 338	99.49 143	96.42 304	99.34 170	99.65 186	98.28 92			99.69 123	99.72 103

PMMVS2		86.87 358	85.37 362	91.35 370	90.21 399	83.80 389	98.89 339	97.45 386	83.13 391	91.67 388	95.03 388	48.49 401	94.70 394	85.86 393	77.62 393	95.54 389

miper_lstm_enhance		98.00 220	97.91 211	98.28 286	99.34 223	97.43 267	98.88 340	99.36 240	96.48 299	98.80 268	99.55 226	95.98 166	98.91 352	97.27 267	95.50 322	98.51 328

MVS-HIRNet		95.75 329	95.16 334	97.51 329	99.30 233	93.69 367	98.88 340	95.78 394	85.09 389	98.78 271	92.65 392	91.29 323	99.37 275	94.85 341	99.85 69	99.46 186

TR-MVS		97.76 255	97.41 272	98.82 226	99.06 290	97.87 250	98.87 342	98.56 364	96.63 287	98.68 285	99.22 313	92.49 294	99.65 235	95.40 332	97.79 244	98.95 242

testdata1							98.85 343		98.32 111

ET-MVSNet_ETH3D		96.49 315	95.64 329	99.05 180	99.53 163	98.82 181	98.84 344	97.51 385	97.63 195	84.77 390	99.21 316	92.09 303	98.91 352	98.98 90	92.21 367	99.41 195

our_test_3		97.65 276	97.68 237	97.55 328	98.62 348	94.97 349	98.84 344	99.30 275	96.83 274	98.19 321	99.34 290	97.01 133	99.02 336	95.00 340	96.01 305	98.64 298

MS-PatchMatch		97.24 300	97.32 284	96.99 341	98.45 358	93.51 370	98.82 346	99.32 267	97.41 222	98.13 324	99.30 300	88.99 346	99.56 249	95.68 325	99.80 97	97.90 370

c3_l		98.12 199	98.04 197	98.38 275	99.30 233	97.69 261	98.81 347	99.33 257	96.67 281	98.83 264	99.34 290	97.11 128	98.99 340	97.58 242	95.34 324	98.48 330

ppachtmachnet_test		97.49 290	97.45 261	97.61 326	98.62 348	95.24 343	98.80 348	99.46 182	96.11 326	98.22 320	99.62 203	96.45 153	98.97 348	93.77 353	95.97 310	98.61 318

PAPR		98.63 160	98.34 170	99.51 113	99.40 208	99.03 145	98.80 348	99.36 240	96.33 307	99.00 239	99.12 326	98.46 81	99.84 151	95.23 336	99.37 156	99.66 125

test0.0.03 1		97.71 267	97.42 271	98.56 251	98.41 360	97.82 253	98.78 350	98.63 362	97.34 227	98.05 329	98.98 339	94.45 238	98.98 341	95.04 339	97.15 286	98.89 243

PVSNet_Blended		99.08 105	98.97 101	99.42 128	99.76 65	98.79 184	98.78 350	99.91 3	96.74 276	99.67 78	99.49 247	97.53 113	99.88 131	98.98 90	99.85 69	99.60 146

PMMVS		98.80 142	98.62 147	99.34 136	99.27 242	98.70 190	98.76 352	99.31 271	97.34 227	99.21 198	99.07 328	97.20 125	99.82 169	98.56 158	98.87 191	99.52 167

test123		39.01 370	42.50 372	28.53 385	39.17 408	20.91 410	98.75 353	19.17 410	19.83 403	38.57 402	66.67 400	33.16 405	15.42 404	37.50 404	29.66 402	49.26 399

MSDG		98.98 116	98.80 123	99.53 105	99.76 65	99.19 120	98.75 353	99.55 77	97.25 235	99.47 131	99.77 129	97.82 107	99.87 136	96.93 290	99.90 39	99.54 161

CLD-MVS		98.16 194	98.10 188	98.33 278	99.29 237	96.82 302	98.75 353	99.44 201	97.83 172	99.13 212	99.55 226	92.92 277	99.67 227	98.32 181	97.69 247	98.48 330
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		98.18 192	98.10 188	98.41 271	99.23 250	97.72 257	98.72 356	99.31 271	96.60 290	98.88 256	99.29 302	97.29 123	99.13 320	97.60 240	95.99 307	98.38 343

cl____		98.01 218	97.84 219	98.55 253	99.25 248	97.97 242	98.71 357	99.34 250	96.47 301	98.59 300	99.54 231	95.65 183	99.21 311	97.21 270	95.77 313	98.46 335

DIV-MVS_self_test		98.01 218	97.85 218	98.48 259	99.24 249	97.95 246	98.71 357	99.35 246	96.50 295	98.60 299	99.54 231	95.72 180	99.03 334	97.21 270	95.77 313	98.46 335

test-LLR		98.06 205	97.90 212	98.55 253	98.79 326	97.10 280	98.67 359	97.75 380	97.34 227	98.61 297	98.85 349	94.45 238	99.45 257	97.25 268	99.38 149	99.10 218

TESTMET0.1,1		97.55 281	97.27 291	98.40 273	98.93 309	96.53 312	98.67 359	97.61 383	96.96 263	98.64 293	99.28 304	88.63 352	99.45 257	97.30 266	99.38 149	99.21 213

test-mter		97.49 290	97.13 296	98.55 253	98.79 326	97.10 280	98.67 359	97.75 380	96.65 283	98.61 297	98.85 349	88.23 356	99.45 257	97.25 268	99.38 149	99.10 218

IB-MVS	95.67 18	96.22 319	95.44 332	98.57 248	99.21 255	96.70 305	98.65 362	97.74 382	96.71 278	97.27 349	98.54 361	86.03 367	99.92 95	98.47 168	86.30 384	99.10 218
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		98.95 119	98.71 132	99.66 69	99.63 131	99.55 77	98.64 363	99.10 307	97.93 162	99.42 143	99.55 226	98.67 66	99.80 180	95.80 321	99.68 126	99.61 144

thisisatest0515		98.14 196	97.79 221	99.19 165	99.50 179	98.50 212	98.61 364	96.82 389	96.95 265	99.54 119	99.43 263	91.66 316	99.86 139	98.08 199	99.51 142	99.22 212

DeepPCF-MVS	98.18 3	98.81 139	99.37 30	97.12 339	99.60 146	91.75 379	98.61 364	99.44 201	99.35 12	99.83 34	99.85 54	98.70 63	99.81 174	99.02 87	99.91 31	99.81 61

cl22		97.85 240	97.64 243	98.48 259	99.09 284	97.87 250	98.60 366	99.33 257	97.11 250	98.87 259	99.22 313	92.38 300	99.17 315	98.21 186	95.99 307	98.42 338

GA-MVS		97.85 240	97.47 258	99.00 186	99.38 212	97.99 241	98.57 367	99.15 302	97.04 258	98.90 253	99.30 300	89.83 339	99.38 270	96.70 300	98.33 219	99.62 142

TinyColmap		97.12 303	96.89 302	97.83 315	99.07 287	95.52 337	98.57 367	98.74 352	97.58 199	97.81 338	99.79 115	88.16 357	99.56 249	95.10 337	97.21 283	98.39 342

eth_miper_zixun_eth		98.05 210	97.96 205	98.33 278	99.26 244	97.38 268	98.56 369	99.31 271	96.65 283	98.88 256	99.52 238	96.58 147	99.12 324	97.39 262	95.53 321	98.47 332

CMPMVS	69.68 23	94.13 344	94.90 336	91.84 367	97.24 378	80.01 397	98.52 370	99.48 155	89.01 384	91.99 385	99.67 180	85.67 369	99.13 320	95.44 330	97.03 287	96.39 385
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 294	97.20 292	97.75 320	99.07 287	95.20 344	98.51 371	99.04 316	97.99 158	98.31 315	99.86 49	89.02 345	99.55 251	95.67 326	97.36 278	98.49 329

ambc				93.06 365	92.68 396	82.36 390	98.47 372	98.73 357		95.09 373	97.41 379	55.55 397	99.10 327	96.42 309	91.32 369	97.71 371

miper_enhance_ethall		98.16 194	98.08 192	98.41 271	98.96 307	97.72 257	98.45 373	99.32 267	96.95 265	98.97 243	99.17 318	97.06 131	99.22 306	97.86 214	95.99 307	98.29 347

CHOSEN 280x420		99.12 95	99.13 73	99.08 175	99.66 120	97.89 249	98.43 374	99.71 13	98.88 59	99.62 100	99.76 135	96.63 145	99.70 219	99.46 44	99.99 1	99.66 125

testmvs		39.17 369	43.78 371	25.37 386	36.04 409	16.84 411	98.36 375	26.56 408	20.06 402	38.51 403	67.32 399	29.64 406	15.30 405	37.59 403	39.90 401	43.98 400

FPMVS		84.93 360	85.65 361	82.75 381	86.77 402	63.39 407	98.35 376	98.92 328	74.11 393	83.39 392	98.98 339	50.85 400	92.40 396	84.54 394	94.97 332	92.46 391

KD-MVS_2432*1600		94.62 339	93.72 347	97.31 333	97.19 380	95.82 329	98.34 377	99.20 296	95.00 347	97.57 341	98.35 365	87.95 359	98.10 371	92.87 365	77.00 394	98.01 361

miper_refine_blended		94.62 339	93.72 347	97.31 333	97.19 380	95.82 329	98.34 377	99.20 296	95.00 347	97.57 341	98.35 365	87.95 359	98.10 371	92.87 365	77.00 394	98.01 361

CL-MVSNet_self_test		94.49 341	93.97 345	96.08 355	96.16 384	93.67 368	98.33 379	99.38 231	95.13 341	97.33 348	98.15 369	92.69 288	96.57 388	88.67 382	79.87 392	97.99 364

PVSNet	96.02 17	98.85 135	98.84 120	98.89 209	99.73 87	97.28 270	98.32 380	99.60 54	97.86 167	99.50 126	99.57 220	96.75 142	99.86 139	98.56 158	99.70 122	99.54 161

PAPM		97.59 280	97.09 297	99.07 177	99.06 290	98.26 227	98.30 381	99.10 307	94.88 349	98.08 325	99.34 290	96.27 159	99.64 238	89.87 378	98.92 188	99.31 207

Patchmatch-RL test		95.84 327	95.81 326	95.95 356	95.61 387	90.57 382	98.24 382	98.39 368	95.10 345	95.20 371	98.67 357	94.78 215	97.77 379	96.28 312	90.02 377	99.51 173

UnsupCasMVSNet_bld		93.53 347	92.51 352	96.58 352	97.38 374	93.82 363	98.24 382	99.48 155	91.10 379	93.10 381	96.66 385	74.89 389	98.37 366	94.03 352	87.71 382	97.56 376

LCM-MVSNet		86.80 359	85.22 363	91.53 369	87.81 401	80.96 395	98.23 384	98.99 320	71.05 394	90.13 389	96.51 386	48.45 402	96.88 387	90.51 375	85.30 385	96.76 381

cascas		97.69 269	97.43 270	98.48 259	98.60 351	97.30 269	98.18 385	99.39 223	92.96 371	98.41 309	98.78 354	93.77 262	99.27 297	98.16 192	98.61 203	98.86 244

Effi-MVS+		98.81 139	98.59 154	99.48 117	99.46 191	99.12 134	98.08 386	99.50 135	97.50 211	99.38 158	99.41 269	96.37 156	99.81 174	99.11 78	98.54 211	99.51 173

PCF-MVS	97.08 14	97.66 275	97.06 298	99.47 120	99.61 141	99.09 136	98.04 387	99.25 287	91.24 378	98.51 304	99.70 158	94.55 233	99.91 105	92.76 367	99.85 69	99.42 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 324	95.47 330	97.94 307	99.31 232	94.34 360	97.81 388	99.70 15	97.12 247	97.46 343	98.75 355	89.71 340	99.79 183	97.69 236	81.69 390	99.68 119

E-PMN		80.61 363	79.88 365	82.81 380	90.75 398	76.38 401	97.69 389	95.76 395	66.44 398	83.52 391	92.25 393	62.54 394	87.16 400	68.53 400	61.40 397	84.89 398

ANet_high		77.30 365	74.86 369	84.62 379	75.88 405	77.61 399	97.63 390	93.15 403	88.81 385	64.27 400	89.29 397	36.51 404	83.93 402	75.89 397	52.31 399	92.33 393

EMVS		80.02 364	79.22 366	82.43 382	91.19 397	76.40 400	97.55 391	92.49 405	66.36 399	83.01 393	91.27 395	64.63 393	85.79 401	65.82 401	60.65 398	85.08 397

MVE	76.82 21	76.91 366	74.31 370	84.70 378	85.38 404	76.05 402	96.88 392	93.17 402	67.39 397	71.28 399	89.01 398	21.66 409	87.69 399	71.74 399	72.29 396	90.35 395
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 353	91.36 355	90.31 372	95.85 385	73.72 405	94.89 393	99.25 287	68.39 396	95.82 367	99.02 335	80.50 386	98.95 350	93.64 355	94.89 336	98.25 350

Gipuma		90.99 354	90.15 359	93.51 362	98.73 336	90.12 383	93.98 394	99.45 193	79.32 392	92.28 384	94.91 389	69.61 390	97.98 375	87.42 387	95.67 317	92.45 392
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 367	74.97 368	79.01 383	70.98 406	55.18 408	93.37 395	98.21 373	65.08 400	61.78 401	93.83 391	21.74 408	92.53 395	78.59 395	91.12 372	89.34 396
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 361	81.52 364	86.66 377	66.61 407	68.44 406	92.79 396	97.92 377	68.96 395	80.04 398	99.85 54	85.77 368	96.15 391	97.86 214	43.89 400	95.39 390

wuyk23d		40.18 368	41.29 373	36.84 384	86.18 403	49.12 409	79.73 397	22.81 409	27.64 401	25.46 404	28.45 404	21.98 407	48.89 403	55.80 402	23.56 403	12.51 401

test_blank		0.13 374	0.17 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	1.57 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet_test		0.02 375	0.03 378	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

DCPMVS		0.02 375	0.03 378	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

cdsmvs_eth3d_5k		24.64 371	32.85 374	0.00 387	0.00 410	0.00 412	0.00 398	99.51 115	0.00 405	0.00 406	99.56 223	96.58 147	0.00 406	0.00 405	0.00 404	0.00 402

pcd_1.5k_mvsjas		8.27 373	11.03 376	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	99.01 18	0.00 406	0.00 405	0.00 404	0.00 402

sosnet-low-res		0.02 375	0.03 378	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

sosnet		0.02 375	0.03 378	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uncertanet		0.02 375	0.03 378	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

Regformer		0.02 375	0.03 378	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

ab-mvs-re		8.30 372	11.06 375	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	99.58 216	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet		0.02 375	0.03 378	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.27 406	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

WAC-MVS						97.16 277								95.47 329

MSC_two_6792asdad				99.87 11	99.51 170	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

PC_three_1452									98.18 131	99.84 29	99.70 158	99.31 3	98.52 364	98.30 183	99.80 97	99.81 61

No_MVS				99.87 11	99.51 170	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

test_one_0601					99.81 46	99.88 8		99.49 143	98.97 51	99.65 89	99.81 90	99.09 14

eth-test2					0.00 410

eth-test					0.00 410

ZD-MVS					99.71 96	99.79 30		99.61 48	96.84 272	99.56 114	99.54 231	98.58 72	99.96 30	96.93 290	99.75 112

IU-MVS					99.84 32	99.88 8		99.32 267	98.30 112	99.84 29				98.86 110	99.85 69	99.89 20

test_241102_TWO								99.48 155	99.08 33	99.88 20	99.81 90	98.94 29	99.96 30	98.91 99	99.84 77	99.88 26

test_241102_ONE					99.84 32	99.90 2		99.48 155	99.07 35	99.91 16	99.74 143	99.20 7	99.76 194

test_0728_THIRD									98.99 45	99.81 37	99.80 103	99.09 14	99.96 30	98.85 112	99.90 39	99.88 26

GSMVS																99.52 167

test_part2					99.81 46	99.83 16				99.77 51

sam_mvs1												94.86 209				99.52 167

sam_mvs												94.72 222

MTGPA								99.47 173

test_post											65.99 401	94.65 228	99.73 203

patchmatchnet-post											98.70 356	94.79 214	99.74 197

gm-plane-assit					98.54 355	92.96 373			94.65 355		99.15 321		99.64 238	97.56 247

test9_res														97.49 253	99.72 118	99.75 88

agg_prior2														97.21 270	99.73 117	99.75 88

agg_prior					99.67 111	99.62 65		99.40 220		98.87 259			99.91 105

TestCases				99.31 143	99.86 20	98.48 215		99.61 48	97.85 169	99.36 164	99.85 54	95.95 168	99.85 145	96.66 303	99.83 86	99.59 150

test_prior				99.68 68	99.67 111	99.48 89		99.56 69					99.83 163			99.74 92

新几何1				99.75 58	99.75 73	99.59 70		99.54 85	96.76 275	99.29 179	99.64 192	98.43 83	99.94 69	96.92 292	99.66 128	99.72 103

旧先验1					99.74 80	99.59 70		99.54 85			99.69 168	98.47 80			99.68 126	99.73 97

原ACMM1				99.65 73	99.73 87	99.33 103		99.47 173	97.46 213	99.12 214	99.66 185	98.67 66	99.91 105	97.70 235	99.69 123	99.71 112

testdata2													99.95 59	96.67 302

segment_acmp												98.96 24

testdata				99.54 97	99.75 73	98.95 162		99.51 115	97.07 253	99.43 140	99.70 158	98.87 37	99.94 69	97.76 226	99.64 131	99.72 103

test12				99.75 58	99.64 128	99.61 67		99.29 279		99.21 198		98.38 87	99.89 126		99.74 115	99.74 92

plane_prior7					99.29 237	97.03 290

plane_prior6					99.27 242	96.98 294						92.71 286

plane_prior5								99.47 173					99.69 224	97.78 222	97.63 249	98.67 286

plane_prior4											99.61 207

plane_prior3						97.00 292			98.69 79	99.11 216

plane_prior1					99.26 244

n2								0.00 411

nn								0.00 411

door-mid								98.05 376

lessismore_v0				97.79 319	98.69 342	95.44 340		94.75 398		95.71 368	99.87 44	88.69 349	99.32 288	95.89 318	94.93 334	98.62 309

LGP-MVS_train				98.49 257	99.33 225	97.05 286		99.55 77	97.46 213	99.24 190	99.83 68	92.58 291	99.72 207	98.09 195	97.51 261	98.68 279

test11								99.35 246

door								97.92 377

HQP5-MVS						96.83 300

BP-MVS														97.19 274

HQP4-MVS										98.66 286			99.64 238			98.64 298

HQP3-MVS								99.39 223							97.58 254

HQP2-MVS												92.47 295

NP-MVS					99.23 250	96.92 298					99.40 272

ACMMP++_ref															97.19 284

ACMMP++															97.43 273

Test By Simon												98.75 55

ITE_SJBPF				98.08 297	99.29 237	96.37 317		98.92 328	98.34 108	98.83 264	99.75 138	91.09 325	99.62 244	95.82 319	97.40 275	98.25 350

DeepMVS_CX				93.34 363	99.29 237	82.27 391		99.22 292	85.15 388	96.33 362	99.05 331	90.97 327	99.73 203	93.57 356	97.77 245	98.01 361

High-resolution multi-view benchmark