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.

RA-MVS			99.82 5		99.99 53	99.98 19	100.00 1	99.85 44	98.97 38	100.00 1	100.00 1	99.57 29	100.00 1	100.00 1	100.00 1

fmvsm_l_mol_unc0.5_1		99.69 42	99.59 51	99.99 13	99.84 132	99.99 6	100.00 1	99.35 250	99.02 31	100.00 1	100.00 1	98.09 169	99.99 108	100.00 1	99.99 108	100.00 1

PRO-TEST		99.18 136	99.03 140	99.61 167	99.71 179	99.37 176	100.00 1	99.25 321	97.51 194	99.96 156	100.00 1	95.41 253	99.66 293	99.75 160	99.69 179	99.82 234

onestephybrid01		98.89 193	98.67 198	99.56 179	99.51 279	99.08 208	100.00 1	99.20 367	97.30 220	99.95 188	100.00 1	94.04 294	99.79 262	99.77 153	98.29 258	99.81 251

viewmamba		98.92 185	98.74 183	99.46 193	99.46 309	98.83 236	100.00 1	99.19 371	97.18 229	99.95 188	100.00 1	94.97 267	99.74 281	99.64 200	98.29 258	99.81 251

hybridnocas07		98.85 197	98.63 203	99.53 182	99.52 272	98.95 228	100.00 1	99.19 371	97.15 231	99.93 199	100.00 1	93.83 303	99.82 253	99.67 190	98.38 237	99.82 234

Casviewmamba		98.71 215	98.47 231	99.46 193	99.47 300	98.70 248	100.00 1	99.17 391	96.97 251	99.45 280	100.00 1	93.04 325	99.87 234	99.67 190	98.41 230	99.81 251

dtuplus		98.57 241	98.32 257	99.30 246	99.44 319	98.35 287	100.00 1	99.14 407	96.36 334	98.97 321	100.00 1	93.04 325	99.77 270	99.55 225	98.39 233	99.79 288

hybridcas		98.64 224	98.41 239	99.33 234	99.54 254	98.41 273	100.00 1	99.18 381	96.78 272	99.68 254	100.00 1	92.58 337	99.75 280	99.57 222	98.38 237	99.82 234

hybrid		98.81 201	98.60 210	99.45 197	99.52 272	98.74 244	100.00 1	99.19 371	97.04 243	99.95 188	100.00 1	93.89 302	99.78 268	99.64 200	98.19 276	99.81 251

E5new		98.63 230	98.41 239	99.31 242	99.51 279	98.21 303	99.79 382	99.21 357	96.62 307	99.67 260	100.00 1	93.15 321	99.91 210	99.46 244	98.26 266	99.81 251

E6new		98.64 224	98.41 239	99.30 246	99.46 309	98.19 306	99.79 382	99.21 357	96.62 307	99.68 254	100.00 1	93.24 317	99.91 210	99.47 241	98.26 266	99.81 251

E6		98.64 224	98.41 239	99.30 246	99.46 309	98.19 306	99.79 382	99.21 357	96.62 307	99.68 254	100.00 1	93.24 317	99.91 210	99.47 241	98.26 266	99.81 251

E5		98.63 230	98.41 239	99.31 242	99.51 279	98.21 303	99.79 382	99.21 357	96.62 307	99.67 260	100.00 1	93.15 321	99.91 210	99.46 244	98.26 266	99.81 251

E4		98.68 222	98.46 233	99.33 234	99.51 279	98.27 297	99.96 323	99.21 357	96.66 299	99.68 254	100.00 1	93.38 312	99.91 210	99.49 238	98.27 264	99.81 251

E3new		98.95 181	98.80 174	99.41 207	99.57 244	98.50 268	100.00 1	99.22 338	96.84 265	99.89 210	100.00 1	95.70 248	99.93 202	99.57 222	98.39 233	99.82 234

fmvsm_s_conf0.5_n_11		98.92 185	98.63 203	99.80 124	99.85 130	99.86 92	100.00 1	99.24 328	98.91 57	100.00 1	100.00 1	89.69 394	99.99 108	100.00 1	99.98 120	99.54 326

E2		98.77 204	98.57 215	99.37 220	99.53 258	98.38 280	99.98 305	99.22 338	96.77 275	99.75 243	100.00 1	94.03 295	99.91 210	99.53 232	98.35 244	99.82 234

aaatest				99.99 13	100.00 1	99.98 19	100.00 1	99.95 19	99.10 12	99.99 131	100.00 1		100.00 1	100.00 1	100.00 1	100.00 1

MED-MVS		99.89 1	99.86 2	99.99 13	100.00 1	99.98 19	100.00 1	99.95 19	99.18 6	99.99 131	100.00 1	99.58 27	100.00 1	100.00 1	100.00 1	100.00 1

E3		98.77 204	98.57 215	99.36 222	99.47 300	98.36 284	99.98 305	99.22 338	96.76 276	99.75 243	100.00 1	94.10 292	99.91 210	99.53 232	98.35 244	99.82 234

TestfortrainingZip a		99.85 5	99.81 7	99.99 13	100.00 1	99.98 19	100.00 1	99.95 19	99.18 6	100.00 1	100.00 1	99.45 54	99.99 108	99.68 186	99.99 108	100.00 1

TestfortrainingZip				100.00 1	99.99 53	100.00 1	100.00 1	99.95 19	99.03 25	100.00 1	100.00 1	99.59 24	100.00 1		100.00 1	100.00 1

fmvsm_s_conf0.5_n_10		99.08 147	98.78 176	99.97 41	99.84 132	99.92 62	100.00 1	99.28 296	98.93 51	100.00 1	100.00 1	91.07 357	99.99 108	100.00 1	99.95 130	100.00 1

viewdifsd2359ckpt07		98.72 211	98.52 223	99.34 226	99.47 300	98.28 295	99.99 272	99.20 367	96.98 248	99.60 265	100.00 1	93.45 311	99.93 202	99.58 219	98.36 242	99.82 234

viewdifsd2359ckpt09		98.78 203	98.60 210	99.31 242	99.53 258	98.37 281	100.00 1	99.20 367	96.85 263	99.32 293	100.00 1	94.68 277	99.74 281	99.46 244	98.36 242	99.81 251

viewdifsd2359ckpt13		98.72 211	98.52 223	99.34 226	99.55 251	98.46 270	99.99 272	99.22 338	96.50 321	99.05 316	100.00 1	94.54 281	99.73 285	99.46 244	98.35 244	99.81 251

viewcassd2359sk11		98.90 190	98.73 185	99.40 212	99.57 244	98.47 269	99.99 272	99.22 338	96.79 270	99.82 229	100.00 1	95.24 257	99.91 210	99.54 229	98.38 237	99.82 234

viewdifsd2359ckpt11		97.98 287	97.89 288	98.26 330	99.47 300	94.98 413	99.99 272	99.22 338	96.74 282	99.24 297	100.00 1	90.14 381	99.90 221	99.49 238	96.73 335	99.90 184

viewmacassd2359aftdt		98.57 241	98.31 258	99.33 234	99.49 292	98.31 293	99.89 361	99.21 357	96.87 262	99.10 309	100.00 1	92.48 340	99.88 232	99.50 236	98.28 261	99.81 251

viewmsd2359difaftdt		97.98 287	97.89 288	98.27 327	99.47 300	94.99 412	99.99 272	99.22 338	96.74 282	99.24 297	100.00 1	90.14 381	99.90 221	99.49 238	96.73 335	99.90 184

diffmvs_AUTHOR		98.92 185	98.73 185	99.49 190	99.48 295	98.81 237	99.94 340	99.14 407	97.24 224	99.96 156	100.00 1	94.85 270	99.87 234	99.67 190	98.31 255	99.79 288

fmvsm_l_conf0.5_n_9		99.35 102	99.15 125	99.95 62	99.83 137	99.84 98	100.00 1	99.30 279	98.92 54	100.00 1	100.00 1	94.32 289	100.00 1	100.00 1	99.93 140	100.00 1

icg_test_0407_2		98.30 268	98.45 234	97.85 370	99.38 334	95.36 401	99.99 272	99.18 381	96.72 288	99.58 266	100.00 1	95.17 262	98.45 429	97.84 342	98.15 281	99.74 304

SSM_0407		98.72 211	98.52 223	99.33 234	99.53 258	98.52 264	99.88 364	99.15 400	96.53 314	98.95 322	100.00 1	94.38 286	99.72 287	99.64 200	98.62 216	99.75 297

viewmambaseed2359dif		98.57 241	98.34 256	99.28 252	99.46 309	98.23 300	100.00 1	99.16 394	96.26 342	99.11 308	100.00 1	93.12 324	99.79 262	99.61 212	98.33 251	99.80 282

IMVS_0407		98.36 265	98.42 237	98.19 337	99.38 334	95.36 401	99.73 402	99.18 381	96.72 288	99.58 266	100.00 1	95.17 262	99.47 333	97.84 342	98.15 281	99.74 304

viewmanbaseed2359cas		98.86 195	98.68 197	99.40 212	99.51 279	98.51 267	99.98 305	99.22 338	97.05 242	99.72 250	100.00 1	94.77 273	99.89 223	99.58 219	98.31 255	99.81 251

IMVS_0404		97.87 292	97.89 288	97.81 372	99.38 334	95.36 401	99.84 370	99.18 381	96.72 288	98.41 372	100.00 1	91.43 351	98.32 438	97.84 342	98.15 281	99.74 304

SSM_0404		98.76 207	98.56 218	99.35 224	99.53 258	98.65 253	99.80 381	99.15 400	96.53 314	99.47 277	100.00 1	94.38 286	99.76 275	99.64 200	98.59 219	99.64 322

IMVS_0403		98.37 263	98.39 248	98.29 325	99.38 334	95.36 401	99.97 315	99.18 381	96.72 288	99.68 254	100.00 1	94.61 279	99.77 270	97.84 342	98.15 281	99.74 304

fmvsm_s_conf0.5_n_9		99.04 154	98.78 176	99.81 118	99.86 128	99.44 168	100.00 1	99.32 264	98.94 47	100.00 1	100.00 1	91.00 360	99.99 108	100.00 1	99.94 136	100.00 1

aaEdge-Enhanced		99.87 3	99.83 4	99.99 13	99.99 53	99.98 19	100.00 1	99.95 19	99.05 18	100.00 1	100.00 1	99.50 44	100.00 1	100.00 1	100.00 1	100.00 1

NormalMVS		99.47 85	99.48 78	99.43 203	99.99 53	98.55 259	99.94 340	99.28 296	98.39 105	100.00 1	100.00 1	98.44 157	99.98 143	99.36 253	99.92 143	99.75 297

lecture		99.64 55	99.53 67	99.98 29	99.99 53	99.93 55	100.00 1	99.47 86	98.53 96	100.00 1	100.00 1	97.88 177	100.00 1	99.98 94	99.92 143	100.00 1

SymmetryMVS		99.30 115	99.25 106	99.45 197	99.79 164	98.55 259	99.94 340	99.47 86	98.39 105	100.00 1	100.00 1	98.44 157	99.98 143	99.36 253	97.83 307	99.83 227

AstraMVS		99.03 157	99.01 144	99.09 266	99.46 309	97.66 344	100.00 1	99.23 333	97.83 152	99.95 188	100.00 1	95.52 252	99.86 236	99.74 162	99.39 196	99.74 304

guyue		99.21 133	99.07 136	99.62 165	99.55 251	99.29 184	100.00 1	99.32 264	97.66 168	99.96 156	100.00 1	95.84 244	99.84 247	99.63 207	99.67 182	99.75 297

fmvsm_s_conf0.5_n_8		99.34 106	99.14 127	99.91 84	99.83 137	99.74 114	100.00 1	99.38 228	98.94 47	100.00 1	100.00 1	94.25 291	99.99 108	100.00 1	99.91 148	100.00 1

fmvsm_s_conf0.5_n_7		98.98 175	98.85 169	99.37 220	99.67 198	98.34 288	100.00 1	99.31 273	98.97 38	100.00 1	100.00 1	91.70 348	99.97 152	99.99 79	99.97 124	99.80 282

fmvsm_s_conf0.5_n_6		99.30 115	99.12 131	99.84 110	99.24 351	99.56 140	100.00 1	99.31 273	98.90 61	100.00 1	100.00 1	94.75 275	99.97 152	99.98 94	99.88 154	100.00 1

fmvsm_s_conf0.5_n_5		99.00 166	98.70 193	99.88 96	99.81 146	99.64 130	100.00 1	99.26 317	98.78 85	99.97 148	100.00 1	90.65 367	99.99 108	100.00 1	99.89 151	99.99 126

fmvsm_s_conf0.5_n_4		98.98 175	98.74 183	99.68 155	99.81 146	99.50 155	100.00 1	99.26 317	98.91 57	100.00 1	100.00 1	90.87 364	99.97 152	99.99 79	99.81 170	99.57 324

fmvsm_l_conf0.5_n_3		99.38 96	99.20 119	99.92 83	99.80 159	99.78 106	100.00 1	99.35 250	98.94 47	100.00 1	100.00 1	94.77 273	99.99 108	99.99 79	99.92 143	100.00 1

fmvsm_s_conf0.5_n_3		98.99 170	98.69 195	99.89 91	99.70 182	99.69 125	100.00 1	99.39 225	98.93 51	100.00 1	100.00 1	90.20 379	99.99 108	100.00 1	99.95 130	100.00 1

fmvsm_s_conf0.5_n_2		98.90 190	98.57 215	99.90 88	99.79 164	99.78 106	100.00 1	99.25 321	98.97 38	100.00 1	100.00 1	89.22 402	99.99 108	100.00 1	99.88 154	99.92 169

fmvsm_s_conf0.1_n_2		98.95 181	98.69 195	99.73 145	99.61 228	99.74 114	100.00 1	99.23 333	98.95 44	99.97 148	100.00 1	90.92 363	99.97 152	100.00 1	99.58 190	99.47 331

GDP-MVS		99.38 96	99.26 104	99.77 138	99.53 258	99.55 142	100.00 1	99.11 422	97.14 232	99.96 156	100.00 1	99.83 5	99.89 223	98.47 314	99.26 198	99.87 216

BP-MVS1		99.56 72	99.48 78	99.79 130	99.48 295	99.61 133	100.00 1	99.32 264	97.34 214	99.94 194	100.00 1	99.74 13	99.89 223	99.75 160	99.72 176	99.87 216

reproduce_model		99.76 21	99.69 26	99.98 29	99.96 105	99.93 55	100.00 1	99.42 156	98.81 78	100.00 1	100.00 1	98.98 118	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 21	99.69 26	99.98 29	99.96 105	99.94 49	100.00 1	99.42 156	98.82 74	100.00 1	100.00 1	98.99 115	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 21	99.69 26	99.98 29	99.96 105	99.94 49	100.00 1	99.42 156	98.82 74	100.00 1	100.00 1	98.99 115	100.00 1	100.00 1	100.00 1	100.00 1

MGCFI-Net		99.01 165	98.70 193	99.93 79	99.74 176	99.94 49	100.00 1	99.29 288	97.60 182	100.00 1	100.00 1	95.10 264	99.96 172	99.74 162	96.85 334	99.91 173

sasdasda		99.03 157	98.73 185	99.94 75	99.75 174	99.95 40	100.00 1	99.30 279	97.64 172	100.00 1	100.00 1	95.22 258	99.97 152	99.76 155	96.90 332	99.91 173

fmvsm_l_conf0.5_n_a		99.63 59	99.55 64	99.86 101	99.83 137	99.58 138	100.00 1	99.36 239	98.98 36	100.00 1	100.00 1	97.85 179	99.99 108	100.00 1	99.94 136	100.00 1

fmvsm_l_conf0.5_n		99.63 59	99.56 62	99.86 101	99.81 146	99.59 136	100.00 1	99.36 239	98.98 36	100.00 1	100.00 1	97.92 174	99.99 108	100.00 1	99.95 130	100.00 1

fmvsm_s_conf0.1_n_a		98.71 215	98.36 254	99.78 135	99.09 361	99.42 170	100.00 1	99.26 317	97.42 206	100.00 1	100.00 1	89.78 390	99.96 172	99.82 142	99.85 163	99.97 139

fmvsm_s_conf0.1_n		98.77 204	98.42 237	99.82 113	99.47 300	99.52 151	100.00 1	99.27 311	97.53 190	100.00 1	100.00 1	89.73 392	99.96 172	99.84 136	99.93 140	99.97 139

fmvsm_s_conf0.5_n_a		99.32 111	99.15 125	99.81 118	99.80 159	99.47 164	100.00 1	99.35 250	98.22 118	100.00 1	100.00 1	95.21 260	99.99 108	99.96 108	99.86 160	99.98 129

fmvsm_s_conf0.5_n		99.21 133	99.01 144	99.83 111	99.84 132	99.53 147	100.00 1	99.38 228	98.29 117	100.00 1	100.00 1	93.62 307	99.99 108	99.99 79	99.93 140	99.98 129

MM		99.63 59	99.52 70	99.94 75	99.99 53	99.82 101	100.00 1	99.97 17	99.11 10	100.00 1	100.00 1	96.65 230	100.00 1	100.00 1	99.97 124	100.00 1

Syy-MVS		96.17 384	96.57 345	95.00 450	99.50 288	87.37 492	100.00 1	99.57 75	96.23 343	98.07 393	100.00 1	92.41 341	97.81 477	85.34 497	97.96 295	99.82 234

myMVS_eth3d		98.52 250	98.51 228	98.53 307	99.50 288	97.98 323	100.00 1	99.57 75	96.23 343	98.07 393	100.00 1	99.09 101	97.81 477	96.17 396	97.96 295	99.82 234

test_fmvsmconf_n		99.56 72	99.46 81	99.86 101	99.68 190	99.58 138	100.00 1	99.31 273	98.92 54	99.88 213	100.00 1	97.35 206	99.99 108	99.98 94	99.99 108	100.00 1

test_fmvsmvis_n_1920		99.46 86	99.37 88	99.73 145	98.88 391	99.18 201	100.00 1	99.26 317	98.85 68	99.79 237	100.00 1	97.70 187	100.00 1	99.98 94	99.86 160	100.00 1

dmvs_re		97.54 313	97.88 291	96.54 425	99.55 251	90.35 479	99.86 367	99.46 104	97.00 246	99.41 286	100.00 1	90.78 366	99.30 351	99.60 214	95.24 359	99.96 145

dmvs_testset		93.27 434	95.48 399	86.65 493	98.74 402	68.42 529	99.92 348	98.91 466	96.19 348	93.28 480	100.00 1	91.06 359	91.67 525	89.64 478	91.54 419	99.86 220

test_fmvsm_n_1920		99.55 74	99.49 75	99.73 145	99.85 130	99.19 199	100.00 1	99.41 205	98.87 66	100.00 1	100.00 1	97.34 207	100.00 1	99.98 94	99.90 150	100.00 1

test_cas_vis1_n_1920		98.63 230	98.25 262	99.77 138	99.69 185	99.32 181	100.00 1	99.31 273	98.84 70	99.96 156	100.00 1	87.42 425	99.99 108	99.14 274	99.86 160	100.00 1

test_vis1_n_1920		97.77 299	97.24 321	99.34 226	99.79 164	98.04 320	100.00 1	99.25 321	98.88 63	100.00 1	100.00 1	77.52 479	100.00 1	99.88 126	99.85 163	100.00 1

test_vis1_n		96.69 354	95.81 379	99.32 240	99.14 355	97.98 323	99.97 315	98.98 460	98.45 102	100.00 1	100.00 1	66.44 505	99.99 108	99.78 151	99.57 192	100.00 1

test_fmvs1_n		97.43 318	96.86 334	99.15 263	99.68 190	97.48 350	99.99 272	98.98 460	98.82 74	100.00 1	100.00 1	74.85 488	99.96 172	99.67 190	99.70 178	100.00 1

mvsany_test1		99.57 71	99.48 78	99.85 105	99.86 128	99.54 145	100.00 1	99.36 239	98.94 47	100.00 1	100.00 1	97.97 171	100.00 1	99.88 126	99.28 197	100.00 1

test_fmvs1		98.37 263	98.04 281	99.34 226	99.84 132	98.07 316	100.00 1	99.00 456	98.85 68	100.00 1	100.00 1	85.11 446	99.96 172	99.69 185	99.88 154	100.00 1

patch_mono-2		99.04 154	99.79 10	96.81 419	99.92 117	90.47 478	100.00 1	99.41 205	98.95 44	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 151

test2506		99.48 83	99.38 85	99.75 141	99.89 123	99.51 153	99.45 439	100.00 1	98.38 107	99.83 220	100.00 1	98.86 133	99.81 257	99.25 265	98.78 211	99.94 156

test1111		98.42 258	98.12 272	99.29 249	99.88 125	98.15 309	99.46 437	100.00 1	98.36 111	99.42 281	100.00 1	87.91 418	99.79 262	99.31 262	98.78 211	99.94 156

ECVR-MVS		98.43 256	98.14 271	99.32 240	99.89 123	98.21 303	99.46 437	100.00 1	98.38 107	99.47 277	100.00 1	87.91 418	99.80 261	99.35 257	98.78 211	99.94 156

DVP-MVS++		99.81 14	99.75 18	100.00 1	100.00 1	99.99 6	100.00 1	99.42 156	98.79 82	100.00 1	100.00 1	99.54 33	100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 79	100.00 1	100.00 1	99.54 33	100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 6		99.42 156	98.72 87	100.00 1	100.00 1	99.60 21

GeoE		98.06 283	97.65 303	99.29 249	99.47 300	98.41 273	100.00 1	99.19 371	94.85 388	98.88 329	100.00 1	91.21 353	99.59 299	97.02 372	98.19 276	99.88 205

ZD-MVS					100.00 1	99.98 19		99.80 49	97.31 219	100.00 1	100.00 1	99.32 75	99.99 108	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 59	99.52 70	99.97 41	99.99 53	99.91 66	100.00 1	99.42 156	97.62 175	100.00 1	100.00 1	98.65 147	99.99 108	99.99 79	100.00 1	100.00 1

RE-MVS-def			99.55 64		99.99 53	99.91 66	100.00 1	99.42 156	97.62 175	100.00 1	100.00 1	98.94 126		99.99 79	100.00 1	100.00 1

SED-MVS		99.83 10	99.77 13	100.00 1	100.00 1	99.99 6	100.00 1	99.42 156	99.03 25	100.00 1	100.00 1	99.50 44	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 33	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 156	99.03 25	100.00 1	100.00 1	99.56 30	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 6		99.42 156	99.03 25	100.00 1	100.00 1	99.50 44	100.00 1

SF-MVS		99.66 52	99.57 57	99.95 62	99.99 53	99.85 96	100.00 1	99.42 156	97.67 167	100.00 1	100.00 1	99.05 108	99.99 108	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 36	99.62 48	99.97 41	99.99 53	99.90 73	100.00 1	99.79 51	97.97 141	99.97 148	100.00 1	98.97 120	100.00 1	99.94 116	100.00 1	100.00 1

dcpmvs_2		98.87 194	99.53 67	96.90 407	99.87 127	90.88 474	99.94 340	99.07 436	98.20 121	100.00 1	100.00 1	98.69 146	99.86 236	100.00 1	100.00 1	99.95 151

9.14			99.57 57		99.99 53		100.00 1	99.42 156	97.54 187	100.00 1	100.00 1	99.15 97	99.99 108	100.00 1	100.00 1

ET-MVSNet_ETH3D		96.41 367	95.48 399	99.20 261	99.81 146	99.75 111	100.00 1	99.02 453	97.30 220	78.33 525	100.00 1	97.73 185	97.94 474	99.70 176	87.41 464	99.92 169

EIA-MVS		99.26 124	99.19 120	99.45 197	99.63 218	98.75 241	100.00 1	99.27 311	96.93 255	99.95 188	100.00 1	97.47 200	99.79 262	99.74 162	99.72 176	99.82 234

ETV-MVS		99.34 106	99.24 110	99.64 162	99.58 240	99.33 180	100.00 1	99.25 321	97.57 185	99.96 156	100.00 1	97.44 203	99.79 262	99.70 176	99.65 185	99.81 251

CS-MVS		99.33 109	99.27 100	99.50 186	99.99 53	99.00 221	100.00 1	99.13 414	97.26 223	99.96 156	100.00 1	97.79 184	99.64 295	99.64 200	99.67 182	99.87 216

DVP-MVS		99.83 10	99.78 11	100.00 1	100.00 1	99.99 6	100.00 1	99.42 156	99.04 20	100.00 1	100.00 1	99.53 36	100.00 1	100.00 1	100.00 1	100.00 1
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_THIRD									98.79 82	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 6	100.00 1	99.42 156	99.04 20	100.00 1	100.00 1	99.53 36

SR-MVS		99.68 47	99.58 54	99.98 29	100.00 1	99.95 40	100.00 1	99.64 71	97.59 183	100.00 1	100.00 1	98.99 115	99.99 108	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 59	99.51 72	100.00 1	99.90 121	100.00 1	100.00 1	99.43 135	99.00 33	100.00 1	100.00 1	99.58 27	100.00 1	97.64 351	100.00 1	100.00 1

GST-MVS		99.64 55	99.53 67	99.95 62	100.00 1	99.86 92	100.00 1	99.79 51	97.72 162	99.95 188	100.00 1	98.39 160	100.00 1	99.96 108	99.99 108	100.00 1

test_yl		99.51 76	99.37 88	99.95 62	99.82 140	99.90 73	100.00 1	99.47 86	97.48 198	100.00 1	100.00 1	99.80 6	100.00 1	99.98 94	97.75 314	99.94 156

thisisatest0530		99.37 100	99.27 100	99.69 152	99.59 235	99.41 171	100.00 1	99.46 104	96.46 323	99.90 206	100.00 1	99.44 57	99.85 243	98.97 285	99.58 190	99.80 282

Anonymous20240529		96.93 344	96.22 362	99.05 269	99.79 164	97.30 360	99.16 477	99.47 86	88.51 477	98.69 342	100.00 1	83.50 457	100.00 1	99.83 137	97.02 329	99.83 227

DCV-MVSNet		99.51 76	99.37 88	99.95 62	99.82 140	99.90 73	100.00 1	99.47 86	97.48 198	100.00 1	100.00 1	99.80 6	100.00 1	99.98 94	97.75 314	99.94 156

tttt0517		99.34 106	99.23 113	99.67 156	99.57 244	99.38 173	100.00 1	99.46 104	96.33 338	99.89 210	100.00 1	99.44 57	99.84 247	98.93 287	99.46 194	99.78 293

thisisatest0515		99.42 91	99.31 96	99.74 142	99.59 235	99.55 142	100.00 1	99.46 104	96.65 301	99.92 201	100.00 1	99.44 57	99.85 243	99.09 280	99.63 188	99.81 251

SMA-MVS		99.69 42	99.59 51	99.98 29	99.99 53	99.93 55	100.00 1	99.43 135	97.50 196	100.00 1	100.00 1	99.43 61	100.00 1	100.00 1	100.00 1	100.00 1
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
DPE-MVS		99.79 17	99.73 21	99.99 13	99.99 53	99.98 19	100.00 1	99.42 156	98.91 57	100.00 1	100.00 1	99.22 89	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CHOSEN 280x420		99.85 5	99.87 1	99.80 124	99.99 53	99.97 29	99.97 315	99.98 16	98.96 41	100.00 1	100.00 1	99.96 4	99.42 343	100.00 1	100.00 1	100.00 1

CANet		99.40 93	99.24 110	99.89 91	99.99 53	99.76 110	100.00 1	99.73 62	98.40 104	99.78 239	100.00 1	95.28 255	99.96 172	100.00 1	99.99 108	99.96 145

CANet_DTU		99.02 163	98.90 166	99.41 207	99.88 125	98.71 246	100.00 1	99.29 288	98.84 70	100.00 1	100.00 1	94.02 297	100.00 1	98.08 330	99.96 128	99.52 328

MGCNet		99.72 32	99.65 38	99.93 79	99.99 53	99.79 105	100.00 1	99.91 40	99.17 8	100.00 1	100.00 1	97.84 181	100.00 1	100.00 1	99.95 130	100.00 1

MP-MVS-pluss		99.61 66	99.50 73	99.97 41	99.98 95	99.92 62	100.00 1	99.42 156	97.53 190	99.77 240	100.00 1	98.77 142	100.00 1	99.99 79	100.00 1	99.99 126
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.81 14	99.77 13	99.94 75	100.00 1	99.86 92	100.00 1	99.42 156	98.87 66	100.00 1	100.00 1	99.65 19	99.96 172	100.00 1	100.00 1	100.00 1
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
TSAR-MVS + MP.		99.82 12	99.77 13	99.99 13	100.00 1	99.96 32	100.00 1	99.43 135	99.05 18	100.00 1	100.00 1	99.45 54	99.99 108	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xiu_mvs_v1_base_debu		99.35 102	99.21 115	99.79 130	99.67 198	99.71 119	99.78 387	99.36 239	98.13 127	100.00 1	100.00 1	97.00 217	100.00 1	99.83 137	99.07 203	99.66 318

ACMMP_NAP		99.67 50	99.57 57	99.97 41	99.98 95	99.92 62	100.00 1	99.42 156	97.83 152	100.00 1	100.00 1	98.89 132	100.00 1	99.98 94	100.00 1	100.00 1

SPE-MVS-test		99.31 113	99.27 100	99.43 203	99.99 53	98.77 240	100.00 1	99.19 371	97.24 224	99.96 156	100.00 1	97.56 195	99.70 291	99.68 186	99.81 170	99.82 234

xiu_mvs_v2_base		99.51 76	99.41 82	99.82 113	99.70 182	99.73 116	99.92 348	99.40 209	98.15 125	100.00 1	100.00 1	98.50 155	100.00 1	99.85 133	99.13 201	99.74 304

xiu_mvs_v1_base		99.35 102	99.21 115	99.79 130	99.67 198	99.71 119	99.78 387	99.36 239	98.13 127	100.00 1	100.00 1	97.00 217	100.00 1	99.83 137	99.07 203	99.66 318

xiu_mvs_v1_base_debi		99.35 102	99.21 115	99.79 130	99.67 198	99.71 119	99.78 387	99.36 239	98.13 127	100.00 1	100.00 1	97.00 217	100.00 1	99.83 137	99.07 203	99.66 318

MTAPA		99.68 47	99.59 51	99.97 41	99.99 53	99.91 66	100.00 1	99.42 156	98.32 115	99.94 194	100.00 1	98.65 147	100.00 1	99.96 108	100.00 1	100.00 1

gm-plane-assit					99.52 272	97.26 362			95.86 357		100.00 1		99.43 341	98.76 297

TEST9					100.00 1	99.95 40	100.00 1	99.42 156	97.65 170	100.00 1	100.00 1	99.53 36	99.97 152

train_agg		99.71 36	99.63 45	99.97 41	100.00 1	99.95 40	100.00 1	99.42 156	97.70 164	100.00 1	100.00 1	99.51 40	99.97 152	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 66	100.00 1	99.42 156	97.70 164	100.00 1	100.00 1	99.51 40	99.98 143

cdsmvs_eth3d_5k		24.41 525	32.55 528	0.00 543	0.00 568	0.00 571	0.00 556	99.39 225	0.00 563	0.00 564	100.00 1	93.55 309	0.00 565	0.00 563	0.00 563	0.00 559

tmp_tt		75.80 497	74.26 500	80.43 512	52.91 566	53.67 547	87.42 547	97.98 498	61.80 527	67.04 542	100.00 1	76.43 484	96.40 495	96.47 388	28.26 555	91.23 525

canonicalmvs		99.03 157	98.73 185	99.94 75	99.75 174	99.95 40	100.00 1	99.30 279	97.64 172	100.00 1	100.00 1	95.22 258	99.97 152	99.76 155	96.90 332	99.91 173

alignmvs		99.38 96	99.21 115	99.91 84	99.73 177	99.92 62	100.00 1	99.51 83	97.61 179	100.00 1	100.00 1	99.06 106	99.93 202	99.83 137	97.12 326	99.90 184

UA-Net		99.06 151	98.83 170	99.74 142	99.52 272	99.40 172	99.08 488	99.45 112	97.64 172	99.83 220	100.00 1	95.80 245	99.94 198	98.35 319	99.80 173	99.88 205

HFP-MVS		99.74 28	99.67 34	99.96 53	100.00 1	99.89 80	100.00 1	99.76 55	97.95 145	100.00 1	100.00 1	99.31 77	100.00 1	99.99 79	100.00 1	100.00 1

region2R		99.72 32	99.64 41	99.97 41	100.00 1	99.90 73	100.00 1	99.74 61	97.86 151	100.00 1	100.00 1	99.19 92	100.00 1	99.99 79	100.00 1	100.00 1

PS-MVSNAJ		99.64 55	99.57 57	99.85 105	99.78 169	99.81 102	99.95 332	99.42 156	98.38 107	100.00 1	100.00 1	98.75 143	100.00 1	99.88 126	99.99 108	99.74 304

EI-MVSNet-UG-set		99.69 42	99.63 45	99.87 98	99.99 53	99.64 130	99.95 332	99.44 126	98.35 113	100.00 1	100.00 1	98.98 118	99.97 152	99.98 94	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 41	99.87 98	100.00 1	99.64 130	99.98 305	99.44 126	98.35 113	99.99 131	100.00 1	99.04 111	99.96 172	99.98 94	100.00 1	100.00 1

HPM-MVS++		99.82 12	99.76 16	99.99 13	99.99 53	99.98 19	100.00 1	99.83 45	98.88 63	99.96 156	100.00 1	99.21 90	100.00 1	100.00 1	100.00 1	99.99 126

XVS		99.79 17	99.73 21	99.98 29	100.00 1	99.94 49	100.00 1	99.75 58	98.67 90	100.00 1	100.00 1	99.16 95	100.00 1	100.00 1	100.00 1	100.00 1

test_prior2							100.00 1		98.82 74	100.00 1	100.00 1	99.47 52		100.00 1	100.00 1

新几何1				99.99 13	100.00 1	99.96 32		99.81 48	97.89 148	100.00 1	100.00 1	99.20 91	100.00 1	97.91 339	100.00 1	100.00 1

旧先验1					99.99 53	99.88 87		99.82 46			100.00 1	99.27 86			100.00 1	100.00 1

原ACMM1				99.93 79	100.00 1	99.80 104		99.66 70	98.18 122	100.00 1	100.00 1	99.43 61	100.00 1	99.50 236	100.00 1	100.00 1

test222					99.99 53	99.90 73	100.00 1	99.69 68	97.66 168	100.00 1	100.00 1	99.30 82			100.00 1	100.00 1

testdata				99.66 159	99.99 53	98.97 225		99.73 62	97.96 144	100.00 1	100.00 1	99.42 65	100.00 1	99.28 264	100.00 1	100.00 1

1314		99.38 96	99.19 120	99.96 53	98.88 391	99.89 80	99.24 461	99.93 35	98.88 63	98.79 339	100.00 1	97.02 213	100.00 1	100.00 1	100.00 1	100.00 1

LFMVS		97.42 319	96.62 343	99.81 118	99.80 159	99.50 155	99.16 477	99.56 77	94.48 404	100.00 1	100.00 1	79.35 473	100.00 1	99.89 124	97.37 323	99.94 156

VDD-MVS		96.58 359	95.99 371	98.34 322	99.52 272	95.33 405	99.18 471	99.38 228	96.64 302	99.77 240	100.00 1	72.51 494	100.00 1	100.00 1	96.94 331	99.70 314

VDDNet		96.39 371	95.55 394	98.90 282	99.27 348	97.45 351	99.15 479	99.92 39	91.28 458	99.98 141	100.00 1	73.55 490	100.00 1	99.85 133	96.98 330	99.24 337

MVS		99.22 132	98.96 153	99.98 29	99.00 377	99.95 40	99.24 461	99.94 27	98.14 126	98.88 329	100.00 1	95.63 250	100.00 1	99.85 133	100.00 1	100.00 1

SD-MVS		99.81 14	99.75 18	99.99 13	99.99 53	99.96 32	100.00 1	99.42 156	99.01 32	100.00 1	100.00 1	99.33 72	100.00 1	100.00 1	100.00 1	100.00 1
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
MSLP-MVS++		99.89 1	99.85 3	99.99 13	100.00 1	99.96 32	100.00 1	99.95 19	99.11 10	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 9	99.78 11	99.99 13	100.00 1	99.98 19	100.00 1	99.44 126	99.06 16	100.00 1	100.00 1	99.56 30	99.99 108	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		99.65 53	99.55 64	99.97 41	99.99 53	99.91 66	100.00 1	99.48 85	97.54 187	100.00 1	100.00 1	98.97 120	99.99 108	99.98 94	100.00 1	100.00 1

EI-MVSNet		97.98 287	97.93 287	98.16 341	99.11 358	97.84 337	99.74 397	99.29 288	94.39 407	98.65 347	100.00 1	97.21 208	98.88 386	97.62 355	95.31 354	97.75 364

CVMVSNet		98.56 244	98.47 231	98.82 289	99.11 358	97.67 343	99.74 397	99.47 86	97.57 185	99.06 315	100.00 1	95.72 247	98.97 374	98.21 327	97.33 324	99.83 227

TESTMET0.1,1		99.08 147	98.96 153	99.44 200	99.63 218	99.38 173	100.00 1	99.45 112	95.53 369	99.48 274	100.00 1	99.71 15	99.02 366	96.84 379	99.99 108	99.91 173

ACMMPR		99.74 28	99.67 34	99.96 53	100.00 1	99.89 80	100.00 1	99.76 55	97.95 145	100.00 1	100.00 1	99.29 83	100.00 1	99.99 79	100.00 1	100.00 1

MP-MVS		99.61 66	99.49 75	99.98 29	99.99 53	99.94 49	100.00 1	99.42 156	97.82 154	99.99 131	100.00 1	98.20 164	100.00 1	99.99 79	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		80.17 487	81.95 484	74.80 516	58.54 564	59.58 544	100.00 1	87.14 544	76.09 518	99.61 264	100.00 1	67.06 504	74.19 552	98.84 292	50.30 543	90.64 526

PGM-MVS		99.69 42	99.61 49	99.95 62	99.99 53	99.85 96	100.00 1	99.58 74	97.69 166	100.00 1	100.00 1	99.44 57	100.00 1	99.79 145	100.00 1	100.00 1

MCST-MVS		99.85 5	99.80 8	100.00 1	100.00 1	99.99 6	100.00 1	99.73 62	99.19 5	100.00 1	100.00 1	99.31 77	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 31	99.64 41	99.99 13	100.00 1	99.97 29	100.00 1	99.42 156	98.02 135	100.00 1	100.00 1	99.32 75	99.99 108	100.00 1	100.00 1	100.00 1

casdiffmvs		98.65 223	98.38 250	99.46 193	99.52 272	98.74 244	100.00 1	99.15 400	96.91 258	99.05 316	100.00 1	92.75 331	99.83 250	99.70 176	98.38 237	99.81 251
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		98.96 178	98.73 185	99.63 163	99.54 254	99.16 203	100.00 1	99.18 381	97.33 216	99.96 156	100.00 1	94.60 280	99.91 210	99.66 197	98.33 251	99.82 234
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		98.99 170	98.93 160	99.18 262	99.26 350	99.15 204	100.00 1	99.46 104	96.71 293	96.79 441	100.00 1	99.42 65	99.25 354	98.75 298	99.94 136	99.15 339

TSAR-MVS + GP.		99.61 66	99.69 26	99.35 224	99.99 53	98.06 318	100.00 1	99.36 239	99.83 2	100.00 1	100.00 1	98.95 124	99.99 108	100.00 1	99.11 202	100.00 1

mPP-MVS		99.69 42	99.60 50	99.97 41	100.00 1	99.91 66	100.00 1	99.42 156	97.91 147	100.00 1	100.00 1	99.04 111	100.00 1	100.00 1	100.00 1	100.00 1

XVG-OURS-SEG-HR		98.27 274	98.31 258	98.14 342	99.59 235	95.92 392	100.00 1	99.36 239	98.48 100	99.21 300	100.00 1	89.27 401	99.94 198	99.76 155	99.17 199	98.56 349

MVSFormer		98.94 183	98.82 171	99.28 252	99.45 317	99.49 159	100.00 1	99.13 414	95.46 376	99.97 148	100.00 1	96.76 225	98.59 415	98.63 306	100.00 1	99.74 304

jason		99.11 145	98.96 153	99.59 172	99.17 354	99.31 183	100.00 1	99.13 414	97.38 209	99.83 220	100.00 1	95.54 251	99.72 287	99.57 222	99.97 124	99.74 304
jason: jason.
lupinMVS		99.29 119	99.16 124	99.69 152	99.45 317	99.49 159	100.00 1	99.15 400	97.45 202	99.97 148	100.00 1	96.76 225	99.76 275	99.67 190	100.00 1	99.81 251

HPM-MVS_fast		99.60 69	99.49 75	99.91 84	99.99 53	99.78 106	100.00 1	99.42 156	97.09 237	100.00 1	100.00 1	98.95 124	99.96 172	99.98 94	100.00 1	100.00 1

HPM-MVS		99.59 70	99.50 73	99.89 91	100.00 1	99.70 123	100.00 1	99.42 156	97.46 200	100.00 1	100.00 1	98.60 150	99.96 172	99.99 79	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.30 268	98.36 254	98.13 345	99.58 240	95.91 393	100.00 1	99.36 239	98.69 88	99.23 299	100.00 1	91.20 354	99.92 208	99.34 259	97.82 308	98.56 349

casdiffmvs_mvg		98.64 224	98.39 248	99.40 212	99.50 288	98.60 256	100.00 1	99.22 338	96.85 263	99.10 309	100.00 1	92.75 331	99.78 268	99.71 172	98.35 244	99.81 251
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		98.69 220	98.45 234	99.41 207	99.52 272	98.67 250	100.00 1	99.17 391	97.03 244	99.13 306	100.00 1	93.17 319	99.74 281	99.70 176	98.34 248	99.81 251

CHOSEN 1792x2688		99.00 166	98.91 163	99.25 257	99.90 121	97.79 340	100.00 1	99.99 13	98.79 82	98.28 383	100.00 1	93.63 306	99.95 185	99.66 197	99.95 130	100.00 1

EPNet		99.62 64	99.69 26	99.42 206	99.99 53	98.37 281	100.00 1	99.89 42	98.83 72	100.00 1	100.00 1	98.97 120	100.00 1	99.90 122	99.61 189	99.89 192
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS		99.68 47	99.58 54	99.97 41	99.99 53	99.96 32	100.00 1	99.42 156	97.53 190	100.00 1	100.00 1	99.27 86	99.97 152	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.85 5	99.80 8	100.00 1	100.00 1	99.99 6	100.00 1	99.77 54	99.07 14	100.00 1	100.00 1	99.39 70	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 4	99.82 5	100.00 1	100.00 1	99.99 6	100.00 1	99.71 67	99.07 14	100.00 1	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 94	99.25 106	99.81 118	99.97 99	99.48 163	100.00 1	99.42 156	95.53 369	100.00 1	100.00 1	98.37 161	99.95 185	99.97 106	100.00 1	100.00 1

CP-MVS		99.67 50	99.58 54	99.95 62	100.00 1	99.84 98	100.00 1	99.42 156	97.77 159	100.00 1	100.00 1	99.07 105	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 19	99.71 24	99.98 29	99.76 172	99.95 40	100.00 1	99.42 156	98.69 88	100.00 1	100.00 1	99.52 39	99.99 108	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
BH-w/o		98.82 200	98.81 173	98.88 284	99.62 225	96.71 379	100.00 1	99.28 296	97.09 237	98.81 337	100.00 1	94.91 269	99.96 172	99.54 229	100.00 1	99.96 145

DELS-MVS		99.62 64	99.56 62	99.82 113	99.92 117	99.45 165	100.00 1	99.78 53	98.92 54	99.73 249	100.00 1	97.70 187	100.00 1	99.93 118	100.00 1	100.00 1
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
BH-untuned		98.64 224	98.65 200	98.60 303	99.59 235	96.17 389	100.00 1	99.28 296	96.67 298	98.41 372	100.00 1	94.52 282	99.83 250	99.41 250	100.00 1	99.81 251

CPTT-MVS		99.49 81	99.38 85	99.85 105	100.00 1	99.54 145	100.00 1	99.42 156	97.58 184	99.98 141	100.00 1	97.43 204	100.00 1	99.99 79	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 109	99.18 123	99.78 135	99.82 140	99.49 159	100.00 1	99.95 19	97.36 210	99.63 263	100.00 1	96.45 236	99.95 185	99.79 145	99.65 185	99.89 192

PVSNet_Blended		99.48 83	99.36 91	99.83 111	99.98 95	99.60 134	100.00 1	100.00 1	97.79 157	100.00 1	100.00 1	96.57 232	99.99 108	100.00 1	99.88 154	99.90 184

BH-RMVSNet		98.46 254	98.08 277	99.59 172	99.61 228	99.19 199	100.00 1	99.28 296	97.06 241	98.95 322	100.00 1	88.99 405	99.82 253	98.83 294	100.00 1	99.77 294

WTY-MVS		99.54 75	99.40 83	99.95 62	99.81 146	99.93 55	100.00 1	100.00 1	97.98 139	99.84 217	100.00 1	98.94 126	99.98 143	99.86 130	98.21 272	99.94 156

EC-MVSNet		99.19 135	99.09 135	99.48 191	99.42 323	99.07 209	100.00 1	99.21 357	96.95 253	99.96 156	100.00 1	96.88 223	99.48 331	99.64 200	99.79 174	99.88 205

1112_ss		98.91 188	98.71 191	99.51 183	99.69 185	98.75 241	99.99 272	99.15 400	96.82 267	98.84 334	100.00 1	97.45 201	99.89 223	98.66 301	97.75 314	99.89 192

ab-mvs-re		8.33 526	11.11 530	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	100.00 1	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

DP-MVS Recon		99.76 21	99.69 26	99.98 29	100.00 1	99.95 40	100.00 1	99.52 79	97.99 137	99.99 131	100.00 1	99.72 14	100.00 1	99.96 108	100.00 1	100.00 1

MVS_111021_LR		99.70 39	99.65 38	99.88 96	99.96 105	99.70 123	100.00 1	99.97 17	98.96 41	100.00 1	100.00 1	97.93 173	99.95 185	99.99 79	100.00 1	100.00 1

DP-MVS		98.86 195	98.54 220	99.81 118	99.97 99	99.45 165	99.52 432	99.40 209	94.35 408	98.36 375	100.00 1	96.13 239	99.97 152	99.12 277	100.00 1	100.00 1

QAPM		98.99 170	98.66 199	99.96 53	99.01 372	99.87 89	99.88 364	99.93 35	97.99 137	98.68 344	100.00 1	93.17 319	100.00 1	99.32 261	100.00 1	100.00 1

Vis-MVSNet		98.52 250	98.25 262	99.34 226	99.68 190	98.55 259	99.68 412	99.41 205	97.34 214	99.94 194	100.00 1	90.38 378	99.70 291	99.03 282	98.84 209	99.76 296
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		99.08 147	98.91 163	99.59 172	99.65 209	99.38 173	99.78 387	99.24 328	96.70 294	99.51 271	100.00 1	98.44 157	99.52 324	98.47 314	98.39 233	99.88 205

PAPM_NR		99.74 28	99.66 37	99.99 13	100.00 1	99.96 32	100.00 1	99.47 86	97.87 150	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 21	99.68 32	99.99 13	100.00 1	99.96 32	100.00 1	99.47 86	98.16 123	100.00 1	100.00 1	99.51 40	100.00 1	100.00 1	100.00 1	100.00 1

RPSCF		97.37 321	98.24 265	94.76 455	99.80 159	84.57 499	99.99 272	99.05 446	94.95 386	99.82 229	100.00 1	94.03 295	100.00 1	98.15 329	98.38 237	99.70 314

Vis-MVSNet (Re-imp)		98.99 170	98.89 167	99.29 249	99.64 215	98.89 232	99.98 305	99.31 273	96.74 282	99.48 274	100.00 1	98.11 167	99.10 361	98.39 317	98.34 248	99.89 192

MVS_111021_HR		99.71 36	99.63 45	99.93 79	99.95 109	99.83 100	100.00 1	100.00 1	98.89 62	100.00 1	100.00 1	97.85 179	99.95 185	100.00 1	100.00 1	100.00 1

CSCG		99.28 121	99.35 93	99.05 269	99.99 53	97.15 367	100.00 1	99.47 86	97.44 204	99.42 281	100.00 1	97.83 183	100.00 1	99.99 79	100.00 1	100.00 1

API-MVS		99.72 32	99.70 25	99.79 130	99.97 99	99.37 176	99.96 323	99.94 27	98.48 100	100.00 1	100.00 1	98.92 129	100.00 1	100.00 1	100.00 1	100.00 1

EPP-MVSNet		99.10 146	99.00 147	99.40 212	99.51 279	98.68 249	99.92 348	99.43 135	95.47 375	99.65 262	100.00 1	99.51 40	99.76 275	99.53 232	98.00 291	99.75 297

PMMVS		99.12 144	98.97 152	99.58 176	99.57 244	98.98 223	100.00 1	99.30 279	97.14 232	99.96 156	100.00 1	96.53 235	99.82 253	99.70 176	98.49 224	99.94 156

PAPM		99.78 19	99.76 16	99.85 105	99.01 372	99.95 40	100.00 1	99.75 58	99.37 3	99.99 131	100.00 1	99.76 12	99.60 297	100.00 1	100.00 1	100.00 1

ACMMP		99.65 53	99.57 57	99.89 91	99.99 53	99.66 128	99.75 396	99.73 62	98.16 123	99.75 243	100.00 1	98.90 131	100.00 1	99.96 108	99.88 154	100.00 1
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
CNLPA		99.72 32	99.65 38	99.91 84	99.97 99	99.72 118	100.00 1	99.47 86	98.43 103	99.88 213	100.00 1	99.14 98	100.00 1	99.97 106	100.00 1	100.00 1

PHI-MVS		99.50 79	99.39 84	99.82 113	100.00 1	99.45 165	100.00 1	99.94 27	96.38 331	100.00 1	100.00 1	98.18 165	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 115	99.25 106	99.44 200	100.00 1	98.32 291	100.00 1	99.86 43	98.04 134	100.00 1	100.00 1	96.10 240	100.00 1	99.55 225	99.73 175	100.00 1

F-COLMAP		99.64 55	99.64 41	99.67 156	99.99 53	99.07 209	100.00 1	99.44 126	98.30 116	99.90 206	100.00 1	99.18 93	99.99 108	99.91 121	100.00 1	99.94 156

DeepPCF-MVS	98.03 4	98.54 248	99.72 23	94.98 452	99.99 53	84.94 498	100.00 1	99.42 156	99.98 1	100.00 1	100.00 1	98.11 167	100.00 1	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 122	99.38 85	98.96 278	99.95 109	97.06 371	100.00 1	99.40 209	98.83 72	99.88 213	100.00 1	97.01 214	99.86 236	99.47 241	99.84 165	99.97 139

DeepC-MVS	97.84 5	99.00 166	98.80 174	99.60 170	99.93 114	99.03 214	100.00 1	99.40 209	98.61 94	99.33 292	100.00 1	92.23 342	99.95 185	99.74 162	99.96 128	99.83 227
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	98.92 1	99.75 26	99.67 34	99.99 13	99.99 53	99.96 32	99.73 402	99.52 79	99.06 16	100.00 1	100.00 1	98.80 140	100.00 1	99.95 114	100.00 1	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.75 26	99.68 32	99.97 41	100.00 1	99.91 66	99.98 305	99.47 86	99.09 13	100.00 1	100.00 1	98.59 151	100.00 1	99.95 114	100.00 1	100.00 1

AdaColmap		99.44 89	99.26 104	99.95 62	100.00 1	99.86 92	99.70 408	99.99 13	98.53 96	99.90 206	100.00 1	95.34 254	100.00 1	99.92 119	100.00 1	100.00 1

PLC	98.56 2	99.70 39	99.74 20	99.58 176	100.00 1	98.79 239	100.00 1	99.54 78	98.58 95	99.96 156	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 156
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	98.23 3	98.69 220	98.37 252	99.62 165	99.78 169	99.02 216	99.23 468	99.06 444	96.43 324	98.08 392	100.00 1	94.72 276	99.95 185	98.16 328	99.91 148	99.90 184
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		99.49 81	99.36 91	99.89 91	99.97 99	99.66 128	99.74 397	99.95 19	97.89 148	100.00 1	100.00 1	96.71 229	100.00 1	100.00 1	100.00 1	100.00 1
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
3Dnovator+	95.58 15	99.03 157	98.71 191	99.96 53	98.99 380	99.89 80	100.00 1	99.51 83	98.96 41	98.32 380	100.00 1	92.78 330	100.00 1	99.87 129	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 151	98.76 180	99.96 53	98.86 396	99.90 73	99.98 305	99.93 35	98.95 44	98.49 367	100.00 1	92.91 328	100.00 1	99.71 172	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 303	97.37 313	98.45 312	99.94 112	95.70 398	100.00 1	99.40 209	97.65 170	99.53 269	100.00 1	99.31 77	99.66 293	80.48 513	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	95.20 17	98.76 207	98.41 239	99.78 135	98.89 390	99.81 102	99.99 272	99.76 55	98.02 135	98.02 398	100.00 1	91.44 350	100.00 1	99.63 207	99.97 124	99.55 325

MSDG		98.90 190	98.63 203	99.70 151	99.92 117	99.25 191	100.00 1	99.37 233	95.71 362	99.40 287	100.00 1	96.58 231	99.95 185	96.80 382	99.94 136	99.91 173

LS3D		99.31 113	99.13 128	99.87 98	99.99 53	99.71 119	99.55 428	99.46 104	97.32 217	99.82 229	100.00 1	96.85 224	99.97 152	99.14 274	100.00 1	99.92 169

COLMAP_ROB	97.10 7	98.29 271	98.17 270	98.65 299	99.94 112	97.39 353	99.30 456	99.40 209	95.64 364	97.75 412	100.00 1	92.69 335	99.95 185	98.89 289	99.92 143	98.62 348
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Elysia		98.12 280	97.72 299	99.34 226	99.30 344	98.96 226	99.95 332	99.28 296	96.64 302	99.75 243	99.99 246	88.71 410	99.81 257	95.99 398	99.84 165	99.26 335

StellarMVS		98.12 280	97.72 299	99.34 226	99.30 344	98.96 226	99.95 332	99.28 296	96.64 302	99.75 243	99.99 246	88.71 410	99.81 257	95.99 398	99.84 165	99.26 335

KinetiMVS		98.61 234	98.26 261	99.65 161	99.46 309	99.24 193	99.96 323	99.44 126	97.54 187	99.99 131	99.99 246	90.83 365	99.95 185	97.18 368	99.92 143	99.75 297

testing3		98.44 255	98.37 252	98.65 299	99.51 279	98.32 291	100.00 1	99.62 73	96.43 324	97.93 402	99.99 246	99.11 99	97.81 477	94.88 424	97.80 310	99.82 234

tt0805		96.52 360	96.23 361	97.40 382	99.30 344	93.55 448	99.32 452	99.45 112	96.75 279	97.88 405	99.99 246	79.99 471	99.59 299	97.39 363	95.98 343	99.06 342

Anonymous202405211		97.87 292	97.53 305	98.90 282	99.81 146	96.70 380	99.35 450	99.46 104	92.98 445	98.83 336	99.99 246	90.63 369	100.00 1	99.70 176	97.03 328	100.00 1

VNet		99.04 154	98.75 181	99.90 88	99.81 146	99.75 111	99.50 434	99.47 86	98.36 111	100.00 1	99.99 246	94.66 278	100.00 1	99.90 122	97.09 327	99.96 145

baseline1		98.91 188	98.61 207	99.81 118	99.71 179	99.77 109	99.78 387	99.44 126	97.51 194	98.81 337	99.99 246	98.25 163	99.76 275	98.60 309	95.41 350	99.89 192

mamba_0408		98.63 230	98.40 245	99.34 226	99.53 258	98.52 264	99.24 461	99.16 394	96.43 324	98.95 322	99.98 254	94.47 283	99.76 275	99.21 271	98.62 216	99.75 297

SSM_04072		98.59 237	98.40 245	99.15 263	99.53 258	98.52 264	99.24 461	99.16 394	96.43 324	98.95 322	99.98 254	94.47 283	99.19 358	99.21 271	98.62 216	99.75 297

LuminaMVS		99.07 150	98.92 162	99.50 186	98.87 394	99.12 206	99.92 348	99.22 338	97.45 202	99.82 229	99.98 254	96.29 238	99.85 243	99.71 172	99.05 206	99.52 328

kuosan		98.55 245	98.53 222	98.62 301	99.66 207	96.16 390	100.00 1	99.44 126	93.93 421	99.81 235	99.98 254	97.58 191	99.81 257	98.08 330	98.28 261	99.89 192

OPM-MVS		97.21 328	97.18 325	97.32 388	98.08 439	94.66 426	100.00 1	99.28 296	98.65 92	98.92 326	99.98 254	86.03 440	99.56 309	98.28 325	95.41 350	97.72 411
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
AllTest		98.55 245	98.40 245	98.99 275	99.93 114	97.35 356	100.00 1	99.40 209	97.08 239	99.09 311	99.98 254	93.37 313	99.95 185	96.94 374	99.84 165	99.68 316

TestCases				98.99 275	99.93 114	97.35 356		99.40 209	97.08 239	99.09 311	99.98 254	93.37 313	99.95 185	96.94 374	99.84 165	99.68 316

LPG-MVS_test		97.31 325	97.32 315	97.28 391	98.85 397	94.60 430	100.00 1	99.37 233	97.35 211	98.85 332	99.98 254	86.66 432	99.56 309	99.55 225	95.26 356	97.70 420

LGP-MVS_train				97.28 391	98.85 397	94.60 430		99.37 233	97.35 211	98.85 332	99.98 254	86.66 432	99.56 309	99.55 225	95.26 356	97.70 420

cascas		98.43 256	98.07 279	99.50 186	99.65 209	99.02 216	100.00 1	99.22 338	94.21 412	99.72 250	99.98 254	92.03 346	99.93 202	99.68 186	98.12 285	99.54 326

ACMP	97.00 8	97.19 329	97.16 327	97.27 393	98.97 383	94.58 433	100.00 1	99.32 264	97.97 141	97.45 424	99.98 254	85.79 442	99.56 309	99.70 176	95.24 359	97.67 431
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	97.17 6	97.37 321	97.40 311	97.29 390	99.01 372	94.64 428	100.00 1	99.25 321	98.07 133	98.44 371	99.98 254	87.38 426	99.55 315	99.25 265	95.19 362	97.69 425
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
casdiffseed414692147		98.31 267	97.94 286	99.40 212	99.46 309	98.67 250	99.91 355	99.17 391	96.33 338	98.66 346	99.97 266	90.47 376	99.71 289	99.36 253	98.16 280	99.81 251

testing3-2		99.45 87	99.31 96	99.86 101	99.70 182	99.73 116	100.00 1	99.47 86	97.46 200	99.97 148	99.97 266	99.48 51	100.00 1	99.78 151	97.99 292	99.85 222

myMVS_eth3d28		99.41 92	99.28 98	99.80 124	99.69 185	99.53 147	100.00 1	99.43 135	97.12 236	99.98 141	99.97 266	99.41 67	100.00 1	99.81 144	98.07 288	99.88 205

dongtai		98.29 271	98.25 262	98.42 316	99.58 240	95.86 395	100.00 1	99.44 126	93.46 434	99.69 253	99.97 266	97.53 196	99.51 326	96.28 395	98.27 264	99.89 192

MVSMamba_PlusPlus		99.39 94	99.25 106	99.80 124	99.68 190	99.59 136	99.99 272	99.30 279	96.66 299	99.96 156	99.97 266	97.89 176	99.92 208	99.76 155	100.00 1	99.90 184

test_fmvsmconf0.1_n		99.25 128	99.05 138	99.82 113	98.92 387	99.55 142	100.00 1	99.23 333	98.91 57	99.75 243	99.97 266	94.79 272	99.94 198	99.94 116	99.99 108	99.97 139

BridgeMVS		99.43 90	99.28 98	99.85 105	99.68 190	99.68 126	99.97 315	99.28 296	97.03 244	99.96 156	99.97 266	97.90 175	99.93 202	99.77 153	100.00 1	99.94 156

balanced_ft_v1		98.70 218	98.61 207	98.94 279	99.67 198	96.90 373	99.91 355	99.30 279	96.73 286	99.96 156	99.97 266	92.18 343	99.93 202	99.86 130	99.95 130	100.00 1

HQP_MVS		97.71 302	97.82 294	97.37 384	99.00 377	94.80 420	100.00 1	99.40 209	99.00 33	99.08 313	99.97 266	88.58 415	99.55 315	99.79 145	95.57 348	97.76 353

plane_prior4											99.97 266

mvsmamba		99.05 153	98.98 150	99.27 255	99.57 244	98.10 314	100.00 1	99.28 296	95.92 354	99.96 156	99.97 266	96.73 228	99.89 223	99.72 168	99.65 185	99.81 251

SixPastTwentyTwo		95.71 400	95.49 397	96.38 429	97.42 474	93.01 454	99.84 370	98.23 486	94.75 391	95.98 457	99.97 266	85.35 445	98.43 430	94.71 425	93.17 390	97.69 425

NP-MVS					99.07 363	94.81 419					99.97 266

HQP-MVS		97.73 300	97.85 292	97.39 383	99.07 363	94.82 417	100.00 1	99.40 209	99.04 20	99.17 301	99.97 266	88.61 413	99.57 305	99.79 145	95.58 344	97.77 351

ITE_SJBPF				96.84 411	98.96 384	93.49 449		98.12 490	98.12 130	98.35 377	99.97 266	84.45 448	99.56 309	95.63 410	95.25 358	97.49 453

ACMH+	96.20 13	96.49 365	96.33 358	97.00 401	99.06 367	93.80 446	99.81 376	99.31 273	97.32 217	95.89 459	99.97 266	82.62 461	99.54 318	98.34 320	94.63 377	97.65 437

CLD-MVS		97.64 303	97.74 296	97.36 385	99.01 372	94.76 425	100.00 1	99.34 258	99.30 4	99.00 319	99.97 266	87.49 424	99.57 305	99.96 108	95.58 344	97.75 364
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMH	96.25 11	96.77 348	96.62 343	97.21 394	98.96 384	94.43 437	99.64 415	99.33 261	97.43 205	96.55 446	99.97 266	83.52 456	99.54 318	99.07 281	95.13 366	97.66 432
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
dtuonly		97.85 294	97.46 307	99.02 273	98.44 413	97.89 333	99.99 272	97.62 508	96.53 314	99.49 273	99.96 284	94.01 298	99.58 303	92.75 449	98.32 253	99.59 323

test_fmvsmconf0.01_n		98.60 236	98.24 265	99.67 156	96.90 481	99.21 197	99.99 272	99.04 449	98.80 79	99.57 268	99.96 284	90.12 384	99.91 210	99.89 124	99.89 151	99.90 184

thres100view900		99.25 128	99.01 144	99.95 62	99.81 146	99.87 89	100.00 1	99.94 27	97.13 234	99.83 220	99.96 284	97.01 214	100.00 1	99.59 216	97.85 304	99.98 129

tfpn200view9		99.26 124	99.03 140	99.96 53	99.81 146	99.89 80	100.00 1	99.94 27	97.23 226	99.83 220	99.96 284	97.04 210	100.00 1	99.59 216	97.85 304	99.98 129

thres600view7		99.24 131	99.00 147	99.95 62	99.81 146	99.87 89	100.00 1	99.94 27	97.13 234	99.83 220	99.96 284	97.01 214	100.00 1	99.54 229	97.77 313	99.97 139

thres400		99.26 124	99.03 140	99.95 62	99.81 146	99.89 80	100.00 1	99.94 27	97.23 226	99.83 220	99.96 284	97.04 210	100.00 1	99.59 216	97.85 304	99.97 139

thres200		99.27 122	99.04 139	99.96 53	99.81 146	99.90 73	100.00 1	99.94 27	97.30 220	99.83 220	99.96 284	97.04 210	100.00 1	99.62 209	97.88 302	99.98 129

EPNet_dtu		98.53 249	98.23 268	99.43 203	99.92 117	99.01 218	99.96 323	99.47 86	98.80 79	99.96 156	99.96 284	98.56 152	99.30 351	87.78 489	99.68 180	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ETVMVS		99.16 140	98.98 150	99.69 152	99.67 198	99.56 140	100.00 1	99.45 112	96.36 334	99.98 141	99.95 292	98.65 147	99.64 295	99.11 278	97.63 321	99.88 205

Fast-Effi-MVS+		98.40 261	98.02 283	99.55 181	99.63 218	99.06 211	100.00 1	99.15 400	95.07 383	99.42 281	99.95 292	93.26 316	99.73 285	97.44 359	98.24 270	99.87 216

nrg030		97.64 303	97.27 319	98.75 296	98.34 417	99.53 147	100.00 1	99.22 338	96.21 347	98.27 385	99.95 292	94.40 285	98.98 372	99.23 268	89.78 441	97.75 364

RRT-MVS		98.75 210	98.52 223	99.44 200	99.65 209	98.57 258	99.90 357	99.08 431	96.51 319	99.96 156	99.95 292	92.59 336	99.96 172	99.60 214	99.45 195	99.81 251

test0.0.03 1		98.12 280	98.03 282	98.39 318	99.11 358	98.07 316	100.00 1	99.93 35	96.70 294	96.91 437	99.95 292	99.31 77	98.19 451	91.93 456	98.44 227	98.91 343

OurMVSNet-221017-0		96.14 388	95.98 372	96.62 423	97.49 470	93.44 450	99.92 348	98.16 488	95.86 357	97.65 414	99.95 292	85.71 443	98.78 391	94.93 423	94.18 381	97.64 440

nomal-1		98.99 170	99.02 143	98.88 284	99.47 300	97.25 365	100.00 1	99.38 228	96.38 331	99.90 206	99.94 298	98.78 141	99.56 309	99.40 252	97.94 298	99.83 227

testing11		99.26 124	99.19 120	99.46 193	99.64 215	98.61 255	100.00 1	99.43 135	96.94 254	99.92 201	99.94 298	99.43 61	99.97 152	99.67 190	97.79 312	99.82 234

APD_test1		93.07 437	94.14 417	89.85 483	99.18 353	72.49 519	99.76 394	98.90 468	92.86 449	96.35 448	99.94 298	75.56 486	99.91 210	86.73 492	97.98 293	97.15 466

sss		99.45 87	99.34 95	99.80 124	99.76 172	99.50 155	100.00 1	99.91 40	97.72 162	99.98 141	99.94 298	98.45 156	100.00 1	99.53 232	98.75 214	99.89 192

TR-MVS		98.14 279	97.74 296	99.33 234	99.59 235	98.28 295	99.27 457	99.21 357	96.42 328	99.15 305	99.94 298	88.87 408	99.79 262	98.88 290	98.29 258	99.93 167

USDC		95.90 396	95.70 387	96.50 426	98.60 407	92.56 462	100.00 1	98.30 485	97.77 159	96.92 435	99.94 298	81.25 468	99.45 339	93.54 442	94.96 373	97.49 453

testing915		99.30 115	99.13 128	99.80 124	99.62 225	99.52 151	100.00 1	99.43 135	96.98 248	99.98 141	99.93 304	98.29 162	99.89 223	99.76 155	98.21 272	100.00 1

SD_0403		97.92 291	98.43 236	96.39 428	99.68 190	89.74 484	99.92 348	99.34 258	96.75 279	99.39 288	99.93 304	93.54 310	99.51 326	99.11 278	98.21 272	99.92 169

FBQ-MVS		99.13 143	99.11 132	99.21 260	99.64 215	97.94 328	100.00 1	99.43 135	96.78 272	99.97 148	99.92 306	99.03 114	99.84 247	99.18 273	98.01 290	99.86 220

testing91		99.18 136	99.10 133	99.41 207	99.60 231	98.43 271	100.00 1	99.43 135	96.76 276	99.82 229	99.92 306	99.05 108	99.98 143	99.62 209	97.67 318	99.81 251

testing99		99.18 136	99.10 133	99.41 207	99.60 231	98.43 271	100.00 1	99.43 135	96.76 276	99.84 217	99.92 306	99.06 106	99.98 143	99.62 209	97.67 318	99.81 251

UBG		99.36 101	99.27 100	99.63 163	99.63 218	99.01 218	100.00 1	99.43 135	96.99 247	100.00 1	99.92 306	99.69 17	99.99 108	99.74 162	98.06 289	99.88 205

lessismore_v0				96.05 437	97.55 466	91.80 467		99.22 338		91.87 485	99.91 310	83.50 457	98.68 399	92.48 453	90.42 437	97.68 427

HyFIR lowres test		99.32 111	99.24 110	99.58 176	99.95 109	99.26 189	100.00 1	99.99 13	96.72 288	99.29 295	99.91 310	99.49 47	99.47 333	99.74 162	98.08 287	100.00 1

testing222		99.14 142	98.94 158	99.73 145	99.67 198	99.51 153	100.00 1	99.43 135	96.90 260	99.99 131	99.90 312	98.55 153	99.86 236	98.85 291	97.18 325	99.81 251

WB-MVSnew		97.02 341	97.24 321	96.37 430	99.44 319	97.36 355	100.00 1	99.43 135	96.12 350	99.35 291	99.89 313	93.60 308	98.42 431	88.91 486	98.39 233	93.33 518

Effi-MVS+		98.58 239	98.24 265	99.61 167	99.60 231	99.26 189	97.85 519	99.10 425	96.22 346	99.97 148	99.89 313	93.75 304	99.77 270	99.43 248	98.34 248	99.81 251

VPNet		96.41 367	95.76 384	98.33 323	98.61 406	98.30 294	99.48 435	99.45 112	96.98 248	98.87 331	99.88 315	81.57 465	98.93 378	99.22 270	87.82 461	97.76 353

TinyColmap		95.50 403	95.12 410	96.64 422	98.69 403	93.00 455	99.40 445	97.75 505	96.40 330	96.14 453	99.87 316	79.47 472	99.50 329	93.62 441	94.72 376	97.40 459

LF4IMVS		96.19 381	96.18 363	96.23 434	98.26 427	92.09 465	100.00 1	97.89 501	97.82 154	97.94 401	99.87 316	82.71 460	99.38 345	97.41 361	93.71 384	97.20 464

UWE-MVS		99.18 136	99.06 137	99.51 183	99.67 198	98.80 238	100.00 1	99.43 135	96.80 269	99.93 199	99.86 318	99.79 8	99.94 198	97.78 347	98.32 253	99.80 282

SDMVSNet		98.49 253	98.08 277	99.73 145	99.82 140	99.53 147	99.99 272	99.45 112	97.62 175	99.38 289	99.86 318	90.06 387	99.88 232	99.92 119	96.61 339	99.79 288

sd_testset		97.81 297	97.48 306	98.79 293	99.82 140	96.80 377	99.32 452	99.45 112	97.62 175	99.38 289	99.86 318	85.56 444	99.77 270	99.72 168	96.61 339	99.79 288

testgi		96.18 382	95.93 374	96.93 406	98.98 381	94.20 444	100.00 1	99.07 436	97.16 230	96.06 456	99.86 318	84.08 454	97.79 480	90.38 471	97.80 310	98.81 344

MVS_Test		98.93 184	98.65 200	99.77 138	99.62 225	99.50 155	99.99 272	99.19 371	95.52 371	99.96 156	99.86 318	96.54 234	99.98 143	98.65 303	98.48 225	99.82 234

test_djsdf		97.55 312	97.38 312	98.07 348	97.50 468	97.99 322	100.00 1	99.13 414	95.46 376	98.47 368	99.85 323	92.01 347	98.59 415	98.63 306	95.36 352	97.62 443

HY-MVS	96.53 9	99.50 79	99.35 93	99.96 53	99.81 146	99.93 55	99.64 415	100.00 1	97.97 141	99.84 217	99.85 323	98.94 126	99.99 108	99.86 130	98.23 271	99.95 151

TDRefinement		91.93 445	90.48 456	96.27 433	81.60 553	92.65 461	99.10 485	97.61 509	93.96 420	93.77 477	99.85 323	80.03 469	99.53 323	97.82 346	70.59 524	96.63 481

UWE-MVS-28		99.29 119	99.23 113	99.48 191	99.73 177	98.86 233	100.00 1	99.43 135	96.97 251	99.99 131	99.83 326	99.43 61	99.77 270	99.35 257	98.31 255	99.80 282

XXY-MVS		97.14 333	96.63 342	98.67 298	98.65 404	98.92 229	99.54 430	99.29 288	95.57 368	97.63 415	99.83 326	87.79 422	99.35 348	98.39 317	92.95 393	97.75 364

CDS-MVSNet		98.96 178	98.95 157	99.01 274	99.48 295	98.36 284	99.93 346	99.37 233	96.79 270	99.31 294	99.83 326	99.77 11	98.91 380	98.07 332	97.98 293	99.77 294
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DeepMVS_CX				89.98 482	98.90 389	71.46 521		99.18 381	97.61 179	96.92 435	99.83 326	86.07 438	99.83 250	96.02 397	97.65 320	98.65 347

FIs		97.95 290	97.73 298	98.62 301	98.53 411	99.24 193	100.00 1	99.43 135	96.74 282	97.87 406	99.82 330	95.27 256	98.89 383	98.78 295	93.07 391	97.74 392

FC-MVSNet-test		97.84 295	97.63 304	98.45 312	98.30 423	99.05 212	100.00 1	99.43 135	96.63 306	97.61 418	99.82 330	95.19 261	98.57 419	98.64 304	93.05 392	97.73 404

mvs_anonymous		98.80 202	98.60 210	99.38 219	99.57 244	99.24 193	100.00 1	99.21 357	95.87 355	98.92 326	99.82 330	96.39 237	99.03 365	99.13 276	98.50 223	99.88 205

0.3-1-1-0.015		97.60 307	97.19 324	98.83 288	99.13 356	96.55 385	100.00 1	99.40 209	94.19 414	99.83 220	99.81 333	99.18 93	99.97 152	99.70 176	83.50 490	99.98 129

ab-mvs		98.42 258	98.02 283	99.61 167	99.71 179	99.00 221	99.10 485	99.64 71	96.70 294	99.04 318	99.81 333	90.64 368	99.98 143	99.64 200	97.93 299	99.84 224

TAMVS		98.76 207	98.73 185	98.86 286	99.44 319	97.69 342	99.57 425	99.34 258	96.57 311	99.12 307	99.81 333	98.83 137	99.16 359	97.97 338	97.91 300	99.73 313

PatchMatch-RL		99.02 163	98.78 176	99.74 142	99.99 53	99.29 184	100.00 1	100.00 1	98.38 107	99.89 210	99.81 333	93.14 323	99.99 108	97.85 341	99.98 120	99.95 151

0.4-1-1-0.1		97.56 310	97.15 328	98.79 293	99.01 372	96.44 388	100.00 1	99.40 209	94.11 417	99.81 235	99.80 337	99.09 101	99.97 152	99.65 199	83.48 492	99.98 129

0.4-1-1-0.2		97.60 307	97.18 325	98.86 286	99.05 369	96.62 383	100.00 1	99.40 209	94.24 409	99.82 229	99.80 337	99.09 101	99.97 152	99.70 176	83.50 490	99.98 129

hse-mvs2		96.79 347	96.38 354	98.04 358	99.68 190	95.54 400	99.81 376	99.42 156	98.21 119	100.00 1	99.80 337	97.49 198	99.46 338	99.72 168	73.27 518	99.12 340

AUN-MVS		96.26 378	95.67 391	98.06 352	99.68 190	95.60 399	99.82 375	99.42 156	96.78 272	99.88 213	99.80 337	94.84 271	99.47 333	97.48 358	73.29 517	99.12 340

MVSTER		98.58 239	98.52 223	98.77 295	99.65 209	99.68 126	100.00 1	99.29 288	95.63 365	98.65 347	99.80 337	99.78 9	98.88 386	98.59 310	95.31 354	97.73 404

sc_t1		92.52 441	91.34 447	96.09 436	97.80 452	89.86 483	98.61 506	99.12 420	77.73 512	96.09 454	99.79 342	68.64 501	98.94 377	96.94 374	87.31 466	99.46 332

h-mvs33		97.03 339	96.53 346	98.51 308	99.79 164	95.90 394	99.45 439	99.45 112	98.21 119	100.00 1	99.78 343	97.49 198	99.99 108	99.72 168	74.92 514	99.65 321

EU-MVSNet		96.63 356	96.53 346	96.94 405	97.59 464	96.87 375	99.76 394	99.47 86	96.35 336	96.85 439	99.78 343	92.57 338	96.27 498	95.33 416	91.08 427	97.68 427

PVSNet_0	93.57 19	96.41 367	95.74 385	98.41 317	99.84 132	95.22 407	100.00 1	100.00 1	98.08 132	97.55 422	99.78 343	84.40 449	100.00 1	100.00 1	81.99 497	100.00 1

ArgMatch-Sym		94.50 414	94.12 418	95.63 442	98.16 437	90.84 475	100.00 1	99.00 456	97.42 206	97.22 430	99.76 346	73.91 489	99.05 364	91.22 461	90.43 436	97.01 470

K. test v3		95.46 404	95.14 409	96.40 427	97.53 467	93.40 451	99.99 272	99.23 333	95.49 374	92.70 484	99.73 347	84.26 450	98.12 457	93.94 438	93.38 389	97.68 427

IterMVS-SCA-FT		96.72 352	96.42 353	97.62 377	99.40 330	96.83 376	99.99 272	99.14 407	94.65 398	97.55 422	99.72 348	89.65 396	98.31 439	95.62 411	92.05 408	97.73 404

pm-mvs1		95.76 398	95.01 411	98.00 360	98.23 431	97.45 351	99.24 461	99.04 449	93.13 444	95.93 458	99.72 348	86.28 436	98.84 388	95.62 411	87.92 459	97.72 411

tfpnnormal		96.36 372	95.69 390	98.37 320	98.55 409	98.71 246	99.69 410	99.45 112	93.16 443	96.69 445	99.71 350	88.44 417	98.99 371	94.17 433	91.38 424	97.41 458

pmmvs4		97.17 330	96.80 336	98.27 327	97.68 459	98.64 254	100.00 1	99.18 381	94.22 411	98.55 355	99.71 350	93.67 305	98.47 427	95.66 409	92.57 401	97.71 419

TransMVSNet (Re)		94.78 412	93.72 421	97.93 366	98.34 417	97.88 334	99.23 468	97.98 498	91.60 456	94.55 470	99.71 350	87.89 420	98.36 435	89.30 482	84.92 480	97.56 449

test_fmvs2		95.17 410	95.23 407	95.01 449	98.95 386	88.99 488	99.99 272	97.77 504	97.79 157	98.58 353	99.70 353	73.36 491	99.34 349	95.88 400	95.03 369	96.70 479

ADS-MVSNet2		98.28 273	98.51 228	97.62 377	99.51 279	95.03 411	99.24 461	99.41 205	95.52 371	99.96 156	99.70 353	97.57 193	97.94 474	97.11 370	98.54 221	99.88 205

ADS-MVSNet		98.70 218	98.51 228	99.28 252	99.51 279	98.39 277	99.24 461	99.44 126	95.52 371	99.96 156	99.70 353	97.57 193	99.58 303	97.11 370	98.54 221	99.88 205

jajsoiax		97.07 336	96.79 338	97.89 368	97.28 478	97.12 368	99.95 332	99.19 371	96.55 312	97.31 427	99.69 356	87.35 428	98.91 380	98.70 300	95.12 367	97.66 432

mvs_tets		97.00 342	96.69 340	97.94 364	97.41 476	97.27 361	99.60 422	99.18 381	96.51 319	97.35 426	99.69 356	86.53 434	98.91 380	98.84 292	95.09 368	97.65 437

test123		79.44 491	79.23 493	80.05 514	80.03 555	71.72 520	100.00 1	77.93 554	62.52 525	94.81 466	99.69 356	78.21 477	74.53 551	92.57 451	27.33 556	93.90 514

IB-MVS	96.24 12	97.54 313	96.95 331	99.33 234	99.67 198	98.10 314	100.00 1	99.47 86	97.42 206	99.26 296	99.69 356	98.83 137	99.89 223	99.43 248	78.77 511	100.00 1
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
GG-mvs-BLEND				99.59 172	99.54 254	99.49 159	99.17 476	99.52 79		99.96 156	99.68 360	100.00 1	99.33 350	99.71 172	99.99 108	99.96 145

WR-MVS		97.09 334	96.64 341	98.46 311	98.43 414	99.09 207	99.97 315	99.33 261	95.62 366	97.76 409	99.67 361	91.17 355	98.56 421	98.49 313	89.28 448	97.74 392

tpm2		98.64 224	98.58 214	98.81 292	99.42 323	97.12 368	99.69 410	99.37 233	93.63 428	99.94 194	99.67 361	98.96 123	99.47 333	98.62 308	97.95 297	99.83 227

ArgMatch-SfM		93.74 426	93.14 429	95.54 444	98.57 408	90.54 477	99.97 315	98.86 471	97.35 211	97.60 419	99.66 363	71.88 496	99.02 366	90.18 473	84.16 484	97.07 469

UniMVSNet_ETH3D		95.28 407	94.41 415	97.89 368	98.91 388	95.14 408	99.13 481	99.35 250	92.11 453	97.17 432	99.66 363	70.28 499	99.36 346	97.88 340	95.18 363	99.16 338

IterMVS		96.76 349	96.46 351	97.63 375	99.41 325	96.89 374	99.99 272	99.13 414	94.74 393	97.59 421	99.66 363	89.63 398	98.28 444	95.71 405	92.31 405	97.72 411
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-CasMVS		96.34 374	95.78 383	98.03 359	98.18 435	98.27 297	99.71 406	99.32 264	94.75 391	96.82 440	99.65 366	86.98 431	98.15 453	97.74 348	88.85 453	97.66 432

DU-MVS		96.93 344	96.49 349	98.22 334	98.31 421	98.41 273	100.00 1	99.37 233	96.41 329	97.76 409	99.65 366	92.14 344	98.50 424	97.98 335	86.84 471	97.75 364

CP-MVSNet		96.73 350	96.25 360	98.18 338	98.21 432	98.67 250	99.77 392	99.32 264	95.06 384	97.20 431	99.65 366	90.10 385	98.19 451	98.06 333	88.90 452	97.66 432

NR-MVSNet		96.63 356	96.04 369	98.38 319	98.31 421	98.98 223	99.22 470	99.35 250	95.87 355	94.43 473	99.65 366	92.73 333	98.40 432	96.78 383	88.05 458	97.75 364

GA-MVS		97.72 301	97.27 319	99.06 267	99.24 351	97.93 330	100.00 1	99.24 328	95.80 361	98.99 320	99.64 370	89.77 391	99.36 346	95.12 421	97.62 322	99.89 192

UniMVSNet_NR-MVSNet		97.16 331	96.80 336	98.22 334	98.38 416	98.41 273	100.00 1	99.45 112	96.14 349	97.76 409	99.64 370	95.05 265	98.50 424	97.98 335	86.84 471	97.75 364

TranMVSNet+NR-MVSNet		96.45 366	96.01 370	97.79 373	98.00 444	97.62 346	100.00 1	99.35 250	95.98 352	97.31 427	99.64 370	90.09 386	98.00 470	96.89 378	86.80 474	97.75 364

tpmrst		98.98 175	98.93 160	99.14 265	99.61 228	97.74 341	99.52 432	99.36 239	96.05 351	99.98 141	99.64 370	99.04 111	99.86 236	98.94 286	98.19 276	99.82 234

cl____		97.54 313	97.32 315	98.18 338	99.47 300	98.14 311	100.00 1	99.10 425	94.16 416	97.60 419	99.63 374	97.52 197	98.65 403	96.47 388	91.97 411	97.76 353

DIV-MVS_self_test		97.52 316	97.35 314	98.05 356	99.46 309	98.11 312	100.00 1	99.10 425	94.21 412	97.62 417	99.63 374	97.65 189	98.29 443	96.47 388	91.98 410	97.76 353

Fast-Effi-MVS+-dtu		98.38 262	98.56 218	97.82 371	99.58 240	94.44 436	100.00 1	99.16 394	96.75 279	99.51 271	99.63 374	95.03 266	99.60 297	97.71 349	99.67 182	99.42 333

MDTV_nov1_ep13			98.94 158		99.53 258	98.36 284	99.39 446	99.46 104	96.54 313	99.99 131	99.63 374	98.92 129	99.86 236	98.30 324	98.71 215

ppachtmachnet_test		96.17 384	95.89 375	97.02 400	97.61 462	95.24 406	99.99 272	99.24 328	93.31 439	96.71 444	99.62 378	94.34 288	98.07 465	89.87 475	92.30 406	97.75 364

anonymousdsp		97.16 331	96.88 333	98.00 360	97.08 480	98.06 318	99.81 376	99.15 400	94.58 399	97.84 408	99.62 378	90.49 371	98.60 413	97.98 335	95.32 353	97.33 462

miper_lstm_enhance		97.40 320	97.28 317	97.75 374	99.48 295	97.52 348	100.00 1	99.07 436	94.08 418	98.01 399	99.61 380	97.38 205	97.98 472	96.44 391	91.47 423	97.76 353

pmmvs6		93.64 430	92.87 434	95.94 439	97.47 472	91.41 470	98.92 494	99.02 453	87.84 483	95.01 465	99.61 380	77.24 481	98.77 394	94.33 431	86.41 476	97.63 441

FE-MVS		99.16 140	98.99 149	99.66 159	99.65 209	99.18 201	99.58 424	99.43 135	95.24 381	99.91 204	99.59 382	99.37 71	99.97 152	98.31 321	99.81 170	99.83 227

PS-MVSNAJss		98.03 285	98.06 280	97.94 364	97.63 460	97.33 359	99.89 361	99.23 333	96.27 341	98.03 396	99.59 382	98.75 143	98.78 391	98.52 312	94.61 378	97.70 420

mvs5depth		93.81 423	93.00 432	96.23 434	94.25 513	93.33 452	97.43 525	98.07 494	93.47 433	94.15 475	99.58 384	77.52 479	98.97 374	93.64 440	88.92 451	96.39 487

SCA		98.30 268	97.98 285	99.23 258	99.41 325	98.25 299	99.99 272	99.45 112	96.91 258	99.76 242	99.58 384	89.65 396	99.54 318	98.31 321	98.79 210	99.91 173

Patchmatch-test		97.83 296	97.42 309	99.06 267	99.08 362	97.66 344	98.66 504	99.21 357	93.65 427	98.25 387	99.58 384	99.47 52	99.57 305	90.25 472	98.59 219	99.95 151

PatchmatchNet		99.03 157	98.96 153	99.26 256	99.49 292	98.33 289	99.38 447	99.45 112	96.64 302	99.96 156	99.58 384	99.49 47	99.50 329	97.63 352	99.00 207	99.93 167
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		95.52 402	94.99 412	97.08 397	97.49 470	96.45 387	100.00 1	99.25 321	93.82 422	96.17 452	99.57 388	87.81 421	97.18 486	94.57 428	86.26 477	97.62 443

eth_miper_zixun_eth		97.47 317	97.28 317	98.06 352	99.41 325	97.94 328	99.62 420	99.08 431	94.46 405	98.19 390	99.56 389	96.91 222	98.50 424	96.78 383	91.49 421	97.74 392

VortexMVS		98.23 276	98.11 273	98.59 304	99.56 250	99.37 176	99.95 332	99.03 452	96.47 322	98.69 342	99.55 390	95.91 241	98.66 401	99.01 284	94.80 374	97.73 404

PEN-MVS		96.01 393	95.48 399	97.58 379	97.74 455	97.26 362	99.90 357	99.29 288	94.55 400	96.79 441	99.55 390	87.38 426	97.84 476	96.92 377	87.24 468	97.65 437

CMPMVS	66.12 22	90.65 456	92.04 445	86.46 494	96.18 488	66.87 534	98.03 517	99.38 228	83.38 503	85.49 511	99.55 390	77.59 478	98.80 390	94.44 430	94.31 380	93.72 516
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IterMVS-LS		97.56 310	97.44 308	97.92 367	99.38 334	97.90 331	99.89 361	99.10 425	94.41 406	98.32 380	99.54 393	97.21 208	98.11 459	97.50 357	91.62 418	97.75 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt0320-xc		91.69 450	90.50 455	95.26 446	98.04 440	90.12 482	98.60 507	98.70 478	76.63 515	94.66 469	99.52 394	68.57 502	97.99 471	94.61 427	85.18 479	97.66 432

dp		98.72 211	98.61 207	99.03 272	99.53 258	97.39 353	99.45 439	99.39 225	95.62 366	99.94 194	99.52 394	98.83 137	99.82 253	96.77 385	98.42 229	99.89 192

LTVRE_ROB	95.29 16	96.32 375	96.10 366	96.99 402	98.55 409	93.88 445	99.45 439	99.28 296	94.50 403	96.46 447	99.52 394	84.86 447	99.48 331	97.26 367	95.03 369	97.59 447
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
tt0320		92.36 443	91.28 448	95.58 443	98.30 423	90.65 476	98.69 503	99.14 407	76.73 513	96.07 455	99.50 397	72.28 495	98.39 433	93.29 445	87.56 463	97.70 420

v7n		96.06 392	95.42 404	97.99 362	97.58 465	97.35 356	99.86 367	99.11 422	92.81 450	97.91 404	99.49 398	90.99 361	98.92 379	92.51 452	88.49 456	97.70 420

test_0402		94.35 416	93.70 422	96.32 432	97.92 446	93.60 447	99.61 421	98.85 472	88.19 481	94.68 468	99.48 399	80.01 470	98.58 418	89.39 481	95.15 365	96.77 475

dtuonlycased		95.07 411	95.43 402	93.98 465	98.26 427	85.63 496	99.98 305	98.92 465	94.83 389	94.13 476	99.47 400	82.60 462	97.61 484	94.66 426	96.01 342	98.70 345

Baseline_NR-MVSNet		96.16 386	95.70 387	97.56 380	98.28 426	96.79 378	100.00 1	97.86 502	91.93 455	97.63 415	99.47 400	92.14 344	98.35 436	97.13 369	86.83 473	97.54 450

RoMa-SfM		90.39 459	89.63 462	92.66 474	97.47 472	83.18 503	98.81 498	98.21 487	85.44 498	89.21 495	99.46 402	63.72 506	98.30 442	87.11 490	87.25 467	96.51 483

Anonymous20231211		96.29 376	95.70 387	98.07 348	99.80 159	97.49 349	99.15 479	99.40 209	89.11 474	97.75 412	99.45 403	88.93 407	98.98 372	98.26 326	89.47 445	97.73 404

pmmvs5		95.94 395	95.61 392	96.95 404	97.42 474	94.66 426	100.00 1	98.08 493	93.60 429	97.05 433	99.43 404	87.02 429	98.46 428	95.76 403	92.12 407	97.72 411

SSC-MVS3.2		95.32 405	94.97 413	96.37 430	98.29 425	92.75 458	100.00 1	99.30 279	95.46 376	98.36 375	99.42 405	78.92 475	98.63 407	93.28 446	91.72 416	97.72 411

v148		96.29 376	95.84 378	97.63 375	97.74 455	96.53 386	100.00 1	99.07 436	93.52 431	98.01 399	99.42 405	91.22 352	98.60 413	96.37 392	87.22 469	97.75 364

WBMVS		98.19 278	98.10 276	98.47 310	99.63 218	99.03 214	100.00 1	99.32 264	95.46 376	98.39 374	99.40 407	99.69 17	98.61 410	98.64 304	92.39 403	97.76 353

miper_enhance_ethall		98.33 266	98.27 260	98.51 308	99.66 207	99.04 213	100.00 1	99.22 338	97.53 190	98.51 365	99.38 408	99.49 47	98.75 396	98.02 334	92.61 398	97.76 353

FA-MVS(test-final)		99.00 166	98.75 181	99.73 145	99.63 218	99.43 169	99.83 372	99.43 135	95.84 360	99.52 270	99.37 409	97.84 181	99.96 172	97.63 352	99.68 180	99.79 288

CostFormer		98.84 198	98.77 179	99.04 271	99.41 325	97.58 347	99.67 413	99.35 250	94.66 397	99.96 156	99.36 410	99.28 85	99.74 281	99.41 250	97.81 309	99.81 251

tpm		98.24 275	98.22 269	98.32 324	99.13 356	95.79 396	99.53 431	99.12 420	95.20 382	99.96 156	99.36 410	97.58 191	99.28 353	97.41 361	96.67 337	99.88 205

EPMVS		99.25 128	99.13 128	99.60 170	99.60 231	99.20 198	99.60 422	100.00 1	96.93 255	99.92 201	99.36 410	99.05 108	99.71 289	98.77 296	98.94 208	99.90 184

XVG-ACMP-BASELINE		96.60 358	96.52 348	96.84 411	98.41 415	93.29 453	99.99 272	99.32 264	97.76 161	98.51 365	99.29 413	81.95 464	99.54 318	98.40 316	95.03 369	97.68 427

ttmdpeth		96.24 379	95.88 376	97.32 388	97.80 452	96.61 384	99.95 332	98.77 476	97.80 156	93.42 479	99.28 414	86.42 435	99.01 368	97.63 352	91.84 413	96.33 488

tpmvs		98.59 237	98.38 250	99.23 258	99.69 185	97.90 331	99.31 455	99.47 86	94.52 402	99.68 254	99.28 414	97.64 190	99.89 223	97.71 349	98.17 279	99.89 192

RoMa-HiRes		87.37 469	86.72 474	89.32 485	95.81 495	78.25 510	98.63 505	97.01 515	82.18 505	86.32 507	99.25 416	56.48 518	94.79 508	83.17 504	81.62 500	94.91 509

reproduce_monomvs		98.61 234	98.54 220	98.82 289	99.97 99	99.28 186	100.00 1	99.33 261	98.51 99	97.87 406	99.24 417	99.98 3	99.45 339	99.02 283	92.93 394	97.74 392

MonoMVSNet		98.55 245	98.64 202	98.26 330	98.21 432	95.76 397	99.94 340	99.16 394	96.23 343	99.47 277	99.24 417	96.75 227	99.22 355	99.61 212	99.17 199	99.81 251

v1921920		96.16 386	95.50 395	98.14 342	97.88 449	97.96 326	99.99 272	99.07 436	93.33 438	98.60 351	99.24 417	89.37 400	98.71 398	91.28 460	90.74 431	97.75 364

cl22		98.23 276	98.11 273	98.58 306	99.82 140	99.01 218	100.00 1	99.28 296	96.92 257	98.33 379	99.21 420	98.09 169	98.97 374	98.72 299	92.61 398	97.76 353

miper_ehance_all_eth		97.81 297	97.66 302	98.23 333	99.49 292	98.37 281	99.99 272	99.11 422	94.78 390	98.25 387	99.21 420	98.18 165	98.57 419	97.35 365	92.61 398	97.76 353

c3_l		97.58 309	97.42 309	98.06 352	99.48 295	98.16 308	99.96 323	99.10 425	94.54 401	98.13 391	99.20 422	97.87 178	98.25 446	97.28 366	91.20 426	97.75 364

MVS_clip		68.81 505	72.22 505	58.58 534	84.27 546	34.51 563	80.78 552	61.23 562	34.94 558	86.68 506	99.12 423	55.61 520	50.86 562	80.33 514	66.99 537	90.36 527

test-LLR		99.03 157	98.91 163	99.40 212	99.40 330	99.28 186	100.00 1	99.45 112	96.70 294	99.42 281	99.12 423	99.31 77	99.01 368	96.82 380	99.99 108	99.91 173

test-mter		98.96 178	98.82 171	99.40 212	99.40 330	99.28 186	100.00 1	99.45 112	95.44 380	99.42 281	99.12 423	99.70 16	99.01 368	96.82 380	99.99 108	99.91 173

v144192		96.40 370	95.81 379	98.17 340	97.89 448	98.11 312	99.99 272	99.06 444	93.39 436	98.75 340	99.09 426	90.43 377	98.66 401	93.10 447	90.55 433	97.75 364

v2v482		96.70 353	96.18 363	98.27 327	98.04 440	98.39 277	100.00 1	99.13 414	94.19 414	98.58 353	99.08 427	90.48 372	98.67 400	95.69 406	90.44 435	97.75 364

our_test_3		96.51 362	96.35 356	96.98 403	97.61 462	95.05 410	99.98 305	99.01 455	94.68 396	96.77 443	99.06 428	95.87 243	98.14 455	91.81 457	92.37 404	97.75 364

Test_1112_low_res		98.83 199	98.60 210	99.51 183	99.69 185	98.75 241	99.99 272	99.14 407	96.81 268	98.84 334	99.06 428	97.45 201	99.89 223	98.66 301	97.75 314	99.89 192

V42		96.65 355	96.16 365	98.11 347	98.17 436	98.23 300	99.99 272	99.09 430	93.97 419	98.74 341	99.05 430	91.09 356	98.82 389	95.46 415	89.90 439	97.27 463

MVStest1		94.27 417	93.30 427	97.19 395	98.83 399	97.18 366	99.93 346	98.79 475	86.80 492	84.88 514	99.04 431	94.32 289	98.25 446	90.55 468	86.57 475	96.12 494

MVP-Stereo		96.51 362	96.48 350	96.60 424	95.65 498	94.25 442	98.84 497	98.16 488	95.85 359	95.23 463	99.04 431	92.54 339	99.13 360	92.98 448	99.98 120	96.43 486
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		98.41 260	98.11 273	99.31 242	99.54 254	98.55 259	99.18 471	100.00 1	98.64 93	99.79 237	99.04 431	87.61 423	100.00 1	99.30 263	99.89 151	99.40 334
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
D2MVS		97.63 306	97.83 293	97.05 398	98.83 399	94.60 430	100.00 1	99.82 46	96.89 261	98.28 383	99.03 434	94.05 293	99.47 333	98.58 311	94.97 372	97.09 467

PVSNet_BlendedMVS		98.71 215	98.62 206	98.98 277	99.98 95	99.60 134	100.00 1	100.00 1	97.23 226	100.00 1	99.03 434	96.57 232	99.99 108	100.00 1	94.75 375	97.35 461

MS-PatchMatch		95.66 401	95.87 377	95.05 448	97.80 452	89.25 486	98.88 496	99.30 279	96.35 336	96.86 438	99.01 436	81.35 467	99.43 341	93.30 444	99.98 120	96.46 485

v8		96.35 373	95.73 386	98.21 336	98.11 438	98.23 300	99.94 340	99.07 436	92.66 451	98.29 382	99.00 437	91.46 349	98.77 394	94.17 433	88.83 454	97.62 443

VLMVS		69.79 503	73.02 504	60.12 533	72.70 561	33.43 566	87.87 546	83.71 547	40.13 550	86.04 508	98.98 438	34.57 542	58.39 560	85.00 498	68.17 532	88.54 530

v1144		96.51 362	95.97 373	98.13 345	97.98 445	98.04 320	99.99 272	99.08 431	93.51 432	98.62 350	98.98 438	90.98 362	98.62 409	93.79 439	90.79 430	97.74 392

CR-MVSNet		98.02 286	97.71 301	98.93 280	99.31 341	98.86 233	99.13 481	99.00 456	96.53 314	99.96 156	98.98 438	96.94 220	98.10 463	91.18 462	98.40 231	99.84 224

Patchmtry		96.81 346	96.37 355	98.14 342	99.31 341	98.55 259	98.91 495	99.00 456	90.45 466	97.92 403	98.98 438	96.94 220	98.12 457	94.27 432	91.53 420	97.75 364

v1192		96.18 382	95.49 397	98.26 330	98.01 443	98.15 309	99.99 272	99.08 431	93.36 437	98.54 356	98.97 442	89.47 399	98.89 383	91.15 463	90.82 429	97.75 364

v1240		95.96 394	95.25 406	98.07 348	97.91 447	97.87 336	99.96 323	99.07 436	93.24 441	98.64 349	98.96 443	88.98 406	98.61 410	89.58 480	90.92 428	97.75 364

v10		96.14 388	95.50 395	98.07 348	98.19 434	97.96 326	99.83 372	99.07 436	92.10 454	98.07 393	98.94 444	91.07 357	98.61 410	92.41 455	89.82 440	97.63 441

VPA-MVSNet		97.03 339	96.43 352	98.82 289	98.64 405	99.32 181	99.38 447	99.47 86	96.73 286	98.91 328	98.94 444	87.00 430	99.40 344	99.23 268	89.59 442	97.76 353

FMVSNet3		97.30 326	96.95 331	98.37 320	99.65 209	99.25 191	99.71 406	99.28 296	94.23 410	98.53 361	98.91 446	93.30 315	98.11 459	95.31 417	93.60 385	97.73 404

UniMVSNet (Re)		97.29 327	96.85 335	98.59 304	98.49 412	99.13 205	100.00 1	99.42 156	96.52 318	98.24 389	98.90 447	94.93 268	98.89 383	97.54 356	87.61 462	97.75 364

usedtu_dtu_shiyan1		97.34 323	96.97 329	98.43 314	97.82 450	98.91 230	100.00 1	99.29 288	94.70 394	98.46 369	98.89 448	93.95 300	98.64 405	95.86 402	93.75 382	97.74 392

FE-MVSNET3		97.34 323	96.97 329	98.43 314	97.82 450	98.91 230	100.00 1	99.29 288	94.70 394	98.46 369	98.89 448	93.95 300	98.64 405	95.88 400	93.75 382	97.74 392

test20.03		93.11 435	92.85 435	93.88 466	95.19 506	91.83 466	100.00 1	98.87 469	93.68 426	92.76 482	98.88 450	89.20 403	92.71 520	77.88 522	89.19 449	97.09 467

MASt3R-SfM		91.92 446	92.47 444	90.28 481	96.64 485	75.61 515	99.63 417	98.31 484	95.70 363	95.42 461	98.84 451	67.34 503	99.22 355	89.92 474	90.47 434	96.01 496

FMVSNet2		96.22 380	95.60 393	98.06 352	99.53 258	98.33 289	99.45 439	99.27 311	93.71 423	98.03 396	98.84 451	84.23 451	98.10 463	93.97 437	93.40 388	97.73 404

GBi-Net		96.07 390	95.80 381	96.89 408	99.53 258	94.87 414	99.18 471	99.27 311	93.71 423	98.53 361	98.81 453	84.23 451	98.07 465	95.31 417	93.60 385	97.72 411

test1		96.07 390	95.80 381	96.89 408	99.53 258	94.87 414	99.18 471	99.27 311	93.71 423	98.53 361	98.81 453	84.23 451	98.07 465	95.31 417	93.60 385	97.72 411

FMVSNet1		94.45 415	93.63 423	96.89 408	98.87 394	94.87 414	99.18 471	99.27 311	90.95 462	97.31 427	98.81 453	72.89 493	98.07 465	92.61 450	92.81 395	97.72 411

Effi-MVS+-dtu		98.51 252	98.86 168	97.47 381	99.77 171	94.21 443	100.00 1	98.94 462	97.61 179	99.91 204	98.75 456	95.89 242	99.51 326	99.36 253	99.48 193	98.68 346

EGC-MVSNET		79.46 490	74.04 501	95.72 441	96.00 492	92.73 459	99.09 487	99.04 449	5.08 562	16.72 562	98.71 457	73.03 492	98.74 397	82.05 508	96.64 338	95.69 501

tpm cat1		98.05 284	97.76 295	98.92 281	99.50 288	97.10 370	99.77 392	99.30 279	90.20 470	99.72 250	98.71 457	97.71 186	99.86 236	96.75 386	98.20 275	99.81 251

WR-MVS_H		96.73 350	96.32 359	97.95 363	98.26 427	97.88 334	99.72 405	99.43 135	95.06 384	96.99 434	98.68 459	93.02 327	98.53 422	97.43 360	88.33 457	97.43 457

EG-PatchMatch MVS		92.94 438	92.49 443	94.29 461	95.87 494	87.07 493	99.07 490	98.11 491	93.19 442	88.98 496	98.66 460	70.89 497	99.08 362	92.43 454	95.21 361	96.72 477

UnsupCasMVSNet_eth		94.25 418	93.89 419	95.34 445	97.63 460	92.13 464	99.73 402	99.36 239	94.88 387	92.78 481	98.63 461	82.72 459	96.53 494	94.57 428	84.73 481	97.36 460

blend_shiyan4		95.76 398	95.40 405	96.82 417	95.50 501	94.40 438	100.00 1	99.22 338	87.12 487	98.67 345	98.59 462	99.09 101	98.31 439	96.31 393	84.14 485	97.75 364

Anonymous20231206		93.45 432	93.17 428	94.30 460	95.00 509	89.69 485	99.98 305	98.43 483	93.30 440	94.50 472	98.59 462	90.52 370	95.73 504	77.46 524	90.73 432	97.48 456

N_pmnet		91.88 448	93.37 426	87.40 491	97.24 479	66.33 536	99.90 357	91.05 536	89.77 473	95.65 460	98.58 464	90.05 388	98.11 459	85.39 496	92.72 397	97.75 364

wanda-best-256-512		93.76 424	92.74 437	96.84 411	95.22 503	94.54 434	100.00 1	99.22 338	87.22 485	98.54 356	98.56 465	90.48 372	98.22 448	95.67 407	69.73 526	97.75 364

FE-blended-shiyan7		93.76 424	92.74 437	96.84 411	95.22 503	94.54 434	100.00 1	99.22 338	87.22 485	98.54 356	98.56 465	90.48 372	98.22 448	95.67 407	69.73 526	97.75 364

blended_shiyan6		93.70 429	92.67 442	96.78 421	95.17 507	94.38 441	100.00 1	99.22 338	87.03 490	98.54 356	98.56 465	90.14 381	98.22 448	95.62 411	69.73 526	97.75 364

usedtu_blend_shiyan5		92.75 439	91.39 446	96.82 417	95.22 503	94.40 438	99.05 492	98.64 480	75.98 519	98.54 356	98.56 465	90.48 372	98.31 439	96.31 393	69.73 526	97.75 364

blended_shiyan8		93.73 427	92.69 440	96.84 411	95.17 507	94.40 438	100.00 1	99.20 367	87.05 488	98.60 351	98.54 469	90.15 380	98.39 433	95.54 414	69.93 525	97.74 392

MIMVSNet		97.06 337	96.73 339	98.05 356	99.38 334	96.64 382	98.47 510	99.35 250	93.41 435	99.48 274	98.53 470	89.66 395	97.70 483	94.16 435	98.11 286	99.80 282

test_method		91.04 455	91.10 451	90.85 479	98.34 417	77.63 511	100.00 1	98.93 464	76.69 514	96.25 451	98.52 471	70.44 498	97.98 472	89.02 485	91.74 414	96.92 473

DKM		88.67 464	87.74 470	91.44 477	97.38 477	82.60 504	98.95 493	97.94 500	87.54 484	87.00 504	98.48 472	55.08 521	95.81 503	86.05 495	81.29 504	95.91 498

LCM-MVSNet-Re		96.52 360	97.21 323	94.44 457	99.27 348	85.80 495	99.85 369	96.61 522	95.98 352	92.75 483	98.48 472	93.97 299	97.55 485	99.58 219	98.43 228	99.98 129

gbinet_0.2-2-1-0.02		93.73 427	92.69 440	96.84 411	94.91 511	94.62 429	100.00 1	99.28 296	87.02 491	98.53 361	98.45 474	89.72 393	98.15 453	96.65 387	69.64 530	97.74 392

FMVSNet5		95.32 405	95.43 402	94.99 451	99.39 333	92.99 456	99.25 460	99.24 328	90.45 466	97.44 425	98.45 474	95.78 246	94.39 510	87.02 491	91.88 412	97.59 447

MIMVSNet1		91.96 444	91.20 449	94.23 462	94.94 510	91.69 468	99.34 451	99.22 338	88.23 478	94.18 474	98.45 474	75.52 487	93.41 518	79.37 516	91.49 421	97.60 446

DenseAffine		90.43 458	89.28 466	93.87 467	97.71 458	86.21 494	99.13 481	98.10 492	87.86 482	90.15 493	98.43 477	60.76 509	98.65 403	84.48 501	86.90 470	96.74 476

YYNet1		92.44 442	90.92 453	97.03 399	96.20 487	97.06 371	99.99 272	99.14 407	88.21 480	67.93 540	98.43 477	88.63 412	96.28 497	90.64 465	89.08 450	97.74 392

MDA-MVSNet-bldmvs		91.65 451	89.94 461	96.79 420	96.72 482	96.70 380	99.42 444	98.94 462	88.89 475	66.97 543	98.37 479	81.43 466	95.91 501	89.24 483	89.46 446	97.75 364

FPMVS		77.92 495	79.45 492	73.34 520	76.87 557	46.81 550	98.24 512	99.05 446	59.89 529	73.55 532	98.34 480	36.81 540	86.55 533	80.96 511	91.35 425	86.65 533

MDA-MVSNet_test_wron		92.61 440	91.09 452	97.19 395	96.71 483	97.26 362	100.00 1	99.14 407	88.61 476	67.90 541	98.32 481	89.03 404	96.57 493	90.47 470	89.59 442	97.74 392

VLMVS_CLIP		69.45 504	71.86 506	62.23 527	66.80 562	30.24 569	87.12 549	87.67 542	33.62 559	82.03 521	98.28 482	28.75 552	67.69 557	88.35 487	74.12 516	88.74 529

DKM-HiRes		87.00 471	86.38 475	88.84 487	96.71 483	79.05 508	98.73 502	97.57 511	84.56 500	84.00 516	98.23 483	52.90 526	92.48 521	84.95 499	79.77 506	95.00 507

Anonymous20240521		93.29 433	92.76 436	94.90 454	95.64 499	91.27 471	99.97 315	98.82 473	87.04 489	94.71 467	98.19 484	83.86 455	96.80 489	84.04 503	92.56 402	96.64 480

WB-MVS		88.24 467	90.09 458	82.68 509	91.56 525	69.51 524	100.00 1	98.73 477	90.72 465	87.29 503	98.12 485	92.87 329	85.01 539	62.19 539	89.34 447	93.54 517

SSC-MVS		87.61 468	89.47 463	82.04 510	90.63 529	68.77 528	99.99 272	98.66 479	90.34 468	86.70 505	98.08 486	92.72 334	84.12 540	59.41 542	88.71 455	93.22 521

testf1		84.40 477	84.79 478	83.23 507	95.71 496	58.71 545	98.79 499	97.75 505	81.58 506	84.94 512	98.07 487	45.33 533	97.73 481	77.09 525	83.85 486	93.24 519

APD_test2		84.40 477	84.79 478	83.23 507	95.71 496	58.71 545	98.79 499	97.75 505	81.58 506	84.94 512	98.07 487	45.33 533	97.73 481	77.09 525	83.85 486	93.24 519

new_pmnet		94.11 422	93.47 425	96.04 438	96.60 486	92.82 457	99.97 315	98.91 466	90.21 469	95.26 462	98.05 489	85.89 441	98.14 455	84.28 502	92.01 409	97.16 465

patchmatchnet-post											97.79 490	99.41 67	99.54 318

KD-MVS_2432*1600		94.15 419	93.08 430	97.35 386	99.53 258	97.83 338	99.63 417	99.19 371	92.88 447	96.29 449	97.68 491	98.84 135	96.70 490	89.73 476	63.92 539	97.53 451

miper_refine_blended		94.15 419	93.08 430	97.35 386	99.53 258	97.83 338	99.63 417	99.19 371	92.88 447	96.29 449	97.68 491	98.84 135	96.70 490	89.73 476	63.92 539	97.53 451

Patchmatch-RL test		93.49 431	93.63 423	93.05 471	91.78 522	83.41 501	98.21 513	96.95 517	91.58 457	91.05 487	97.64 493	99.40 69	95.83 502	94.11 436	81.95 498	99.91 173

DSMNet-mixed		95.18 409	95.21 408	95.08 447	96.03 491	90.21 481	99.65 414	93.64 532	92.91 446	98.34 378	97.40 494	90.05 388	95.51 506	91.02 464	97.86 303	99.51 330

mmtdpeth		94.58 413	94.18 416	95.81 440	98.82 401	91.09 473	99.99 272	98.61 481	96.38 331	100.00 1	97.23 495	76.52 483	99.85 243	99.82 142	80.22 505	96.48 484

test_vis1_rt		93.10 436	92.93 433	93.58 468	99.63 218	85.07 497	99.99 272	93.71 531	97.49 197	90.96 488	97.10 496	60.40 510	99.95 185	99.24 267	97.90 301	95.72 500

CL-MVSNet_self_test		91.07 454	90.35 457	93.24 469	93.27 515	89.16 487	99.55 428	99.25 321	92.34 452	95.23 463	97.05 497	88.86 409	93.59 516	80.67 512	66.95 538	96.96 472

OpenMVS_ROB	88.34 20	91.89 447	91.12 450	94.19 463	95.55 500	87.63 491	99.26 459	98.03 495	86.61 494	90.65 492	96.82 498	70.14 500	98.78 391	86.54 493	96.50 341	96.15 492

usedtu_dtu_shiyan2		85.34 474	83.22 482	91.71 476	88.10 539	83.34 502	98.75 501	97.59 510	76.21 517	91.11 486	96.80 499	58.14 514	94.30 511	75.00 529	67.24 536	97.49 453

LoFTR		88.61 465	87.13 472	93.06 470	96.18 488	83.87 500	99.48 435	97.21 513	86.37 495	82.32 520	96.66 500	58.07 515	98.59 415	81.76 509	86.15 478	96.72 477

pmmvs3		90.62 457	89.36 465	94.40 458	90.53 531	91.49 469	100.00 1	96.73 520	84.21 501	93.65 478	96.65 501	82.56 463	94.83 507	82.28 507	77.62 512	96.89 474

PMatch-SfM		81.57 485	79.80 490	86.88 492	92.36 518	73.86 517	97.50 524	92.66 535	80.39 508	73.10 533	96.35 502	33.54 546	91.86 523	81.28 510	71.01 523	94.92 508

PMatch-Up-SfM		79.27 492	77.62 496	84.22 503	90.58 530	69.08 527	96.98 527	90.47 538	76.44 516	71.47 536	96.27 503	30.15 551	88.77 532	78.74 517	67.46 533	94.81 511

MatchFormer		86.71 473	84.75 480	92.57 475	96.14 490	82.52 505	99.27 457	97.86 502	80.17 509	78.74 524	96.16 504	54.81 522	98.63 407	75.87 527	83.75 489	96.56 482

mvsany_test3		89.36 463	88.96 467	90.56 480	91.95 521	78.97 509	99.74 397	96.59 523	96.84 265	89.25 494	96.07 505	52.59 527	97.11 487	95.17 420	82.44 496	95.58 505

ELoFTR		83.63 479	81.67 487	89.53 484	92.30 519	75.98 514	98.27 511	96.74 519	83.38 503	74.05 531	95.78 506	43.66 535	98.11 459	78.01 520	72.80 520	94.48 512

PM-MVS		88.39 466	87.41 471	91.31 478	91.73 523	82.02 507	99.79 382	96.62 521	91.06 461	90.71 491	95.73 507	48.60 530	95.96 500	90.56 467	81.91 499	95.97 497

pmmvs-eth3d		91.73 449	90.67 454	94.92 453	91.63 524	92.71 460	99.90 357	98.54 482	91.19 459	88.08 500	95.50 508	79.31 474	96.13 499	90.55 468	81.32 503	95.91 498

FE-MVSNET		89.50 461	88.33 468	93.00 472	88.89 535	90.24 480	99.96 323	96.86 518	88.23 478	88.46 498	95.47 509	77.03 482	93.37 519	78.54 519	81.56 502	95.39 506

ALIKED-NN		82.28 483	81.49 488	84.63 501	99.44 319	67.26 533	97.36 526	90.47 538	62.09 526	81.26 523	95.45 510	59.17 511	93.89 514	63.93 538	84.26 482	92.75 522

FE-MVSNET2		91.15 453	90.00 460	94.58 456	90.74 528	92.52 463	99.56 426	98.87 469	90.82 463	88.96 497	95.40 511	76.26 485	95.56 505	87.84 488	81.59 501	95.66 503

ambc				88.45 488	86.84 542	70.76 522	97.79 521	98.02 497		90.91 489	95.14 512	38.69 537	98.51 423	94.97 422	84.23 483	96.09 495

RPMNet		95.26 408	93.82 420	99.56 179	99.31 341	98.86 233	99.13 481	99.42 156	79.82 511	99.96 156	95.13 513	95.69 249	99.98 143	77.54 523	98.40 231	99.84 224

PDCNetPlus		75.87 496	73.92 502	81.72 511	89.55 534	74.48 516	98.59 508	62.34 559	72.19 521	76.04 527	95.03 514	47.66 531	86.31 535	77.97 521	45.88 545	84.35 536

new-patchmatchnet		90.30 460	89.46 464	92.84 473	90.77 527	88.55 490	99.83 372	98.80 474	90.07 471	87.86 501	95.00 515	78.77 476	94.30 511	84.86 500	79.15 508	95.68 502

PatchT		95.90 396	94.95 414	98.75 296	99.03 370	98.39 277	99.08 488	99.32 264	85.52 496	99.96 156	94.99 516	97.94 172	98.05 469	80.20 515	98.47 226	99.81 251

KD-MVS_self_test		91.16 452	90.09 458	94.35 459	94.44 512	91.27 471	99.74 397	99.08 431	90.82 463	94.53 471	94.91 517	86.11 437	94.78 509	82.67 506	68.52 531	96.99 471

ALIKED-LG		80.86 486	79.70 491	84.33 502	98.33 420	69.33 525	97.59 523	90.14 541	65.38 524	76.03 528	94.87 518	54.78 523	93.65 515	57.59 544	82.61 495	90.01 528

UnsupCasMVSNet_bld		89.50 461	88.00 469	93.99 464	95.30 502	88.86 489	98.52 509	99.28 296	85.50 497	87.80 502	94.11 519	61.63 507	96.96 488	90.63 466	79.26 507	96.15 492

test_fmvs3		87.19 470	87.02 473	87.71 490	92.69 517	76.64 512	99.96 323	97.27 512	93.55 430	90.82 490	94.03 520	38.00 539	92.19 522	93.49 443	83.35 494	94.32 513

LCM-MVSNet		79.01 494	76.93 498	85.27 495	78.28 556	68.01 531	96.57 529	98.03 495	55.10 535	82.03 521	93.27 521	31.99 550	93.95 513	82.72 505	74.37 515	93.84 515

test_f		86.87 472	86.06 476	89.28 486	91.45 526	76.37 513	99.87 366	97.11 514	91.10 460	88.46 498	93.05 522	38.31 538	96.66 492	91.77 458	83.46 493	94.82 510

SP-DiffGlue		85.17 475	85.16 477	85.22 496	93.54 514	69.16 526	97.83 520	95.33 526	60.61 528	86.04 508	92.86 523	61.04 508	90.90 529	89.62 479	89.57 444	95.59 504

ALIKED-MNN		79.54 489	78.11 495	83.80 506	99.29 347	66.55 535	97.70 522	90.37 540	57.60 531	74.96 530	92.30 524	53.12 525	93.57 517	58.80 543	78.89 510	91.27 524

PMMVS2		79.15 493	77.28 497	84.76 500	82.34 550	72.66 518	99.70 408	95.11 529	71.68 522	84.78 515	90.87 525	32.05 549	89.99 531	75.53 528	63.45 541	91.64 523

MVS_baseline		35.10 523	36.24 527	31.67 541	45.91 567	12.01 570	34.47 555	7.88 569	5.62 561	47.50 550	90.75 526	11.45 566	7.89 564	46.41 547	36.20 550	75.11 543

SP-NN		83.33 480	82.73 483	85.13 498	98.98 381	65.96 537	97.92 518	95.13 528	56.43 533	83.71 517	90.52 527	58.27 512	91.69 524	71.99 530	91.66 417	97.74 392

Gipuma		84.73 476	83.50 481	88.40 489	97.50 468	82.21 506	88.87 542	99.05 446	65.81 523	85.71 510	90.49 528	53.70 524	96.31 496	78.64 518	91.74 414	86.67 532
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SP-MNN		81.80 484	81.08 489	83.94 504	98.26 427	64.81 540	98.20 514	93.56 533	55.15 534	77.43 526	90.43 529	56.33 519	90.69 530	70.11 534	90.27 438	96.32 489

SP-SuperGlue		82.71 482	81.92 485	85.07 499	98.02 442	67.96 532	98.10 515	95.26 527	57.79 530	82.47 519	90.37 530	57.02 516	91.04 527	70.34 533	87.92 459	96.23 490

JIA-IIPM		97.09 334	96.34 357	99.36 222	98.88 391	98.59 257	99.81 376	99.43 135	84.81 499	99.96 156	90.34 531	98.55 153	99.52 324	97.00 373	98.28 261	99.98 129

XFeat-NN		75.54 498	76.00 499	74.19 518	93.25 516	52.63 549	95.93 531	81.98 550	46.32 541	75.32 529	90.27 532	56.80 517	85.05 538	71.26 532	72.85 519	84.87 535

SP-LightGlue		82.73 481	81.92 485	85.19 497	97.73 457	68.40 530	98.05 516	94.51 530	56.95 532	82.72 518	90.14 533	58.20 513	90.97 528	71.57 531	87.38 465	96.20 491

XFeat-MNN		73.39 499	73.10 503	74.25 517	89.63 533	53.35 548	96.25 530	84.01 546	43.66 542	69.74 537	89.91 534	52.56 528	85.32 536	64.72 537	67.44 534	84.08 537

test_post											89.05 535	99.49 47	99.59 299

PMVS	60.66 23	65.98 509	65.05 512	68.75 523	55.06 565	38.40 562	88.19 545	96.98 516	48.30 540	44.82 554	88.52 536	12.22 564	86.49 534	67.58 535	83.79 488	81.35 540
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_post1							99.32 452				88.24 537	99.33 72	99.59 299	98.31 321

GLUNet-SfM		70.22 501	66.87 510	80.24 513	84.13 547	61.64 543	96.72 528	82.62 548	51.83 536	60.24 547	88.02 538	36.12 541	91.44 526	67.32 536	34.86 553	87.65 531

MVS-HIRNet		94.12 421	92.73 439	98.29 325	99.33 340	95.95 391	99.38 447	99.19 371	74.54 520	98.26 386	86.34 539	86.07 438	99.06 363	91.60 459	99.87 159	99.85 222

E-PMN		70.72 500	70.06 507	72.69 521	83.92 548	65.48 539	99.95 332	92.72 534	49.88 538	72.30 534	86.26 540	47.17 532	77.43 548	53.83 545	44.49 546	75.17 542

SIFT-NN		67.52 506	68.28 509	65.25 524	96.00 492	45.92 551	93.38 534	80.01 551	43.05 543	69.06 538	85.13 541	39.13 536	85.13 537	32.15 549	76.58 513	64.70 545

EMVS		69.88 502	69.09 508	72.24 522	84.70 545	65.82 538	99.96 323	87.08 545	49.82 539	71.51 535	84.74 542	49.30 529	75.32 550	50.97 546	43.71 547	75.59 541

test_vis3_rt		79.61 488	78.19 494	83.86 505	88.68 538	69.56 523	99.81 376	82.19 549	86.78 493	68.57 539	84.51 543	25.06 557	98.26 445	89.18 484	78.94 509	83.75 538

SIFT-MNN		64.77 510	65.11 511	63.77 525	92.18 520	44.02 553	91.93 536	78.84 552	41.80 545	61.69 545	84.03 544	33.92 545	81.69 542	29.20 554	72.39 521	65.59 544

gg-mvs-nofinetune		96.95 343	96.10 366	99.50 186	99.41 325	99.36 179	99.07 490	99.52 79	83.69 502	99.96 156	83.60 545	100.00 1	99.20 357	99.68 186	99.99 108	99.96 145

SIFT-NN-NCMNet		64.49 511	64.92 513	63.20 526	88.84 536	44.41 552	92.37 535	78.67 553	41.90 544	62.62 544	83.27 546	34.31 543	81.88 541	30.88 550	71.40 522	63.31 547

SIFT-NN-CMatch		60.63 512	60.17 516	62.02 528	86.89 541	43.32 555	90.70 539	71.03 555	41.60 547	61.16 546	83.16 547	33.45 547	78.31 546	30.28 551	43.26 548	64.44 546

SIFT-ConvMatch		56.83 516	55.72 520	60.16 531	88.80 537	43.02 557	88.55 543	64.15 558	40.75 548	45.84 552	83.12 548	27.00 554	77.01 549	28.36 555	34.89 552	60.45 551

SIFT-UMatch		55.48 517	53.92 521	60.16 531	85.84 544	42.45 558	89.09 541	61.68 561	39.97 552	41.34 556	82.92 549	26.90 555	77.66 547	27.36 556	30.17 554	60.37 552

SIFT-UM-Cal		51.73 519	50.25 523	56.15 537	85.87 543	41.10 561	88.21 544	50.44 565	39.83 553	33.54 559	82.23 550	23.59 558	71.25 556	27.05 558	21.52 559	56.10 555

SIFT-NN-UMatch		59.27 514	58.65 518	61.13 530	83.27 549	43.66 554	91.00 538	70.69 556	41.78 546	44.38 555	82.21 551	34.17 544	79.10 544	30.07 552	50.25 544	60.64 550

SIFT-CM-Cal		53.99 518	52.89 522	57.28 536	87.31 540	41.77 559	86.71 550	54.86 564	39.82 554	45.09 553	82.10 552	25.89 556	71.72 555	27.27 557	26.97 557	58.36 553

SIFT-NCM-Cal		59.75 513	59.15 517	61.53 529	90.12 532	43.18 556	91.26 537	70.04 557	40.34 549	38.39 557	81.51 553	27.19 553	79.90 543	26.25 559	67.30 535	61.50 549

MVE	68.59 21	67.22 507	64.68 514	74.84 515	74.67 560	62.32 542	95.84 532	90.87 537	50.98 537	58.72 548	81.05 554	12.20 565	78.95 545	61.06 541	56.75 542	83.24 539
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN		57.34 515	56.95 519	58.53 535	82.11 551	41.35 560	90.36 540	61.72 560	40.01 551	54.78 549	80.99 555	32.74 548	72.39 553	29.64 553	40.16 549	61.83 548

ANet_high		66.05 508	63.44 515	73.88 519	61.14 563	63.45 541	95.68 533	87.18 543	79.93 510	47.35 551	80.68 556	22.35 560	72.33 554	61.24 540	35.42 551	85.88 534

SIFT-PCN-Cal		47.97 521	47.56 525	49.19 539	81.85 552	33.99 565	86.00 551	49.11 566	36.44 556	32.13 560	77.60 557	22.63 559	62.04 558	23.11 560	19.17 560	51.55 556

SIFT-PointCN		49.44 520	48.89 524	51.12 538	81.24 554	34.25 564	87.16 548	56.78 563	36.95 555	33.84 558	76.32 558	20.17 561	61.65 559	21.99 561	25.53 558	57.46 554

SIFT-NCMNet		41.74 522	41.17 526	43.45 540	76.48 558	31.10 568	80.74 553	30.14 567	35.07 557	28.33 561	71.87 559	16.32 562	52.56 561	19.72 562	11.82 562	46.67 557

X-MVStestdata		97.04 338	96.06 368	99.98 29	100.00 1	99.94 49	100.00 1	99.75 58	98.67 90	100.00 1	66.97 560	99.16 95	100.00 1	100.00 1	100.00 1	100.00 1

wuyk23d		28.28 524	29.73 529	23.92 542	75.89 559	32.61 567	66.50 554	12.88 568	16.09 560	14.59 563	16.59 561	12.35 563	32.36 563	39.36 548	13.36 561	6.79 558

test_blank		0.07 528	0.09 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.79 562	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

mmdepth		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

monomultidepth		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

uanet_test		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

DCPMVS		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

pcd_1.5k_mvsjas		8.24 527	10.99 531	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	98.75 143	0.00 565	0.00 563	0.00 563	0.00 559

sosnet-low-res		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

sosnet		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

uncertanet		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

Regformer		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

uanet		0.01 529	0.02 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.14 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

Meshroom													0.00 565
: In preparation.
AliceVision / Meshro													0.00 565
: In preparation.
AliceVision_Meshroom													0.00 565
: In preparation.
PatchmatchNet2					0.00 568	95.13 409	99.92 348	99.16 394	89.91 472
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														86.42 494	92.76 396	97.75 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													98.34 437
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					100.00 1	99.98 19		99.69 68		100.00 1		99.45 54	100.00 1	100.00 1	100.00 1

WAC-MVS						97.98 323								95.74 404

FOURS1					100.00 1	99.97 29	100.00 1	99.42 156	98.52 98	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 156					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 156					100.00 1	100.00 1	100.00 1	100.00 1

eth-test2					0.00 568

eth-test					0.00 568

IU-MVS					100.00 1	99.99 6		99.42 156	99.12 9	100.00 1				100.00 1	100.00 1	100.00 1

save fliter					99.99 53	99.93 55	100.00 1	99.42 156	98.93 51

test_0728_SECOND				100.00 1	99.99 53	99.99 6	100.00 1	99.42 156					100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 173

test_part2					100.00 1	99.99 6				100.00 1

sam_mvs1												99.29 83				99.91 173

sam_mvs												99.33 72

MTGPA								99.42 156

MTMP							100.00 1	99.18 381

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 87		99.42 156		100.00 1			99.97 152

test_prior4						99.93 55	100.00 1

test_prior				99.90 88	100.00 1	99.75 111		99.73 62					99.97 152			100.00 1

旧先验2							100.00 1		98.11 131	100.00 1			100.00 1	99.67 190

新几何2							100.00 1

无先验							100.00 1	99.80 49	97.98 139				100.00 1	99.33 260		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 364

segment_acmp												99.55 32

testdata1							100.00 1		98.77 86

test12				99.95 62	99.99 53	99.89 80		99.42 156		100.00 1		99.24 88	99.97 152		100.00 1	100.00 1

plane_prior7					99.00 377	94.78 424

plane_prior6					99.06 367	94.80 420						88.58 415

plane_prior5								99.40 209					99.55 315	99.79 145	95.57 348	97.76 353

plane_prior3						94.79 423			99.03 25	99.08 313

plane_prior2							100.00 1		99.00 33

plane_prior1					99.02 371

plane_prior						94.80 420	100.00 1		99.03 25						95.58 344

n2								0.00 570

nn								0.00 570

door-mid								96.32 524

test11								99.42 156

door								96.13 525

HQP5-MVS						94.82 417

HQP-NCC					99.07 363		100.00 1		99.04 20	99.17 301

ACMP_Plane					99.07 363		100.00 1		99.04 20	99.17 301

BP-MVS														99.79 145

HQP4-MVS										99.17 301			99.57 305			97.77 351

HQP3-MVS								99.40 209							95.58 344

HQP2-MVS												88.61 413

MDTV_nov1_ep13_2view						99.24 193	99.56 426		96.31 340	99.96 156		98.86 133		98.92 288		99.89 192

ACMMP++_ref															94.58 379

ACMMP++															95.17 364

Test By Simon												99.10 100

High-resolution multi-view benchmark