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.

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 27	99.85 16	99.11 65	99.90 1	99.78 36	99.63 29	99.78 40	99.67 30	99.48 10	99.81 223	99.30 63	99.97 21	99.77 50
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
3Dnovator	98.27 2	98.81 125	98.73 126	99.05 143	98.76 333	97.81 187	99.25 43	99.30 232	98.57 169	98.55 284	99.33 116	97.95 136	99.90 82	97.16 236	99.67 223	99.44 204

3Dnovator+	97.89 3	98.69 149	98.51 168	99.24 107	98.81 328	98.40 118	99.02 70	99.19 268	98.99 122	98.07 326	99.28 127	97.11 211	99.84 175	96.84 269	99.32 322	99.47 193

DeepC-MVS	97.60 4	98.97 95	98.93 99	99.10 129	99.35 192	97.98 163	98.01 210	99.46 156	97.56 261	99.54 79	99.50 69	98.97 28	99.84 175	98.06 158	99.92 69	99.49 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	96.93 5	98.32 217	98.01 247	99.23 109	98.39 396	98.97 74	95.03 445	99.18 272	96.88 326	99.33 131	98.78 273	98.16 118	99.28 450	96.74 277	99.62 244	99.44 204

DeepC-MVS_fast	96.85 6	98.30 220	98.15 232	98.75 205	98.61 367	97.23 231	97.76 253	99.09 291	97.31 291	98.75 254	98.66 303	97.56 174	99.64 356	96.10 334	99.55 271	99.39 226
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVS	96.65 7	97.09 331	96.68 342	98.32 281	98.32 399	97.16 243	98.86 92	99.37 195	89.48 470	96.29 431	99.15 168	96.56 247	99.90 82	92.90 422	99.20 344	97.89 442

ACMH	96.65 7	99.25 41	99.24 54	99.26 102	99.72 44	98.38 120	99.07 64	99.55 114	98.30 188	99.65 64	99.45 85	99.22 17	99.76 268	98.44 129	99.77 162	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 77	99.00 92	99.33 90	99.71 48	98.83 87	98.60 121	99.58 94	99.11 99	99.53 83	99.18 158	98.81 38	99.67 332	96.71 282	99.77 162	99.50 167

COLMAP_ROB	96.50 10	98.99 90	98.85 115	99.41 70	99.58 93	99.10 66	98.74 99	99.56 110	99.09 109	99.33 131	99.19 154	98.40 84	99.72 302	95.98 337	99.76 177	99.42 213
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TAPA-MVS	96.21 11	96.63 353	95.95 364	98.65 222	98.93 299	98.09 147	96.93 348	99.28 244	83.58 483	98.13 320	97.78 388	96.13 267	99.40 431	93.52 411	99.29 329	98.45 408
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 103	98.73 126	99.48 57	99.55 116	99.14 58	98.07 197	99.37 195	97.62 252	99.04 192	98.96 228	98.84 36	99.79 245	97.43 220	99.65 232	99.49 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 380	95.35 388	97.55 362	97.95 419	94.79 354	98.81 98	96.94 429	92.28 449	95.17 454	98.57 319	89.90 390	99.75 279	91.20 451	97.33 450	98.10 431

OpenMVS_ROB	95.38 14	95.84 383	95.18 396	97.81 328	98.41 395	97.15 244	97.37 314	98.62 366	83.86 482	98.65 265	98.37 344	94.29 330	99.68 328	88.41 466	98.62 402	96.60 473

ACMP	95.32 15	98.41 199	98.09 237	99.36 75	99.51 130	98.79 90	97.68 264	99.38 191	95.76 381	98.81 244	98.82 265	98.36 87	99.82 206	94.75 373	99.77 162	99.48 185
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 356	95.73 369	98.85 176	98.75 335	97.91 172	96.42 379	99.06 294	90.94 463	95.59 443	97.38 412	94.41 325	99.59 376	90.93 455	98.04 429	99.05 326
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 387	95.70 370	95.57 437	98.83 322	88.57 464	92.50 481	97.72 402	92.69 444	96.49 428	96.44 433	93.72 343	99.43 427	93.61 408	99.28 330	98.71 385

PCF-MVS	92.86 18	94.36 410	93.00 428	98.42 269	98.70 347	97.56 203	93.16 479	99.11 288	79.59 487	97.55 365	97.43 409	92.19 366	99.73 292	79.85 485	99.45 298	97.97 439
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 446	90.90 450	96.27 418	97.22 457	91.24 446	94.36 465	93.33 473	92.37 447	92.24 482	94.58 468	66.20 484	99.89 98	93.16 419	94.63 479	97.66 455
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
PMVS	91.26 20	97.86 269	97.94 256	97.65 348	99.71 48	97.94 169	98.52 130	98.68 361	98.99 122	97.52 368	99.35 109	97.41 190	98.18 481	91.59 444	99.67 223	96.82 470
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 452	90.30 454	93.70 461	97.72 429	84.34 485	90.24 485	97.42 411	90.20 467	93.79 473	93.09 477	90.90 383	98.89 470	86.57 474	72.76 491	97.87 444

MVE	83.40 22	92.50 441	91.92 443	94.25 453	98.83 322	91.64 435	92.71 480	83.52 493	95.92 375	86.46 491	95.46 454	95.20 303	95.40 489	80.51 484	98.64 399	95.73 482
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 364	95.44 383	98.84 181	96.25 482	98.69 99	97.02 341	99.12 286	88.90 473	97.83 346	98.86 252	89.51 394	98.90 469	91.92 436	99.51 283	98.92 353
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
usedtu_dtu_shiyan2		98.99 90	98.86 112	99.39 73	99.73 37	98.71 98	99.05 67	99.47 151	99.16 93	99.49 95	99.12 176	96.34 259	99.93 54	98.05 160	99.36 314	99.54 142

blended_shiyan8		95.98 377	95.33 389	97.94 318	97.05 464	94.87 352	95.34 435	98.59 368	96.17 360	97.09 390	92.39 482	87.62 409	99.76 268	97.65 198	96.05 472	99.20 296

E5new		99.05 80	99.11 72	98.85 176	99.60 87	97.30 222	98.42 151	99.63 73	98.73 147	99.26 149	99.39 100	98.71 50	99.70 310	98.43 131	99.84 112	99.54 142

FE-blended-shiyan7		95.48 394	94.74 406	97.68 343	96.53 474	94.12 377	94.17 468	98.57 371	95.84 377	96.71 413	91.16 487	86.05 420	99.76 268	97.57 206	96.09 467	99.17 310

E6new		99.05 80	99.11 72	98.85 176	99.60 87	97.30 222	98.42 151	99.63 73	98.73 147	99.26 149	99.39 100	98.71 50	99.70 310	98.43 131	99.84 112	99.54 142

blended_shiyan6		95.99 376	95.33 389	97.95 317	97.06 462	94.89 350	95.34 435	98.58 369	96.17 360	97.06 392	92.41 481	87.64 408	99.76 268	97.64 199	96.09 467	99.19 302

usedtu_blend_shiyan5		96.20 370	95.62 373	97.94 318	96.53 474	94.93 348	98.83 96	99.59 91	98.89 136	96.71 413	91.16 487	86.05 420	99.73 292	96.70 283	96.09 467	99.17 310

blend_shiyan4		92.09 448	90.16 455	97.88 323	96.78 469	94.93 348	95.24 439	98.58 369	96.22 358	96.07 436	91.42 486	63.46 491	99.73 292	96.70 283	76.98 490	98.98 340

E6		99.05 80	99.11 72	98.85 176	99.60 87	97.30 222	98.42 151	99.63 73	98.73 147	99.26 149	99.39 100	98.71 50	99.70 310	98.43 131	99.84 112	99.54 142

E5		99.05 80	99.11 72	98.85 176	99.60 87	97.30 222	98.42 151	99.63 73	98.73 147	99.26 149	99.39 100	98.71 50	99.70 310	98.43 131	99.84 112	99.54 142

FE-MVSNET3		97.37 309	97.13 314	98.11 303	99.03 279	95.40 330	94.47 462	98.99 312	96.87 327	97.97 335	97.81 387	92.12 368	99.75 279	97.49 218	99.43 306	99.16 315

E4		98.87 110	98.88 105	98.81 186	99.52 127	97.23 231	97.62 275	99.61 82	98.58 167	99.18 171	99.33 116	98.29 96	99.69 318	97.99 168	99.83 123	99.52 159

E3new		98.41 199	98.34 200	98.62 230	99.19 237	96.90 261	97.32 318	99.50 132	97.40 282	98.63 267	98.92 236	97.21 205	99.65 352	97.34 224	99.52 280	99.31 264

FE-MVSNET2		99.15 58	99.22 55	98.94 162	99.70 56	97.49 206	98.62 118	99.67 64	98.85 143	99.34 128	99.54 63	98.47 76	99.81 223	98.93 93	99.91 78	99.51 163

fmvsm_s_conf0.5_n_11		99.21 48	99.34 36	98.80 189	99.48 152	96.56 280	97.97 223	99.69 54	99.63 29	99.84 30	99.54 63	98.21 111	99.94 42	99.76 23	99.95 38	99.88 20

E2		98.70 145	98.68 139	98.73 211	99.40 177	97.10 247	97.48 297	99.57 101	98.09 217	99.00 197	99.20 151	97.90 139	99.67 332	97.73 193	99.77 162	99.43 208

MED-MVS test				99.45 64	99.58 93	98.93 80	98.68 109	99.60 84	96.46 348	99.53 83	98.77 275		99.83 193	96.67 287	99.64 234	99.58 115

MED-MVS		98.90 105	98.72 128	99.45 64	99.58 93	98.93 80	98.68 109	99.60 84	98.14 214	99.53 83	98.77 275	97.87 145	99.83 193	96.67 287	99.64 234	99.58 115

E3		98.69 149	98.68 139	98.73 211	99.40 177	97.10 247	97.48 297	99.57 101	98.09 217	99.00 197	99.20 151	97.90 139	99.67 332	97.73 193	99.77 162	99.43 208

TestfortrainingZip a		98.95 98	98.72 128	99.64 9	99.58 93	99.32 22	98.68 109	99.60 84	96.46 348	99.53 83	98.77 275	97.87 145	99.83 193	98.39 136	99.64 234	99.77 50

TestfortrainingZip							98.68 109

fmvsm_s_conf0.5_n_10		99.15 58	99.27 48	98.78 196	99.47 155	96.56 280	97.75 256	99.71 47	99.60 36	99.74 47	99.44 86	97.96 135	99.95 26	99.86 4	99.94 50	99.82 36

viewdifsd2359ckpt07		98.71 140	98.86 112	98.26 287	99.43 170	95.65 314	97.20 332	99.66 65	99.20 83	99.29 141	99.01 212	98.29 96	99.73 292	97.92 173	99.75 181	99.39 226

viewdifsd2359ckpt09		98.13 243	97.92 259	98.77 201	99.18 245	97.35 217	97.29 322	99.53 123	95.81 379	98.09 324	98.47 334	96.34 259	99.66 345	97.02 248	99.51 283	99.29 270

viewdifsd2359ckpt13		98.39 208	98.29 210	98.70 215	99.26 220	97.19 238	97.51 293	99.48 142	96.94 321	98.58 278	98.82 265	97.47 188	99.55 392	97.21 233	99.33 320	99.34 251

viewcassd2359sk11		98.55 180	98.51 168	98.67 220	99.29 205	96.99 253	97.39 308	99.54 119	97.73 244	98.81 244	99.08 187	97.55 175	99.66 345	97.52 212	99.67 223	99.36 244

viewdifsd2359ckpt11		98.84 117	99.04 85	98.24 291	99.56 110	95.51 320	97.38 310	99.70 52	99.16 93	99.57 72	99.40 97	98.26 102	99.71 303	98.55 124	99.82 128	99.50 167

viewmacassd2359aftdt		98.86 114	98.87 108	98.83 182	99.53 124	97.32 221	97.70 262	99.64 71	98.22 196	99.25 157	99.27 129	98.40 84	99.61 369	97.98 169	99.87 98	99.55 136

viewmsd2359difaftdt		98.84 117	99.04 85	98.24 291	99.56 110	95.51 320	97.38 310	99.70 52	99.16 93	99.57 72	99.40 97	98.26 102	99.71 303	98.55 124	99.82 128	99.50 167

diffmvs_AUTHOR		98.50 191	98.59 158	98.23 294	99.35 192	95.48 324	96.61 366	99.60 84	98.37 180	98.90 224	99.00 216	97.37 193	99.76 268	98.22 146	99.85 107	99.46 195

FE-MVSNET		98.59 172	98.50 171	98.87 173	99.58 93	97.30 222	98.08 193	99.74 43	96.94 321	98.97 206	99.10 181	96.94 221	99.74 285	97.33 226	99.86 105	99.55 136

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 156	99.59 91	97.18 240	97.44 305	99.83 25	99.56 40	99.91 12	99.34 113	99.36 13	99.93 54	99.83 10	99.98 12	99.85 30

mamba_0408		98.80 127	98.88 105	98.55 248	99.27 211	96.50 283	98.00 211	99.60 84	98.93 130	99.22 162	98.84 260	98.59 66	99.89 98	97.74 191	99.72 193	99.27 274

icg_test_0407_2		98.20 235	98.38 194	97.65 348	99.03 279	94.03 382	95.78 418	99.45 160	98.16 208	99.06 182	98.71 287	98.27 100	99.68 328	97.50 213	99.45 298	99.22 291

SSM_04072		98.80 127	98.88 105	98.56 246	99.27 211	96.50 283	98.00 211	99.60 84	98.93 130	99.22 162	98.84 260	98.59 66	99.90 82	97.74 191	99.72 193	99.27 274

SSM_0407		98.86 114	98.96 98	98.55 248	99.27 211	96.50 283	98.04 202	99.66 65	99.09 109	99.22 162	99.02 201	98.79 42	99.87 135	97.87 179	99.72 193	99.27 274

viewmambaseed2359dif		98.19 236	98.26 215	97.99 315	99.02 285	95.03 345	96.59 368	99.53 123	96.21 359	99.00 197	98.99 218	97.62 168	99.61 369	97.62 201	99.72 193	99.33 257

IMVS_0407		98.39 208	98.64 147	97.66 346	99.03 279	94.03 382	98.10 190	99.45 160	98.16 208	99.06 182	98.71 287	98.27 100	99.71 303	97.50 213	99.45 298	99.22 291

viewmanbaseed2359cas		98.58 174	98.54 164	98.70 215	99.28 208	97.13 246	97.47 301	99.55 114	97.55 263	98.96 211	98.92 236	97.77 155	99.59 376	97.59 205	99.77 162	99.39 226

IMVS_0404		98.07 248	98.20 222	97.69 342	99.03 279	94.03 382	96.67 362	99.45 160	98.16 208	98.03 331	98.71 287	96.80 232	99.82 206	97.50 213	99.45 298	99.22 291

SSM_0404		98.90 105	99.01 90	98.57 241	99.42 172	96.59 275	98.13 183	99.66 65	99.09 109	99.30 140	99.02 201	98.79 42	99.89 98	97.87 179	99.80 145	99.23 286

IMVS_0403		98.34 212	98.56 161	97.66 346	99.03 279	94.03 382	97.98 219	99.45 160	98.16 208	98.89 227	98.71 287	97.90 139	99.74 285	97.50 213	99.45 298	99.22 291

SD_0403		96.28 365	95.83 366	97.64 351	98.72 339	94.30 370	98.87 89	98.77 350	97.80 239	96.53 422	98.02 373	97.34 195	99.47 419	76.93 488	99.48 294	99.16 315

fmvsm_s_conf0.5_n_9		99.17 53	99.38 29	98.53 255	99.51 130	95.82 310	97.62 275	99.78 36	99.72 15	99.90 14	99.48 76	98.66 58	99.89 98	99.85 6	99.93 56	99.89 16

ME-MVS		98.61 168	98.33 205	99.44 66	99.24 222	98.93 80	97.45 303	99.06 294	98.14 214	99.06 182	98.77 275	96.97 220	99.82 206	96.67 287	99.64 234	99.58 115

NormalMVS		98.26 226	97.97 253	99.15 122	99.64 76	97.83 179	98.28 165	99.43 174	99.24 76	98.80 246	98.85 255	89.76 391	99.94 42	98.04 161	99.67 223	99.68 71

lecture		99.25 41	99.12 71	99.62 10	99.64 76	99.40 12	98.89 88	99.51 129	99.19 88	99.37 121	99.25 140	98.36 87	99.88 116	98.23 145	99.67 223	99.59 107

SymmetryMVS		98.05 250	97.71 275	99.09 133	99.29 205	97.83 179	98.28 165	97.64 409	99.24 76	98.80 246	98.85 255	89.76 391	99.94 42	98.04 161	99.50 291	99.49 174

Elysia		99.15 58	99.14 69	99.18 114	99.63 82	97.92 170	98.50 137	99.43 174	99.67 21	99.70 52	99.13 173	96.66 242	99.98 4	99.54 44	99.96 28	99.64 84

StellarMVS		99.15 58	99.14 69	99.18 114	99.63 82	97.92 170	98.50 137	99.43 174	99.67 21	99.70 52	99.13 173	96.66 242	99.98 4	99.54 44	99.96 28	99.64 84

KinetiMVS		99.03 85	99.02 88	99.03 146	99.70 56	97.48 209	98.43 148	99.29 240	99.70 16	99.60 71	99.07 188	96.13 267	99.94 42	99.42 56	99.87 98	99.68 71

LuminaMVS		98.39 208	98.20 222	98.98 156	99.50 136	97.49 206	97.78 247	97.69 404	98.75 146	99.49 95	99.25 140	92.30 365	99.94 42	99.14 76	99.88 94	99.50 167

VortexMVS		97.98 259	98.31 207	97.02 390	98.88 313	91.45 438	98.03 204	99.47 151	98.65 155	99.55 77	99.47 79	91.49 376	99.81 223	99.32 61	99.91 78	99.80 42

AstraMVS		98.16 242	98.07 242	98.41 270	99.51 130	95.86 307	98.00 211	95.14 458	98.97 125	99.43 107	99.24 142	93.25 345	99.84 175	99.21 71	99.87 98	99.54 142

guyue		98.01 254	97.93 258	98.26 287	99.45 163	95.48 324	98.08 193	96.24 441	98.89 136	99.34 128	99.14 171	91.32 378	99.82 206	99.07 81	99.83 123	99.48 185

sc_t1		99.62 7	99.66 8	99.53 39	99.82 19	99.09 69	99.50 11	99.63 73	99.88 4	99.86 24	99.80 11	99.03 24	99.89 98	99.48 53	99.93 56	99.60 100

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 44	98.98 72	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 82	99.54 44	99.95 38	99.61 98

tt0320		99.61 8	99.65 9	99.48 57	99.71 48	98.94 79	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 82	99.54 44	99.95 38	99.59 107

fmvsm_s_conf0.5_n_8		99.13 67	99.26 51	98.74 209	99.51 130	96.44 287	97.65 270	99.65 69	99.66 24	99.78 40	99.48 76	97.92 138	99.93 54	99.72 30	99.95 38	99.87 22

fmvsm_s_conf0.5_n_7		98.83 120	99.04 85	98.20 296	99.30 202	94.83 353	97.23 327	99.36 199	98.64 156	99.84 30	99.43 89	98.10 123	99.91 75	99.56 41	99.96 28	99.87 22

fmvsm_s_conf0.5_n_6		99.08 77	99.21 58	98.69 217	99.36 187	96.51 282	97.62 275	99.68 60	98.43 178	99.85 27	99.10 181	99.12 23	99.88 116	99.77 22	99.92 69	99.67 76

fmvsm_s_conf0.5_n_5		99.07 79	99.10 78	98.99 152	99.47 155	97.22 234	97.40 307	99.83 25	97.61 255	99.85 27	99.30 123	98.80 40	99.95 26	99.71 32	99.90 86	99.78 47

fmvsm_s_conf0.5_n_4		99.01 87	99.22 55	98.38 274	99.31 198	95.48 324	97.56 286	99.73 44	98.87 138	99.75 45	99.27 129	98.80 40	99.86 144	99.80 17	99.90 86	99.81 40

SSC-MVS3.2		98.53 185	98.79 120	97.74 337	99.46 158	93.62 405	96.45 375	99.34 211	99.33 66	98.93 220	98.70 294	97.90 139	99.90 82	99.12 77	99.92 69	99.69 70

testing3-2		93.78 422	93.91 414	93.39 465	98.82 325	81.72 492	97.76 253	95.28 456	98.60 163	96.54 421	96.66 427	65.85 486	99.62 362	96.65 291	98.99 372	98.82 366

myMVS_eth3d28		92.92 437	92.31 433	94.77 448	97.84 424	87.59 471	96.19 393	96.11 444	97.08 313	94.27 464	93.49 475	66.07 485	98.78 472	91.78 439	97.93 432	97.92 441

UWE-MVS-28		90.22 453	89.28 456	93.02 469	94.50 490	82.87 488	96.52 372	87.51 488	95.21 398	92.36 481	96.04 438	71.57 472	98.25 480	72.04 490	97.77 434	97.94 440

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 84	99.59 91	98.21 137	97.82 241	99.84 22	99.41 58	99.92 8	99.41 94	99.51 8	99.95 26	99.84 9	99.97 21	99.87 22

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 196	99.46 158	96.58 278	97.65 270	99.72 45	99.47 48	99.86 24	99.50 69	98.94 30	99.89 98	99.75 26	99.97 21	99.86 28

fmvsm_s_conf0.5_n_2		99.14 63	99.31 42	98.63 228	99.49 144	96.08 300	97.38 310	99.81 31	99.48 45	99.84 30	99.57 49	98.46 80	99.89 98	99.82 12	99.97 21	99.91 13

fmvsm_s_conf0.1_n_2		99.20 51	99.38 29	98.65 222	99.69 60	96.08 300	97.49 296	99.90 11	99.53 42	99.88 21	99.64 37	98.51 75	99.90 82	99.83 10	99.98 12	99.97 4

GDP-MVS		97.50 295	97.11 315	98.67 220	99.02 285	96.85 263	98.16 180	99.71 47	98.32 186	98.52 289	98.54 321	83.39 442	99.95 26	98.79 102	99.56 267	99.19 302

BP-MVS1		97.40 307	96.97 321	98.71 214	99.07 267	96.81 265	98.34 163	97.18 419	98.58 167	98.17 313	98.61 314	84.01 438	99.94 42	98.97 90	99.78 156	99.37 237

reproduce_monomvs		95.00 404	95.25 392	94.22 454	97.51 449	83.34 486	97.86 237	98.44 378	98.51 174	99.29 141	99.30 123	67.68 479	99.56 388	98.89 97	99.81 134	99.77 50

mmtdpeth		99.30 34	99.42 25	98.92 168	99.58 93	96.89 262	99.48 13	99.92 7	99.92 2	98.26 310	99.80 11	98.33 93	99.91 75	99.56 41	99.95 38	99.97 4

reproduce_model		99.15 58	98.97 96	99.67 4	99.33 196	99.44 10	98.15 181	99.47 151	99.12 98	99.52 88	99.32 121	98.31 94	99.90 82	97.78 185	99.73 185	99.66 78

reproduce-ours		99.09 73	98.90 102	99.67 4	99.27 211	99.49 6	98.00 211	99.42 180	99.05 116	99.48 97	99.27 129	98.29 96	99.89 98	97.61 202	99.71 202	99.62 90

our_new_method		99.09 73	98.90 102	99.67 4	99.27 211	99.49 6	98.00 211	99.42 180	99.05 116	99.48 97	99.27 129	98.29 96	99.89 98	97.61 202	99.71 202	99.62 90

mmdepth		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

monomultidepth		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

mvs5depth		99.30 34	99.59 12	98.44 267	99.65 70	95.35 332	99.82 3	99.94 2	99.83 7	99.42 111	99.94 2	98.13 121	99.96 14	99.63 36	99.96 28	100.00 1

MVStest1		95.86 381	95.60 375	96.63 408	95.87 486	91.70 434	97.93 225	98.94 315	98.03 220	99.56 74	99.66 32	71.83 471	98.26 479	99.35 59	99.24 336	99.91 13

ttmdpeth		97.91 261	98.02 246	97.58 357	98.69 352	94.10 378	98.13 183	98.90 324	97.95 226	97.32 383	99.58 47	95.95 282	98.75 473	96.41 314	99.22 340	99.87 22

WBMVS		95.18 399	94.78 404	96.37 414	97.68 437	89.74 461	95.80 417	98.73 358	97.54 265	98.30 304	98.44 337	70.06 473	99.82 206	96.62 293	99.87 98	99.54 142

dongtai		76.24 457	75.95 460	77.12 474	92.39 492	67.91 498	90.16 486	59.44 499	82.04 485	89.42 487	94.67 467	49.68 496	81.74 492	48.06 492	77.66 489	81.72 488

kuosan		69.30 458	68.95 461	70.34 475	87.68 496	65.00 499	91.11 484	59.90 498	69.02 488	74.46 493	88.89 490	48.58 497	68.03 494	28.61 493	72.33 492	77.99 489

MVSMamba_PlusPlus		98.83 120	98.98 95	98.36 278	99.32 197	96.58 278	98.90 84	99.41 184	99.75 11	98.72 257	99.50 69	96.17 265	99.94 42	99.27 65	99.78 156	98.57 401

MGCFI-Net		98.34 212	98.28 211	98.51 257	98.47 385	97.59 202	98.96 78	99.48 142	99.18 91	97.40 378	95.50 451	98.66 58	99.50 410	98.18 149	98.71 392	98.44 411

testing91		93.32 429	92.27 434	96.47 412	97.54 442	91.25 445	96.17 397	96.76 433	97.18 307	93.65 475	93.50 474	65.11 488	99.63 359	93.04 420	97.45 441	98.53 402

testing11		93.08 434	92.02 439	96.26 419	97.56 440	90.83 453	96.32 385	95.70 452	96.47 347	92.66 479	93.73 471	64.36 489	99.59 376	93.77 406	97.57 437	98.37 420

testing99		93.04 435	91.98 442	96.23 421	97.53 444	90.70 455	96.35 383	95.94 448	96.87 327	93.41 476	93.43 476	63.84 490	99.59 376	93.24 418	97.19 451	98.40 416

UBG		93.25 431	92.32 432	96.04 428	97.72 429	90.16 458	95.92 411	95.91 449	96.03 370	93.95 472	93.04 478	69.60 475	99.52 404	90.72 459	97.98 430	98.45 408

UWE-MVS		92.38 443	91.76 446	94.21 455	97.16 458	84.65 481	95.42 432	88.45 487	95.96 373	96.17 432	95.84 446	66.36 482	99.71 303	91.87 438	98.64 399	98.28 423

ETVMVS		92.60 440	91.08 449	97.18 382	97.70 434	93.65 404	96.54 369	95.70 452	96.51 343	94.68 460	92.39 482	61.80 492	99.50 410	86.97 471	97.41 444	98.40 416

sasdasda		98.34 212	98.26 215	98.58 238	98.46 387	97.82 184	98.96 78	99.46 156	99.19 88	97.46 373	95.46 454	98.59 66	99.46 422	98.08 156	98.71 392	98.46 405

testing222		91.96 449	90.37 452	96.72 407	97.47 451	92.59 420	96.11 399	94.76 460	96.83 330	92.90 478	92.87 479	57.92 493	99.55 392	86.93 472	97.52 438	98.00 438

WB-MVSnew		95.73 386	95.57 378	96.23 421	96.70 471	90.70 455	96.07 401	93.86 470	95.60 385	97.04 394	95.45 457	96.00 274	99.55 392	91.04 453	98.31 411	98.43 413

fmvsm_l_conf0.5_n_a		99.19 52	99.27 48	98.94 162	99.65 70	97.05 249	97.80 245	99.76 39	98.70 154	99.78 40	99.11 178	98.79 42	99.95 26	99.85 6	99.96 28	99.83 33

fmvsm_l_conf0.5_n		99.21 48	99.28 47	99.02 149	99.64 76	97.28 228	97.82 241	99.76 39	98.73 147	99.82 34	99.09 186	98.81 38	99.95 26	99.86 4	99.96 28	99.83 33

fmvsm_s_conf0.1_n_a		99.17 53	99.30 45	98.80 189	99.75 34	96.59 275	97.97 223	99.86 16	98.22 196	99.88 21	99.71 22	98.59 66	99.84 175	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 57	99.33 38	98.64 224	99.71 48	96.10 295	97.87 236	99.85 18	98.56 172	99.90 14	99.68 25	98.69 56	99.85 157	99.72 30	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 72	99.20 59	98.78 196	99.55 116	96.59 275	97.79 246	99.82 30	98.21 198	99.81 37	99.53 65	98.46 80	99.84 175	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 73	99.26 51	98.61 234	99.55 116	96.09 298	97.74 257	99.81 31	98.55 173	99.85 27	99.55 57	98.60 65	99.84 175	99.69 35	99.98 12	99.89 16

MM		98.22 231	97.99 249	98.91 169	98.66 362	96.97 254	97.89 232	94.44 463	99.54 41	98.95 212	99.14 171	93.50 344	99.92 66	99.80 17	99.96 28	99.85 30

WAC-MVS						90.90 451								91.37 448

Syy-MVS		96.04 373	95.56 379	97.49 368	97.10 460	94.48 365	96.18 395	96.58 436	95.65 383	94.77 458	92.29 484	91.27 379	99.36 436	98.17 151	98.05 427	98.63 395

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 84	99.78 24	98.11 144	97.77 250	99.90 11	99.33 66	99.97 3	99.66 32	99.71 3	99.96 14	99.79 19	99.99 5	99.96 8

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 75	99.87 12	98.13 143	98.08 193	99.95 1	99.45 51	99.98 2	99.75 16	99.80 1	99.97 7	99.82 12	99.99 5	99.99 2

myMVS_eth3d		91.92 450	90.45 451	96.30 416	97.10 460	90.90 451	96.18 395	96.58 436	95.65 383	94.77 458	92.29 484	53.88 494	99.36 436	89.59 464	98.05 427	98.63 395

testing3		93.51 426	92.09 437	97.75 335	98.60 369	94.40 367	97.32 318	95.26 457	97.56 261	96.79 411	95.50 451	53.57 495	99.77 262	95.26 363	98.97 376	99.08 322

SSC-MVS		98.71 140	98.74 124	98.62 230	99.72 44	96.08 300	98.74 99	98.64 365	99.74 13	99.67 60	99.24 142	94.57 322	99.95 26	99.11 78	99.24 336	99.82 36

test_fmvsmconf_n		99.44 19	99.48 18	99.31 95	99.64 76	98.10 146	97.68 264	99.84 22	99.29 72	99.92 8	99.57 49	99.60 5	99.96 14	99.74 27	99.98 12	99.89 16

WB-MVS		98.52 189	98.55 162	98.43 268	99.65 70	95.59 315	98.52 130	98.77 350	99.65 26	99.52 88	99.00 216	94.34 328	99.93 54	98.65 115	98.83 384	99.76 56

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 253	99.66 69	96.97 254	98.00 211	99.85 18	99.24 76	99.92 8	99.50 69	99.39 12	99.95 26	99.89 3	99.98 12	98.71 385

dmvs_re		95.98 377	95.39 386	97.74 337	98.86 316	97.45 212	98.37 159	95.69 454	97.95 226	96.56 420	95.95 441	90.70 384	97.68 484	88.32 467	96.13 466	98.11 430

SDMVSNet		99.23 46	99.32 40	98.96 159	99.68 63	97.35 217	98.84 95	99.48 142	99.69 18	99.63 67	99.68 25	99.03 24	99.96 14	97.97 170	99.92 69	99.57 123

dmvs_testset		92.94 436	92.21 436	95.13 445	98.59 372	90.99 450	97.65 270	92.09 478	96.95 320	94.00 470	93.55 473	92.34 364	96.97 487	72.20 489	92.52 484	97.43 462

sd_testset		99.28 37	99.31 42	99.19 113	99.68 63	98.06 156	99.41 17	99.30 232	99.69 18	99.63 67	99.68 25	99.25 16	99.96 14	97.25 231	99.92 69	99.57 123

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 152	99.57 102	97.73 194	97.93 225	99.83 25	99.22 79	99.93 6	99.30 123	99.42 11	99.96 14	99.85 6	99.99 5	99.29 270

test_cas_vis1_n_1920		98.33 216	98.68 139	97.27 379	99.69 60	92.29 428	98.03 204	99.85 18	97.62 252	99.96 4	99.62 40	93.98 337	99.74 285	99.52 50	99.86 105	99.79 44

test_vis1_n_1920		98.40 202	98.92 100	96.81 403	99.74 36	90.76 454	98.15 181	99.91 9	98.33 184	99.89 18	99.55 57	95.07 307	99.88 116	99.76 23	99.93 56	99.79 44

test_vis1_n		98.31 219	98.50 171	97.73 340	99.76 30	94.17 375	98.68 109	99.91 9	96.31 355	99.79 39	99.57 49	92.85 357	99.42 429	99.79 19	99.84 112	99.60 100

test_fmvs1_n		98.09 246	98.28 211	97.52 365	99.68 63	93.47 407	98.63 116	99.93 5	95.41 394	99.68 58	99.64 37	91.88 372	99.48 416	99.82 12	99.87 98	99.62 90

mvsany_test1		97.60 289	97.54 287	97.77 331	97.72 429	95.35 332	95.36 434	97.13 422	94.13 423	99.71 50	99.33 116	97.93 137	99.30 446	97.60 204	98.94 379	98.67 393

APD_test1		98.83 120	98.66 144	99.34 84	99.78 24	99.47 9	98.42 151	99.45 160	98.28 193	98.98 202	99.19 154	97.76 156	99.58 383	96.57 298	99.55 271	98.97 344

test_vis1_rt		97.75 279	97.72 274	97.83 326	98.81 328	96.35 290	97.30 321	99.69 54	94.61 410	97.87 342	98.05 371	96.26 263	98.32 478	98.74 108	98.18 416	98.82 366

test_vis3_rt		99.14 63	99.17 61	99.07 136	99.78 24	98.38 120	98.92 83	99.94 2	97.80 239	99.91 12	99.67 30	97.15 208	98.91 468	99.76 23	99.56 267	99.92 12

test_fmvs2		98.70 145	98.97 96	97.89 322	99.54 121	94.05 379	98.55 126	99.92 7	96.78 333	99.72 48	99.78 13	96.60 246	99.67 332	99.91 2	99.90 86	99.94 10

test_fmvs1		97.72 281	97.94 256	97.07 389	98.66 362	92.39 425	97.68 264	99.81 31	95.20 399	99.54 79	99.44 86	91.56 375	99.41 430	99.78 21	99.77 162	99.40 225

test_fmvs3		99.12 70	99.41 26	98.25 289	99.76 30	95.07 344	99.05 67	99.94 2	97.78 242	99.82 34	99.84 3	98.56 72	99.71 303	99.96 1	99.96 28	99.97 4

mvsany_test3		98.87 110	98.92 100	98.74 209	99.38 180	96.94 258	98.58 123	99.10 289	96.49 345	99.96 4	99.81 8	98.18 114	99.45 424	98.97 90	99.79 151	99.83 33

testf1		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 106	99.69 54	98.90 134	99.43 107	99.35 109	98.86 34	99.67 332	97.81 182	99.81 134	99.24 284

APD_test2		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 106	99.69 54	98.90 134	99.43 107	99.35 109	98.86 34	99.67 332	97.81 182	99.81 134	99.24 284

test_f		98.67 158	98.87 108	98.05 311	99.72 44	95.59 315	98.51 135	99.81 31	96.30 357	99.78 40	99.82 5	96.14 266	98.63 475	99.82 12	99.93 56	99.95 9

FE-MVS		95.66 388	94.95 401	97.77 331	98.53 381	95.28 335	99.40 19	96.09 445	93.11 438	97.96 336	99.26 135	79.10 460	99.77 262	92.40 434	98.71 392	98.27 424

FA-MVS(test-final)		96.99 340	96.82 333	97.50 367	98.70 347	94.78 355	99.34 23	96.99 425	95.07 400	98.48 292	99.33 116	88.41 405	99.65 352	96.13 333	98.92 381	98.07 433

balanced_conf03		98.63 164	98.72 128	98.38 274	98.66 362	96.68 274	98.90 84	99.42 180	98.99 122	98.97 206	99.19 154	95.81 287	99.85 157	98.77 106	99.77 162	98.60 397

MonoMVSNet		96.25 367	96.53 353	95.39 442	96.57 473	91.01 449	98.82 97	97.68 406	98.57 169	98.03 331	99.37 104	90.92 382	97.78 483	94.99 367	93.88 482	97.38 463

patch_mono-2		98.51 190	98.63 149	98.17 299	99.38 180	94.78 355	97.36 315	99.69 54	98.16 208	98.49 291	99.29 126	97.06 212	99.97 7	98.29 142	99.91 78	99.76 56

EGC-MVSNET		85.24 454	80.54 457	99.34 84	99.77 27	99.20 40	99.08 61	99.29 240	12.08 493	20.84 494	99.42 90	97.55 175	99.85 157	97.08 244	99.72 193	98.96 346

test2506		92.39 442	91.89 444	93.89 459	99.38 180	82.28 490	99.32 26	66.03 497	99.08 113	98.77 251	99.57 49	66.26 483	99.84 175	98.71 111	99.95 38	99.54 142

test1111		96.49 359	96.82 333	95.52 438	99.42 172	87.08 473	99.22 45	87.14 489	99.11 99	99.46 102	99.58 47	88.69 399	99.86 144	98.80 101	99.95 38	99.62 90

ECVR-MVS		96.42 361	96.61 347	95.85 430	99.38 180	88.18 468	99.22 45	86.00 491	99.08 113	99.36 124	99.57 49	88.47 404	99.82 206	98.52 126	99.95 38	99.54 142

test_blank		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

tt0805		98.69 149	98.62 151	98.90 172	99.75 34	99.30 23	99.15 56	96.97 426	98.86 140	98.87 235	97.62 399	98.63 62	98.96 465	99.41 57	98.29 412	98.45 408

DVP-MVS++		98.90 105	98.70 136	99.51 49	98.43 391	99.15 53	99.43 15	99.32 219	98.17 205	99.26 149	99.02 201	98.18 114	99.88 116	97.07 245	99.45 298	99.49 174

FOURS1					99.73 37	99.67 3	99.43 15	99.54 119	99.43 55	99.26 149

MSC_two_6792asdad				99.32 92	98.43 391	98.37 122		98.86 335					99.89 98	97.14 239	99.60 251	99.71 63

PC_three_1452									93.27 435	99.40 116	98.54 321	98.22 109	97.00 486	95.17 364	99.45 298	99.49 174

No_MVS				99.32 92	98.43 391	98.37 122		98.86 335					99.89 98	97.14 239	99.60 251	99.71 63

test_one_0601					99.39 179	99.20 40		99.31 224	98.49 175	98.66 264	99.02 201	97.64 166

eth-test2					0.00 501

eth-test					0.00 501

GeoE		99.05 80	98.99 94	99.25 105	99.44 165	98.35 126	98.73 103	99.56 110	98.42 179	98.91 223	98.81 268	98.94 30	99.91 75	98.35 138	99.73 185	99.49 174

test_method		79.78 455	79.50 458	80.62 472	80.21 497	45.76 500	70.82 489	98.41 382	31.08 492	80.89 492	97.71 392	84.85 429	97.37 485	91.51 446	80.03 488	98.75 382

Anonymous20240521		98.69 149	98.87 108	98.16 301	99.77 27	95.11 343	99.08 61	99.44 168	99.34 65	99.33 131	99.55 57	94.10 336	99.94 42	99.25 68	99.96 28	99.42 213

h-mvs33		97.77 278	97.33 302	99.10 129	99.21 230	97.84 178	98.35 161	98.57 371	99.11 99	98.58 278	99.02 201	88.65 402	99.96 14	98.11 153	96.34 462	99.49 174

hse-mvs2		97.46 300	97.07 316	98.64 224	98.73 337	97.33 219	97.45 303	97.64 409	99.11 99	98.58 278	97.98 376	88.65 402	99.79 245	98.11 153	97.39 445	98.81 371

CL-MVSNet_self_test		97.44 303	97.22 307	98.08 307	98.57 376	95.78 312	94.30 466	98.79 347	96.58 342	98.60 274	98.19 360	94.74 320	99.64 356	96.41 314	98.84 383	98.82 366

KD-MVS_2432*1600		92.87 438	91.99 440	95.51 439	91.37 493	89.27 462	94.07 469	98.14 392	95.42 391	97.25 385	96.44 433	67.86 477	99.24 452	91.28 449	96.08 470	98.02 435

KD-MVS_self_test		99.25 41	99.18 60	99.44 66	99.63 82	99.06 71	98.69 108	99.54 119	99.31 69	99.62 70	99.53 65	97.36 194	99.86 144	99.24 70	99.71 202	99.39 226

AUN-MVS		96.24 369	95.45 382	98.60 236	98.70 347	97.22 234	97.38 310	97.65 407	95.95 374	95.53 450	97.96 380	82.11 450	99.79 245	96.31 320	97.44 442	98.80 376

ZD-MVS					99.01 287	98.84 86		99.07 293	94.10 424	98.05 329	98.12 364	96.36 258	99.86 144	92.70 430	99.19 347

SR-MVS-dyc-post		98.81 125	98.55 162	99.57 22	99.20 234	99.38 13	98.48 143	99.30 232	98.64 156	98.95 212	98.96 228	97.49 186	99.86 144	96.56 302	99.39 310	99.45 200

RE-MVS-def			98.58 159		99.20 234	99.38 13	98.48 143	99.30 232	98.64 156	98.95 212	98.96 228	97.75 157		96.56 302	99.39 310	99.45 200

SED-MVS		98.91 103	98.72 128	99.49 55	99.49 144	99.17 45	98.10 190	99.31 224	98.03 220	99.66 61	99.02 201	98.36 87	99.88 116	96.91 258	99.62 244	99.41 216

IU-MVS					99.49 144	99.15 53		98.87 330	92.97 439	99.41 113				96.76 275	99.62 244	99.66 78

OPU-MVS				98.82 184	98.59 372	98.30 127	98.10 190				98.52 325	98.18 114	98.75 473	94.62 377	99.48 294	99.41 216

test_241102_TWO								99.30 232	98.03 220	99.26 149	99.02 201	97.51 182	99.88 116	96.91 258	99.60 251	99.66 78

test_241102_ONE					99.49 144	99.17 45		99.31 224	97.98 223	99.66 61	98.90 242	98.36 87	99.48 416

SF-MVS		98.53 185	98.27 214	99.32 92	99.31 198	98.75 91	98.19 175	99.41 184	96.77 334	98.83 239	98.90 242	97.80 153	99.82 206	95.68 353	99.52 280	99.38 235

cl22		95.79 384	95.39 386	96.98 393	96.77 470	92.79 417	94.40 464	98.53 374	94.59 411	97.89 340	98.17 361	82.82 447	99.24 452	96.37 316	99.03 365	98.92 353

miper_ehance_all_eth		97.06 333	97.03 318	97.16 386	97.83 425	93.06 411	94.66 455	99.09 291	95.99 372	98.69 259	98.45 336	92.73 360	99.61 369	96.79 271	99.03 365	98.82 366

miper_enhance_ethall		96.01 374	95.74 368	96.81 403	96.41 480	92.27 429	93.69 476	98.89 327	91.14 461	98.30 304	97.35 415	90.58 385	99.58 383	96.31 320	99.03 365	98.60 397

ZNCC-MVS		98.68 155	98.40 189	99.54 32	99.57 102	99.21 34	98.46 145	99.29 240	97.28 294	98.11 322	98.39 341	98.00 130	99.87 135	96.86 268	99.64 234	99.55 136

dcpmvs_2		98.78 131	99.11 72	97.78 330	99.56 110	93.67 402	99.06 65	99.86 16	99.50 44	99.66 61	99.26 135	97.21 205	99.99 2	98.00 166	99.91 78	99.68 71

cl____		97.02 336	96.83 332	97.58 357	97.82 426	94.04 381	94.66 455	99.16 279	97.04 315	98.63 267	98.71 287	88.68 401	99.69 318	97.00 250	99.81 134	99.00 338

DIV-MVS_self_test		97.02 336	96.84 331	97.58 357	97.82 426	94.03 382	94.66 455	99.16 279	97.04 315	98.63 267	98.71 287	88.69 399	99.69 318	97.00 250	99.81 134	99.01 334

eth_miper_zixun_eth		97.23 322	97.25 305	97.17 384	98.00 418	92.77 418	94.71 452	99.18 272	97.27 295	98.56 282	98.74 283	91.89 371	99.69 318	97.06 247	99.81 134	99.05 326

9.14			97.78 268		99.07 267		97.53 290	99.32 219	95.53 388	98.54 286	98.70 294	97.58 172	99.76 268	94.32 390	99.46 296

uanet_test		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

DCPMVS		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

save fliter					99.11 258	97.97 164	96.53 371	99.02 306	98.24 194

ET-MVSNet_ETH3D		94.30 413	93.21 424	97.58 357	98.14 411	94.47 366	94.78 451	93.24 474	94.72 408	89.56 486	95.87 444	78.57 463	99.81 223	96.91 258	97.11 454	98.46 405

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 20	99.69 5	99.58 94	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 88	99.95 38	99.78 47

EIA-MVS		98.00 255	97.74 271	98.80 189	98.72 339	98.09 147	98.05 200	99.60 84	97.39 283	96.63 417	95.55 449	97.68 160	99.80 232	96.73 279	99.27 331	98.52 403

miper_refine_blended		92.87 438	91.99 440	95.51 439	91.37 493	89.27 462	94.07 469	98.14 392	95.42 391	97.25 385	96.44 433	67.86 477	99.24 452	91.28 449	96.08 470	98.02 435

miper_lstm_enhance		97.18 326	97.16 310	97.25 381	98.16 409	92.85 416	95.15 443	99.31 224	97.25 297	98.74 256	98.78 273	90.07 388	99.78 256	97.19 234	99.80 145	99.11 321

ETV-MVS		98.03 251	97.86 265	98.56 246	98.69 352	98.07 153	97.51 293	99.50 132	98.10 216	97.50 370	95.51 450	98.41 83	99.88 116	96.27 323	99.24 336	97.71 454

CS-MVS		99.13 67	99.10 78	99.24 107	99.06 272	99.15 53	99.36 22	99.88 14	99.36 64	98.21 312	98.46 335	98.68 57	99.93 54	99.03 86	99.85 107	98.64 394

D2MVS		97.84 275	97.84 266	97.83 326	99.14 254	94.74 357	96.94 346	98.88 328	95.84 377	98.89 227	98.96 228	94.40 326	99.69 318	97.55 207	99.95 38	99.05 326

DVP-MVS		98.77 134	98.52 167	99.52 45	99.50 136	99.21 34	98.02 207	98.84 339	97.97 224	99.08 180	99.02 201	97.61 170	99.88 116	96.99 252	99.63 241	99.48 185
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									98.17 205	99.08 180	99.02 201	97.89 143	99.88 116	97.07 245	99.71 202	99.70 68

test_0728_SECOND				99.60 16	99.50 136	99.23 32	98.02 207	99.32 219					99.88 116	96.99 252	99.63 241	99.68 71

test0726					99.50 136	99.21 34	98.17 179	99.35 205	97.97 224	99.26 149	99.06 189	97.61 170

SR-MVS		98.71 140	98.43 185	99.57 22	99.18 245	99.35 17	98.36 160	99.29 240	98.29 191	98.88 231	98.85 255	97.53 179	99.87 135	96.14 331	99.31 324	99.48 185

DPM-MVS		96.32 363	95.59 377	98.51 257	98.76 333	97.21 236	94.54 461	98.26 386	91.94 451	96.37 429	97.25 416	93.06 352	99.43 427	91.42 447	98.74 388	98.89 358

GST-MVS		98.61 168	98.30 208	99.52 45	99.51 130	99.20 40	98.26 169	99.25 253	97.44 279	98.67 262	98.39 341	97.68 160	99.85 157	96.00 335	99.51 283	99.52 159

test_yl		96.69 349	96.29 359	97.90 320	98.28 401	95.24 336	97.29 322	97.36 413	98.21 198	98.17 313	97.86 383	86.27 415	99.55 392	94.87 371	98.32 409	98.89 358

thisisatest0530		95.27 397	94.45 408	97.74 337	99.19 237	94.37 368	97.86 237	90.20 484	97.17 308	98.22 311	97.65 396	73.53 470	99.90 82	96.90 263	99.35 317	98.95 347

Anonymous20240529		98.93 101	98.87 108	99.12 125	99.19 237	98.22 136	99.01 71	98.99 312	99.25 75	99.54 79	99.37 104	97.04 213	99.80 232	97.89 174	99.52 280	99.35 249

Anonymous202405211		97.90 262	97.50 290	99.08 134	98.90 307	98.25 130	98.53 129	96.16 442	98.87 138	99.11 175	98.86 252	90.40 387	99.78 256	97.36 223	99.31 324	99.19 302

DCV-MVSNet		96.69 349	96.29 359	97.90 320	98.28 401	95.24 336	97.29 322	97.36 413	98.21 198	98.17 313	97.86 383	86.27 415	99.55 392	94.87 371	98.32 409	98.89 358

tttt0517		95.64 389	94.98 399	97.64 351	99.36 187	93.81 397	98.72 104	90.47 483	98.08 219	98.67 262	98.34 348	73.88 469	99.92 66	97.77 186	99.51 283	99.20 296

our_test_3		97.39 308	97.73 273	96.34 415	98.70 347	89.78 460	94.61 458	98.97 314	96.50 344	99.04 192	98.85 255	95.98 279	99.84 175	97.26 230	99.67 223	99.41 216

thisisatest0515		94.12 417	93.16 425	96.97 394	98.60 369	92.90 415	93.77 475	90.61 482	94.10 424	96.91 401	95.87 444	74.99 468	99.80 232	94.52 380	99.12 358	98.20 426

ppachtmachnet_test		97.50 295	97.74 271	96.78 405	98.70 347	91.23 447	94.55 460	99.05 298	96.36 352	99.21 165	98.79 271	96.39 254	99.78 256	96.74 277	99.82 128	99.34 251

SMA-MVS		98.40 202	98.03 245	99.51 49	99.16 249	99.21 34	98.05 200	99.22 261	94.16 422	98.98 202	99.10 181	97.52 181	99.79 245	96.45 312	99.64 234	99.53 156
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
GSMVS																98.81 371

DPE-MVS		98.59 172	98.26 215	99.57 22	99.27 211	99.15 53	97.01 342	99.39 189	97.67 248	99.44 106	98.99 218	97.53 179	99.89 98	95.40 361	99.68 217	99.66 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.36 187	99.10 66				99.05 190

thres100view900		94.19 414	93.67 419	95.75 433	99.06 272	91.35 441	98.03 204	94.24 467	98.33 184	97.40 378	94.98 462	79.84 454	99.62 362	83.05 479	98.08 424	96.29 474

tfpnnormal		98.90 105	98.90 102	98.91 169	99.67 67	97.82 184	99.00 73	99.44 168	99.45 51	99.51 93	99.24 142	98.20 113	99.86 144	95.92 339	99.69 212	99.04 330

tfpn200view9		94.03 418	93.44 421	95.78 432	98.93 299	91.44 439	97.60 281	94.29 465	97.94 228	97.10 388	94.31 469	79.67 456	99.62 362	83.05 479	98.08 424	96.29 474

c3_l		97.36 310	97.37 298	97.31 376	98.09 414	93.25 409	95.01 446	99.16 279	97.05 314	98.77 251	98.72 286	92.88 355	99.64 356	96.93 257	99.76 177	99.05 326

CHOSEN 280x420		95.51 393	95.47 380	95.65 436	98.25 403	88.27 467	93.25 478	98.88 328	93.53 432	94.65 461	97.15 419	86.17 417	99.93 54	97.41 221	99.93 56	98.73 384

CANet		97.87 268	97.76 269	98.19 298	97.75 428	95.51 320	96.76 357	99.05 298	97.74 243	96.93 398	98.21 358	95.59 293	99.89 98	97.86 181	99.93 56	99.19 302

Fast-Effi-MVS+-dtu		98.27 224	98.09 237	98.81 186	98.43 391	98.11 144	97.61 280	99.50 132	98.64 156	97.39 380	97.52 404	98.12 122	99.95 26	96.90 263	98.71 392	98.38 418

Effi-MVS+-dtu		98.26 226	97.90 262	99.35 81	98.02 417	99.49 6	98.02 207	99.16 279	98.29 191	97.64 357	97.99 375	96.44 253	99.95 26	96.66 290	98.93 380	98.60 397

CANet_DTU		97.26 318	97.06 317	97.84 325	97.57 439	94.65 362	96.19 393	98.79 347	97.23 303	95.14 455	98.24 355	93.22 347	99.84 175	97.34 224	99.84 112	99.04 330

MGCNet		97.44 303	97.01 320	98.72 213	96.42 479	96.74 270	97.20 332	91.97 479	98.46 177	98.30 304	98.79 271	92.74 359	99.91 75	99.30 63	99.94 50	99.52 159

MP-MVS-pluss		98.57 175	98.23 220	99.60 16	99.69 60	99.35 17	97.16 337	99.38 191	94.87 406	98.97 206	98.99 218	98.01 129	99.88 116	97.29 228	99.70 209	99.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 202	98.00 248	99.61 14	99.57 102	99.25 30	98.57 124	99.35 205	97.55 263	99.31 139	97.71 392	94.61 321	99.88 116	96.14 331	99.19 347	99.70 68
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
sam_mvs1												84.74 431				98.81 371

sam_mvs												84.29 437

IterMVS-SCA-FT		97.85 274	98.18 227	96.87 399	99.27 211	91.16 448	95.53 426	99.25 253	99.10 106	99.41 113	99.35 109	93.10 350	99.96 14	98.65 115	99.94 50	99.49 174

TSAR-MVS + MP.		98.63 164	98.49 176	99.06 142	99.64 76	97.90 173	98.51 135	98.94 315	96.96 319	99.24 159	98.89 248	97.83 148	99.81 223	96.88 265	99.49 293	99.48 185
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		97.86 269	98.17 228	96.92 396	98.98 292	93.91 392	96.45 375	99.17 276	97.85 236	98.41 298	97.14 420	98.47 76	99.92 66	98.02 163	99.05 361	96.92 467

OPM-MVS		98.56 176	98.32 206	99.25 105	99.41 175	98.73 95	97.13 339	99.18 272	97.10 312	98.75 254	98.92 236	98.18 114	99.65 352	96.68 286	99.56 267	99.37 237
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 136	98.48 177	99.57 22	99.58 93	99.29 25	97.82 241	99.25 253	96.94 321	98.78 248	99.12 176	98.02 128	99.84 175	97.13 241	99.67 223	99.59 107

ambc				98.24 291	98.82 325	95.97 304	98.62 118	99.00 311		99.27 145	99.21 149	96.99 218	99.50 410	96.55 305	99.50 291	99.26 280

MTGPA								99.20 264

SPE-MVS-test		99.13 67	99.09 80	99.26 102	99.13 256	98.97 74	99.31 30	99.88 14	99.44 53	98.16 316	98.51 326	98.64 60	99.93 54	98.91 94	99.85 107	98.88 361

Effi-MVS+		98.02 252	97.82 267	98.62 230	98.53 381	97.19 238	97.33 317	99.68 60	97.30 292	96.68 415	97.46 408	98.56 72	99.80 232	96.63 292	98.20 415	98.86 363

xiu_mvs_v2_base		97.16 328	97.49 291	96.17 424	98.54 379	92.46 423	95.45 430	98.84 339	97.25 297	97.48 372	96.49 430	98.31 94	99.90 82	96.34 319	98.68 397	96.15 478

xiu_mvs_v1_base		97.86 269	98.17 228	96.92 396	98.98 292	93.91 392	96.45 375	99.17 276	97.85 236	98.41 298	97.14 420	98.47 76	99.92 66	98.02 163	99.05 361	96.92 467

new-patchmatchnet		98.35 211	98.74 124	97.18 382	99.24 222	92.23 430	96.42 379	99.48 142	98.30 188	99.69 56	99.53 65	97.44 189	99.82 206	98.84 100	99.77 162	99.49 174

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 28	99.53 9	99.76 39	99.64 27	99.84 30	99.83 4	99.50 9	99.87 135	99.36 58	99.92 69	99.64 84

pmmvs5		97.64 287	97.49 291	98.08 307	99.14 254	95.12 342	96.70 361	99.05 298	93.77 429	98.62 270	98.83 262	93.23 346	99.75 279	98.33 141	99.76 177	99.36 244

test_post1							97.59 283				20.48 495	83.07 445	99.66 345	94.16 391

test_post											21.25 494	83.86 440	99.70 310

Fast-Effi-MVS+		97.67 285	97.38 297	98.57 241	98.71 343	97.43 214	97.23 327	99.45 160	94.82 407	96.13 433	96.51 429	98.52 74	99.91 75	96.19 327	98.83 384	98.37 420

patchmatchnet-post											98.77 275	84.37 434	99.85 157

Anonymous20231211		99.27 38	99.27 48	99.26 102	99.29 205	98.18 138	99.49 12	99.51 129	99.70 16	99.80 38	99.68 25	96.84 226	99.83 193	99.21 71	99.91 78	99.77 50

pmmvs-eth3d		98.47 194	98.34 200	98.86 175	99.30 202	97.76 190	97.16 337	99.28 244	95.54 387	99.42 111	99.19 154	97.27 200	99.63 359	97.89 174	99.97 21	99.20 296

GG-mvs-BLEND				94.76 449	94.54 489	92.13 431	99.31 30	80.47 495		88.73 489	91.01 489	67.59 480	98.16 482	82.30 483	94.53 480	93.98 485

xiu_mvs_v1_base_debi		97.86 269	98.17 228	96.92 396	98.98 292	93.91 392	96.45 375	99.17 276	97.85 236	98.41 298	97.14 420	98.47 76	99.92 66	98.02 163	99.05 361	96.92 467

Anonymous20231206		98.21 233	98.21 221	98.20 296	99.51 130	95.43 329	98.13 183	99.32 219	96.16 364	98.93 220	98.82 265	96.00 274	99.83 193	97.32 227	99.73 185	99.36 244

MTAPA		98.88 109	98.64 147	99.61 14	99.67 67	99.36 16	98.43 148	99.20 264	98.83 145	98.89 227	98.90 242	96.98 219	99.92 66	97.16 236	99.70 209	99.56 129

MTMP							97.93 225	91.91 480

gm-plane-assit					94.83 488	81.97 491			88.07 476		94.99 461		99.60 372	91.76 440

test9_res														93.28 417	99.15 352	99.38 235

MVP-Stereo		98.08 247	97.92 259	98.57 241	98.96 295	96.79 266	97.90 231	99.18 272	96.41 351	98.46 293	98.95 232	95.93 283	99.60 372	96.51 308	98.98 375	99.31 264
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 343	98.08 151	95.96 406	99.03 303	91.40 457	95.85 440	97.53 402	96.52 249	99.76 268

train_agg		97.10 330	96.45 355	99.07 136	98.71 343	98.08 151	95.96 406	99.03 303	91.64 452	95.85 440	97.53 402	96.47 251	99.76 268	93.67 407	99.16 350	99.36 244

gg-mvs-nofinetune		92.37 444	91.20 448	95.85 430	95.80 487	92.38 426	99.31 30	81.84 494	99.75 11	91.83 483	99.74 18	68.29 476	99.02 462	87.15 470	97.12 453	96.16 477

SCA		96.41 362	96.66 345	95.67 434	98.24 404	88.35 466	95.85 415	96.88 431	96.11 365	97.67 356	98.67 300	93.10 350	99.85 157	94.16 391	99.22 340	98.81 371

Patchmatch-test		96.55 355	96.34 357	97.17 384	98.35 397	93.06 411	98.40 156	97.79 400	97.33 288	98.41 298	98.67 300	83.68 441	99.69 318	95.16 365	99.31 324	98.77 379

test_8					98.67 357	98.01 159	95.91 412	99.02 306	91.64 452	95.79 442	97.50 405	96.47 251	99.76 268

MS-PatchMatch		97.68 284	97.75 270	97.45 371	98.23 406	93.78 398	97.29 322	98.84 339	96.10 366	98.64 266	98.65 305	96.04 271	99.36 436	96.84 269	99.14 353	99.20 296

Patchmatch-RL test		97.26 318	97.02 319	97.99 315	99.52 127	95.53 319	96.13 398	99.71 47	97.47 271	99.27 145	99.16 164	84.30 436	99.62 362	97.89 174	99.77 162	98.81 371

cdsmvs_eth3d_5k		24.66 459	32.88 462	0.00 478	0.00 501	0.00 503	0.00 490	99.10 289	0.00 496	0.00 497	97.58 400	99.21 18	0.00 497	0.00 496	0.00 495	0.00 493

pcd_1.5k_mvsjas		8.17 462	10.90 465	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	98.07 124	0.00 497	0.00 496	0.00 495	0.00 493

agg_prior2														92.50 433	99.16 350	99.37 237

agg_prior					98.68 356	97.99 160		99.01 309		95.59 443			99.77 262

tmp_tt		78.77 456	78.73 459	78.90 473	58.45 498	74.76 497	94.20 467	78.26 496	39.16 491	86.71 490	92.82 480	80.50 452	75.19 493	86.16 475	92.29 485	86.74 487

canonicalmvs		98.34 212	98.26 215	98.58 238	98.46 387	97.82 184	98.96 78	99.46 156	99.19 88	97.46 373	95.46 454	98.59 66	99.46 422	98.08 156	98.71 392	98.46 405

anonymousdsp		99.51 14	99.47 21	99.62 10	99.88 9	99.08 70	99.34 23	99.69 54	98.93 130	99.65 64	99.72 21	98.93 32	99.95 26	99.11 78	100.00 1	99.82 36

alignmvs		97.35 311	96.88 328	98.78 196	98.54 379	98.09 147	97.71 260	97.69 404	99.20 83	97.59 361	95.90 443	88.12 407	99.55 392	98.18 149	98.96 377	98.70 388

nrg030		99.40 26	99.35 34	99.54 32	99.58 93	99.13 61	98.98 76	99.48 142	99.68 20	99.46 102	99.26 135	98.62 63	99.73 292	99.17 75	99.92 69	99.76 56

v144192		98.54 183	98.57 160	98.45 265	99.21 230	95.98 303	97.63 274	99.36 199	97.15 311	99.32 137	99.18 158	95.84 286	99.84 175	99.50 51	99.91 78	99.54 142

FIs		99.14 63	99.09 80	99.29 96	99.70 56	98.28 128	99.13 58	99.52 128	99.48 45	99.24 159	99.41 94	96.79 233	99.82 206	98.69 113	99.88 94	99.76 56

v1921920		98.54 183	98.60 156	98.38 274	99.20 234	95.76 313	97.56 286	99.36 199	97.23 303	99.38 119	99.17 162	96.02 272	99.84 175	99.57 39	99.90 86	99.54 142

UA-Net		99.47 16	99.40 27	99.70 2	99.49 144	99.29 25	99.80 4	99.72 45	99.82 8	99.04 192	99.81 8	98.05 127	99.96 14	98.85 99	99.99 5	99.86 28

v1192		98.60 170	98.66 144	98.41 270	99.27 211	95.88 306	97.52 291	99.36 199	97.41 280	99.33 131	99.20 151	96.37 257	99.82 206	99.57 39	99.92 69	99.55 136

FC-MVSNet-test		99.27 38	99.25 53	99.34 84	99.77 27	98.37 122	99.30 35	99.57 101	99.61 35	99.40 116	99.50 69	97.12 209	99.85 157	99.02 87	99.94 50	99.80 42

v1144		98.60 170	98.66 144	98.41 270	99.36 187	95.90 305	97.58 284	99.34 211	97.51 267	99.27 145	99.15 168	96.34 259	99.80 232	99.47 54	99.93 56	99.51 163

sosnet-low-res		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

HFP-MVS		98.71 140	98.44 184	99.51 49	99.49 144	99.16 49	98.52 130	99.31 224	97.47 271	98.58 278	98.50 330	97.97 134	99.85 157	96.57 298	99.59 255	99.53 156

v148		98.45 196	98.60 156	98.00 314	99.44 165	94.98 346	97.44 305	99.06 294	98.30 188	99.32 137	98.97 225	96.65 244	99.62 362	98.37 137	99.85 107	99.39 226

sosnet		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

uncertanet		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

AllTest		98.44 197	98.20 222	99.16 119	99.50 136	98.55 108	98.25 170	99.58 94	96.80 331	98.88 231	99.06 189	97.65 163	99.57 385	94.45 383	99.61 249	99.37 237

TestCases				99.16 119	99.50 136	98.55 108		99.58 94	96.80 331	98.88 231	99.06 189	97.65 163	99.57 385	94.45 383	99.61 249	99.37 237

v7n		99.53 12	99.57 13	99.41 70	99.88 9	98.54 111	99.45 14	99.61 82	99.66 24	99.68 58	99.66 32	98.44 82	99.95 26	99.73 28	99.96 28	99.75 60

region2R		98.69 149	98.40 189	99.54 32	99.53 124	99.17 45	98.52 130	99.31 224	97.46 276	98.44 295	98.51 326	97.83 148	99.88 116	96.46 311	99.58 260	99.58 115

RRT-MVS		97.88 266	97.98 250	97.61 354	98.15 410	93.77 399	98.97 77	99.64 71	99.16 93	98.69 259	99.42 90	91.60 373	99.89 98	97.63 200	98.52 406	99.16 315

mamv4		99.44 19	99.39 28	99.58 21	99.30 202	99.74 2	99.04 69	99.81 31	99.77 10	99.82 34	99.57 49	97.82 151	99.98 4	99.53 48	99.89 92	99.01 334

PS-MVSNAJss		99.46 17	99.49 16	99.35 81	99.90 4	98.15 140	99.20 48	99.65 69	99.48 45	99.92 8	99.71 22	98.07 124	99.96 14	99.53 48	100.00 1	99.93 11

PS-MVSNAJ		97.08 332	97.39 296	96.16 426	98.56 377	92.46 423	95.24 439	98.85 338	97.25 297	97.49 371	95.99 440	98.07 124	99.90 82	96.37 316	98.67 398	96.12 479

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 103	99.28 40	99.66 65	99.09 109	99.89 18	99.68 25	99.53 7	99.97 7	99.50 51	99.99 5	99.87 22

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 103	99.34 23	99.71 47	99.27 74	99.90 14	99.74 18	99.68 4	99.97 7	99.55 43	99.99 5	99.88 20

EI-MVSNet-UG-set		98.69 149	98.71 133	98.62 230	99.10 260	96.37 289	97.23 327	98.87 330	99.20 83	99.19 167	98.99 218	97.30 197	99.85 157	98.77 106	99.79 151	99.65 83

EI-MVSNet-Vis-set		98.68 155	98.70 136	98.63 228	99.09 263	96.40 288	97.23 327	98.86 335	99.20 83	99.18 171	98.97 225	97.29 199	99.85 157	98.72 110	99.78 156	99.64 84

HPM-MVS++		98.10 244	97.64 282	99.48 57	99.09 263	99.13 61	97.52 291	98.75 355	97.46 276	96.90 404	97.83 386	96.01 273	99.84 175	95.82 347	99.35 317	99.46 195

test_prior4						97.97 164	95.86 413

XVS		98.72 139	98.45 182	99.53 39	99.46 158	99.21 34	98.65 114	99.34 211	98.62 161	97.54 366	98.63 310	97.50 183	99.83 193	96.79 271	99.53 277	99.56 129

v1240		98.55 180	98.62 151	98.32 281	99.22 228	95.58 317	97.51 293	99.45 160	97.16 309	99.45 105	99.24 142	96.12 269	99.85 157	99.60 37	99.88 94	99.55 136

pm-mvs1		99.44 19	99.48 18	99.33 90	99.80 21	98.63 100	99.29 36	99.63 73	99.30 71	99.65 64	99.60 45	99.16 22	99.82 206	99.07 81	99.83 123	99.56 129

test_prior2							95.74 420		96.48 346	96.11 434	97.63 398	95.92 284		94.16 391	99.20 344

X-MVStestdata		94.32 411	92.59 430	99.53 39	99.46 158	99.21 34	98.65 114	99.34 211	98.62 161	97.54 366	45.85 491	97.50 183	99.83 193	96.79 271	99.53 277	99.56 129

test_prior				98.95 161	98.69 352	97.95 168		99.03 303					99.59 376			99.30 268

旧先验2							95.76 419		88.56 475	97.52 368			99.66 345	94.48 381

新几何2							95.93 409

新几何1				98.91 169	98.94 297	97.76 190		98.76 352	87.58 477	96.75 412	98.10 366	94.80 317	99.78 256	92.73 429	99.00 370	99.20 296

旧先验1					98.82 325	97.45 212		98.76 352			98.34 348	95.50 297			99.01 369	99.23 286

无先验							95.74 420	98.74 357	89.38 471				99.73 292	92.38 435		99.22 291

原ACMM2							95.53 426

原ACMM1				98.35 279	98.90 307	96.25 293		98.83 343	92.48 446	96.07 436	98.10 366	95.39 300	99.71 303	92.61 432	98.99 372	99.08 322

test222					98.92 303	96.93 259	95.54 425	98.78 349	85.72 480	96.86 407	98.11 365	94.43 324			99.10 360	99.23 286

testdata2													99.79 245	92.80 427

segment_acmp												97.02 216

testdata				98.09 304	98.93 299	95.40 330		98.80 346	90.08 468	97.45 375	98.37 344	95.26 302	99.70 310	93.58 410	98.95 378	99.17 310

testdata1							95.44 431		96.32 354

v8		99.01 87	99.16 63	98.57 241	99.47 155	96.31 292	98.90 84	99.47 151	99.03 119	99.52 88	99.57 49	96.93 222	99.81 223	99.60 37	99.98 12	99.60 100

1314		95.74 385	95.60 375	96.17 424	97.53 444	92.75 419	98.07 197	98.31 385	91.22 459	94.25 465	96.68 426	95.53 294	99.03 461	91.64 443	97.18 452	96.74 471

LFMVS		97.20 324	96.72 339	98.64 224	98.72 339	96.95 257	98.93 82	94.14 469	99.74 13	98.78 248	99.01 212	84.45 433	99.73 292	97.44 219	99.27 331	99.25 281

VDD-MVS		98.56 176	98.39 192	99.07 136	99.13 256	98.07 153	98.59 122	97.01 424	99.59 37	99.11 175	99.27 129	94.82 314	99.79 245	98.34 139	99.63 241	99.34 251

VDDNet		98.21 233	97.95 254	99.01 150	99.58 93	97.74 192	99.01 71	97.29 417	99.67 21	98.97 206	99.50 69	90.45 386	99.80 232	97.88 177	99.20 344	99.48 185

v10		98.97 95	99.11 72	98.55 248	99.44 165	96.21 294	98.90 84	99.55 114	98.73 147	99.48 97	99.60 45	96.63 245	99.83 193	99.70 33	99.99 5	99.61 98

VPNet		98.87 110	98.83 116	99.01 150	99.70 56	97.62 201	98.43 148	99.35 205	99.47 48	99.28 143	99.05 196	96.72 239	99.82 206	98.09 155	99.36 314	99.59 107

MVS		93.19 432	92.09 437	96.50 411	96.91 465	94.03 382	98.07 197	98.06 396	68.01 489	94.56 463	96.48 431	95.96 281	99.30 446	83.84 478	96.89 457	96.17 476

v2v482		98.56 176	98.62 151	98.37 277	99.42 172	95.81 311	97.58 284	99.16 279	97.90 232	99.28 143	99.01 212	95.98 279	99.79 245	99.33 60	99.90 86	99.51 163

V42		98.78 131	98.78 122	98.76 203	99.44 165	97.04 250	98.27 168	99.19 268	97.87 234	99.25 157	99.16 164	96.84 226	99.78 256	99.21 71	99.84 112	99.46 195

SD-MVS		98.40 202	98.68 139	97.54 363	98.96 295	97.99 160	97.88 233	99.36 199	98.20 202	99.63 67	99.04 198	98.76 45	95.33 490	96.56 302	99.74 182	99.31 264
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
GA-MVS		95.86 381	95.32 391	97.49 368	98.60 369	94.15 376	93.83 474	97.93 398	95.49 389	96.68 415	97.42 410	83.21 443	99.30 446	96.22 325	98.55 405	99.01 334

MSLP-MVS++		98.02 252	98.14 234	97.64 351	98.58 374	95.19 339	97.48 297	99.23 260	97.47 271	97.90 339	98.62 312	97.04 213	98.81 471	97.55 207	99.41 308	98.94 351

APDe-MVS		98.99 90	98.79 120	99.60 16	99.21 230	99.15 53	98.87 89	99.48 142	97.57 259	99.35 126	99.24 142	97.83 148	99.89 98	97.88 177	99.70 209	99.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.84 117	98.61 155	99.53 39	99.19 237	99.27 28	98.49 140	99.33 217	98.64 156	99.03 195	98.98 223	97.89 143	99.85 157	96.54 306	99.42 307	99.46 195

ADS-MVSNet2		95.43 395	94.98 399	96.76 406	98.14 411	91.74 433	97.92 228	97.76 401	90.23 464	96.51 425	98.91 239	85.61 424	99.85 157	92.88 423	96.90 455	98.69 389

EI-MVSNet		98.40 202	98.51 168	98.04 312	99.10 260	94.73 358	97.20 332	98.87 330	98.97 125	99.06 182	99.02 201	96.00 274	99.80 232	98.58 118	99.82 128	99.60 100

Regformer		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

CVMVSNet		96.25 367	97.21 308	93.38 466	99.10 260	80.56 494	97.20 332	98.19 391	96.94 321	99.00 197	99.02 201	89.50 395	99.80 232	96.36 318	99.59 255	99.78 47

pmmvs4		97.58 292	97.28 303	98.51 257	98.84 320	96.93 259	95.40 433	98.52 375	93.60 431	98.61 272	98.65 305	95.10 306	99.60 372	96.97 255	99.79 151	98.99 339

EU-MVSNet		97.66 286	98.50 171	95.13 445	99.63 82	85.84 476	98.35 161	98.21 388	98.23 195	99.54 79	99.46 81	95.02 308	99.68 328	98.24 143	99.87 98	99.87 22

VNet		98.42 198	98.30 208	98.79 193	98.79 332	97.29 227	98.23 171	98.66 362	99.31 69	98.85 236	98.80 269	94.80 317	99.78 256	98.13 152	99.13 355	99.31 264

test-LLR		93.90 420	93.85 415	94.04 456	96.53 474	84.62 482	94.05 471	92.39 476	96.17 360	94.12 467	95.07 458	82.30 448	99.67 332	95.87 343	98.18 416	97.82 445

TESTMET0.1,1		92.19 447	91.77 445	93.46 463	96.48 478	82.80 489	94.05 471	91.52 481	94.45 416	94.00 470	94.88 464	66.65 481	99.56 388	95.78 348	98.11 422	98.02 435

test-mter		92.33 445	91.76 446	94.04 456	96.53 474	84.62 482	94.05 471	92.39 476	94.00 427	94.12 467	95.07 458	65.63 487	99.67 332	95.87 343	98.18 416	97.82 445

VPA-MVSNet		99.30 34	99.30 45	99.28 97	99.49 144	98.36 125	99.00 73	99.45 160	99.63 29	99.52 88	99.44 86	98.25 104	99.88 116	99.09 80	99.84 112	99.62 90

ACMMPR		98.70 145	98.42 187	99.54 32	99.52 127	99.14 58	98.52 130	99.31 224	97.47 271	98.56 282	98.54 321	97.75 157	99.88 116	96.57 298	99.59 255	99.58 115

testgi		98.32 217	98.39 192	98.13 302	99.57 102	95.54 318	97.78 247	99.49 140	97.37 285	99.19 167	97.65 396	98.96 29	99.49 413	96.50 309	98.99 372	99.34 251

test20.03		98.78 131	98.77 123	98.78 196	99.46 158	97.20 237	97.78 247	99.24 258	99.04 118	99.41 113	98.90 242	97.65 163	99.76 268	97.70 195	99.79 151	99.39 226

thres600view7		94.45 409	93.83 416	96.29 417	99.06 272	91.53 436	97.99 218	94.24 467	98.34 183	97.44 376	95.01 460	79.84 454	99.67 332	84.33 477	98.23 413	97.66 455

ADS-MVSNet		95.24 398	94.93 402	96.18 423	98.14 411	90.10 459	97.92 228	97.32 416	90.23 464	96.51 425	98.91 239	85.61 424	99.74 285	92.88 423	96.90 455	98.69 389

MP-MVS		98.46 195	98.09 237	99.54 32	99.57 102	99.22 33	98.50 137	99.19 268	97.61 255	97.58 362	98.66 303	97.40 191	99.88 116	94.72 376	99.60 251	99.54 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 460	20.53 463	6.87 477	12.05 499	4.20 502	93.62 477	6.73 500	4.62 495	10.41 495	24.33 492	8.28 499	3.56 496	9.69 495	15.07 493	12.86 492

thres400		94.14 416	93.44 421	96.24 420	98.93 299	91.44 439	97.60 281	94.29 465	97.94 228	97.10 388	94.31 469	79.67 456	99.62 362	83.05 479	98.08 424	97.66 455

test123		17.04 461	20.11 464	7.82 476	10.25 500	4.91 501	94.80 450	4.47 501	4.93 494	10.00 496	24.28 493	9.69 498	3.64 495	10.14 494	12.43 494	14.92 491

thres200		93.72 424	93.14 426	95.46 441	98.66 362	91.29 443	96.61 366	94.63 462	97.39 283	96.83 408	93.71 472	79.88 453	99.56 388	82.40 482	98.13 421	95.54 483

test0.0.03 1		94.51 408	93.69 418	96.99 392	96.05 483	93.61 406	94.97 447	93.49 471	96.17 360	97.57 364	94.88 464	82.30 448	99.01 464	93.60 409	94.17 481	98.37 420

pmmvs3		95.03 402	94.40 409	96.93 395	97.70 434	92.53 422	95.08 444	97.71 403	88.57 474	97.71 353	98.08 369	79.39 458	99.82 206	96.19 327	99.11 359	98.43 413

EMVS		93.83 421	94.02 413	93.23 467	96.83 468	84.96 479	89.77 488	96.32 440	97.92 230	97.43 377	96.36 436	86.17 417	98.93 467	87.68 469	97.73 435	95.81 481

E-PMN		94.17 415	94.37 410	93.58 462	96.86 466	85.71 478	90.11 487	97.07 423	98.17 205	97.82 348	97.19 417	84.62 432	98.94 466	89.77 462	97.68 436	96.09 480

PGM-MVS		98.66 159	98.37 196	99.55 29	99.53 124	99.18 44	98.23 171	99.49 140	97.01 318	98.69 259	98.88 249	98.00 130	99.89 98	95.87 343	99.59 255	99.58 115

LCM-MVSNet-Re		98.64 162	98.48 177	99.11 127	98.85 319	98.51 113	98.49 140	99.83 25	98.37 180	99.69 56	99.46 81	98.21 111	99.92 66	94.13 395	99.30 327	98.91 356

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 16	100.00 1	99.85 30

MCST-MVS		98.00 255	97.63 283	99.10 129	99.24 222	98.17 139	96.89 351	98.73 358	95.66 382	97.92 337	97.70 394	97.17 207	99.66 345	96.18 329	99.23 339	99.47 193

mvs_anonymous		97.83 277	98.16 231	96.87 399	98.18 408	91.89 432	97.31 320	98.90 324	97.37 285	98.83 239	99.46 81	96.28 262	99.79 245	98.90 95	98.16 419	98.95 347

MVS_Test		98.18 238	98.36 197	97.67 344	98.48 384	94.73 358	98.18 176	99.02 306	97.69 247	98.04 330	99.11 178	97.22 204	99.56 388	98.57 120	98.90 382	98.71 385

MDA-MVSNet-bldmvs		97.94 260	97.91 261	98.06 309	99.44 165	94.96 347	96.63 365	99.15 284	98.35 182	98.83 239	99.11 178	94.31 329	99.85 157	96.60 295	98.72 390	99.37 237

CDPH-MVS		97.26 318	96.66 345	99.07 136	99.00 288	98.15 140	96.03 402	99.01 309	91.21 460	97.79 349	97.85 385	96.89 224	99.69 318	92.75 428	99.38 313	99.39 226

test12				98.93 165	98.58 374	97.83 179		98.66 362		96.53 422		95.51 296	99.69 318		99.13 355	99.27 274

casdiffmvs		98.95 98	99.00 92	98.81 186	99.38 180	97.33 219	97.82 241	99.57 101	99.17 92	99.35 126	99.17 162	98.35 91	99.69 318	98.46 128	99.73 185	99.41 216
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		98.22 231	98.24 219	98.17 299	99.00 288	95.44 328	96.38 381	99.58 94	97.79 241	98.53 287	98.50 330	96.76 236	99.74 285	97.95 172	99.64 234	99.34 251
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		93.73 423	92.83 429	96.42 413	97.70 434	91.28 444	96.84 353	89.77 485	93.96 428	92.44 480	95.93 442	79.14 459	99.77 262	92.94 421	96.76 459	98.21 425

baseline1		95.96 379	95.44 383	97.52 365	98.51 383	93.99 389	98.39 157	96.09 445	98.21 198	98.40 302	97.76 390	86.88 411	99.63 359	95.42 360	89.27 487	98.95 347

YYNet1		97.60 289	97.67 277	97.39 375	99.04 276	93.04 414	95.27 437	98.38 383	97.25 297	98.92 222	98.95 232	95.48 298	99.73 292	96.99 252	98.74 388	99.41 216

PMMVS2		98.07 248	98.08 240	98.04 312	99.41 175	94.59 364	94.59 459	99.40 187	97.50 268	98.82 242	98.83 262	96.83 228	99.84 175	97.50 213	99.81 134	99.71 63

MDA-MVSNet_test_wron		97.60 289	97.66 280	97.41 374	99.04 276	93.09 410	95.27 437	98.42 380	97.26 296	98.88 231	98.95 232	95.43 299	99.73 292	97.02 248	98.72 390	99.41 216

tpmvs		95.02 403	95.25 392	94.33 452	96.39 481	85.87 475	98.08 193	96.83 432	95.46 390	95.51 451	98.69 296	85.91 422	99.53 400	94.16 391	96.23 464	97.58 458

PM-MVS		98.82 123	98.72 128	99.12 125	99.64 76	98.54 111	97.98 219	99.68 60	97.62 252	99.34 128	99.18 158	97.54 177	99.77 262	97.79 184	99.74 182	99.04 330

HQP_MVS		97.99 258	97.67 277	98.93 165	99.19 237	97.65 198	97.77 250	99.27 247	98.20 202	97.79 349	97.98 376	94.90 310	99.70 310	94.42 385	99.51 283	99.45 200

plane_prior7					99.19 237	97.87 175

plane_prior6					98.99 291	97.70 196						94.90 310

plane_prior5								99.27 247					99.70 310	94.42 385	99.51 283	99.45 200

plane_prior4											97.98 376

plane_prior3						97.78 189			97.41 280	97.79 349

plane_prior2							97.77 250		98.20 202

plane_prior1					99.05 275

plane_prior						97.65 198	97.07 340		96.72 336						99.36 314

PS-CasMVS		99.40 26	99.33 38	99.62 10	99.71 48	99.10 66	99.29 36	99.53 123	99.53 42	99.46 102	99.41 94	98.23 106	99.95 26	98.89 97	99.95 38	99.81 40

UniMVSNet_NR-MVSNet		98.86 114	98.68 139	99.40 72	99.17 247	98.74 92	97.68 264	99.40 187	99.14 97	99.06 182	98.59 317	96.71 240	99.93 54	98.57 120	99.77 162	99.53 156

PEN-MVS		99.41 25	99.34 36	99.62 10	99.73 37	99.14 58	99.29 36	99.54 119	99.62 33	99.56 74	99.42 90	98.16 118	99.96 14	98.78 103	99.93 56	99.77 50

TransMVSNet (Re)		99.44 19	99.47 21	99.36 75	99.80 21	98.58 106	99.27 42	99.57 101	99.39 59	99.75 45	99.62 40	99.17 20	99.83 193	99.06 83	99.62 244	99.66 78

DTE-MVSNet		99.43 23	99.35 34	99.66 7	99.71 48	99.30 23	99.31 30	99.51 129	99.64 27	99.56 74	99.46 81	98.23 106	99.97 7	98.78 103	99.93 56	99.72 62

DU-MVS		98.82 123	98.63 149	99.39 73	99.16 249	98.74 92	97.54 289	99.25 253	98.84 144	99.06 182	98.76 281	96.76 236	99.93 54	98.57 120	99.77 162	99.50 167

UniMVSNet (Re)		98.87 110	98.71 133	99.35 81	99.24 222	98.73 95	97.73 259	99.38 191	98.93 130	99.12 174	98.73 284	96.77 234	99.86 144	98.63 117	99.80 145	99.46 195

CP-MVSNet		99.21 48	99.09 80	99.56 27	99.65 70	98.96 78	99.13 58	99.34 211	99.42 56	99.33 131	99.26 135	97.01 217	99.94 42	98.74 108	99.93 56	99.79 44

WR-MVS_H		99.33 31	99.22 55	99.65 8	99.71 48	99.24 31	99.32 26	99.55 114	99.46 50	99.50 94	99.34 113	97.30 197	99.93 54	98.90 95	99.93 56	99.77 50

WR-MVS		98.40 202	98.19 226	99.03 146	99.00 288	97.65 198	96.85 352	98.94 315	98.57 169	98.89 227	98.50 330	95.60 292	99.85 157	97.54 209	99.85 107	99.59 107

NR-MVSNet		98.95 98	98.82 117	99.36 75	99.16 249	98.72 97	99.22 45	99.20 264	99.10 106	99.72 48	98.76 281	96.38 256	99.86 144	98.00 166	99.82 128	99.50 167

Baseline_NR-MVSNet		98.98 94	98.86 112	99.36 75	99.82 19	98.55 108	97.47 301	99.57 101	99.37 61	99.21 165	99.61 43	96.76 236	99.83 193	98.06 158	99.83 123	99.71 63

TranMVSNet+NR-MVSNet		99.17 53	99.07 83	99.46 63	99.37 186	98.87 85	98.39 157	99.42 180	99.42 56	99.36 124	99.06 189	98.38 86	99.95 26	98.34 139	99.90 86	99.57 123

TSAR-MVS + GP.		98.18 238	97.98 250	98.77 201	98.71 343	97.88 174	96.32 385	98.66 362	96.33 353	99.23 161	98.51 326	97.48 187	99.40 431	97.16 236	99.46 296	99.02 333

n2								0.00 502

nn								0.00 502

mPP-MVS		98.64 162	98.34 200	99.54 32	99.54 121	99.17 45	98.63 116	99.24 258	97.47 271	98.09 324	98.68 298	97.62 168	99.89 98	96.22 325	99.62 244	99.57 123

door-mid								99.57 101

XVG-OURS-SEG-HR		98.49 192	98.28 211	99.14 123	99.49 144	98.83 87	96.54 369	99.48 142	97.32 290	99.11 175	98.61 314	99.33 15	99.30 446	96.23 324	98.38 408	99.28 273

mvsmamba		97.57 293	97.26 304	98.51 257	98.69 352	96.73 271	98.74 99	97.25 418	97.03 317	97.88 341	99.23 147	90.95 381	99.87 135	96.61 294	99.00 370	98.91 356

MVSFormer		98.26 226	98.43 185	97.77 331	98.88 313	93.89 395	99.39 20	99.56 110	99.11 99	98.16 316	98.13 362	93.81 340	99.97 7	99.26 66	99.57 264	99.43 208

jason		97.45 302	97.35 300	97.76 334	99.24 222	93.93 391	95.86 413	98.42 380	94.24 420	98.50 290	98.13 362	94.82 314	99.91 75	97.22 232	99.73 185	99.43 208
jason: jason.
lupinMVS		97.06 333	96.86 329	97.65 348	98.88 313	93.89 395	95.48 429	97.97 397	93.53 432	98.16 316	97.58 400	93.81 340	99.91 75	96.77 274	99.57 264	99.17 310

test_djsdf		99.52 13	99.51 15	99.53 39	99.86 14	98.74 92	99.39 20	99.56 110	99.11 99	99.70 52	99.73 20	99.00 27	99.97 7	99.26 66	99.98 12	99.89 16

HPM-MVS_fast		99.01 87	98.82 117	99.57 22	99.71 48	99.35 17	99.00 73	99.50 132	97.33 288	98.94 219	98.86 252	98.75 46	99.82 206	97.53 210	99.71 202	99.56 129

K. test v3		98.00 255	97.66 280	99.03 146	99.79 23	97.56 203	99.19 52	92.47 475	99.62 33	99.52 88	99.66 32	89.61 393	99.96 14	99.25 68	99.81 134	99.56 129

lessismore_v0				98.97 158	99.73 37	97.53 205		86.71 490		99.37 121	99.52 68	89.93 389	99.92 66	98.99 89	99.72 193	99.44 204

SixPastTwentyTwo		98.75 136	98.62 151	99.16 119	99.83 18	97.96 167	99.28 40	98.20 389	99.37 61	99.70 52	99.65 36	92.65 361	99.93 54	99.04 85	99.84 112	99.60 100

OurMVSNet-221017-0		99.37 29	99.31 42	99.53 39	99.91 3	98.98 72	99.63 7	99.58 94	99.44 53	99.78 40	99.76 15	96.39 254	99.92 66	99.44 55	99.92 69	99.68 71

HPM-MVS		98.79 129	98.53 166	99.59 20	99.65 70	99.29 25	99.16 54	99.43 174	96.74 335	98.61 272	98.38 343	98.62 63	99.87 135	96.47 310	99.67 223	99.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 185	98.34 200	99.11 127	99.50 136	98.82 89	95.97 404	99.50 132	97.30 292	99.05 190	98.98 223	99.35 14	99.32 443	95.72 350	99.68 217	99.18 306

XVG-ACMP-BASELINE		98.56 176	98.34 200	99.22 110	99.54 121	98.59 105	97.71 260	99.46 156	97.25 297	98.98 202	98.99 218	97.54 177	99.84 175	95.88 340	99.74 182	99.23 286

casdiffmvs_mvg		99.12 70	99.16 63	98.99 152	99.43 170	97.73 194	98.00 211	99.62 79	99.22 79	99.55 77	99.22 148	98.93 32	99.75 279	98.66 114	99.81 134	99.50 167
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		98.71 140	98.46 181	99.47 61	99.57 102	98.97 74	98.23 171	99.48 142	96.60 340	99.10 178	99.06 189	98.71 50	99.83 193	95.58 357	99.78 156	99.62 90

LGP-MVS_train				99.47 61	99.57 102	98.97 74		99.48 142	96.60 340	99.10 178	99.06 189	98.71 50	99.83 193	95.58 357	99.78 156	99.62 90

baseline		98.96 97	99.02 88	98.76 203	99.38 180	97.26 230	98.49 140	99.50 132	98.86 140	99.19 167	99.06 189	98.23 106	99.69 318	98.71 111	99.76 177	99.33 257

test11								98.87 330

door								99.41 184

EPNet_dtu		94.93 405	94.78 404	95.38 443	93.58 491	87.68 470	96.78 355	95.69 454	97.35 287	89.14 488	98.09 368	88.15 406	99.49 413	94.95 370	99.30 327	98.98 340
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 298	97.14 313	98.54 253	99.68 63	96.09 298	96.50 373	99.62 79	91.58 454	98.84 238	98.97 225	92.36 363	99.88 116	96.76 275	99.95 38	99.67 76

EPNet		96.14 371	95.44 383	98.25 289	90.76 495	95.50 323	97.92 228	94.65 461	98.97 125	92.98 477	98.85 255	89.12 397	99.87 135	95.99 336	99.68 217	99.39 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 266

HQP-NCC					98.67 357		96.29 387		96.05 367	95.55 446

ACMP_Plane					98.67 357		96.29 387		96.05 367	95.55 446

APD-MVS		98.10 244	97.67 277	99.42 68	99.11 258	98.93 80	97.76 253	99.28 244	94.97 403	98.72 257	98.77 275	97.04 213	99.85 157	93.79 405	99.54 273	99.49 174
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 425

HQP4-MVS										95.56 445			99.54 398			99.32 260

HQP3-MVS								99.04 301							99.26 334

HQP2-MVS												93.84 338

CNVR-MVS		98.17 240	97.87 264	99.07 136	98.67 357	98.24 131	97.01 342	98.93 318	97.25 297	97.62 358	98.34 348	97.27 200	99.57 385	96.42 313	99.33 320	99.39 226

NCCC		97.86 269	97.47 294	99.05 143	98.61 367	98.07 153	96.98 344	98.90 324	97.63 251	97.04 394	97.93 381	95.99 278	99.66 345	95.31 362	98.82 386	99.43 208

114514_t		96.50 358	95.77 367	98.69 217	99.48 152	97.43 214	97.84 240	99.55 114	81.42 486	96.51 425	98.58 318	95.53 294	99.67 332	93.41 415	99.58 260	98.98 340

CP-MVS		98.70 145	98.42 187	99.52 45	99.36 187	99.12 63	98.72 104	99.36 199	97.54 265	98.30 304	98.40 340	97.86 147	99.89 98	96.53 307	99.72 193	99.56 129

DSMNet-mixed		97.42 305	97.60 285	96.87 399	99.15 253	91.46 437	98.54 128	99.12 286	92.87 442	97.58 362	99.63 39	96.21 264	99.90 82	95.74 349	99.54 273	99.27 274

tpm2		93.09 433	92.58 431	94.62 450	97.56 440	86.53 474	97.66 268	95.79 451	86.15 479	94.07 469	98.23 357	75.95 466	99.53 400	90.91 456	96.86 458	97.81 447

NP-MVS					98.84 320	97.39 216					96.84 423

EG-PatchMatch MVS		98.99 90	99.01 90	98.94 162	99.50 136	97.47 210	98.04 202	99.59 91	98.15 213	99.40 116	99.36 108	98.58 71	99.76 268	98.78 103	99.68 217	99.59 107

tpm cat1		93.29 430	93.13 427	93.75 460	97.39 453	84.74 480	97.39 308	97.65 407	83.39 484	94.16 466	98.41 339	82.86 446	99.39 433	91.56 445	95.35 476	97.14 466

SteuartSystems-ACMMP		98.79 129	98.54 164	99.54 32	99.73 37	99.16 49	98.23 171	99.31 224	97.92 230	98.90 224	98.90 242	98.00 130	99.88 116	96.15 330	99.72 193	99.58 115
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 419	93.78 417	94.51 451	97.53 444	85.83 477	97.98 219	95.96 447	89.29 472	94.99 457	98.63 310	78.63 462	99.62 362	94.54 379	96.50 460	98.09 432

CR-MVSNet		96.28 365	95.95 364	97.28 378	97.71 432	94.22 371	98.11 188	98.92 321	92.31 448	96.91 401	99.37 104	85.44 427	99.81 223	97.39 222	97.36 448	97.81 447

JIA-IIPM		95.52 392	95.03 398	97.00 391	96.85 467	94.03 382	96.93 348	95.82 450	99.20 83	94.63 462	99.71 22	83.09 444	99.60 372	94.42 385	94.64 478	97.36 464

Patchmtry		97.35 311	96.97 321	98.50 261	97.31 455	96.47 286	98.18 176	98.92 321	98.95 129	98.78 248	99.37 104	85.44 427	99.85 157	95.96 338	99.83 123	99.17 310

PatchT		96.65 352	96.35 356	97.54 363	97.40 452	95.32 334	97.98 219	96.64 435	99.33 66	96.89 405	99.42 90	84.32 435	99.81 223	97.69 197	97.49 439	97.48 460

tpmrst		95.07 401	95.46 381	93.91 458	97.11 459	84.36 484	97.62 275	96.96 427	94.98 402	96.35 430	98.80 269	85.46 426	99.59 376	95.60 355	96.23 464	97.79 450

BH-w/o		95.13 400	94.89 403	95.86 429	98.20 407	91.31 442	95.65 422	97.37 412	93.64 430	96.52 424	95.70 447	93.04 353	99.02 462	88.10 468	95.82 473	97.24 465

tpm		94.67 407	94.34 411	95.66 435	97.68 437	88.42 465	97.88 233	94.90 459	94.46 414	96.03 439	98.56 320	78.66 461	99.79 245	95.88 340	95.01 477	98.78 378

DELS-MVS		98.27 224	98.20 222	98.48 262	98.86 316	96.70 272	95.60 424	99.20 264	97.73 244	98.45 294	98.71 287	97.50 183	99.82 206	98.21 147	99.59 255	98.93 352
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		96.83 345	96.75 338	97.08 387	98.74 336	93.33 408	96.71 360	98.26 386	96.72 336	98.44 295	97.37 413	95.20 303	99.47 419	91.89 437	97.43 443	98.44 411

RPMNet		97.02 336	96.93 323	97.30 377	97.71 432	94.22 371	98.11 188	99.30 232	99.37 61	96.91 401	99.34 113	86.72 412	99.87 135	97.53 210	97.36 448	97.81 447

MVSTER		96.86 344	96.55 351	97.79 329	97.91 422	94.21 373	97.56 286	98.87 330	97.49 270	99.06 182	99.05 196	80.72 451	99.80 232	98.44 129	99.82 128	99.37 237

CPTT-MVS		97.84 275	97.36 299	99.27 100	99.31 198	98.46 116	98.29 164	99.27 247	94.90 405	97.83 346	98.37 344	94.90 310	99.84 175	93.85 404	99.54 273	99.51 163

GBi-Net		98.65 160	98.47 179	99.17 116	98.90 307	98.24 131	99.20 48	99.44 168	98.59 164	98.95 212	99.55 57	94.14 332	99.86 144	97.77 186	99.69 212	99.41 216

PVSNet_Blended_VisFu		98.17 240	98.15 232	98.22 295	99.73 37	95.15 340	97.36 315	99.68 60	94.45 416	98.99 201	99.27 129	96.87 225	99.94 42	97.13 241	99.91 78	99.57 123

PVSNet_BlendedMVS		97.55 294	97.53 288	97.60 355	98.92 303	93.77 399	96.64 364	99.43 174	94.49 412	97.62 358	99.18 158	96.82 229	99.67 332	94.73 374	99.93 56	99.36 244

UnsupCasMVSNet_eth		97.89 264	97.60 285	98.75 205	99.31 198	97.17 242	97.62 275	99.35 205	98.72 153	98.76 253	98.68 298	92.57 362	99.74 285	97.76 190	95.60 474	99.34 251

UnsupCasMVSNet_bld		97.30 315	96.92 325	98.45 265	99.28 208	96.78 269	96.20 392	99.27 247	95.42 391	98.28 308	98.30 352	93.16 348	99.71 303	94.99 367	97.37 446	98.87 362

PVSNet_Blended		96.88 343	96.68 342	97.47 370	98.92 303	93.77 399	94.71 452	99.43 174	90.98 462	97.62 358	97.36 414	96.82 229	99.67 332	94.73 374	99.56 267	98.98 340

FMVSNet5		96.01 374	95.20 395	98.41 270	97.53 444	96.10 295	98.74 99	99.50 132	97.22 306	98.03 331	99.04 198	69.80 474	99.88 116	97.27 229	99.71 202	99.25 281

test1		98.65 160	98.47 179	99.17 116	98.90 307	98.24 131	99.20 48	99.44 168	98.59 164	98.95 212	99.55 57	94.14 332	99.86 144	97.77 186	99.69 212	99.41 216

new_pmnet		96.99 340	96.76 337	97.67 344	98.72 339	94.89 350	95.95 408	98.20 389	92.62 445	98.55 284	98.54 321	94.88 313	99.52 404	93.96 399	99.44 305	98.59 400

FMVSNet3		97.50 295	97.24 306	98.29 285	98.08 415	95.83 309	97.86 237	98.91 323	97.89 233	98.95 212	98.95 232	87.06 410	99.81 223	97.77 186	99.69 212	99.23 286

dp		93.47 427	93.59 420	93.13 468	96.64 472	81.62 493	97.66 268	96.42 439	92.80 443	96.11 434	98.64 308	78.55 464	99.59 376	93.31 416	92.18 486	98.16 428

FMVSNet2		98.49 192	98.40 189	98.75 205	98.90 307	97.14 245	98.61 120	99.13 285	98.59 164	99.19 167	99.28 127	94.14 332	99.82 206	97.97 170	99.80 145	99.29 270

FMVSNet1		99.17 53	99.17 61	99.17 116	99.55 116	98.24 131	99.20 48	99.44 168	99.21 81	99.43 107	99.55 57	97.82 151	99.86 144	98.42 135	99.89 92	99.41 216

N_pmnet		97.63 288	97.17 309	98.99 152	99.27 211	97.86 176	95.98 403	93.41 472	95.25 396	99.47 101	98.90 242	95.63 291	99.85 157	96.91 258	99.73 185	99.27 274

cascas		94.79 406	94.33 412	96.15 427	96.02 485	92.36 427	92.34 483	99.26 252	85.34 481	95.08 456	94.96 463	92.96 354	98.53 476	94.41 388	98.59 403	97.56 459

BH-RMVSNet		96.83 345	96.58 350	97.58 357	98.47 385	94.05 379	96.67 362	97.36 413	96.70 338	97.87 342	97.98 376	95.14 305	99.44 426	90.47 460	98.58 404	99.25 281

UGNet		98.53 185	98.45 182	98.79 193	97.94 420	96.96 256	99.08 61	98.54 373	99.10 106	96.82 409	99.47 79	96.55 248	99.84 175	98.56 123	99.94 50	99.55 136
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
WTY-MVS		96.67 351	96.27 361	97.87 324	98.81 328	94.61 363	96.77 356	97.92 399	94.94 404	97.12 387	97.74 391	91.11 380	99.82 206	93.89 401	98.15 420	99.18 306

XXY-MVS		99.14 63	99.15 68	99.10 129	99.76 30	97.74 192	98.85 93	99.62 79	98.48 176	99.37 121	99.49 75	98.75 46	99.86 144	98.20 148	99.80 145	99.71 63

EC-MVSNet		99.09 73	99.05 84	99.20 111	99.28 208	98.93 80	99.24 44	99.84 22	99.08 113	98.12 321	98.37 344	98.72 49	99.90 82	99.05 84	99.77 162	98.77 379

sss		97.21 323	96.93 323	98.06 309	98.83 322	95.22 338	96.75 358	98.48 377	94.49 412	97.27 384	97.90 382	92.77 358	99.80 232	96.57 298	99.32 322	99.16 315

Test_1112_low_res		96.99 340	96.55 351	98.31 283	99.35 192	95.47 327	95.84 416	99.53 123	91.51 456	96.80 410	98.48 333	91.36 377	99.83 193	96.58 296	99.53 277	99.62 90

1112_ss		97.29 317	96.86 329	98.58 238	99.34 195	96.32 291	96.75 358	99.58 94	93.14 437	96.89 405	97.48 406	92.11 369	99.86 144	96.91 258	99.54 273	99.57 123

ab-mvs-re		8.12 463	10.83 466	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	97.48 406	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

ab-mvs		98.41 199	98.36 197	98.59 237	99.19 237	97.23 231	99.32 26	98.81 344	97.66 249	98.62 270	99.40 97	96.82 229	99.80 232	95.88 340	99.51 283	98.75 382

TR-MVS		95.55 391	95.12 397	96.86 402	97.54 442	93.94 390	96.49 374	96.53 438	94.36 419	97.03 396	96.61 428	94.26 331	99.16 458	86.91 473	96.31 463	97.47 461

MDTV_nov1_ep13_2view						74.92 496	97.69 263		90.06 469	97.75 352		85.78 423		93.52 411		98.69 389

MDTV_nov1_ep13			95.22 394		97.06 462	83.20 487	97.74 257	96.16 442	94.37 418	96.99 397	98.83 262	83.95 439	99.53 400	93.90 400	97.95 431

MIMVSNet1		99.38 28	99.32 40	99.55 29	99.86 14	99.19 43	99.41 17	99.59 91	99.59 37	99.71 50	99.57 49	97.12 209	99.90 82	99.21 71	99.87 98	99.54 142

MIMVSNet		96.62 354	96.25 362	97.71 341	99.04 276	94.66 361	99.16 54	96.92 430	97.23 303	97.87 342	99.10 181	86.11 419	99.65 352	91.65 442	99.21 343	98.82 366

IterMVS-LS		98.55 180	98.70 136	98.09 304	99.48 152	94.73 358	97.22 331	99.39 189	98.97 125	99.38 119	99.31 122	96.00 274	99.93 54	98.58 118	99.97 21	99.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		97.69 283	97.35 300	98.69 217	98.73 337	97.02 252	96.92 350	98.75 355	95.89 376	98.59 276	98.67 300	92.08 370	99.74 285	96.72 280	99.81 134	99.32 260
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 162

IterMVS		97.73 280	98.11 236	96.57 409	99.24 222	90.28 457	95.52 428	99.21 262	98.86 140	99.33 131	99.33 116	93.11 349	99.94 42	98.49 127	99.94 50	99.48 185
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 313	96.92 325	98.57 241	99.09 263	97.99 160	96.79 354	99.35 205	93.18 436	97.71 353	98.07 370	95.00 309	99.31 444	93.97 398	99.13 355	98.42 415

MVS_111021_LR		98.30 220	98.12 235	98.83 182	99.16 249	98.03 158	96.09 400	99.30 232	97.58 258	98.10 323	98.24 355	98.25 104	99.34 440	96.69 285	99.65 232	99.12 320

DP-MVS		98.93 101	98.81 119	99.28 97	99.21 230	98.45 117	98.46 145	99.33 217	99.63 29	99.48 97	99.15 168	97.23 203	99.75 279	97.17 235	99.66 231	99.63 89

ACMMP++															99.68 217

HQP-MVS		97.00 339	96.49 354	98.55 248	98.67 357	96.79 266	96.29 387	99.04 301	96.05 367	95.55 446	96.84 423	93.84 338	99.54 398	92.82 425	99.26 334	99.32 260

QAPM		97.31 314	96.81 335	98.82 184	98.80 331	97.49 206	99.06 65	99.19 268	90.22 466	97.69 355	99.16 164	96.91 223	99.90 82	90.89 457	99.41 308	99.07 324

Vis-MVSNet		99.34 30	99.36 33	99.27 100	99.73 37	98.26 129	99.17 53	99.78 36	99.11 99	99.27 145	99.48 76	98.82 37	99.95 26	98.94 92	99.93 56	99.59 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		94.32 411	95.62 373	90.42 471	98.46 387	75.36 495	96.29 387	89.13 486	95.25 396	95.38 452	99.75 16	92.88 355	99.19 456	94.07 397	99.39 310	96.72 472

IS-MVSNet		98.19 236	97.90 262	99.08 134	99.57 102	97.97 164	99.31 30	98.32 384	99.01 121	98.98 202	99.03 200	91.59 374	99.79 245	95.49 359	99.80 145	99.48 185

HyFIR lowres test		97.19 325	96.60 349	98.96 159	99.62 86	97.28 228	95.17 441	99.50 132	94.21 421	99.01 196	98.32 351	86.61 413	99.99 2	97.10 243	99.84 112	99.60 100

EPMVS		93.72 424	93.27 423	95.09 447	96.04 484	87.76 469	98.13 183	85.01 492	94.69 409	96.92 399	98.64 308	78.47 465	99.31 444	95.04 366	96.46 461	98.20 426

PAPM_NR		96.82 347	96.32 358	98.30 284	99.07 267	96.69 273	97.48 297	98.76 352	95.81 379	96.61 419	96.47 432	94.12 335	99.17 457	90.82 458	97.78 433	99.06 325

TAMVS		98.24 230	98.05 243	98.80 189	99.07 267	97.18 240	97.88 233	98.81 344	96.66 339	99.17 173	99.21 149	94.81 316	99.77 262	96.96 256	99.88 94	99.44 204

PAPR		95.29 396	94.47 407	97.75 335	97.50 450	95.14 341	94.89 449	98.71 360	91.39 458	95.35 453	95.48 453	94.57 322	99.14 460	84.95 476	97.37 446	98.97 344

RPSCF		98.62 167	98.36 197	99.42 68	99.65 70	99.42 11	98.55 126	99.57 101	97.72 246	98.90 224	99.26 135	96.12 269	99.52 404	95.72 350	99.71 202	99.32 260

Vis-MVSNet (Re-imp)		97.46 300	97.16 310	98.34 280	99.55 116	96.10 295	98.94 81	98.44 378	98.32 186	98.16 316	98.62 312	88.76 398	99.73 292	93.88 402	99.79 151	99.18 306

test_0402		98.76 135	98.71 133	98.93 165	99.56 110	98.14 142	98.45 147	99.34 211	99.28 73	98.95 212	98.91 239	98.34 92	99.79 245	95.63 354	99.91 78	98.86 363

MVS_111021_HR		98.25 229	98.08 240	98.75 205	99.09 263	97.46 211	95.97 404	99.27 247	97.60 257	97.99 334	98.25 354	98.15 120	99.38 435	96.87 266	99.57 264	99.42 213

CSCG		98.68 155	98.50 171	99.20 111	99.45 163	98.63 100	98.56 125	99.57 101	97.87 234	98.85 236	98.04 372	97.66 162	99.84 175	96.72 280	99.81 134	99.13 319

PatchMatch-RL		97.24 321	96.78 336	98.61 234	99.03 279	97.83 179	96.36 382	99.06 294	93.49 434	97.36 382	97.78 388	95.75 288	99.49 413	93.44 414	98.77 387	98.52 403

API-MVS		97.04 335	96.91 327	97.42 373	97.88 423	98.23 135	98.18 176	98.50 376	97.57 259	97.39 380	96.75 425	96.77 234	99.15 459	90.16 461	99.02 368	94.88 484

Test By Simon												96.52 249

TDRefinement		99.42 24	99.38 29	99.55 29	99.76 30	99.33 21	99.68 6	99.71 47	99.38 60	99.53 83	99.61 43	98.64 60	99.80 232	98.24 143	99.84 112	99.52 159

USDC		97.41 306	97.40 295	97.44 372	98.94 297	93.67 402	95.17 441	99.53 123	94.03 426	98.97 206	99.10 181	95.29 301	99.34 440	95.84 346	99.73 185	99.30 268

EPP-MVSNet		98.30 220	98.04 244	99.07 136	99.56 110	97.83 179	99.29 36	98.07 395	99.03 119	98.59 276	99.13 173	92.16 367	99.90 82	96.87 266	99.68 217	99.49 174

PMMVS		96.51 356	95.98 363	98.09 304	97.53 444	95.84 308	94.92 448	98.84 339	91.58 454	96.05 438	95.58 448	95.68 290	99.66 345	95.59 356	98.09 423	98.76 381

PAPM		91.88 451	90.34 453	96.51 410	98.06 416	92.56 421	92.44 482	97.17 420	86.35 478	90.38 485	96.01 439	86.61 413	99.21 455	70.65 491	95.43 475	97.75 451

ACMMP		98.75 136	98.50 171	99.52 45	99.56 110	99.16 49	98.87 89	99.37 195	97.16 309	98.82 242	99.01 212	97.71 159	99.87 135	96.29 322	99.69 212	99.54 142
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		97.17 327	96.71 340	98.55 248	98.56 377	98.05 157	96.33 384	98.93 318	96.91 325	97.06 392	97.39 411	94.38 327	99.45 424	91.66 441	99.18 349	98.14 429

PatchmatchNet		95.58 390	95.67 372	95.30 444	97.34 454	87.32 472	97.65 270	96.65 434	95.30 395	97.07 391	98.69 296	84.77 430	99.75 279	94.97 369	98.64 399	98.83 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 223	97.95 254	99.34 84	98.44 390	99.16 49	98.12 187	99.38 191	96.01 371	98.06 327	98.43 338	97.80 153	99.67 332	95.69 352	99.58 260	99.20 296

F-COLMAP		97.30 315	96.68 342	99.14 123	99.19 237	98.39 119	97.27 326	99.30 232	92.93 440	96.62 418	98.00 374	95.73 289	99.68 328	92.62 431	98.46 407	99.35 249

ANet_high		99.57 10	99.67 6	99.28 97	99.89 6	98.09 147	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 42	99.31 62	100.00 1	99.82 36

wuyk23d		96.06 372	97.62 284	91.38 470	98.65 366	98.57 107	98.85 93	96.95 428	96.86 329	99.90 14	99.16 164	99.18 19	98.40 477	89.23 465	99.77 162	77.18 490

OMC-MVS		97.88 266	97.49 291	99.04 145	98.89 312	98.63 100	96.94 346	99.25 253	95.02 401	98.53 287	98.51 326	97.27 200	99.47 419	93.50 413	99.51 283	99.01 334

MG-MVS		96.77 348	96.61 347	97.26 380	98.31 400	93.06 411	95.93 409	98.12 394	96.45 350	97.92 337	98.73 284	93.77 342	99.39 433	91.19 452	99.04 364	99.33 257

AdaColmap		97.14 329	96.71 340	98.46 264	98.34 398	97.80 188	96.95 345	98.93 318	95.58 386	96.92 399	97.66 395	95.87 285	99.53 400	90.97 454	99.14 353	98.04 434

uanet		0.00 464	0.00 467	0.00 478	0.00 501	0.00 503	0.00 490	0.00 502	0.00 496	0.00 497	0.00 496	0.00 500	0.00 497	0.00 496	0.00 495	0.00 493

ITE_SJBPF				98.87 173	99.22 228	98.48 115		99.35 205	97.50 268	98.28 308	98.60 316	97.64 166	99.35 439	93.86 403	99.27 331	98.79 377

DeepMVS_CX				93.44 464	98.24 404	94.21 373		94.34 464	64.28 490	91.34 484	94.87 466	89.45 396	92.77 491	77.54 487	93.14 483	93.35 486

TinyColmap		97.89 264	97.98 250	97.60 355	98.86 316	94.35 369	96.21 391	99.44 168	97.45 278	99.06 182	98.88 249	97.99 133	99.28 450	94.38 389	99.58 260	99.18 306

MAR-MVS		96.47 360	95.70 370	98.79 193	97.92 421	99.12 63	98.28 165	98.60 367	92.16 450	95.54 449	96.17 437	94.77 319	99.52 404	89.62 463	98.23 413	97.72 453
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
LF4IMVS		97.90 262	97.69 276	98.52 256	99.17 247	97.66 197	97.19 336	99.47 151	96.31 355	97.85 345	98.20 359	96.71 240	99.52 404	94.62 377	99.72 193	98.38 418

MSDG		97.71 282	97.52 289	98.28 286	98.91 306	96.82 264	94.42 463	99.37 195	97.65 250	98.37 303	98.29 353	97.40 191	99.33 442	94.09 396	99.22 340	98.68 392

LS3D		98.63 164	98.38 194	99.36 75	97.25 456	99.38 13	99.12 60	99.32 219	99.21 81	98.44 295	98.88 249	97.31 196	99.80 232	96.58 296	99.34 319	98.92 353

CLD-MVS		97.49 298	97.16 310	98.48 262	99.07 267	97.03 251	94.71 452	99.21 262	94.46 414	98.06 327	97.16 418	97.57 173	99.48 416	94.46 382	99.78 156	98.95 347
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 428	92.23 435	97.08 387	99.25 221	97.86 176	95.61 423	97.16 421	92.90 441	93.76 474	98.65 305	75.94 467	95.66 488	79.30 486	97.49 439	97.73 452

Gipuma		99.03 85	99.16 63	98.64 224	99.94 2	98.51 113	99.32 26	99.75 42	99.58 39	98.60 274	99.62 40	98.22 109	99.51 409	97.70 195	99.73 185	97.89 442
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark