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 26	99.85 16	99.11 64	99.90 1	99.78 36	99.63 28	99.78 39	99.67 30	99.48 10	99.81 223	99.30 62	99.97 21	99.77 53
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	98.73 128	99.05 142	98.76 338	97.81 195	99.25 43	99.30 238	98.57 171	98.55 295	99.33 118	97.95 137	99.90 81	97.16 245	99.67 233	99.44 206

3Dnovator+	97.89 3	98.69 149	98.51 168	99.24 106	98.81 332	98.40 119	99.02 70	99.19 276	98.99 122	98.07 339	99.28 129	97.11 211	99.84 176	96.84 278	99.32 336	99.47 195

DeepC-MVS	97.60 4	98.97 97	98.93 99	99.10 128	99.35 194	97.98 169	98.01 211	99.46 161	97.56 265	99.54 79	99.50 68	98.97 29	99.84 176	98.06 159	99.92 71	99.49 176
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	98.32 218	98.01 249	99.23 108	98.39 402	98.97 73	95.03 457	99.18 280	96.88 332	99.33 133	98.78 280	98.16 119	99.28 461	96.74 286	99.62 254	99.44 206

DeepC-MVS_fast	96.85 6	98.30 221	98.15 234	98.75 208	98.61 372	97.23 243	97.76 254	99.09 300	97.31 295	98.75 262	98.66 309	97.56 173	99.64 359	96.10 346	99.55 282	99.39 228
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	97.09 340	96.68 352	98.32 286	98.32 405	97.16 256	98.86 92	99.37 201	89.48 494	96.29 451	99.15 173	96.56 247	99.90 81	92.90 443	99.20 360	97.89 452

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 44	98.38 121	99.07 65	99.55 118	98.30 191	99.65 63	99.45 84	99.22 17	99.76 269	98.44 129	99.77 168	99.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 78	99.00 92	99.33 89	99.71 48	98.83 86	98.60 121	99.58 97	99.11 98	99.53 83	99.18 161	98.81 39	99.67 335	96.71 291	99.77 168	99.50 168

COLMAP_ROB	96.50 10	98.99 92	98.85 116	99.41 69	99.58 93	99.10 65	98.74 99	99.56 114	99.09 108	99.33 133	99.19 157	98.40 85	99.72 305	95.98 349	99.76 184	99.42 215
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	96.63 364	95.95 381	98.65 225	98.93 303	98.09 152	96.93 354	99.28 250	83.58 509	98.13 333	97.78 404	96.13 268	99.40 442	93.52 426	99.29 344	98.45 417
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 104	98.73 128	99.48 57	99.55 116	99.14 57	98.07 198	99.37 201	97.62 256	99.04 196	98.96 234	98.84 37	99.79 245	97.43 226	99.65 242	99.49 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 398	95.35 405	97.55 375	97.95 432	94.79 374	98.81 98	96.94 449	92.28 470	95.17 476	98.57 327	89.90 409	99.75 281	91.20 473	97.33 472	98.10 441

OpenMVS_ROB	95.38 14	95.84 401	95.18 414	97.81 339	98.41 401	97.15 257	97.37 317	98.62 379	83.86 508	98.65 275	98.37 352	94.29 337	99.68 331	88.41 491	98.62 419	96.60 491

ACMP	95.32 15	98.41 200	98.09 239	99.36 74	99.51 131	98.79 89	97.68 266	99.38 197	95.76 394	98.81 252	98.82 272	98.36 88	99.82 206	94.75 386	99.77 168	99.48 187
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 368	95.73 386	98.85 178	98.75 340	97.91 179	96.42 387	99.06 304	90.94 486	95.59 464	97.38 430	94.41 330	99.59 381	90.93 477	98.04 450	99.05 333
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 405	95.70 387	95.57 460	98.83 326	88.57 490	92.50 501	97.72 418	92.69 465	96.49 448	96.44 453	93.72 353	99.43 438	93.61 422	99.28 345	98.71 393

PCF-MVS	92.86 18	94.36 431	93.00 449	98.42 274	98.70 352	97.56 212	93.16 499	99.11 297	79.59 513	97.55 379	97.43 427	92.19 381	99.73 295	79.85 510	99.45 309	97.97 449
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 468	90.90 472	96.27 437	97.22 474	91.24 469	94.36 479	93.33 497	92.37 468	92.24 506	94.58 490	66.20 508	99.89 97	93.16 437	94.63 503	97.66 467
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	97.86 273	97.94 259	97.65 360	99.71 48	97.94 176	98.52 130	98.68 374	98.99 122	97.52 382	99.35 111	97.41 189	98.18 498	91.59 466	99.67 233	96.82 487
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 474	90.30 476	93.70 487	97.72 444	84.34 511	90.24 507	97.42 427	90.20 490	93.79 497	93.09 499	90.90 401	98.89 485	86.57 499	72.76 518	97.87 454

MVE	83.40 22	92.50 463	91.92 465	94.25 479	98.83 326	91.64 457	92.71 500	83.52 520	95.92 383	86.46 515	95.46 475	95.20 305	95.40 515	80.51 509	98.64 416	95.73 502
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 380	95.44 400	98.84 184	96.25 502	98.69 98	97.02 345	99.12 295	88.90 498	97.83 360	98.86 259	89.51 413	98.90 484	91.92 458	99.51 294	98.92 360
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GLUNet-SfM		86.26 479	84.68 481	91.01 497	80.58 523	83.56 512	78.04 514	93.59 494	76.70 514	95.29 475	94.72 488	77.51 488	94.26 517	66.39 517	99.33 333	95.20 504

PDCNetPlus		95.22 419	94.73 426	96.70 424	97.85 437	91.14 472	93.94 490	99.97 1	93.06 459	98.95 216	98.89 254	74.32 492	99.14 471	95.63 366	99.93 57	99.82 36

RoMa-SfM		98.46 195	98.27 215	99.02 148	99.35 194	98.32 128	97.56 288	99.70 52	95.88 385	99.38 119	98.65 311	96.41 254	99.46 431	97.78 187	99.71 210	99.28 276

DKM		98.18 240	97.95 256	98.85 178	99.35 194	98.31 129	96.68 369	99.69 55	96.90 331	98.61 282	98.77 282	94.41 330	98.93 481	97.32 234	99.84 113	99.32 262

ELoFTR		97.81 282	97.74 275	98.04 320	99.39 180	95.79 329	97.28 329	99.58 97	94.13 441	99.38 119	99.37 104	93.31 358	99.60 376	97.23 240	99.96 28	98.74 391

MatchFormer		97.07 342	96.92 333	97.49 381	98.44 395	95.92 321	96.79 360	99.14 293	93.08 458	99.32 139	99.10 186	93.89 347	99.03 474	92.78 449	99.78 161	97.52 472

LoFTR		97.97 263	97.79 271	98.53 258	98.80 335	97.47 220	97.01 346	99.55 118	95.55 401	99.46 101	99.22 150	94.22 339	99.44 436	96.45 321	99.82 132	98.68 400

ALIKED-LG		97.10 338	96.63 357	98.50 265	97.96 431	98.68 99	97.75 257	99.68 62	95.86 386	98.36 316	98.33 359	91.58 391	99.04 473	90.87 480	99.31 338	97.77 461

SP-DiffGlue		96.87 354	96.76 346	97.21 396	95.17 509	96.88 276	96.12 409	98.93 329	96.51 350	98.37 314	97.55 418	93.65 355	97.83 502	96.11 345	98.45 427	96.92 483

SP-LightGlue		97.22 330	97.01 327	97.88 333	97.33 471	97.19 250	96.38 389	99.08 302	97.28 298	96.53 441	97.50 422	92.36 377	98.70 490	97.84 183	98.76 404	97.74 463

SP-SuperGlue		97.31 320	97.23 312	97.57 374	96.96 482	97.24 242	96.26 400	98.76 364	97.68 251	96.88 424	97.85 399	94.32 335	98.01 500	97.76 193	98.57 422	97.45 475

SIFT-UMatch		96.33 377	96.47 367	95.89 450	98.29 408	97.95 174	93.84 492	97.24 436	95.78 393	98.72 265	98.04 384	93.45 357	96.81 512	93.14 438	99.73 192	92.91 512

SIFT-NCMNet		96.30 379	96.40 370	96.03 448	97.80 442	97.68 205	92.34 503	96.94 449	95.55 401	98.84 245	98.63 317	94.17 340	97.63 506	93.57 425	99.71 210	92.77 513

SIFT-ConvMatch		96.57 366	96.62 358	96.43 430	98.20 415	98.27 132	93.88 491	96.88 452	95.29 412	98.88 236	98.25 364	95.18 307	97.43 507	93.22 436	99.83 125	93.59 509

SIFT-PointCN		96.45 374	96.47 367	96.39 432	98.13 424	97.54 214	93.31 497	97.23 437	94.67 427	98.68 271	98.32 360	94.64 325	97.81 503	93.50 428	99.77 168	93.83 507

XFeat-MNN		93.41 450	92.98 450	94.68 475	92.63 515	92.92 436	89.72 511	95.81 473	92.10 472	97.23 402	96.29 457	84.95 450	97.31 509	89.60 488	98.54 424	93.81 508

ALIKED-MNN		95.97 395	95.30 409	98.00 323	97.66 454	98.12 148	96.98 349	99.41 189	91.11 484	94.04 493	97.30 434	91.56 392	98.61 492	89.99 485	99.63 250	97.28 480

SP-MNN		96.46 373	96.24 378	97.10 402	96.71 489	95.98 318	96.00 414	97.33 432	95.82 390	94.93 480	97.10 442	93.70 354	98.01 500	96.30 332	98.30 432	97.30 479

SIFT-MNN		95.92 397	95.97 380	95.74 456	98.18 417	98.00 165	94.17 482	96.99 444	95.74 395	97.16 403	97.90 395	90.71 402	95.79 513	93.71 420	99.21 358	93.44 510

casdiffseed414692147		99.09 72	99.12 70	99.01 150	99.55 116	97.91 179	98.30 164	99.68 62	99.04 117	99.19 170	99.37 104	98.98 28	99.61 372	98.13 152	99.83 125	99.50 168

gbinet_0.2-2-1-0.02		95.44 414	94.55 427	98.14 307	95.99 506	95.34 353	94.71 464	98.29 400	96.00 379	96.05 458	90.50 513	84.99 449	99.79 245	97.33 232	97.07 477	99.28 276

0.3-1-1-0.015		87.27 478	84.50 482	95.57 460	91.70 517	90.77 478	89.41 512	92.04 504	88.98 497	82.46 518	81.35 516	60.36 519	99.50 416	92.96 440	81.23 514	96.45 492

0.4-1-1-0.1		88.42 476	85.91 479	95.94 449	93.08 514	91.54 458	90.99 506	92.04 504	89.96 493	84.83 516	83.25 515	63.75 515	99.52 409	93.25 434	82.07 512	96.75 488

0.4-1-1-0.2		87.49 477	84.89 480	95.31 468	91.33 520	90.08 485	88.47 513	92.07 503	88.70 499	84.06 517	81.08 517	63.62 516	99.49 420	92.93 442	81.71 513	96.37 493

wanda-best-256-512		95.48 412	94.74 424	97.68 354	96.53 493	94.12 397	94.17 482	98.57 384	95.84 387	96.71 431	91.16 509	86.05 439	99.76 269	97.57 210	96.09 490	99.17 315

usedtu_dtu_shiyan2		98.99 92	98.86 113	99.39 72	99.73 37	98.71 97	99.05 68	99.47 156	99.16 92	99.49 94	99.12 181	96.34 260	99.93 53	98.05 161	99.36 326	99.54 143

usedtu_dtu_shiyan1		97.37 314	97.13 320	98.11 309	99.03 282	95.40 348	94.47 475	98.99 322	96.87 333	97.97 348	97.81 402	92.12 383	99.75 281	97.49 223	99.43 317	99.16 321

blended_shiyan8		95.98 393	95.33 406	97.94 328	97.05 481	94.87 372	95.34 447	98.59 381	96.17 367	97.09 407	92.39 504	87.62 428	99.76 269	97.65 202	96.05 496	99.20 301

E5new		99.05 81	99.11 72	98.85 178	99.60 87	97.30 233	98.42 151	99.63 77	98.73 149	99.26 152	99.39 100	98.71 51	99.70 313	98.43 131	99.84 113	99.54 143

FE-blended-shiyan7		95.48 412	94.74 424	97.68 354	96.53 493	94.12 397	94.17 482	98.57 384	95.84 387	96.71 431	91.16 509	86.05 439	99.76 269	97.57 210	96.09 490	99.17 315

E6new		99.05 81	99.11 72	98.85 178	99.60 87	97.30 233	98.42 151	99.63 77	98.73 149	99.26 152	99.39 100	98.71 51	99.70 313	98.43 131	99.84 113	99.54 143

blended_shiyan6		95.99 392	95.33 406	97.95 327	97.06 479	94.89 370	95.34 447	98.58 382	96.17 367	97.06 409	92.41 503	87.64 427	99.76 269	97.64 203	96.09 490	99.19 307

usedtu_blend_shiyan5		96.20 386	95.62 390	97.94 328	96.53 493	94.93 368	98.83 96	99.59 94	98.89 136	96.71 431	91.16 509	86.05 439	99.73 295	96.70 292	96.09 490	99.17 315

blend_shiyan4		92.09 470	90.16 477	97.88 333	96.78 487	94.93 368	95.24 451	98.58 382	96.22 365	96.07 456	91.42 508	63.46 517	99.73 295	96.70 292	76.98 517	98.98 347

E6		99.05 81	99.11 72	98.85 178	99.60 87	97.30 233	98.42 151	99.63 77	98.73 149	99.26 152	99.39 100	98.71 51	99.70 313	98.43 131	99.84 113	99.54 143

E5		99.05 81	99.11 72	98.85 178	99.60 87	97.30 233	98.42 151	99.63 77	98.73 149	99.26 152	99.39 100	98.71 51	99.70 313	98.43 131	99.84 113	99.54 143

FE-MVSNET3		97.37 314	97.13 320	98.11 309	99.03 282	95.40 348	94.47 475	98.99 322	96.87 333	97.97 348	97.81 402	92.12 383	99.75 281	97.49 223	99.43 317	99.16 321

E4		98.87 110	98.88 106	98.81 189	99.52 128	97.23 243	97.62 277	99.61 86	98.58 169	99.18 175	99.33 118	98.29 97	99.69 321	97.99 169	99.83 125	99.52 160

E3new		98.41 200	98.34 201	98.62 233	99.19 240	96.90 274	97.32 321	99.50 137	97.40 286	98.63 277	98.92 242	97.21 204	99.65 355	97.34 230	99.52 291	99.31 267

FE-MVSNET2		99.15 57	99.22 54	98.94 164	99.70 56	97.49 216	98.62 118	99.67 68	98.85 143	99.34 130	99.54 62	98.47 77	99.81 223	98.93 92	99.91 80	99.51 164

fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 192	99.48 153	96.56 294	97.97 224	99.69 55	99.63 28	99.84 30	99.54 62	98.21 112	99.94 41	99.76 23	99.95 39	99.88 20

E2		98.70 145	98.68 139	98.73 214	99.40 178	97.10 260	97.48 300	99.57 105	98.09 219	99.00 201	99.20 154	97.90 140	99.67 335	97.73 197	99.77 168	99.43 210

MED-MVS test				99.45 64	99.58 93	98.93 79	98.68 109	99.60 88	96.46 356	99.53 83	98.77 282		99.83 194	96.67 296	99.64 244	99.58 117

MED-MVS		99.01 88	98.84 117	99.52 44	99.58 93	98.93 79	98.68 109	99.60 88	98.85 143	99.53 83	99.16 167	97.87 146	99.83 194	96.67 296	99.64 244	99.81 41

E3		98.69 149	98.68 139	98.73 214	99.40 178	97.10 260	97.48 300	99.57 105	98.09 219	99.00 201	99.20 154	97.90 140	99.67 335	97.73 197	99.77 168	99.43 210

TestfortrainingZip a		99.09 72	98.92 100	99.61 13	99.58 93	99.17 43	98.68 109	99.27 253	98.85 143	99.61 70	99.16 167	97.14 208	99.86 144	98.39 136	99.57 274	99.81 41

TestfortrainingZip				98.97 159	98.30 407	98.43 118	98.68 109	98.26 401	97.76 245	98.86 242	98.16 373	95.15 308	99.47 427		97.55 459	99.02 340

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 199	99.47 156	96.56 294	97.75 257	99.71 47	99.60 35	99.74 46	99.44 85	97.96 136	99.95 25	99.86 4	99.94 51	99.82 36

viewdifsd2359ckpt07		98.71 140	98.86 113	98.26 292	99.43 171	95.65 332	97.20 336	99.66 69	99.20 82	99.29 144	99.01 218	98.29 97	99.73 295	97.92 174	99.75 188	99.39 228

viewdifsd2359ckpt09		98.13 246	97.92 262	98.77 204	99.18 248	97.35 228	97.29 325	99.53 128	95.81 391	98.09 337	98.47 342	96.34 260	99.66 348	97.02 257	99.51 294	99.29 273

viewdifsd2359ckpt13		98.39 209	98.29 211	98.70 218	99.26 223	97.19 250	97.51 296	99.48 147	96.94 326	98.58 289	98.82 272	97.47 187	99.55 397	97.21 242	99.33 333	99.34 253

viewcassd2359sk11		98.55 180	98.51 168	98.67 223	99.29 208	96.99 266	97.39 311	99.54 124	97.73 247	98.81 252	99.08 193	97.55 174	99.66 348	97.52 217	99.67 233	99.36 246

viewdifsd2359ckpt11		98.84 117	99.04 85	98.24 296	99.56 110	95.51 338	97.38 313	99.70 52	99.16 92	99.57 72	99.40 97	98.26 103	99.71 306	98.55 124	99.82 132	99.50 168

viewmacassd2359aftdt		98.86 114	98.87 109	98.83 185	99.53 125	97.32 232	97.70 264	99.64 75	98.22 199	99.25 160	99.27 131	98.40 85	99.61 372	97.98 170	99.87 99	99.55 137

viewmsd2359difaftdt		98.84 117	99.04 85	98.24 296	99.56 110	95.51 338	97.38 313	99.70 52	99.16 92	99.57 72	99.40 97	98.26 103	99.71 306	98.55 124	99.82 132	99.50 168

diffmvs_AUTHOR		98.50 191	98.59 158	98.23 299	99.35 194	95.48 342	96.61 374	99.60 88	98.37 182	98.90 229	99.00 222	97.37 192	99.76 269	98.22 146	99.85 108	99.46 197

FE-MVSNET		98.59 172	98.50 171	98.87 175	99.58 93	97.30 233	98.08 194	99.74 43	96.94 326	98.97 210	99.10 186	96.94 221	99.74 288	97.33 232	99.86 106	99.55 137

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 157	99.59 91	97.18 253	97.44 308	99.83 26	99.56 39	99.91 12	99.34 115	99.36 13	99.93 53	99.83 10	99.98 12	99.85 30

mamba_0408		98.80 127	98.88 106	98.55 251	99.27 214	96.50 297	98.00 212	99.60 88	98.93 130	99.22 165	98.84 267	98.59 67	99.89 97	97.74 195	99.72 201	99.27 279

icg_test_0407_2		98.20 237	98.38 194	97.65 360	99.03 282	94.03 403	95.78 430	99.45 165	98.16 211	99.06 186	98.71 293	98.27 101	99.68 331	97.50 218	99.45 309	99.22 296

SSM_04072		98.80 127	98.88 106	98.56 249	99.27 214	96.50 297	98.00 212	99.60 88	98.93 130	99.22 165	98.84 267	98.59 67	99.90 81	97.74 195	99.72 201	99.27 279

SSM_0407		98.86 114	98.96 98	98.55 251	99.27 214	96.50 297	98.04 203	99.66 69	99.09 108	99.22 165	99.02 207	98.79 43	99.87 135	97.87 180	99.72 201	99.27 279

viewmambaseed2359dif		98.19 238	98.26 217	97.99 325	99.02 289	95.03 365	96.59 376	99.53 128	96.21 366	99.00 201	98.99 224	97.62 167	99.61 372	97.62 205	99.72 201	99.33 259

IMVS_0407		98.39 209	98.64 147	97.66 358	99.03 282	94.03 403	98.10 191	99.45 165	98.16 211	99.06 186	98.71 293	98.27 101	99.71 306	97.50 218	99.45 309	99.22 296

viewmanbaseed2359cas		98.58 174	98.54 164	98.70 218	99.28 211	97.13 259	97.47 304	99.55 118	97.55 267	98.96 215	98.92 242	97.77 154	99.59 381	97.59 209	99.77 168	99.39 228

IMVS_0404		98.07 251	98.20 224	97.69 353	99.03 282	94.03 403	96.67 370	99.45 165	98.16 211	98.03 344	98.71 293	96.80 232	99.82 206	97.50 218	99.45 309	99.22 296

SSM_0404		98.90 106	99.01 90	98.57 244	99.42 173	96.59 289	98.13 184	99.66 69	99.09 108	99.30 143	99.02 207	98.79 43	99.89 97	97.87 180	99.80 150	99.23 291

IMVS_0403		98.34 213	98.56 161	97.66 358	99.03 282	94.03 403	97.98 220	99.45 165	98.16 211	98.89 232	98.71 293	97.90 140	99.74 288	97.50 218	99.45 309	99.22 296

SD_0403		96.28 381	95.83 383	97.64 363	98.72 344	94.30 390	98.87 89	98.77 362	97.80 241	96.53 441	98.02 386	97.34 194	99.47 427	76.93 513	99.48 305	99.16 321

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 258	99.51 131	95.82 327	97.62 277	99.78 36	99.72 14	99.90 14	99.48 75	98.66 59	99.89 97	99.85 6	99.93 57	99.89 16

ME-MVS		98.61 168	98.33 206	99.44 65	99.24 225	98.93 79	97.45 306	99.06 304	98.14 217	99.06 186	98.77 282	96.97 220	99.82 206	96.67 296	99.64 244	99.58 117

NormalMVS		98.26 228	97.97 255	99.15 121	99.64 76	97.83 187	98.28 166	99.43 179	99.24 75	98.80 254	98.85 262	89.76 410	99.94 41	98.04 162	99.67 233	99.68 73

lecture		99.25 40	99.12 70	99.62 9	99.64 76	99.40 11	98.89 88	99.51 134	99.19 87	99.37 123	99.25 142	98.36 88	99.88 115	98.23 145	99.67 233	99.59 109

SymmetryMVS		98.05 253	97.71 280	99.09 132	99.29 208	97.83 187	98.28 166	97.64 425	99.24 75	98.80 254	98.85 262	89.76 410	99.94 41	98.04 162	99.50 302	99.49 176

Elysia		99.15 57	99.14 68	99.18 113	99.63 82	97.92 177	98.50 137	99.43 179	99.67 20	99.70 51	99.13 178	96.66 242	99.98 4	99.54 44	99.96 28	99.64 86

StellarMVS		99.15 57	99.14 68	99.18 113	99.63 82	97.92 177	98.50 137	99.43 179	99.67 20	99.70 51	99.13 178	96.66 242	99.98 4	99.54 44	99.96 28	99.64 86

KinetiMVS		99.03 86	99.02 88	99.03 145	99.70 56	97.48 219	98.43 148	99.29 246	99.70 15	99.60 71	99.07 194	96.13 268	99.94 41	99.42 55	99.87 99	99.68 73

LuminaMVS		98.39 209	98.20 224	98.98 157	99.50 137	97.49 216	97.78 248	97.69 420	98.75 148	99.49 94	99.25 142	92.30 380	99.94 41	99.14 75	99.88 95	99.50 168

VortexMVS		97.98 262	98.31 208	97.02 406	98.88 317	91.45 461	98.03 205	99.47 156	98.65 157	99.55 77	99.47 78	91.49 394	99.81 223	99.32 60	99.91 80	99.80 45

AstraMVS		98.16 245	98.07 244	98.41 275	99.51 131	95.86 324	98.00 212	95.14 481	98.97 125	99.43 107	99.24 144	93.25 359	99.84 176	99.21 70	99.87 99	99.54 143

guyue		98.01 257	97.93 261	98.26 292	99.45 164	95.48 342	98.08 194	96.24 463	98.89 136	99.34 130	99.14 176	91.32 396	99.82 206	99.07 80	99.83 125	99.48 187

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 77	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 52	99.93 57	99.60 102

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 44	98.98 71	99.51 10	99.85 19	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 39	99.61 100

tt0320		99.61 8	99.65 9	99.48 57	99.71 48	98.94 78	99.54 8	99.83 26	99.87 5	99.89 18	99.82 5	98.75 47	99.90 81	99.54 44	99.95 39	99.59 109

fmvsm_s_conf0.5_n_8		99.13 66	99.26 50	98.74 212	99.51 131	96.44 301	97.65 272	99.65 73	99.66 23	99.78 39	99.48 75	97.92 139	99.93 53	99.72 30	99.95 39	99.87 22

fmvsm_s_conf0.5_n_7		98.83 120	99.04 85	98.20 301	99.30 206	94.83 373	97.23 331	99.36 205	98.64 158	99.84 30	99.43 88	98.10 124	99.91 74	99.56 41	99.96 28	99.87 22

fmvsm_s_conf0.5_n_6		99.08 78	99.21 57	98.69 220	99.36 189	96.51 296	97.62 277	99.68 62	98.43 180	99.85 27	99.10 186	99.12 23	99.88 115	99.77 22	99.92 71	99.67 78

fmvsm_s_conf0.5_n_5		99.07 80	99.10 78	98.99 153	99.47 156	97.22 246	97.40 310	99.83 26	97.61 259	99.85 27	99.30 125	98.80 41	99.95 25	99.71 32	99.90 88	99.78 50

fmvsm_s_conf0.5_n_4		99.01 88	99.22 54	98.38 279	99.31 202	95.48 342	97.56 288	99.73 44	98.87 138	99.75 44	99.27 131	98.80 41	99.86 144	99.80 17	99.90 88	99.81 41

SSC-MVS3.2		98.53 185	98.79 122	97.74 348	99.46 159	93.62 426	96.45 383	99.34 217	99.33 65	98.93 225	98.70 300	97.90 140	99.90 81	99.12 76	99.92 71	99.69 72

testing3-2		93.78 443	93.91 435	93.39 491	98.82 329	81.72 519	97.76 254	95.28 479	98.60 165	96.54 440	96.66 447	65.85 510	99.62 365	96.65 300	98.99 388	98.82 373

myMVS_eth3d28		92.92 459	92.31 455	94.77 473	97.84 438	87.59 497	96.19 403	96.11 466	97.08 318	94.27 487	93.49 497	66.07 509	98.78 487	91.78 461	97.93 453	97.92 451

UWE-MVS-28		90.22 475	89.28 478	93.02 495	94.50 512	82.87 515	96.52 380	87.51 515	95.21 415	92.36 505	96.04 459	71.57 496	98.25 497	72.04 515	97.77 455	97.94 450

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 91	98.21 141	97.82 242	99.84 23	99.41 57	99.92 8	99.41 94	99.51 8	99.95 25	99.84 9	99.97 21	99.87 22

fmvsm_s_conf0.5_n_3		99.22 46	99.37 31	98.78 199	99.46 159	96.58 292	97.65 272	99.72 45	99.47 47	99.86 24	99.50 68	98.94 31	99.89 97	99.75 26	99.97 21	99.86 28

fmvsm_s_conf0.5_n_2		99.14 62	99.31 41	98.63 231	99.49 145	96.08 315	97.38 313	99.81 32	99.48 44	99.84 30	99.57 49	98.46 81	99.89 97	99.82 12	99.97 21	99.91 13

fmvsm_s_conf0.1_n_2		99.20 50	99.38 28	98.65 225	99.69 60	96.08 315	97.49 299	99.90 12	99.53 41	99.88 21	99.64 37	98.51 76	99.90 81	99.83 10	99.98 12	99.97 4

GDP-MVS		97.50 300	97.11 322	98.67 223	99.02 289	96.85 277	98.16 181	99.71 47	98.32 189	98.52 300	98.54 329	83.39 464	99.95 25	98.79 101	99.56 278	99.19 307

BP-MVS1		97.40 312	96.97 329	98.71 217	99.07 270	96.81 279	98.34 163	97.18 438	98.58 169	98.17 326	98.61 322	84.01 460	99.94 41	98.97 89	99.78 161	99.37 239

reproduce_monomvs		95.00 425	95.25 410	94.22 480	97.51 465	83.34 513	97.86 238	98.44 392	98.51 176	99.29 144	99.30 125	67.68 503	99.56 393	98.89 96	99.81 139	99.77 53

mmtdpeth		99.30 33	99.42 25	98.92 170	99.58 93	96.89 275	99.48 13	99.92 8	99.92 2	98.26 323	99.80 11	98.33 94	99.91 74	99.56 41	99.95 39	99.97 4

reproduce_model		99.15 57	98.97 96	99.67 4	99.33 200	99.44 9	98.15 182	99.47 156	99.12 97	99.52 87	99.32 123	98.31 95	99.90 81	97.78 187	99.73 192	99.66 80

reproduce-ours		99.09 72	98.90 103	99.67 4	99.27 214	99.49 5	98.00 212	99.42 185	99.05 115	99.48 96	99.27 131	98.29 97	99.89 97	97.61 206	99.71 210	99.62 92

our_new_method		99.09 72	98.90 103	99.67 4	99.27 214	99.49 5	98.00 212	99.42 185	99.05 115	99.48 96	99.27 131	98.29 97	99.89 97	97.61 206	99.71 210	99.62 92

mmdepth		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

monomultidepth		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

mvs5depth		99.30 33	99.59 12	98.44 272	99.65 70	95.35 351	99.82 3	99.94 3	99.83 7	99.42 111	99.94 2	98.13 122	99.96 13	99.63 36	99.96 28	100.00 1

MVStest1		95.86 399	95.60 392	96.63 425	95.87 507	91.70 456	97.93 226	98.94 326	98.03 222	99.56 74	99.66 32	71.83 495	98.26 496	99.35 58	99.24 351	99.91 13

ttmdpeth		97.91 265	98.02 248	97.58 369	98.69 357	94.10 399	98.13 184	98.90 336	97.95 228	97.32 398	99.58 47	95.95 283	98.75 488	96.41 324	99.22 355	99.87 22

WBMVS		95.18 420	94.78 422	96.37 433	97.68 452	89.74 487	95.80 429	98.73 371	97.54 269	98.30 317	98.44 345	70.06 497	99.82 206	96.62 302	99.87 99	99.54 143

dongtai		76.24 483	75.95 486	77.12 501	92.39 516	67.91 525	90.16 508	59.44 526	82.04 511	89.42 511	94.67 489	49.68 523	81.74 519	48.06 518	77.66 516	81.72 515

kuosan		69.30 484	68.95 487	70.34 502	87.68 522	65.00 526	91.11 505	59.90 525	69.02 515	74.46 520	88.89 514	48.58 524	68.03 521	28.61 519	72.33 519	77.99 516

MVSMamba_PlusPlus		98.83 120	98.98 95	98.36 283	99.32 201	96.58 292	98.90 84	99.41 189	99.75 10	98.72 265	99.50 68	96.17 266	99.94 41	99.27 64	99.78 161	98.57 410

MGCFI-Net		98.34 213	98.28 212	98.51 261	98.47 390	97.59 211	98.96 78	99.48 147	99.18 90	97.40 393	95.50 472	98.66 59	99.50 416	98.18 149	98.71 409	98.44 420

testing91		93.32 451	92.27 456	96.47 429	97.54 458	91.25 468	96.17 407	96.76 455	97.18 312	93.65 499	93.50 496	65.11 512	99.63 362	93.04 439	97.45 463	98.53 411

testing11		93.08 456	92.02 461	96.26 438	97.56 456	90.83 477	96.32 394	95.70 475	96.47 355	92.66 503	93.73 493	64.36 513	99.59 381	93.77 419	97.57 458	98.37 429

testing99		93.04 457	91.98 464	96.23 440	97.53 460	90.70 480	96.35 392	95.94 470	96.87 333	93.41 500	93.43 498	63.84 514	99.59 381	93.24 435	97.19 473	98.40 425

UBG		93.25 453	92.32 454	96.04 447	97.72 444	90.16 483	95.92 423	95.91 471	96.03 377	93.95 496	93.04 500	69.60 499	99.52 409	90.72 482	97.98 451	98.45 417

UWE-MVS		92.38 465	91.76 468	94.21 481	97.16 475	84.65 507	95.42 444	88.45 514	95.96 381	96.17 452	95.84 467	66.36 506	99.71 306	91.87 460	98.64 416	98.28 432

ETVMVS		92.60 462	91.08 471	97.18 397	97.70 449	93.65 425	96.54 377	95.70 475	96.51 350	94.68 483	92.39 504	61.80 518	99.50 416	86.97 496	97.41 466	98.40 425

sasdasda		98.34 213	98.26 217	98.58 241	98.46 392	97.82 192	98.96 78	99.46 161	99.19 87	97.46 387	95.46 475	98.59 67	99.46 431	98.08 157	98.71 409	98.46 414

testing222		91.96 471	90.37 474	96.72 423	97.47 467	92.59 442	96.11 410	94.76 483	96.83 337	92.90 502	92.87 501	57.92 520	99.55 397	86.93 497	97.52 460	98.00 448

WB-MVSnew		95.73 404	95.57 395	96.23 440	96.70 490	90.70 480	96.07 412	93.86 493	95.60 399	97.04 411	95.45 478	96.00 275	99.55 397	91.04 475	98.31 431	98.43 422

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 164	99.65 70	97.05 262	97.80 246	99.76 39	98.70 156	99.78 39	99.11 183	98.79 43	99.95 25	99.85 6	99.96 28	99.83 33

fmvsm_l_conf0.5_n		99.21 47	99.28 46	99.02 148	99.64 76	97.28 239	97.82 242	99.76 39	98.73 149	99.82 34	99.09 192	98.81 39	99.95 25	99.86 4	99.96 28	99.83 33

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 192	99.75 34	96.59 289	97.97 224	99.86 17	98.22 199	99.88 21	99.71 22	98.59 67	99.84 176	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 227	99.71 48	96.10 310	97.87 237	99.85 19	98.56 174	99.90 14	99.68 25	98.69 57	99.85 158	99.72 30	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 71	99.20 58	98.78 199	99.55 116	96.59 289	97.79 247	99.82 31	98.21 201	99.81 36	99.53 64	98.46 81	99.84 176	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 72	99.26 50	98.61 237	99.55 116	96.09 313	97.74 259	99.81 32	98.55 175	99.85 27	99.55 56	98.60 66	99.84 176	99.69 35	99.98 12	99.89 16

MM		98.22 233	97.99 251	98.91 171	98.66 367	96.97 267	97.89 233	94.44 486	99.54 40	98.95 216	99.14 176	93.50 356	99.92 65	99.80 17	99.96 28	99.85 30

WAC-MVS						90.90 475								91.37 470

Syy-MVS		96.04 389	95.56 396	97.49 381	97.10 477	94.48 385	96.18 405	96.58 458	95.65 397	94.77 481	92.29 506	91.27 397	99.36 447	98.17 151	98.05 448	98.63 404

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 149	97.77 251	99.90 12	99.33 65	99.97 3	99.66 32	99.71 3	99.96 13	99.79 19	99.99 5	99.96 8

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 147	98.08 194	99.95 2	99.45 50	99.98 2	99.75 16	99.80 1	99.97 6	99.82 12	99.99 5	99.99 2

myMVS_eth3d		91.92 472	90.45 473	96.30 435	97.10 477	90.90 475	96.18 405	96.58 458	95.65 397	94.77 481	92.29 506	53.88 521	99.36 447	89.59 489	98.05 448	98.63 404

testing3		93.51 447	92.09 459	97.75 346	98.60 374	94.40 387	97.32 321	95.26 480	97.56 265	96.79 429	95.50 472	53.57 522	99.77 263	95.26 376	98.97 392	99.08 329

SSC-MVS		98.71 140	98.74 126	98.62 233	99.72 44	96.08 315	98.74 99	98.64 378	99.74 12	99.67 59	99.24 144	94.57 327	99.95 25	99.11 77	99.24 351	99.82 36

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 76	98.10 151	97.68 266	99.84 23	99.29 71	99.92 8	99.57 49	99.60 5	99.96 13	99.74 27	99.98 12	99.89 16

WB-MVS		98.52 189	98.55 162	98.43 273	99.65 70	95.59 333	98.52 130	98.77 362	99.65 25	99.52 87	99.00 222	94.34 334	99.93 53	98.65 114	98.83 400	99.76 58

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 256	99.66 69	96.97 267	98.00 212	99.85 19	99.24 75	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 393

dmvs_re		95.98 393	95.39 403	97.74 348	98.86 320	97.45 223	98.37 159	95.69 477	97.95 228	96.56 439	95.95 462	90.70 403	97.68 505	88.32 492	96.13 489	98.11 440

SDMVSNet		99.23 45	99.32 39	98.96 161	99.68 63	97.35 228	98.84 95	99.48 147	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 171	99.92 71	99.57 124

dmvs_testset		92.94 458	92.21 458	95.13 470	98.59 377	90.99 474	97.65 272	92.09 502	96.95 325	94.00 494	93.55 495	92.34 379	96.97 511	72.20 514	92.52 508	97.43 476

sd_testset		99.28 36	99.31 41	99.19 112	99.68 63	98.06 161	99.41 17	99.30 238	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 239	99.92 71	99.57 124

test_fmvsm_n_1920		99.33 30	99.45 23	98.99 153	99.57 102	97.73 202	97.93 226	99.83 26	99.22 78	99.93 6	99.30 125	99.42 11	99.96 13	99.85 6	99.99 5	99.29 273

test_cas_vis1_n_1920		98.33 217	98.68 139	97.27 393	99.69 60	92.29 450	98.03 205	99.85 19	97.62 256	99.96 4	99.62 40	93.98 346	99.74 288	99.52 49	99.86 106	99.79 47

test_vis1_n_1920		98.40 203	98.92 100	96.81 419	99.74 36	90.76 479	98.15 182	99.91 10	98.33 187	99.89 18	99.55 56	95.07 311	99.88 115	99.76 23	99.93 57	99.79 47

test_vis1_n		98.31 220	98.50 171	97.73 351	99.76 30	94.17 395	98.68 109	99.91 10	96.31 362	99.79 38	99.57 49	92.85 371	99.42 440	99.79 19	99.84 113	99.60 102

test_fmvs1_n		98.09 249	98.28 212	97.52 378	99.68 63	93.47 428	98.63 116	99.93 6	95.41 410	99.68 57	99.64 37	91.88 388	99.48 424	99.82 12	99.87 99	99.62 92

mvsany_test1		97.60 294	97.54 292	97.77 342	97.72 444	95.35 351	95.36 446	97.13 441	94.13 441	99.71 49	99.33 118	97.93 138	99.30 457	97.60 208	98.94 395	98.67 402

APD_test1		98.83 120	98.66 144	99.34 83	99.78 24	99.47 8	98.42 151	99.45 165	98.28 196	98.98 206	99.19 157	97.76 155	99.58 388	96.57 307	99.55 282	98.97 351

test_vis1_rt		97.75 284	97.72 279	97.83 337	98.81 332	96.35 304	97.30 324	99.69 55	94.61 428	97.87 356	98.05 383	96.26 264	98.32 495	98.74 107	98.18 437	98.82 373

test_vis3_rt		99.14 62	99.17 60	99.07 135	99.78 24	98.38 121	98.92 83	99.94 3	97.80 241	99.91 12	99.67 30	97.15 207	98.91 483	99.76 23	99.56 278	99.92 12

test_fmvs2		98.70 145	98.97 96	97.89 332	99.54 122	94.05 400	98.55 126	99.92 8	96.78 340	99.72 47	99.78 13	96.60 246	99.67 335	99.91 2	99.90 88	99.94 10

test_fmvs1		97.72 286	97.94 259	97.07 405	98.66 367	92.39 447	97.68 266	99.81 32	95.20 416	99.54 79	99.44 85	91.56 392	99.41 441	99.78 21	99.77 168	99.40 227

test_fmvs3		99.12 69	99.41 26	98.25 294	99.76 30	95.07 364	99.05 68	99.94 3	97.78 244	99.82 34	99.84 3	98.56 73	99.71 306	99.96 1	99.96 28	99.97 4

mvsany_test3		98.87 110	98.92 100	98.74 212	99.38 182	96.94 271	98.58 123	99.10 298	96.49 353	99.96 4	99.81 8	98.18 115	99.45 434	98.97 89	99.79 156	99.83 33

testf1		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 55	98.90 134	99.43 107	99.35 111	98.86 35	99.67 335	97.81 184	99.81 139	99.24 289

APD_test2		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 55	98.90 134	99.43 107	99.35 111	98.86 35	99.67 335	97.81 184	99.81 139	99.24 289

test_f		98.67 158	98.87 109	98.05 319	99.72 44	95.59 333	98.51 135	99.81 32	96.30 364	99.78 39	99.82 5	96.14 267	98.63 491	99.82 12	99.93 57	99.95 9

FE-MVS		95.66 406	94.95 419	97.77 342	98.53 386	95.28 355	99.40 19	96.09 467	93.11 457	97.96 350	99.26 137	79.10 482	99.77 263	92.40 456	98.71 409	98.27 433

FA-MVS(test-final)		96.99 350	96.82 342	97.50 380	98.70 352	94.78 375	99.34 23	96.99 444	95.07 417	98.48 303	99.33 118	88.41 424	99.65 355	96.13 344	98.92 397	98.07 443

BridgeMVS		98.63 164	98.72 130	98.38 279	98.66 367	96.68 288	98.90 84	99.42 185	98.99 122	98.97 210	99.19 157	95.81 288	99.85 158	98.77 105	99.77 168	98.60 406

MonoMVSNet		96.25 383	96.53 365	95.39 466	96.57 492	91.01 473	98.82 97	97.68 422	98.57 171	98.03 344	99.37 104	90.92 400	97.78 504	94.99 380	93.88 506	97.38 477

patch_mono-2		98.51 190	98.63 149	98.17 304	99.38 182	94.78 375	97.36 318	99.69 55	98.16 211	98.49 302	99.29 128	97.06 212	99.97 6	98.29 142	99.91 80	99.76 58

EGC-MVSNET		85.24 480	80.54 483	99.34 83	99.77 27	99.20 38	99.08 62	99.29 246	12.08 520	20.84 521	99.42 89	97.55 174	99.85 158	97.08 253	99.72 201	98.96 353

test2506		92.39 464	91.89 466	93.89 485	99.38 182	82.28 517	99.32 26	66.03 524	99.08 112	98.77 259	99.57 49	66.26 507	99.84 176	98.71 110	99.95 39	99.54 143

test1111		96.49 371	96.82 342	95.52 462	99.42 173	87.08 499	99.22 46	87.14 516	99.11 98	99.46 101	99.58 47	88.69 418	99.86 144	98.80 100	99.95 39	99.62 92

ECVR-MVS		96.42 375	96.61 359	95.85 452	99.38 182	88.18 494	99.22 46	86.00 518	99.08 112	99.36 126	99.57 49	88.47 423	99.82 206	98.52 126	99.95 39	99.54 143

test_blank		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

tt0805		98.69 149	98.62 151	98.90 174	99.75 34	99.30 21	99.15 57	96.97 446	98.86 140	98.87 241	97.62 415	98.63 63	98.96 479	99.41 56	98.29 433	98.45 417

DVP-MVS++		98.90 106	98.70 136	99.51 49	98.43 397	99.15 52	99.43 15	99.32 225	98.17 208	99.26 152	99.02 207	98.18 115	99.88 115	97.07 254	99.45 309	99.49 176

FOURS1					99.73 37	99.67 2	99.43 15	99.54 124	99.43 54	99.26 152

MSC_two_6792asdad				99.32 91	98.43 397	98.37 123		98.86 347					99.89 97	97.14 248	99.60 261	99.71 65

PC_three_1452									93.27 454	99.40 116	98.54 329	98.22 110	97.00 510	95.17 377	99.45 309	99.49 176

No_MVS				99.32 91	98.43 397	98.37 123		98.86 347					99.89 97	97.14 248	99.60 261	99.71 65

test_one_0601					99.39 180	99.20 38		99.31 230	98.49 177	98.66 274	99.02 207	97.64 165

eth-test2					0.00 528

eth-test					0.00 528

GeoE		99.05 81	98.99 94	99.25 104	99.44 166	98.35 127	98.73 103	99.56 114	98.42 181	98.91 228	98.81 275	98.94 31	99.91 74	98.35 138	99.73 192	99.49 176

test_method		79.78 481	79.50 484	80.62 499	80.21 524	45.76 527	70.82 515	98.41 396	31.08 519	80.89 519	97.71 408	84.85 451	97.37 508	91.51 468	80.03 515	98.75 389

Anonymous20240521		98.69 149	98.87 109	98.16 306	99.77 27	95.11 363	99.08 62	99.44 173	99.34 64	99.33 133	99.55 56	94.10 345	99.94 41	99.25 67	99.96 28	99.42 215

h-mvs33		97.77 283	97.33 307	99.10 128	99.21 233	97.84 186	98.35 161	98.57 384	99.11 98	98.58 289	99.02 207	88.65 421	99.96 13	98.11 154	96.34 485	99.49 176

hse-mvs2		97.46 305	97.07 323	98.64 227	98.73 342	97.33 230	97.45 306	97.64 425	99.11 98	98.58 289	97.98 389	88.65 421	99.79 245	98.11 154	97.39 467	98.81 378

CL-MVSNet_self_test		97.44 308	97.22 313	98.08 315	98.57 381	95.78 330	94.30 480	98.79 359	96.58 349	98.60 285	98.19 371	94.74 324	99.64 359	96.41 324	98.84 399	98.82 373

KD-MVS_2432*1600		92.87 460	91.99 462	95.51 463	91.37 518	89.27 488	94.07 485	98.14 408	95.42 407	97.25 400	96.44 453	67.86 501	99.24 463	91.28 471	96.08 494	98.02 445

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 82	99.06 70	98.69 108	99.54 124	99.31 68	99.62 69	99.53 64	97.36 193	99.86 144	99.24 69	99.71 210	99.39 228

AUN-MVS		96.24 385	95.45 399	98.60 239	98.70 352	97.22 246	97.38 313	97.65 423	95.95 382	95.53 471	97.96 393	82.11 472	99.79 245	96.31 330	97.44 464	98.80 383

ZD-MVS					99.01 291	98.84 85		99.07 303	94.10 443	98.05 342	98.12 376	96.36 259	99.86 144	92.70 452	99.19 363

SR-MVS-dyc-post		98.81 125	98.55 162	99.57 21	99.20 237	99.38 12	98.48 143	99.30 238	98.64 158	98.95 216	98.96 234	97.49 185	99.86 144	96.56 311	99.39 322	99.45 202

RE-MVS-def			98.58 159		99.20 237	99.38 12	98.48 143	99.30 238	98.64 158	98.95 216	98.96 234	97.75 156		96.56 311	99.39 322	99.45 202

SED-MVS		98.91 104	98.72 130	99.49 55	99.49 145	99.17 43	98.10 191	99.31 230	98.03 222	99.66 60	99.02 207	98.36 88	99.88 115	96.91 267	99.62 254	99.41 218

IU-MVS					99.49 145	99.15 52		98.87 342	92.97 460	99.41 113				96.76 284	99.62 254	99.66 80

OPU-MVS				98.82 187	98.59 377	98.30 130	98.10 191				98.52 333	98.18 115	98.75 488	94.62 390	99.48 305	99.41 218

test_241102_TWO								99.30 238	98.03 222	99.26 152	99.02 207	97.51 181	99.88 115	96.91 267	99.60 261	99.66 80

test_241102_ONE					99.49 145	99.17 43		99.31 230	97.98 225	99.66 60	98.90 248	98.36 88	99.48 424

SF-MVS		98.53 185	98.27 215	99.32 91	99.31 202	98.75 90	98.19 176	99.41 189	96.77 341	98.83 247	98.90 248	97.80 152	99.82 206	95.68 365	99.52 291	99.38 237

cl22		95.79 402	95.39 403	96.98 409	96.77 488	92.79 439	94.40 478	98.53 388	94.59 429	97.89 354	98.17 372	82.82 469	99.24 463	96.37 326	99.03 381	98.92 360

miper_ehance_all_eth		97.06 343	97.03 325	97.16 401	97.83 439	93.06 432	94.66 468	99.09 300	95.99 380	98.69 268	98.45 344	92.73 374	99.61 372	96.79 280	99.03 381	98.82 373

miper_enhance_ethall		96.01 390	95.74 385	96.81 419	96.41 500	92.27 451	93.69 495	98.89 339	91.14 483	98.30 317	97.35 433	90.58 404	99.58 388	96.31 330	99.03 381	98.60 406

ZNCC-MVS		98.68 155	98.40 189	99.54 31	99.57 102	99.21 32	98.46 145	99.29 246	97.28 298	98.11 335	98.39 349	98.00 131	99.87 135	96.86 277	99.64 244	99.55 137

dcpmvs_2		98.78 131	99.11 72	97.78 341	99.56 110	93.67 423	99.06 66	99.86 17	99.50 43	99.66 60	99.26 137	97.21 204	99.99 2	98.00 167	99.91 80	99.68 73

cl____		97.02 346	96.83 341	97.58 369	97.82 440	94.04 402	94.66 468	99.16 287	97.04 320	98.63 277	98.71 293	88.68 420	99.69 321	97.00 259	99.81 139	99.00 345

DIV-MVS_self_test		97.02 346	96.84 340	97.58 369	97.82 440	94.03 403	94.66 468	99.16 287	97.04 320	98.63 277	98.71 293	88.69 418	99.69 321	97.00 259	99.81 139	99.01 342

eth_miper_zixun_eth		97.23 329	97.25 310	97.17 399	98.00 430	92.77 440	94.71 464	99.18 280	97.27 300	98.56 293	98.74 289	91.89 387	99.69 321	97.06 256	99.81 139	99.05 333

9.14			97.78 272		99.07 270		97.53 293	99.32 225	95.53 404	98.54 297	98.70 300	97.58 171	99.76 269	94.32 403	99.46 307

uanet_test		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

DCPMVS		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

save fliter					99.11 261	97.97 170	96.53 379	99.02 316	98.24 197

ET-MVSNet_ETH3D		94.30 434	93.21 445	97.58 369	98.14 421	94.47 386	94.78 463	93.24 498	94.72 425	89.56 510	95.87 465	78.57 485	99.81 223	96.91 267	97.11 476	98.46 414

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 97	99.90 3	99.86 24	99.78 13	99.58 6	99.95 25	99.00 87	99.95 39	99.78 50

EIA-MVS		98.00 258	97.74 275	98.80 192	98.72 344	98.09 152	98.05 201	99.60 88	97.39 287	96.63 436	95.55 470	97.68 159	99.80 232	96.73 288	99.27 346	98.52 412

miper_refine_blended		92.87 460	91.99 462	95.51 463	91.37 518	89.27 488	94.07 485	98.14 408	95.42 407	97.25 400	96.44 453	67.86 501	99.24 463	91.28 471	96.08 494	98.02 445

miper_lstm_enhance		97.18 334	97.16 316	97.25 395	98.16 419	92.85 438	95.15 455	99.31 230	97.25 302	98.74 264	98.78 280	90.07 407	99.78 257	97.19 243	99.80 150	99.11 328

ETV-MVS		98.03 254	97.86 268	98.56 249	98.69 357	98.07 158	97.51 296	99.50 137	98.10 218	97.50 384	95.51 471	98.41 84	99.88 115	96.27 334	99.24 351	97.71 466

CS-MVS		99.13 66	99.10 78	99.24 106	99.06 275	99.15 52	99.36 22	99.88 15	99.36 63	98.21 325	98.46 343	98.68 58	99.93 53	99.03 85	99.85 108	98.64 403

D2MVS		97.84 279	97.84 269	97.83 337	99.14 257	94.74 377	96.94 352	98.88 340	95.84 387	98.89 232	98.96 234	94.40 332	99.69 321	97.55 212	99.95 39	99.05 333

DVP-MVS		98.77 134	98.52 167	99.52 44	99.50 137	99.21 32	98.02 208	98.84 351	97.97 226	99.08 184	99.02 207	97.61 169	99.88 115	96.99 261	99.63 250	99.48 187
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									98.17 208	99.08 184	99.02 207	97.89 144	99.88 115	97.07 254	99.71 210	99.70 70

test_0728_SECOND				99.60 16	99.50 137	99.23 30	98.02 208	99.32 225					99.88 115	96.99 261	99.63 250	99.68 73

test0726					99.50 137	99.21 32	98.17 180	99.35 211	97.97 226	99.26 152	99.06 195	97.61 169

SR-MVS		98.71 140	98.43 185	99.57 21	99.18 248	99.35 16	98.36 160	99.29 246	98.29 194	98.88 236	98.85 262	97.53 178	99.87 135	96.14 342	99.31 338	99.48 187

DPM-MVS		96.32 378	95.59 394	98.51 261	98.76 338	97.21 248	94.54 474	98.26 401	91.94 473	96.37 449	97.25 435	93.06 366	99.43 438	91.42 469	98.74 405	98.89 365

GST-MVS		98.61 168	98.30 209	99.52 44	99.51 131	99.20 38	98.26 170	99.25 261	97.44 283	98.67 272	98.39 349	97.68 159	99.85 158	96.00 347	99.51 294	99.52 160

test_yl		96.69 360	96.29 374	97.90 330	98.28 409	95.24 356	97.29 325	97.36 429	98.21 201	98.17 326	97.86 397	86.27 434	99.55 397	94.87 384	98.32 429	98.89 365

thisisatest0530		95.27 417	94.45 429	97.74 348	99.19 240	94.37 388	97.86 238	90.20 511	97.17 313	98.22 324	97.65 412	73.53 494	99.90 81	96.90 272	99.35 329	98.95 354

Anonymous20240529		98.93 102	98.87 109	99.12 124	99.19 240	98.22 140	99.01 71	98.99 322	99.25 74	99.54 79	99.37 104	97.04 213	99.80 232	97.89 175	99.52 291	99.35 251

Anonymous202405211		97.90 266	97.50 295	99.08 133	98.90 311	98.25 134	98.53 129	96.16 464	98.87 138	99.11 179	98.86 259	90.40 406	99.78 257	97.36 229	99.31 338	99.19 307

DCV-MVSNet		96.69 360	96.29 374	97.90 330	98.28 409	95.24 356	97.29 325	97.36 429	98.21 201	98.17 326	97.86 397	86.27 434	99.55 397	94.87 384	98.32 429	98.89 365

tttt0517		95.64 407	94.98 417	97.64 363	99.36 189	93.81 418	98.72 104	90.47 510	98.08 221	98.67 272	98.34 356	73.88 493	99.92 65	97.77 189	99.51 294	99.20 301

our_test_3		97.39 313	97.73 278	96.34 434	98.70 352	89.78 486	94.61 471	98.97 325	96.50 352	99.04 196	98.85 262	95.98 280	99.84 176	97.26 238	99.67 233	99.41 218

thisisatest0515		94.12 438	93.16 446	96.97 410	98.60 374	92.90 437	93.77 494	90.61 509	94.10 443	96.91 418	95.87 465	74.99 491	99.80 232	94.52 393	99.12 374	98.20 435

ppachtmachnet_test		97.50 300	97.74 275	96.78 421	98.70 352	91.23 470	94.55 473	99.05 308	96.36 359	99.21 168	98.79 278	96.39 255	99.78 257	96.74 286	99.82 132	99.34 253

SMA-MVS		98.40 203	98.03 247	99.51 49	99.16 252	99.21 32	98.05 201	99.22 269	94.16 440	98.98 206	99.10 186	97.52 180	99.79 245	96.45 321	99.64 244	99.53 157
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																98.81 378

DPE-MVS		98.59 172	98.26 217	99.57 21	99.27 214	99.15 52	97.01 346	99.39 195	97.67 252	99.44 106	98.99 224	97.53 178	99.89 97	95.40 374	99.68 227	99.66 80
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.36 189	99.10 65				99.05 194

thres100view900		94.19 435	93.67 440	95.75 455	99.06 275	91.35 464	98.03 205	94.24 490	98.33 187	97.40 393	94.98 483	79.84 476	99.62 365	83.05 504	98.08 445	96.29 494

tfpnnormal		98.90 106	98.90 103	98.91 171	99.67 67	97.82 192	99.00 73	99.44 173	99.45 50	99.51 92	99.24 144	98.20 114	99.86 144	95.92 351	99.69 222	99.04 337

tfpn200view9		94.03 439	93.44 442	95.78 454	98.93 303	91.44 462	97.60 283	94.29 488	97.94 230	97.10 405	94.31 491	79.67 478	99.62 365	83.05 504	98.08 445	96.29 494

c3_l		97.36 316	97.37 303	97.31 390	98.09 425	93.25 430	95.01 458	99.16 287	97.05 319	98.77 259	98.72 292	92.88 369	99.64 359	96.93 266	99.76 184	99.05 333

CHOSEN 280x420		95.51 411	95.47 397	95.65 459	98.25 411	88.27 493	93.25 498	98.88 340	93.53 451	94.65 484	97.15 438	86.17 436	99.93 53	97.41 227	99.93 57	98.73 392

CANet		97.87 272	97.76 273	98.19 303	97.75 443	95.51 338	96.76 364	99.05 308	97.74 246	96.93 415	98.21 369	95.59 294	99.89 97	97.86 182	99.93 57	99.19 307

Fast-Effi-MVS+-dtu		98.27 226	98.09 239	98.81 189	98.43 397	98.11 149	97.61 282	99.50 137	98.64 158	97.39 395	97.52 421	98.12 123	99.95 25	96.90 272	98.71 409	98.38 427

Effi-MVS+-dtu		98.26 228	97.90 265	99.35 80	98.02 429	99.49 5	98.02 208	99.16 287	98.29 194	97.64 371	97.99 388	96.44 253	99.95 25	96.66 299	98.93 396	98.60 406

CANet_DTU		97.26 325	97.06 324	97.84 336	97.57 455	94.65 382	96.19 403	98.79 359	97.23 308	95.14 477	98.24 366	93.22 361	99.84 176	97.34 230	99.84 113	99.04 337

MGCNet		97.44 308	97.01 327	98.72 216	96.42 499	96.74 284	97.20 336	91.97 506	98.46 179	98.30 317	98.79 278	92.74 373	99.91 74	99.30 62	99.94 51	99.52 160

MP-MVS-pluss		98.57 175	98.23 222	99.60 16	99.69 60	99.35 16	97.16 341	99.38 197	94.87 423	98.97 210	98.99 224	98.01 130	99.88 115	97.29 236	99.70 219	99.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 203	98.00 250	99.61 13	99.57 102	99.25 28	98.57 124	99.35 211	97.55 267	99.31 142	97.71 408	94.61 326	99.88 115	96.14 342	99.19 363	99.70 70
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												84.74 453				98.81 378

sam_mvs												84.29 459

IterMVS-SCA-FT		97.85 278	98.18 229	96.87 415	99.27 214	91.16 471	95.53 438	99.25 261	99.10 105	99.41 113	99.35 111	93.10 364	99.96 13	98.65 114	99.94 51	99.49 176

TSAR-MVS + MP.		98.63 164	98.49 176	99.06 141	99.64 76	97.90 181	98.51 135	98.94 326	96.96 324	99.24 162	98.89 254	97.83 148	99.81 223	96.88 274	99.49 304	99.48 187
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		97.86 273	98.17 230	96.92 412	98.98 296	93.91 413	96.45 383	99.17 284	97.85 238	98.41 309	97.14 439	98.47 77	99.92 65	98.02 164	99.05 377	96.92 483

OPM-MVS		98.56 176	98.32 207	99.25 104	99.41 176	98.73 94	97.13 343	99.18 280	97.10 317	98.75 262	98.92 242	98.18 115	99.65 355	96.68 295	99.56 278	99.37 239
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 21	99.58 93	99.29 23	97.82 242	99.25 261	96.94 326	98.78 256	99.12 181	98.02 129	99.84 176	97.13 250	99.67 233	99.59 109

ambc				98.24 296	98.82 329	95.97 320	98.62 118	99.00 321		99.27 148	99.21 152	96.99 218	99.50 416	96.55 314	99.50 302	99.26 285

MTGPA								99.20 272

SPE-MVS-test		99.13 66	99.09 80	99.26 101	99.13 259	98.97 73	99.31 30	99.88 15	99.44 52	98.16 329	98.51 334	98.64 61	99.93 53	98.91 93	99.85 108	98.88 368

Effi-MVS+		98.02 255	97.82 270	98.62 233	98.53 386	97.19 250	97.33 320	99.68 62	97.30 296	96.68 434	97.46 426	98.56 73	99.80 232	96.63 301	98.20 436	98.86 370

xiu_mvs_v2_base		97.16 336	97.49 296	96.17 443	98.54 384	92.46 445	95.45 442	98.84 351	97.25 302	97.48 386	96.49 450	98.31 95	99.90 81	96.34 329	98.68 414	96.15 498

xiu_mvs_v1_base		97.86 273	98.17 230	96.92 412	98.98 296	93.91 413	96.45 383	99.17 284	97.85 238	98.41 309	97.14 439	98.47 77	99.92 65	98.02 164	99.05 377	96.92 483

new-patchmatchnet		98.35 212	98.74 126	97.18 397	99.24 225	92.23 452	96.42 387	99.48 147	98.30 191	99.69 55	99.53 64	97.44 188	99.82 206	98.84 99	99.77 168	99.49 176

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 26	99.53 9	99.76 39	99.64 26	99.84 30	99.83 4	99.50 9	99.87 135	99.36 57	99.92 71	99.64 86

pmmvs5		97.64 292	97.49 296	98.08 315	99.14 257	95.12 362	96.70 368	99.05 308	93.77 448	98.62 280	98.83 269	93.23 360	99.75 281	98.33 141	99.76 184	99.36 246

test_post1							97.59 285				20.48 522	83.07 467	99.66 348	94.16 404

test_post											21.25 521	83.86 462	99.70 313

Fast-Effi-MVS+		97.67 290	97.38 302	98.57 244	98.71 348	97.43 225	97.23 331	99.45 165	94.82 424	96.13 453	96.51 449	98.52 75	99.91 74	96.19 338	98.83 400	98.37 429

patchmatchnet-post											98.77 282	84.37 456	99.85 158

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 208	98.18 142	99.49 12	99.51 134	99.70 15	99.80 37	99.68 25	96.84 226	99.83 194	99.21 70	99.91 80	99.77 53

pmmvs-eth3d		98.47 194	98.34 201	98.86 177	99.30 206	97.76 198	97.16 341	99.28 250	95.54 403	99.42 111	99.19 157	97.27 199	99.63 362	97.89 175	99.97 21	99.20 301

GG-mvs-BLEND				94.76 474	94.54 511	92.13 453	99.31 30	80.47 522		88.73 513	91.01 512	67.59 504	98.16 499	82.30 508	94.53 504	93.98 506

xiu_mvs_v1_base_debi		97.86 273	98.17 230	96.92 412	98.98 296	93.91 413	96.45 383	99.17 284	97.85 238	98.41 309	97.14 439	98.47 77	99.92 65	98.02 164	99.05 377	96.92 483

Anonymous20231206		98.21 235	98.21 223	98.20 301	99.51 131	95.43 347	98.13 184	99.32 225	96.16 371	98.93 225	98.82 272	96.00 275	99.83 194	97.32 234	99.73 192	99.36 246

MTAPA		98.88 109	98.64 147	99.61 13	99.67 67	99.36 15	98.43 148	99.20 272	98.83 147	98.89 232	98.90 248	96.98 219	99.92 65	97.16 245	99.70 219	99.56 130

MTMP							97.93 226	91.91 507

gm-plane-assit					94.83 510	81.97 518			88.07 502		94.99 482		99.60 376	91.76 462

test9_res														93.28 433	99.15 368	99.38 237

MVP-Stereo		98.08 250	97.92 262	98.57 244	98.96 299	96.79 280	97.90 232	99.18 280	96.41 358	98.46 304	98.95 238	95.93 284	99.60 376	96.51 317	98.98 391	99.31 267
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 348	98.08 156	95.96 418	99.03 313	91.40 479	95.85 461	97.53 419	96.52 249	99.76 269

train_agg		97.10 338	96.45 369	99.07 135	98.71 348	98.08 156	95.96 418	99.03 313	91.64 474	95.85 461	97.53 419	96.47 251	99.76 269	93.67 421	99.16 366	99.36 246

gg-mvs-nofinetune		92.37 466	91.20 470	95.85 452	95.80 508	92.38 448	99.31 30	81.84 521	99.75 10	91.83 507	99.74 18	68.29 500	99.02 476	87.15 495	97.12 475	96.16 497

SCA		96.41 376	96.66 355	95.67 457	98.24 412	88.35 492	95.85 427	96.88 452	96.11 372	97.67 370	98.67 306	93.10 364	99.85 158	94.16 404	99.22 355	98.81 378

Patchmatch-test		96.55 367	96.34 372	97.17 399	98.35 403	93.06 432	98.40 156	97.79 416	97.33 292	98.41 309	98.67 306	83.68 463	99.69 321	95.16 378	99.31 338	98.77 386

test_8					98.67 362	98.01 164	95.91 424	99.02 316	91.64 474	95.79 463	97.50 422	96.47 251	99.76 269

MS-PatchMatch		97.68 289	97.75 274	97.45 385	98.23 414	93.78 419	97.29 325	98.84 351	96.10 373	98.64 276	98.65 311	96.04 272	99.36 447	96.84 278	99.14 369	99.20 301

Patchmatch-RL test		97.26 325	97.02 326	97.99 325	99.52 128	95.53 337	96.13 408	99.71 47	97.47 275	99.27 148	99.16 167	84.30 458	99.62 365	97.89 175	99.77 168	98.81 378

cdsmvs_eth3d_5k		24.66 485	32.88 488	0.00 505	0.00 528	0.00 530	0.00 516	99.10 298	0.00 523	0.00 524	97.58 416	99.21 18	0.00 524	0.00 522	0.00 522	0.00 520

pcd_1.5k_mvsjas		8.17 488	10.90 491	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	98.07 125	0.00 524	0.00 522	0.00 522	0.00 520

agg_prior2														92.50 455	99.16 366	99.37 239

agg_prior					98.68 361	97.99 166		99.01 319		95.59 464			99.77 263

tmp_tt		78.77 482	78.73 485	78.90 500	58.45 525	74.76 524	94.20 481	78.26 523	39.16 518	86.71 514	92.82 502	80.50 474	75.19 520	86.16 500	92.29 509	86.74 514

canonicalmvs		98.34 213	98.26 217	98.58 241	98.46 392	97.82 192	98.96 78	99.46 161	99.19 87	97.46 387	95.46 475	98.59 67	99.46 431	98.08 157	98.71 409	98.46 414

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 55	98.93 130	99.65 63	99.72 21	98.93 33	99.95 25	99.11 77	100.00 1	99.82 36

alignmvs		97.35 317	96.88 337	98.78 199	98.54 384	98.09 152	97.71 262	97.69 420	99.20 82	97.59 375	95.90 464	88.12 426	99.55 397	98.18 149	98.96 393	98.70 396

nrg030		99.40 25	99.35 33	99.54 31	99.58 93	99.13 60	98.98 76	99.48 147	99.68 19	99.46 101	99.26 137	98.62 64	99.73 295	99.17 74	99.92 71	99.76 58

v144192		98.54 183	98.57 160	98.45 270	99.21 233	95.98 318	97.63 276	99.36 205	97.15 316	99.32 139	99.18 161	95.84 287	99.84 176	99.50 50	99.91 80	99.54 143

FIs		99.14 62	99.09 80	99.29 95	99.70 56	98.28 131	99.13 59	99.52 133	99.48 44	99.24 162	99.41 94	96.79 233	99.82 206	98.69 112	99.88 95	99.76 58

v1921920		98.54 183	98.60 156	98.38 279	99.20 237	95.76 331	97.56 288	99.36 205	97.23 308	99.38 119	99.17 165	96.02 273	99.84 176	99.57 39	99.90 88	99.54 143

UA-Net		99.47 16	99.40 27	99.70 2	99.49 145	99.29 23	99.80 4	99.72 45	99.82 8	99.04 196	99.81 8	98.05 128	99.96 13	98.85 98	99.99 5	99.86 28

v1192		98.60 170	98.66 144	98.41 275	99.27 214	95.88 323	97.52 294	99.36 205	97.41 284	99.33 133	99.20 154	96.37 258	99.82 206	99.57 39	99.92 71	99.55 137

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 123	99.30 35	99.57 105	99.61 34	99.40 116	99.50 68	97.12 209	99.85 158	99.02 86	99.94 51	99.80 45

v1144		98.60 170	98.66 144	98.41 275	99.36 189	95.90 322	97.58 286	99.34 217	97.51 271	99.27 148	99.15 173	96.34 260	99.80 232	99.47 53	99.93 57	99.51 164

sosnet-low-res		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

HFP-MVS		98.71 140	98.44 184	99.51 49	99.49 145	99.16 48	98.52 130	99.31 230	97.47 275	98.58 289	98.50 338	97.97 135	99.85 158	96.57 307	99.59 265	99.53 157

v148		98.45 197	98.60 156	98.00 323	99.44 166	94.98 366	97.44 308	99.06 304	98.30 191	99.32 139	98.97 231	96.65 244	99.62 365	98.37 137	99.85 108	99.39 228

sosnet		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

uncertanet		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

AllTest		98.44 198	98.20 224	99.16 118	99.50 137	98.55 108	98.25 171	99.58 97	96.80 338	98.88 236	99.06 195	97.65 162	99.57 390	94.45 396	99.61 259	99.37 239

TestCases				99.16 118	99.50 137	98.55 108		99.58 97	96.80 338	98.88 236	99.06 195	97.65 162	99.57 390	94.45 396	99.61 259	99.37 239

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 111	99.45 14	99.61 86	99.66 23	99.68 57	99.66 32	98.44 83	99.95 25	99.73 28	99.96 28	99.75 62

region2R		98.69 149	98.40 189	99.54 31	99.53 125	99.17 43	98.52 130	99.31 230	97.46 280	98.44 306	98.51 334	97.83 148	99.88 115	96.46 320	99.58 270	99.58 117

RRT-MVS		97.88 270	97.98 252	97.61 366	98.15 420	93.77 420	98.97 77	99.64 75	99.16 92	98.69 268	99.42 89	91.60 389	99.89 97	97.63 204	98.52 425	99.16 321

balanced_ft_v1		98.28 225	98.35 200	98.10 311	98.08 426	96.23 308	99.23 45	99.26 259	98.34 185	97.46 387	99.42 89	95.38 302	99.88 115	98.60 117	99.34 331	98.17 437

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 144	99.20 49	99.65 73	99.48 44	99.92 8	99.71 22	98.07 125	99.96 13	99.53 48	100.00 1	99.93 11

PS-MVSNAJ		97.08 341	97.39 301	96.16 445	98.56 382	92.46 445	95.24 451	98.85 350	97.25 302	97.49 385	95.99 461	98.07 125	99.90 81	96.37 326	98.67 415	96.12 499

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 103	99.28 40	99.66 69	99.09 108	99.89 18	99.68 25	99.53 7	99.97 6	99.50 50	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 73	99.90 14	99.74 18	99.68 4	99.97 6	99.55 43	99.99 5	99.88 20

EI-MVSNet-UG-set		98.69 149	98.71 133	98.62 233	99.10 263	96.37 303	97.23 331	98.87 342	99.20 82	99.19 170	98.99 224	97.30 196	99.85 158	98.77 105	99.79 156	99.65 85

EI-MVSNet-Vis-set		98.68 155	98.70 136	98.63 231	99.09 266	96.40 302	97.23 331	98.86 347	99.20 82	99.18 175	98.97 231	97.29 198	99.85 158	98.72 109	99.78 161	99.64 86

HPM-MVS++		98.10 247	97.64 287	99.48 57	99.09 266	99.13 60	97.52 294	98.75 368	97.46 280	96.90 421	97.83 401	96.01 274	99.84 176	95.82 359	99.35 329	99.46 197

test_prior4						97.97 170	95.86 425

XVS		98.72 139	98.45 182	99.53 38	99.46 159	99.21 32	98.65 114	99.34 217	98.62 163	97.54 380	98.63 317	97.50 182	99.83 194	96.79 280	99.53 288	99.56 130

v1240		98.55 180	98.62 151	98.32 286	99.22 231	95.58 335	97.51 296	99.45 165	97.16 314	99.45 105	99.24 144	96.12 270	99.85 158	99.60 37	99.88 95	99.55 137

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 100	99.29 36	99.63 77	99.30 70	99.65 63	99.60 45	99.16 22	99.82 206	99.07 80	99.83 125	99.56 130

test_prior2							95.74 432		96.48 354	96.11 454	97.63 414	95.92 285		94.16 404	99.20 360

X-MVStestdata		94.32 432	92.59 452	99.53 38	99.46 159	99.21 32	98.65 114	99.34 217	98.62 163	97.54 380	45.85 518	97.50 182	99.83 194	96.79 280	99.53 288	99.56 130

test_prior				98.95 163	98.69 357	97.95 174		99.03 313					99.59 381			99.30 271

旧先验2							95.76 431		88.56 501	97.52 382			99.66 348	94.48 394

新几何2							95.93 421

新几何1				98.91 171	98.94 301	97.76 198		98.76 364	87.58 503	96.75 430	98.10 378	94.80 321	99.78 257	92.73 451	99.00 386	99.20 301

旧先验1					98.82 329	97.45 223		98.76 364			98.34 356	95.50 298			99.01 385	99.23 291

无先验							95.74 432	98.74 370	89.38 495				99.73 295	92.38 457		99.22 296

原ACMM2							95.53 438

原ACMM1				98.35 284	98.90 311	96.25 307		98.83 355	92.48 467	96.07 456	98.10 378	95.39 301	99.71 306	92.61 454	98.99 388	99.08 329

test222					98.92 307	96.93 272	95.54 437	98.78 361	85.72 506	96.86 425	98.11 377	94.43 329			99.10 376	99.23 291

testdata2													99.79 245	92.80 448

segment_acmp												97.02 216

testdata				98.09 312	98.93 303	95.40 348		98.80 358	90.08 491	97.45 390	98.37 352	95.26 304	99.70 313	93.58 424	98.95 394	99.17 315

testdata1							95.44 443		96.32 361

v8		99.01 88	99.16 62	98.57 244	99.47 156	96.31 306	98.90 84	99.47 156	99.03 119	99.52 87	99.57 49	96.93 222	99.81 223	99.60 37	99.98 12	99.60 102

1314		95.74 403	95.60 392	96.17 443	97.53 460	92.75 441	98.07 198	98.31 399	91.22 481	94.25 488	96.68 446	95.53 295	99.03 474	91.64 465	97.18 474	96.74 489

LFMVS		97.20 332	96.72 349	98.64 227	98.72 344	96.95 270	98.93 82	94.14 492	99.74 12	98.78 256	99.01 218	84.45 455	99.73 295	97.44 225	99.27 346	99.25 286

VDD-MVS		98.56 176	98.39 192	99.07 135	99.13 259	98.07 158	98.59 122	97.01 443	99.59 36	99.11 179	99.27 131	94.82 318	99.79 245	98.34 139	99.63 250	99.34 253

VDDNet		98.21 235	97.95 256	99.01 150	99.58 93	97.74 200	99.01 71	97.29 434	99.67 20	98.97 210	99.50 68	90.45 405	99.80 232	97.88 178	99.20 360	99.48 187

v10		98.97 97	99.11 72	98.55 251	99.44 166	96.21 309	98.90 84	99.55 118	98.73 149	99.48 96	99.60 45	96.63 245	99.83 194	99.70 33	99.99 5	99.61 100

VPNet		98.87 110	98.83 118	99.01 150	99.70 56	97.62 210	98.43 148	99.35 211	99.47 47	99.28 146	99.05 202	96.72 239	99.82 206	98.09 156	99.36 326	99.59 109

MVS		93.19 454	92.09 459	96.50 428	96.91 483	94.03 403	98.07 198	98.06 412	68.01 516	94.56 486	96.48 451	95.96 282	99.30 457	83.84 503	96.89 480	96.17 496

v2v482		98.56 176	98.62 151	98.37 282	99.42 173	95.81 328	97.58 286	99.16 287	97.90 234	99.28 146	99.01 218	95.98 280	99.79 245	99.33 59	99.90 88	99.51 164

V42		98.78 131	98.78 124	98.76 206	99.44 166	97.04 263	98.27 169	99.19 276	97.87 236	99.25 160	99.16 167	96.84 226	99.78 257	99.21 70	99.84 113	99.46 197

SD-MVS		98.40 203	98.68 139	97.54 376	98.96 299	97.99 166	97.88 234	99.36 205	98.20 205	99.63 66	99.04 204	98.76 46	95.33 516	96.56 311	99.74 189	99.31 267
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		95.86 399	95.32 408	97.49 381	98.60 374	94.15 396	93.83 493	97.93 414	95.49 405	96.68 434	97.42 428	83.21 465	99.30 457	96.22 336	98.55 423	99.01 342

MSLP-MVS++		98.02 255	98.14 236	97.64 363	98.58 379	95.19 359	97.48 300	99.23 268	97.47 275	97.90 353	98.62 320	97.04 213	98.81 486	97.55 212	99.41 320	98.94 358

APDe-MVS		98.99 92	98.79 122	99.60 16	99.21 233	99.15 52	98.87 89	99.48 147	97.57 263	99.35 128	99.24 144	97.83 148	99.89 97	97.88 178	99.70 219	99.75 62
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 38	99.19 240	99.27 26	98.49 140	99.33 223	98.64 158	99.03 199	98.98 229	97.89 144	99.85 158	96.54 315	99.42 319	99.46 197

ADS-MVSNet2		95.43 415	94.98 417	96.76 422	98.14 421	91.74 455	97.92 229	97.76 417	90.23 487	96.51 445	98.91 245	85.61 444	99.85 158	92.88 444	96.90 478	98.69 397

EI-MVSNet		98.40 203	98.51 168	98.04 320	99.10 263	94.73 378	97.20 336	98.87 342	98.97 125	99.06 186	99.02 207	96.00 275	99.80 232	98.58 118	99.82 132	99.60 102

Regformer		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

CVMVSNet		96.25 383	97.21 314	93.38 492	99.10 263	80.56 521	97.20 336	98.19 407	96.94 326	99.00 201	99.02 207	89.50 414	99.80 232	96.36 328	99.59 265	99.78 50

pmmvs4		97.58 297	97.28 308	98.51 261	98.84 324	96.93 272	95.40 445	98.52 389	93.60 450	98.61 282	98.65 311	95.10 310	99.60 376	96.97 264	99.79 156	98.99 346

EU-MVSNet		97.66 291	98.50 171	95.13 470	99.63 82	85.84 502	98.35 161	98.21 404	98.23 198	99.54 79	99.46 80	95.02 312	99.68 331	98.24 143	99.87 99	99.87 22

VNet		98.42 199	98.30 209	98.79 196	98.79 337	97.29 238	98.23 172	98.66 375	99.31 68	98.85 243	98.80 276	94.80 321	99.78 257	98.13 152	99.13 371	99.31 267

test-LLR		93.90 441	93.85 436	94.04 482	96.53 493	84.62 508	94.05 487	92.39 500	96.17 367	94.12 490	95.07 479	82.30 470	99.67 335	95.87 355	98.18 437	97.82 455

TESTMET0.1,1		92.19 469	91.77 467	93.46 489	96.48 498	82.80 516	94.05 487	91.52 508	94.45 434	94.00 494	94.88 485	66.65 505	99.56 393	95.78 360	98.11 443	98.02 445

test-mter		92.33 467	91.76 468	94.04 482	96.53 493	84.62 508	94.05 487	92.39 500	94.00 446	94.12 490	95.07 479	65.63 511	99.67 335	95.87 355	98.18 437	97.82 455

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 145	98.36 126	99.00 73	99.45 165	99.63 28	99.52 87	99.44 85	98.25 105	99.88 115	99.09 79	99.84 113	99.62 92

ACMMPR		98.70 145	98.42 187	99.54 31	99.52 128	99.14 57	98.52 130	99.31 230	97.47 275	98.56 293	98.54 329	97.75 156	99.88 115	96.57 307	99.59 265	99.58 117

testgi		98.32 218	98.39 192	98.13 308	99.57 102	95.54 336	97.78 248	99.49 145	97.37 289	99.19 170	97.65 412	98.96 30	99.49 420	96.50 318	98.99 388	99.34 253

test20.03		98.78 131	98.77 125	98.78 199	99.46 159	97.20 249	97.78 248	99.24 266	99.04 117	99.41 113	98.90 248	97.65 162	99.76 269	97.70 199	99.79 156	99.39 228

thres600view7		94.45 430	93.83 437	96.29 436	99.06 275	91.53 459	97.99 219	94.24 490	98.34 185	97.44 391	95.01 481	79.84 476	99.67 335	84.33 502	98.23 434	97.66 467

ADS-MVSNet		95.24 418	94.93 420	96.18 442	98.14 421	90.10 484	97.92 229	97.32 433	90.23 487	96.51 445	98.91 245	85.61 444	99.74 288	92.88 444	96.90 478	98.69 397

MP-MVS		98.46 195	98.09 239	99.54 31	99.57 102	99.22 31	98.50 137	99.19 276	97.61 259	97.58 376	98.66 309	97.40 190	99.88 115	94.72 389	99.60 261	99.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 486	20.53 489	6.87 504	12.05 526	4.20 529	93.62 496	6.73 527	4.62 522	10.41 522	24.33 519	8.28 526	3.56 523	9.69 521	15.07 520	12.86 519

thres400		94.14 437	93.44 442	96.24 439	98.93 303	91.44 462	97.60 283	94.29 488	97.94 230	97.10 405	94.31 491	79.67 478	99.62 365	83.05 504	98.08 445	97.66 467

test123		17.04 487	20.11 490	7.82 503	10.25 527	4.91 528	94.80 462	4.47 528	4.93 521	10.00 523	24.28 520	9.69 525	3.64 522	10.14 520	12.43 521	14.92 518

thres200		93.72 445	93.14 447	95.46 465	98.66 367	91.29 466	96.61 374	94.63 485	97.39 287	96.83 426	93.71 494	79.88 475	99.56 393	82.40 507	98.13 442	95.54 503

test0.0.03 1		94.51 429	93.69 439	96.99 408	96.05 503	93.61 427	94.97 459	93.49 495	96.17 367	97.57 378	94.88 485	82.30 470	99.01 478	93.60 423	94.17 505	98.37 429

pmmvs3		95.03 423	94.40 430	96.93 411	97.70 449	92.53 444	95.08 456	97.71 419	88.57 500	97.71 367	98.08 381	79.39 480	99.82 206	96.19 338	99.11 375	98.43 422

EMVS		93.83 442	94.02 434	93.23 493	96.83 486	84.96 505	89.77 510	96.32 462	97.92 232	97.43 392	96.36 456	86.17 436	98.93 481	87.68 494	97.73 456	95.81 501

E-PMN		94.17 436	94.37 431	93.58 488	96.86 484	85.71 504	90.11 509	97.07 442	98.17 208	97.82 362	97.19 436	84.62 454	98.94 480	89.77 486	97.68 457	96.09 500

PGM-MVS		98.66 159	98.37 196	99.55 28	99.53 125	99.18 42	98.23 172	99.49 145	97.01 323	98.69 268	98.88 256	98.00 131	99.89 97	95.87 355	99.59 265	99.58 117

LCM-MVSNet-Re		98.64 162	98.48 177	99.11 126	98.85 323	98.51 113	98.49 140	99.83 26	98.37 182	99.69 55	99.46 80	98.21 112	99.92 65	94.13 408	99.30 342	98.91 363

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 14	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		98.00 258	97.63 288	99.10 128	99.24 225	98.17 143	96.89 357	98.73 371	95.66 396	97.92 351	97.70 410	97.17 206	99.66 348	96.18 340	99.23 354	99.47 195

mvs_anonymous		97.83 281	98.16 233	96.87 415	98.18 417	91.89 454	97.31 323	98.90 336	97.37 289	98.83 247	99.46 80	96.28 263	99.79 245	98.90 94	98.16 440	98.95 354

MVS_Test		98.18 240	98.36 197	97.67 356	98.48 389	94.73 378	98.18 177	99.02 316	97.69 250	98.04 343	99.11 183	97.22 203	99.56 393	98.57 120	98.90 398	98.71 393

MDA-MVSNet-bldmvs		97.94 264	97.91 264	98.06 317	99.44 166	94.96 367	96.63 373	99.15 292	98.35 184	98.83 247	99.11 183	94.31 336	99.85 158	96.60 304	98.72 407	99.37 239

CDPH-MVS		97.26 325	96.66 355	99.07 135	99.00 292	98.15 144	96.03 413	99.01 319	91.21 482	97.79 363	97.85 399	96.89 224	99.69 321	92.75 450	99.38 325	99.39 228

test12				98.93 167	98.58 379	97.83 187		98.66 375		96.53 441		95.51 297	99.69 321		99.13 371	99.27 279

casdiffmvs		98.95 100	99.00 92	98.81 189	99.38 182	97.33 230	97.82 242	99.57 105	99.17 91	99.35 128	99.17 165	98.35 92	99.69 321	98.46 128	99.73 192	99.41 218
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		98.22 233	98.24 221	98.17 304	99.00 292	95.44 346	96.38 389	99.58 97	97.79 243	98.53 298	98.50 338	96.76 236	99.74 288	97.95 173	99.64 244	99.34 253
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		93.73 444	92.83 451	96.42 431	97.70 449	91.28 467	96.84 359	89.77 512	93.96 447	92.44 504	95.93 463	79.14 481	99.77 263	92.94 441	96.76 482	98.21 434

baseline1		95.96 396	95.44 400	97.52 378	98.51 388	93.99 410	98.39 157	96.09 467	98.21 201	98.40 313	97.76 406	86.88 430	99.63 362	95.42 373	89.27 511	98.95 354

YYNet1		97.60 294	97.67 282	97.39 389	99.04 279	93.04 435	95.27 449	98.38 397	97.25 302	98.92 227	98.95 238	95.48 299	99.73 295	96.99 261	98.74 405	99.41 218

PMMVS2		98.07 251	98.08 242	98.04 320	99.41 176	94.59 384	94.59 472	99.40 193	97.50 272	98.82 250	98.83 269	96.83 228	99.84 176	97.50 218	99.81 139	99.71 65

MDA-MVSNet_test_wron		97.60 294	97.66 285	97.41 388	99.04 279	93.09 431	95.27 449	98.42 394	97.26 301	98.88 236	98.95 238	95.43 300	99.73 295	97.02 257	98.72 407	99.41 218

tpmvs		95.02 424	95.25 410	94.33 478	96.39 501	85.87 501	98.08 194	96.83 454	95.46 406	95.51 472	98.69 302	85.91 442	99.53 405	94.16 404	96.23 487	97.58 470

PM-MVS		98.82 123	98.72 130	99.12 124	99.64 76	98.54 111	97.98 220	99.68 62	97.62 256	99.34 130	99.18 161	97.54 176	99.77 263	97.79 186	99.74 189	99.04 337

HQP_MVS		97.99 261	97.67 282	98.93 167	99.19 240	97.65 207	97.77 251	99.27 253	98.20 205	97.79 363	97.98 389	94.90 314	99.70 313	94.42 398	99.51 294	99.45 202

plane_prior7					99.19 240	97.87 183

plane_prior6					98.99 295	97.70 204						94.90 314

plane_prior5								99.27 253					99.70 313	94.42 398	99.51 294	99.45 202

plane_prior4											97.98 389

plane_prior3						97.78 197			97.41 284	97.79 363

plane_prior2							97.77 251		98.20 205

plane_prior1					99.05 278

plane_prior						97.65 207	97.07 344		96.72 343						99.36 326

PS-CasMVS		99.40 25	99.33 37	99.62 9	99.71 48	99.10 65	99.29 36	99.53 128	99.53 41	99.46 101	99.41 94	98.23 107	99.95 25	98.89 96	99.95 39	99.81 41

UniMVSNet_NR-MVSNet		98.86 114	98.68 139	99.40 71	99.17 250	98.74 91	97.68 266	99.40 193	99.14 96	99.06 186	98.59 325	96.71 240	99.93 53	98.57 120	99.77 168	99.53 157

PEN-MVS		99.41 24	99.34 35	99.62 9	99.73 37	99.14 57	99.29 36	99.54 124	99.62 32	99.56 74	99.42 89	98.16 119	99.96 13	98.78 102	99.93 57	99.77 53

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 106	99.27 42	99.57 105	99.39 58	99.75 44	99.62 40	99.17 20	99.83 194	99.06 82	99.62 254	99.66 80

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 48	99.30 21	99.31 30	99.51 134	99.64 26	99.56 74	99.46 80	98.23 107	99.97 6	98.78 102	99.93 57	99.72 64

DU-MVS		98.82 123	98.63 149	99.39 72	99.16 252	98.74 91	97.54 292	99.25 261	98.84 146	99.06 186	98.76 287	96.76 236	99.93 53	98.57 120	99.77 168	99.50 168

UniMVSNet (Re)		98.87 110	98.71 133	99.35 80	99.24 225	98.73 94	97.73 261	99.38 197	98.93 130	99.12 178	98.73 290	96.77 234	99.86 144	98.63 116	99.80 150	99.46 197

CP-MVSNet		99.21 47	99.09 80	99.56 26	99.65 70	98.96 77	99.13 59	99.34 217	99.42 55	99.33 133	99.26 137	97.01 217	99.94 41	98.74 107	99.93 57	99.79 47

WR-MVS_H		99.33 30	99.22 54	99.65 8	99.71 48	99.24 29	99.32 26	99.55 118	99.46 49	99.50 93	99.34 115	97.30 196	99.93 53	98.90 94	99.93 57	99.77 53

WR-MVS		98.40 203	98.19 228	99.03 145	99.00 292	97.65 207	96.85 358	98.94 326	98.57 171	98.89 232	98.50 338	95.60 293	99.85 158	97.54 214	99.85 108	99.59 109

NR-MVSNet		98.95 100	98.82 119	99.36 74	99.16 252	98.72 96	99.22 46	99.20 272	99.10 105	99.72 47	98.76 287	96.38 257	99.86 144	98.00 167	99.82 132	99.50 168

Baseline_NR-MVSNet		98.98 96	98.86 113	99.36 74	99.82 19	98.55 108	97.47 304	99.57 105	99.37 60	99.21 168	99.61 43	96.76 236	99.83 194	98.06 159	99.83 125	99.71 65

TranMVSNet+NR-MVSNet		99.17 52	99.07 83	99.46 63	99.37 188	98.87 84	98.39 157	99.42 185	99.42 55	99.36 126	99.06 195	98.38 87	99.95 25	98.34 139	99.90 88	99.57 124

TSAR-MVS + GP.		98.18 240	97.98 252	98.77 204	98.71 348	97.88 182	96.32 394	98.66 375	96.33 360	99.23 164	98.51 334	97.48 186	99.40 442	97.16 245	99.46 307	99.02 340

n2								0.00 529

nn								0.00 529

mPP-MVS		98.64 162	98.34 201	99.54 31	99.54 122	99.17 43	98.63 116	99.24 266	97.47 275	98.09 337	98.68 304	97.62 167	99.89 97	96.22 336	99.62 254	99.57 124

door-mid								99.57 105

XVG-OURS-SEG-HR		98.49 192	98.28 212	99.14 122	99.49 145	98.83 86	96.54 377	99.48 147	97.32 294	99.11 179	98.61 322	99.33 15	99.30 457	96.23 335	98.38 428	99.28 276

mvsmamba		97.57 298	97.26 309	98.51 261	98.69 357	96.73 285	98.74 99	97.25 435	97.03 322	97.88 355	99.23 149	90.95 399	99.87 135	96.61 303	99.00 386	98.91 363

MVSFormer		98.26 228	98.43 185	97.77 342	98.88 317	93.89 416	99.39 20	99.56 114	99.11 98	98.16 329	98.13 374	93.81 350	99.97 6	99.26 65	99.57 274	99.43 210

jason		97.45 307	97.35 305	97.76 345	99.24 225	93.93 412	95.86 425	98.42 394	94.24 438	98.50 301	98.13 374	94.82 318	99.91 74	97.22 241	99.73 192	99.43 210
jason: jason.
lupinMVS		97.06 343	96.86 338	97.65 360	98.88 317	93.89 416	95.48 441	97.97 413	93.53 451	98.16 329	97.58 416	93.81 350	99.91 74	96.77 283	99.57 274	99.17 315

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 91	99.39 20	99.56 114	99.11 98	99.70 51	99.73 20	99.00 27	99.97 6	99.26 65	99.98 12	99.89 16

HPM-MVS_fast		99.01 88	98.82 119	99.57 21	99.71 48	99.35 16	99.00 73	99.50 137	97.33 292	98.94 224	98.86 259	98.75 47	99.82 206	97.53 215	99.71 210	99.56 130

K. test v3		98.00 258	97.66 285	99.03 145	99.79 23	97.56 212	99.19 53	92.47 499	99.62 32	99.52 87	99.66 32	89.61 412	99.96 13	99.25 67	99.81 139	99.56 130

lessismore_v0				98.97 159	99.73 37	97.53 215		86.71 517		99.37 123	99.52 67	89.93 408	99.92 65	98.99 88	99.72 201	99.44 206

SixPastTwentyTwo		98.75 136	98.62 151	99.16 118	99.83 18	97.96 173	99.28 40	98.20 405	99.37 60	99.70 51	99.65 36	92.65 375	99.93 53	99.04 84	99.84 113	99.60 102

OurMVSNet-221017-0		99.37 28	99.31 41	99.53 38	99.91 3	98.98 71	99.63 7	99.58 97	99.44 52	99.78 39	99.76 15	96.39 255	99.92 65	99.44 54	99.92 71	99.68 73

HPM-MVS		98.79 129	98.53 166	99.59 20	99.65 70	99.29 23	99.16 55	99.43 179	96.74 342	98.61 282	98.38 351	98.62 64	99.87 135	96.47 319	99.67 233	99.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 185	98.34 201	99.11 126	99.50 137	98.82 88	95.97 416	99.50 137	97.30 296	99.05 194	98.98 229	99.35 14	99.32 454	95.72 362	99.68 227	99.18 311

XVG-ACMP-BASELINE		98.56 176	98.34 201	99.22 109	99.54 122	98.59 105	97.71 262	99.46 161	97.25 302	98.98 206	98.99 224	97.54 176	99.84 176	95.88 352	99.74 189	99.23 291

casdiffmvs_mvg		99.12 69	99.16 62	98.99 153	99.43 171	97.73 202	98.00 212	99.62 83	99.22 78	99.55 77	99.22 150	98.93 33	99.75 281	98.66 113	99.81 139	99.50 168
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 73	98.23 172	99.48 147	96.60 347	99.10 182	99.06 195	98.71 51	99.83 194	95.58 370	99.78 161	99.62 92

LGP-MVS_train				99.47 61	99.57 102	98.97 73		99.48 147	96.60 347	99.10 182	99.06 195	98.71 51	99.83 194	95.58 370	99.78 161	99.62 92

baseline		98.96 99	99.02 88	98.76 206	99.38 182	97.26 241	98.49 140	99.50 137	98.86 140	99.19 170	99.06 195	98.23 107	99.69 321	98.71 110	99.76 184	99.33 259

test11								98.87 342

door								99.41 189

EPNet_dtu		94.93 426	94.78 422	95.38 467	93.58 513	87.68 496	96.78 362	95.69 477	97.35 291	89.14 512	98.09 380	88.15 425	99.49 420	94.95 383	99.30 342	98.98 347
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 303	97.14 319	98.54 256	99.68 63	96.09 313	96.50 381	99.62 83	91.58 476	98.84 245	98.97 231	92.36 377	99.88 115	96.76 284	99.95 39	99.67 78

EPNet		96.14 387	95.44 400	98.25 294	90.76 521	95.50 341	97.92 229	94.65 484	98.97 125	92.98 501	98.85 262	89.12 416	99.87 135	95.99 348	99.68 227	99.39 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 280

HQP-NCC					98.67 362		96.29 396		96.05 374	95.55 467

ACMP_Plane					98.67 362		96.29 396		96.05 374	95.55 467

APD-MVS		98.10 247	97.67 282	99.42 67	99.11 261	98.93 79	97.76 254	99.28 250	94.97 420	98.72 265	98.77 282	97.04 213	99.85 158	93.79 418	99.54 284	99.49 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 446

HQP4-MVS										95.56 466			99.54 403			99.32 262

HQP3-MVS								99.04 311							99.26 349

HQP2-MVS												93.84 348

CNVR-MVS		98.17 243	97.87 267	99.07 135	98.67 362	98.24 135	97.01 346	98.93 329	97.25 302	97.62 372	98.34 356	97.27 199	99.57 390	96.42 323	99.33 333	99.39 228

NCCC		97.86 273	97.47 299	99.05 142	98.61 372	98.07 158	96.98 349	98.90 336	97.63 255	97.04 411	97.93 394	95.99 279	99.66 348	95.31 375	98.82 402	99.43 210

114514_t		96.50 370	95.77 384	98.69 220	99.48 153	97.43 225	97.84 241	99.55 118	81.42 512	96.51 445	98.58 326	95.53 295	99.67 335	93.41 431	99.58 270	98.98 347

CP-MVS		98.70 145	98.42 187	99.52 44	99.36 189	99.12 62	98.72 104	99.36 205	97.54 269	98.30 317	98.40 348	97.86 147	99.89 97	96.53 316	99.72 201	99.56 130

DSMNet-mixed		97.42 310	97.60 290	96.87 415	99.15 256	91.46 460	98.54 128	99.12 295	92.87 463	97.58 376	99.63 39	96.21 265	99.90 81	95.74 361	99.54 284	99.27 279

tpm2		93.09 455	92.58 453	94.62 476	97.56 456	86.53 500	97.66 270	95.79 474	86.15 505	94.07 492	98.23 368	75.95 489	99.53 405	90.91 478	96.86 481	97.81 457

NP-MVS					98.84 324	97.39 227					96.84 443

EG-PatchMatch MVS		98.99 92	99.01 90	98.94 164	99.50 137	97.47 220	98.04 203	99.59 94	98.15 216	99.40 116	99.36 110	98.58 72	99.76 269	98.78 102	99.68 227	99.59 109

tpm cat1		93.29 452	93.13 448	93.75 486	97.39 469	84.74 506	97.39 311	97.65 423	83.39 510	94.16 489	98.41 347	82.86 468	99.39 444	91.56 467	95.35 500	97.14 482

SteuartSystems-ACMMP		98.79 129	98.54 164	99.54 31	99.73 37	99.16 48	98.23 172	99.31 230	97.92 232	98.90 229	98.90 248	98.00 131	99.88 115	96.15 341	99.72 201	99.58 117
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 440	93.78 438	94.51 477	97.53 460	85.83 503	97.98 220	95.96 469	89.29 496	94.99 479	98.63 317	78.63 484	99.62 365	94.54 392	96.50 483	98.09 442

CR-MVSNet		96.28 381	95.95 381	97.28 392	97.71 447	94.22 391	98.11 189	98.92 333	92.31 469	96.91 418	99.37 104	85.44 447	99.81 223	97.39 228	97.36 470	97.81 457

JIA-IIPM		95.52 410	95.03 416	97.00 407	96.85 485	94.03 403	96.93 354	95.82 472	99.20 82	94.63 485	99.71 22	83.09 466	99.60 376	94.42 398	94.64 502	97.36 478

Patchmtry		97.35 317	96.97 329	98.50 265	97.31 472	96.47 300	98.18 177	98.92 333	98.95 129	98.78 256	99.37 104	85.44 447	99.85 158	95.96 350	99.83 125	99.17 315

PatchT		96.65 363	96.35 371	97.54 376	97.40 468	95.32 354	97.98 220	96.64 457	99.33 65	96.89 422	99.42 89	84.32 457	99.81 223	97.69 201	97.49 461	97.48 473

tpmrst		95.07 422	95.46 398	93.91 484	97.11 476	84.36 510	97.62 277	96.96 447	94.98 419	96.35 450	98.80 276	85.46 446	99.59 381	95.60 368	96.23 487	97.79 460

BH-w/o		95.13 421	94.89 421	95.86 451	98.20 415	91.31 465	95.65 434	97.37 428	93.64 449	96.52 444	95.70 468	93.04 367	99.02 476	88.10 493	95.82 497	97.24 481

tpm		94.67 428	94.34 432	95.66 458	97.68 452	88.42 491	97.88 234	94.90 482	94.46 432	96.03 460	98.56 328	78.66 483	99.79 245	95.88 352	95.01 501	98.78 385

DELS-MVS		98.27 226	98.20 224	98.48 267	98.86 320	96.70 286	95.60 436	99.20 272	97.73 247	98.45 305	98.71 293	97.50 182	99.82 206	98.21 147	99.59 265	98.93 359
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		96.83 356	96.75 348	97.08 403	98.74 341	93.33 429	96.71 367	98.26 401	96.72 343	98.44 306	97.37 431	95.20 305	99.47 427	91.89 459	97.43 465	98.44 420

RPMNet		97.02 346	96.93 331	97.30 391	97.71 447	94.22 391	98.11 189	99.30 238	99.37 60	96.91 418	99.34 115	86.72 431	99.87 135	97.53 215	97.36 470	97.81 457

MVSTER		96.86 355	96.55 363	97.79 340	97.91 435	94.21 393	97.56 288	98.87 342	97.49 274	99.06 186	99.05 202	80.72 473	99.80 232	98.44 129	99.82 132	99.37 239

CPTT-MVS		97.84 279	97.36 304	99.27 99	99.31 202	98.46 116	98.29 165	99.27 253	94.90 422	97.83 360	98.37 352	94.90 314	99.84 176	93.85 417	99.54 284	99.51 164

GBi-Net		98.65 160	98.47 179	99.17 115	98.90 311	98.24 135	99.20 49	99.44 173	98.59 166	98.95 216	99.55 56	94.14 341	99.86 144	97.77 189	99.69 222	99.41 218

PVSNet_Blended_VisFu		98.17 243	98.15 234	98.22 300	99.73 37	95.15 360	97.36 318	99.68 62	94.45 434	98.99 205	99.27 131	96.87 225	99.94 41	97.13 250	99.91 80	99.57 124

PVSNet_BlendedMVS		97.55 299	97.53 293	97.60 367	98.92 307	93.77 420	96.64 372	99.43 179	94.49 430	97.62 372	99.18 161	96.82 229	99.67 335	94.73 387	99.93 57	99.36 246

UnsupCasMVSNet_eth		97.89 268	97.60 290	98.75 208	99.31 202	97.17 255	97.62 277	99.35 211	98.72 155	98.76 261	98.68 304	92.57 376	99.74 288	97.76 193	95.60 498	99.34 253

UnsupCasMVSNet_bld		97.30 322	96.92 333	98.45 270	99.28 211	96.78 283	96.20 402	99.27 253	95.42 407	98.28 321	98.30 362	93.16 362	99.71 306	94.99 380	97.37 468	98.87 369

PVSNet_Blended		96.88 353	96.68 352	97.47 384	98.92 307	93.77 420	94.71 464	99.43 179	90.98 485	97.62 372	97.36 432	96.82 229	99.67 335	94.73 387	99.56 278	98.98 347

FMVSNet5		96.01 390	95.20 413	98.41 275	97.53 460	96.10 310	98.74 99	99.50 137	97.22 311	98.03 344	99.04 204	69.80 498	99.88 115	97.27 237	99.71 210	99.25 286

test1		98.65 160	98.47 179	99.17 115	98.90 311	98.24 135	99.20 49	99.44 173	98.59 166	98.95 216	99.55 56	94.14 341	99.86 144	97.77 189	99.69 222	99.41 218

new_pmnet		96.99 350	96.76 346	97.67 356	98.72 344	94.89 370	95.95 420	98.20 405	92.62 466	98.55 295	98.54 329	94.88 317	99.52 409	93.96 412	99.44 316	98.59 409

FMVSNet3		97.50 300	97.24 311	98.29 290	98.08 426	95.83 326	97.86 238	98.91 335	97.89 235	98.95 216	98.95 238	87.06 429	99.81 223	97.77 189	99.69 222	99.23 291

dp		93.47 448	93.59 441	93.13 494	96.64 491	81.62 520	97.66 270	96.42 461	92.80 464	96.11 454	98.64 315	78.55 486	99.59 381	93.31 432	92.18 510	98.16 438

FMVSNet2		98.49 192	98.40 189	98.75 208	98.90 311	97.14 258	98.61 120	99.13 294	98.59 166	99.19 170	99.28 129	94.14 341	99.82 206	97.97 171	99.80 150	99.29 273

FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 116	98.24 135	99.20 49	99.44 173	99.21 80	99.43 107	99.55 56	97.82 151	99.86 144	98.42 135	99.89 94	99.41 218

N_pmnet		97.63 293	97.17 315	98.99 153	99.27 214	97.86 184	95.98 415	93.41 496	95.25 413	99.47 100	98.90 248	95.63 292	99.85 158	96.91 267	99.73 192	99.27 279

cascas		94.79 427	94.33 433	96.15 446	96.02 505	92.36 449	92.34 503	99.26 259	85.34 507	95.08 478	94.96 484	92.96 368	98.53 493	94.41 401	98.59 420	97.56 471

BH-RMVSNet		96.83 356	96.58 362	97.58 369	98.47 390	94.05 400	96.67 370	97.36 429	96.70 345	97.87 356	97.98 389	95.14 309	99.44 436	90.47 483	98.58 421	99.25 286

UGNet		98.53 185	98.45 182	98.79 196	97.94 433	96.96 269	99.08 62	98.54 387	99.10 105	96.82 427	99.47 78	96.55 248	99.84 176	98.56 123	99.94 51	99.55 137
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		96.67 362	96.27 376	97.87 335	98.81 332	94.61 383	96.77 363	97.92 415	94.94 421	97.12 404	97.74 407	91.11 398	99.82 206	93.89 414	98.15 441	99.18 311

XXY-MVS		99.14 62	99.15 67	99.10 128	99.76 30	97.74 200	98.85 93	99.62 83	98.48 178	99.37 123	99.49 74	98.75 47	99.86 144	98.20 148	99.80 150	99.71 65

EC-MVSNet		99.09 72	99.05 84	99.20 110	99.28 211	98.93 79	99.24 44	99.84 23	99.08 112	98.12 334	98.37 352	98.72 50	99.90 81	99.05 83	99.77 168	98.77 386

sss		97.21 331	96.93 331	98.06 317	98.83 326	95.22 358	96.75 365	98.48 391	94.49 430	97.27 399	97.90 395	92.77 372	99.80 232	96.57 307	99.32 336	99.16 321

Test_1112_low_res		96.99 350	96.55 363	98.31 288	99.35 194	95.47 345	95.84 428	99.53 128	91.51 478	96.80 428	98.48 341	91.36 395	99.83 194	96.58 305	99.53 288	99.62 92

1112_ss		97.29 324	96.86 338	98.58 241	99.34 199	96.32 305	96.75 365	99.58 97	93.14 456	96.89 422	97.48 424	92.11 385	99.86 144	96.91 267	99.54 284	99.57 124

ab-mvs-re		8.12 489	10.83 492	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	97.48 424	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

ab-mvs		98.41 200	98.36 197	98.59 240	99.19 240	97.23 243	99.32 26	98.81 356	97.66 253	98.62 280	99.40 97	96.82 229	99.80 232	95.88 352	99.51 294	98.75 389

TR-MVS		95.55 409	95.12 415	96.86 418	97.54 458	93.94 411	96.49 382	96.53 460	94.36 437	97.03 413	96.61 448	94.26 338	99.16 469	86.91 498	96.31 486	97.47 474

MDTV_nov1_ep13_2view						74.92 523	97.69 265		90.06 492	97.75 366		85.78 443		93.52 426		98.69 397

MDTV_nov1_ep13			95.22 412		97.06 479	83.20 514	97.74 259	96.16 464	94.37 436	96.99 414	98.83 269	83.95 461	99.53 405	93.90 413	97.95 452

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 41	99.41 17	99.59 94	99.59 36	99.71 49	99.57 49	97.12 209	99.90 81	99.21 70	99.87 99	99.54 143

MIMVSNet		96.62 365	96.25 377	97.71 352	99.04 279	94.66 381	99.16 55	96.92 451	97.23 308	97.87 356	99.10 186	86.11 438	99.65 355	91.65 464	99.21 358	98.82 373

IterMVS-LS		98.55 180	98.70 136	98.09 312	99.48 153	94.73 378	97.22 335	99.39 195	98.97 125	99.38 119	99.31 124	96.00 275	99.93 53	98.58 118	99.97 21	99.60 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		97.69 288	97.35 305	98.69 220	98.73 342	97.02 265	96.92 356	98.75 368	95.89 384	98.59 287	98.67 306	92.08 386	99.74 288	96.72 289	99.81 139	99.32 262
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 168

IterMVS		97.73 285	98.11 238	96.57 426	99.24 225	90.28 482	95.52 440	99.21 270	98.86 140	99.33 133	99.33 118	93.11 363	99.94 41	98.49 127	99.94 51	99.48 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 319	96.92 333	98.57 244	99.09 266	97.99 166	96.79 360	99.35 211	93.18 455	97.71 367	98.07 382	95.00 313	99.31 455	93.97 411	99.13 371	98.42 424

MVS_111021_LR		98.30 221	98.12 237	98.83 185	99.16 252	98.03 163	96.09 411	99.30 238	97.58 262	98.10 336	98.24 366	98.25 105	99.34 451	96.69 294	99.65 242	99.12 327

DP-MVS		98.93 102	98.81 121	99.28 96	99.21 233	98.45 117	98.46 145	99.33 223	99.63 28	99.48 96	99.15 173	97.23 202	99.75 281	97.17 244	99.66 241	99.63 91

ACMMP++															99.68 227

HQP-MVS		97.00 349	96.49 366	98.55 251	98.67 362	96.79 280	96.29 396	99.04 311	96.05 374	95.55 467	96.84 443	93.84 348	99.54 403	92.82 446	99.26 349	99.32 262

QAPM		97.31 320	96.81 344	98.82 187	98.80 335	97.49 216	99.06 66	99.19 276	90.22 489	97.69 369	99.16 167	96.91 223	99.90 81	90.89 479	99.41 320	99.07 331

Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 37	98.26 133	99.17 54	99.78 36	99.11 98	99.27 148	99.48 75	98.82 38	99.95 25	98.94 91	99.93 57	99.59 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		94.32 432	95.62 390	90.42 498	98.46 392	75.36 522	96.29 396	89.13 513	95.25 413	95.38 473	99.75 16	92.88 369	99.19 467	94.07 410	99.39 322	96.72 490

IS-MVSNet		98.19 238	97.90 265	99.08 133	99.57 102	97.97 170	99.31 30	98.32 398	99.01 121	98.98 206	99.03 206	91.59 390	99.79 245	95.49 372	99.80 150	99.48 187

HyFIR lowres test		97.19 333	96.60 361	98.96 161	99.62 86	97.28 239	95.17 453	99.50 137	94.21 439	99.01 200	98.32 360	86.61 432	99.99 2	97.10 252	99.84 113	99.60 102

EPMVS		93.72 445	93.27 444	95.09 472	96.04 504	87.76 495	98.13 184	85.01 519	94.69 426	96.92 416	98.64 315	78.47 487	99.31 455	95.04 379	96.46 484	98.20 435

PAPM_NR		96.82 358	96.32 373	98.30 289	99.07 270	96.69 287	97.48 300	98.76 364	95.81 391	96.61 438	96.47 452	94.12 344	99.17 468	90.82 481	97.78 454	99.06 332

TAMVS		98.24 232	98.05 245	98.80 192	99.07 270	97.18 253	97.88 234	98.81 356	96.66 346	99.17 177	99.21 152	94.81 320	99.77 263	96.96 265	99.88 95	99.44 206

PAPR		95.29 416	94.47 428	97.75 346	97.50 466	95.14 361	94.89 461	98.71 373	91.39 480	95.35 474	95.48 474	94.57 327	99.14 471	84.95 501	97.37 468	98.97 351

RPSCF		98.62 167	98.36 197	99.42 67	99.65 70	99.42 10	98.55 126	99.57 105	97.72 249	98.90 229	99.26 137	96.12 270	99.52 409	95.72 362	99.71 210	99.32 262

Vis-MVSNet (Re-imp)		97.46 305	97.16 316	98.34 285	99.55 116	96.10 310	98.94 81	98.44 392	98.32 189	98.16 329	98.62 320	88.76 417	99.73 295	93.88 415	99.79 156	99.18 311

test_0402		98.76 135	98.71 133	98.93 167	99.56 110	98.14 146	98.45 147	99.34 217	99.28 72	98.95 216	98.91 245	98.34 93	99.79 245	95.63 366	99.91 80	98.86 370

MVS_111021_HR		98.25 231	98.08 242	98.75 208	99.09 266	97.46 222	95.97 416	99.27 253	97.60 261	97.99 347	98.25 364	98.15 121	99.38 446	96.87 275	99.57 274	99.42 215

CSCG		98.68 155	98.50 171	99.20 110	99.45 164	98.63 100	98.56 125	99.57 105	97.87 236	98.85 243	98.04 384	97.66 161	99.84 176	96.72 289	99.81 139	99.13 326

PatchMatch-RL		97.24 328	96.78 345	98.61 237	99.03 282	97.83 187	96.36 391	99.06 304	93.49 453	97.36 397	97.78 404	95.75 289	99.49 420	93.44 430	98.77 403	98.52 412

API-MVS		97.04 345	96.91 336	97.42 387	97.88 436	98.23 139	98.18 177	98.50 390	97.57 263	97.39 395	96.75 445	96.77 234	99.15 470	90.16 484	99.02 384	94.88 505

Test By Simon												96.52 249

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 47	99.38 59	99.53 83	99.61 43	98.64 61	99.80 232	98.24 143	99.84 113	99.52 160

USDC		97.41 311	97.40 300	97.44 386	98.94 301	93.67 423	95.17 453	99.53 128	94.03 445	98.97 210	99.10 186	95.29 303	99.34 451	95.84 358	99.73 192	99.30 271

EPP-MVSNet		98.30 221	98.04 246	99.07 135	99.56 110	97.83 187	99.29 36	98.07 411	99.03 119	98.59 287	99.13 178	92.16 382	99.90 81	96.87 275	99.68 227	99.49 176

PMMVS		96.51 368	95.98 379	98.09 312	97.53 460	95.84 325	94.92 460	98.84 351	91.58 476	96.05 458	95.58 469	95.68 291	99.66 348	95.59 369	98.09 444	98.76 388

PAPM		91.88 473	90.34 475	96.51 427	98.06 428	92.56 443	92.44 502	97.17 439	86.35 504	90.38 509	96.01 460	86.61 432	99.21 466	70.65 516	95.43 499	97.75 462

ACMMP		98.75 136	98.50 171	99.52 44	99.56 110	99.16 48	98.87 89	99.37 201	97.16 314	98.82 250	99.01 218	97.71 158	99.87 135	96.29 333	99.69 222	99.54 143
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		97.17 335	96.71 350	98.55 251	98.56 382	98.05 162	96.33 393	98.93 329	96.91 330	97.06 409	97.39 429	94.38 333	99.45 434	91.66 463	99.18 365	98.14 439

PatchmatchNet		95.58 408	95.67 389	95.30 469	97.34 470	87.32 498	97.65 272	96.65 456	95.30 411	97.07 408	98.69 302	84.77 452	99.75 281	94.97 382	98.64 416	98.83 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 224	97.95 256	99.34 83	98.44 395	99.16 48	98.12 188	99.38 197	96.01 378	98.06 340	98.43 346	97.80 152	99.67 335	95.69 364	99.58 270	99.20 301

F-COLMAP		97.30 322	96.68 352	99.14 122	99.19 240	98.39 120	97.27 330	99.30 238	92.93 461	96.62 437	98.00 387	95.73 290	99.68 331	92.62 453	98.46 426	99.35 251

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 152	99.14 58	99.93 6	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 61	100.00 1	99.82 36

wuyk23d		96.06 388	97.62 289	91.38 496	98.65 371	98.57 107	98.85 93	96.95 448	96.86 336	99.90 14	99.16 167	99.18 19	98.40 494	89.23 490	99.77 168	77.18 517

OMC-MVS		97.88 270	97.49 296	99.04 144	98.89 316	98.63 100	96.94 352	99.25 261	95.02 418	98.53 298	98.51 334	97.27 199	99.47 427	93.50 428	99.51 294	99.01 342

MG-MVS		96.77 359	96.61 359	97.26 394	98.31 406	93.06 432	95.93 421	98.12 410	96.45 357	97.92 351	98.73 290	93.77 352	99.39 444	91.19 474	99.04 380	99.33 259

AdaColmap		97.14 337	96.71 350	98.46 269	98.34 404	97.80 196	96.95 351	98.93 329	95.58 400	96.92 416	97.66 411	95.87 286	99.53 405	90.97 476	99.14 369	98.04 444

uanet		0.00 490	0.00 493	0.00 505	0.00 528	0.00 530	0.00 516	0.00 529	0.00 523	0.00 524	0.00 523	0.00 527	0.00 524	0.00 522	0.00 522	0.00 520

ITE_SJBPF				98.87 175	99.22 231	98.48 115		99.35 211	97.50 272	98.28 321	98.60 324	97.64 165	99.35 450	93.86 416	99.27 346	98.79 384

DeepMVS_CX				93.44 490	98.24 412	94.21 393		94.34 487	64.28 517	91.34 508	94.87 487	89.45 415	92.77 518	77.54 512	93.14 507	93.35 511

TinyColmap		97.89 268	97.98 252	97.60 367	98.86 320	94.35 389	96.21 401	99.44 173	97.45 282	99.06 186	98.88 256	97.99 134	99.28 461	94.38 402	99.58 270	99.18 311

MAR-MVS		96.47 372	95.70 387	98.79 196	97.92 434	99.12 62	98.28 166	98.60 380	92.16 471	95.54 470	96.17 458	94.77 323	99.52 409	89.62 487	98.23 434	97.72 465
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		97.90 266	97.69 281	98.52 260	99.17 250	97.66 206	97.19 340	99.47 156	96.31 362	97.85 359	98.20 370	96.71 240	99.52 409	94.62 390	99.72 201	98.38 427

MSDG		97.71 287	97.52 294	98.28 291	98.91 310	96.82 278	94.42 477	99.37 201	97.65 254	98.37 314	98.29 363	97.40 190	99.33 453	94.09 409	99.22 355	98.68 400

LS3D		98.63 164	98.38 194	99.36 74	97.25 473	99.38 12	99.12 61	99.32 225	99.21 80	98.44 306	98.88 256	97.31 195	99.80 232	96.58 305	99.34 331	98.92 360

CLD-MVS		97.49 303	97.16 316	98.48 267	99.07 270	97.03 264	94.71 464	99.21 270	94.46 432	98.06 340	97.16 437	97.57 172	99.48 424	94.46 395	99.78 161	98.95 354
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 449	92.23 457	97.08 403	99.25 224	97.86 184	95.61 435	97.16 440	92.90 462	93.76 498	98.65 311	75.94 490	95.66 514	79.30 511	97.49 461	97.73 464

Gipuma		99.03 86	99.16 62	98.64 227	99.94 2	98.51 113	99.32 26	99.75 42	99.58 38	98.60 285	99.62 40	98.22 110	99.51 415	97.70 199	99.73 192	97.89 452
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark