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	99.81 224	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	98.81 127	98.73 130	99.05 143	98.76 349	97.81 202	99.25 43	99.30 249	98.57 174	98.55 305	99.33 118	97.95 140	99.90 81	97.16 251	99.67 243	99.44 208

3Dnovator+	97.89 3	98.69 151	98.51 171	99.24 106	98.81 342	98.40 120	99.02 70	99.19 287	98.99 124	98.07 353	99.28 129	97.11 216	99.84 177	96.84 286	99.32 351	99.47 195

DeepC-MVS	97.60 4	98.97 99	98.93 101	99.10 128	99.35 197	97.98 174	98.01 213	99.46 172	97.56 270	99.54 79	99.50 68	98.97 29	99.84 177	98.06 162	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	98.32 221	98.01 256	99.23 108	98.39 413	98.97 73	95.03 466	99.18 291	96.88 339	99.33 137	98.78 287	98.16 122	99.28 472	96.74 295	99.62 265	99.44 208

DeepC-MVS_fast	96.85 6	98.30 226	98.15 241	98.75 211	98.61 383	97.23 256	97.76 256	99.09 311	97.31 302	98.75 268	98.66 318	97.56 177	99.64 367	96.10 356	99.55 294	99.39 230
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	97.09 350	96.68 363	98.32 291	98.32 417	97.16 269	98.86 92	99.37 212	89.48 515	96.29 468	99.15 175	96.56 254	99.90 81	92.90 457	99.20 376	97.89 463

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 45	98.38 122	99.07 65	99.55 124	98.30 194	99.65 63	99.45 84	99.22 17	99.76 270	98.44 131	99.77 172	99.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 79	99.00 94	99.33 89	99.71 49	98.83 86	98.60 121	99.58 101	99.11 100	99.53 83	99.18 163	98.81 39	99.67 342	96.71 300	99.77 172	99.50 168

COLMAP_ROB	96.50 10	98.99 94	98.85 118	99.41 69	99.58 94	99.10 65	98.74 99	99.56 119	99.09 110	99.33 137	99.19 159	98.40 86	99.72 306	95.98 359	99.76 188	99.42 217
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	96.63 374	95.95 397	98.65 230	98.93 313	98.09 155	96.93 356	99.28 261	83.58 533	98.13 347	97.78 421	96.13 278	99.40 452	93.52 440	99.29 359	98.45 428
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 106	98.73 130	99.48 57	99.55 117	99.14 57	98.07 200	99.37 212	97.62 261	99.04 201	98.96 240	98.84 37	99.79 246	97.43 231	99.65 253	99.49 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 415	95.35 423	97.55 384	97.95 450	94.79 395	98.81 98	96.94 465	92.28 491	95.17 494	98.57 336	89.90 430	99.75 282	91.20 493	97.33 494	98.10 452

OpenMVS_ROB	95.38 14	95.84 418	95.18 434	97.81 348	98.41 412	97.15 270	97.37 319	98.62 391	83.86 532	98.65 283	98.37 363	94.29 354	99.68 337	88.41 512	98.62 438	96.60 504

ACMP	95.32 15	98.41 203	98.09 246	99.36 74	99.51 134	98.79 89	97.68 268	99.38 208	95.76 403	98.81 258	98.82 279	98.36 90	99.82 207	94.75 399	99.77 172	99.48 187
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 379	95.73 403	98.85 180	98.75 351	97.91 185	96.42 395	99.06 315	90.94 507	95.59 482	97.38 449	94.41 346	99.59 391	90.93 498	98.04 470	99.05 342
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 422	95.70 404	95.57 480	98.83 336	88.57 513	92.50 523	97.72 432	92.69 486	96.49 465	96.44 474	93.72 370	99.43 448	93.61 435	99.28 360	98.71 404

PCF-MVS	92.86 18	94.36 452	93.00 471	98.42 279	98.70 363	97.56 223	93.16 520	99.11 308	79.59 537	97.55 395	97.43 446	92.19 401	99.73 296	79.85 534	99.45 322	97.97 460
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 491	90.90 495	96.27 448	97.22 493	91.24 490	94.36 491	93.33 521	92.37 489	92.24 528	94.58 513	66.20 532	99.89 97	93.16 451	94.63 527	97.66 478
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	97.86 282	97.94 266	97.65 369	99.71 49	97.94 181	98.52 130	98.68 385	98.99 124	97.52 398	99.35 111	97.41 194	98.18 509	91.59 486	99.67 243	96.82 500
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 497	90.30 499	93.70 509	97.72 463	84.34 534	90.24 530	97.42 442	90.20 511	93.79 517	93.09 523	90.90 422	98.89 496	86.57 521	72.76 542	97.87 465

MVE	83.40 22	92.50 486	91.92 488	94.25 500	98.83 336	91.64 478	92.71 521	83.52 544	95.92 392	86.46 539	95.46 497	95.20 318	95.40 536	80.51 533	98.64 434	95.73 516
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 394	95.44 418	98.84 186	96.25 523	98.69 98	97.02 347	99.12 306	88.90 519	97.83 375	98.86 266	89.51 435	98.90 495	91.92 478	99.51 306	98.92 370
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
nocashy02		98.57 177	98.66 146	98.31 293	99.20 242	95.89 338	96.92 358	99.57 109	98.71 158	99.02 205	99.04 208	97.48 190	99.71 308	98.28 145	99.70 226	99.35 256

PMatch-SfM		97.89 276	97.64 296	98.66 228	99.26 227	97.44 236	96.08 420	99.51 142	96.72 350	98.47 315	99.13 181	93.62 373	99.70 316	97.14 254	98.80 420	98.83 382

DenseAffine		98.10 253	97.86 275	98.84 186	99.32 204	97.93 182	96.62 379	99.76 39	96.68 354	98.65 283	98.72 299	94.46 344	99.33 463	96.76 292	99.75 192	99.25 294

ArgMatch-SfM		97.96 271	97.72 287	98.66 228	99.02 298	97.33 242	96.49 389	99.52 140	95.46 416	98.71 275	98.29 377	96.14 276	99.69 326	96.30 341	99.56 289	98.97 360

MASt3R-SfM		96.02 406	95.82 400	96.60 436	97.03 501	94.90 390	94.26 494	98.53 400	88.40 524	98.41 322	98.67 314	92.39 396	97.62 519	95.31 386	99.41 334	97.29 491

hybridnocas07		98.32 221	98.37 200	98.17 310	99.14 264	95.51 354	96.67 374	99.56 119	97.85 242	98.75 268	98.95 244	96.65 250	99.63 370	98.00 170	99.78 164	99.37 242

cashybrid2		99.12 69	99.12 71	99.09 132	99.53 127	98.08 159	98.34 163	99.66 70	99.35 64	99.35 130	99.23 150	98.39 88	99.72 306	98.46 129	99.81 140	99.47 195

dtuonlycased		97.70 297	98.19 234	96.24 450	99.75 34	89.51 510	94.69 478	99.64 78	98.23 201	99.46 101	98.57 336	98.25 107	99.85 158	95.65 376	99.44 329	99.36 250

dtuonly		96.49 382	97.28 318	94.10 503	98.80 345	83.27 537	93.66 510	99.48 157	95.10 429	97.87 370	98.30 374	95.61 304	99.68 337	96.98 271	99.75 192	99.33 266

dtuplus		98.32 221	98.39 195	98.10 318	99.15 262	95.29 372	96.68 372	99.51 142	97.32 300	99.18 179	99.15 175	97.61 172	99.62 374	97.19 248	99.74 195	99.38 239

SIFT-UM-Cal		96.49 382	96.62 370	96.12 461	98.13 441	97.89 188	93.35 516	98.44 405	95.48 415	98.63 286	98.34 367	95.45 312	97.45 520	92.22 475	99.50 314	93.02 531

SIFT-NCM-Cal		96.56 377	96.68 363	96.20 454	98.27 424	98.44 118	94.40 489	96.67 472	95.29 423	97.63 387	98.17 388	96.40 262	96.59 532	93.61 435	99.66 251	93.57 524

SIFT-CM-Cal		96.28 395	96.31 388	96.16 458	98.39 413	98.11 151	93.46 515	96.47 478	94.81 439	98.49 312	98.43 356	94.48 343	97.34 523	92.60 469	99.70 226	93.02 531

SIFT-PCN-Cal		96.34 390	96.46 382	96.01 465	98.17 435	96.89 288	93.48 514	97.35 447	94.84 437	99.35 130	98.30 374	94.70 338	97.92 513	92.03 476	99.88 95	93.21 530

SIFT-NN-UMatch		95.38 435	95.26 428	95.75 474	98.25 425	97.78 204	93.24 519	95.66 498	94.01 464	95.10 496	97.47 444	93.12 381	96.78 529	92.42 472	98.04 470	92.69 536

SIFT-NN-NCMNet		95.39 434	95.22 431	95.92 467	98.29 420	98.34 129	93.58 512	94.60 506	94.07 462	94.84 500	97.53 436	94.37 350	96.62 530	91.01 496	98.64 434	92.80 534

SIFT-NN-CMatch		95.63 425	95.48 414	96.08 462	98.24 427	98.00 169	92.71 521	94.29 510	94.20 456	95.85 478	97.26 454	95.72 301	97.01 525	91.99 477	99.02 400	93.23 528

SIFT-NN-PointCN		96.06 403	96.11 394	95.91 468	97.88 454	97.73 210	93.49 513	97.51 441	93.22 474	96.57 455	98.26 379	96.23 273	96.60 531	92.54 470	99.27 361	93.40 526

XFeat-NN		89.63 499	89.13 502	91.14 520	90.93 544	90.02 507	84.90 537	94.05 516	88.10 525	92.89 523	93.33 522	78.74 506	90.89 541	83.46 527	95.72 521	92.52 537

ALIKED-NN		94.29 456	93.41 465	96.94 420	96.18 524	97.66 215	94.90 470	98.68 385	88.85 520	90.43 532	96.81 465	89.82 431	96.59 532	86.67 520	98.33 448	96.58 505

SP-NN		94.67 448	94.44 450	95.36 488	95.12 532	95.23 377	94.27 493	96.10 485	94.46 447	90.91 531	95.76 489	91.47 415	93.87 539	95.23 389	96.62 505	97.00 495

SIFT-NN		92.96 480	92.79 474	93.46 511	96.92 503	96.45 315	91.89 527	94.39 508	92.91 482	92.54 525	95.46 497	88.26 447	90.71 542	85.22 523	97.52 481	93.22 529

hybridcas		99.08 79	99.13 70	98.92 171	99.54 123	97.61 221	98.22 177	99.66 70	99.27 74	99.40 117	99.24 144	98.47 77	99.70 316	98.59 118	99.80 152	99.46 198

GLUNet-SfM		86.26 503	84.68 505	91.01 521	80.58 547	83.56 535	78.04 538	93.59 518	76.70 538	95.29 493	94.72 511	77.51 512	94.26 538	66.39 541	99.33 348	95.20 518

PDCNetPlus		95.22 439	94.73 446	96.70 434	97.85 456	91.14 493	93.94 504	99.97 1	93.06 479	98.95 222	98.89 261	74.32 516	99.14 482	95.63 377	99.93 57	99.82 36

hybrid		98.22 238	98.27 220	98.08 323	99.13 267	95.24 374	96.61 380	99.53 134	97.43 289	98.46 316	98.97 236	96.75 244	99.65 362	97.84 187	99.69 231	99.35 256

RoMa-SfM		98.46 198	98.27 220	99.02 149	99.35 197	98.32 130	97.56 290	99.70 53	95.88 394	99.38 121	98.65 320	96.41 261	99.46 441	97.78 192	99.71 217	99.28 284

DKM		98.18 246	97.95 263	98.85 180	99.35 197	98.31 131	96.68 372	99.69 56	96.90 338	98.61 292	98.77 289	94.41 346	98.93 492	97.32 239	99.84 114	99.32 270

ELoFTR		97.81 291	97.74 283	98.04 329	99.39 183	95.79 345	97.28 331	99.58 101	94.13 458	99.38 121	99.37 104	93.31 376	99.60 386	97.23 245	99.96 28	98.74 402

MatchFormer		97.07 352	96.92 344	97.49 390	98.44 406	95.92 336	96.79 363	99.14 304	93.08 478	99.32 143	99.10 190	93.89 364	99.03 485	92.78 463	99.78 164	97.52 483

LoFTR		97.97 270	97.79 279	98.53 263	98.80 345	97.47 231	97.01 348	99.55 124	95.55 410	99.46 101	99.22 152	94.22 356	99.44 446	96.45 330	99.82 133	98.68 411

ALIKED-LG		97.10 348	96.63 369	98.50 270	97.96 449	98.68 99	97.75 259	99.68 63	95.86 395	98.36 330	98.33 371	91.58 411	99.04 484	90.87 501	99.31 353	97.77 472

SP-DiffGlue		96.87 364	96.76 357	97.21 405	95.17 531	96.88 290	96.12 417	98.93 340	96.51 359	98.37 328	97.55 435	93.65 372	97.83 514	96.11 355	98.45 446	96.92 496

SP-LightGlue		97.22 340	97.01 338	97.88 342	97.33 490	97.19 263	96.38 397	99.08 313	97.28 305	96.53 458	97.50 440	92.36 397	98.70 501	97.84 187	98.76 422	97.74 474

SP-SuperGlue		97.31 330	97.23 323	97.57 383	96.96 502	97.24 255	96.26 408	98.76 375	97.68 256	96.88 440	97.85 416	94.32 352	98.01 511	97.76 198	98.57 441	97.45 486

SIFT-UMatch		96.33 391	96.47 380	95.89 469	98.29 420	97.95 179	93.84 506	97.24 452	95.78 402	98.72 272	98.04 401	93.45 375	96.81 528	93.14 452	99.73 199	92.91 533

SIFT-NCMNet		96.30 393	96.40 384	96.03 464	97.80 461	97.68 214	92.34 525	96.94 465	95.55 410	98.84 251	98.63 326	94.17 357	97.63 518	93.57 439	99.71 217	92.77 535

SIFT-ConvMatch		96.57 376	96.62 370	96.43 441	98.20 431	98.27 134	93.88 505	96.88 468	95.29 423	98.88 242	98.25 380	95.18 320	97.43 521	93.22 450	99.83 126	93.59 523

SIFT-PointCN		96.45 387	96.47 380	96.39 443	98.13 441	97.54 225	93.31 517	97.23 453	94.67 442	98.68 279	98.32 372	94.64 339	97.81 515	93.50 442	99.77 172	93.83 521

XFeat-MNN		93.41 472	92.98 472	94.68 496	92.63 538	92.92 457	89.72 534	95.81 492	92.10 493	97.23 418	96.29 478	84.95 473	97.31 524	89.60 509	98.54 443	93.81 522

ALIKED-MNN		95.97 412	95.30 427	98.00 332	97.66 473	98.12 150	96.98 351	99.41 200	91.11 505	94.04 513	97.30 453	91.56 412	98.61 503	89.99 506	99.63 261	97.28 492

SP-MNN		96.46 386	96.24 393	97.10 411	96.71 510	95.98 333	96.00 423	97.33 448	95.82 399	94.93 499	97.10 462	93.70 371	98.01 511	96.30 341	98.30 452	97.30 490

SIFT-MNN		95.92 414	95.97 396	95.74 476	98.18 433	98.00 169	94.17 496	96.99 460	95.74 404	97.16 419	97.90 412	90.71 423	95.79 534	93.71 433	99.21 374	93.44 525

casdiffseed414692147		99.09 73	99.12 71	99.01 151	99.55 117	97.91 185	98.30 165	99.68 63	99.04 119	99.19 174	99.37 104	98.98 28	99.61 382	98.13 155	99.83 126	99.50 168

gbinet_0.2-2-1-0.02		95.44 432	94.55 447	98.14 314	95.99 528	95.34 370	94.71 474	98.29 414	96.00 388	96.05 475	90.50 537	84.99 472	99.79 246	97.33 237	97.07 499	99.28 284

0.3-1-1-0.015		87.27 502	84.50 506	95.57 480	91.70 540	90.77 499	89.41 535	92.04 528	88.98 518	82.46 542	81.35 540	60.36 543	99.50 426	92.96 454	81.23 538	96.45 506

0.4-1-1-0.1		88.42 500	85.91 503	95.94 466	93.08 537	91.54 479	90.99 529	92.04 528	89.96 514	84.83 540	83.25 539	63.75 539	99.52 419	93.25 448	82.07 536	96.75 501

0.4-1-1-0.2		87.49 501	84.89 504	95.31 489	91.33 543	90.08 506	88.47 536	92.07 527	88.70 521	84.06 541	81.08 541	63.62 540	99.49 430	92.93 456	81.71 537	96.37 507

wanda-best-256-512		95.48 430	94.74 444	97.68 363	96.53 514	94.12 418	94.17 496	98.57 396	95.84 396	96.71 447	91.16 533	86.05 462	99.76 270	97.57 215	96.09 513	99.17 324

usedtu_dtu_shiyan2		98.99 94	98.86 115	99.39 72	99.73 38	98.71 97	99.05 68	99.47 167	99.16 94	99.49 94	99.12 185	96.34 268	99.93 53	98.05 164	99.36 341	99.54 143

usedtu_dtu_shiyan1		97.37 324	97.13 331	98.11 316	99.03 291	95.40 365	94.47 486	98.99 333	96.87 340	97.97 362	97.81 419	92.12 403	99.75 282	97.49 228	99.43 331	99.16 330

blended_shiyan8		95.98 410	95.33 424	97.94 337	97.05 500	94.87 393	95.34 456	98.59 393	96.17 376	97.09 423	92.39 528	87.62 451	99.76 270	97.65 207	96.05 519	99.20 310

E5new		99.05 83	99.11 74	98.85 180	99.60 88	97.30 246	98.42 151	99.63 81	98.73 151	99.26 156	99.39 100	98.71 51	99.70 316	98.43 133	99.84 114	99.54 143

FE-blended-shiyan7		95.48 430	94.74 444	97.68 363	96.53 514	94.12 418	94.17 496	98.57 396	95.84 396	96.71 447	91.16 533	86.05 462	99.76 270	97.57 215	96.09 513	99.17 324

E6new		99.05 83	99.11 74	98.85 180	99.60 88	97.30 246	98.42 151	99.63 81	98.73 151	99.26 156	99.39 100	98.71 51	99.70 316	98.43 133	99.84 114	99.54 143

blended_shiyan6		95.99 409	95.33 424	97.95 336	97.06 498	94.89 391	95.34 456	98.58 394	96.17 376	97.06 425	92.41 527	87.64 450	99.76 270	97.64 208	96.09 513	99.19 316

usedtu_blend_shiyan5		96.20 401	95.62 407	97.94 337	96.53 514	94.93 388	98.83 96	99.59 98	98.89 138	96.71 447	91.16 533	86.05 462	99.73 296	96.70 301	96.09 513	99.17 324

blend_shiyan4		92.09 493	90.16 500	97.88 342	96.78 508	94.93 388	95.24 460	98.58 394	96.22 374	96.07 473	91.42 532	63.46 541	99.73 296	96.70 301	76.98 541	98.98 356

E6		99.05 83	99.11 74	98.85 180	99.60 88	97.30 246	98.42 151	99.63 81	98.73 151	99.26 156	99.39 100	98.71 51	99.70 316	98.43 133	99.84 114	99.54 143

E5		99.05 83	99.11 74	98.85 180	99.60 88	97.30 246	98.42 151	99.63 81	98.73 151	99.26 156	99.39 100	98.71 51	99.70 316	98.43 133	99.84 114	99.54 143

FE-MVSNET3		97.37 324	97.13 331	98.11 316	99.03 291	95.40 365	94.47 486	98.99 333	96.87 340	97.97 362	97.81 419	92.12 403	99.75 282	97.49 228	99.43 331	99.16 330

E4		98.87 112	98.88 108	98.81 192	99.52 131	97.23 256	97.62 279	99.61 90	98.58 172	99.18 179	99.33 118	98.29 99	99.69 326	97.99 173	99.83 126	99.52 160

E3new		98.41 203	98.34 206	98.62 238	99.19 246	96.90 287	97.32 323	99.50 147	97.40 292	98.63 286	98.92 249	97.21 209	99.65 362	97.34 235	99.52 303	99.31 275

FE-MVSNET2		99.15 57	99.22 54	98.94 165	99.70 57	97.49 227	98.62 118	99.67 69	98.85 145	99.34 134	99.54 62	98.47 77	99.81 224	98.93 92	99.91 80	99.51 164

fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 195	99.48 156	96.56 308	97.97 226	99.69 56	99.63 28	99.84 30	99.54 62	98.21 115	99.94 41	99.76 23	99.95 39	99.88 20

E2		98.70 147	98.68 141	98.73 217	99.40 181	97.10 273	97.48 302	99.57 109	98.09 223	99.00 207	99.20 156	97.90 143	99.67 342	97.73 202	99.77 172	99.43 212

MED-MVS test				99.45 64	99.58 94	98.93 79	98.68 109	99.60 92	96.46 365	99.53 83	98.77 289		99.83 195	96.67 305	99.64 255	99.58 117

MED-MVS		99.01 90	98.84 119	99.52 44	99.58 94	98.93 79	98.68 109	99.60 92	98.85 145	99.53 83	99.16 169	97.87 149	99.83 195	96.67 305	99.64 255	99.81 41

E3		98.69 151	98.68 141	98.73 217	99.40 181	97.10 273	97.48 302	99.57 109	98.09 223	99.00 207	99.20 156	97.90 143	99.67 342	97.73 202	99.77 172	99.43 212

TestfortrainingZip a		99.09 73	98.92 102	99.61 13	99.58 94	99.17 43	98.68 109	99.27 264	98.85 145	99.61 70	99.16 169	97.14 213	99.86 144	98.39 138	99.57 285	99.81 41

TestfortrainingZip				98.97 160	98.30 419	98.43 119	98.68 109	98.26 415	97.76 250	98.86 248	98.16 390	95.15 321	99.47 437		97.55 480	99.02 349

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 202	99.47 159	96.56 308	97.75 259	99.71 48	99.60 35	99.74 46	99.44 85	97.96 139	99.95 25	99.86 4	99.94 51	99.82 36

viewdifsd2359ckpt07		98.71 142	98.86 115	98.26 298	99.43 174	95.65 348	97.20 338	99.66 70	99.20 84	99.29 148	99.01 223	98.29 99	99.73 296	97.92 178	99.75 192	99.39 230

viewdifsd2359ckpt09		98.13 252	97.92 269	98.77 207	99.18 254	97.35 240	97.29 327	99.53 134	95.81 400	98.09 351	98.47 352	96.34 268	99.66 355	97.02 264	99.51 306	99.29 281

viewdifsd2359ckpt13		98.39 212	98.29 216	98.70 221	99.26 227	97.19 263	97.51 298	99.48 157	96.94 333	98.58 299	98.82 279	97.47 192	99.55 407	97.21 247	99.33 348	99.34 260

viewcassd2359sk11		98.55 183	98.51 171	98.67 226	99.29 212	96.99 279	97.39 313	99.54 130	97.73 252	98.81 258	99.08 197	97.55 178	99.66 355	97.52 222	99.67 243	99.36 250

viewdifsd2359ckpt11		98.84 119	99.04 87	98.24 302	99.56 111	95.51 354	97.38 315	99.70 53	99.16 94	99.57 72	99.40 97	98.26 105	99.71 308	98.55 125	99.82 133	99.50 168

viewmacassd2359aftdt		98.86 116	98.87 111	98.83 188	99.53 127	97.32 245	97.70 266	99.64 78	98.22 203	99.25 164	99.27 131	98.40 86	99.61 382	97.98 174	99.87 100	99.55 137

viewmsd2359difaftdt		98.84 119	99.04 87	98.24 302	99.56 111	95.51 354	97.38 315	99.70 53	99.16 94	99.57 72	99.40 97	98.26 105	99.71 308	98.55 125	99.82 133	99.50 168

diffmvs_AUTHOR		98.50 194	98.59 161	98.23 305	99.35 197	95.48 359	96.61 380	99.60 92	98.37 185	98.90 235	99.00 227	97.37 197	99.76 270	98.22 149	99.85 109	99.46 198

FE-MVSNET		98.59 174	98.50 174	98.87 177	99.58 94	97.30 246	98.08 196	99.74 44	96.94 333	98.97 216	99.10 190	96.94 226	99.74 289	97.33 237	99.86 107	99.55 137

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 158	99.59 92	97.18 266	97.44 310	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		98.80 129	98.88 108	98.55 256	99.27 218	96.50 311	98.00 214	99.60 92	98.93 132	99.22 169	98.84 274	98.59 67	99.89 97	97.74 200	99.72 208	99.27 287

icg_test_0407_2		98.20 243	98.38 198	97.65 369	99.03 291	94.03 424	95.78 439	99.45 176	98.16 215	99.06 191	98.71 301	98.27 103	99.68 337	97.50 223	99.45 322	99.22 305

SSM_04072		98.80 129	98.88 108	98.56 254	99.27 218	96.50 311	98.00 214	99.60 92	98.93 132	99.22 169	98.84 274	98.59 67	99.90 81	97.74 200	99.72 208	99.27 287

SSM_0407		98.86 116	98.96 100	98.55 256	99.27 218	96.50 311	98.04 205	99.66 70	99.09 110	99.22 169	99.02 212	98.79 43	99.87 135	97.87 184	99.72 208	99.27 287

viewmambaseed2359dif		98.19 244	98.26 223	97.99 334	99.02 298	95.03 385	96.59 383	99.53 134	96.21 375	99.00 207	98.99 229	97.62 170	99.61 382	97.62 210	99.72 208	99.33 266

IMVS_0407		98.39 212	98.64 150	97.66 367	99.03 291	94.03 424	98.10 193	99.45 176	98.16 215	99.06 191	98.71 301	98.27 103	99.71 308	97.50 223	99.45 322	99.22 305

viewmanbaseed2359cas		98.58 176	98.54 167	98.70 221	99.28 215	97.13 272	97.47 306	99.55 124	97.55 272	98.96 221	98.92 249	97.77 157	99.59 391	97.59 214	99.77 172	99.39 230

IMVS_0404		98.07 258	98.20 230	97.69 362	99.03 291	94.03 424	96.67 374	99.45 176	98.16 215	98.03 358	98.71 301	96.80 237	99.82 207	97.50 223	99.45 322	99.22 305

SSM_0404		98.90 108	99.01 92	98.57 249	99.42 176	96.59 303	98.13 186	99.66 70	99.09 110	99.30 147	99.02 212	98.79 43	99.89 97	97.87 184	99.80 152	99.23 300

IMVS_0403		98.34 216	98.56 164	97.66 367	99.03 291	94.03 424	97.98 222	99.45 176	98.16 215	98.89 238	98.71 301	97.90 143	99.74 289	97.50 223	99.45 322	99.22 305

SD_0403		96.28 395	95.83 399	97.64 372	98.72 355	94.30 411	98.87 89	98.77 373	97.80 246	96.53 458	98.02 403	97.34 199	99.47 437	76.93 537	99.48 318	99.16 330

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 263	99.51 134	95.82 343	97.62 279	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		98.61 170	98.33 211	99.44 65	99.24 230	98.93 79	97.45 308	99.06 315	98.14 221	99.06 191	98.77 289	96.97 225	99.82 207	96.67 305	99.64 255	99.58 117

NormalMVS		98.26 233	97.97 262	99.15 121	99.64 77	97.83 194	98.28 167	99.43 190	99.24 77	98.80 260	98.85 269	89.76 432	99.94 41	98.04 165	99.67 243	99.68 73

lecture		99.25 40	99.12 71	99.62 9	99.64 77	99.40 11	98.89 88	99.51 142	99.19 89	99.37 125	99.25 142	98.36 90	99.88 115	98.23 148	99.67 243	99.59 109

SymmetryMVS		98.05 260	97.71 289	99.09 132	99.29 212	97.83 194	98.28 167	97.64 439	99.24 77	98.80 260	98.85 269	89.76 432	99.94 41	98.04 165	99.50 314	99.49 176

Elysia		99.15 57	99.14 68	99.18 113	99.63 83	97.92 183	98.50 137	99.43 190	99.67 20	99.70 51	99.13 181	96.66 248	99.98 4	99.54 44	99.96 28	99.64 86

StellarMVS		99.15 57	99.14 68	99.18 113	99.63 83	97.92 183	98.50 137	99.43 190	99.67 20	99.70 51	99.13 181	96.66 248	99.98 4	99.54 44	99.96 28	99.64 86

KinetiMVS		99.03 88	99.02 90	99.03 146	99.70 57	97.48 230	98.43 148	99.29 257	99.70 15	99.60 71	99.07 198	96.13 278	99.94 41	99.42 55	99.87 100	99.68 73

LuminaMVS		98.39 212	98.20 230	98.98 158	99.50 140	97.49 227	97.78 250	97.69 434	98.75 150	99.49 94	99.25 142	92.30 400	99.94 41	99.14 75	99.88 95	99.50 168

VortexMVS		97.98 269	98.31 213	97.02 415	98.88 327	91.45 482	98.03 207	99.47 167	98.65 160	99.55 77	99.47 78	91.49 414	99.81 224	99.32 60	99.91 80	99.80 45

AstraMVS		98.16 251	98.07 251	98.41 280	99.51 134	95.86 340	98.00 214	95.14 501	98.97 127	99.43 108	99.24 144	93.25 377	99.84 177	99.21 70	99.87 100	99.54 143

guyue		98.01 264	97.93 268	98.26 298	99.45 167	95.48 359	98.08 196	96.24 481	98.89 138	99.34 134	99.14 179	91.32 417	99.82 207	99.07 80	99.83 126	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 81	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 45	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 49	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	98.74 215	99.51 134	96.44 316	97.65 274	99.65 76	99.66 23	99.78 39	99.48 75	97.92 142	99.93 53	99.72 30	99.95 39	99.87 22

fmvsm_s_conf0.5_n_7		98.83 122	99.04 87	98.20 307	99.30 210	94.83 394	97.23 333	99.36 216	98.64 161	99.84 30	99.43 88	98.10 127	99.91 74	99.56 41	99.96 28	99.87 22

fmvsm_s_conf0.5_n_6		99.08 79	99.21 57	98.69 223	99.36 192	96.51 310	97.62 279	99.68 63	98.43 183	99.85 27	99.10 190	99.12 23	99.88 115	99.77 22	99.92 71	99.67 78

fmvsm_s_conf0.5_n_5		99.07 82	99.10 80	98.99 154	99.47 159	97.22 259	97.40 312	99.83 26	97.61 264	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 90	99.22 54	98.38 284	99.31 206	95.48 359	97.56 290	99.73 45	98.87 140	99.75 44	99.27 131	98.80 41	99.86 144	99.80 17	99.90 88	99.81 41

SSC-MVS3.2		98.53 188	98.79 124	97.74 357	99.46 162	93.62 447	96.45 391	99.34 228	99.33 66	98.93 231	98.70 308	97.90 143	99.90 81	99.12 76	99.92 71	99.69 72

testing3-2		93.78 465	93.91 456	93.39 514	98.82 339	81.72 543	97.76 256	95.28 499	98.60 168	96.54 457	96.66 468	65.85 534	99.62 374	96.65 309	98.99 405	98.82 384

myMVS_eth3d28		92.92 482	92.31 478	94.77 494	97.84 457	87.59 520	96.19 411	96.11 484	97.08 325	94.27 507	93.49 520	66.07 533	98.78 498	91.78 481	97.93 474	97.92 462

UWE-MVS-28		90.22 498	89.28 501	93.02 518	94.50 535	82.87 539	96.52 387	87.51 539	95.21 427	92.36 527	96.04 480	71.57 520	98.25 508	72.04 539	97.77 476	97.94 461

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 92	98.21 143	97.82 244	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	98.78 202	99.46 162	96.58 306	97.65 274	99.72 46	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	98.63 236	99.49 148	96.08 330	97.38 315	99.81 32	99.48 44	99.84 30	99.57 49	98.46 82	99.89 97	99.82 12	99.97 21	99.91 13

fmvsm_s_conf0.1_n_2		99.20 50	99.38 28	98.65 230	99.69 61	96.08 330	97.49 301	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		97.50 310	97.11 333	98.67 226	99.02 298	96.85 291	98.16 183	99.71 48	98.32 192	98.52 310	98.54 339	83.39 487	99.95 25	98.79 101	99.56 289	99.19 316

BP-MVS1		97.40 322	96.97 340	98.71 220	99.07 279	96.81 293	98.34 163	97.18 454	98.58 172	98.17 340	98.61 331	84.01 483	99.94 41	98.97 89	99.78 164	99.37 242

reproduce_monomvs		95.00 445	95.25 429	94.22 501	97.51 484	83.34 536	97.86 240	98.44 405	98.51 179	99.29 148	99.30 125	67.68 527	99.56 403	98.89 96	99.81 140	99.77 53

mmtdpeth		99.30 33	99.42 25	98.92 171	99.58 94	96.89 288	99.48 13	99.92 8	99.92 2	98.26 337	99.80 11	98.33 96	99.91 74	99.56 41	99.95 39	99.97 4

reproduce_model		99.15 57	98.97 98	99.67 4	99.33 203	99.44 9	98.15 184	99.47 167	99.12 99	99.52 87	99.32 123	98.31 97	99.90 81	97.78 192	99.73 199	99.66 80

reproduce-ours		99.09 73	98.90 105	99.67 4	99.27 218	99.49 5	98.00 214	99.42 196	99.05 117	99.48 96	99.27 131	98.29 99	99.89 97	97.61 211	99.71 217	99.62 92

our_new_method		99.09 73	98.90 105	99.67 4	99.27 218	99.49 5	98.00 214	99.42 196	99.05 117	99.48 96	99.27 131	98.29 99	99.89 97	97.61 211	99.71 217	99.62 92

mmdepth		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

monomultidepth		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

mvs5depth		99.30 33	99.59 12	98.44 277	99.65 71	95.35 368	99.82 3	99.94 3	99.83 7	99.42 112	99.94 2	98.13 125	99.96 13	99.63 36	99.96 28	100.00 1

MVStest1		95.86 416	95.60 409	96.63 435	95.87 529	91.70 477	97.93 228	98.94 337	98.03 226	99.56 74	99.66 32	71.83 519	98.26 507	99.35 58	99.24 367	99.91 13

ttmdpeth		97.91 273	98.02 255	97.58 378	98.69 368	94.10 420	98.13 186	98.90 347	97.95 232	97.32 414	99.58 47	95.95 293	98.75 499	96.41 333	99.22 371	99.87 22

WBMVS		95.18 440	94.78 442	96.37 444	97.68 471	89.74 509	95.80 438	98.73 382	97.54 274	98.30 331	98.44 355	70.06 521	99.82 207	96.62 311	99.87 100	99.54 143

dongtai		76.24 507	75.95 510	77.12 525	92.39 539	67.91 549	90.16 531	59.44 550	82.04 535	89.42 535	94.67 512	49.68 547	81.74 543	48.06 542	77.66 540	81.72 539

kuosan		69.30 508	68.95 511	70.34 526	87.68 546	65.00 550	91.11 528	59.90 549	69.02 539	74.46 544	88.89 538	48.58 548	68.03 545	28.61 543	72.33 543	77.99 540

MVSMamba_PlusPlus		98.83 122	98.98 97	98.36 288	99.32 204	96.58 306	98.90 84	99.41 200	99.75 10	98.72 272	99.50 68	96.17 275	99.94 41	99.27 64	99.78 164	98.57 421

MGCFI-Net		98.34 216	98.28 217	98.51 266	98.47 401	97.59 222	98.96 78	99.48 157	99.18 92	97.40 409	95.50 494	98.66 59	99.50 426	98.18 152	98.71 427	98.44 431

testing91		93.32 473	92.27 479	96.47 440	97.54 477	91.25 489	96.17 415	96.76 471	97.18 319	93.65 519	93.50 519	65.11 536	99.63 370	93.04 453	97.45 485	98.53 422

testing11		93.08 478	92.02 484	96.26 449	97.56 475	90.83 498	96.32 402	95.70 494	96.47 364	92.66 524	93.73 516	64.36 537	99.59 391	93.77 432	97.57 479	98.37 440

testing99		93.04 479	91.98 487	96.23 452	97.53 479	90.70 501	96.35 400	95.94 489	96.87 340	93.41 520	93.43 521	63.84 538	99.59 391	93.24 449	97.19 495	98.40 436

UBG		93.25 475	92.32 477	96.04 463	97.72 463	90.16 504	95.92 432	95.91 490	96.03 386	93.95 516	93.04 524	69.60 523	99.52 419	90.72 503	97.98 472	98.45 428

UWE-MVS		92.38 488	91.76 491	94.21 502	97.16 494	84.65 530	95.42 453	88.45 538	95.96 390	96.17 469	95.84 488	66.36 530	99.71 308	91.87 480	98.64 434	98.28 443

ETVMVS		92.60 485	91.08 494	97.18 406	97.70 468	93.65 446	96.54 384	95.70 494	96.51 359	94.68 503	92.39 528	61.80 542	99.50 426	86.97 517	97.41 488	98.40 436

sasdasda		98.34 216	98.26 223	98.58 246	98.46 403	97.82 199	98.96 78	99.46 172	99.19 89	97.46 403	95.46 497	98.59 67	99.46 441	98.08 160	98.71 427	98.46 425

testing222		91.96 494	90.37 497	96.72 433	97.47 486	92.59 463	96.11 418	94.76 503	96.83 344	92.90 522	92.87 525	57.92 544	99.55 407	86.93 518	97.52 481	98.00 459

WB-MVSnew		95.73 421	95.57 412	96.23 452	96.70 511	90.70 501	96.07 421	93.86 517	95.60 408	97.04 427	95.45 501	96.00 285	99.55 407	91.04 495	98.31 451	98.43 433

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 165	99.65 71	97.05 275	97.80 248	99.76 39	98.70 159	99.78 39	99.11 187	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	99.02 149	99.64 77	97.28 252	97.82 244	99.76 39	98.73 151	99.82 34	99.09 196	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	98.80 195	99.75 34	96.59 303	97.97 226	99.86 17	98.22 203	99.88 21	99.71 22	98.59 67	99.84 177	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 232	99.71 49	96.10 325	97.87 239	99.85 19	98.56 177	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 72	99.20 58	98.78 202	99.55 117	96.59 303	97.79 249	99.82 31	98.21 205	99.81 36	99.53 64	98.46 82	99.84 177	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 73	99.26 50	98.61 242	99.55 117	96.09 328	97.74 261	99.81 32	98.55 178	99.85 27	99.55 56	98.60 66	99.84 177	99.69 35	99.98 12	99.89 16

MM		98.22 238	97.99 258	98.91 173	98.66 378	96.97 280	97.89 235	94.44 507	99.54 40	98.95 222	99.14 179	93.50 374	99.92 65	99.80 17	99.96 28	99.85 30

WAC-MVS						90.90 496								91.37 490

Syy-MVS		96.04 405	95.56 413	97.49 390	97.10 496	94.48 406	96.18 413	96.58 475	95.65 406	94.77 501	92.29 530	91.27 418	99.36 457	98.17 154	98.05 468	98.63 415

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 151	97.77 253	99.90 12	99.33 66	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	98.13 149	98.08 196	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		91.92 495	90.45 496	96.30 446	97.10 496	90.90 496	96.18 413	96.58 475	95.65 406	94.77 501	92.29 530	53.88 545	99.36 457	89.59 510	98.05 468	98.63 415

testing3		93.51 469	92.09 482	97.75 355	98.60 385	94.40 408	97.32 323	95.26 500	97.56 270	96.79 445	95.50 494	53.57 546	99.77 264	95.26 388	98.97 409	99.08 338

SSC-MVS		98.71 142	98.74 128	98.62 238	99.72 45	96.08 330	98.74 99	98.64 390	99.74 12	99.67 59	99.24 144	94.57 341	99.95 25	99.11 77	99.24 367	99.82 36

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 77	98.10 154	97.68 268	99.84 23	99.29 72	99.92 8	99.57 49	99.60 5	99.96 13	99.74 27	99.98 12	99.89 16

WB-MVS		98.52 192	98.55 165	98.43 278	99.65 71	95.59 349	98.52 130	98.77 373	99.65 25	99.52 87	99.00 227	94.34 351	99.93 53	98.65 114	98.83 417	99.76 58

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 261	99.66 70	96.97 280	98.00 214	99.85 19	99.24 77	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 404

dmvs_re		95.98 410	95.39 421	97.74 357	98.86 330	97.45 234	98.37 159	95.69 496	97.95 232	96.56 456	95.95 483	90.70 424	97.68 517	88.32 513	96.13 512	98.11 451

SDMVSNet		99.23 45	99.32 39	98.96 162	99.68 64	97.35 240	98.84 95	99.48 157	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 175	99.92 71	99.57 124

dmvs_testset		92.94 481	92.21 481	95.13 491	98.59 388	90.99 495	97.65 274	92.09 526	96.95 332	94.00 514	93.55 518	92.34 399	96.97 527	72.20 538	92.52 532	97.43 487

sd_testset		99.28 36	99.31 41	99.19 112	99.68 64	98.06 165	99.41 17	99.30 249	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 244	99.92 71	99.57 124

test_fmvsm_n_1920		99.33 30	99.45 23	98.99 154	99.57 103	97.73 210	97.93 228	99.83 26	99.22 80	99.93 6	99.30 125	99.42 11	99.96 13	99.85 6	99.99 5	99.29 281

test_cas_vis1_n_1920		98.33 220	98.68 141	97.27 402	99.69 61	92.29 471	98.03 207	99.85 19	97.62 261	99.96 4	99.62 40	93.98 363	99.74 289	99.52 49	99.86 107	99.79 47

test_vis1_n_1920		98.40 206	98.92 102	96.81 429	99.74 37	90.76 500	98.15 184	99.91 10	98.33 190	99.89 18	99.55 56	95.07 324	99.88 115	99.76 23	99.93 57	99.79 47

test_vis1_n		98.31 225	98.50 174	97.73 360	99.76 30	94.17 416	98.68 109	99.91 10	96.31 371	99.79 38	99.57 49	92.85 390	99.42 450	99.79 19	99.84 114	99.60 102

test_fmvs1_n		98.09 256	98.28 217	97.52 387	99.68 64	93.47 449	98.63 116	99.93 6	95.41 421	99.68 57	99.64 37	91.88 408	99.48 434	99.82 12	99.87 100	99.62 92

mvsany_test1		97.60 304	97.54 302	97.77 351	97.72 463	95.35 368	95.36 455	97.13 457	94.13 458	99.71 49	99.33 118	97.93 141	99.30 468	97.60 213	98.94 412	98.67 413

APD_test1		98.83 122	98.66 146	99.34 83	99.78 24	99.47 8	98.42 151	99.45 176	98.28 199	98.98 212	99.19 159	97.76 158	99.58 398	96.57 316	99.55 294	98.97 360

test_vis1_rt		97.75 293	97.72 287	97.83 346	98.81 342	96.35 319	97.30 326	99.69 56	94.61 443	97.87 370	98.05 400	96.26 272	98.32 506	98.74 107	98.18 457	98.82 384

test_vis3_rt		99.14 62	99.17 60	99.07 136	99.78 24	98.38 122	98.92 83	99.94 3	97.80 246	99.91 12	99.67 30	97.15 212	98.91 494	99.76 23	99.56 289	99.92 12

test_fmvs2		98.70 147	98.97 98	97.89 341	99.54 123	94.05 421	98.55 126	99.92 8	96.78 347	99.72 47	99.78 13	96.60 253	99.67 342	99.91 2	99.90 88	99.94 10

test_fmvs1		97.72 295	97.94 266	97.07 414	98.66 378	92.39 468	97.68 268	99.81 32	95.20 428	99.54 79	99.44 85	91.56 412	99.41 451	99.78 21	99.77 172	99.40 229

test_fmvs3		99.12 69	99.41 26	98.25 300	99.76 30	95.07 384	99.05 68	99.94 3	97.78 249	99.82 34	99.84 3	98.56 73	99.71 308	99.96 1	99.96 28	99.97 4

mvsany_test3		98.87 112	98.92 102	98.74 215	99.38 185	96.94 284	98.58 123	99.10 309	96.49 362	99.96 4	99.81 8	98.18 118	99.45 444	98.97 89	99.79 159	99.83 33

testf1		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 56	98.90 136	99.43 108	99.35 111	98.86 35	99.67 342	97.81 189	99.81 140	99.24 298

APD_test2		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 56	98.90 136	99.43 108	99.35 111	98.86 35	99.67 342	97.81 189	99.81 140	99.24 298

test_f		98.67 160	98.87 111	98.05 328	99.72 45	95.59 349	98.51 135	99.81 32	96.30 373	99.78 39	99.82 5	96.14 276	98.63 502	99.82 12	99.93 57	99.95 9

FE-MVS		95.66 423	94.95 439	97.77 351	98.53 397	95.28 373	99.40 19	96.09 486	93.11 477	97.96 364	99.26 137	79.10 505	99.77 264	92.40 473	98.71 427	98.27 444

FA-MVS(test-final)		96.99 360	96.82 353	97.50 389	98.70 363	94.78 396	99.34 23	96.99 460	95.07 430	98.48 314	99.33 118	88.41 446	99.65 362	96.13 354	98.92 414	98.07 454

BridgeMVS		98.63 166	98.72 132	98.38 284	98.66 378	96.68 302	98.90 84	99.42 196	98.99 124	98.97 216	99.19 159	95.81 298	99.85 158	98.77 105	99.77 172	98.60 417

MonoMVSNet		96.25 398	96.53 378	95.39 486	96.57 513	91.01 494	98.82 97	97.68 436	98.57 174	98.03 358	99.37 104	90.92 421	97.78 516	94.99 393	93.88 530	97.38 488

patch_mono-2		98.51 193	98.63 152	98.17 310	99.38 185	94.78 396	97.36 320	99.69 56	98.16 215	98.49 312	99.29 128	97.06 217	99.97 6	98.29 144	99.91 80	99.76 58

EGC-MVSNET		85.24 504	80.54 507	99.34 83	99.77 27	99.20 38	99.08 62	99.29 257	12.08 544	20.84 545	99.42 89	97.55 178	99.85 158	97.08 260	99.72 208	98.96 363

test2506		92.39 487	91.89 489	93.89 507	99.38 185	82.28 541	99.32 26	66.03 548	99.08 114	98.77 265	99.57 49	66.26 531	99.84 177	98.71 110	99.95 39	99.54 143

test1111		96.49 382	96.82 353	95.52 482	99.42 176	87.08 522	99.22 46	87.14 540	99.11 100	99.46 101	99.58 47	88.69 440	99.86 144	98.80 100	99.95 39	99.62 92

ECVR-MVS		96.42 388	96.61 372	95.85 471	99.38 185	88.18 517	99.22 46	86.00 542	99.08 114	99.36 128	99.57 49	88.47 445	99.82 207	98.52 127	99.95 39	99.54 143

test_blank		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

tt0805		98.69 151	98.62 154	98.90 176	99.75 34	99.30 21	99.15 57	96.97 462	98.86 142	98.87 247	97.62 432	98.63 63	98.96 490	99.41 56	98.29 453	98.45 428

DVP-MVS++		98.90 108	98.70 138	99.51 49	98.43 408	99.15 52	99.43 15	99.32 236	98.17 212	99.26 156	99.02 212	98.18 118	99.88 115	97.07 261	99.45 322	99.49 176

FOURS1					99.73 38	99.67 2	99.43 15	99.54 130	99.43 54	99.26 156

MSC_two_6792asdad				99.32 91	98.43 408	98.37 124		98.86 358					99.89 97	97.14 254	99.60 272	99.71 65

PC_three_1452									93.27 473	99.40 117	98.54 339	98.22 113	97.00 526	95.17 390	99.45 322	99.49 176

No_MVS				99.32 91	98.43 408	98.37 124		98.86 358					99.89 97	97.14 254	99.60 272	99.71 65

test_one_0601					99.39 183	99.20 38		99.31 241	98.49 180	98.66 282	99.02 212	97.64 168

eth-test2					0.00 552

eth-test					0.00 552

GeoE		99.05 83	98.99 96	99.25 104	99.44 169	98.35 128	98.73 103	99.56 119	98.42 184	98.91 234	98.81 282	98.94 31	99.91 74	98.35 140	99.73 199	99.49 176

test_method		79.78 505	79.50 508	80.62 523	80.21 548	45.76 551	70.82 539	98.41 410	31.08 543	80.89 543	97.71 425	84.85 474	97.37 522	91.51 488	80.03 539	98.75 400

Anonymous20240521		98.69 151	98.87 111	98.16 313	99.77 27	95.11 383	99.08 62	99.44 184	99.34 65	99.33 137	99.55 56	94.10 362	99.94 41	99.25 67	99.96 28	99.42 217

h-mvs33		97.77 292	97.33 317	99.10 128	99.21 238	97.84 193	98.35 161	98.57 396	99.11 100	98.58 299	99.02 212	88.65 443	99.96 13	98.11 157	96.34 508	99.49 176

hse-mvs2		97.46 315	97.07 334	98.64 232	98.73 353	97.33 242	97.45 308	97.64 439	99.11 100	98.58 299	97.98 406	88.65 443	99.79 246	98.11 157	97.39 489	98.81 389

CL-MVSNet_self_test		97.44 318	97.22 324	98.08 323	98.57 392	95.78 346	94.30 492	98.79 370	96.58 358	98.60 295	98.19 387	94.74 337	99.64 367	96.41 333	98.84 416	98.82 384

KD-MVS_2432*1600		92.87 483	91.99 485	95.51 483	91.37 541	89.27 511	94.07 499	98.14 422	95.42 418	97.25 416	96.44 474	67.86 525	99.24 474	91.28 491	96.08 517	98.02 456

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 83	99.06 70	98.69 108	99.54 130	99.31 69	99.62 69	99.53 64	97.36 198	99.86 144	99.24 69	99.71 217	99.39 230

AUN-MVS		96.24 400	95.45 417	98.60 244	98.70 363	97.22 259	97.38 315	97.65 437	95.95 391	95.53 489	97.96 410	82.11 495	99.79 246	96.31 339	97.44 486	98.80 394

ZD-MVS					99.01 301	98.84 85		99.07 314	94.10 460	98.05 356	98.12 393	96.36 267	99.86 144	92.70 466	99.19 379

SR-MVS-dyc-post		98.81 127	98.55 165	99.57 21	99.20 242	99.38 12	98.48 143	99.30 249	98.64 161	98.95 222	98.96 240	97.49 189	99.86 144	96.56 320	99.39 337	99.45 204

RE-MVS-def			98.58 162		99.20 242	99.38 12	98.48 143	99.30 249	98.64 161	98.95 222	98.96 240	97.75 159		96.56 320	99.39 337	99.45 204

SED-MVS		98.91 106	98.72 132	99.49 55	99.49 148	99.17 43	98.10 193	99.31 241	98.03 226	99.66 60	99.02 212	98.36 90	99.88 115	96.91 275	99.62 265	99.41 220

IU-MVS					99.49 148	99.15 52		98.87 353	92.97 480	99.41 114				96.76 292	99.62 265	99.66 80

OPU-MVS				98.82 190	98.59 388	98.30 132	98.10 193				98.52 343	98.18 118	98.75 499	94.62 403	99.48 318	99.41 220

test_241102_TWO								99.30 249	98.03 226	99.26 156	99.02 212	97.51 185	99.88 115	96.91 275	99.60 272	99.66 80

test_241102_ONE					99.49 148	99.17 43		99.31 241	97.98 229	99.66 60	98.90 255	98.36 90	99.48 434

SF-MVS		98.53 188	98.27 220	99.32 91	99.31 206	98.75 90	98.19 178	99.41 200	96.77 348	98.83 253	98.90 255	97.80 155	99.82 207	95.68 375	99.52 303	99.38 239

cl22		95.79 419	95.39 421	96.98 418	96.77 509	92.79 460	94.40 489	98.53 400	94.59 444	97.89 368	98.17 388	82.82 492	99.24 474	96.37 335	99.03 397	98.92 370

miper_ehance_all_eth		97.06 353	97.03 336	97.16 410	97.83 458	93.06 453	94.66 479	99.09 311	95.99 389	98.69 276	98.45 354	92.73 393	99.61 382	96.79 288	99.03 397	98.82 384

miper_enhance_ethall		96.01 407	95.74 402	96.81 429	96.41 521	92.27 472	93.69 509	98.89 350	91.14 504	98.30 331	97.35 452	90.58 425	99.58 398	96.31 339	99.03 397	98.60 417

ZNCC-MVS		98.68 157	98.40 192	99.54 31	99.57 103	99.21 32	98.46 145	99.29 257	97.28 305	98.11 349	98.39 360	98.00 134	99.87 135	96.86 285	99.64 255	99.55 137

dcpmvs_2		98.78 133	99.11 74	97.78 350	99.56 111	93.67 444	99.06 66	99.86 17	99.50 43	99.66 60	99.26 137	97.21 209	99.99 2	98.00 170	99.91 80	99.68 73

cl____		97.02 356	96.83 352	97.58 378	97.82 459	94.04 423	94.66 479	99.16 298	97.04 327	98.63 286	98.71 301	88.68 442	99.69 326	97.00 266	99.81 140	99.00 354

DIV-MVS_self_test		97.02 356	96.84 351	97.58 378	97.82 459	94.03 424	94.66 479	99.16 298	97.04 327	98.63 286	98.71 301	88.69 440	99.69 326	97.00 266	99.81 140	99.01 351

eth_miper_zixun_eth		97.23 339	97.25 321	97.17 408	98.00 448	92.77 461	94.71 474	99.18 291	97.27 307	98.56 303	98.74 296	91.89 407	99.69 326	97.06 263	99.81 140	99.05 342

9.14			97.78 280		99.07 279		97.53 295	99.32 236	95.53 413	98.54 307	98.70 308	97.58 175	99.76 270	94.32 416	99.46 320

uanet_test		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

DCPMVS		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

save fliter					99.11 270	97.97 175	96.53 386	99.02 327	98.24 200

ET-MVSNet_ETH3D		94.30 455	93.21 467	97.58 378	98.14 438	94.47 407	94.78 473	93.24 522	94.72 440	89.56 534	95.87 486	78.57 509	99.81 224	96.91 275	97.11 498	98.46 425

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 101	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		98.00 265	97.74 283	98.80 195	98.72 355	98.09 155	98.05 203	99.60 92	97.39 293	96.63 452	95.55 492	97.68 162	99.80 233	96.73 297	99.27 361	98.52 423

miper_refine_blended		92.87 483	91.99 485	95.51 483	91.37 541	89.27 511	94.07 499	98.14 422	95.42 418	97.25 416	96.44 474	67.86 525	99.24 474	91.28 491	96.08 517	98.02 456

miper_lstm_enhance		97.18 344	97.16 327	97.25 404	98.16 436	92.85 459	95.15 464	99.31 241	97.25 309	98.74 271	98.78 287	90.07 428	99.78 258	97.19 248	99.80 152	99.11 337

ETV-MVS		98.03 261	97.86 275	98.56 254	98.69 368	98.07 162	97.51 298	99.50 147	98.10 222	97.50 400	95.51 493	98.41 85	99.88 115	96.27 344	99.24 367	97.71 477

CS-MVS		99.13 66	99.10 80	99.24 106	99.06 284	99.15 52	99.36 22	99.88 15	99.36 63	98.21 339	98.46 353	98.68 58	99.93 53	99.03 85	99.85 109	98.64 414

D2MVS		97.84 288	97.84 277	97.83 346	99.14 264	94.74 398	96.94 354	98.88 351	95.84 396	98.89 238	98.96 240	94.40 348	99.69 326	97.55 217	99.95 39	99.05 342

DVP-MVS		98.77 136	98.52 170	99.52 44	99.50 140	99.21 32	98.02 210	98.84 362	97.97 230	99.08 189	99.02 212	97.61 172	99.88 115	96.99 268	99.63 261	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									98.17 212	99.08 189	99.02 212	97.89 147	99.88 115	97.07 261	99.71 217	99.70 70

test_0728_SECOND				99.60 16	99.50 140	99.23 30	98.02 210	99.32 236					99.88 115	96.99 268	99.63 261	99.68 73

test0726					99.50 140	99.21 32	98.17 182	99.35 222	97.97 230	99.26 156	99.06 199	97.61 172

SR-MVS		98.71 142	98.43 188	99.57 21	99.18 254	99.35 16	98.36 160	99.29 257	98.29 197	98.88 242	98.85 269	97.53 182	99.87 135	96.14 352	99.31 353	99.48 187

DPM-MVS		96.32 392	95.59 411	98.51 266	98.76 349	97.21 261	94.54 485	98.26 415	91.94 494	96.37 466	97.25 455	93.06 385	99.43 448	91.42 489	98.74 423	98.89 375

GST-MVS		98.61 170	98.30 214	99.52 44	99.51 134	99.20 38	98.26 171	99.25 272	97.44 288	98.67 280	98.39 360	97.68 162	99.85 158	96.00 357	99.51 306	99.52 160

test_yl		96.69 370	96.29 389	97.90 339	98.28 422	95.24 374	97.29 327	97.36 444	98.21 205	98.17 340	97.86 414	86.27 457	99.55 407	94.87 397	98.32 449	98.89 375

thisisatest0530		95.27 437	94.45 449	97.74 357	99.19 246	94.37 409	97.86 240	90.20 535	97.17 320	98.22 338	97.65 429	73.53 518	99.90 81	96.90 280	99.35 344	98.95 364

Anonymous20240529		98.93 104	98.87 111	99.12 124	99.19 246	98.22 142	99.01 71	98.99 333	99.25 76	99.54 79	99.37 104	97.04 218	99.80 233	97.89 179	99.52 303	99.35 256

Anonymous202405211		97.90 274	97.50 305	99.08 134	98.90 321	98.25 136	98.53 129	96.16 482	98.87 140	99.11 184	98.86 266	90.40 427	99.78 258	97.36 234	99.31 353	99.19 316

DCV-MVSNet		96.69 370	96.29 389	97.90 339	98.28 422	95.24 374	97.29 327	97.36 444	98.21 205	98.17 340	97.86 414	86.27 457	99.55 407	94.87 397	98.32 449	98.89 375

tttt0517		95.64 424	94.98 437	97.64 372	99.36 192	93.81 439	98.72 104	90.47 534	98.08 225	98.67 280	98.34 367	73.88 517	99.92 65	97.77 194	99.51 306	99.20 310

our_test_3		97.39 323	97.73 286	96.34 445	98.70 363	89.78 508	94.61 482	98.97 336	96.50 361	99.04 201	98.85 269	95.98 290	99.84 177	97.26 243	99.67 243	99.41 220

thisisatest0515		94.12 460	93.16 468	96.97 419	98.60 385	92.90 458	93.77 508	90.61 533	94.10 460	96.91 434	95.87 486	74.99 515	99.80 233	94.52 406	99.12 390	98.20 446

ppachtmachnet_test		97.50 310	97.74 283	96.78 431	98.70 363	91.23 491	94.55 484	99.05 319	96.36 368	99.21 172	98.79 285	96.39 263	99.78 258	96.74 295	99.82 133	99.34 260

SMA-MVS		98.40 206	98.03 254	99.51 49	99.16 258	99.21 32	98.05 203	99.22 280	94.16 457	98.98 212	99.10 190	97.52 184	99.79 246	96.45 330	99.64 255	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																98.81 389

DPE-MVS		98.59 174	98.26 223	99.57 21	99.27 218	99.15 52	97.01 348	99.39 206	97.67 257	99.44 107	98.99 229	97.53 182	99.89 97	95.40 385	99.68 237	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					99.36 192	99.10 65				99.05 199

thres100view900		94.19 457	93.67 461	95.75 474	99.06 284	91.35 485	98.03 207	94.24 513	98.33 190	97.40 409	94.98 506	79.84 499	99.62 374	83.05 528	98.08 465	96.29 508

tfpnnormal		98.90 108	98.90 105	98.91 173	99.67 68	97.82 199	99.00 73	99.44 184	99.45 50	99.51 92	99.24 144	98.20 117	99.86 144	95.92 361	99.69 231	99.04 346

tfpn200view9		94.03 461	93.44 463	95.78 473	98.93 313	91.44 483	97.60 285	94.29 510	97.94 234	97.10 421	94.31 514	79.67 501	99.62 374	83.05 528	98.08 465	96.29 508

c3_l		97.36 326	97.37 313	97.31 399	98.09 443	93.25 451	95.01 467	99.16 298	97.05 326	98.77 265	98.72 299	92.88 388	99.64 367	96.93 274	99.76 188	99.05 342

CHOSEN 280x420		95.51 429	95.47 415	95.65 479	98.25 425	88.27 516	93.25 518	98.88 351	93.53 470	94.65 504	97.15 458	86.17 459	99.93 53	97.41 232	99.93 57	98.73 403

CANet		97.87 281	97.76 281	98.19 309	97.75 462	95.51 354	96.76 367	99.05 319	97.74 251	96.93 431	98.21 385	95.59 306	99.89 97	97.86 186	99.93 57	99.19 316

Fast-Effi-MVS+-dtu		98.27 231	98.09 246	98.81 192	98.43 408	98.11 151	97.61 284	99.50 147	98.64 161	97.39 411	97.52 439	98.12 126	99.95 25	96.90 280	98.71 427	98.38 438

Effi-MVS+-dtu		98.26 233	97.90 272	99.35 80	98.02 447	99.49 5	98.02 210	99.16 298	98.29 197	97.64 386	97.99 405	96.44 260	99.95 25	96.66 308	98.93 413	98.60 417

CANet_DTU		97.26 335	97.06 335	97.84 345	97.57 474	94.65 403	96.19 411	98.79 370	97.23 315	95.14 495	98.24 382	93.22 379	99.84 177	97.34 235	99.84 114	99.04 346

MGCNet		97.44 318	97.01 338	98.72 219	96.42 520	96.74 298	97.20 338	91.97 530	98.46 182	98.30 331	98.79 285	92.74 392	99.91 74	99.30 62	99.94 51	99.52 160

MP-MVS-pluss		98.57 177	98.23 228	99.60 16	99.69 61	99.35 16	97.16 343	99.38 208	94.87 436	98.97 216	98.99 229	98.01 133	99.88 115	97.29 241	99.70 226	99.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 206	98.00 257	99.61 13	99.57 103	99.25 28	98.57 124	99.35 222	97.55 272	99.31 146	97.71 425	94.61 340	99.88 115	96.14 352	99.19 379	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												84.74 476				98.81 389

sam_mvs												84.29 482

IterMVS-SCA-FT		97.85 287	98.18 236	96.87 425	99.27 218	91.16 492	95.53 447	99.25 272	99.10 107	99.41 114	99.35 111	93.10 383	99.96 13	98.65 114	99.94 51	99.49 176

TSAR-MVS + MP.		98.63 166	98.49 179	99.06 142	99.64 77	97.90 187	98.51 135	98.94 337	96.96 331	99.24 166	98.89 261	97.83 151	99.81 224	96.88 282	99.49 317	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		97.86 282	98.17 237	96.92 422	98.98 306	93.91 434	96.45 391	99.17 295	97.85 242	98.41 322	97.14 459	98.47 77	99.92 65	98.02 167	99.05 393	96.92 496

OPM-MVS		98.56 179	98.32 212	99.25 104	99.41 179	98.73 94	97.13 345	99.18 291	97.10 324	98.75 268	98.92 249	98.18 118	99.65 362	96.68 304	99.56 289	99.37 242
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 138	98.48 180	99.57 21	99.58 94	99.29 23	97.82 244	99.25 272	96.94 333	98.78 262	99.12 185	98.02 132	99.84 177	97.13 257	99.67 243	99.59 109

ambc				98.24 302	98.82 339	95.97 335	98.62 118	99.00 332		99.27 152	99.21 154	96.99 223	99.50 426	96.55 323	99.50 314	99.26 293

MTGPA								99.20 283

SPE-MVS-test		99.13 66	99.09 82	99.26 101	99.13 267	98.97 73	99.31 30	99.88 15	99.44 52	98.16 343	98.51 344	98.64 61	99.93 53	98.91 93	99.85 109	98.88 378

Effi-MVS+		98.02 262	97.82 278	98.62 238	98.53 397	97.19 263	97.33 322	99.68 63	97.30 303	96.68 450	97.46 445	98.56 73	99.80 233	96.63 310	98.20 456	98.86 380

xiu_mvs_v2_base		97.16 346	97.49 306	96.17 456	98.54 395	92.46 466	95.45 451	98.84 362	97.25 309	97.48 402	96.49 471	98.31 97	99.90 81	96.34 338	98.68 432	96.15 512

xiu_mvs_v1_base		97.86 282	98.17 237	96.92 422	98.98 306	93.91 434	96.45 391	99.17 295	97.85 242	98.41 322	97.14 459	98.47 77	99.92 65	98.02 167	99.05 393	96.92 496

new-patchmatchnet		98.35 215	98.74 128	97.18 406	99.24 230	92.23 473	96.42 395	99.48 157	98.30 194	99.69 55	99.53 64	97.44 193	99.82 207	98.84 99	99.77 172	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		97.64 302	97.49 306	98.08 323	99.14 264	95.12 382	96.70 371	99.05 319	93.77 467	98.62 290	98.83 276	93.23 378	99.75 282	98.33 143	99.76 188	99.36 250

test_post1							97.59 287				20.48 546	83.07 490	99.66 355	94.16 417

test_post											21.25 545	83.86 485	99.70 316

Fast-Effi-MVS+		97.67 300	97.38 312	98.57 249	98.71 359	97.43 237	97.23 333	99.45 176	94.82 438	96.13 470	96.51 470	98.52 75	99.91 74	96.19 348	98.83 417	98.37 440

patchmatchnet-post											98.77 289	84.37 479	99.85 158

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 212	98.18 144	99.49 12	99.51 142	99.70 15	99.80 37	99.68 25	96.84 231	99.83 195	99.21 70	99.91 80	99.77 53

pmmvs-eth3d		98.47 197	98.34 206	98.86 179	99.30 210	97.76 206	97.16 343	99.28 261	95.54 412	99.42 112	99.19 159	97.27 204	99.63 370	97.89 179	99.97 21	99.20 310

GG-mvs-BLEND				94.76 495	94.54 534	92.13 474	99.31 30	80.47 546		88.73 537	91.01 536	67.59 528	98.16 510	82.30 532	94.53 528	93.98 520

xiu_mvs_v1_base_debi		97.86 282	98.17 237	96.92 422	98.98 306	93.91 434	96.45 391	99.17 295	97.85 242	98.41 322	97.14 459	98.47 77	99.92 65	98.02 167	99.05 393	96.92 496

Anonymous20231206		98.21 241	98.21 229	98.20 307	99.51 134	95.43 364	98.13 186	99.32 236	96.16 380	98.93 231	98.82 279	96.00 285	99.83 195	97.32 239	99.73 199	99.36 250

MTAPA		98.88 111	98.64 150	99.61 13	99.67 68	99.36 15	98.43 148	99.20 283	98.83 149	98.89 238	98.90 255	96.98 224	99.92 65	97.16 251	99.70 226	99.56 130

MTMP							97.93 228	91.91 531

gm-plane-assit					94.83 533	81.97 542			88.07 526		94.99 505		99.60 386	91.76 482

test9_res														93.28 447	99.15 384	99.38 239

MVP-Stereo		98.08 257	97.92 269	98.57 249	98.96 309	96.79 294	97.90 234	99.18 291	96.41 367	98.46 316	98.95 244	95.93 294	99.60 386	96.51 326	98.98 408	99.31 275
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 359	98.08 159	95.96 427	99.03 324	91.40 500	95.85 478	97.53 436	96.52 256	99.76 270

train_agg		97.10 348	96.45 383	99.07 136	98.71 359	98.08 159	95.96 427	99.03 324	91.64 495	95.85 478	97.53 436	96.47 258	99.76 270	93.67 434	99.16 382	99.36 250

gg-mvs-nofinetune		92.37 489	91.20 493	95.85 471	95.80 530	92.38 469	99.31 30	81.84 545	99.75 10	91.83 529	99.74 18	68.29 524	99.02 487	87.15 516	97.12 497	96.16 511

SCA		96.41 389	96.66 367	95.67 477	98.24 427	88.35 515	95.85 436	96.88 468	96.11 381	97.67 385	98.67 314	93.10 383	99.85 158	94.16 417	99.22 371	98.81 389

Patchmatch-test		96.55 378	96.34 386	97.17 408	98.35 415	93.06 453	98.40 156	97.79 430	97.33 298	98.41 322	98.67 314	83.68 486	99.69 326	95.16 391	99.31 353	98.77 397

test_8					98.67 373	98.01 168	95.91 433	99.02 327	91.64 495	95.79 481	97.50 440	96.47 258	99.76 270

MS-PatchMatch		97.68 299	97.75 282	97.45 394	98.23 430	93.78 440	97.29 327	98.84 362	96.10 382	98.64 285	98.65 320	96.04 282	99.36 457	96.84 286	99.14 385	99.20 310

Patchmatch-RL test		97.26 335	97.02 337	97.99 334	99.52 131	95.53 353	96.13 416	99.71 48	97.47 280	99.27 152	99.16 169	84.30 481	99.62 374	97.89 179	99.77 172	98.81 389

cdsmvs_eth3d_5k		24.66 509	32.88 512	0.00 529	0.00 552	0.00 554	0.00 540	99.10 309	0.00 547	0.00 548	97.58 433	99.21 18	0.00 548	0.00 546	0.00 546	0.00 544

pcd_1.5k_mvsjas		8.17 512	10.90 515	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	98.07 128	0.00 548	0.00 546	0.00 546	0.00 544

agg_prior2														92.50 471	99.16 382	99.37 242

agg_prior					98.68 372	97.99 171		99.01 330		95.59 482			99.77 264

tmp_tt		78.77 506	78.73 509	78.90 524	58.45 549	74.76 548	94.20 495	78.26 547	39.16 542	86.71 538	92.82 526	80.50 497	75.19 544	86.16 522	92.29 533	86.74 538

canonicalmvs		98.34 216	98.26 223	98.58 246	98.46 403	97.82 199	98.96 78	99.46 172	99.19 89	97.46 403	95.46 497	98.59 67	99.46 441	98.08 160	98.71 427	98.46 425

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 56	98.93 132	99.65 63	99.72 21	98.93 33	99.95 25	99.11 77	100.00 1	99.82 36

alignmvs		97.35 327	96.88 348	98.78 202	98.54 395	98.09 155	97.71 264	97.69 434	99.20 84	97.59 391	95.90 485	88.12 449	99.55 407	98.18 152	98.96 410	98.70 407

nrg030		99.40 25	99.35 33	99.54 31	99.58 94	99.13 60	98.98 76	99.48 157	99.68 19	99.46 101	99.26 137	98.62 64	99.73 296	99.17 74	99.92 71	99.76 58

v144192		98.54 186	98.57 163	98.45 275	99.21 238	95.98 333	97.63 278	99.36 216	97.15 323	99.32 143	99.18 163	95.84 297	99.84 177	99.50 50	99.91 80	99.54 143

FIs		99.14 62	99.09 82	99.29 95	99.70 57	98.28 133	99.13 59	99.52 140	99.48 44	99.24 166	99.41 94	96.79 238	99.82 207	98.69 112	99.88 95	99.76 58

v1921920		98.54 186	98.60 159	98.38 284	99.20 242	95.76 347	97.56 290	99.36 216	97.23 315	99.38 121	99.17 167	96.02 283	99.84 177	99.57 39	99.90 88	99.54 143

UA-Net		99.47 16	99.40 27	99.70 2	99.49 148	99.29 23	99.80 4	99.72 46	99.82 8	99.04 201	99.81 8	98.05 131	99.96 13	98.85 98	99.99 5	99.86 28

v1192		98.60 172	98.66 146	98.41 280	99.27 218	95.88 339	97.52 296	99.36 216	97.41 290	99.33 137	99.20 156	96.37 266	99.82 207	99.57 39	99.92 71	99.55 137

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 124	99.30 35	99.57 109	99.61 34	99.40 117	99.50 68	97.12 214	99.85 158	99.02 86	99.94 51	99.80 45

v1144		98.60 172	98.66 146	98.41 280	99.36 192	95.90 337	97.58 288	99.34 228	97.51 276	99.27 152	99.15 175	96.34 268	99.80 233	99.47 53	99.93 57	99.51 164

sosnet-low-res		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

HFP-MVS		98.71 142	98.44 187	99.51 49	99.49 148	99.16 48	98.52 130	99.31 241	97.47 280	98.58 299	98.50 348	97.97 138	99.85 158	96.57 316	99.59 276	99.53 157

v148		98.45 200	98.60 159	98.00 332	99.44 169	94.98 386	97.44 310	99.06 315	98.30 194	99.32 143	98.97 236	96.65 250	99.62 374	98.37 139	99.85 109	99.39 230

sosnet		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

uncertanet		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

AllTest		98.44 201	98.20 230	99.16 118	99.50 140	98.55 108	98.25 172	99.58 101	96.80 345	98.88 242	99.06 199	97.65 165	99.57 400	94.45 409	99.61 270	99.37 242

TestCases				99.16 118	99.50 140	98.55 108		99.58 101	96.80 345	98.88 242	99.06 199	97.65 165	99.57 400	94.45 409	99.61 270	99.37 242

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 111	99.45 14	99.61 90	99.66 23	99.68 57	99.66 32	98.44 84	99.95 25	99.73 28	99.96 28	99.75 62

region2R		98.69 151	98.40 192	99.54 31	99.53 127	99.17 43	98.52 130	99.31 241	97.46 285	98.44 319	98.51 344	97.83 151	99.88 115	96.46 329	99.58 281	99.58 117

RRT-MVS		97.88 279	97.98 259	97.61 375	98.15 437	93.77 441	98.97 77	99.64 78	99.16 94	98.69 276	99.42 89	91.60 409	99.89 97	97.63 209	98.52 444	99.16 330

balanced_ft_v1		98.28 230	98.35 205	98.10 318	98.08 444	96.23 323	99.23 45	99.26 270	98.34 188	97.46 403	99.42 89	95.38 315	99.88 115	98.60 117	99.34 346	98.17 448

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 146	99.20 49	99.65 76	99.48 44	99.92 8	99.71 22	98.07 128	99.96 13	99.53 48	100.00 1	99.93 11

PS-MVSNAJ		97.08 351	97.39 311	96.16 458	98.56 393	92.46 466	95.24 460	98.85 361	97.25 309	97.49 401	95.99 482	98.07 128	99.90 81	96.37 335	98.67 433	96.12 513

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 103	99.28 40	99.66 70	99.09 110	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 48	99.27 74	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		98.69 151	98.71 135	98.62 238	99.10 272	96.37 318	97.23 333	98.87 353	99.20 84	99.19 174	98.99 229	97.30 201	99.85 158	98.77 105	99.79 159	99.65 85

EI-MVSNet-Vis-set		98.68 157	98.70 138	98.63 236	99.09 275	96.40 317	97.23 333	98.86 358	99.20 84	99.18 179	98.97 236	97.29 203	99.85 158	98.72 109	99.78 164	99.64 86

HPM-MVS++		98.10 253	97.64 296	99.48 57	99.09 275	99.13 60	97.52 296	98.75 379	97.46 285	96.90 437	97.83 418	96.01 284	99.84 177	95.82 369	99.35 344	99.46 198

test_prior4						97.97 175	95.86 434

XVS		98.72 141	98.45 185	99.53 38	99.46 162	99.21 32	98.65 114	99.34 228	98.62 166	97.54 396	98.63 326	97.50 186	99.83 195	96.79 288	99.53 300	99.56 130

v1240		98.55 183	98.62 154	98.32 291	99.22 236	95.58 351	97.51 298	99.45 176	97.16 321	99.45 106	99.24 144	96.12 280	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 81	99.30 71	99.65 63	99.60 45	99.16 22	99.82 207	99.07 80	99.83 126	99.56 130

test_prior2							95.74 441		96.48 363	96.11 471	97.63 431	95.92 295		94.16 417	99.20 376

X-MVStestdata		94.32 453	92.59 475	99.53 38	99.46 162	99.21 32	98.65 114	99.34 228	98.62 166	97.54 396	45.85 542	97.50 186	99.83 195	96.79 288	99.53 300	99.56 130

test_prior				98.95 164	98.69 368	97.95 179		99.03 324					99.59 391			99.30 279

旧先验2							95.76 440		88.56 523	97.52 398			99.66 355	94.48 407

新几何2							95.93 430

新几何1				98.91 173	98.94 311	97.76 206		98.76 375	87.58 527	96.75 446	98.10 395	94.80 334	99.78 258	92.73 465	99.00 403	99.20 310

旧先验1					98.82 339	97.45 234		98.76 375			98.34 367	95.50 310			99.01 402	99.23 300

无先验							95.74 441	98.74 381	89.38 516				99.73 296	92.38 474		99.22 305

原ACMM2							95.53 447

原ACMM1				98.35 289	98.90 321	96.25 322		98.83 366	92.48 488	96.07 473	98.10 395	95.39 314	99.71 308	92.61 468	98.99 405	99.08 338

test222					98.92 317	96.93 285	95.54 446	98.78 372	85.72 530	96.86 441	98.11 394	94.43 345			99.10 392	99.23 300

testdata2													99.79 246	92.80 462

segment_acmp												97.02 221

testdata				98.09 320	98.93 313	95.40 365		98.80 369	90.08 512	97.45 406	98.37 363	95.26 317	99.70 316	93.58 438	98.95 411	99.17 324

testdata1							95.44 452		96.32 370

v8		99.01 90	99.16 62	98.57 249	99.47 159	96.31 321	98.90 84	99.47 167	99.03 121	99.52 87	99.57 49	96.93 227	99.81 224	99.60 37	99.98 12	99.60 102

1314		95.74 420	95.60 409	96.17 456	97.53 479	92.75 462	98.07 200	98.31 413	91.22 502	94.25 508	96.68 467	95.53 307	99.03 485	91.64 485	97.18 496	96.74 502

LFMVS		97.20 342	96.72 360	98.64 232	98.72 355	96.95 283	98.93 82	94.14 515	99.74 12	98.78 262	99.01 223	84.45 478	99.73 296	97.44 230	99.27 361	99.25 294

VDD-MVS		98.56 179	98.39 195	99.07 136	99.13 267	98.07 162	98.59 122	97.01 459	99.59 36	99.11 184	99.27 131	94.82 331	99.79 246	98.34 141	99.63 261	99.34 260

VDDNet		98.21 241	97.95 263	99.01 151	99.58 94	97.74 208	99.01 71	97.29 450	99.67 20	98.97 216	99.50 68	90.45 426	99.80 233	97.88 182	99.20 376	99.48 187

v10		98.97 99	99.11 74	98.55 256	99.44 169	96.21 324	98.90 84	99.55 124	98.73 151	99.48 96	99.60 45	96.63 252	99.83 195	99.70 33	99.99 5	99.61 100

VPNet		98.87 112	98.83 120	99.01 151	99.70 57	97.62 220	98.43 148	99.35 222	99.47 47	99.28 150	99.05 206	96.72 245	99.82 207	98.09 159	99.36 341	99.59 109

MVS		93.19 476	92.09 482	96.50 439	96.91 504	94.03 424	98.07 200	98.06 426	68.01 540	94.56 506	96.48 472	95.96 292	99.30 468	83.84 526	96.89 502	96.17 510

v2v482		98.56 179	98.62 154	98.37 287	99.42 176	95.81 344	97.58 288	99.16 298	97.90 238	99.28 150	99.01 223	95.98 290	99.79 246	99.33 59	99.90 88	99.51 164

V42		98.78 133	98.78 126	98.76 209	99.44 169	97.04 276	98.27 170	99.19 287	97.87 240	99.25 164	99.16 169	96.84 231	99.78 258	99.21 70	99.84 114	99.46 198

SD-MVS		98.40 206	98.68 141	97.54 385	98.96 309	97.99 171	97.88 236	99.36 216	98.20 209	99.63 66	99.04 208	98.76 46	95.33 537	96.56 320	99.74 195	99.31 275
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		95.86 416	95.32 426	97.49 390	98.60 385	94.15 417	93.83 507	97.93 428	95.49 414	96.68 450	97.42 447	83.21 488	99.30 468	96.22 346	98.55 442	99.01 351

MSLP-MVS++		98.02 262	98.14 243	97.64 372	98.58 390	95.19 379	97.48 302	99.23 279	97.47 280	97.90 367	98.62 329	97.04 218	98.81 497	97.55 217	99.41 334	98.94 368

APDe-MVS		98.99 94	98.79 124	99.60 16	99.21 238	99.15 52	98.87 89	99.48 157	97.57 268	99.35 130	99.24 144	97.83 151	99.89 97	97.88 182	99.70 226	99.75 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.84 119	98.61 158	99.53 38	99.19 246	99.27 26	98.49 140	99.33 234	98.64 161	99.03 204	98.98 234	97.89 147	99.85 158	96.54 324	99.42 333	99.46 198

ADS-MVSNet2		95.43 433	94.98 437	96.76 432	98.14 438	91.74 476	97.92 231	97.76 431	90.23 508	96.51 462	98.91 252	85.61 467	99.85 158	92.88 458	96.90 500	98.69 408

EI-MVSNet		98.40 206	98.51 171	98.04 329	99.10 272	94.73 399	97.20 338	98.87 353	98.97 127	99.06 191	99.02 212	96.00 285	99.80 233	98.58 119	99.82 133	99.60 102

Regformer		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

CVMVSNet		96.25 398	97.21 325	93.38 515	99.10 272	80.56 545	97.20 338	98.19 421	96.94 333	99.00 207	99.02 212	89.50 436	99.80 233	96.36 337	99.59 276	99.78 50

pmmvs4		97.58 307	97.28 318	98.51 266	98.84 334	96.93 285	95.40 454	98.52 402	93.60 469	98.61 292	98.65 320	95.10 323	99.60 386	96.97 272	99.79 159	98.99 355

EU-MVSNet		97.66 301	98.50 174	95.13 491	99.63 83	85.84 525	98.35 161	98.21 418	98.23 201	99.54 79	99.46 80	95.02 325	99.68 337	98.24 146	99.87 100	99.87 22

VNet		98.42 202	98.30 214	98.79 199	98.79 348	97.29 251	98.23 173	98.66 387	99.31 69	98.85 249	98.80 283	94.80 334	99.78 258	98.13 155	99.13 387	99.31 275

test-LLR		93.90 463	93.85 457	94.04 504	96.53 514	84.62 531	94.05 501	92.39 524	96.17 376	94.12 510	95.07 502	82.30 493	99.67 342	95.87 365	98.18 457	97.82 466

TESTMET0.1,1		92.19 492	91.77 490	93.46 511	96.48 519	82.80 540	94.05 501	91.52 532	94.45 450	94.00 514	94.88 508	66.65 529	99.56 403	95.78 370	98.11 463	98.02 456

test-mter		92.33 490	91.76 491	94.04 504	96.53 514	84.62 531	94.05 501	92.39 524	94.00 465	94.12 510	95.07 502	65.63 535	99.67 342	95.87 365	98.18 457	97.82 466

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 148	98.36 127	99.00 73	99.45 176	99.63 28	99.52 87	99.44 85	98.25 107	99.88 115	99.09 79	99.84 114	99.62 92

ACMMPR		98.70 147	98.42 190	99.54 31	99.52 131	99.14 57	98.52 130	99.31 241	97.47 280	98.56 303	98.54 339	97.75 159	99.88 115	96.57 316	99.59 276	99.58 117

testgi		98.32 221	98.39 195	98.13 315	99.57 103	95.54 352	97.78 250	99.49 155	97.37 295	99.19 174	97.65 429	98.96 30	99.49 430	96.50 327	98.99 405	99.34 260

test20.03		98.78 133	98.77 127	98.78 202	99.46 162	97.20 262	97.78 250	99.24 277	99.04 119	99.41 114	98.90 255	97.65 165	99.76 270	97.70 204	99.79 159	99.39 230

thres600view7		94.45 451	93.83 458	96.29 447	99.06 284	91.53 480	97.99 221	94.24 513	98.34 188	97.44 407	95.01 504	79.84 499	99.67 342	84.33 525	98.23 454	97.66 478

ADS-MVSNet		95.24 438	94.93 440	96.18 455	98.14 438	90.10 505	97.92 231	97.32 449	90.23 508	96.51 462	98.91 252	85.61 467	99.74 289	92.88 458	96.90 500	98.69 408

MP-MVS		98.46 198	98.09 246	99.54 31	99.57 103	99.22 31	98.50 137	99.19 287	97.61 264	97.58 392	98.66 318	97.40 195	99.88 115	94.72 402	99.60 272	99.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 510	20.53 513	6.87 528	12.05 550	4.20 553	93.62 511	6.73 551	4.62 546	10.41 546	24.33 543	8.28 550	3.56 547	9.69 545	15.07 544	12.86 543

thres400		94.14 459	93.44 463	96.24 450	98.93 313	91.44 483	97.60 285	94.29 510	97.94 234	97.10 421	94.31 514	79.67 501	99.62 374	83.05 528	98.08 465	97.66 478

test123		17.04 511	20.11 514	7.82 527	10.25 551	4.91 552	94.80 472	4.47 552	4.93 545	10.00 547	24.28 544	9.69 549	3.64 546	10.14 544	12.43 545	14.92 542

thres200		93.72 467	93.14 469	95.46 485	98.66 378	91.29 487	96.61 380	94.63 505	97.39 293	96.83 442	93.71 517	79.88 498	99.56 403	82.40 531	98.13 462	95.54 517

test0.0.03 1		94.51 450	93.69 460	96.99 417	96.05 525	93.61 448	94.97 468	93.49 519	96.17 376	97.57 394	94.88 508	82.30 493	99.01 489	93.60 437	94.17 529	98.37 440

pmmvs3		95.03 443	94.40 451	96.93 421	97.70 468	92.53 465	95.08 465	97.71 433	88.57 522	97.71 382	98.08 398	79.39 503	99.82 207	96.19 348	99.11 391	98.43 433

EMVS		93.83 464	94.02 455	93.23 516	96.83 507	84.96 528	89.77 533	96.32 480	97.92 236	97.43 408	96.36 477	86.17 459	98.93 492	87.68 515	97.73 477	95.81 515

E-PMN		94.17 458	94.37 452	93.58 510	96.86 505	85.71 527	90.11 532	97.07 458	98.17 212	97.82 377	97.19 456	84.62 477	98.94 491	89.77 507	97.68 478	96.09 514

PGM-MVS		98.66 161	98.37 200	99.55 28	99.53 127	99.18 42	98.23 173	99.49 155	97.01 330	98.69 276	98.88 263	98.00 134	99.89 97	95.87 365	99.59 276	99.58 117

LCM-MVSNet-Re		98.64 164	98.48 180	99.11 126	98.85 333	98.51 113	98.49 140	99.83 26	98.37 185	99.69 55	99.46 80	98.21 115	99.92 65	94.13 421	99.30 357	98.91 373

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		98.00 265	97.63 298	99.10 128	99.24 230	98.17 145	96.89 360	98.73 382	95.66 405	97.92 365	97.70 427	97.17 211	99.66 355	96.18 350	99.23 370	99.47 195

mvs_anonymous		97.83 290	98.16 240	96.87 425	98.18 433	91.89 475	97.31 325	98.90 347	97.37 295	98.83 253	99.46 80	96.28 271	99.79 246	98.90 94	98.16 460	98.95 364

MVS_Test		98.18 246	98.36 202	97.67 365	98.48 400	94.73 399	98.18 179	99.02 327	97.69 255	98.04 357	99.11 187	97.22 208	99.56 403	98.57 121	98.90 415	98.71 404

MDA-MVSNet-bldmvs		97.94 272	97.91 271	98.06 326	99.44 169	94.96 387	96.63 378	99.15 303	98.35 187	98.83 253	99.11 187	94.31 353	99.85 158	96.60 313	98.72 425	99.37 242

CDPH-MVS		97.26 335	96.66 367	99.07 136	99.00 302	98.15 146	96.03 422	99.01 330	91.21 503	97.79 378	97.85 416	96.89 229	99.69 326	92.75 464	99.38 340	99.39 230

test12				98.93 168	98.58 390	97.83 194		98.66 387		96.53 458		95.51 309	99.69 326		99.13 387	99.27 287

casdiffmvs		98.95 102	99.00 94	98.81 192	99.38 185	97.33 242	97.82 244	99.57 109	99.17 93	99.35 130	99.17 167	98.35 94	99.69 326	98.46 129	99.73 199	99.41 220
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		98.22 238	98.24 227	98.17 310	99.00 302	95.44 363	96.38 397	99.58 101	97.79 248	98.53 308	98.50 348	96.76 241	99.74 289	97.95 177	99.64 255	99.34 260
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		93.73 466	92.83 473	96.42 442	97.70 468	91.28 488	96.84 362	89.77 536	93.96 466	92.44 526	95.93 484	79.14 504	99.77 264	92.94 455	96.76 504	98.21 445

baseline1		95.96 413	95.44 418	97.52 387	98.51 399	93.99 431	98.39 157	96.09 486	98.21 205	98.40 327	97.76 423	86.88 453	99.63 370	95.42 384	89.27 535	98.95 364

YYNet1		97.60 304	97.67 291	97.39 398	99.04 288	93.04 456	95.27 458	98.38 411	97.25 309	98.92 233	98.95 244	95.48 311	99.73 296	96.99 268	98.74 423	99.41 220

PMMVS2		98.07 258	98.08 249	98.04 329	99.41 179	94.59 405	94.59 483	99.40 204	97.50 277	98.82 256	98.83 276	96.83 233	99.84 177	97.50 223	99.81 140	99.71 65

MDA-MVSNet_test_wron		97.60 304	97.66 294	97.41 397	99.04 288	93.09 452	95.27 458	98.42 408	97.26 308	98.88 242	98.95 244	95.43 313	99.73 296	97.02 264	98.72 425	99.41 220

tpmvs		95.02 444	95.25 429	94.33 499	96.39 522	85.87 524	98.08 196	96.83 470	95.46 416	95.51 490	98.69 310	85.91 465	99.53 415	94.16 417	96.23 510	97.58 481

PM-MVS		98.82 125	98.72 132	99.12 124	99.64 77	98.54 111	97.98 222	99.68 63	97.62 261	99.34 134	99.18 163	97.54 180	99.77 264	97.79 191	99.74 195	99.04 346

HQP_MVS		97.99 268	97.67 291	98.93 168	99.19 246	97.65 217	97.77 253	99.27 264	98.20 209	97.79 378	97.98 406	94.90 327	99.70 316	94.42 411	99.51 306	99.45 204

plane_prior7					99.19 246	97.87 190

plane_prior6					98.99 305	97.70 213						94.90 327

plane_prior5								99.27 264					99.70 316	94.42 411	99.51 306	99.45 204

plane_prior4											97.98 406

plane_prior3						97.78 204			97.41 290	97.79 378

plane_prior2							97.77 253		98.20 209

plane_prior1					99.05 287

plane_prior						97.65 217	97.07 346		96.72 350						99.36 341

PS-CasMVS		99.40 25	99.33 37	99.62 9	99.71 49	99.10 65	99.29 36	99.53 134	99.53 41	99.46 101	99.41 94	98.23 110	99.95 25	98.89 96	99.95 39	99.81 41

UniMVSNet_NR-MVSNet		98.86 116	98.68 141	99.40 71	99.17 256	98.74 91	97.68 268	99.40 204	99.14 98	99.06 191	98.59 334	96.71 246	99.93 53	98.57 121	99.77 172	99.53 157

PEN-MVS		99.41 24	99.34 35	99.62 9	99.73 38	99.14 57	99.29 36	99.54 130	99.62 32	99.56 74	99.42 89	98.16 122	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	99.57 109	99.39 58	99.75 44	99.62 40	99.17 20	99.83 195	99.06 82	99.62 265	99.66 80

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 49	99.30 21	99.31 30	99.51 142	99.64 26	99.56 74	99.46 80	98.23 110	99.97 6	98.78 102	99.93 57	99.72 64

DU-MVS		98.82 125	98.63 152	99.39 72	99.16 258	98.74 91	97.54 294	99.25 272	98.84 148	99.06 191	98.76 294	96.76 241	99.93 53	98.57 121	99.77 172	99.50 168

UniMVSNet (Re)		98.87 112	98.71 135	99.35 80	99.24 230	98.73 94	97.73 263	99.38 208	98.93 132	99.12 183	98.73 297	96.77 239	99.86 144	98.63 116	99.80 152	99.46 198

CP-MVSNet		99.21 47	99.09 82	99.56 26	99.65 71	98.96 77	99.13 59	99.34 228	99.42 55	99.33 137	99.26 137	97.01 222	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 49	99.24 29	99.32 26	99.55 124	99.46 49	99.50 93	99.34 115	97.30 201	99.93 53	98.90 94	99.93 57	99.77 53

WR-MVS		98.40 206	98.19 234	99.03 146	99.00 302	97.65 217	96.85 361	98.94 337	98.57 174	98.89 238	98.50 348	95.60 305	99.85 158	97.54 219	99.85 109	99.59 109

NR-MVSNet		98.95 102	98.82 121	99.36 74	99.16 258	98.72 96	99.22 46	99.20 283	99.10 107	99.72 47	98.76 294	96.38 265	99.86 144	98.00 170	99.82 133	99.50 168

Baseline_NR-MVSNet		98.98 98	98.86 115	99.36 74	99.82 19	98.55 108	97.47 306	99.57 109	99.37 60	99.21 172	99.61 43	96.76 241	99.83 195	98.06 162	99.83 126	99.71 65

TranMVSNet+NR-MVSNet		99.17 52	99.07 85	99.46 63	99.37 191	98.87 84	98.39 157	99.42 196	99.42 55	99.36 128	99.06 199	98.38 89	99.95 25	98.34 141	99.90 88	99.57 124

TSAR-MVS + GP.		98.18 246	97.98 259	98.77 207	98.71 359	97.88 189	96.32 402	98.66 387	96.33 369	99.23 168	98.51 344	97.48 190	99.40 452	97.16 251	99.46 320	99.02 349

n2								0.00 553

nn								0.00 553

mPP-MVS		98.64 164	98.34 206	99.54 31	99.54 123	99.17 43	98.63 116	99.24 277	97.47 280	98.09 351	98.68 312	97.62 170	99.89 97	96.22 346	99.62 265	99.57 124

door-mid								99.57 109

XVG-OURS-SEG-HR		98.49 195	98.28 217	99.14 122	99.49 148	98.83 86	96.54 384	99.48 157	97.32 300	99.11 184	98.61 331	99.33 15	99.30 468	96.23 345	98.38 447	99.28 284

mvsmamba		97.57 308	97.26 320	98.51 266	98.69 368	96.73 299	98.74 99	97.25 451	97.03 329	97.88 369	99.23 150	90.95 420	99.87 135	96.61 312	99.00 403	98.91 373

MVSFormer		98.26 233	98.43 188	97.77 351	98.88 327	93.89 437	99.39 20	99.56 119	99.11 100	98.16 343	98.13 391	93.81 367	99.97 6	99.26 65	99.57 285	99.43 212

jason		97.45 317	97.35 315	97.76 354	99.24 230	93.93 433	95.86 434	98.42 408	94.24 454	98.50 311	98.13 391	94.82 331	99.91 74	97.22 246	99.73 199	99.43 212
jason: jason.
lupinMVS		97.06 353	96.86 349	97.65 369	98.88 327	93.89 437	95.48 450	97.97 427	93.53 470	98.16 343	97.58 433	93.81 367	99.91 74	96.77 291	99.57 285	99.17 324

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 91	99.39 20	99.56 119	99.11 100	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 90	98.82 121	99.57 21	99.71 49	99.35 16	99.00 73	99.50 147	97.33 298	98.94 230	98.86 266	98.75 47	99.82 207	97.53 220	99.71 217	99.56 130

K. test v3		98.00 265	97.66 294	99.03 146	99.79 23	97.56 223	99.19 53	92.47 523	99.62 32	99.52 87	99.66 32	89.61 434	99.96 13	99.25 67	99.81 140	99.56 130

lessismore_v0				98.97 160	99.73 38	97.53 226		86.71 541		99.37 125	99.52 67	89.93 429	99.92 65	98.99 88	99.72 208	99.44 208

SixPastTwentyTwo		98.75 138	98.62 154	99.16 118	99.83 18	97.96 178	99.28 40	98.20 419	99.37 60	99.70 51	99.65 36	92.65 394	99.93 53	99.04 84	99.84 114	99.60 102

OurMVSNet-221017-0		99.37 28	99.31 41	99.53 38	99.91 3	98.98 71	99.63 7	99.58 101	99.44 52	99.78 39	99.76 15	96.39 263	99.92 65	99.44 54	99.92 71	99.68 73

HPM-MVS		98.79 131	98.53 169	99.59 20	99.65 71	99.29 23	99.16 55	99.43 190	96.74 349	98.61 292	98.38 362	98.62 64	99.87 135	96.47 328	99.67 243	99.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 188	98.34 206	99.11 126	99.50 140	98.82 88	95.97 425	99.50 147	97.30 303	99.05 199	98.98 234	99.35 14	99.32 465	95.72 372	99.68 237	99.18 320

XVG-ACMP-BASELINE		98.56 179	98.34 206	99.22 109	99.54 123	98.59 105	97.71 264	99.46 172	97.25 309	98.98 212	98.99 229	97.54 180	99.84 177	95.88 362	99.74 195	99.23 300

casdiffmvs_mvg		99.12 69	99.16 62	98.99 154	99.43 174	97.73 210	98.00 214	99.62 87	99.22 80	99.55 77	99.22 152	98.93 33	99.75 282	98.66 113	99.81 140	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		98.71 142	98.46 184	99.47 61	99.57 103	98.97 73	98.23 173	99.48 157	96.60 356	99.10 187	99.06 199	98.71 51	99.83 195	95.58 381	99.78 164	99.62 92

LGP-MVS_train				99.47 61	99.57 103	98.97 73		99.48 157	96.60 356	99.10 187	99.06 199	98.71 51	99.83 195	95.58 381	99.78 164	99.62 92

baseline		98.96 101	99.02 90	98.76 209	99.38 185	97.26 254	98.49 140	99.50 147	98.86 142	99.19 174	99.06 199	98.23 110	99.69 326	98.71 110	99.76 188	99.33 266

test11								98.87 353

door								99.41 200

EPNet_dtu		94.93 446	94.78 442	95.38 487	93.58 536	87.68 519	96.78 365	95.69 496	97.35 297	89.14 536	98.09 397	88.15 448	99.49 430	94.95 396	99.30 357	98.98 356
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 313	97.14 330	98.54 261	99.68 64	96.09 328	96.50 388	99.62 87	91.58 497	98.84 251	98.97 236	92.36 397	99.88 115	96.76 292	99.95 39	99.67 78

EPNet		96.14 402	95.44 418	98.25 300	90.76 545	95.50 358	97.92 231	94.65 504	98.97 127	92.98 521	98.85 269	89.12 438	99.87 135	95.99 358	99.68 237	99.39 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 294

HQP-NCC					98.67 373		96.29 404		96.05 383	95.55 485

ACMP_Plane					98.67 373		96.29 404		96.05 383	95.55 485

APD-MVS		98.10 253	97.67 291	99.42 67	99.11 270	98.93 79	97.76 256	99.28 261	94.97 433	98.72 272	98.77 289	97.04 218	99.85 158	93.79 431	99.54 296	99.49 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 460

HQP4-MVS										95.56 484			99.54 413			99.32 270

HQP3-MVS								99.04 322							99.26 365

HQP2-MVS												93.84 365

CNVR-MVS		98.17 249	97.87 274	99.07 136	98.67 373	98.24 137	97.01 348	98.93 340	97.25 309	97.62 388	98.34 367	97.27 204	99.57 400	96.42 332	99.33 348	99.39 230

NCCC		97.86 282	97.47 309	99.05 143	98.61 383	98.07 162	96.98 351	98.90 347	97.63 260	97.04 427	97.93 411	95.99 289	99.66 355	95.31 386	98.82 419	99.43 212

114514_t		96.50 381	95.77 401	98.69 223	99.48 156	97.43 237	97.84 243	99.55 124	81.42 536	96.51 462	98.58 335	95.53 307	99.67 342	93.41 445	99.58 281	98.98 356

CP-MVS		98.70 147	98.42 190	99.52 44	99.36 192	99.12 62	98.72 104	99.36 216	97.54 274	98.30 331	98.40 359	97.86 150	99.89 97	96.53 325	99.72 208	99.56 130

DSMNet-mixed		97.42 320	97.60 300	96.87 425	99.15 262	91.46 481	98.54 128	99.12 306	92.87 484	97.58 392	99.63 39	96.21 274	99.90 81	95.74 371	99.54 296	99.27 287

tpm2		93.09 477	92.58 476	94.62 497	97.56 475	86.53 523	97.66 272	95.79 493	86.15 529	94.07 512	98.23 384	75.95 513	99.53 415	90.91 499	96.86 503	97.81 468

NP-MVS					98.84 334	97.39 239					96.84 463

EG-PatchMatch MVS		98.99 94	99.01 92	98.94 165	99.50 140	97.47 231	98.04 205	99.59 98	98.15 220	99.40 117	99.36 110	98.58 72	99.76 270	98.78 102	99.68 237	99.59 109

tpm cat1		93.29 474	93.13 470	93.75 508	97.39 488	84.74 529	97.39 313	97.65 437	83.39 534	94.16 509	98.41 358	82.86 491	99.39 454	91.56 487	95.35 524	97.14 494

SteuartSystems-ACMMP		98.79 131	98.54 167	99.54 31	99.73 38	99.16 48	98.23 173	99.31 241	97.92 236	98.90 235	98.90 255	98.00 134	99.88 115	96.15 351	99.72 208	99.58 117
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CostFormer		93.97 462	93.78 459	94.51 498	97.53 479	85.83 526	97.98 222	95.96 488	89.29 517	94.99 498	98.63 326	78.63 508	99.62 374	94.54 405	96.50 506	98.09 453

CR-MVSNet		96.28 395	95.95 397	97.28 401	97.71 466	94.22 412	98.11 191	98.92 344	92.31 490	96.91 434	99.37 104	85.44 470	99.81 224	97.39 233	97.36 492	97.81 468

JIA-IIPM		95.52 428	95.03 436	97.00 416	96.85 506	94.03 424	96.93 356	95.82 491	99.20 84	94.63 505	99.71 22	83.09 489	99.60 386	94.42 411	94.64 526	97.36 489

Patchmtry		97.35 327	96.97 340	98.50 270	97.31 491	96.47 314	98.18 179	98.92 344	98.95 131	98.78 262	99.37 104	85.44 470	99.85 158	95.96 360	99.83 126	99.17 324

PatchT		96.65 373	96.35 385	97.54 385	97.40 487	95.32 371	97.98 222	96.64 474	99.33 66	96.89 438	99.42 89	84.32 480	99.81 224	97.69 206	97.49 483	97.48 484

tpmrst		95.07 442	95.46 416	93.91 506	97.11 495	84.36 533	97.62 279	96.96 463	94.98 432	96.35 467	98.80 283	85.46 469	99.59 391	95.60 379	96.23 510	97.79 471

BH-w/o		95.13 441	94.89 441	95.86 470	98.20 431	91.31 486	95.65 443	97.37 443	93.64 468	96.52 461	95.70 490	93.04 386	99.02 487	88.10 514	95.82 520	97.24 493

tpm		94.67 448	94.34 453	95.66 478	97.68 471	88.42 514	97.88 236	94.90 502	94.46 447	96.03 477	98.56 338	78.66 507	99.79 246	95.88 362	95.01 525	98.78 396

DELS-MVS		98.27 231	98.20 230	98.48 272	98.86 330	96.70 300	95.60 445	99.20 283	97.73 252	98.45 318	98.71 301	97.50 186	99.82 207	98.21 150	99.59 276	98.93 369
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		96.83 366	96.75 359	97.08 412	98.74 352	93.33 450	96.71 370	98.26 415	96.72 350	98.44 319	97.37 450	95.20 318	99.47 437	91.89 479	97.43 487	98.44 431

RPMNet		97.02 356	96.93 342	97.30 400	97.71 466	94.22 412	98.11 191	99.30 249	99.37 60	96.91 434	99.34 115	86.72 454	99.87 135	97.53 220	97.36 492	97.81 468

MVSTER		96.86 365	96.55 376	97.79 349	97.91 453	94.21 414	97.56 290	98.87 353	97.49 279	99.06 191	99.05 206	80.72 496	99.80 233	98.44 131	99.82 133	99.37 242

CPTT-MVS		97.84 288	97.36 314	99.27 99	99.31 206	98.46 116	98.29 166	99.27 264	94.90 435	97.83 375	98.37 363	94.90 327	99.84 177	93.85 430	99.54 296	99.51 164

GBi-Net		98.65 162	98.47 182	99.17 115	98.90 321	98.24 137	99.20 49	99.44 184	98.59 169	98.95 222	99.55 56	94.14 358	99.86 144	97.77 194	99.69 231	99.41 220

PVSNet_Blended_VisFu		98.17 249	98.15 241	98.22 306	99.73 38	95.15 380	97.36 320	99.68 63	94.45 450	98.99 211	99.27 131	96.87 230	99.94 41	97.13 257	99.91 80	99.57 124

PVSNet_BlendedMVS		97.55 309	97.53 303	97.60 376	98.92 317	93.77 441	96.64 377	99.43 190	94.49 445	97.62 388	99.18 163	96.82 234	99.67 342	94.73 400	99.93 57	99.36 250

UnsupCasMVSNet_eth		97.89 276	97.60 300	98.75 211	99.31 206	97.17 268	97.62 279	99.35 222	98.72 157	98.76 267	98.68 312	92.57 395	99.74 289	97.76 198	95.60 522	99.34 260

UnsupCasMVSNet_bld		97.30 332	96.92 344	98.45 275	99.28 215	96.78 297	96.20 410	99.27 264	95.42 418	98.28 335	98.30 374	93.16 380	99.71 308	94.99 393	97.37 490	98.87 379

PVSNet_Blended		96.88 363	96.68 363	97.47 393	98.92 317	93.77 441	94.71 474	99.43 190	90.98 506	97.62 388	97.36 451	96.82 234	99.67 342	94.73 400	99.56 289	98.98 356

FMVSNet5		96.01 407	95.20 433	98.41 280	97.53 479	96.10 325	98.74 99	99.50 147	97.22 318	98.03 358	99.04 208	69.80 522	99.88 115	97.27 242	99.71 217	99.25 294

test1		98.65 162	98.47 182	99.17 115	98.90 321	98.24 137	99.20 49	99.44 184	98.59 169	98.95 222	99.55 56	94.14 358	99.86 144	97.77 194	99.69 231	99.41 220

new_pmnet		96.99 360	96.76 357	97.67 365	98.72 355	94.89 391	95.95 429	98.20 419	92.62 487	98.55 305	98.54 339	94.88 330	99.52 419	93.96 425	99.44 329	98.59 420

FMVSNet3		97.50 310	97.24 322	98.29 296	98.08 444	95.83 342	97.86 240	98.91 346	97.89 239	98.95 222	98.95 244	87.06 452	99.81 224	97.77 194	99.69 231	99.23 300

dp		93.47 470	93.59 462	93.13 517	96.64 512	81.62 544	97.66 272	96.42 479	92.80 485	96.11 471	98.64 324	78.55 510	99.59 391	93.31 446	92.18 534	98.16 449

FMVSNet2		98.49 195	98.40 192	98.75 211	98.90 321	97.14 271	98.61 120	99.13 305	98.59 169	99.19 174	99.28 129	94.14 358	99.82 207	97.97 175	99.80 152	99.29 281

FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 117	98.24 137	99.20 49	99.44 184	99.21 82	99.43 108	99.55 56	97.82 154	99.86 144	98.42 137	99.89 94	99.41 220

N_pmnet		97.63 303	97.17 326	98.99 154	99.27 218	97.86 191	95.98 424	93.41 520	95.25 425	99.47 100	98.90 255	95.63 303	99.85 158	96.91 275	99.73 199	99.27 287

cascas		94.79 447	94.33 454	96.15 460	96.02 527	92.36 470	92.34 525	99.26 270	85.34 531	95.08 497	94.96 507	92.96 387	98.53 504	94.41 414	98.59 439	97.56 482

BH-RMVSNet		96.83 366	96.58 375	97.58 378	98.47 401	94.05 421	96.67 374	97.36 444	96.70 353	97.87 370	97.98 406	95.14 322	99.44 446	90.47 504	98.58 440	99.25 294

UGNet		98.53 188	98.45 185	98.79 199	97.94 451	96.96 282	99.08 62	98.54 399	99.10 107	96.82 443	99.47 78	96.55 255	99.84 177	98.56 124	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		96.67 372	96.27 391	97.87 344	98.81 342	94.61 404	96.77 366	97.92 429	94.94 434	97.12 420	97.74 424	91.11 419	99.82 207	93.89 427	98.15 461	99.18 320

XXY-MVS		99.14 62	99.15 67	99.10 128	99.76 30	97.74 208	98.85 93	99.62 87	98.48 181	99.37 125	99.49 74	98.75 47	99.86 144	98.20 151	99.80 152	99.71 65

EC-MVSNet		99.09 73	99.05 86	99.20 110	99.28 215	98.93 79	99.24 44	99.84 23	99.08 114	98.12 348	98.37 363	98.72 50	99.90 81	99.05 83	99.77 172	98.77 397

sss		97.21 341	96.93 342	98.06 326	98.83 336	95.22 378	96.75 368	98.48 404	94.49 445	97.27 415	97.90 412	92.77 391	99.80 233	96.57 316	99.32 351	99.16 330

Test_1112_low_res		96.99 360	96.55 376	98.31 293	99.35 197	95.47 362	95.84 437	99.53 134	91.51 499	96.80 444	98.48 351	91.36 416	99.83 195	96.58 314	99.53 300	99.62 92

1112_ss		97.29 334	96.86 349	98.58 246	99.34 202	96.32 320	96.75 368	99.58 101	93.14 476	96.89 438	97.48 442	92.11 405	99.86 144	96.91 275	99.54 296	99.57 124

ab-mvs-re		8.12 513	10.83 516	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	97.48 442	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

ab-mvs		98.41 203	98.36 202	98.59 245	99.19 246	97.23 256	99.32 26	98.81 367	97.66 258	98.62 290	99.40 97	96.82 234	99.80 233	95.88 362	99.51 306	98.75 400

TR-MVS		95.55 427	95.12 435	96.86 428	97.54 477	93.94 432	96.49 389	96.53 477	94.36 453	97.03 429	96.61 469	94.26 355	99.16 480	86.91 519	96.31 509	97.47 485

MDTV_nov1_ep13_2view						74.92 547	97.69 267		90.06 513	97.75 381		85.78 466		93.52 440		98.69 408

MDTV_nov1_ep13			95.22 431		97.06 498	83.20 538	97.74 261	96.16 482	94.37 452	96.99 430	98.83 276	83.95 484	99.53 415	93.90 426	97.95 473

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 41	99.41 17	99.59 98	99.59 36	99.71 49	99.57 49	97.12 214	99.90 81	99.21 70	99.87 100	99.54 143

MIMVSNet		96.62 375	96.25 392	97.71 361	99.04 288	94.66 402	99.16 55	96.92 467	97.23 315	97.87 370	99.10 190	86.11 461	99.65 362	91.65 484	99.21 374	98.82 384

IterMVS-LS		98.55 183	98.70 138	98.09 320	99.48 156	94.73 399	97.22 337	99.39 206	98.97 127	99.38 121	99.31 124	96.00 285	99.93 53	98.58 119	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		97.69 298	97.35 315	98.69 223	98.73 353	97.02 278	96.92 358	98.75 379	95.89 393	98.59 297	98.67 314	92.08 406	99.74 289	96.72 298	99.81 140	99.32 270
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 172

IterMVS		97.73 294	98.11 245	96.57 437	99.24 230	90.28 503	95.52 449	99.21 281	98.86 142	99.33 137	99.33 118	93.11 382	99.94 41	98.49 128	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		97.33 329	96.92 344	98.57 249	99.09 275	97.99 171	96.79 363	99.35 222	93.18 475	97.71 382	98.07 399	95.00 326	99.31 466	93.97 424	99.13 387	98.42 435

MVS_111021_LR		98.30 226	98.12 244	98.83 188	99.16 258	98.03 167	96.09 419	99.30 249	97.58 267	98.10 350	98.24 382	98.25 107	99.34 461	96.69 303	99.65 253	99.12 336

DP-MVS		98.93 104	98.81 123	99.28 96	99.21 238	98.45 117	98.46 145	99.33 234	99.63 28	99.48 96	99.15 175	97.23 207	99.75 282	97.17 250	99.66 251	99.63 91

ACMMP++															99.68 237

HQP-MVS		97.00 359	96.49 379	98.55 256	98.67 373	96.79 294	96.29 404	99.04 322	96.05 383	95.55 485	96.84 463	93.84 365	99.54 413	92.82 460	99.26 365	99.32 270

QAPM		97.31 330	96.81 355	98.82 190	98.80 345	97.49 227	99.06 66	99.19 287	90.22 510	97.69 384	99.16 169	96.91 228	99.90 81	90.89 500	99.41 334	99.07 340

Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 38	98.26 135	99.17 54	99.78 36	99.11 100	99.27 152	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		94.32 453	95.62 407	90.42 522	98.46 403	75.36 546	96.29 404	89.13 537	95.25 425	95.38 491	99.75 16	92.88 388	99.19 478	94.07 423	99.39 337	96.72 503

IS-MVSNet		98.19 244	97.90 272	99.08 134	99.57 103	97.97 175	99.31 30	98.32 412	99.01 123	98.98 212	99.03 211	91.59 410	99.79 246	95.49 383	99.80 152	99.48 187

HyFIR lowres test		97.19 343	96.60 374	98.96 162	99.62 87	97.28 252	95.17 462	99.50 147	94.21 455	99.01 206	98.32 372	86.61 455	99.99 2	97.10 259	99.84 114	99.60 102

EPMVS		93.72 467	93.27 466	95.09 493	96.04 526	87.76 518	98.13 186	85.01 543	94.69 441	96.92 432	98.64 324	78.47 511	99.31 466	95.04 392	96.46 507	98.20 446

PAPM_NR		96.82 368	96.32 387	98.30 295	99.07 279	96.69 301	97.48 302	98.76 375	95.81 400	96.61 454	96.47 473	94.12 361	99.17 479	90.82 502	97.78 475	99.06 341

TAMVS		98.24 237	98.05 252	98.80 195	99.07 279	97.18 266	97.88 236	98.81 367	96.66 355	99.17 182	99.21 154	94.81 333	99.77 264	96.96 273	99.88 95	99.44 208

PAPR		95.29 436	94.47 448	97.75 355	97.50 485	95.14 381	94.89 471	98.71 384	91.39 501	95.35 492	95.48 496	94.57 341	99.14 482	84.95 524	97.37 490	98.97 360

RPSCF		98.62 169	98.36 202	99.42 67	99.65 71	99.42 10	98.55 126	99.57 109	97.72 254	98.90 235	99.26 137	96.12 280	99.52 419	95.72 372	99.71 217	99.32 270

Vis-MVSNet (Re-imp)		97.46 315	97.16 327	98.34 290	99.55 117	96.10 325	98.94 81	98.44 405	98.32 192	98.16 343	98.62 329	88.76 439	99.73 296	93.88 428	99.79 159	99.18 320

test_0402		98.76 137	98.71 135	98.93 168	99.56 111	98.14 148	98.45 147	99.34 228	99.28 73	98.95 222	98.91 252	98.34 95	99.79 246	95.63 377	99.91 80	98.86 380

MVS_111021_HR		98.25 236	98.08 249	98.75 211	99.09 275	97.46 233	95.97 425	99.27 264	97.60 266	97.99 361	98.25 380	98.15 124	99.38 456	96.87 283	99.57 285	99.42 217

CSCG		98.68 157	98.50 174	99.20 110	99.45 167	98.63 100	98.56 125	99.57 109	97.87 240	98.85 249	98.04 401	97.66 164	99.84 177	96.72 298	99.81 140	99.13 335

PatchMatch-RL		97.24 338	96.78 356	98.61 242	99.03 291	97.83 194	96.36 399	99.06 315	93.49 472	97.36 413	97.78 421	95.75 299	99.49 430	93.44 444	98.77 421	98.52 423

API-MVS		97.04 355	96.91 347	97.42 396	97.88 454	98.23 141	98.18 179	98.50 403	97.57 268	97.39 411	96.75 466	96.77 239	99.15 481	90.16 505	99.02 400	94.88 519

Test By Simon												96.52 256

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 48	99.38 59	99.53 83	99.61 43	98.64 61	99.80 233	98.24 146	99.84 114	99.52 160

USDC		97.41 321	97.40 310	97.44 395	98.94 311	93.67 444	95.17 462	99.53 134	94.03 463	98.97 216	99.10 190	95.29 316	99.34 461	95.84 368	99.73 199	99.30 279

EPP-MVSNet		98.30 226	98.04 253	99.07 136	99.56 111	97.83 194	99.29 36	98.07 425	99.03 121	98.59 297	99.13 181	92.16 402	99.90 81	96.87 283	99.68 237	99.49 176

PMMVS		96.51 379	95.98 395	98.09 320	97.53 479	95.84 341	94.92 469	98.84 362	91.58 497	96.05 475	95.58 491	95.68 302	99.66 355	95.59 380	98.09 464	98.76 399

PAPM		91.88 496	90.34 498	96.51 438	98.06 446	92.56 464	92.44 524	97.17 455	86.35 528	90.38 533	96.01 481	86.61 455	99.21 477	70.65 540	95.43 523	97.75 473

ACMMP		98.75 138	98.50 174	99.52 44	99.56 111	99.16 48	98.87 89	99.37 212	97.16 321	98.82 256	99.01 223	97.71 161	99.87 135	96.29 343	99.69 231	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		97.17 345	96.71 361	98.55 256	98.56 393	98.05 166	96.33 401	98.93 340	96.91 337	97.06 425	97.39 448	94.38 349	99.45 444	91.66 483	99.18 381	98.14 450

PatchmatchNet		95.58 426	95.67 406	95.30 490	97.34 489	87.32 521	97.65 274	96.65 473	95.30 422	97.07 424	98.69 310	84.77 475	99.75 282	94.97 395	98.64 434	98.83 382
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 229	97.95 263	99.34 83	98.44 406	99.16 48	98.12 190	99.38 208	96.01 387	98.06 354	98.43 356	97.80 155	99.67 342	95.69 374	99.58 281	99.20 310

F-COLMAP		97.30 332	96.68 363	99.14 122	99.19 246	98.39 121	97.27 332	99.30 249	92.93 481	96.62 453	98.00 404	95.73 300	99.68 337	92.62 467	98.46 445	99.35 256

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 155	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		96.06 403	97.62 299	91.38 519	98.65 382	98.57 107	98.85 93	96.95 464	96.86 343	99.90 14	99.16 169	99.18 19	98.40 505	89.23 511	99.77 172	77.18 541

OMC-MVS		97.88 279	97.49 306	99.04 145	98.89 326	98.63 100	96.94 354	99.25 272	95.02 431	98.53 308	98.51 344	97.27 204	99.47 437	93.50 442	99.51 306	99.01 351

MG-MVS		96.77 369	96.61 372	97.26 403	98.31 418	93.06 453	95.93 430	98.12 424	96.45 366	97.92 365	98.73 297	93.77 369	99.39 454	91.19 494	99.04 396	99.33 266

AdaColmap		97.14 347	96.71 361	98.46 274	98.34 416	97.80 203	96.95 353	98.93 340	95.58 409	96.92 432	97.66 428	95.87 296	99.53 415	90.97 497	99.14 385	98.04 455

uanet		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

ITE_SJBPF				98.87 177	99.22 236	98.48 115		99.35 222	97.50 277	98.28 335	98.60 333	97.64 168	99.35 460	93.86 429	99.27 361	98.79 395

DeepMVS_CX				93.44 513	98.24 427	94.21 414		94.34 509	64.28 541	91.34 530	94.87 510	89.45 437	92.77 540	77.54 536	93.14 531	93.35 527

TinyColmap		97.89 276	97.98 259	97.60 376	98.86 330	94.35 410	96.21 409	99.44 184	97.45 287	99.06 191	98.88 263	97.99 137	99.28 472	94.38 415	99.58 281	99.18 320

MAR-MVS		96.47 385	95.70 404	98.79 199	97.92 452	99.12 62	98.28 167	98.60 392	92.16 492	95.54 488	96.17 479	94.77 336	99.52 419	89.62 508	98.23 454	97.72 476
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		97.90 274	97.69 290	98.52 265	99.17 256	97.66 215	97.19 342	99.47 167	96.31 371	97.85 374	98.20 386	96.71 246	99.52 419	94.62 403	99.72 208	98.38 438

MSDG		97.71 296	97.52 304	98.28 297	98.91 320	96.82 292	94.42 488	99.37 212	97.65 259	98.37 328	98.29 377	97.40 195	99.33 463	94.09 422	99.22 371	98.68 411

LS3D		98.63 166	98.38 198	99.36 74	97.25 492	99.38 12	99.12 61	99.32 236	99.21 82	98.44 319	98.88 263	97.31 200	99.80 233	96.58 314	99.34 346	98.92 370

CLD-MVS		97.49 313	97.16 327	98.48 272	99.07 279	97.03 277	94.71 474	99.21 281	94.46 447	98.06 354	97.16 457	97.57 176	99.48 434	94.46 408	99.78 164	98.95 364
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 471	92.23 480	97.08 412	99.25 229	97.86 191	95.61 444	97.16 456	92.90 483	93.76 518	98.65 320	75.94 514	95.66 535	79.30 535	97.49 483	97.73 475

Gipuma		99.03 88	99.16 62	98.64 232	99.94 2	98.51 113	99.32 26	99.75 43	99.58 38	98.60 295	99.62 40	98.22 113	99.51 425	97.70 204	99.73 199	97.89 463
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark