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	99.81 227	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	99.05 145	98.76 355	97.81 205	99.25 43	99.30 254	98.57 174	98.55 310	99.33 119	97.95 140	99.90 81	97.16 256	99.67 246	99.44 210

3Dnovator+	97.89 3	98.69 151	98.51 172	99.24 106	98.81 348	98.40 121	99.02 70	99.19 294	98.99 124	98.07 358	99.28 130	97.11 216	99.84 180	96.84 293	99.32 358	99.47 197

DeepC-MVS	97.60 4	98.97 99	98.93 101	99.10 130	99.35 199	97.98 177	98.01 214	99.46 175	97.56 272	99.54 79	99.50 68	98.97 29	99.84 180	98.06 164	99.92 71	99.49 177
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	98.32 223	98.01 259	99.23 108	98.39 419	98.97 74	95.03 473	99.18 298	96.88 344	99.33 138	98.78 290	98.16 122	99.28 481	96.74 302	99.62 267	99.44 210

DeepC-MVS_fast	96.85 6	98.30 228	98.15 244	98.75 213	98.61 389	97.23 261	97.76 258	99.09 318	97.31 305	98.75 271	98.66 322	97.56 177	99.64 373	96.10 363	99.55 298	99.39 232
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	97.09 356	96.68 369	98.32 296	98.32 423	97.16 274	98.86 92	99.37 217	89.48 523	96.29 475	99.15 177	96.56 256	99.90 81	92.90 466	99.20 383	97.89 471

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 45	98.38 124	99.07 65	99.55 126	98.30 195	99.65 63	99.45 84	99.22 17	99.76 273	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 87	98.60 121	99.58 103	99.11 100	99.53 83	99.18 164	98.81 39	99.67 348	96.71 307	99.77 172	99.50 169

COLMAP_ROB	96.50 10	98.99 94	98.85 118	99.41 69	99.58 94	99.10 65	98.74 99	99.56 121	99.09 110	99.33 138	99.19 160	98.40 86	99.72 310	95.98 366	99.76 188	99.42 219
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	96.63 380	95.95 403	98.65 234	98.93 319	98.09 158	96.93 358	99.28 266	83.58 541	98.13 352	97.78 427	96.13 281	99.40 461	93.52 449	99.29 366	98.45 435
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	98.07 201	99.37 217	97.62 263	99.04 203	98.96 243	98.84 37	99.79 249	97.43 236	99.65 256	99.49 177
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 421	95.35 429	97.55 389	97.95 457	94.79 401	98.81 98	96.94 473	92.28 499	95.17 501	98.57 341	89.90 437	99.75 285	91.20 502	97.33 503	98.10 460

OpenMVS_ROB	95.38 14	95.84 424	95.18 440	97.81 353	98.41 418	97.15 275	97.37 321	98.62 398	83.86 540	98.65 287	98.37 368	94.29 359	99.68 343	88.41 521	98.62 446	96.60 512

ACMP	95.32 15	98.41 204	98.09 249	99.36 74	99.51 134	98.79 90	97.68 270	99.38 213	95.76 409	98.81 261	98.82 282	98.36 90	99.82 210	94.75 407	99.77 172	99.48 188
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 385	95.73 409	98.85 182	98.75 357	97.91 188	96.42 400	99.06 322	90.94 515	95.59 489	97.38 455	94.41 351	99.59 398	90.93 507	98.04 478	99.05 346
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 428	95.70 410	95.57 487	98.83 342	88.57 522	92.50 531	97.72 440	92.69 494	96.49 472	96.44 480	93.72 375	99.43 457	93.61 444	99.28 367	98.71 409

PCF-MVS	92.86 18	94.36 458	93.00 478	98.42 283	98.70 369	97.56 228	93.16 528	99.11 315	79.59 545	97.55 401	97.43 452	92.19 407	99.73 299	79.85 543	99.45 328	97.97 468
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 498	90.90 502	96.27 455	97.22 501	91.24 498	94.36 498	93.33 530	92.37 497	92.24 536	94.58 520	66.20 540	99.89 97	93.16 460	94.63 536	97.66 486
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	97.86 286	97.94 269	97.65 374	99.71 49	97.94 184	98.52 130	98.68 392	98.99 124	97.52 404	99.35 112	97.41 194	98.18 518	91.59 495	99.67 246	96.82 508
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 504	90.30 506	93.70 516	97.72 471	84.34 543	90.24 538	97.42 450	90.20 519	93.79 525	93.09 530	90.90 429	98.89 505	86.57 530	72.76 552	97.87 473

MVE	83.40 22	92.50 493	91.92 495	94.25 507	98.83 342	91.64 486	92.71 529	83.52 553	95.92 398	86.46 547	95.46 503	95.20 322	95.40 545	80.51 542	98.64 442	95.73 524
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 400	95.44 424	98.84 188	96.25 531	98.69 99	97.02 349	99.12 313	88.90 527	97.83 381	98.86 269	89.51 442	98.90 504	91.92 487	99.51 311	98.92 374
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
nomal-1		94.03 467	93.02 477	97.07 420	97.95 457	92.86 466	96.66 381	95.37 507	96.16 385	94.89 507	94.68 518	69.16 531	99.73 299	94.43 419	97.86 483	98.62 423

MVS_clip		56.94 517	60.93 519	44.97 535	71.47 557	51.70 560	61.73 548	21.77 561	28.88 553	86.09 548	92.75 534	48.89 556	27.00 556	61.70 551	75.08 551	56.23 550

MVS_baseline		25.61 519	31.27 523	8.63 537	32.09 561	3.00 566	22.13 551	5.43 564	1.36 558	58.03 555	69.99 551	18.40 560	0.00 560	18.79 556	55.18 555	22.88 553

VLMVS_CLIP		57.57 516	58.80 520	53.85 534	47.22 559	42.89 562	60.06 549	76.87 557	39.44 550	65.76 554	80.47 550	36.24 558	64.75 555	58.06 552	65.11 554	53.91 551

PatchmatchNet2					0.00 564	90.12 513	94.29 500	98.12 431	94.40 459
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														96.95 280	99.71 217	99.28 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.85 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
VLMVS		32.15 518	34.06 521	26.43 536	35.38 560	29.60 563	32.69 550	19.27 562	3.29 557	44.01 556	60.07 552	35.02 559	20.44 557	22.64 555	54.15 556	29.25 552

PRO-TEST		97.94 275	98.16 242	97.26 408	98.17 441	93.56 455	98.36 160	99.22 286	98.46 182	97.93 370	99.41 94	94.82 335	99.87 135	97.64 212	99.45 328	98.35 450

test-260524					99.33 205	99.02 71		99.25 277		99.23 169		96.59 255	99.85 159	98.10 160	99.62 267

RoMa-HiRes		98.68 157	98.52 170	99.16 118	99.50 141	98.35 130	98.01 214	99.71 48	96.94 336	99.35 130	98.66 322	96.38 267	99.63 376	98.39 138	99.71 217	99.48 188

DKM-HiRes		98.14 254	97.80 282	99.16 118	99.51 134	98.40 121	96.70 374	99.63 82	97.55 274	97.45 412	98.74 299	93.27 382	99.54 421	97.78 194	99.55 298	99.53 157

ArgMatch-Sym		97.83 294	97.54 306	98.71 223	98.98 311	97.65 221	96.25 414	99.43 193	95.60 414	98.85 251	97.98 411	95.72 304	99.56 410	95.54 390	99.50 319	98.92 374

PMatch-Up-SfM		97.79 297	97.48 314	98.72 221	99.03 295	97.78 207	96.05 428	99.48 159	96.90 342	98.72 275	99.18 164	92.00 413	99.71 312	97.15 259	98.77 428	98.69 413

onestephybrid01		98.40 207	98.39 196	98.42 283	99.05 290	96.23 328	96.73 372	99.41 204	98.18 213	98.65 287	99.02 214	97.02 221	99.69 331	97.73 205	99.70 228	99.33 268

viewmamba		98.57 178	98.66 146	98.31 298	99.20 245	95.89 344	96.92 360	99.57 111	98.71 158	99.02 207	99.04 210	97.48 190	99.71 312	98.28 146	99.70 228	99.35 258

PMatch-SfM		97.89 280	97.64 300	98.66 232	99.26 230	97.44 241	96.08 426	99.51 144	96.72 355	98.47 320	99.13 183	93.62 378	99.70 321	97.14 260	98.80 427	98.83 387

DenseAffine		98.10 256	97.86 278	98.84 188	99.32 207	97.93 185	96.62 384	99.76 39	96.68 359	98.65 287	98.72 303	94.46 349	99.33 472	96.76 299	99.75 192	99.25 298

ArgMatch-SfM		97.96 274	97.72 291	98.66 232	99.02 303	97.33 247	96.49 394	99.52 142	95.46 423	98.71 279	98.29 382	96.14 279	99.69 331	96.30 348	99.56 293	98.97 364

MASt3R-SfM		96.02 412	95.82 406	96.60 443	97.03 509	94.90 396	94.26 502	98.53 407	88.40 532	98.41 327	98.67 318	92.39 402	97.62 528	95.31 394	99.41 341	97.29 499

hybridnocas07		98.32 223	98.37 202	98.17 315	99.14 267	95.51 360	96.67 378	99.56 121	97.85 244	98.75 271	98.95 247	96.65 251	99.63 376	98.00 172	99.78 164	99.37 244

Casviewmamba		99.12 69	99.12 71	99.09 134	99.53 127	98.08 162	98.34 164	99.66 71	99.35 64	99.35 130	99.23 151	98.39 88	99.72 310	98.46 129	99.81 140	99.47 197

dtuonlycased		97.70 303	98.19 236	96.24 457	99.75 34	89.51 519	94.69 485	99.64 79	98.23 202	99.46 101	98.57 341	98.25 107	99.85 159	95.65 383	99.44 336	99.36 252

dtuonly		96.49 388	97.28 324	94.10 510	98.80 351	83.27 546	93.66 518	99.48 159	95.10 436	97.87 376	98.30 379	95.61 308	99.68 343	96.98 277	99.75 192	99.33 268

dtuplus		98.32 223	98.39 196	98.10 323	99.15 265	95.29 378	96.68 376	99.51 144	97.32 303	99.18 181	99.15 177	97.61 172	99.62 381	97.19 253	99.74 195	99.38 241

SIFT-UM-Cal		96.49 388	96.62 376	96.12 468	98.13 448	97.89 191	93.35 524	98.44 412	95.48 422	98.63 291	98.34 372	95.45 316	97.45 529	92.22 484	99.50 319	93.02 539

SIFT-NCM-Cal		96.56 383	96.68 369	96.20 461	98.27 430	98.44 119	94.40 496	96.67 480	95.29 430	97.63 393	98.17 393	96.40 264	96.59 541	93.61 444	99.66 254	93.57 532

SIFT-CM-Cal		96.28 401	96.31 394	96.16 465	98.39 419	98.11 154	93.46 523	96.47 486	94.81 446	98.49 317	98.43 361	94.48 348	97.34 532	92.60 478	99.70 228	93.02 539

SIFT-PCN-Cal		96.34 396	96.46 388	96.01 472	98.17 441	96.89 293	93.48 522	97.35 455	94.84 444	99.35 130	98.30 379	94.70 343	97.92 522	92.03 485	99.88 95	93.21 538

SIFT-NN-UMatch		95.38 441	95.26 434	95.75 481	98.25 431	97.78 207	93.24 527	95.66 506	94.01 472	95.10 503	97.47 450	93.12 387	96.78 538	92.42 481	98.04 478	92.69 544

SIFT-NN-NCMNet		95.39 440	95.22 437	95.92 474	98.29 426	98.34 132	93.58 520	94.60 515	94.07 470	94.84 508	97.53 442	94.37 355	96.62 539	91.01 505	98.64 442	92.80 542

SIFT-NN-CMatch		95.63 431	95.48 420	96.08 469	98.24 433	98.00 172	92.71 529	94.29 519	94.20 464	95.85 485	97.26 460	95.72 304	97.01 534	91.99 486	99.02 407	93.23 536

SIFT-NN-PointCN		96.06 409	96.11 400	95.91 475	97.88 462	97.73 214	93.49 521	97.51 449	93.22 482	96.57 462	98.26 384	96.23 276	96.60 540	92.54 479	99.27 368	93.40 534

XFeat-NN		89.63 506	89.13 509	91.14 527	90.93 552	90.02 516	84.90 545	94.05 525	88.10 533	92.89 531	93.33 529	78.74 513	90.89 550	83.46 536	95.72 530	92.52 545

ALIKED-NN		94.29 462	93.41 471	96.94 427	96.18 532	97.66 219	94.90 477	98.68 392	88.85 528	90.43 540	96.81 471	89.82 438	96.59 541	86.67 529	98.33 456	96.58 513

SP-NN		94.67 454	94.44 456	95.36 495	95.12 540	95.23 383	94.27 501	96.10 493	94.46 454	90.91 539	95.76 495	91.47 422	93.87 548	95.23 397	96.62 514	97.00 503

SIFT-NN		92.96 487	92.79 481	93.46 518	96.92 511	96.45 320	91.89 535	94.39 517	92.91 490	92.54 533	95.46 503	88.26 454	90.71 551	85.22 532	97.52 490	93.22 537

hybridcas		99.08 79	99.13 70	98.92 173	99.54 123	97.61 226	98.22 178	99.66 71	99.27 74	99.40 117	99.24 145	98.47 77	99.70 321	98.59 118	99.80 152	99.46 200

GLUNet-SfM		86.26 510	84.68 512	91.01 528	80.58 555	83.56 544	78.04 546	93.59 527	76.70 546	95.29 500	94.72 517	77.51 519	94.26 547	66.39 550	99.33 355	95.20 526

PDCNetPlus		95.22 445	94.73 452	96.70 441	97.85 464	91.14 501	93.94 512	99.97 1	93.06 487	98.95 224	98.89 264	74.32 523	99.14 491	95.63 384	99.93 57	99.82 36

hybrid		98.22 240	98.27 222	98.08 328	99.13 270	95.24 380	96.61 385	99.53 136	97.43 292	98.46 321	98.97 239	96.75 245	99.65 368	97.84 189	99.69 234	99.35 258

RoMa-SfM		98.46 199	98.27 222	99.02 151	99.35 199	98.32 133	97.56 292	99.70 54	95.88 400	99.38 121	98.65 325	96.41 263	99.46 450	97.78 194	99.71 217	99.28 287

DKM		98.18 248	97.95 266	98.85 182	99.35 199	98.31 134	96.68 376	99.69 57	96.90 342	98.61 297	98.77 292	94.41 351	98.93 501	97.32 244	99.84 114	99.32 273

ELoFTR		97.81 296	97.74 287	98.04 334	99.39 185	95.79 351	97.28 333	99.58 103	94.13 466	99.38 121	99.37 105	93.31 381	99.60 393	97.23 250	99.96 28	98.74 407

MatchFormer		97.07 358	96.92 350	97.49 395	98.44 412	95.92 342	96.79 365	99.14 311	93.08 486	99.32 144	99.10 192	93.89 369	99.03 494	92.78 472	99.78 164	97.52 491

LoFTR		97.97 273	97.79 283	98.53 267	98.80 351	97.47 236	97.01 350	99.55 126	95.55 417	99.46 101	99.22 153	94.22 361	99.44 455	96.45 337	99.82 133	98.68 417

ALIKED-LG		97.10 354	96.63 375	98.50 274	97.96 456	98.68 100	97.75 261	99.68 64	95.86 401	98.36 335	98.33 376	91.58 418	99.04 493	90.87 510	99.31 360	97.77 480

SP-DiffGlue		96.87 370	96.76 363	97.21 411	95.17 539	96.88 295	96.12 423	98.93 347	96.51 364	98.37 333	97.55 441	93.65 377	97.83 523	96.11 362	98.45 454	96.92 504

SP-LightGlue		97.22 346	97.01 344	97.88 347	97.33 498	97.19 268	96.38 402	99.08 320	97.28 308	96.53 465	97.50 446	92.36 403	98.70 510	97.84 189	98.76 430	97.74 482

SP-SuperGlue		97.31 336	97.23 329	97.57 388	96.96 510	97.24 260	96.26 413	98.76 382	97.68 258	96.88 447	97.85 422	94.32 357	98.01 520	97.76 201	98.57 449	97.45 494

SIFT-UMatch		96.33 397	96.47 386	95.89 476	98.29 426	97.95 182	93.84 514	97.24 460	95.78 408	98.72 275	98.04 406	93.45 380	96.81 537	93.14 461	99.73 199	92.91 541

SIFT-NCMNet		96.30 399	96.40 390	96.03 471	97.80 469	97.68 218	92.34 533	96.94 473	95.55 417	98.84 254	98.63 331	94.17 362	97.63 527	93.57 448	99.71 217	92.77 543

SIFT-ConvMatch		96.57 382	96.62 376	96.43 448	98.20 437	98.27 137	93.88 513	96.88 476	95.29 430	98.88 244	98.25 385	95.18 324	97.43 530	93.22 459	99.83 126	93.59 531

SIFT-PointCN		96.45 393	96.47 386	96.39 450	98.13 448	97.54 230	93.31 525	97.23 461	94.67 449	98.68 283	98.32 377	94.64 344	97.81 524	93.50 451	99.77 172	93.83 529

XFeat-MNN		93.41 479	92.98 479	94.68 503	92.63 546	92.92 464	89.72 542	95.81 500	92.10 501	97.23 425	96.29 484	84.95 480	97.31 533	89.60 518	98.54 451	93.81 530

ALIKED-MNN		95.97 418	95.30 433	98.00 337	97.66 481	98.12 153	96.98 353	99.41 204	91.11 513	94.04 521	97.30 459	91.56 419	98.61 512	89.99 515	99.63 263	97.28 500

SP-MNN		96.46 392	96.24 399	97.10 417	96.71 518	95.98 339	96.00 430	97.33 456	95.82 405	94.93 506	97.10 468	93.70 376	98.01 520	96.30 348	98.30 460	97.30 498

SIFT-MNN		95.92 420	95.97 402	95.74 483	98.18 439	98.00 172	94.17 504	96.99 468	95.74 410	97.16 426	97.90 418	90.71 430	95.79 543	93.71 442	99.21 381	93.44 533

casdiffseed414692147		99.09 73	99.12 71	99.01 153	99.55 117	97.91 188	98.30 166	99.68 64	99.04 119	99.19 176	99.37 105	98.98 28	99.61 389	98.13 156	99.83 126	99.50 169

gbinet_0.2-2-1-0.02		95.44 438	94.55 453	98.14 319	95.99 536	95.34 376	94.71 481	98.29 421	96.00 394	96.05 482	90.50 545	84.99 479	99.79 249	97.33 242	97.07 508	99.28 287

0.3-1-1-0.015		87.27 509	84.50 513	95.57 487	91.70 548	90.77 507	89.41 543	92.04 537	88.98 526	82.46 551	81.35 548	60.36 551	99.50 435	92.96 463	81.23 547	96.45 514

0.4-1-1-0.1		88.42 507	85.91 510	95.94 473	93.08 545	91.54 487	90.99 537	92.04 537	89.96 522	84.83 549	83.25 547	63.75 547	99.52 428	93.25 457	82.07 545	96.75 509

0.4-1-1-0.2		87.49 508	84.89 511	95.31 496	91.33 551	90.08 515	88.47 544	92.07 536	88.70 529	84.06 550	81.08 549	63.62 548	99.49 439	92.93 465	81.71 546	96.37 515

wanda-best-256-512		95.48 436	94.74 450	97.68 368	96.53 522	94.12 424	94.17 504	98.57 403	95.84 402	96.71 454	91.16 541	86.05 469	99.76 273	97.57 220	96.09 522	99.17 328

usedtu_dtu_shiyan2		98.99 94	98.86 115	99.39 72	99.73 38	98.71 98	99.05 68	99.47 170	99.16 94	99.49 94	99.12 187	96.34 271	99.93 53	98.05 166	99.36 348	99.54 143

usedtu_dtu_shiyan1		97.37 330	97.13 337	98.11 321	99.03 295	95.40 371	94.47 493	98.99 340	96.87 345	97.97 367	97.81 425	92.12 409	99.75 285	97.49 233	99.43 338	99.16 334

blended_shiyan8		95.98 416	95.33 430	97.94 342	97.05 508	94.87 399	95.34 463	98.59 400	96.17 381	97.09 430	92.39 536	87.62 458	99.76 273	97.65 211	96.05 528	99.20 314

E5new		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

FE-blended-shiyan7		95.48 436	94.74 450	97.68 368	96.53 522	94.12 424	94.17 504	98.57 403	95.84 402	96.71 454	91.16 541	86.05 469	99.76 273	97.57 220	96.09 522	99.17 328

E6new		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

blended_shiyan6		95.99 415	95.33 430	97.95 341	97.06 506	94.89 397	95.34 463	98.58 401	96.17 381	97.06 432	92.41 535	87.64 457	99.76 273	97.64 212	96.09 522	99.19 320

usedtu_blend_shiyan5		96.20 407	95.62 413	97.94 342	96.53 522	94.93 394	98.83 96	99.59 100	98.89 138	96.71 454	91.16 541	86.05 469	99.73 299	96.70 308	96.09 522	99.17 328

blend_shiyan4		92.09 500	90.16 507	97.88 347	96.78 516	94.93 394	95.24 467	98.58 401	96.22 379	96.07 480	91.42 540	63.46 549	99.73 299	96.70 308	76.98 550	98.98 360

E6		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

E5		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 101	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

FE-MVSNET3		97.37 330	97.13 337	98.11 321	99.03 295	95.40 371	94.47 493	98.99 340	96.87 345	97.97 367	97.81 425	92.12 409	99.75 285	97.49 233	99.43 338	99.16 334

E4		98.87 112	98.88 108	98.81 194	99.52 131	97.23 261	97.62 281	99.61 92	98.58 172	99.18 181	99.33 119	98.29 99	99.69 331	97.99 175	99.83 126	99.52 161

E3new		98.41 204	98.34 208	98.62 242	99.19 249	96.90 292	97.32 325	99.50 149	97.40 295	98.63 291	98.92 252	97.21 209	99.65 368	97.34 240	99.52 308	99.31 278

FE-MVSNET2		99.15 57	99.22 54	98.94 167	99.70 57	97.49 232	98.62 118	99.67 70	98.85 145	99.34 135	99.54 62	98.47 77	99.81 227	98.93 92	99.91 80	99.51 165

fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 197	99.48 158	96.56 313	97.97 228	99.69 57	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	98.73 219	99.40 183	97.10 278	97.48 304	99.57 111	98.09 225	99.00 209	99.20 157	97.90 143	99.67 348	97.73 205	99.77 172	99.43 214

aaatest				99.45 64	99.58 94	98.93 80	98.68 109	99.60 94	96.46 370	99.53 83	98.77 292		99.83 198	96.67 312	99.64 258	99.58 117

MED-MVS		99.01 90	98.84 119	99.52 44	99.58 94	98.93 80	98.68 109	99.60 94	98.85 145	99.53 83	99.16 171	97.87 149	99.83 198	96.67 312	99.62 267	99.81 41

E3		98.69 151	98.68 141	98.73 219	99.40 183	97.10 278	97.48 304	99.57 111	98.09 225	99.00 209	99.20 157	97.90 143	99.67 348	97.73 205	99.77 172	99.43 214

TestfortrainingZip a		99.09 73	98.92 102	99.61 13	99.58 94	99.17 43	98.68 109	99.27 269	98.85 145	99.61 70	99.16 171	97.14 213	99.86 145	98.39 138	99.57 289	99.81 41

TestfortrainingZip				98.97 162	98.30 425	98.43 120	98.68 109	98.26 422	97.76 252	98.86 250	98.16 395	95.15 325	99.47 446		97.55 489	99.02 353

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 204	99.47 161	96.56 313	97.75 261	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	98.26 303	99.43 176	95.65 354	97.20 340	99.66 71	99.20 84	99.29 149	99.01 226	98.29 99	99.73 299	97.92 180	99.75 192	99.39 232

viewdifsd2359ckpt09		98.13 255	97.92 272	98.77 209	99.18 257	97.35 245	97.29 329	99.53 136	95.81 406	98.09 356	98.47 357	96.34 271	99.66 361	97.02 270	99.51 311	99.29 284

viewdifsd2359ckpt13		98.39 214	98.29 218	98.70 225	99.26 230	97.19 268	97.51 300	99.48 159	96.94 336	98.58 304	98.82 282	97.47 192	99.55 415	97.21 252	99.33 355	99.34 262

viewcassd2359sk11		98.55 184	98.51 172	98.67 230	99.29 215	96.99 284	97.39 315	99.54 132	97.73 254	98.81 261	99.08 199	97.55 178	99.66 361	97.52 227	99.67 246	99.36 252

viewdifsd2359ckpt11		98.84 119	99.04 87	98.24 307	99.56 111	95.51 360	97.38 317	99.70 54	99.16 94	99.57 72	99.40 98	98.26 105	99.71 312	98.55 125	99.82 133	99.50 169

viewmacassd2359aftdt		98.86 116	98.87 111	98.83 190	99.53 127	97.32 250	97.70 268	99.64 79	98.22 204	99.25 165	99.27 132	98.40 86	99.61 389	97.98 176	99.87 100	99.55 137

viewmsd2359difaftdt		98.84 119	99.04 87	98.24 307	99.56 111	95.51 360	97.38 317	99.70 54	99.16 94	99.57 72	99.40 98	98.26 105	99.71 312	98.55 125	99.82 133	99.50 169

diffmvs_AUTHOR		98.50 195	98.59 161	98.23 310	99.35 199	95.48 365	96.61 385	99.60 94	98.37 186	98.90 237	99.00 230	97.37 197	99.76 273	98.22 150	99.85 109	99.46 200

FE-MVSNET		98.59 175	98.50 175	98.87 179	99.58 94	97.30 251	98.08 197	99.74 44	96.94 336	98.97 218	99.10 192	96.94 227	99.74 292	97.33 242	99.86 107	99.55 137

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 160	99.59 92	97.18 271	97.44 312	99.83 26	99.56 39	99.91 12	99.34 116	99.36 13	99.93 53	99.83 10	99.98 12	99.85 30

mamba_0408		98.80 129	98.88 108	98.55 260	99.27 221	96.50 316	98.00 216	99.60 94	98.93 132	99.22 171	98.84 277	98.59 67	99.89 97	97.74 203	99.72 208	99.27 291

icg_test_0407_2		98.20 245	98.38 200	97.65 374	99.03 295	94.03 430	95.78 446	99.45 179	98.16 217	99.06 193	98.71 305	98.27 103	99.68 343	97.50 228	99.45 328	99.22 309

SSM_04072		98.80 129	98.88 108	98.56 258	99.27 221	96.50 316	98.00 216	99.60 94	98.93 132	99.22 171	98.84 277	98.59 67	99.90 81	97.74 203	99.72 208	99.27 291

SSM_0407		98.86 116	98.96 100	98.55 260	99.27 221	96.50 316	98.04 206	99.66 71	99.09 110	99.22 171	99.02 214	98.79 43	99.87 135	97.87 186	99.72 208	99.27 291

viewmambaseed2359dif		98.19 246	98.26 225	97.99 339	99.02 303	95.03 391	96.59 388	99.53 136	96.21 380	99.00 209	98.99 232	97.62 170	99.61 389	97.62 215	99.72 208	99.33 268

IMVS_0407		98.39 214	98.64 150	97.66 372	99.03 295	94.03 430	98.10 194	99.45 179	98.16 217	99.06 193	98.71 305	98.27 103	99.71 312	97.50 228	99.45 328	99.22 309

viewmanbaseed2359cas		98.58 177	98.54 167	98.70 225	99.28 218	97.13 277	97.47 308	99.55 126	97.55 274	98.96 223	98.92 252	97.77 157	99.59 398	97.59 219	99.77 172	99.39 232

IMVS_0404		98.07 261	98.20 232	97.69 367	99.03 295	94.03 430	96.67 378	99.45 179	98.16 217	98.03 363	98.71 305	96.80 238	99.82 210	97.50 228	99.45 328	99.22 309

SSM_0404		98.90 108	99.01 92	98.57 253	99.42 178	96.59 308	98.13 187	99.66 71	99.09 110	99.30 148	99.02 214	98.79 43	99.89 97	97.87 186	99.80 152	99.23 304

IMVS_0403		98.34 218	98.56 164	97.66 372	99.03 295	94.03 430	97.98 224	99.45 179	98.16 217	98.89 240	98.71 305	97.90 143	99.74 292	97.50 228	99.45 328	99.22 309

SD_0403		96.28 401	95.83 405	97.64 377	98.72 361	94.30 417	98.87 89	98.77 380	97.80 248	96.53 465	98.02 408	97.34 199	99.47 446	76.93 546	99.48 324	99.16 334

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 267	99.51 134	95.82 349	97.62 281	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

aaEdge-Enhanced		98.61 171	98.33 213	99.44 65	99.24 233	98.93 80	97.45 310	99.06 322	98.14 223	99.06 193	98.77 292	96.97 226	99.82 210	96.67 312	99.64 258	99.58 117

NormalMVS		98.26 235	97.97 265	99.15 123	99.64 77	97.83 197	98.28 168	99.43 193	99.24 77	98.80 263	98.85 272	89.76 439	99.94 41	98.04 167	99.67 246	99.68 73

lecture		99.25 40	99.12 71	99.62 9	99.64 77	99.40 11	98.89 88	99.51 144	99.19 89	99.37 125	99.25 143	98.36 90	99.88 115	98.23 149	99.67 246	99.59 109

SymmetryMVS		98.05 263	97.71 293	99.09 134	99.29 215	97.83 197	98.28 168	97.64 447	99.24 77	98.80 263	98.85 272	89.76 439	99.94 41	98.04 167	99.50 319	99.49 177

Elysia		99.15 57	99.14 68	99.18 113	99.63 83	97.92 186	98.50 137	99.43 193	99.67 20	99.70 51	99.13 183	96.66 249	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	97.92 186	98.50 137	99.43 193	99.67 20	99.70 51	99.13 183	96.66 249	99.98 4	99.54 44	99.96 28	99.64 86

KinetiMVS		99.03 88	99.02 90	99.03 148	99.70 57	97.48 235	98.43 148	99.29 262	99.70 15	99.60 71	99.07 200	96.13 281	99.94 41	99.42 55	99.87 100	99.68 73

LuminaMVS		98.39 214	98.20 232	98.98 160	99.50 141	97.49 232	97.78 252	97.69 442	98.75 150	99.49 94	99.25 143	92.30 406	99.94 41	99.14 75	99.88 95	99.50 169

VortexMVS		97.98 272	98.31 215	97.02 422	98.88 333	91.45 490	98.03 208	99.47 170	98.65 160	99.55 77	99.47 78	91.49 421	99.81 227	99.32 60	99.91 80	99.80 45

AstraMVS		98.16 253	98.07 254	98.41 285	99.51 134	95.86 346	98.00 216	95.14 510	98.97 127	99.43 108	99.24 145	93.25 383	99.84 180	99.21 70	99.87 100	99.54 143

guyue		98.01 267	97.93 271	98.26 303	99.45 169	95.48 365	98.08 197	96.24 489	98.89 138	99.34 135	99.14 181	91.32 424	99.82 210	99.07 80	99.83 126	99.48 188

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 82	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 72	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 79	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	98.74 217	99.51 134	96.44 321	97.65 276	99.65 77	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	98.20 312	99.30 213	94.83 400	97.23 335	99.36 221	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	98.69 227	99.36 194	96.51 315	97.62 281	99.68 64	98.43 184	99.85 27	99.10 192	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	98.99 156	99.47 161	97.22 264	97.40 314	99.83 26	97.61 266	99.85 27	99.30 126	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	98.38 289	99.31 209	95.48 365	97.56 292	99.73 45	98.87 140	99.75 44	99.27 132	98.80 41	99.86 145	99.80 17	99.90 88	99.81 41

SSC-MVS3.2		98.53 189	98.79 124	97.74 362	99.46 164	93.62 453	96.45 396	99.34 233	99.33 66	98.93 233	98.70 312	97.90 143	99.90 81	99.12 76	99.92 71	99.69 72

testing3-2		93.78 472	93.91 462	93.39 521	98.82 345	81.72 552	97.76 258	95.28 508	98.60 168	96.54 464	96.66 474	65.85 542	99.62 381	96.65 316	98.99 412	98.82 389

myMVS_eth3d28		92.92 489	92.31 485	94.77 501	97.84 465	87.59 529	96.19 417	96.11 492	97.08 328	94.27 515	93.49 527	66.07 541	98.78 507	91.78 490	97.93 482	97.92 470

UWE-MVS-28		90.22 505	89.28 508	93.02 525	94.50 543	82.87 548	96.52 392	87.51 548	95.21 434	92.36 535	96.04 486	71.57 527	98.25 517	72.04 548	97.77 485	97.94 469

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 92	98.21 146	97.82 246	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	98.78 204	99.46 164	96.58 311	97.65 276	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	98.63 240	99.49 150	96.08 336	97.38 317	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	98.65 234	99.69 61	96.08 336	97.49 303	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		97.50 316	97.11 339	98.67 230	99.02 303	96.85 296	98.16 184	99.71 48	98.32 193	98.52 315	98.54 344	83.39 494	99.95 25	98.79 101	99.56 293	99.19 320

BP-MVS1		97.40 328	96.97 346	98.71 223	99.07 282	96.81 298	98.34 164	97.18 462	98.58 172	98.17 345	98.61 336	84.01 490	99.94 41	98.97 89	99.78 164	99.37 244

reproduce_monomvs		95.00 451	95.25 435	94.22 508	97.51 492	83.34 545	97.86 242	98.44 412	98.51 179	99.29 149	99.30 126	67.68 535	99.56 410	98.89 96	99.81 140	99.77 53

mmtdpeth		99.30 33	99.42 25	98.92 173	99.58 94	96.89 293	99.48 13	99.92 8	99.92 2	98.26 342	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	99.33 205	99.44 9	98.15 185	99.47 170	99.12 99	99.52 87	99.32 124	98.31 97	99.90 81	97.78 194	99.73 199	99.66 80

reproduce-ours		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 216	99.42 200	99.05 117	99.48 96	99.27 132	98.29 99	99.89 97	97.61 216	99.71 217	99.62 92

our_new_method		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 216	99.42 200	99.05 117	99.48 96	99.27 132	98.29 99	99.89 97	97.61 216	99.71 217	99.62 92

mmdepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

monomultidepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

mvs5depth		99.30 33	99.59 12	98.44 281	99.65 71	95.35 374	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		95.86 422	95.60 415	96.63 442	95.87 537	91.70 485	97.93 230	98.94 344	98.03 228	99.56 74	99.66 32	71.83 526	98.26 516	99.35 58	99.24 374	99.91 13

ttmdpeth		97.91 277	98.02 258	97.58 383	98.69 374	94.10 426	98.13 187	98.90 354	97.95 234	97.32 421	99.58 47	95.95 296	98.75 508	96.41 340	99.22 378	99.87 22

WBMVS		95.18 446	94.78 448	96.37 451	97.68 479	89.74 518	95.80 445	98.73 389	97.54 277	98.30 336	98.44 360	70.06 528	99.82 210	96.62 318	99.87 100	99.54 143

dongtai		76.24 514	75.95 517	77.12 532	92.39 547	67.91 558	90.16 539	59.44 560	82.04 543	89.42 543	94.67 519	49.68 555	81.74 552	48.06 553	77.66 549	81.72 547

kuosan		69.30 515	68.95 518	70.34 533	87.68 554	65.00 559	91.11 536	59.90 559	69.02 547	74.46 553	88.89 546	48.58 557	68.03 554	28.61 554	72.33 553	77.99 548

MVSMamba_PlusPlus		98.83 122	98.98 97	98.36 293	99.32 207	96.58 311	98.90 84	99.41 204	99.75 10	98.72 275	99.50 68	96.17 278	99.94 41	99.27 64	99.78 164	98.57 428

MGCFI-Net		98.34 218	98.28 219	98.51 270	98.47 407	97.59 227	98.96 78	99.48 159	99.18 92	97.40 416	95.50 500	98.66 59	99.50 435	98.18 153	98.71 435	98.44 438

testing91		93.32 480	92.27 486	96.47 447	97.54 485	91.25 497	96.17 421	96.76 479	97.18 322	93.65 527	93.50 526	65.11 544	99.63 376	93.04 462	97.45 494	98.53 429

testing11		93.08 485	92.02 491	96.26 456	97.56 483	90.83 506	96.32 407	95.70 502	96.47 369	92.66 532	93.73 523	64.36 545	99.59 398	93.77 441	97.57 488	98.37 447

testing99		93.04 486	91.98 494	96.23 459	97.53 487	90.70 509	96.35 405	95.94 497	96.87 345	93.41 528	93.43 528	63.84 546	99.59 398	93.24 458	97.19 504	98.40 443

UBG		93.25 482	92.32 484	96.04 470	97.72 471	90.16 512	95.92 439	95.91 498	96.03 392	93.95 524	93.04 531	69.60 530	99.52 428	90.72 512	97.98 480	98.45 435

UWE-MVS		92.38 495	91.76 498	94.21 509	97.16 502	84.65 539	95.42 460	88.45 547	95.96 396	96.17 476	95.84 494	66.36 538	99.71 312	91.87 489	98.64 442	98.28 451

ETVMVS		92.60 492	91.08 501	97.18 412	97.70 476	93.65 452	96.54 389	95.70 502	96.51 364	94.68 511	92.39 536	61.80 550	99.50 435	86.97 526	97.41 497	98.40 443

sasdasda		98.34 218	98.26 225	98.58 250	98.46 409	97.82 202	98.96 78	99.46 175	99.19 89	97.46 409	95.46 503	98.59 67	99.46 450	98.08 162	98.71 435	98.46 432

testing222		91.96 501	90.37 504	96.72 440	97.47 494	92.59 471	96.11 424	94.76 512	96.83 349	92.90 530	92.87 532	57.92 552	99.55 415	86.93 527	97.52 490	98.00 467

WB-MVSnew		95.73 427	95.57 418	96.23 459	96.70 519	90.70 509	96.07 427	93.86 526	95.60 414	97.04 434	95.45 507	96.00 288	99.55 415	91.04 504	98.31 459	98.43 440

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 167	99.65 71	97.05 280	97.80 250	99.76 39	98.70 159	99.78 39	99.11 189	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	99.02 151	99.64 77	97.28 257	97.82 246	99.76 39	98.73 151	99.82 34	99.09 198	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	98.80 197	99.75 34	96.59 308	97.97 228	99.86 17	98.22 204	99.88 21	99.71 22	98.59 67	99.84 180	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 236	99.71 49	96.10 331	97.87 241	99.85 19	98.56 177	99.90 14	99.68 25	98.69 57	99.85 159	99.72 30	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 72	99.20 58	98.78 204	99.55 117	96.59 308	97.79 251	99.82 31	98.21 206	99.81 36	99.53 64	98.46 82	99.84 180	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 73	99.26 50	98.61 246	99.55 117	96.09 334	97.74 263	99.81 32	98.55 178	99.85 27	99.55 56	98.60 66	99.84 180	99.69 35	99.98 12	99.89 16

MM		98.22 240	97.99 261	98.91 175	98.66 384	96.97 285	97.89 237	94.44 516	99.54 40	98.95 224	99.14 181	93.50 379	99.92 65	99.80 17	99.96 28	99.85 30

WAC-MVS						90.90 504								91.37 499

Syy-MVS		96.04 411	95.56 419	97.49 395	97.10 504	94.48 412	96.18 419	96.58 483	95.65 412	94.77 509	92.29 538	91.27 425	99.36 466	98.17 155	98.05 476	98.63 421

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 154	97.77 255	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	98.13 152	98.08 197	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		91.92 502	90.45 503	96.30 453	97.10 504	90.90 504	96.18 419	96.58 483	95.65 412	94.77 509	92.29 538	53.88 553	99.36 466	89.59 519	98.05 476	98.63 421

testing3		93.51 476	92.09 489	97.75 360	98.60 391	94.40 414	97.32 325	95.26 509	97.56 272	96.79 452	95.50 500	53.57 554	99.77 267	95.26 396	98.97 416	99.08 342

SSC-MVS		98.71 142	98.74 128	98.62 242	99.72 45	96.08 336	98.74 99	98.64 397	99.74 12	99.67 59	99.24 145	94.57 346	99.95 25	99.11 77	99.24 374	99.82 36

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 77	98.10 157	97.68 270	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		98.52 193	98.55 165	98.43 282	99.65 71	95.59 355	98.52 130	98.77 380	99.65 25	99.52 87	99.00 230	94.34 356	99.93 53	98.65 114	98.83 424	99.76 58

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 265	99.66 70	96.97 285	98.00 216	99.85 19	99.24 77	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 409

dmvs_re		95.98 416	95.39 427	97.74 362	98.86 336	97.45 239	98.37 159	95.69 504	97.95 234	96.56 463	95.95 489	90.70 431	97.68 526	88.32 522	96.13 521	98.11 459

SDMVSNet		99.23 45	99.32 39	98.96 164	99.68 64	97.35 245	98.84 95	99.48 159	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 177	99.92 71	99.57 124

dmvs_testset		92.94 488	92.21 488	95.13 498	98.59 394	90.99 503	97.65 276	92.09 535	96.95 335	94.00 522	93.55 525	92.34 405	96.97 536	72.20 547	92.52 541	97.43 495

sd_testset		99.28 36	99.31 41	99.19 112	99.68 64	98.06 168	99.41 17	99.30 254	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 249	99.92 71	99.57 124

test_fmvsm_n_1920		99.33 30	99.45 23	98.99 156	99.57 103	97.73 214	97.93 230	99.83 26	99.22 80	99.93 6	99.30 126	99.42 11	99.96 13	99.85 6	99.99 5	99.29 284

test_cas_vis1_n_1920		98.33 222	98.68 141	97.27 407	99.69 61	92.29 479	98.03 208	99.85 19	97.62 263	99.96 4	99.62 40	93.98 368	99.74 292	99.52 49	99.86 107	99.79 47

test_vis1_n_1920		98.40 207	98.92 102	96.81 436	99.74 37	90.76 508	98.15 185	99.91 10	98.33 191	99.89 18	99.55 56	95.07 328	99.88 115	99.76 23	99.93 57	99.79 47

test_vis1_n		98.31 227	98.50 175	97.73 365	99.76 30	94.17 422	98.68 109	99.91 10	96.31 376	99.79 38	99.57 49	92.85 396	99.42 459	99.79 19	99.84 114	99.60 102

test_fmvs1_n		98.09 259	98.28 219	97.52 392	99.68 64	93.47 456	98.63 116	99.93 6	95.41 428	99.68 57	99.64 37	91.88 415	99.48 443	99.82 12	99.87 100	99.62 92

mvsany_test1		97.60 310	97.54 306	97.77 356	97.72 471	95.35 374	95.36 462	97.13 465	94.13 466	99.71 49	99.33 119	97.93 141	99.30 477	97.60 218	98.94 419	98.67 419

APD_test1		98.83 122	98.66 146	99.34 83	99.78 24	99.47 8	98.42 151	99.45 179	98.28 200	98.98 214	99.19 160	97.76 158	99.58 405	96.57 323	99.55 298	98.97 364

test_vis1_rt		97.75 299	97.72 291	97.83 351	98.81 348	96.35 324	97.30 328	99.69 57	94.61 450	97.87 376	98.05 405	96.26 275	98.32 515	98.74 107	98.18 465	98.82 389

test_vis3_rt		99.14 62	99.17 60	99.07 138	99.78 24	98.38 124	98.92 83	99.94 3	97.80 248	99.91 12	99.67 30	97.15 212	98.91 503	99.76 23	99.56 293	99.92 12

test_fmvs2		98.70 147	98.97 98	97.89 346	99.54 123	94.05 427	98.55 126	99.92 8	96.78 352	99.72 47	99.78 13	96.60 254	99.67 348	99.91 2	99.90 88	99.94 10

test_fmvs1		97.72 301	97.94 269	97.07 420	98.66 384	92.39 476	97.68 270	99.81 32	95.20 435	99.54 79	99.44 85	91.56 419	99.41 460	99.78 21	99.77 172	99.40 231

test_fmvs3		99.12 69	99.41 26	98.25 305	99.76 30	95.07 390	99.05 68	99.94 3	97.78 251	99.82 34	99.84 3	98.56 73	99.71 312	99.96 1	99.96 28	99.97 4

mvsany_test3		98.87 112	98.92 102	98.74 217	99.38 187	96.94 289	98.58 123	99.10 316	96.49 367	99.96 4	99.81 8	98.18 118	99.45 453	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 57	98.90 136	99.43 108	99.35 112	98.86 35	99.67 348	97.81 191	99.81 140	99.24 302

APD_test2		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 57	98.90 136	99.43 108	99.35 112	98.86 35	99.67 348	97.81 191	99.81 140	99.24 302

test_f		98.67 161	98.87 111	98.05 333	99.72 45	95.59 355	98.51 135	99.81 32	96.30 378	99.78 39	99.82 5	96.14 279	98.63 511	99.82 12	99.93 57	99.95 9

FE-MVS		95.66 429	94.95 445	97.77 356	98.53 403	95.28 379	99.40 19	96.09 494	93.11 485	97.96 369	99.26 138	79.10 512	99.77 267	92.40 482	98.71 435	98.27 452

FA-MVS(test-final)		96.99 366	96.82 359	97.50 394	98.70 369	94.78 402	99.34 23	96.99 468	95.07 437	98.48 319	99.33 119	88.41 453	99.65 368	96.13 361	98.92 421	98.07 462

BridgeMVS		98.63 167	98.72 132	98.38 289	98.66 384	96.68 307	98.90 84	99.42 200	98.99 124	98.97 218	99.19 160	95.81 301	99.85 159	98.77 105	99.77 172	98.60 424

MonoMVSNet		96.25 404	96.53 384	95.39 493	96.57 521	91.01 502	98.82 97	97.68 444	98.57 174	98.03 363	99.37 105	90.92 428	97.78 525	94.99 401	93.88 539	97.38 496

patch_mono-2		98.51 194	98.63 152	98.17 315	99.38 187	94.78 402	97.36 322	99.69 57	98.16 217	98.49 317	99.29 129	97.06 217	99.97 6	98.29 145	99.91 80	99.76 58

EGC-MVSNET		85.24 511	80.54 514	99.34 83	99.77 27	99.20 38	99.08 62	99.29 262	12.08 554	20.84 557	99.42 89	97.55 178	99.85 159	97.08 266	99.72 208	98.96 367

test2506		92.39 494	91.89 496	93.89 514	99.38 187	82.28 550	99.32 26	66.03 558	99.08 114	98.77 268	99.57 49	66.26 539	99.84 180	98.71 110	99.95 39	99.54 143

test1111		96.49 388	96.82 359	95.52 489	99.42 178	87.08 531	99.22 46	87.14 549	99.11 100	99.46 101	99.58 47	88.69 447	99.86 145	98.80 100	99.95 39	99.62 92

ECVR-MVS		96.42 394	96.61 378	95.85 478	99.38 187	88.18 526	99.22 46	86.00 551	99.08 114	99.36 128	99.57 49	88.47 452	99.82 210	98.52 127	99.95 39	99.54 143

test_blank		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

tt0805		98.69 151	98.62 154	98.90 178	99.75 34	99.30 21	99.15 57	96.97 470	98.86 142	98.87 249	97.62 438	98.63 63	98.96 499	99.41 56	98.29 461	98.45 435

DVP-MVS++		98.90 108	98.70 138	99.51 49	98.43 414	99.15 52	99.43 15	99.32 241	98.17 214	99.26 157	99.02 214	98.18 118	99.88 115	97.07 267	99.45 328	99.49 177

FOURS1					99.73 38	99.67 2	99.43 15	99.54 132	99.43 54	99.26 157

MSC_two_6792asdad				99.32 91	98.43 414	98.37 126		98.86 365					99.89 97	97.14 260	99.60 276	99.71 65

PC_three_1452									93.27 481	99.40 117	98.54 344	98.22 113	97.00 535	95.17 398	99.45 328	99.49 177

No_MVS				99.32 91	98.43 414	98.37 126		98.86 365					99.89 97	97.14 260	99.60 276	99.71 65

test_one_0601					99.39 185	99.20 38		99.31 246	98.49 180	98.66 286	99.02 214	97.64 168

eth-test2					0.00 564

eth-test					0.00 564

GeoE		99.05 83	98.99 96	99.25 104	99.44 171	98.35 130	98.73 103	99.56 121	98.42 185	98.91 236	98.81 285	98.94 31	99.91 74	98.35 141	99.73 199	99.49 177

test_method		79.78 512	79.50 515	80.62 530	80.21 556	45.76 561	70.82 547	98.41 417	31.08 552	80.89 552	97.71 431	84.85 481	97.37 531	91.51 497	80.03 548	98.75 405

Anonymous20240521		98.69 151	98.87 111	98.16 318	99.77 27	95.11 389	99.08 62	99.44 187	99.34 65	99.33 138	99.55 56	94.10 367	99.94 41	99.25 67	99.96 28	99.42 219

h-mvs33		97.77 298	97.33 323	99.10 130	99.21 241	97.84 196	98.35 162	98.57 403	99.11 100	98.58 304	99.02 214	88.65 450	99.96 13	98.11 158	96.34 517	99.49 177

hse-mvs2		97.46 321	97.07 340	98.64 236	98.73 359	97.33 247	97.45 310	97.64 447	99.11 100	98.58 304	97.98 411	88.65 450	99.79 249	98.11 158	97.39 498	98.81 394

CL-MVSNet_self_test		97.44 324	97.22 330	98.08 328	98.57 398	95.78 352	94.30 499	98.79 377	96.58 363	98.60 300	98.19 392	94.74 342	99.64 373	96.41 340	98.84 423	98.82 389

KD-MVS_2432*1600		92.87 490	91.99 492	95.51 490	91.37 549	89.27 520	94.07 507	98.14 429	95.42 425	97.25 423	96.44 480	67.86 533	99.24 483	91.28 500	96.08 526	98.02 464

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 83	99.06 70	98.69 108	99.54 132	99.31 69	99.62 69	99.53 64	97.36 198	99.86 145	99.24 69	99.71 217	99.39 232

AUN-MVS		96.24 406	95.45 423	98.60 248	98.70 369	97.22 264	97.38 317	97.65 445	95.95 397	95.53 496	97.96 416	82.11 502	99.79 249	96.31 346	97.44 495	98.80 399

ZD-MVS					99.01 306	98.84 86		99.07 321	94.10 468	98.05 361	98.12 398	96.36 270	99.86 145	92.70 475	99.19 386

SR-MVS-dyc-post		98.81 127	98.55 165	99.57 21	99.20 245	99.38 12	98.48 143	99.30 254	98.64 161	98.95 224	98.96 243	97.49 189	99.86 145	96.56 327	99.39 344	99.45 206

RE-MVS-def			98.58 162		99.20 245	99.38 12	98.48 143	99.30 254	98.64 161	98.95 224	98.96 243	97.75 159		96.56 327	99.39 344	99.45 206

SED-MVS		98.91 106	98.72 132	99.49 55	99.49 150	99.17 43	98.10 194	99.31 246	98.03 228	99.66 60	99.02 214	98.36 90	99.88 115	96.91 282	99.62 267	99.41 222

IU-MVS					99.49 150	99.15 52		98.87 360	92.97 488	99.41 114				96.76 299	99.62 267	99.66 80

OPU-MVS				98.82 192	98.59 394	98.30 135	98.10 194				98.52 348	98.18 118	98.75 508	94.62 411	99.48 324	99.41 222

test_241102_TWO								99.30 254	98.03 228	99.26 157	99.02 214	97.51 185	99.88 115	96.91 282	99.60 276	99.66 80

test_241102_ONE					99.49 150	99.17 43		99.31 246	97.98 231	99.66 60	98.90 258	98.36 90	99.48 443

SF-MVS		98.53 189	98.27 222	99.32 91	99.31 209	98.75 91	98.19 179	99.41 204	96.77 353	98.83 256	98.90 258	97.80 155	99.82 210	95.68 382	99.52 308	99.38 241

cl22		95.79 425	95.39 427	96.98 425	96.77 517	92.79 468	94.40 496	98.53 407	94.59 451	97.89 374	98.17 393	82.82 499	99.24 483	96.37 342	99.03 404	98.92 374

miper_ehance_all_eth		97.06 359	97.03 342	97.16 416	97.83 466	93.06 460	94.66 486	99.09 318	95.99 395	98.69 280	98.45 359	92.73 399	99.61 389	96.79 295	99.03 404	98.82 389

miper_enhance_ethall		96.01 413	95.74 408	96.81 436	96.41 529	92.27 480	93.69 517	98.89 357	91.14 512	98.30 336	97.35 458	90.58 432	99.58 405	96.31 346	99.03 404	98.60 424

ZNCC-MVS		98.68 157	98.40 193	99.54 31	99.57 103	99.21 32	98.46 145	99.29 262	97.28 308	98.11 354	98.39 365	98.00 134	99.87 135	96.86 292	99.64 258	99.55 137

dcpmvs_2		98.78 133	99.11 74	97.78 355	99.56 111	93.67 450	99.06 66	99.86 17	99.50 43	99.66 60	99.26 138	97.21 209	99.99 2	98.00 172	99.91 80	99.68 73

cl____		97.02 362	96.83 358	97.58 383	97.82 467	94.04 429	94.66 486	99.16 305	97.04 330	98.63 291	98.71 305	88.68 449	99.69 331	97.00 272	99.81 140	99.00 358

DIV-MVS_self_test		97.02 362	96.84 357	97.58 383	97.82 467	94.03 430	94.66 486	99.16 305	97.04 330	98.63 291	98.71 305	88.69 447	99.69 331	97.00 272	99.81 140	99.01 355

eth_miper_zixun_eth		97.23 345	97.25 327	97.17 414	98.00 455	92.77 469	94.71 481	99.18 298	97.27 310	98.56 308	98.74 299	91.89 414	99.69 331	97.06 269	99.81 140	99.05 346

9.14			97.78 284		99.07 282		97.53 297	99.32 241	95.53 420	98.54 312	98.70 312	97.58 175	99.76 273	94.32 425	99.46 326

uanet_test		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

DCPMVS		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

save fliter					99.11 273	97.97 178	96.53 391	99.02 334	98.24 201

ET-MVSNet_ETH3D		94.30 461	93.21 473	97.58 383	98.14 445	94.47 413	94.78 480	93.24 531	94.72 447	89.56 542	95.87 492	78.57 516	99.81 227	96.91 282	97.11 507	98.46 432

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 103	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		98.00 268	97.74 287	98.80 197	98.72 361	98.09 158	98.05 204	99.60 94	97.39 296	96.63 459	95.55 498	97.68 162	99.80 236	96.73 304	99.27 368	98.52 430

miper_refine_blended		92.87 490	91.99 492	95.51 490	91.37 549	89.27 520	94.07 507	98.14 429	95.42 425	97.25 423	96.44 480	67.86 533	99.24 483	91.28 500	96.08 526	98.02 464

miper_lstm_enhance		97.18 350	97.16 333	97.25 410	98.16 443	92.85 467	95.15 471	99.31 246	97.25 312	98.74 274	98.78 290	90.07 435	99.78 261	97.19 253	99.80 152	99.11 341

ETV-MVS		98.03 264	97.86 278	98.56 258	98.69 374	98.07 165	97.51 300	99.50 149	98.10 224	97.50 406	95.51 499	98.41 85	99.88 115	96.27 351	99.24 374	97.71 485

CS-MVS		99.13 66	99.10 80	99.24 106	99.06 287	99.15 52	99.36 22	99.88 15	99.36 63	98.21 344	98.46 358	98.68 58	99.93 53	99.03 85	99.85 109	98.64 420

D2MVS		97.84 292	97.84 280	97.83 351	99.14 267	94.74 404	96.94 356	98.88 358	95.84 402	98.89 240	98.96 243	94.40 353	99.69 331	97.55 222	99.95 39	99.05 346

DVP-MVS		98.77 136	98.52 170	99.52 44	99.50 141	99.21 32	98.02 211	98.84 369	97.97 232	99.08 191	99.02 214	97.61 172	99.88 115	96.99 274	99.63 263	99.48 188
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									98.17 214	99.08 191	99.02 214	97.89 147	99.88 115	97.07 267	99.71 217	99.70 70

test_0728_SECOND				99.60 16	99.50 141	99.23 30	98.02 211	99.32 241					99.88 115	96.99 274	99.63 263	99.68 73

test0726					99.50 141	99.21 32	98.17 183	99.35 227	97.97 232	99.26 157	99.06 201	97.61 172

SR-MVS		98.71 142	98.43 189	99.57 21	99.18 257	99.35 16	98.36 160	99.29 262	98.29 198	98.88 244	98.85 272	97.53 182	99.87 135	96.14 359	99.31 360	99.48 188

DPM-MVS		96.32 398	95.59 417	98.51 270	98.76 355	97.21 266	94.54 492	98.26 422	91.94 502	96.37 473	97.25 461	93.06 391	99.43 457	91.42 498	98.74 431	98.89 380

GST-MVS		98.61 171	98.30 216	99.52 44	99.51 134	99.20 38	98.26 172	99.25 277	97.44 291	98.67 284	98.39 365	97.68 162	99.85 159	96.00 364	99.51 311	99.52 161

test_yl		96.69 376	96.29 395	97.90 344	98.28 428	95.24 380	97.29 329	97.36 452	98.21 206	98.17 345	97.86 420	86.27 464	99.55 415	94.87 405	98.32 457	98.89 380

thisisatest0530		95.27 443	94.45 455	97.74 362	99.19 249	94.37 415	97.86 242	90.20 544	97.17 323	98.22 343	97.65 435	73.53 525	99.90 81	96.90 287	99.35 351	98.95 368

Anonymous20240529		98.93 104	98.87 111	99.12 126	99.19 249	98.22 145	99.01 71	98.99 340	99.25 76	99.54 79	99.37 105	97.04 218	99.80 236	97.89 181	99.52 308	99.35 258

Anonymous202405211		97.90 278	97.50 310	99.08 136	98.90 327	98.25 139	98.53 129	96.16 490	98.87 140	99.11 186	98.86 269	90.40 434	99.78 261	97.36 239	99.31 360	99.19 320

DCV-MVSNet		96.69 376	96.29 395	97.90 344	98.28 428	95.24 380	97.29 329	97.36 452	98.21 206	98.17 345	97.86 420	86.27 464	99.55 415	94.87 405	98.32 457	98.89 380

tttt0517		95.64 430	94.98 443	97.64 377	99.36 194	93.81 445	98.72 104	90.47 543	98.08 227	98.67 284	98.34 372	73.88 524	99.92 65	97.77 197	99.51 311	99.20 314

our_test_3		97.39 329	97.73 290	96.34 452	98.70 369	89.78 517	94.61 489	98.97 343	96.50 366	99.04 203	98.85 272	95.98 293	99.84 180	97.26 248	99.67 246	99.41 222

thisisatest0515		94.12 466	93.16 474	96.97 426	98.60 391	92.90 465	93.77 516	90.61 542	94.10 468	96.91 441	95.87 492	74.99 522	99.80 236	94.52 414	99.12 397	98.20 454

ppachtmachnet_test		97.50 316	97.74 287	96.78 438	98.70 369	91.23 499	94.55 491	99.05 326	96.36 373	99.21 174	98.79 288	96.39 265	99.78 261	96.74 302	99.82 133	99.34 262

SMA-MVS		98.40 207	98.03 257	99.51 49	99.16 261	99.21 32	98.05 204	99.22 286	94.16 465	98.98 214	99.10 192	97.52 184	99.79 249	96.45 337	99.64 258	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																98.81 394

DPE-MVS		98.59 175	98.26 225	99.57 21	99.27 221	99.15 52	97.01 350	99.39 211	97.67 259	99.44 107	98.99 232	97.53 182	99.89 97	95.40 393	99.68 240	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					99.36 194	99.10 65				99.05 201

thres100view900		94.19 463	93.67 467	95.75 481	99.06 287	91.35 493	98.03 208	94.24 522	98.33 191	97.40 416	94.98 512	79.84 506	99.62 381	83.05 537	98.08 473	96.29 516

tfpnnormal		98.90 108	98.90 105	98.91 175	99.67 68	97.82 202	99.00 73	99.44 187	99.45 50	99.51 92	99.24 145	98.20 117	99.86 145	95.92 368	99.69 234	99.04 350

tfpn200view9		94.03 467	93.44 469	95.78 480	98.93 319	91.44 491	97.60 287	94.29 519	97.94 236	97.10 428	94.31 521	79.67 508	99.62 381	83.05 537	98.08 473	96.29 516

c3_l		97.36 332	97.37 319	97.31 404	98.09 450	93.25 458	95.01 474	99.16 305	97.05 329	98.77 268	98.72 303	92.88 394	99.64 373	96.93 281	99.76 188	99.05 346

CHOSEN 280x420		95.51 435	95.47 421	95.65 486	98.25 431	88.27 525	93.25 526	98.88 358	93.53 478	94.65 512	97.15 464	86.17 466	99.93 53	97.41 237	99.93 57	98.73 408

CANet		97.87 285	97.76 285	98.19 314	97.75 470	95.51 360	96.76 369	99.05 326	97.74 253	96.93 438	98.21 390	95.59 310	99.89 97	97.86 188	99.93 57	99.19 320

Fast-Effi-MVS+-dtu		98.27 233	98.09 249	98.81 194	98.43 414	98.11 154	97.61 286	99.50 149	98.64 161	97.39 418	97.52 445	98.12 126	99.95 25	96.90 287	98.71 435	98.38 445

Effi-MVS+-dtu		98.26 235	97.90 275	99.35 80	98.02 454	99.49 5	98.02 211	99.16 305	98.29 198	97.64 392	97.99 410	96.44 262	99.95 25	96.66 315	98.93 420	98.60 424

CANet_DTU		97.26 341	97.06 341	97.84 350	97.57 482	94.65 409	96.19 417	98.79 377	97.23 318	95.14 502	98.24 387	93.22 385	99.84 180	97.34 240	99.84 114	99.04 350

MGCNet		97.44 324	97.01 344	98.72 221	96.42 528	96.74 303	97.20 340	91.97 539	98.46 182	98.30 336	98.79 288	92.74 398	99.91 74	99.30 62	99.94 51	99.52 161

MP-MVS-pluss		98.57 178	98.23 230	99.60 16	99.69 61	99.35 16	97.16 345	99.38 213	94.87 443	98.97 218	98.99 232	98.01 133	99.88 115	97.29 246	99.70 228	99.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 207	98.00 260	99.61 13	99.57 103	99.25 28	98.57 124	99.35 227	97.55 274	99.31 147	97.71 431	94.61 345	99.88 115	96.14 359	99.19 386	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												84.74 483				98.81 394

sam_mvs												84.29 489

IterMVS-SCA-FT		97.85 291	98.18 238	96.87 432	99.27 221	91.16 500	95.53 454	99.25 277	99.10 107	99.41 114	99.35 112	93.10 389	99.96 13	98.65 114	99.94 51	99.49 177

TSAR-MVS + MP.		98.63 167	98.49 180	99.06 144	99.64 77	97.90 190	98.51 135	98.94 344	96.96 334	99.24 167	98.89 264	97.83 151	99.81 227	96.88 289	99.49 323	99.48 188
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		97.86 286	98.17 239	96.92 429	98.98 311	93.91 440	96.45 396	99.17 302	97.85 244	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 504

OPM-MVS		98.56 180	98.32 214	99.25 104	99.41 181	98.73 95	97.13 347	99.18 298	97.10 327	98.75 271	98.92 252	98.18 118	99.65 368	96.68 311	99.56 293	99.37 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 138	98.48 181	99.57 21	99.58 94	99.29 23	97.82 246	99.25 277	96.94 336	98.78 265	99.12 187	98.02 132	99.84 180	97.13 263	99.67 246	99.59 109

ambc				98.24 307	98.82 345	95.97 341	98.62 118	99.00 339		99.27 153	99.21 155	96.99 224	99.50 435	96.55 330	99.50 319	99.26 297

MTGPA								99.20 290

SPE-MVS-test		99.13 66	99.09 82	99.26 101	99.13 270	98.97 74	99.31 30	99.88 15	99.44 52	98.16 348	98.51 349	98.64 61	99.93 53	98.91 93	99.85 109	98.88 383

Effi-MVS+		98.02 265	97.82 281	98.62 242	98.53 403	97.19 268	97.33 324	99.68 64	97.30 306	96.68 457	97.46 451	98.56 73	99.80 236	96.63 317	98.20 464	98.86 385

xiu_mvs_v2_base		97.16 352	97.49 311	96.17 463	98.54 401	92.46 474	95.45 458	98.84 369	97.25 312	97.48 408	96.49 477	98.31 97	99.90 81	96.34 345	98.68 440	96.15 520

xiu_mvs_v1_base		97.86 286	98.17 239	96.92 429	98.98 311	93.91 440	96.45 396	99.17 302	97.85 244	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 504

new-patchmatchnet		98.35 217	98.74 128	97.18 412	99.24 233	92.23 481	96.42 400	99.48 159	98.30 195	99.69 55	99.53 64	97.44 193	99.82 210	98.84 99	99.77 172	99.49 177

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		97.64 308	97.49 311	98.08 328	99.14 267	95.12 388	96.70 374	99.05 326	93.77 475	98.62 295	98.83 279	93.23 384	99.75 285	98.33 144	99.76 188	99.36 252

test_post1							97.59 289				20.48 557	83.07 497	99.66 361	94.16 426

test_post											21.25 556	83.86 492	99.70 321

Fast-Effi-MVS+		97.67 306	97.38 318	98.57 253	98.71 365	97.43 242	97.23 335	99.45 179	94.82 445	96.13 477	96.51 476	98.52 75	99.91 74	96.19 355	98.83 424	98.37 447

patchmatchnet-post											98.77 292	84.37 486	99.85 159

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 215	98.18 147	99.49 12	99.51 144	99.70 15	99.80 37	99.68 25	96.84 232	99.83 198	99.21 70	99.91 80	99.77 53

pmmvs-eth3d		98.47 198	98.34 208	98.86 181	99.30 213	97.76 210	97.16 345	99.28 266	95.54 419	99.42 112	99.19 160	97.27 204	99.63 376	97.89 181	99.97 21	99.20 314

GG-mvs-BLEND				94.76 502	94.54 542	92.13 482	99.31 30	80.47 555		88.73 545	91.01 544	67.59 536	98.16 519	82.30 541	94.53 537	93.98 528

xiu_mvs_v1_base_debi		97.86 286	98.17 239	96.92 429	98.98 311	93.91 440	96.45 396	99.17 302	97.85 244	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 504

Anonymous20231206		98.21 243	98.21 231	98.20 312	99.51 134	95.43 370	98.13 187	99.32 241	96.16 385	98.93 233	98.82 282	96.00 288	99.83 198	97.32 244	99.73 199	99.36 252

MTAPA		98.88 111	98.64 150	99.61 13	99.67 68	99.36 15	98.43 148	99.20 290	98.83 149	98.89 240	98.90 258	96.98 225	99.92 65	97.16 256	99.70 228	99.56 130

MTMP							97.93 230	91.91 540

gm-plane-assit					94.83 541	81.97 551			88.07 534		94.99 511		99.60 393	91.76 491

test9_res														93.28 456	99.15 391	99.38 241

MVP-Stereo		98.08 260	97.92 272	98.57 253	98.96 315	96.79 299	97.90 236	99.18 298	96.41 372	98.46 321	98.95 247	95.93 297	99.60 393	96.51 333	98.98 415	99.31 278
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 365	98.08 162	95.96 434	99.03 331	91.40 508	95.85 485	97.53 442	96.52 258	99.76 273

train_agg		97.10 354	96.45 389	99.07 138	98.71 365	98.08 162	95.96 434	99.03 331	91.64 503	95.85 485	97.53 442	96.47 260	99.76 273	93.67 443	99.16 389	99.36 252

gg-mvs-nofinetune		92.37 496	91.20 500	95.85 478	95.80 538	92.38 477	99.31 30	81.84 554	99.75 10	91.83 537	99.74 18	68.29 532	99.02 496	87.15 525	97.12 506	96.16 519

SCA		96.41 395	96.66 373	95.67 484	98.24 433	88.35 524	95.85 443	96.88 476	96.11 387	97.67 391	98.67 318	93.10 389	99.85 159	94.16 426	99.22 378	98.81 394

Patchmatch-test		96.55 384	96.34 392	97.17 414	98.35 421	93.06 460	98.40 156	97.79 438	97.33 301	98.41 327	98.67 318	83.68 493	99.69 331	95.16 399	99.31 360	98.77 402

test_8					98.67 379	98.01 171	95.91 440	99.02 334	91.64 503	95.79 488	97.50 446	96.47 260	99.76 273

MS-PatchMatch		97.68 305	97.75 286	97.45 399	98.23 436	93.78 446	97.29 329	98.84 369	96.10 388	98.64 290	98.65 325	96.04 285	99.36 466	96.84 293	99.14 392	99.20 314

Patchmatch-RL test		97.26 341	97.02 343	97.99 339	99.52 131	95.53 359	96.13 422	99.71 48	97.47 283	99.27 153	99.16 171	84.30 488	99.62 381	97.89 181	99.77 172	98.81 394

cdsmvs_eth3d_5k		24.66 520	32.88 522	0.00 540	0.00 564	0.00 567	0.00 552	99.10 316	0.00 559	0.00 560	97.58 439	99.21 18	0.00 560	0.00 559	0.00 559	0.00 556

pcd_1.5k_mvsjas		8.17 523	10.90 526	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	98.07 128	0.00 560	0.00 559	0.00 559	0.00 556

agg_prior2														92.50 480	99.16 389	99.37 244

agg_prior					98.68 378	97.99 174		99.01 337		95.59 489			99.77 267

tmp_tt		78.77 513	78.73 516	78.90 531	58.45 558	74.76 557	94.20 503	78.26 556	39.16 551	86.71 546	92.82 533	80.50 504	75.19 553	86.16 531	92.29 542	86.74 546

canonicalmvs		98.34 218	98.26 225	98.58 250	98.46 409	97.82 202	98.96 78	99.46 175	99.19 89	97.46 409	95.46 503	98.59 67	99.46 450	98.08 162	98.71 435	98.46 432

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 57	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		97.35 333	96.88 354	98.78 204	98.54 401	98.09 158	97.71 266	97.69 442	99.20 84	97.59 397	95.90 491	88.12 456	99.55 415	98.18 153	98.96 417	98.70 412

nrg030		99.40 25	99.35 33	99.54 31	99.58 94	99.13 60	98.98 76	99.48 159	99.68 19	99.46 101	99.26 138	98.62 64	99.73 299	99.17 74	99.92 71	99.76 58

v144192		98.54 187	98.57 163	98.45 279	99.21 241	95.98 339	97.63 280	99.36 221	97.15 326	99.32 144	99.18 164	95.84 300	99.84 180	99.50 50	99.91 80	99.54 143

FIs		99.14 62	99.09 82	99.29 95	99.70 57	98.28 136	99.13 59	99.52 142	99.48 44	99.24 167	99.41 94	96.79 239	99.82 210	98.69 112	99.88 95	99.76 58

v1921920		98.54 187	98.60 159	98.38 289	99.20 245	95.76 353	97.56 292	99.36 221	97.23 318	99.38 121	99.17 169	96.02 286	99.84 180	99.57 39	99.90 88	99.54 143

UA-Net		99.47 16	99.40 27	99.70 2	99.49 150	99.29 23	99.80 4	99.72 46	99.82 8	99.04 203	99.81 8	98.05 131	99.96 13	98.85 98	99.99 5	99.86 28

v1192		98.60 173	98.66 146	98.41 285	99.27 221	95.88 345	97.52 298	99.36 221	97.41 293	99.33 138	99.20 157	96.37 269	99.82 210	99.57 39	99.92 71	99.55 137

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 126	99.30 35	99.57 111	99.61 34	99.40 117	99.50 68	97.12 214	99.85 159	99.02 86	99.94 51	99.80 45

v1144		98.60 173	98.66 146	98.41 285	99.36 194	95.90 343	97.58 290	99.34 233	97.51 279	99.27 153	99.15 177	96.34 271	99.80 236	99.47 53	99.93 57	99.51 165

sosnet-low-res		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

HFP-MVS		98.71 142	98.44 188	99.51 49	99.49 150	99.16 48	98.52 130	99.31 246	97.47 283	98.58 304	98.50 353	97.97 138	99.85 159	96.57 323	99.59 280	99.53 157

v148		98.45 201	98.60 159	98.00 337	99.44 171	94.98 392	97.44 312	99.06 322	98.30 195	99.32 144	98.97 239	96.65 251	99.62 381	98.37 140	99.85 109	99.39 232

sosnet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uncertanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

AllTest		98.44 202	98.20 232	99.16 118	99.50 141	98.55 109	98.25 173	99.58 103	96.80 350	98.88 244	99.06 201	97.65 165	99.57 407	94.45 417	99.61 274	99.37 244

TestCases				99.16 118	99.50 141	98.55 109		99.58 103	96.80 350	98.88 244	99.06 201	97.65 165	99.57 407	94.45 417	99.61 274	99.37 244

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 112	99.45 14	99.61 92	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	98.40 193	99.54 31	99.53 127	99.17 43	98.52 130	99.31 246	97.46 288	98.44 324	98.51 349	97.83 151	99.88 115	96.46 336	99.58 285	99.58 117

RRT-MVS		97.88 283	97.98 262	97.61 380	98.15 444	93.77 447	98.97 77	99.64 79	99.16 94	98.69 280	99.42 89	91.60 416	99.89 97	97.63 214	98.52 452	99.16 334

balanced_ft_v1		98.28 232	98.35 207	98.10 323	98.08 451	96.23 328	99.23 45	99.26 275	98.34 189	97.46 409	99.42 89	95.38 319	99.88 115	98.60 117	99.34 353	98.17 456

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 149	99.20 49	99.65 77	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		97.08 357	97.39 317	96.16 465	98.56 399	92.46 474	95.24 467	98.85 368	97.25 312	97.49 407	95.99 488	98.07 128	99.90 81	96.37 342	98.67 441	96.12 521

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 104	99.28 40	99.66 71	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	98.61 104	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	98.62 242	99.10 275	96.37 323	97.23 335	98.87 360	99.20 84	99.19 176	98.99 232	97.30 201	99.85 159	98.77 105	99.79 159	99.65 85

EI-MVSNet-Vis-set		98.68 157	98.70 138	98.63 240	99.09 278	96.40 322	97.23 335	98.86 365	99.20 84	99.18 181	98.97 239	97.29 203	99.85 159	98.72 109	99.78 164	99.64 86

HPM-MVS++		98.10 256	97.64 300	99.48 57	99.09 278	99.13 60	97.52 298	98.75 386	97.46 288	96.90 444	97.83 424	96.01 287	99.84 180	95.82 376	99.35 351	99.46 200

test_prior4						97.97 178	95.86 441

XVS		98.72 141	98.45 186	99.53 38	99.46 164	99.21 32	98.65 114	99.34 233	98.62 166	97.54 402	98.63 331	97.50 186	99.83 198	96.79 295	99.53 305	99.56 130

v1240		98.55 184	98.62 154	98.32 296	99.22 239	95.58 357	97.51 300	99.45 179	97.16 324	99.45 106	99.24 145	96.12 283	99.85 159	99.60 37	99.88 95	99.55 137

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 101	99.29 36	99.63 82	99.30 71	99.65 63	99.60 45	99.16 22	99.82 210	99.07 80	99.83 126	99.56 130

test_prior2							95.74 448		96.48 368	96.11 478	97.63 437	95.92 298		94.16 426	99.20 383

X-MVStestdata		94.32 459	92.59 482	99.53 38	99.46 164	99.21 32	98.65 114	99.34 233	98.62 166	97.54 402	45.85 553	97.50 186	99.83 198	96.79 295	99.53 305	99.56 130

test_prior				98.95 166	98.69 374	97.95 182		99.03 331					99.59 398			99.30 282

旧先验2							95.76 447		88.56 531	97.52 404			99.66 361	94.48 415

新几何2							95.93 437

新几何1				98.91 175	98.94 317	97.76 210		98.76 382	87.58 535	96.75 453	98.10 400	94.80 339	99.78 261	92.73 474	99.00 410	99.20 314

旧先验1					98.82 345	97.45 239		98.76 382			98.34 372	95.50 314			99.01 409	99.23 304

无先验							95.74 448	98.74 388	89.38 524				99.73 299	92.38 483		99.22 309

原ACMM2							95.53 454

原ACMM1				98.35 294	98.90 327	96.25 327		98.83 373	92.48 496	96.07 480	98.10 400	95.39 318	99.71 312	92.61 477	98.99 412	99.08 342

test222					98.92 323	96.93 290	95.54 453	98.78 379	85.72 538	96.86 448	98.11 399	94.43 350			99.10 399	99.23 304

testdata2													99.79 249	92.80 471

segment_acmp												97.02 221

testdata				98.09 325	98.93 319	95.40 371		98.80 376	90.08 520	97.45 412	98.37 368	95.26 321	99.70 321	93.58 447	98.95 418	99.17 328

testdata1							95.44 459		96.32 375

v8		99.01 90	99.16 62	98.57 253	99.47 161	96.31 326	98.90 84	99.47 170	99.03 121	99.52 87	99.57 49	96.93 228	99.81 227	99.60 37	99.98 12	99.60 102

1314		95.74 426	95.60 415	96.17 463	97.53 487	92.75 470	98.07 201	98.31 420	91.22 510	94.25 516	96.68 473	95.53 311	99.03 494	91.64 494	97.18 505	96.74 510

LFMVS		97.20 348	96.72 366	98.64 236	98.72 361	96.95 288	98.93 82	94.14 524	99.74 12	98.78 265	99.01 226	84.45 485	99.73 299	97.44 235	99.27 368	99.25 298

VDD-MVS		98.56 180	98.39 196	99.07 138	99.13 270	98.07 165	98.59 122	97.01 467	99.59 36	99.11 186	99.27 132	94.82 335	99.79 249	98.34 142	99.63 263	99.34 262

VDDNet		98.21 243	97.95 266	99.01 153	99.58 94	97.74 212	99.01 71	97.29 458	99.67 20	98.97 218	99.50 68	90.45 433	99.80 236	97.88 184	99.20 383	99.48 188

v10		98.97 99	99.11 74	98.55 260	99.44 171	96.21 330	98.90 84	99.55 126	98.73 151	99.48 96	99.60 45	96.63 253	99.83 198	99.70 33	99.99 5	99.61 100

VPNet		98.87 112	98.83 120	99.01 153	99.70 57	97.62 225	98.43 148	99.35 227	99.47 47	99.28 151	99.05 208	96.72 246	99.82 210	98.09 161	99.36 348	99.59 109

MVS		93.19 483	92.09 489	96.50 446	96.91 512	94.03 430	98.07 201	98.06 434	68.01 548	94.56 514	96.48 478	95.96 295	99.30 477	83.84 535	96.89 511	96.17 518

v2v482		98.56 180	98.62 154	98.37 292	99.42 178	95.81 350	97.58 290	99.16 305	97.90 240	99.28 151	99.01 226	95.98 293	99.79 249	99.33 59	99.90 88	99.51 165

V42		98.78 133	98.78 126	98.76 211	99.44 171	97.04 281	98.27 171	99.19 294	97.87 242	99.25 165	99.16 171	96.84 232	99.78 261	99.21 70	99.84 114	99.46 200

SD-MVS		98.40 207	98.68 141	97.54 390	98.96 315	97.99 174	97.88 238	99.36 221	98.20 210	99.63 66	99.04 210	98.76 46	95.33 546	96.56 327	99.74 195	99.31 278
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		95.86 422	95.32 432	97.49 395	98.60 391	94.15 423	93.83 515	97.93 436	95.49 421	96.68 457	97.42 453	83.21 495	99.30 477	96.22 353	98.55 450	99.01 355

MSLP-MVS++		98.02 265	98.14 246	97.64 377	98.58 396	95.19 385	97.48 304	99.23 285	97.47 283	97.90 373	98.62 334	97.04 218	98.81 506	97.55 222	99.41 341	98.94 372

APDe-MVS		98.99 94	98.79 124	99.60 16	99.21 241	99.15 52	98.87 89	99.48 159	97.57 270	99.35 130	99.24 145	97.83 151	99.89 97	97.88 184	99.70 228	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	99.19 249	99.27 26	98.49 140	99.33 239	98.64 161	99.03 206	98.98 237	97.89 147	99.85 159	96.54 331	99.42 340	99.46 200

ADS-MVSNet2		95.43 439	94.98 443	96.76 439	98.14 445	91.74 484	97.92 233	97.76 439	90.23 516	96.51 469	98.91 255	85.61 474	99.85 159	92.88 467	96.90 509	98.69 413

EI-MVSNet		98.40 207	98.51 172	98.04 334	99.10 275	94.73 405	97.20 340	98.87 360	98.97 127	99.06 193	99.02 214	96.00 288	99.80 236	98.58 119	99.82 133	99.60 102

Regformer		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

CVMVSNet		96.25 404	97.21 331	93.38 522	99.10 275	80.56 554	97.20 340	98.19 428	96.94 336	99.00 209	99.02 214	89.50 443	99.80 236	96.36 344	99.59 280	99.78 50

pmmvs4		97.58 313	97.28 324	98.51 270	98.84 340	96.93 290	95.40 461	98.52 409	93.60 477	98.61 297	98.65 325	95.10 327	99.60 393	96.97 278	99.79 159	98.99 359

EU-MVSNet		97.66 307	98.50 175	95.13 498	99.63 83	85.84 534	98.35 162	98.21 425	98.23 202	99.54 79	99.46 80	95.02 329	99.68 343	98.24 147	99.87 100	99.87 22

VNet		98.42 203	98.30 216	98.79 201	98.79 354	97.29 256	98.23 174	98.66 394	99.31 69	98.85 251	98.80 286	94.80 339	99.78 261	98.13 156	99.13 394	99.31 278

test-LLR		93.90 470	93.85 463	94.04 511	96.53 522	84.62 540	94.05 509	92.39 533	96.17 381	94.12 518	95.07 508	82.30 500	99.67 348	95.87 372	98.18 465	97.82 474

TESTMET0.1,1		92.19 499	91.77 497	93.46 518	96.48 527	82.80 549	94.05 509	91.52 541	94.45 457	94.00 522	94.88 514	66.65 537	99.56 410	95.78 377	98.11 471	98.02 464

test-mter		92.33 497	91.76 498	94.04 511	96.53 522	84.62 540	94.05 509	92.39 533	94.00 473	94.12 518	95.07 508	65.63 543	99.67 348	95.87 372	98.18 465	97.82 474

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 150	98.36 129	99.00 73	99.45 179	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	98.42 191	99.54 31	99.52 131	99.14 57	98.52 130	99.31 246	97.47 283	98.56 308	98.54 344	97.75 159	99.88 115	96.57 323	99.59 280	99.58 117

testgi		98.32 223	98.39 196	98.13 320	99.57 103	95.54 358	97.78 252	99.49 157	97.37 298	99.19 176	97.65 435	98.96 30	99.49 439	96.50 334	98.99 412	99.34 262

test20.03		98.78 133	98.77 127	98.78 204	99.46 164	97.20 267	97.78 252	99.24 283	99.04 119	99.41 114	98.90 258	97.65 165	99.76 273	97.70 208	99.79 159	99.39 232

thres600view7		94.45 457	93.83 464	96.29 454	99.06 287	91.53 488	97.99 223	94.24 522	98.34 189	97.44 414	95.01 510	79.84 506	99.67 348	84.33 534	98.23 462	97.66 486

ADS-MVSNet		95.24 444	94.93 446	96.18 462	98.14 445	90.10 514	97.92 233	97.32 457	90.23 516	96.51 469	98.91 255	85.61 474	99.74 292	92.88 467	96.90 509	98.69 413

MP-MVS		98.46 199	98.09 249	99.54 31	99.57 103	99.22 31	98.50 137	99.19 294	97.61 266	97.58 398	98.66 322	97.40 195	99.88 115	94.72 410	99.60 276	99.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 521	20.53 524	6.87 539	12.05 562	4.20 565	93.62 519	6.73 563	4.62 556	10.41 558	24.33 554	8.28 562	3.56 559	9.69 558	15.07 557	12.86 555

thres400		94.14 465	93.44 469	96.24 457	98.93 319	91.44 491	97.60 287	94.29 519	97.94 236	97.10 428	94.31 521	79.67 508	99.62 381	83.05 537	98.08 473	97.66 486

test123		17.04 522	20.11 525	7.82 538	10.25 563	4.91 564	94.80 479	4.47 565	4.93 555	10.00 559	24.28 555	9.69 561	3.64 558	10.14 557	12.43 558	14.92 554

thres200		93.72 474	93.14 475	95.46 492	98.66 384	91.29 495	96.61 385	94.63 514	97.39 296	96.83 449	93.71 524	79.88 505	99.56 410	82.40 540	98.13 470	95.54 525

test0.0.03 1		94.51 456	93.69 466	96.99 424	96.05 533	93.61 454	94.97 475	93.49 528	96.17 381	97.57 400	94.88 514	82.30 500	99.01 498	93.60 446	94.17 538	98.37 447

pmmvs3		95.03 449	94.40 457	96.93 428	97.70 476	92.53 473	95.08 472	97.71 441	88.57 530	97.71 388	98.08 403	79.39 510	99.82 210	96.19 355	99.11 398	98.43 440

EMVS		93.83 471	94.02 461	93.23 523	96.83 515	84.96 537	89.77 541	96.32 488	97.92 238	97.43 415	96.36 483	86.17 466	98.93 501	87.68 524	97.73 486	95.81 523

E-PMN		94.17 464	94.37 458	93.58 517	96.86 513	85.71 536	90.11 540	97.07 466	98.17 214	97.82 383	97.19 462	84.62 484	98.94 500	89.77 516	97.68 487	96.09 522

PGM-MVS		98.66 162	98.37 202	99.55 28	99.53 127	99.18 42	98.23 174	99.49 157	97.01 333	98.69 280	98.88 266	98.00 134	99.89 97	95.87 372	99.59 280	99.58 117

LCM-MVSNet-Re		98.64 165	98.48 181	99.11 128	98.85 339	98.51 114	98.49 140	99.83 26	98.37 186	99.69 55	99.46 80	98.21 115	99.92 65	94.13 430	99.30 364	98.91 378

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		98.00 268	97.63 302	99.10 130	99.24 233	98.17 148	96.89 362	98.73 389	95.66 411	97.92 371	97.70 433	97.17 211	99.66 361	96.18 357	99.23 377	99.47 197

mvs_anonymous		97.83 294	98.16 242	96.87 432	98.18 439	91.89 483	97.31 327	98.90 354	97.37 298	98.83 256	99.46 80	96.28 274	99.79 249	98.90 94	98.16 468	98.95 368

MVS_Test		98.18 248	98.36 204	97.67 370	98.48 406	94.73 405	98.18 180	99.02 334	97.69 257	98.04 362	99.11 189	97.22 208	99.56 410	98.57 121	98.90 422	98.71 409

MDA-MVSNet-bldmvs		97.94 275	97.91 274	98.06 331	99.44 171	94.96 393	96.63 383	99.15 310	98.35 188	98.83 256	99.11 189	94.31 358	99.85 159	96.60 320	98.72 433	99.37 244

CDPH-MVS		97.26 341	96.66 373	99.07 138	99.00 307	98.15 149	96.03 429	99.01 337	91.21 511	97.79 384	97.85 422	96.89 230	99.69 331	92.75 473	99.38 347	99.39 232

test12				98.93 170	98.58 396	97.83 197		98.66 394		96.53 465		95.51 313	99.69 331		99.13 394	99.27 291

casdiffmvs		98.95 102	99.00 94	98.81 194	99.38 187	97.33 247	97.82 246	99.57 111	99.17 93	99.35 130	99.17 169	98.35 94	99.69 331	98.46 129	99.73 199	99.41 222
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		98.22 240	98.24 229	98.17 315	99.00 307	95.44 369	96.38 402	99.58 103	97.79 250	98.53 313	98.50 353	96.76 242	99.74 292	97.95 179	99.64 258	99.34 262
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		93.73 473	92.83 480	96.42 449	97.70 476	91.28 496	96.84 364	89.77 545	93.96 474	92.44 534	95.93 490	79.14 511	99.77 267	92.94 464	96.76 513	98.21 453

baseline1		95.96 419	95.44 424	97.52 392	98.51 405	93.99 437	98.39 157	96.09 494	98.21 206	98.40 332	97.76 429	86.88 460	99.63 376	95.42 392	89.27 544	98.95 368

YYNet1		97.60 310	97.67 295	97.39 403	99.04 292	93.04 463	95.27 465	98.38 418	97.25 312	98.92 235	98.95 247	95.48 315	99.73 299	96.99 274	98.74 431	99.41 222

PMMVS2		98.07 261	98.08 252	98.04 334	99.41 181	94.59 411	94.59 490	99.40 209	97.50 280	98.82 259	98.83 279	96.83 234	99.84 180	97.50 228	99.81 140	99.71 65

MDA-MVSNet_test_wron		97.60 310	97.66 298	97.41 402	99.04 292	93.09 459	95.27 465	98.42 415	97.26 311	98.88 244	98.95 247	95.43 317	99.73 299	97.02 270	98.72 433	99.41 222

tpmvs		95.02 450	95.25 435	94.33 506	96.39 530	85.87 533	98.08 197	96.83 478	95.46 423	95.51 497	98.69 314	85.91 472	99.53 424	94.16 426	96.23 519	97.58 489

PM-MVS		98.82 125	98.72 132	99.12 126	99.64 77	98.54 112	97.98 224	99.68 64	97.62 263	99.34 135	99.18 164	97.54 180	99.77 267	97.79 193	99.74 195	99.04 350

HQP_MVS		97.99 271	97.67 295	98.93 170	99.19 249	97.65 221	97.77 255	99.27 269	98.20 210	97.79 384	97.98 411	94.90 331	99.70 321	94.42 420	99.51 311	99.45 206

plane_prior7					99.19 249	97.87 193

plane_prior6					98.99 310	97.70 217						94.90 331

plane_prior5								99.27 269					99.70 321	94.42 420	99.51 311	99.45 206

plane_prior4											97.98 411

plane_prior3						97.78 207			97.41 293	97.79 384

plane_prior2							97.77 255		98.20 210

plane_prior1					99.05 290

plane_prior						97.65 221	97.07 348		96.72 355						99.36 348

PS-CasMVS		99.40 25	99.33 37	99.62 9	99.71 49	99.10 65	99.29 36	99.53 136	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	99.17 259	98.74 92	97.68 270	99.40 209	99.14 98	99.06 193	98.59 339	96.71 247	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	99.54 132	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	98.58 107	99.27 42	99.57 111	99.39 58	99.75 44	99.62 40	99.17 20	99.83 198	99.06 82	99.62 267	99.66 80

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 49	99.30 21	99.31 30	99.51 144	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	99.16 261	98.74 92	97.54 296	99.25 277	98.84 148	99.06 193	98.76 297	96.76 242	99.93 53	98.57 121	99.77 172	99.50 169

UniMVSNet (Re)		98.87 112	98.71 135	99.35 80	99.24 233	98.73 95	97.73 265	99.38 213	98.93 132	99.12 185	98.73 301	96.77 240	99.86 145	98.63 116	99.80 152	99.46 200

CP-MVSNet		99.21 47	99.09 82	99.56 26	99.65 71	98.96 78	99.13 59	99.34 233	99.42 55	99.33 138	99.26 138	97.01 223	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	99.55 126	99.46 49	99.50 93	99.34 116	97.30 201	99.93 53	98.90 94	99.93 57	99.77 53

WR-MVS		98.40 207	98.19 236	99.03 148	99.00 307	97.65 221	96.85 363	98.94 344	98.57 174	98.89 240	98.50 353	95.60 309	99.85 159	97.54 224	99.85 109	99.59 109

NR-MVSNet		98.95 102	98.82 121	99.36 74	99.16 261	98.72 97	99.22 46	99.20 290	99.10 107	99.72 47	98.76 297	96.38 267	99.86 145	98.00 172	99.82 133	99.50 169

Baseline_NR-MVSNet		98.98 98	98.86 115	99.36 74	99.82 19	98.55 109	97.47 308	99.57 111	99.37 60	99.21 174	99.61 43	96.76 242	99.83 198	98.06 164	99.83 126	99.71 65

TranMVSNet+NR-MVSNet		99.17 52	99.07 85	99.46 63	99.37 193	98.87 85	98.39 157	99.42 200	99.42 55	99.36 128	99.06 201	98.38 89	99.95 25	98.34 142	99.90 88	99.57 124

TSAR-MVS + GP.		98.18 248	97.98 262	98.77 209	98.71 365	97.88 192	96.32 407	98.66 394	96.33 374	99.23 169	98.51 349	97.48 190	99.40 461	97.16 256	99.46 326	99.02 353

n2								0.00 566

nn								0.00 566

mPP-MVS		98.64 165	98.34 208	99.54 31	99.54 123	99.17 43	98.63 116	99.24 283	97.47 283	98.09 356	98.68 316	97.62 170	99.89 97	96.22 353	99.62 267	99.57 124

door-mid								99.57 111

XVG-OURS-SEG-HR		98.49 196	98.28 219	99.14 124	99.49 150	98.83 87	96.54 389	99.48 159	97.32 303	99.11 186	98.61 336	99.33 15	99.30 477	96.23 352	98.38 455	99.28 287

mvsmamba		97.57 314	97.26 326	98.51 270	98.69 374	96.73 304	98.74 99	97.25 459	97.03 332	97.88 375	99.23 151	90.95 427	99.87 135	96.61 319	99.00 410	98.91 378

MVSFormer		98.26 235	98.43 189	97.77 356	98.88 333	93.89 443	99.39 20	99.56 121	99.11 100	98.16 348	98.13 396	93.81 372	99.97 6	99.26 65	99.57 289	99.43 214

jason		97.45 323	97.35 321	97.76 359	99.24 233	93.93 439	95.86 441	98.42 415	94.24 462	98.50 316	98.13 396	94.82 335	99.91 74	97.22 251	99.73 199	99.43 214
jason: jason.
lupinMVS		97.06 359	96.86 355	97.65 374	98.88 333	93.89 443	95.48 457	97.97 435	93.53 478	98.16 348	97.58 439	93.81 372	99.91 74	96.77 298	99.57 289	99.17 328

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 92	99.39 20	99.56 121	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	99.50 149	97.33 301	98.94 232	98.86 269	98.75 47	99.82 210	97.53 225	99.71 217	99.56 130

K. test v3		98.00 268	97.66 298	99.03 148	99.79 23	97.56 228	99.19 53	92.47 532	99.62 32	99.52 87	99.66 32	89.61 441	99.96 13	99.25 67	99.81 140	99.56 130

lessismore_v0				98.97 162	99.73 38	97.53 231		86.71 550		99.37 125	99.52 67	89.93 436	99.92 65	98.99 88	99.72 208	99.44 210

SixPastTwentyTwo		98.75 138	98.62 154	99.16 118	99.83 18	97.96 181	99.28 40	98.20 426	99.37 60	99.70 51	99.65 36	92.65 400	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 72	99.63 7	99.58 103	99.44 52	99.78 39	99.76 15	96.39 265	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	99.43 193	96.74 354	98.61 297	98.38 367	98.62 64	99.87 135	96.47 335	99.67 246	99.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 189	98.34 208	99.11 128	99.50 141	98.82 89	95.97 432	99.50 149	97.30 306	99.05 201	98.98 237	99.35 14	99.32 474	95.72 379	99.68 240	99.18 324

XVG-ACMP-BASELINE		98.56 180	98.34 208	99.22 109	99.54 123	98.59 106	97.71 266	99.46 175	97.25 312	98.98 214	98.99 232	97.54 180	99.84 180	95.88 369	99.74 195	99.23 304

casdiffmvs_mvg		99.12 69	99.16 62	98.99 156	99.43 176	97.73 214	98.00 216	99.62 89	99.22 80	99.55 77	99.22 153	98.93 33	99.75 285	98.66 113	99.81 140	99.50 169
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	98.46 185	99.47 61	99.57 103	98.97 74	98.23 174	99.48 159	96.60 361	99.10 189	99.06 201	98.71 51	99.83 198	95.58 388	99.78 164	99.62 92

LGP-MVS_train				99.47 61	99.57 103	98.97 74		99.48 159	96.60 361	99.10 189	99.06 201	98.71 51	99.83 198	95.58 388	99.78 164	99.62 92

baseline		98.96 101	99.02 90	98.76 211	99.38 187	97.26 259	98.49 140	99.50 149	98.86 142	99.19 176	99.06 201	98.23 110	99.69 331	98.71 110	99.76 188	99.33 268

test11								98.87 360

door								99.41 204

EPNet_dtu		94.93 452	94.78 448	95.38 494	93.58 544	87.68 528	96.78 367	95.69 504	97.35 300	89.14 544	98.09 402	88.15 455	99.49 439	94.95 404	99.30 364	98.98 360
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 319	97.14 336	98.54 265	99.68 64	96.09 334	96.50 393	99.62 89	91.58 505	98.84 254	98.97 239	92.36 403	99.88 115	96.76 299	99.95 39	99.67 78

EPNet		96.14 408	95.44 424	98.25 305	90.76 553	95.50 364	97.92 233	94.65 513	98.97 127	92.98 529	98.85 272	89.12 445	99.87 135	95.99 365	99.68 240	99.39 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 299

HQP-NCC					98.67 379		96.29 409		96.05 389	95.55 492

ACMP_Plane					98.67 379		96.29 409		96.05 389	95.55 492

APD-MVS		98.10 256	97.67 295	99.42 67	99.11 273	98.93 80	97.76 258	99.28 266	94.97 440	98.72 275	98.77 292	97.04 218	99.85 159	93.79 440	99.54 301	99.49 177
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 469

HQP4-MVS										95.56 491			99.54 421			99.32 273

HQP3-MVS								99.04 329							99.26 372

HQP2-MVS												93.84 370

CNVR-MVS		98.17 251	97.87 277	99.07 138	98.67 379	98.24 140	97.01 350	98.93 347	97.25 312	97.62 394	98.34 372	97.27 204	99.57 407	96.42 339	99.33 355	99.39 232

NCCC		97.86 286	97.47 315	99.05 145	98.61 389	98.07 165	96.98 353	98.90 354	97.63 262	97.04 434	97.93 417	95.99 292	99.66 361	95.31 394	98.82 426	99.43 214

114514_t		96.50 387	95.77 407	98.69 227	99.48 158	97.43 242	97.84 245	99.55 126	81.42 544	96.51 469	98.58 340	95.53 311	99.67 348	93.41 454	99.58 285	98.98 360

CP-MVS		98.70 147	98.42 191	99.52 44	99.36 194	99.12 62	98.72 104	99.36 221	97.54 277	98.30 336	98.40 364	97.86 150	99.89 97	96.53 332	99.72 208	99.56 130

DSMNet-mixed		97.42 326	97.60 304	96.87 432	99.15 265	91.46 489	98.54 128	99.12 313	92.87 492	97.58 398	99.63 39	96.21 277	99.90 81	95.74 378	99.54 301	99.27 291

tpm2		93.09 484	92.58 483	94.62 504	97.56 483	86.53 532	97.66 274	95.79 501	86.15 537	94.07 520	98.23 389	75.95 520	99.53 424	90.91 508	96.86 512	97.81 476

NP-MVS					98.84 340	97.39 244					96.84 469

EG-PatchMatch MVS		98.99 94	99.01 92	98.94 167	99.50 141	97.47 236	98.04 206	99.59 100	98.15 222	99.40 117	99.36 111	98.58 72	99.76 273	98.78 102	99.68 240	99.59 109

tpm cat1		93.29 481	93.13 476	93.75 515	97.39 496	84.74 538	97.39 315	97.65 445	83.39 542	94.16 517	98.41 363	82.86 498	99.39 463	91.56 496	95.35 533	97.14 502

SteuartSystems-ACMMP		98.79 131	98.54 167	99.54 31	99.73 38	99.16 48	98.23 174	99.31 246	97.92 238	98.90 237	98.90 258	98.00 134	99.88 115	96.15 358	99.72 208	99.58 117
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 469	93.78 465	94.51 505	97.53 487	85.83 535	97.98 224	95.96 496	89.29 525	94.99 505	98.63 331	78.63 515	99.62 381	94.54 413	96.50 515	98.09 461

CR-MVSNet		96.28 401	95.95 403	97.28 406	97.71 474	94.22 418	98.11 192	98.92 351	92.31 498	96.91 441	99.37 105	85.44 477	99.81 227	97.39 238	97.36 501	97.81 476

JIA-IIPM		95.52 434	95.03 442	97.00 423	96.85 514	94.03 430	96.93 358	95.82 499	99.20 84	94.63 513	99.71 22	83.09 496	99.60 393	94.42 420	94.64 535	97.36 497

Patchmtry		97.35 333	96.97 346	98.50 274	97.31 499	96.47 319	98.18 180	98.92 351	98.95 131	98.78 265	99.37 105	85.44 477	99.85 159	95.96 367	99.83 126	99.17 328

PatchT		96.65 379	96.35 391	97.54 390	97.40 495	95.32 377	97.98 224	96.64 482	99.33 66	96.89 445	99.42 89	84.32 487	99.81 227	97.69 210	97.49 492	97.48 492

tpmrst		95.07 448	95.46 422	93.91 513	97.11 503	84.36 542	97.62 281	96.96 471	94.98 439	96.35 474	98.80 286	85.46 476	99.59 398	95.60 386	96.23 519	97.79 479

BH-w/o		95.13 447	94.89 447	95.86 477	98.20 437	91.31 494	95.65 450	97.37 451	93.64 476	96.52 468	95.70 496	93.04 392	99.02 496	88.10 523	95.82 529	97.24 501

tpm		94.67 454	94.34 459	95.66 485	97.68 479	88.42 523	97.88 238	94.90 511	94.46 454	96.03 484	98.56 343	78.66 514	99.79 249	95.88 369	95.01 534	98.78 401

DELS-MVS		98.27 233	98.20 232	98.48 276	98.86 336	96.70 305	95.60 452	99.20 290	97.73 254	98.45 323	98.71 305	97.50 186	99.82 210	98.21 151	99.59 280	98.93 373
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		96.83 372	96.75 365	97.08 418	98.74 358	93.33 457	96.71 373	98.26 422	96.72 355	98.44 324	97.37 456	95.20 322	99.47 446	91.89 488	97.43 496	98.44 438

RPMNet		97.02 362	96.93 348	97.30 405	97.71 474	94.22 418	98.11 192	99.30 254	99.37 60	96.91 441	99.34 116	86.72 461	99.87 135	97.53 225	97.36 501	97.81 476

MVSTER		96.86 371	96.55 382	97.79 354	97.91 461	94.21 420	97.56 292	98.87 360	97.49 282	99.06 193	99.05 208	80.72 503	99.80 236	98.44 131	99.82 133	99.37 244

CPTT-MVS		97.84 292	97.36 320	99.27 99	99.31 209	98.46 117	98.29 167	99.27 269	94.90 442	97.83 381	98.37 368	94.90 331	99.84 180	93.85 439	99.54 301	99.51 165

GBi-Net		98.65 163	98.47 183	99.17 115	98.90 327	98.24 140	99.20 49	99.44 187	98.59 169	98.95 224	99.55 56	94.14 363	99.86 145	97.77 197	99.69 234	99.41 222

PVSNet_Blended_VisFu		98.17 251	98.15 244	98.22 311	99.73 38	95.15 386	97.36 322	99.68 64	94.45 457	98.99 213	99.27 132	96.87 231	99.94 41	97.13 263	99.91 80	99.57 124

PVSNet_BlendedMVS		97.55 315	97.53 308	97.60 381	98.92 323	93.77 447	96.64 382	99.43 193	94.49 452	97.62 394	99.18 164	96.82 235	99.67 348	94.73 408	99.93 57	99.36 252

UnsupCasMVSNet_eth		97.89 280	97.60 304	98.75 213	99.31 209	97.17 273	97.62 281	99.35 227	98.72 157	98.76 270	98.68 316	92.57 401	99.74 292	97.76 201	95.60 531	99.34 262

UnsupCasMVSNet_bld		97.30 338	96.92 350	98.45 279	99.28 218	96.78 302	96.20 416	99.27 269	95.42 425	98.28 340	98.30 379	93.16 386	99.71 312	94.99 401	97.37 499	98.87 384

PVSNet_Blended		96.88 369	96.68 369	97.47 398	98.92 323	93.77 447	94.71 481	99.43 193	90.98 514	97.62 394	97.36 457	96.82 235	99.67 348	94.73 408	99.56 293	98.98 360

FMVSNet5		96.01 413	95.20 439	98.41 285	97.53 487	96.10 331	98.74 99	99.50 149	97.22 321	98.03 363	99.04 210	69.80 529	99.88 115	97.27 247	99.71 217	99.25 298

test1		98.65 163	98.47 183	99.17 115	98.90 327	98.24 140	99.20 49	99.44 187	98.59 169	98.95 224	99.55 56	94.14 363	99.86 145	97.77 197	99.69 234	99.41 222

new_pmnet		96.99 366	96.76 363	97.67 370	98.72 361	94.89 397	95.95 436	98.20 426	92.62 495	98.55 310	98.54 344	94.88 334	99.52 428	93.96 434	99.44 336	98.59 427

FMVSNet3		97.50 316	97.24 328	98.29 301	98.08 451	95.83 348	97.86 242	98.91 353	97.89 241	98.95 224	98.95 247	87.06 459	99.81 227	97.77 197	99.69 234	99.23 304

dp		93.47 477	93.59 468	93.13 524	96.64 520	81.62 553	97.66 274	96.42 487	92.80 493	96.11 478	98.64 329	78.55 517	99.59 398	93.31 455	92.18 543	98.16 457

FMVSNet2		98.49 196	98.40 193	98.75 213	98.90 327	97.14 276	98.61 120	99.13 312	98.59 169	99.19 176	99.28 130	94.14 363	99.82 210	97.97 177	99.80 152	99.29 284

FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 117	98.24 140	99.20 49	99.44 187	99.21 82	99.43 108	99.55 56	97.82 154	99.86 145	98.42 137	99.89 94	99.41 222

N_pmnet		97.63 309	97.17 332	98.99 156	99.27 221	97.86 194	95.98 431	93.41 529	95.25 432	99.47 100	98.90 258	95.63 307	99.85 159	96.91 282	99.73 199	99.27 291

cascas		94.79 453	94.33 460	96.15 467	96.02 535	92.36 478	92.34 533	99.26 275	85.34 539	95.08 504	94.96 513	92.96 393	98.53 513	94.41 423	98.59 447	97.56 490

BH-RMVSNet		96.83 372	96.58 381	97.58 383	98.47 407	94.05 427	96.67 378	97.36 452	96.70 358	97.87 376	97.98 411	95.14 326	99.44 455	90.47 513	98.58 448	99.25 298

UGNet		98.53 189	98.45 186	98.79 201	97.94 459	96.96 287	99.08 62	98.54 406	99.10 107	96.82 450	99.47 78	96.55 257	99.84 180	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		96.67 378	96.27 397	97.87 349	98.81 348	94.61 410	96.77 368	97.92 437	94.94 441	97.12 427	97.74 430	91.11 426	99.82 210	93.89 436	98.15 469	99.18 324

XXY-MVS		99.14 62	99.15 67	99.10 130	99.76 30	97.74 212	98.85 93	99.62 89	98.48 181	99.37 125	99.49 74	98.75 47	99.86 145	98.20 152	99.80 152	99.71 65

EC-MVSNet		99.09 73	99.05 86	99.20 110	99.28 218	98.93 80	99.24 44	99.84 23	99.08 114	98.12 353	98.37 368	98.72 50	99.90 81	99.05 83	99.77 172	98.77 402

sss		97.21 347	96.93 348	98.06 331	98.83 342	95.22 384	96.75 370	98.48 411	94.49 452	97.27 422	97.90 418	92.77 397	99.80 236	96.57 323	99.32 358	99.16 334

Test_1112_low_res		96.99 366	96.55 382	98.31 298	99.35 199	95.47 368	95.84 444	99.53 136	91.51 507	96.80 451	98.48 356	91.36 423	99.83 198	96.58 321	99.53 305	99.62 92

1112_ss		97.29 340	96.86 355	98.58 250	99.34 204	96.32 325	96.75 370	99.58 103	93.14 484	96.89 445	97.48 448	92.11 411	99.86 145	96.91 282	99.54 301	99.57 124

ab-mvs-re		8.12 524	10.83 527	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	97.48 448	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

ab-mvs		98.41 204	98.36 204	98.59 249	99.19 249	97.23 261	99.32 26	98.81 374	97.66 260	98.62 295	99.40 98	96.82 235	99.80 236	95.88 369	99.51 311	98.75 405

TR-MVS		95.55 433	95.12 441	96.86 435	97.54 485	93.94 438	96.49 394	96.53 485	94.36 461	97.03 436	96.61 475	94.26 360	99.16 489	86.91 528	96.31 518	97.47 493

MDTV_nov1_ep13_2view						74.92 556	97.69 269		90.06 521	97.75 387		85.78 473		93.52 449		98.69 413

MDTV_nov1_ep13			95.22 437		97.06 506	83.20 547	97.74 263	96.16 490	94.37 460	96.99 437	98.83 279	83.95 491	99.53 424	93.90 435	97.95 481

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 41	99.41 17	99.59 100	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		96.62 381	96.25 398	97.71 366	99.04 292	94.66 408	99.16 55	96.92 475	97.23 318	97.87 376	99.10 192	86.11 468	99.65 368	91.65 493	99.21 381	98.82 389

IterMVS-LS		98.55 184	98.70 138	98.09 325	99.48 158	94.73 405	97.22 339	99.39 211	98.97 127	99.38 121	99.31 125	96.00 288	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		97.69 304	97.35 321	98.69 227	98.73 359	97.02 283	96.92 360	98.75 386	95.89 399	98.59 302	98.67 318	92.08 412	99.74 292	96.72 305	99.81 140	99.32 273
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 172

IterMVS		97.73 300	98.11 248	96.57 444	99.24 233	90.28 511	95.52 456	99.21 288	98.86 142	99.33 138	99.33 119	93.11 388	99.94 41	98.49 128	99.94 51	99.48 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 335	96.92 350	98.57 253	99.09 278	97.99 174	96.79 365	99.35 227	93.18 483	97.71 388	98.07 404	95.00 330	99.31 475	93.97 433	99.13 394	98.42 442

MVS_111021_LR		98.30 228	98.12 247	98.83 190	99.16 261	98.03 170	96.09 425	99.30 254	97.58 269	98.10 355	98.24 387	98.25 107	99.34 470	96.69 310	99.65 256	99.12 340

DP-MVS		98.93 104	98.81 123	99.28 96	99.21 241	98.45 118	98.46 145	99.33 239	99.63 28	99.48 96	99.15 177	97.23 207	99.75 285	97.17 255	99.66 254	99.63 91

ACMMP++															99.68 240

HQP-MVS		97.00 365	96.49 385	98.55 260	98.67 379	96.79 299	96.29 409	99.04 329	96.05 389	95.55 492	96.84 469	93.84 370	99.54 421	92.82 469	99.26 372	99.32 273

QAPM		97.31 336	96.81 361	98.82 192	98.80 351	97.49 232	99.06 66	99.19 294	90.22 518	97.69 390	99.16 171	96.91 229	99.90 81	90.89 509	99.41 341	99.07 344

Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 38	98.26 138	99.17 54	99.78 36	99.11 100	99.27 153	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		94.32 459	95.62 413	90.42 529	98.46 409	75.36 555	96.29 409	89.13 546	95.25 432	95.38 498	99.75 16	92.88 394	99.19 487	94.07 432	99.39 344	96.72 511

IS-MVSNet		98.19 246	97.90 275	99.08 136	99.57 103	97.97 178	99.31 30	98.32 419	99.01 123	98.98 214	99.03 213	91.59 417	99.79 249	95.49 391	99.80 152	99.48 188

HyFIR lowres test		97.19 349	96.60 380	98.96 164	99.62 87	97.28 257	95.17 469	99.50 149	94.21 463	99.01 208	98.32 377	86.61 462	99.99 2	97.10 265	99.84 114	99.60 102

EPMVS		93.72 474	93.27 472	95.09 500	96.04 534	87.76 527	98.13 187	85.01 552	94.69 448	96.92 439	98.64 329	78.47 518	99.31 475	95.04 400	96.46 516	98.20 454

PAPM_NR		96.82 374	96.32 393	98.30 300	99.07 282	96.69 306	97.48 304	98.76 382	95.81 406	96.61 461	96.47 479	94.12 366	99.17 488	90.82 511	97.78 484	99.06 345

TAMVS		98.24 239	98.05 255	98.80 197	99.07 282	97.18 271	97.88 238	98.81 374	96.66 360	99.17 184	99.21 155	94.81 338	99.77 267	96.96 279	99.88 95	99.44 210

PAPR		95.29 442	94.47 454	97.75 360	97.50 493	95.14 387	94.89 478	98.71 391	91.39 509	95.35 499	95.48 502	94.57 346	99.14 491	84.95 533	97.37 499	98.97 364

RPSCF		98.62 170	98.36 204	99.42 67	99.65 71	99.42 10	98.55 126	99.57 111	97.72 256	98.90 237	99.26 138	96.12 283	99.52 428	95.72 379	99.71 217	99.32 273

Vis-MVSNet (Re-imp)		97.46 321	97.16 333	98.34 295	99.55 117	96.10 331	98.94 81	98.44 412	98.32 193	98.16 348	98.62 334	88.76 446	99.73 299	93.88 437	99.79 159	99.18 324

test_0402		98.76 137	98.71 135	98.93 170	99.56 111	98.14 151	98.45 147	99.34 233	99.28 73	98.95 224	98.91 255	98.34 95	99.79 249	95.63 384	99.91 80	98.86 385

MVS_111021_HR		98.25 238	98.08 252	98.75 213	99.09 278	97.46 238	95.97 432	99.27 269	97.60 268	97.99 366	98.25 385	98.15 124	99.38 465	96.87 290	99.57 289	99.42 219

CSCG		98.68 157	98.50 175	99.20 110	99.45 169	98.63 101	98.56 125	99.57 111	97.87 242	98.85 251	98.04 406	97.66 164	99.84 180	96.72 305	99.81 140	99.13 339

PatchMatch-RL		97.24 344	96.78 362	98.61 246	99.03 295	97.83 197	96.36 404	99.06 322	93.49 480	97.36 420	97.78 427	95.75 302	99.49 439	93.44 453	98.77 428	98.52 430

API-MVS		97.04 361	96.91 353	97.42 401	97.88 462	98.23 144	98.18 180	98.50 410	97.57 270	97.39 418	96.75 472	96.77 240	99.15 490	90.16 514	99.02 407	94.88 527

Test By Simon												96.52 258

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	99.80 236	98.24 147	99.84 114	99.52 161

USDC		97.41 327	97.40 316	97.44 400	98.94 317	93.67 450	95.17 469	99.53 136	94.03 471	98.97 218	99.10 192	95.29 320	99.34 470	95.84 375	99.73 199	99.30 282

EPP-MVSNet		98.30 228	98.04 256	99.07 138	99.56 111	97.83 197	99.29 36	98.07 433	99.03 121	98.59 302	99.13 183	92.16 408	99.90 81	96.87 290	99.68 240	99.49 177

PMMVS		96.51 385	95.98 401	98.09 325	97.53 487	95.84 347	94.92 476	98.84 369	91.58 505	96.05 482	95.58 497	95.68 306	99.66 361	95.59 387	98.09 472	98.76 404

PAPM		91.88 503	90.34 505	96.51 445	98.06 453	92.56 472	92.44 532	97.17 463	86.35 536	90.38 541	96.01 487	86.61 462	99.21 486	70.65 549	95.43 532	97.75 481

ACMMP		98.75 138	98.50 175	99.52 44	99.56 111	99.16 48	98.87 89	99.37 217	97.16 324	98.82 259	99.01 226	97.71 161	99.87 135	96.29 350	99.69 234	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		97.17 351	96.71 367	98.55 260	98.56 399	98.05 169	96.33 406	98.93 347	96.91 341	97.06 432	97.39 454	94.38 354	99.45 453	91.66 492	99.18 388	98.14 458

PatchmatchNet		95.58 432	95.67 412	95.30 497	97.34 497	87.32 530	97.65 276	96.65 481	95.30 429	97.07 431	98.69 314	84.77 482	99.75 285	94.97 403	98.64 442	98.83 387
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 231	97.95 266	99.34 83	98.44 412	99.16 48	98.12 191	99.38 213	96.01 393	98.06 359	98.43 361	97.80 155	99.67 348	95.69 381	99.58 285	99.20 314

F-COLMAP		97.30 338	96.68 369	99.14 124	99.19 249	98.39 123	97.27 334	99.30 254	92.93 489	96.62 460	98.00 409	95.73 303	99.68 343	92.62 476	98.46 453	99.35 258

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 158	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		96.06 409	97.62 303	91.38 526	98.65 388	98.57 108	98.85 93	96.95 472	96.86 348	99.90 14	99.16 171	99.18 19	98.40 514	89.23 520	99.77 172	77.18 549

OMC-MVS		97.88 283	97.49 311	99.04 147	98.89 332	98.63 101	96.94 356	99.25 277	95.02 438	98.53 313	98.51 349	97.27 204	99.47 446	93.50 451	99.51 311	99.01 355

MG-MVS		96.77 375	96.61 378	97.26 408	98.31 424	93.06 460	95.93 437	98.12 431	96.45 371	97.92 371	98.73 301	93.77 374	99.39 463	91.19 503	99.04 403	99.33 268

AdaColmap		97.14 353	96.71 367	98.46 278	98.34 422	97.80 206	96.95 355	98.93 347	95.58 416	96.92 439	97.66 434	95.87 299	99.53 424	90.97 506	99.14 392	98.04 463

uanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

ITE_SJBPF				98.87 179	99.22 239	98.48 116		99.35 227	97.50 280	98.28 340	98.60 338	97.64 168	99.35 469	93.86 438	99.27 368	98.79 400

DeepMVS_CX				93.44 520	98.24 433	94.21 420		94.34 518	64.28 549	91.34 538	94.87 516	89.45 444	92.77 549	77.54 545	93.14 540	93.35 535

TinyColmap		97.89 280	97.98 262	97.60 381	98.86 336	94.35 416	96.21 415	99.44 187	97.45 290	99.06 193	98.88 266	97.99 137	99.28 481	94.38 424	99.58 285	99.18 324

MAR-MVS		96.47 391	95.70 410	98.79 201	97.92 460	99.12 62	98.28 168	98.60 399	92.16 500	95.54 495	96.17 485	94.77 341	99.52 428	89.62 517	98.23 462	97.72 484
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		97.90 278	97.69 294	98.52 269	99.17 259	97.66 219	97.19 344	99.47 170	96.31 376	97.85 380	98.20 391	96.71 247	99.52 428	94.62 411	99.72 208	98.38 445

MSDG		97.71 302	97.52 309	98.28 302	98.91 326	96.82 297	94.42 495	99.37 217	97.65 261	98.37 333	98.29 382	97.40 195	99.33 472	94.09 431	99.22 378	98.68 417

LS3D		98.63 167	98.38 200	99.36 74	97.25 500	99.38 12	99.12 61	99.32 241	99.21 82	98.44 324	98.88 266	97.31 200	99.80 236	96.58 321	99.34 353	98.92 374

CLD-MVS		97.49 319	97.16 333	98.48 276	99.07 282	97.03 282	94.71 481	99.21 288	94.46 454	98.06 359	97.16 463	97.57 176	99.48 443	94.46 416	99.78 164	98.95 368
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 478	92.23 487	97.08 418	99.25 232	97.86 194	95.61 451	97.16 464	92.90 491	93.76 526	98.65 325	75.94 521	95.66 544	79.30 544	97.49 492	97.73 483

Gipuma		99.03 88	99.16 62	98.64 236	99.94 2	98.51 114	99.32 26	99.75 43	99.58 38	98.60 300	99.62 40	98.22 113	99.51 434	97.70 208	99.73 199	97.89 471
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark