High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 36	99.63 28	99.78 39	99.67 30	99.48 10	99.81 224	99.30 62	99.97 21	99.77 53
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
3Dnovator	98.27 2	98.81 126	98.73 129	99.05 142	98.76 343	97.81 200	99.25 43	99.30 243	98.57 172	98.55 300	99.33 118	97.95 139	99.90 81	97.16 248	99.67 239	99.44 207

3Dnovator+	97.89 3	98.69 150	98.51 169	99.24 106	98.81 336	98.40 120	99.02 70	99.19 281	98.99 123	98.07 346	99.28 129	97.11 214	99.84 177	96.84 282	99.32 345	99.47 195

DeepC-MVS	97.60 4	98.97 98	98.93 100	99.10 128	99.35 196	97.98 173	98.01 212	99.46 166	97.56 267	99.54 79	99.50 68	98.97 29	99.84 177	98.06 160	99.92 71	99.49 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	96.93 5	98.32 219	98.01 253	99.23 108	98.39 407	98.97 73	95.03 460	99.18 285	96.88 336	99.33 136	98.78 283	98.16 121	99.28 466	96.74 290	99.62 261	99.44 207

DeepC-MVS_fast	96.85 6	98.30 223	98.15 238	98.75 209	98.61 377	97.23 252	97.76 255	99.09 305	97.31 299	98.75 266	98.66 312	97.56 176	99.64 363	96.10 350	99.55 289	99.39 229
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	97.09 344	96.68 357	98.32 287	98.32 411	97.16 265	98.86 92	99.37 206	89.48 509	96.29 461	99.15 174	96.56 251	99.90 81	92.90 450	99.20 370	97.89 457

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 45	98.38 122	99.07 65	99.55 120	98.30 192	99.65 63	99.45 84	99.22 17	99.76 270	98.44 130	99.77 170	99.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 78	99.00 93	99.33 89	99.71 49	98.83 86	98.60 121	99.58 99	99.11 99	99.53 83	99.18 162	98.81 39	99.67 338	96.71 295	99.77 170	99.50 168

COLMAP_ROB	96.50 10	98.99 93	98.85 117	99.41 69	99.58 94	99.10 65	98.74 99	99.56 116	99.09 109	99.33 136	99.19 158	98.40 86	99.72 306	95.98 353	99.76 186	99.42 216
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	96.63 368	95.95 391	98.65 226	98.93 307	98.09 155	96.93 355	99.28 255	83.58 526	98.13 340	97.78 414	96.13 274	99.40 447	93.52 433	99.29 353	98.45 422
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 105	98.73 129	99.48 57	99.55 117	99.14 57	98.07 199	99.37 206	97.62 258	99.04 200	98.96 237	98.84 37	99.79 246	97.43 228	99.65 249	99.49 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 408	95.35 416	97.55 378	97.95 444	94.79 388	98.81 98	96.94 458	92.28 485	95.17 487	98.57 330	89.90 423	99.75 282	91.20 486	97.33 487	98.10 446

OpenMVS_ROB	95.38 14	95.84 411	95.18 427	97.81 342	98.41 406	97.15 266	97.37 318	98.62 385	83.86 525	98.65 279	98.37 357	94.29 349	99.68 333	88.41 505	98.62 431	96.60 497

ACMP	95.32 15	98.41 201	98.09 243	99.36 74	99.51 133	98.79 89	97.68 267	99.38 202	95.76 398	98.81 256	98.82 275	98.36 89	99.82 207	94.75 392	99.77 170	99.48 187
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 373	95.73 396	98.85 179	98.75 345	97.91 183	96.42 390	99.06 309	90.94 501	95.59 475	97.38 442	94.41 341	99.59 386	90.93 491	98.04 463	99.05 338
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 415	95.70 397	95.57 473	98.83 330	88.57 506	92.50 516	97.72 425	92.69 480	96.49 458	96.44 467	93.72 365	99.43 443	93.61 428	99.28 354	98.71 398

PCF-MVS	92.86 18	94.36 445	93.00 464	98.42 275	98.70 357	97.56 221	93.16 513	99.11 302	79.59 530	97.55 388	97.43 439	92.19 394	99.73 296	79.85 527	99.45 317	97.97 454
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 484	90.90 488	96.27 441	97.22 487	91.24 483	94.36 485	93.33 514	92.37 483	92.24 521	94.58 506	66.20 525	99.89 97	93.16 444	94.63 520	97.66 472
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	97.86 276	97.94 263	97.65 363	99.71 49	97.94 180	98.52 130	98.68 379	98.99 123	97.52 391	99.35 111	97.41 192	98.18 503	91.59 479	99.67 239	96.82 493
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 490	90.30 492	93.70 502	97.72 457	84.34 527	90.24 523	97.42 435	90.20 505	93.79 510	93.09 516	90.90 415	98.89 490	86.57 514	72.76 535	97.87 459

MVE	83.40 22	92.50 479	91.92 481	94.25 493	98.83 330	91.64 471	92.71 514	83.52 537	95.92 387	86.46 532	95.46 490	95.20 314	95.40 529	80.51 526	98.64 427	95.73 509
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 388	95.44 411	98.84 185	96.25 516	98.69 98	97.02 346	99.12 300	88.90 513	97.83 368	98.86 262	89.51 428	98.90 489	91.92 471	99.51 301	98.92 365
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dtuonlycased		97.70 291	98.19 231	96.24 443	99.75 34	89.51 503	94.69 472	99.64 76	98.23 199	99.46 101	98.57 330	98.25 106	99.85 158	95.65 370	99.44 324	99.36 248

dtuonly		96.49 376	97.28 312	94.10 496	98.80 339	83.27 530	93.66 503	99.48 151	95.10 423	97.87 363	98.30 368	95.61 300	99.68 333	96.98 267	99.75 190	99.33 263

dtuplus		98.32 219	98.39 193	98.10 312	99.15 258	95.29 366	96.68 370	99.51 137	97.32 297	99.18 178	99.15 174	97.61 171	99.62 369	97.19 245	99.74 192	99.38 238

SIFT-UM-Cal		96.49 376	96.62 364	96.12 454	98.13 435	97.89 186	93.35 509	98.44 398	95.48 410	98.63 281	98.34 361	95.45 308	97.45 513	92.22 468	99.50 309	93.02 524

SIFT-NCM-Cal		96.56 371	96.68 357	96.20 447	98.27 418	98.44 118	94.40 483	96.67 465	95.29 417	97.63 380	98.17 381	96.40 259	96.59 525	93.61 428	99.66 247	93.57 517

SIFT-CM-Cal		96.28 389	96.31 382	96.16 451	98.39 407	98.11 151	93.46 508	96.47 471	94.81 433	98.49 307	98.43 350	94.48 339	97.34 516	92.60 462	99.70 223	93.02 524

SIFT-PCN-Cal		96.34 384	96.46 376	96.01 458	98.17 429	96.89 284	93.48 507	97.35 440	94.84 431	99.35 130	98.30 368	94.70 334	97.92 507	92.03 469	99.88 95	93.21 523

SIFT-NN-UMatch		95.38 428	95.26 421	95.75 467	98.25 419	97.78 202	93.24 512	95.66 491	94.01 458	95.10 489	97.47 437	93.12 375	96.78 522	92.42 465	98.04 463	92.69 529

SIFT-NN-NCMNet		95.39 427	95.22 424	95.92 460	98.29 414	98.34 129	93.58 505	94.60 499	94.07 456	94.84 493	97.53 429	94.37 345	96.62 523	91.01 489	98.64 427	92.80 527

SIFT-NN-CMatch		95.63 418	95.48 407	96.08 455	98.24 421	98.00 168	92.71 514	94.29 503	94.20 450	95.85 471	97.26 447	95.72 297	97.01 518	91.99 470	99.02 394	93.23 521

SIFT-NN-PointCN		96.06 397	96.11 388	95.91 461	97.88 448	97.73 208	93.49 506	97.51 434	93.22 468	96.57 448	98.26 372	96.23 270	96.60 524	92.54 463	99.27 355	93.40 519

XFeat-NN		89.63 492	89.13 495	91.14 513	90.93 537	90.02 500	84.90 530	94.05 509	88.10 518	92.89 516	93.33 515	78.74 499	90.89 534	83.46 520	95.72 514	92.52 530

ALIKED-NN		94.29 449	93.41 458	96.94 414	96.18 517	97.66 213	94.90 464	98.68 379	88.85 514	90.43 525	96.81 458	89.82 424	96.59 525	86.67 513	98.33 441	96.58 498

SP-NN		94.67 441	94.44 443	95.36 481	95.12 525	95.23 371	94.27 487	96.10 478	94.46 441	90.91 524	95.76 482	91.47 408	93.87 532	95.23 382	96.62 498	97.00 488

SIFT-NN		92.96 473	92.79 467	93.46 504	96.92 496	96.45 311	91.89 520	94.39 501	92.91 476	92.54 518	95.46 490	88.26 440	90.71 535	85.22 516	97.52 474	93.22 522

hybridcas		99.08 78	99.13 70	98.92 170	99.54 123	97.61 219	98.22 176	99.66 69	99.27 73	99.40 117	99.24 144	98.47 77	99.70 314	98.59 118	99.80 151	99.46 197

GLUNet-SfM		86.26 496	84.68 498	91.01 514	80.58 540	83.56 528	78.04 531	93.59 511	76.70 531	95.29 486	94.72 504	77.51 505	94.26 531	66.39 534	99.33 342	95.20 511

PDCNetPlus		95.22 432	94.73 439	96.70 428	97.85 450	91.14 486	93.94 497	99.97 1	93.06 473	98.95 220	98.89 257	74.32 509	99.14 476	95.63 371	99.93 57	99.82 36

hybrid		98.22 235	98.27 217	98.08 317	99.13 262	95.24 368	96.61 376	99.53 130	97.43 286	98.46 310	98.97 233	96.75 242	99.65 358	97.84 184	99.69 227	99.35 254

RoMa-SfM		98.46 196	98.27 217	99.02 148	99.35 196	98.32 130	97.56 289	99.70 52	95.88 389	99.38 121	98.65 314	96.41 258	99.46 436	97.78 189	99.71 214	99.28 281

DKM		98.18 243	97.95 260	98.85 179	99.35 196	98.31 131	96.68 370	99.69 55	96.90 335	98.61 287	98.77 285	94.41 341	98.93 486	97.32 236	99.84 114	99.32 267

ELoFTR		97.81 285	97.74 279	98.04 323	99.39 182	95.79 340	97.28 330	99.58 99	94.13 452	99.38 121	99.37 104	93.31 370	99.60 381	97.23 242	99.96 28	98.74 396

MatchFormer		97.07 346	96.92 338	97.49 384	98.44 400	95.92 332	96.79 361	99.14 298	93.08 472	99.32 142	99.10 188	93.89 359	99.03 479	92.78 456	99.78 163	97.52 477

LoFTR		97.97 266	97.79 275	98.53 259	98.80 339	97.47 229	97.01 347	99.55 120	95.55 405	99.46 101	99.22 151	94.22 351	99.44 441	96.45 325	99.82 133	98.68 405

ALIKED-LG		97.10 342	96.63 363	98.50 266	97.96 443	98.68 99	97.75 258	99.68 62	95.86 390	98.36 323	98.33 365	91.58 404	99.04 478	90.87 494	99.31 347	97.77 466

SP-DiffGlue		96.87 358	96.76 351	97.21 399	95.17 524	96.88 286	96.12 412	98.93 334	96.51 354	98.37 321	97.55 428	93.65 367	97.83 508	96.11 349	98.45 439	96.92 489

SP-LightGlue		97.22 334	97.01 332	97.88 336	97.33 484	97.19 259	96.38 392	99.08 307	97.28 302	96.53 451	97.50 433	92.36 390	98.70 495	97.84 184	98.76 415	97.74 468

SP-SuperGlue		97.31 324	97.23 317	97.57 377	96.96 495	97.24 251	96.26 403	98.76 369	97.68 253	96.88 433	97.85 409	94.32 347	98.01 505	97.76 195	98.57 434	97.45 480

SIFT-UMatch		96.33 385	96.47 374	95.89 462	98.29 414	97.95 178	93.84 499	97.24 445	95.78 397	98.72 269	98.04 394	93.45 369	96.81 521	93.14 445	99.73 196	92.91 526

SIFT-NCMNet		96.30 387	96.40 378	96.03 457	97.80 455	97.68 212	92.34 518	96.94 458	95.55 405	98.84 249	98.63 320	94.17 352	97.63 512	93.57 432	99.71 214	92.77 528

SIFT-ConvMatch		96.57 370	96.62 364	96.43 434	98.20 425	98.27 134	93.88 498	96.88 461	95.29 417	98.88 240	98.25 373	95.18 316	97.43 514	93.22 443	99.83 126	93.59 516

SIFT-PointCN		96.45 381	96.47 374	96.39 436	98.13 435	97.54 223	93.31 510	97.23 446	94.67 436	98.68 275	98.32 366	94.64 335	97.81 509	93.50 435	99.77 170	93.83 514

XFeat-MNN		93.41 465	92.98 465	94.68 489	92.63 531	92.92 450	89.72 527	95.81 485	92.10 487	97.23 411	96.29 471	84.95 466	97.31 517	89.60 502	98.54 436	93.81 515

ALIKED-MNN		95.97 405	95.30 420	98.00 326	97.66 467	98.12 150	96.98 350	99.41 194	91.11 499	94.04 506	97.30 446	91.56 405	98.61 497	89.99 499	99.63 257	97.28 485

SP-MNN		96.46 380	96.24 387	97.10 405	96.71 503	95.98 329	96.00 417	97.33 441	95.82 394	94.93 492	97.10 455	93.70 366	98.01 505	96.30 336	98.30 445	97.30 484

SIFT-MNN		95.92 407	95.97 390	95.74 469	98.18 427	98.00 168	94.17 489	96.99 453	95.74 399	97.16 412	97.90 405	90.71 416	95.79 527	93.71 426	99.21 368	93.44 518

casdiffseed414692147		99.09 72	99.12 71	99.01 150	99.55 117	97.91 183	98.30 164	99.68 62	99.04 118	99.19 173	99.37 104	98.98 28	99.61 377	98.13 153	99.83 126	99.50 168

gbinet_0.2-2-1-0.02		95.44 425	94.55 440	98.14 308	95.99 521	95.34 364	94.71 468	98.29 407	96.00 383	96.05 468	90.50 530	84.99 465	99.79 246	97.33 234	97.07 492	99.28 281

0.3-1-1-0.015		87.27 495	84.50 499	95.57 473	91.70 533	90.77 492	89.41 528	92.04 521	88.98 512	82.46 535	81.35 533	60.36 536	99.50 421	92.96 447	81.23 531	96.45 499

0.4-1-1-0.1		88.42 493	85.91 496	95.94 459	93.08 530	91.54 472	90.99 522	92.04 521	89.96 508	84.83 533	83.25 532	63.75 532	99.52 414	93.25 441	82.07 529	96.75 494

0.4-1-1-0.2		87.49 494	84.89 497	95.31 482	91.33 536	90.08 499	88.47 529	92.07 520	88.70 515	84.06 534	81.08 534	63.62 533	99.49 425	92.93 449	81.71 530	96.37 500

wanda-best-256-512		95.48 423	94.74 437	97.68 357	96.53 507	94.12 411	94.17 489	98.57 390	95.84 391	96.71 440	91.16 526	86.05 455	99.76 270	97.57 212	96.09 506	99.17 320

usedtu_dtu_shiyan2		98.99 93	98.86 114	99.39 72	99.73 38	98.71 97	99.05 68	99.47 161	99.16 93	99.49 94	99.12 183	96.34 265	99.93 53	98.05 162	99.36 335	99.54 143

usedtu_dtu_shiyan1		97.37 318	97.13 325	98.11 310	99.03 286	95.40 359	94.47 480	98.99 327	96.87 337	97.97 355	97.81 412	92.12 396	99.75 282	97.49 225	99.43 326	99.16 326

blended_shiyan8		95.98 403	95.33 417	97.94 331	97.05 494	94.87 386	95.34 450	98.59 387	96.17 371	97.09 416	92.39 521	87.62 444	99.76 270	97.65 204	96.05 512	99.20 306

E5new		99.05 82	99.11 73	98.85 179	99.60 88	97.30 242	98.42 151	99.63 79	98.73 150	99.26 155	99.39 100	98.71 51	99.70 314	98.43 132	99.84 114	99.54 143

FE-blended-shiyan7		95.48 423	94.74 437	97.68 357	96.53 507	94.12 411	94.17 489	98.57 390	95.84 391	96.71 440	91.16 526	86.05 455	99.76 270	97.57 212	96.09 506	99.17 320

E6new		99.05 82	99.11 73	98.85 179	99.60 88	97.30 242	98.42 151	99.63 79	98.73 150	99.26 155	99.39 100	98.71 51	99.70 314	98.43 132	99.84 114	99.54 143

blended_shiyan6		95.99 402	95.33 417	97.95 330	97.06 492	94.89 384	95.34 450	98.58 388	96.17 371	97.06 418	92.41 520	87.64 443	99.76 270	97.64 205	96.09 506	99.19 312

usedtu_blend_shiyan5		96.20 395	95.62 400	97.94 331	96.53 507	94.93 382	98.83 96	99.59 96	98.89 137	96.71 440	91.16 526	86.05 455	99.73 296	96.70 296	96.09 506	99.17 320

blend_shiyan4		92.09 486	90.16 493	97.88 336	96.78 501	94.93 382	95.24 454	98.58 388	96.22 369	96.07 466	91.42 525	63.46 534	99.73 296	96.70 296	76.98 534	98.98 352

E6		99.05 82	99.11 73	98.85 179	99.60 88	97.30 242	98.42 151	99.63 79	98.73 150	99.26 155	99.39 100	98.71 51	99.70 314	98.43 132	99.84 114	99.54 143

E5		99.05 82	99.11 73	98.85 179	99.60 88	97.30 242	98.42 151	99.63 79	98.73 150	99.26 155	99.39 100	98.71 51	99.70 314	98.43 132	99.84 114	99.54 143

FE-MVSNET3		97.37 318	97.13 325	98.11 310	99.03 286	95.40 359	94.47 480	98.99 327	96.87 337	97.97 355	97.81 412	92.12 396	99.75 282	97.49 225	99.43 326	99.16 326

E4		98.87 111	98.88 107	98.81 190	99.52 130	97.23 252	97.62 278	99.61 88	98.58 170	99.18 178	99.33 118	98.29 98	99.69 323	97.99 170	99.83 126	99.52 160

E3new		98.41 201	98.34 203	98.62 234	99.19 242	96.90 283	97.32 322	99.50 141	97.40 289	98.63 281	98.92 245	97.21 207	99.65 358	97.34 232	99.52 298	99.31 272

FE-MVSNET2		99.15 57	99.22 54	98.94 164	99.70 57	97.49 225	98.62 118	99.67 68	98.85 144	99.34 133	99.54 62	98.47 77	99.81 224	98.93 92	99.91 80	99.51 164

fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 193	99.48 155	96.56 304	97.97 225	99.69 55	99.63 28	99.84 30	99.54 62	98.21 114	99.94 41	99.76 23	99.95 39	99.88 20

E2		98.70 146	98.68 140	98.73 215	99.40 180	97.10 269	97.48 301	99.57 107	98.09 221	99.00 205	99.20 155	97.90 142	99.67 338	97.73 199	99.77 170	99.43 211

MED-MVS test				99.45 64	99.58 94	98.93 79	98.68 109	99.60 90	96.46 360	99.53 83	98.77 285		99.83 195	96.67 300	99.64 251	99.58 117

MED-MVS		99.01 89	98.84 118	99.52 44	99.58 94	98.93 79	98.68 109	99.60 90	98.85 144	99.53 83	99.16 168	97.87 148	99.83 195	96.67 300	99.64 251	99.81 41

E3		98.69 150	98.68 140	98.73 215	99.40 180	97.10 269	97.48 301	99.57 107	98.09 221	99.00 205	99.20 155	97.90 142	99.67 338	97.73 199	99.77 170	99.43 211

TestfortrainingZip a		99.09 72	98.92 101	99.61 13	99.58 94	99.17 43	98.68 109	99.27 258	98.85 144	99.61 70	99.16 168	97.14 211	99.86 144	98.39 137	99.57 281	99.81 41

TestfortrainingZip				98.97 159	98.30 413	98.43 119	98.68 109	98.26 408	97.76 247	98.86 246	98.16 383	95.15 317	99.47 432		97.55 473	99.02 345

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 200	99.47 158	96.56 304	97.75 258	99.71 47	99.60 35	99.74 46	99.44 85	97.96 138	99.95 25	99.86 4	99.94 51	99.82 36

viewdifsd2359ckpt07		98.71 141	98.86 114	98.26 293	99.43 173	95.65 343	97.20 337	99.66 69	99.20 83	99.29 147	99.01 220	98.29 98	99.73 296	97.92 175	99.75 190	99.39 229

viewdifsd2359ckpt09		98.13 249	97.92 266	98.77 205	99.18 250	97.35 237	97.29 326	99.53 130	95.81 395	98.09 344	98.47 346	96.34 265	99.66 351	97.02 260	99.51 301	99.29 278

viewdifsd2359ckpt13		98.39 210	98.29 213	98.70 219	99.26 225	97.19 259	97.51 297	99.48 151	96.94 330	98.58 294	98.82 275	97.47 190	99.55 402	97.21 244	99.33 342	99.34 257

viewcassd2359sk11		98.55 181	98.51 169	98.67 224	99.29 210	96.99 275	97.39 312	99.54 126	97.73 249	98.81 256	99.08 195	97.55 177	99.66 351	97.52 219	99.67 239	99.36 248

viewdifsd2359ckpt11		98.84 118	99.04 86	98.24 297	99.56 111	95.51 349	97.38 314	99.70 52	99.16 93	99.57 72	99.40 97	98.26 104	99.71 307	98.55 125	99.82 133	99.50 168

viewmacassd2359aftdt		98.86 115	98.87 110	98.83 186	99.53 127	97.32 241	97.70 265	99.64 76	98.22 201	99.25 163	99.27 131	98.40 86	99.61 377	97.98 171	99.87 100	99.55 137

viewmsd2359difaftdt		98.84 118	99.04 86	98.24 297	99.56 111	95.51 349	97.38 314	99.70 52	99.16 93	99.57 72	99.40 97	98.26 104	99.71 307	98.55 125	99.82 133	99.50 168

diffmvs_AUTHOR		98.50 192	98.59 159	98.23 300	99.35 196	95.48 353	96.61 376	99.60 90	98.37 183	98.90 233	99.00 224	97.37 195	99.76 270	98.22 147	99.85 109	99.46 197

FE-MVSNET		98.59 173	98.50 172	98.87 176	99.58 94	97.30 242	98.08 195	99.74 43	96.94 330	98.97 214	99.10 188	96.94 224	99.74 289	97.33 234	99.86 107	99.55 137

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 157	99.59 92	97.18 262	97.44 309	99.83 26	99.56 39	99.91 12	99.34 115	99.36 13	99.93 53	99.83 10	99.98 12	99.85 30

mamba_0408		98.80 128	98.88 107	98.55 252	99.27 216	96.50 307	98.00 213	99.60 90	98.93 131	99.22 168	98.84 270	98.59 67	99.89 97	97.74 197	99.72 205	99.27 284

icg_test_0407_2		98.20 240	98.38 196	97.65 363	99.03 286	94.03 417	95.78 433	99.45 170	98.16 213	99.06 190	98.71 296	98.27 102	99.68 333	97.50 220	99.45 317	99.22 301

SSM_04072		98.80 128	98.88 107	98.56 250	99.27 216	96.50 307	98.00 213	99.60 90	98.93 131	99.22 168	98.84 270	98.59 67	99.90 81	97.74 197	99.72 205	99.27 284

SSM_0407		98.86 115	98.96 99	98.55 252	99.27 216	96.50 307	98.04 204	99.66 69	99.09 109	99.22 168	99.02 209	98.79 43	99.87 135	97.87 181	99.72 205	99.27 284

viewmambaseed2359dif		98.19 241	98.26 220	97.99 328	99.02 293	95.03 379	96.59 379	99.53 130	96.21 370	99.00 205	98.99 226	97.62 169	99.61 377	97.62 207	99.72 205	99.33 263

IMVS_0407		98.39 210	98.64 148	97.66 361	99.03 286	94.03 417	98.10 192	99.45 170	98.16 213	99.06 190	98.71 296	98.27 102	99.71 307	97.50 220	99.45 317	99.22 301

viewmanbaseed2359cas		98.58 175	98.54 165	98.70 219	99.28 213	97.13 268	97.47 305	99.55 120	97.55 269	98.96 219	98.92 245	97.77 156	99.59 386	97.59 211	99.77 170	99.39 229

IMVS_0404		98.07 254	98.20 227	97.69 356	99.03 286	94.03 417	96.67 372	99.45 170	98.16 213	98.03 351	98.71 296	96.80 235	99.82 207	97.50 220	99.45 317	99.22 301

SSM_0404		98.90 107	99.01 91	98.57 245	99.42 175	96.59 299	98.13 185	99.66 69	99.09 109	99.30 146	99.02 209	98.79 43	99.89 97	97.87 181	99.80 151	99.23 296

IMVS_0403		98.34 214	98.56 162	97.66 361	99.03 286	94.03 417	97.98 221	99.45 170	98.16 213	98.89 236	98.71 296	97.90 142	99.74 289	97.50 220	99.45 317	99.22 301

SD_0403		96.28 389	95.83 393	97.64 366	98.72 349	94.30 404	98.87 89	98.77 367	97.80 243	96.53 451	98.02 396	97.34 197	99.47 432	76.93 530	99.48 313	99.16 326

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 259	99.51 133	95.82 338	97.62 278	99.78 36	99.72 14	99.90 14	99.48 75	98.66 59	99.89 97	99.85 6	99.93 57	99.89 16

ME-MVS		98.61 169	98.33 208	99.44 65	99.24 227	98.93 79	97.45 307	99.06 309	98.14 219	99.06 190	98.77 285	96.97 223	99.82 207	96.67 300	99.64 251	99.58 117

NormalMVS		98.26 230	97.97 259	99.15 121	99.64 77	97.83 192	98.28 166	99.43 184	99.24 76	98.80 258	98.85 265	89.76 425	99.94 41	98.04 163	99.67 239	99.68 73

lecture		99.25 40	99.12 71	99.62 9	99.64 77	99.40 11	98.89 88	99.51 137	99.19 88	99.37 125	99.25 142	98.36 89	99.88 115	98.23 146	99.67 239	99.59 109

SymmetryMVS		98.05 256	97.71 284	99.09 132	99.29 210	97.83 192	98.28 166	97.64 432	99.24 76	98.80 258	98.85 265	89.76 425	99.94 41	98.04 163	99.50 309	99.49 176

Elysia		99.15 57	99.14 68	99.18 113	99.63 83	97.92 181	98.50 137	99.43 184	99.67 20	99.70 51	99.13 180	96.66 246	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	97.92 181	98.50 137	99.43 184	99.67 20	99.70 51	99.13 180	96.66 246	99.98 4	99.54 44	99.96 28	99.64 86

KinetiMVS		99.03 87	99.02 89	99.03 145	99.70 57	97.48 228	98.43 148	99.29 251	99.70 15	99.60 71	99.07 196	96.13 274	99.94 41	99.42 55	99.87 100	99.68 73

LuminaMVS		98.39 210	98.20 227	98.98 157	99.50 139	97.49 225	97.78 249	97.69 427	98.75 149	99.49 94	99.25 142	92.30 393	99.94 41	99.14 75	99.88 95	99.50 168

VortexMVS		97.98 265	98.31 210	97.02 409	98.88 321	91.45 475	98.03 206	99.47 161	98.65 158	99.55 77	99.47 78	91.49 407	99.81 224	99.32 60	99.91 80	99.80 45

AstraMVS		98.16 248	98.07 248	98.41 276	99.51 133	95.86 335	98.00 213	95.14 494	98.97 126	99.43 108	99.24 144	93.25 371	99.84 177	99.21 70	99.87 100	99.54 143

guyue		98.01 260	97.93 265	98.26 293	99.45 166	95.48 353	98.08 195	96.24 474	98.89 137	99.34 133	99.14 178	91.32 410	99.82 207	99.07 80	99.83 126	99.48 187

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 79	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 52	99.93 57	99.60 102

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 45	98.98 71	99.51 10	99.85 19	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 39	99.61 100

tt0320		99.61 8	99.65 9	99.48 57	99.71 49	98.94 78	99.54 8	99.83 26	99.87 5	99.89 18	99.82 5	98.75 47	99.90 81	99.54 44	99.95 39	99.59 109

fmvsm_s_conf0.5_n_8		99.13 66	99.26 50	98.74 213	99.51 133	96.44 312	97.65 273	99.65 74	99.66 23	99.78 39	99.48 75	97.92 141	99.93 53	99.72 30	99.95 39	99.87 22

fmvsm_s_conf0.5_n_7		98.83 121	99.04 86	98.20 302	99.30 208	94.83 387	97.23 332	99.36 210	98.64 159	99.84 30	99.43 88	98.10 126	99.91 74	99.56 41	99.96 28	99.87 22

fmvsm_s_conf0.5_n_6		99.08 78	99.21 57	98.69 221	99.36 191	96.51 306	97.62 278	99.68 62	98.43 181	99.85 27	99.10 188	99.12 23	99.88 115	99.77 22	99.92 71	99.67 78

fmvsm_s_conf0.5_n_5		99.07 81	99.10 79	98.99 153	99.47 158	97.22 255	97.40 311	99.83 26	97.61 261	99.85 27	99.30 125	98.80 41	99.95 25	99.71 32	99.90 88	99.78 50

fmvsm_s_conf0.5_n_4		99.01 89	99.22 54	98.38 280	99.31 204	95.48 353	97.56 289	99.73 44	98.87 139	99.75 44	99.27 131	98.80 41	99.86 144	99.80 17	99.90 88	99.81 41

SSC-MVS3.2		98.53 186	98.79 123	97.74 351	99.46 161	93.62 440	96.45 386	99.34 222	99.33 65	98.93 229	98.70 303	97.90 142	99.90 81	99.12 76	99.92 71	99.69 72

testing3-2		93.78 458	93.91 449	93.39 507	98.82 333	81.72 536	97.76 255	95.28 492	98.60 166	96.54 450	96.66 461	65.85 527	99.62 369	96.65 304	98.99 399	98.82 378

myMVS_eth3d28		92.92 475	92.31 471	94.77 487	97.84 451	87.59 513	96.19 406	96.11 477	97.08 322	94.27 500	93.49 513	66.07 526	98.78 492	91.78 474	97.93 467	97.92 456

UWE-MVS-28		90.22 491	89.28 494	93.02 511	94.50 528	82.87 532	96.52 383	87.51 532	95.21 421	92.36 520	96.04 473	71.57 513	98.25 502	72.04 532	97.77 469	97.94 455

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 92	98.21 143	97.82 243	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	98.78 200	99.46 161	96.58 302	97.65 273	99.72 45	99.47 47	99.86 24	99.50 68	98.94 31	99.89 97	99.75 26	99.97 21	99.86 28

fmvsm_s_conf0.5_n_2		99.14 62	99.31 41	98.63 232	99.49 147	96.08 326	97.38 314	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	98.65 226	99.69 61	96.08 326	97.49 300	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		97.50 304	97.11 327	98.67 224	99.02 293	96.85 287	98.16 182	99.71 47	98.32 190	98.52 305	98.54 333	83.39 480	99.95 25	98.79 101	99.56 285	99.19 312

BP-MVS1		97.40 316	96.97 334	98.71 218	99.07 274	96.81 289	98.34 163	97.18 447	98.58 170	98.17 333	98.61 325	84.01 476	99.94 41	98.97 89	99.78 163	99.37 241

reproduce_monomvs		95.00 438	95.25 422	94.22 494	97.51 478	83.34 529	97.86 239	98.44 398	98.51 177	99.29 147	99.30 125	67.68 520	99.56 398	98.89 96	99.81 140	99.77 53

mmtdpeth		99.30 33	99.42 25	98.92 170	99.58 94	96.89 284	99.48 13	99.92 8	99.92 2	98.26 330	99.80 11	98.33 95	99.91 74	99.56 41	99.95 39	99.97 4

reproduce_model		99.15 57	98.97 97	99.67 4	99.33 202	99.44 9	98.15 183	99.47 161	99.12 98	99.52 87	99.32 123	98.31 96	99.90 81	97.78 189	99.73 196	99.66 80

reproduce-ours		99.09 72	98.90 104	99.67 4	99.27 216	99.49 5	98.00 213	99.42 190	99.05 116	99.48 96	99.27 131	98.29 98	99.89 97	97.61 208	99.71 214	99.62 92

our_new_method		99.09 72	98.90 104	99.67 4	99.27 216	99.49 5	98.00 213	99.42 190	99.05 116	99.48 96	99.27 131	98.29 98	99.89 97	97.61 208	99.71 214	99.62 92

mmdepth		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

monomultidepth		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

mvs5depth		99.30 33	99.59 12	98.44 273	99.65 71	95.35 362	99.82 3	99.94 3	99.83 7	99.42 112	99.94 2	98.13 124	99.96 13	99.63 36	99.96 28	100.00 1

MVStest1		95.86 409	95.60 402	96.63 429	95.87 522	91.70 470	97.93 227	98.94 331	98.03 224	99.56 74	99.66 32	71.83 512	98.26 501	99.35 58	99.24 361	99.91 13

ttmdpeth		97.91 268	98.02 252	97.58 372	98.69 362	94.10 413	98.13 185	98.90 341	97.95 230	97.32 407	99.58 47	95.95 289	98.75 493	96.41 328	99.22 365	99.87 22

WBMVS		95.18 433	94.78 435	96.37 437	97.68 465	89.74 502	95.80 432	98.73 376	97.54 271	98.30 324	98.44 349	70.06 514	99.82 207	96.62 306	99.87 100	99.54 143

dongtai		76.24 500	75.95 503	77.12 518	92.39 532	67.91 542	90.16 524	59.44 543	82.04 528	89.42 528	94.67 505	49.68 540	81.74 536	48.06 535	77.66 533	81.72 532

kuosan		69.30 501	68.95 504	70.34 519	87.68 539	65.00 543	91.11 521	59.90 542	69.02 532	74.46 537	88.89 531	48.58 541	68.03 538	28.61 536	72.33 536	77.99 533

MVSMamba_PlusPlus		98.83 121	98.98 96	98.36 284	99.32 203	96.58 302	98.90 84	99.41 194	99.75 10	98.72 269	99.50 68	96.17 272	99.94 41	99.27 64	99.78 163	98.57 415

MGCFI-Net		98.34 214	98.28 214	98.51 262	98.47 395	97.59 220	98.96 78	99.48 151	99.18 91	97.40 402	95.50 487	98.66 59	99.50 421	98.18 150	98.71 420	98.44 425

testing91		93.32 466	92.27 472	96.47 433	97.54 471	91.25 482	96.17 410	96.76 464	97.18 316	93.65 512	93.50 512	65.11 529	99.63 366	93.04 446	97.45 478	98.53 416

testing11		93.08 471	92.02 477	96.26 442	97.56 469	90.83 491	96.32 397	95.70 487	96.47 359	92.66 517	93.73 509	64.36 530	99.59 386	93.77 425	97.57 472	98.37 434

testing99		93.04 472	91.98 480	96.23 445	97.53 473	90.70 494	96.35 395	95.94 482	96.87 337	93.41 513	93.43 514	63.84 531	99.59 386	93.24 442	97.19 488	98.40 430

UBG		93.25 468	92.32 470	96.04 456	97.72 457	90.16 497	95.92 426	95.91 483	96.03 381	93.95 509	93.04 517	69.60 516	99.52 414	90.72 496	97.98 465	98.45 422

UWE-MVS		92.38 481	91.76 484	94.21 495	97.16 488	84.65 523	95.42 447	88.45 531	95.96 385	96.17 462	95.84 481	66.36 523	99.71 307	91.87 473	98.64 427	98.28 437

ETVMVS		92.60 478	91.08 487	97.18 400	97.70 462	93.65 439	96.54 380	95.70 487	96.51 354	94.68 496	92.39 521	61.80 535	99.50 421	86.97 510	97.41 481	98.40 430

sasdasda		98.34 214	98.26 220	98.58 242	98.46 397	97.82 197	98.96 78	99.46 166	99.19 88	97.46 396	95.46 490	98.59 67	99.46 436	98.08 158	98.71 420	98.46 419

testing222		91.96 487	90.37 490	96.72 427	97.47 480	92.59 456	96.11 413	94.76 496	96.83 341	92.90 515	92.87 518	57.92 537	99.55 402	86.93 511	97.52 474	98.00 453

WB-MVSnew		95.73 414	95.57 405	96.23 445	96.70 504	90.70 494	96.07 415	93.86 510	95.60 403	97.04 420	95.45 494	96.00 281	99.55 402	91.04 488	98.31 444	98.43 427

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 164	99.65 71	97.05 271	97.80 247	99.76 39	98.70 157	99.78 39	99.11 185	98.79 43	99.95 25	99.85 6	99.96 28	99.83 33

fmvsm_l_conf0.5_n		99.21 47	99.28 46	99.02 148	99.64 77	97.28 248	97.82 243	99.76 39	98.73 150	99.82 34	99.09 194	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	98.80 193	99.75 34	96.59 299	97.97 225	99.86 17	98.22 201	99.88 21	99.71 22	98.59 67	99.84 177	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 228	99.71 49	96.10 321	97.87 238	99.85 19	98.56 175	99.90 14	99.68 25	98.69 57	99.85 158	99.72 30	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 71	99.20 58	98.78 200	99.55 117	96.59 299	97.79 248	99.82 31	98.21 203	99.81 36	99.53 64	98.46 82	99.84 177	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 72	99.26 50	98.61 238	99.55 117	96.09 324	97.74 260	99.81 32	98.55 176	99.85 27	99.55 56	98.60 66	99.84 177	99.69 35	99.98 12	99.89 16

MM		98.22 235	97.99 255	98.91 172	98.66 372	96.97 276	97.89 234	94.44 500	99.54 40	98.95 220	99.14 178	93.50 368	99.92 65	99.80 17	99.96 28	99.85 30

WAC-MVS						90.90 489								91.37 483

Syy-MVS		96.04 399	95.56 406	97.49 384	97.10 490	94.48 399	96.18 408	96.58 468	95.65 401	94.77 494	92.29 523	91.27 411	99.36 452	98.17 152	98.05 461	98.63 409

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 151	97.77 252	99.90 12	99.33 65	99.97 3	99.66 32	99.71 3	99.96 13	99.79 19	99.99 5	99.96 8

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 149	98.08 195	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		91.92 488	90.45 489	96.30 439	97.10 490	90.90 489	96.18 408	96.58 468	95.65 401	94.77 494	92.29 523	53.88 538	99.36 452	89.59 503	98.05 461	98.63 409

testing3		93.51 462	92.09 475	97.75 349	98.60 379	94.40 401	97.32 322	95.26 493	97.56 267	96.79 438	95.50 487	53.57 539	99.77 264	95.26 381	98.97 403	99.08 334

SSC-MVS		98.71 141	98.74 127	98.62 234	99.72 45	96.08 326	98.74 99	98.64 384	99.74 12	99.67 59	99.24 144	94.57 337	99.95 25	99.11 77	99.24 361	99.82 36

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 77	98.10 154	97.68 267	99.84 23	99.29 71	99.92 8	99.57 49	99.60 5	99.96 13	99.74 27	99.98 12	99.89 16

WB-MVS		98.52 190	98.55 163	98.43 274	99.65 71	95.59 344	98.52 130	98.77 367	99.65 25	99.52 87	99.00 224	94.34 346	99.93 53	98.65 114	98.83 411	99.76 58

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 257	99.66 70	96.97 276	98.00 213	99.85 19	99.24 76	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 398

dmvs_re		95.98 403	95.39 414	97.74 351	98.86 324	97.45 232	98.37 159	95.69 489	97.95 230	96.56 449	95.95 476	90.70 417	97.68 511	88.32 506	96.13 505	98.11 445

SDMVSNet		99.23 45	99.32 39	98.96 161	99.68 64	97.35 237	98.84 95	99.48 151	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 172	99.92 71	99.57 124

dmvs_testset		92.94 474	92.21 474	95.13 484	98.59 382	90.99 488	97.65 273	92.09 519	96.95 329	94.00 507	93.55 511	92.34 392	96.97 520	72.20 531	92.52 525	97.43 481

sd_testset		99.28 36	99.31 41	99.19 112	99.68 64	98.06 164	99.41 17	99.30 243	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 241	99.92 71	99.57 124

test_fmvsm_n_1920		99.33 30	99.45 23	98.99 153	99.57 103	97.73 208	97.93 227	99.83 26	99.22 79	99.93 6	99.30 125	99.42 11	99.96 13	99.85 6	99.99 5	99.29 278

test_cas_vis1_n_1920		98.33 218	98.68 140	97.27 396	99.69 61	92.29 464	98.03 206	99.85 19	97.62 258	99.96 4	99.62 40	93.98 358	99.74 289	99.52 49	99.86 107	99.79 47

test_vis1_n_1920		98.40 204	98.92 101	96.81 423	99.74 37	90.76 493	98.15 183	99.91 10	98.33 188	99.89 18	99.55 56	95.07 320	99.88 115	99.76 23	99.93 57	99.79 47

test_vis1_n		98.31 222	98.50 172	97.73 354	99.76 30	94.17 409	98.68 109	99.91 10	96.31 366	99.79 38	99.57 49	92.85 384	99.42 445	99.79 19	99.84 114	99.60 102

test_fmvs1_n		98.09 252	98.28 214	97.52 381	99.68 64	93.47 442	98.63 116	99.93 6	95.41 415	99.68 57	99.64 37	91.88 401	99.48 429	99.82 12	99.87 100	99.62 92

mvsany_test1		97.60 298	97.54 296	97.77 345	97.72 457	95.35 362	95.36 449	97.13 450	94.13 452	99.71 49	99.33 118	97.93 140	99.30 462	97.60 210	98.94 406	98.67 407

APD_test1		98.83 121	98.66 145	99.34 83	99.78 24	99.47 8	98.42 151	99.45 170	98.28 197	98.98 210	99.19 158	97.76 157	99.58 393	96.57 311	99.55 289	98.97 356

test_vis1_rt		97.75 287	97.72 283	97.83 340	98.81 336	96.35 315	97.30 325	99.69 55	94.61 437	97.87 363	98.05 393	96.26 269	98.32 500	98.74 107	98.18 450	98.82 378

test_vis3_rt		99.14 62	99.17 60	99.07 135	99.78 24	98.38 122	98.92 83	99.94 3	97.80 243	99.91 12	99.67 30	97.15 210	98.91 488	99.76 23	99.56 285	99.92 12

test_fmvs2		98.70 146	98.97 97	97.89 335	99.54 123	94.05 414	98.55 126	99.92 8	96.78 344	99.72 47	99.78 13	96.60 250	99.67 338	99.91 2	99.90 88	99.94 10

test_fmvs1		97.72 289	97.94 263	97.07 408	98.66 372	92.39 461	97.68 267	99.81 32	95.20 422	99.54 79	99.44 85	91.56 405	99.41 446	99.78 21	99.77 170	99.40 228

test_fmvs3		99.12 69	99.41 26	98.25 295	99.76 30	95.07 378	99.05 68	99.94 3	97.78 246	99.82 34	99.84 3	98.56 73	99.71 307	99.96 1	99.96 28	99.97 4

mvsany_test3		98.87 111	98.92 101	98.74 213	99.38 184	96.94 280	98.58 123	99.10 303	96.49 357	99.96 4	99.81 8	98.18 117	99.45 439	98.97 89	99.79 158	99.83 33

testf1		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 55	98.90 135	99.43 108	99.35 111	98.86 35	99.67 338	97.81 186	99.81 140	99.24 294

APD_test2		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 55	98.90 135	99.43 108	99.35 111	98.86 35	99.67 338	97.81 186	99.81 140	99.24 294

test_f		98.67 159	98.87 110	98.05 322	99.72 45	95.59 344	98.51 135	99.81 32	96.30 368	99.78 39	99.82 5	96.14 273	98.63 496	99.82 12	99.93 57	99.95 9

FE-MVS		95.66 416	94.95 432	97.77 345	98.53 391	95.28 367	99.40 19	96.09 479	93.11 471	97.96 357	99.26 137	79.10 498	99.77 264	92.40 466	98.71 420	98.27 438

FA-MVS(test-final)		96.99 354	96.82 347	97.50 383	98.70 357	94.78 389	99.34 23	96.99 453	95.07 424	98.48 309	99.33 118	88.41 439	99.65 358	96.13 348	98.92 408	98.07 448

BridgeMVS		98.63 165	98.72 131	98.38 280	98.66 372	96.68 298	98.90 84	99.42 190	98.99 123	98.97 214	99.19 158	95.81 294	99.85 158	98.77 105	99.77 170	98.60 411

MonoMVSNet		96.25 392	96.53 372	95.39 479	96.57 506	91.01 487	98.82 97	97.68 429	98.57 172	98.03 351	99.37 104	90.92 414	97.78 510	94.99 386	93.88 523	97.38 482

patch_mono-2		98.51 191	98.63 150	98.17 305	99.38 184	94.78 389	97.36 319	99.69 55	98.16 213	98.49 307	99.29 128	97.06 215	99.97 6	98.29 143	99.91 80	99.76 58

EGC-MVSNET		85.24 497	80.54 500	99.34 83	99.77 27	99.20 38	99.08 62	99.29 251	12.08 537	20.84 538	99.42 89	97.55 177	99.85 158	97.08 256	99.72 205	98.96 358

test2506		92.39 480	91.89 482	93.89 500	99.38 184	82.28 534	99.32 26	66.03 541	99.08 113	98.77 263	99.57 49	66.26 524	99.84 177	98.71 110	99.95 39	99.54 143

test1111		96.49 376	96.82 347	95.52 475	99.42 175	87.08 515	99.22 46	87.14 533	99.11 99	99.46 101	99.58 47	88.69 433	99.86 144	98.80 100	99.95 39	99.62 92

ECVR-MVS		96.42 382	96.61 366	95.85 464	99.38 184	88.18 510	99.22 46	86.00 535	99.08 113	99.36 128	99.57 49	88.47 438	99.82 207	98.52 127	99.95 39	99.54 143

test_blank		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

tt0805		98.69 150	98.62 152	98.90 175	99.75 34	99.30 21	99.15 57	96.97 455	98.86 141	98.87 245	97.62 425	98.63 63	98.96 484	99.41 56	98.29 446	98.45 422

DVP-MVS++		98.90 107	98.70 137	99.51 49	98.43 402	99.15 52	99.43 15	99.32 230	98.17 210	99.26 155	99.02 209	98.18 117	99.88 115	97.07 257	99.45 317	99.49 176

FOURS1					99.73 38	99.67 2	99.43 15	99.54 126	99.43 54	99.26 155

MSC_two_6792asdad				99.32 91	98.43 402	98.37 124		98.86 352					99.89 97	97.14 251	99.60 268	99.71 65

PC_three_1452									93.27 467	99.40 117	98.54 333	98.22 112	97.00 519	95.17 383	99.45 317	99.49 176

No_MVS				99.32 91	98.43 402	98.37 124		98.86 352					99.89 97	97.14 251	99.60 268	99.71 65

test_one_0601					99.39 182	99.20 38		99.31 235	98.49 178	98.66 278	99.02 209	97.64 167

eth-test2					0.00 545

eth-test					0.00 545

GeoE		99.05 82	98.99 95	99.25 104	99.44 168	98.35 128	98.73 103	99.56 116	98.42 182	98.91 232	98.81 278	98.94 31	99.91 74	98.35 139	99.73 196	99.49 176

test_method		79.78 498	79.50 501	80.62 516	80.21 541	45.76 544	70.82 532	98.41 403	31.08 536	80.89 536	97.71 418	84.85 467	97.37 515	91.51 481	80.03 532	98.75 394

Anonymous20240521		98.69 150	98.87 110	98.16 307	99.77 27	95.11 377	99.08 62	99.44 178	99.34 64	99.33 136	99.55 56	94.10 357	99.94 41	99.25 67	99.96 28	99.42 216

h-mvs33		97.77 286	97.33 311	99.10 128	99.21 235	97.84 191	98.35 161	98.57 390	99.11 99	98.58 294	99.02 209	88.65 436	99.96 13	98.11 155	96.34 501	99.49 176

hse-mvs2		97.46 309	97.07 328	98.64 228	98.73 347	97.33 239	97.45 307	97.64 432	99.11 99	98.58 294	97.98 399	88.65 436	99.79 246	98.11 155	97.39 482	98.81 383

CL-MVSNet_self_test		97.44 312	97.22 318	98.08 317	98.57 386	95.78 341	94.30 486	98.79 364	96.58 353	98.60 290	98.19 380	94.74 333	99.64 363	96.41 328	98.84 410	98.82 378

KD-MVS_2432*1600		92.87 476	91.99 478	95.51 476	91.37 534	89.27 504	94.07 492	98.14 415	95.42 412	97.25 409	96.44 467	67.86 518	99.24 468	91.28 484	96.08 510	98.02 450

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 83	99.06 70	98.69 108	99.54 126	99.31 68	99.62 69	99.53 64	97.36 196	99.86 144	99.24 69	99.71 214	99.39 229

AUN-MVS		96.24 394	95.45 410	98.60 240	98.70 357	97.22 255	97.38 314	97.65 430	95.95 386	95.53 482	97.96 403	82.11 488	99.79 246	96.31 334	97.44 479	98.80 388

ZD-MVS					99.01 295	98.84 85		99.07 308	94.10 454	98.05 349	98.12 386	96.36 264	99.86 144	92.70 459	99.19 373

SR-MVS-dyc-post		98.81 126	98.55 163	99.57 21	99.20 239	99.38 12	98.48 143	99.30 243	98.64 159	98.95 220	98.96 237	97.49 188	99.86 144	96.56 315	99.39 331	99.45 203

RE-MVS-def			98.58 160		99.20 239	99.38 12	98.48 143	99.30 243	98.64 159	98.95 220	98.96 237	97.75 158		96.56 315	99.39 331	99.45 203

SED-MVS		98.91 105	98.72 131	99.49 55	99.49 147	99.17 43	98.10 192	99.31 235	98.03 224	99.66 60	99.02 209	98.36 89	99.88 115	96.91 271	99.62 261	99.41 219

IU-MVS					99.49 147	99.15 52		98.87 347	92.97 474	99.41 114				96.76 288	99.62 261	99.66 80

OPU-MVS				98.82 188	98.59 382	98.30 132	98.10 192				98.52 337	98.18 117	98.75 493	94.62 396	99.48 313	99.41 219

test_241102_TWO								99.30 243	98.03 224	99.26 155	99.02 209	97.51 184	99.88 115	96.91 271	99.60 268	99.66 80

test_241102_ONE					99.49 147	99.17 43		99.31 235	97.98 227	99.66 60	98.90 251	98.36 89	99.48 429

SF-MVS		98.53 186	98.27 217	99.32 91	99.31 204	98.75 90	98.19 177	99.41 194	96.77 345	98.83 251	98.90 251	97.80 154	99.82 207	95.68 369	99.52 298	99.38 238

cl22		95.79 412	95.39 414	96.98 412	96.77 502	92.79 453	94.40 483	98.53 394	94.59 438	97.89 361	98.17 381	82.82 485	99.24 468	96.37 330	99.03 391	98.92 365

miper_ehance_all_eth		97.06 347	97.03 330	97.16 404	97.83 452	93.06 446	94.66 473	99.09 305	95.99 384	98.69 272	98.45 348	92.73 387	99.61 377	96.79 284	99.03 391	98.82 378

miper_enhance_ethall		96.01 400	95.74 395	96.81 423	96.41 514	92.27 465	93.69 502	98.89 344	91.14 498	98.30 324	97.35 445	90.58 418	99.58 393	96.31 334	99.03 391	98.60 411

ZNCC-MVS		98.68 156	98.40 190	99.54 31	99.57 103	99.21 32	98.46 145	99.29 251	97.28 302	98.11 342	98.39 354	98.00 133	99.87 135	96.86 281	99.64 251	99.55 137

dcpmvs_2		98.78 132	99.11 73	97.78 344	99.56 111	93.67 437	99.06 66	99.86 17	99.50 43	99.66 60	99.26 137	97.21 207	99.99 2	98.00 168	99.91 80	99.68 73

cl____		97.02 350	96.83 346	97.58 372	97.82 453	94.04 416	94.66 473	99.16 292	97.04 324	98.63 281	98.71 296	88.68 435	99.69 323	97.00 262	99.81 140	99.00 350

DIV-MVS_self_test		97.02 350	96.84 345	97.58 372	97.82 453	94.03 417	94.66 473	99.16 292	97.04 324	98.63 281	98.71 296	88.69 433	99.69 323	97.00 262	99.81 140	99.01 347

eth_miper_zixun_eth		97.23 333	97.25 315	97.17 402	98.00 442	92.77 454	94.71 468	99.18 285	97.27 304	98.56 298	98.74 292	91.89 400	99.69 323	97.06 259	99.81 140	99.05 338

9.14			97.78 276		99.07 274		97.53 294	99.32 230	95.53 408	98.54 302	98.70 303	97.58 174	99.76 270	94.32 409	99.46 315

uanet_test		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

DCPMVS		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

save fliter					99.11 265	97.97 174	96.53 382	99.02 321	98.24 198

ET-MVSNet_ETH3D		94.30 448	93.21 460	97.58 372	98.14 432	94.47 400	94.78 467	93.24 515	94.72 434	89.56 527	95.87 479	78.57 502	99.81 224	96.91 271	97.11 491	98.46 419

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 99	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		98.00 261	97.74 279	98.80 193	98.72 349	98.09 155	98.05 202	99.60 90	97.39 290	96.63 445	95.55 485	97.68 161	99.80 233	96.73 292	99.27 355	98.52 417

miper_refine_blended		92.87 476	91.99 478	95.51 476	91.37 534	89.27 504	94.07 492	98.14 415	95.42 412	97.25 409	96.44 467	67.86 518	99.24 468	91.28 484	96.08 510	98.02 450

miper_lstm_enhance		97.18 338	97.16 321	97.25 398	98.16 430	92.85 452	95.15 458	99.31 235	97.25 306	98.74 268	98.78 283	90.07 421	99.78 258	97.19 245	99.80 151	99.11 333

ETV-MVS		98.03 257	97.86 272	98.56 250	98.69 362	98.07 161	97.51 297	99.50 141	98.10 220	97.50 393	95.51 486	98.41 85	99.88 115	96.27 338	99.24 361	97.71 471

CS-MVS		99.13 66	99.10 79	99.24 106	99.06 279	99.15 52	99.36 22	99.88 15	99.36 63	98.21 332	98.46 347	98.68 58	99.93 53	99.03 85	99.85 109	98.64 408

D2MVS		97.84 282	97.84 273	97.83 340	99.14 260	94.74 391	96.94 353	98.88 345	95.84 391	98.89 236	98.96 237	94.40 343	99.69 323	97.55 214	99.95 39	99.05 338

DVP-MVS		98.77 135	98.52 168	99.52 44	99.50 139	99.21 32	98.02 209	98.84 356	97.97 228	99.08 188	99.02 209	97.61 171	99.88 115	96.99 264	99.63 257	99.48 187
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									98.17 210	99.08 188	99.02 209	97.89 146	99.88 115	97.07 257	99.71 214	99.70 70

test_0728_SECOND				99.60 16	99.50 139	99.23 30	98.02 209	99.32 230					99.88 115	96.99 264	99.63 257	99.68 73

test0726					99.50 139	99.21 32	98.17 181	99.35 216	97.97 228	99.26 155	99.06 197	97.61 171

SR-MVS		98.71 141	98.43 186	99.57 21	99.18 250	99.35 16	98.36 160	99.29 251	98.29 195	98.88 240	98.85 265	97.53 181	99.87 135	96.14 346	99.31 347	99.48 187

DPM-MVS		96.32 386	95.59 404	98.51 262	98.76 343	97.21 257	94.54 479	98.26 408	91.94 488	96.37 459	97.25 448	93.06 379	99.43 443	91.42 482	98.74 416	98.89 370

GST-MVS		98.61 169	98.30 211	99.52 44	99.51 133	99.20 38	98.26 170	99.25 266	97.44 285	98.67 276	98.39 354	97.68 161	99.85 158	96.00 351	99.51 301	99.52 160

test_yl		96.69 364	96.29 383	97.90 333	98.28 416	95.24 368	97.29 326	97.36 437	98.21 203	98.17 333	97.86 407	86.27 450	99.55 402	94.87 390	98.32 442	98.89 370

thisisatest0530		95.27 430	94.45 442	97.74 351	99.19 242	94.37 402	97.86 239	90.20 528	97.17 317	98.22 331	97.65 422	73.53 511	99.90 81	96.90 276	99.35 338	98.95 359

Anonymous20240529		98.93 103	98.87 110	99.12 124	99.19 242	98.22 142	99.01 71	98.99 327	99.25 75	99.54 79	99.37 104	97.04 216	99.80 233	97.89 176	99.52 298	99.35 254

Anonymous202405211		97.90 269	97.50 299	99.08 133	98.90 315	98.25 136	98.53 129	96.16 475	98.87 139	99.11 183	98.86 262	90.40 420	99.78 258	97.36 231	99.31 347	99.19 312

DCV-MVSNet		96.69 364	96.29 383	97.90 333	98.28 416	95.24 368	97.29 326	97.36 437	98.21 203	98.17 333	97.86 407	86.27 450	99.55 402	94.87 390	98.32 442	98.89 370

tttt0517		95.64 417	94.98 430	97.64 366	99.36 191	93.81 432	98.72 104	90.47 527	98.08 223	98.67 276	98.34 361	73.88 510	99.92 65	97.77 191	99.51 301	99.20 306

our_test_3		97.39 317	97.73 282	96.34 438	98.70 357	89.78 501	94.61 476	98.97 330	96.50 356	99.04 200	98.85 265	95.98 286	99.84 177	97.26 240	99.67 239	99.41 219

thisisatest0515		94.12 453	93.16 461	96.97 413	98.60 379	92.90 451	93.77 501	90.61 526	94.10 454	96.91 427	95.87 479	74.99 508	99.80 233	94.52 399	99.12 384	98.20 440

ppachtmachnet_test		97.50 304	97.74 279	96.78 425	98.70 357	91.23 484	94.55 478	99.05 313	96.36 363	99.21 171	98.79 281	96.39 260	99.78 258	96.74 290	99.82 133	99.34 257

SMA-MVS		98.40 204	98.03 251	99.51 49	99.16 254	99.21 32	98.05 202	99.22 274	94.16 451	98.98 210	99.10 188	97.52 183	99.79 246	96.45 325	99.64 251	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																98.81 383

DPE-MVS		98.59 173	98.26 220	99.57 21	99.27 216	99.15 52	97.01 347	99.39 200	97.67 254	99.44 107	98.99 226	97.53 181	99.89 97	95.40 379	99.68 233	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					99.36 191	99.10 65				99.05 198

thres100view900		94.19 450	93.67 454	95.75 467	99.06 279	91.35 478	98.03 206	94.24 506	98.33 188	97.40 402	94.98 499	79.84 492	99.62 369	83.05 521	98.08 458	96.29 501

tfpnnormal		98.90 107	98.90 104	98.91 172	99.67 68	97.82 197	99.00 73	99.44 178	99.45 50	99.51 92	99.24 144	98.20 116	99.86 144	95.92 355	99.69 227	99.04 342

tfpn200view9		94.03 454	93.44 456	95.78 466	98.93 307	91.44 476	97.60 284	94.29 503	97.94 232	97.10 414	94.31 507	79.67 494	99.62 369	83.05 521	98.08 458	96.29 501

c3_l		97.36 320	97.37 307	97.31 393	98.09 437	93.25 444	95.01 461	99.16 292	97.05 323	98.77 263	98.72 295	92.88 382	99.64 363	96.93 270	99.76 186	99.05 338

CHOSEN 280x420		95.51 422	95.47 408	95.65 472	98.25 419	88.27 509	93.25 511	98.88 345	93.53 464	94.65 497	97.15 451	86.17 452	99.93 53	97.41 229	99.93 57	98.73 397

CANet		97.87 275	97.76 277	98.19 304	97.75 456	95.51 349	96.76 365	99.05 313	97.74 248	96.93 424	98.21 378	95.59 302	99.89 97	97.86 183	99.93 57	99.19 312

Fast-Effi-MVS+-dtu		98.27 228	98.09 243	98.81 190	98.43 402	98.11 151	97.61 283	99.50 141	98.64 159	97.39 404	97.52 432	98.12 125	99.95 25	96.90 276	98.71 420	98.38 432

Effi-MVS+-dtu		98.26 230	97.90 269	99.35 80	98.02 441	99.49 5	98.02 209	99.16 292	98.29 195	97.64 379	97.99 398	96.44 257	99.95 25	96.66 303	98.93 407	98.60 411

CANet_DTU		97.26 329	97.06 329	97.84 339	97.57 468	94.65 396	96.19 406	98.79 364	97.23 312	95.14 488	98.24 375	93.22 373	99.84 177	97.34 232	99.84 114	99.04 342

MGCNet		97.44 312	97.01 332	98.72 217	96.42 513	96.74 294	97.20 337	91.97 523	98.46 180	98.30 324	98.79 281	92.74 386	99.91 74	99.30 62	99.94 51	99.52 160

MP-MVS-pluss		98.57 176	98.23 225	99.60 16	99.69 61	99.35 16	97.16 342	99.38 202	94.87 430	98.97 214	98.99 226	98.01 132	99.88 115	97.29 238	99.70 223	99.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 204	98.00 254	99.61 13	99.57 103	99.25 28	98.57 124	99.35 216	97.55 269	99.31 145	97.71 418	94.61 336	99.88 115	96.14 346	99.19 373	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												84.74 469				98.81 383

sam_mvs												84.29 475

IterMVS-SCA-FT		97.85 281	98.18 233	96.87 419	99.27 216	91.16 485	95.53 441	99.25 266	99.10 106	99.41 114	99.35 111	93.10 377	99.96 13	98.65 114	99.94 51	99.49 176

TSAR-MVS + MP.		98.63 165	98.49 177	99.06 141	99.64 77	97.90 185	98.51 135	98.94 331	96.96 328	99.24 165	98.89 257	97.83 150	99.81 224	96.88 278	99.49 312	99.48 187
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xiu_mvs_v1_base_debu		97.86 276	98.17 234	96.92 416	98.98 300	93.91 427	96.45 386	99.17 289	97.85 240	98.41 316	97.14 452	98.47 77	99.92 65	98.02 165	99.05 387	96.92 489

OPM-MVS		98.56 177	98.32 209	99.25 104	99.41 178	98.73 94	97.13 344	99.18 285	97.10 321	98.75 266	98.92 245	98.18 117	99.65 358	96.68 299	99.56 285	99.37 241
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 137	98.48 178	99.57 21	99.58 94	99.29 23	97.82 243	99.25 266	96.94 330	98.78 260	99.12 183	98.02 131	99.84 177	97.13 253	99.67 239	99.59 109

ambc				98.24 297	98.82 333	95.97 331	98.62 118	99.00 326		99.27 151	99.21 153	96.99 221	99.50 421	96.55 318	99.50 309	99.26 290

MTGPA								99.20 277

SPE-MVS-test		99.13 66	99.09 81	99.26 101	99.13 262	98.97 73	99.31 30	99.88 15	99.44 52	98.16 336	98.51 338	98.64 61	99.93 53	98.91 93	99.85 109	98.88 373

Effi-MVS+		98.02 258	97.82 274	98.62 234	98.53 391	97.19 259	97.33 321	99.68 62	97.30 300	96.68 443	97.46 438	98.56 73	99.80 233	96.63 305	98.20 449	98.86 375

xiu_mvs_v2_base		97.16 340	97.49 300	96.17 449	98.54 389	92.46 459	95.45 445	98.84 356	97.25 306	97.48 395	96.49 464	98.31 96	99.90 81	96.34 333	98.68 425	96.15 505

xiu_mvs_v1_base		97.86 276	98.17 234	96.92 416	98.98 300	93.91 427	96.45 386	99.17 289	97.85 240	98.41 316	97.14 452	98.47 77	99.92 65	98.02 165	99.05 387	96.92 489

new-patchmatchnet		98.35 213	98.74 127	97.18 400	99.24 227	92.23 466	96.42 390	99.48 151	98.30 192	99.69 55	99.53 64	97.44 191	99.82 207	98.84 99	99.77 170	99.49 176

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

pmmvs5		97.64 296	97.49 300	98.08 317	99.14 260	95.12 376	96.70 369	99.05 313	93.77 461	98.62 285	98.83 272	93.23 372	99.75 282	98.33 142	99.76 186	99.36 248

test_post1							97.59 286				20.48 539	83.07 483	99.66 351	94.16 410

test_post											21.25 538	83.86 478	99.70 314

Fast-Effi-MVS+		97.67 294	97.38 306	98.57 245	98.71 353	97.43 234	97.23 332	99.45 170	94.82 432	96.13 463	96.51 463	98.52 75	99.91 74	96.19 342	98.83 411	98.37 434

patchmatchnet-post											98.77 285	84.37 472	99.85 158

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 210	98.18 144	99.49 12	99.51 137	99.70 15	99.80 37	99.68 25	96.84 229	99.83 195	99.21 70	99.91 80	99.77 53

pmmvs-eth3d		98.47 195	98.34 203	98.86 178	99.30 208	97.76 204	97.16 342	99.28 255	95.54 407	99.42 112	99.19 158	97.27 202	99.63 366	97.89 176	99.97 21	99.20 306

GG-mvs-BLEND				94.76 488	94.54 527	92.13 467	99.31 30	80.47 539		88.73 530	91.01 529	67.59 521	98.16 504	82.30 525	94.53 521	93.98 513

xiu_mvs_v1_base_debi		97.86 276	98.17 234	96.92 416	98.98 300	93.91 427	96.45 386	99.17 289	97.85 240	98.41 316	97.14 452	98.47 77	99.92 65	98.02 165	99.05 387	96.92 489

Anonymous20231206		98.21 238	98.21 226	98.20 302	99.51 133	95.43 358	98.13 185	99.32 230	96.16 375	98.93 229	98.82 275	96.00 281	99.83 195	97.32 236	99.73 196	99.36 248

MTAPA		98.88 110	98.64 148	99.61 13	99.67 68	99.36 15	98.43 148	99.20 277	98.83 148	98.89 236	98.90 251	96.98 222	99.92 65	97.16 248	99.70 223	99.56 130

MTMP							97.93 227	91.91 524

gm-plane-assit					94.83 526	81.97 535			88.07 519		94.99 498		99.60 381	91.76 475

test9_res														93.28 440	99.15 378	99.38 238

MVP-Stereo		98.08 253	97.92 266	98.57 245	98.96 303	96.79 290	97.90 233	99.18 285	96.41 362	98.46 310	98.95 241	95.93 290	99.60 381	96.51 321	98.98 402	99.31 272
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 353	98.08 159	95.96 421	99.03 318	91.40 494	95.85 471	97.53 429	96.52 253	99.76 270

train_agg		97.10 342	96.45 377	99.07 135	98.71 353	98.08 159	95.96 421	99.03 318	91.64 489	95.85 471	97.53 429	96.47 255	99.76 270	93.67 427	99.16 376	99.36 248

gg-mvs-nofinetune		92.37 482	91.20 486	95.85 464	95.80 523	92.38 462	99.31 30	81.84 538	99.75 10	91.83 522	99.74 18	68.29 517	99.02 481	87.15 509	97.12 490	96.16 504

SCA		96.41 383	96.66 361	95.67 470	98.24 421	88.35 508	95.85 430	96.88 461	96.11 376	97.67 378	98.67 309	93.10 377	99.85 158	94.16 410	99.22 365	98.81 383

Patchmatch-test		96.55 372	96.34 380	97.17 402	98.35 409	93.06 446	98.40 156	97.79 423	97.33 295	98.41 316	98.67 309	83.68 479	99.69 323	95.16 384	99.31 347	98.77 391

test_8					98.67 367	98.01 167	95.91 427	99.02 321	91.64 489	95.79 474	97.50 433	96.47 255	99.76 270

MS-PatchMatch		97.68 293	97.75 278	97.45 388	98.23 424	93.78 433	97.29 326	98.84 356	96.10 377	98.64 280	98.65 314	96.04 278	99.36 452	96.84 282	99.14 379	99.20 306

Patchmatch-RL test		97.26 329	97.02 331	97.99 328	99.52 130	95.53 348	96.13 411	99.71 47	97.47 277	99.27 151	99.16 168	84.30 474	99.62 369	97.89 176	99.77 170	98.81 383

cdsmvs_eth3d_5k		24.66 502	32.88 505	0.00 522	0.00 545	0.00 547	0.00 533	99.10 303	0.00 540	0.00 541	97.58 426	99.21 18	0.00 541	0.00 539	0.00 539	0.00 537

pcd_1.5k_mvsjas		8.17 505	10.90 508	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	98.07 127	0.00 541	0.00 539	0.00 539	0.00 537

agg_prior2														92.50 464	99.16 376	99.37 241

agg_prior					98.68 366	97.99 170		99.01 324		95.59 475			99.77 264

tmp_tt		78.77 499	78.73 502	78.90 517	58.45 542	74.76 541	94.20 488	78.26 540	39.16 535	86.71 531	92.82 519	80.50 490	75.19 537	86.16 515	92.29 526	86.74 531

canonicalmvs		98.34 214	98.26 220	98.58 242	98.46 397	97.82 197	98.96 78	99.46 166	99.19 88	97.46 396	95.46 490	98.59 67	99.46 436	98.08 158	98.71 420	98.46 419

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 55	98.93 131	99.65 63	99.72 21	98.93 33	99.95 25	99.11 77	100.00 1	99.82 36

alignmvs		97.35 321	96.88 342	98.78 200	98.54 389	98.09 155	97.71 263	97.69 427	99.20 83	97.59 384	95.90 478	88.12 442	99.55 402	98.18 150	98.96 404	98.70 401

nrg030		99.40 25	99.35 33	99.54 31	99.58 94	99.13 60	98.98 76	99.48 151	99.68 19	99.46 101	99.26 137	98.62 64	99.73 296	99.17 74	99.92 71	99.76 58

v144192		98.54 184	98.57 161	98.45 271	99.21 235	95.98 329	97.63 277	99.36 210	97.15 320	99.32 142	99.18 162	95.84 293	99.84 177	99.50 50	99.91 80	99.54 143

FIs		99.14 62	99.09 81	99.29 95	99.70 57	98.28 133	99.13 59	99.52 136	99.48 44	99.24 165	99.41 94	96.79 236	99.82 207	98.69 112	99.88 95	99.76 58

v1921920		98.54 184	98.60 157	98.38 280	99.20 239	95.76 342	97.56 289	99.36 210	97.23 312	99.38 121	99.17 166	96.02 279	99.84 177	99.57 39	99.90 88	99.54 143

UA-Net		99.47 16	99.40 27	99.70 2	99.49 147	99.29 23	99.80 4	99.72 45	99.82 8	99.04 200	99.81 8	98.05 130	99.96 13	98.85 98	99.99 5	99.86 28

v1192		98.60 171	98.66 145	98.41 276	99.27 216	95.88 334	97.52 295	99.36 210	97.41 287	99.33 136	99.20 155	96.37 263	99.82 207	99.57 39	99.92 71	99.55 137

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 124	99.30 35	99.57 107	99.61 34	99.40 117	99.50 68	97.12 212	99.85 158	99.02 86	99.94 51	99.80 45

v1144		98.60 171	98.66 145	98.41 276	99.36 191	95.90 333	97.58 287	99.34 222	97.51 273	99.27 151	99.15 174	96.34 265	99.80 233	99.47 53	99.93 57	99.51 164

sosnet-low-res		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

HFP-MVS		98.71 141	98.44 185	99.51 49	99.49 147	99.16 48	98.52 130	99.31 235	97.47 277	98.58 294	98.50 342	97.97 137	99.85 158	96.57 311	99.59 272	99.53 157

v148		98.45 198	98.60 157	98.00 326	99.44 168	94.98 380	97.44 309	99.06 309	98.30 192	99.32 142	98.97 233	96.65 248	99.62 369	98.37 138	99.85 109	99.39 229

sosnet		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

uncertanet		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

AllTest		98.44 199	98.20 227	99.16 118	99.50 139	98.55 108	98.25 171	99.58 99	96.80 342	98.88 240	99.06 197	97.65 164	99.57 395	94.45 402	99.61 266	99.37 241

TestCases				99.16 118	99.50 139	98.55 108		99.58 99	96.80 342	98.88 240	99.06 197	97.65 164	99.57 395	94.45 402	99.61 266	99.37 241

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 111	99.45 14	99.61 88	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		98.69 150	98.40 190	99.54 31	99.53 127	99.17 43	98.52 130	99.31 235	97.46 282	98.44 313	98.51 338	97.83 150	99.88 115	96.46 324	99.58 277	99.58 117

RRT-MVS		97.88 273	97.98 256	97.61 369	98.15 431	93.77 434	98.97 77	99.64 76	99.16 93	98.69 272	99.42 89	91.60 402	99.89 97	97.63 206	98.52 437	99.16 326

balanced_ft_v1		98.28 227	98.35 202	98.10 312	98.08 438	96.23 319	99.23 45	99.26 264	98.34 186	97.46 396	99.42 89	95.38 311	99.88 115	98.60 117	99.34 340	98.17 442

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 146	99.20 49	99.65 74	99.48 44	99.92 8	99.71 22	98.07 127	99.96 13	99.53 48	100.00 1	99.93 11

PS-MVSNAJ		97.08 345	97.39 305	96.16 451	98.56 387	92.46 459	95.24 454	98.85 355	97.25 306	97.49 394	95.99 475	98.07 127	99.90 81	96.37 330	98.67 426	96.12 506

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 103	99.28 40	99.66 69	99.09 109	99.89 18	99.68 25	99.53 7	99.97 6	99.50 50	99.99 5	99.87 22

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 103	99.34 23	99.71 47	99.27 73	99.90 14	99.74 18	99.68 4	99.97 6	99.55 43	99.99 5	99.88 20

EI-MVSNet-UG-set		98.69 150	98.71 134	98.62 234	99.10 267	96.37 314	97.23 332	98.87 347	99.20 83	99.19 173	98.99 226	97.30 199	99.85 158	98.77 105	99.79 158	99.65 85

EI-MVSNet-Vis-set		98.68 156	98.70 137	98.63 232	99.09 270	96.40 313	97.23 332	98.86 352	99.20 83	99.18 178	98.97 233	97.29 201	99.85 158	98.72 109	99.78 163	99.64 86

HPM-MVS++		98.10 250	97.64 291	99.48 57	99.09 270	99.13 60	97.52 295	98.75 373	97.46 282	96.90 430	97.83 411	96.01 280	99.84 177	95.82 363	99.35 338	99.46 197

test_prior4						97.97 174	95.86 428

XVS		98.72 140	98.45 183	99.53 38	99.46 161	99.21 32	98.65 114	99.34 222	98.62 164	97.54 389	98.63 320	97.50 185	99.83 195	96.79 284	99.53 295	99.56 130

v1240		98.55 181	98.62 152	98.32 287	99.22 233	95.58 346	97.51 297	99.45 170	97.16 318	99.45 106	99.24 144	96.12 276	99.85 158	99.60 37	99.88 95	99.55 137

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 100	99.29 36	99.63 79	99.30 70	99.65 63	99.60 45	99.16 22	99.82 207	99.07 80	99.83 126	99.56 130

test_prior2							95.74 435		96.48 358	96.11 464	97.63 424	95.92 291		94.16 410	99.20 370

X-MVStestdata		94.32 446	92.59 468	99.53 38	99.46 161	99.21 32	98.65 114	99.34 222	98.62 164	97.54 389	45.85 535	97.50 185	99.83 195	96.79 284	99.53 295	99.56 130

test_prior				98.95 163	98.69 362	97.95 178		99.03 318					99.59 386			99.30 276

旧先验2							95.76 434		88.56 517	97.52 391			99.66 351	94.48 400

新几何2							95.93 424

新几何1				98.91 172	98.94 305	97.76 204		98.76 369	87.58 520	96.75 439	98.10 388	94.80 330	99.78 258	92.73 458	99.00 397	99.20 306

旧先验1					98.82 333	97.45 232		98.76 369			98.34 361	95.50 306			99.01 396	99.23 296

无先验							95.74 435	98.74 375	89.38 510				99.73 296	92.38 467		99.22 301

原ACMM2							95.53 441

原ACMM1				98.35 285	98.90 315	96.25 318		98.83 360	92.48 482	96.07 466	98.10 388	95.39 310	99.71 307	92.61 461	98.99 399	99.08 334

test222					98.92 311	96.93 281	95.54 440	98.78 366	85.72 523	96.86 434	98.11 387	94.43 340			99.10 386	99.23 296

testdata2													99.79 246	92.80 455

segment_acmp												97.02 219

testdata				98.09 314	98.93 307	95.40 359		98.80 363	90.08 506	97.45 399	98.37 357	95.26 313	99.70 314	93.58 431	98.95 405	99.17 320

testdata1							95.44 446		96.32 365

v8		99.01 89	99.16 62	98.57 245	99.47 158	96.31 317	98.90 84	99.47 161	99.03 120	99.52 87	99.57 49	96.93 225	99.81 224	99.60 37	99.98 12	99.60 102

1314		95.74 413	95.60 402	96.17 449	97.53 473	92.75 455	98.07 199	98.31 406	91.22 496	94.25 501	96.68 460	95.53 303	99.03 479	91.64 478	97.18 489	96.74 495

LFMVS		97.20 336	96.72 354	98.64 228	98.72 349	96.95 279	98.93 82	94.14 508	99.74 12	98.78 260	99.01 220	84.45 471	99.73 296	97.44 227	99.27 355	99.25 291

VDD-MVS		98.56 177	98.39 193	99.07 135	99.13 262	98.07 161	98.59 122	97.01 452	99.59 36	99.11 183	99.27 131	94.82 327	99.79 246	98.34 140	99.63 257	99.34 257

VDDNet		98.21 238	97.95 260	99.01 150	99.58 94	97.74 206	99.01 71	97.29 443	99.67 20	98.97 214	99.50 68	90.45 419	99.80 233	97.88 179	99.20 370	99.48 187

v10		98.97 98	99.11 73	98.55 252	99.44 168	96.21 320	98.90 84	99.55 120	98.73 150	99.48 96	99.60 45	96.63 249	99.83 195	99.70 33	99.99 5	99.61 100

VPNet		98.87 111	98.83 119	99.01 150	99.70 57	97.62 218	98.43 148	99.35 216	99.47 47	99.28 149	99.05 204	96.72 243	99.82 207	98.09 157	99.36 335	99.59 109

MVS		93.19 469	92.09 475	96.50 432	96.91 497	94.03 417	98.07 199	98.06 419	68.01 533	94.56 499	96.48 465	95.96 288	99.30 462	83.84 519	96.89 495	96.17 503

v2v482		98.56 177	98.62 152	98.37 283	99.42 175	95.81 339	97.58 287	99.16 292	97.90 236	99.28 149	99.01 220	95.98 286	99.79 246	99.33 59	99.90 88	99.51 164

V42		98.78 132	98.78 125	98.76 207	99.44 168	97.04 272	98.27 169	99.19 281	97.87 238	99.25 163	99.16 168	96.84 229	99.78 258	99.21 70	99.84 114	99.46 197

SD-MVS		98.40 204	98.68 140	97.54 379	98.96 303	97.99 170	97.88 235	99.36 210	98.20 207	99.63 66	99.04 206	98.76 46	95.33 530	96.56 315	99.74 192	99.31 272
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		95.86 409	95.32 419	97.49 384	98.60 379	94.15 410	93.83 500	97.93 421	95.49 409	96.68 443	97.42 440	83.21 481	99.30 462	96.22 340	98.55 435	99.01 347

MSLP-MVS++		98.02 258	98.14 240	97.64 366	98.58 384	95.19 373	97.48 301	99.23 273	97.47 277	97.90 360	98.62 323	97.04 216	98.81 491	97.55 214	99.41 329	98.94 363

APDe-MVS		98.99 93	98.79 123	99.60 16	99.21 235	99.15 52	98.87 89	99.48 151	97.57 265	99.35 130	99.24 144	97.83 150	99.89 97	97.88 179	99.70 223	99.75 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.84 118	98.61 156	99.53 38	99.19 242	99.27 26	98.49 140	99.33 228	98.64 159	99.03 203	98.98 231	97.89 146	99.85 158	96.54 319	99.42 328	99.46 197

ADS-MVSNet2		95.43 426	94.98 430	96.76 426	98.14 432	91.74 469	97.92 230	97.76 424	90.23 502	96.51 455	98.91 248	85.61 460	99.85 158	92.88 451	96.90 493	98.69 402

EI-MVSNet		98.40 204	98.51 169	98.04 323	99.10 267	94.73 392	97.20 337	98.87 347	98.97 126	99.06 190	99.02 209	96.00 281	99.80 233	98.58 119	99.82 133	99.60 102

Regformer		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

CVMVSNet		96.25 392	97.21 319	93.38 508	99.10 267	80.56 538	97.20 337	98.19 414	96.94 330	99.00 205	99.02 209	89.50 429	99.80 233	96.36 332	99.59 272	99.78 50

pmmvs4		97.58 301	97.28 312	98.51 262	98.84 328	96.93 281	95.40 448	98.52 395	93.60 463	98.61 287	98.65 314	95.10 319	99.60 381	96.97 268	99.79 158	98.99 351

EU-MVSNet		97.66 295	98.50 172	95.13 484	99.63 83	85.84 518	98.35 161	98.21 411	98.23 199	99.54 79	99.46 80	95.02 321	99.68 333	98.24 144	99.87 100	99.87 22

VNet		98.42 200	98.30 211	98.79 197	98.79 342	97.29 247	98.23 172	98.66 381	99.31 68	98.85 247	98.80 279	94.80 330	99.78 258	98.13 153	99.13 381	99.31 272

test-LLR		93.90 456	93.85 450	94.04 497	96.53 507	84.62 524	94.05 494	92.39 517	96.17 371	94.12 503	95.07 495	82.30 486	99.67 338	95.87 359	98.18 450	97.82 460

TESTMET0.1,1		92.19 485	91.77 483	93.46 504	96.48 512	82.80 533	94.05 494	91.52 525	94.45 444	94.00 507	94.88 501	66.65 522	99.56 398	95.78 364	98.11 456	98.02 450

test-mter		92.33 483	91.76 484	94.04 497	96.53 507	84.62 524	94.05 494	92.39 517	94.00 459	94.12 503	95.07 495	65.63 528	99.67 338	95.87 359	98.18 450	97.82 460

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 147	98.36 127	99.00 73	99.45 170	99.63 28	99.52 87	99.44 85	98.25 106	99.88 115	99.09 79	99.84 114	99.62 92

ACMMPR		98.70 146	98.42 188	99.54 31	99.52 130	99.14 57	98.52 130	99.31 235	97.47 277	98.56 298	98.54 333	97.75 158	99.88 115	96.57 311	99.59 272	99.58 117

testgi		98.32 219	98.39 193	98.13 309	99.57 103	95.54 347	97.78 249	99.49 149	97.37 292	99.19 173	97.65 422	98.96 30	99.49 425	96.50 322	98.99 399	99.34 257

test20.03		98.78 132	98.77 126	98.78 200	99.46 161	97.20 258	97.78 249	99.24 271	99.04 118	99.41 114	98.90 251	97.65 164	99.76 270	97.70 201	99.79 158	99.39 229

thres600view7		94.45 444	93.83 451	96.29 440	99.06 279	91.53 473	97.99 220	94.24 506	98.34 186	97.44 400	95.01 497	79.84 492	99.67 338	84.33 518	98.23 447	97.66 472

ADS-MVSNet		95.24 431	94.93 433	96.18 448	98.14 432	90.10 498	97.92 230	97.32 442	90.23 502	96.51 455	98.91 248	85.61 460	99.74 289	92.88 451	96.90 493	98.69 402

MP-MVS		98.46 196	98.09 243	99.54 31	99.57 103	99.22 31	98.50 137	99.19 281	97.61 261	97.58 385	98.66 312	97.40 193	99.88 115	94.72 395	99.60 268	99.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 503	20.53 506	6.87 521	12.05 543	4.20 546	93.62 504	6.73 544	4.62 539	10.41 539	24.33 536	8.28 543	3.56 540	9.69 538	15.07 537	12.86 536

thres400		94.14 452	93.44 456	96.24 443	98.93 307	91.44 476	97.60 284	94.29 503	97.94 232	97.10 414	94.31 507	79.67 494	99.62 369	83.05 521	98.08 458	97.66 472

test123		17.04 504	20.11 507	7.82 520	10.25 544	4.91 545	94.80 466	4.47 545	4.93 538	10.00 540	24.28 537	9.69 542	3.64 539	10.14 537	12.43 538	14.92 535

thres200		93.72 460	93.14 462	95.46 478	98.66 372	91.29 480	96.61 376	94.63 498	97.39 290	96.83 435	93.71 510	79.88 491	99.56 398	82.40 524	98.13 455	95.54 510

test0.0.03 1		94.51 443	93.69 453	96.99 411	96.05 518	93.61 441	94.97 462	93.49 512	96.17 371	97.57 387	94.88 501	82.30 486	99.01 483	93.60 430	94.17 522	98.37 434

pmmvs3		95.03 436	94.40 444	96.93 415	97.70 462	92.53 458	95.08 459	97.71 426	88.57 516	97.71 375	98.08 391	79.39 496	99.82 207	96.19 342	99.11 385	98.43 427

EMVS		93.83 457	94.02 448	93.23 509	96.83 500	84.96 521	89.77 526	96.32 473	97.92 234	97.43 401	96.36 470	86.17 452	98.93 486	87.68 508	97.73 470	95.81 508

E-PMN		94.17 451	94.37 445	93.58 503	96.86 498	85.71 520	90.11 525	97.07 451	98.17 210	97.82 370	97.19 449	84.62 470	98.94 485	89.77 500	97.68 471	96.09 507

PGM-MVS		98.66 160	98.37 198	99.55 28	99.53 127	99.18 42	98.23 172	99.49 149	97.01 327	98.69 272	98.88 259	98.00 133	99.89 97	95.87 359	99.59 272	99.58 117

LCM-MVSNet-Re		98.64 163	98.48 178	99.11 126	98.85 327	98.51 113	98.49 140	99.83 26	98.37 183	99.69 55	99.46 80	98.21 114	99.92 65	94.13 414	99.30 351	98.91 368

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		98.00 261	97.63 292	99.10 128	99.24 227	98.17 145	96.89 358	98.73 376	95.66 400	97.92 358	97.70 420	97.17 209	99.66 351	96.18 344	99.23 364	99.47 195

mvs_anonymous		97.83 284	98.16 237	96.87 419	98.18 427	91.89 468	97.31 324	98.90 341	97.37 292	98.83 251	99.46 80	96.28 268	99.79 246	98.90 94	98.16 453	98.95 359

MVS_Test		98.18 243	98.36 199	97.67 359	98.48 394	94.73 392	98.18 178	99.02 321	97.69 252	98.04 350	99.11 185	97.22 206	99.56 398	98.57 121	98.90 409	98.71 398

MDA-MVSNet-bldmvs		97.94 267	97.91 268	98.06 320	99.44 168	94.96 381	96.63 375	99.15 297	98.35 185	98.83 251	99.11 185	94.31 348	99.85 158	96.60 308	98.72 418	99.37 241

CDPH-MVS		97.26 329	96.66 361	99.07 135	99.00 296	98.15 146	96.03 416	99.01 324	91.21 497	97.79 371	97.85 409	96.89 227	99.69 323	92.75 457	99.38 334	99.39 229

test12				98.93 167	98.58 384	97.83 192		98.66 381		96.53 451		95.51 305	99.69 323		99.13 381	99.27 284

casdiffmvs		98.95 101	99.00 93	98.81 190	99.38 184	97.33 239	97.82 243	99.57 107	99.17 92	99.35 130	99.17 166	98.35 93	99.69 323	98.46 129	99.73 196	99.41 219
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		98.22 235	98.24 224	98.17 305	99.00 296	95.44 357	96.38 392	99.58 99	97.79 245	98.53 303	98.50 342	96.76 239	99.74 289	97.95 174	99.64 251	99.34 257
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		93.73 459	92.83 466	96.42 435	97.70 462	91.28 481	96.84 360	89.77 529	93.96 460	92.44 519	95.93 477	79.14 497	99.77 264	92.94 448	96.76 497	98.21 439

baseline1		95.96 406	95.44 411	97.52 381	98.51 393	93.99 424	98.39 157	96.09 479	98.21 203	98.40 320	97.76 416	86.88 446	99.63 366	95.42 378	89.27 528	98.95 359

YYNet1		97.60 298	97.67 286	97.39 392	99.04 283	93.04 449	95.27 452	98.38 404	97.25 306	98.92 231	98.95 241	95.48 307	99.73 296	96.99 264	98.74 416	99.41 219

PMMVS2		98.07 254	98.08 246	98.04 323	99.41 178	94.59 398	94.59 477	99.40 198	97.50 274	98.82 254	98.83 272	96.83 231	99.84 177	97.50 220	99.81 140	99.71 65

MDA-MVSNet_test_wron		97.60 298	97.66 289	97.41 391	99.04 283	93.09 445	95.27 452	98.42 401	97.26 305	98.88 240	98.95 241	95.43 309	99.73 296	97.02 260	98.72 418	99.41 219

tpmvs		95.02 437	95.25 422	94.33 492	96.39 515	85.87 517	98.08 195	96.83 463	95.46 411	95.51 483	98.69 305	85.91 458	99.53 410	94.16 410	96.23 503	97.58 475

PM-MVS		98.82 124	98.72 131	99.12 124	99.64 77	98.54 111	97.98 221	99.68 62	97.62 258	99.34 133	99.18 162	97.54 179	99.77 264	97.79 188	99.74 192	99.04 342

HQP_MVS		97.99 264	97.67 286	98.93 167	99.19 242	97.65 215	97.77 252	99.27 258	98.20 207	97.79 371	97.98 399	94.90 323	99.70 314	94.42 404	99.51 301	99.45 203

plane_prior7					99.19 242	97.87 188

plane_prior6					98.99 299	97.70 211						94.90 323

plane_prior5								99.27 258					99.70 314	94.42 404	99.51 301	99.45 203

plane_prior4											97.98 399

plane_prior3						97.78 202			97.41 287	97.79 371

plane_prior2							97.77 252		98.20 207

plane_prior1					99.05 282

plane_prior						97.65 215	97.07 345		96.72 347						99.36 335

PS-CasMVS		99.40 25	99.33 37	99.62 9	99.71 49	99.10 65	99.29 36	99.53 130	99.53 41	99.46 101	99.41 94	98.23 109	99.95 25	98.89 96	99.95 39	99.81 41

UniMVSNet_NR-MVSNet		98.86 115	98.68 140	99.40 71	99.17 252	98.74 91	97.68 267	99.40 198	99.14 97	99.06 190	98.59 328	96.71 244	99.93 53	98.57 121	99.77 170	99.53 157

PEN-MVS		99.41 24	99.34 35	99.62 9	99.73 38	99.14 57	99.29 36	99.54 126	99.62 32	99.56 74	99.42 89	98.16 121	99.96 13	98.78 102	99.93 57	99.77 53

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 106	99.27 42	99.57 107	99.39 58	99.75 44	99.62 40	99.17 20	99.83 195	99.06 82	99.62 261	99.66 80

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 49	99.30 21	99.31 30	99.51 137	99.64 26	99.56 74	99.46 80	98.23 109	99.97 6	98.78 102	99.93 57	99.72 64

DU-MVS		98.82 124	98.63 150	99.39 72	99.16 254	98.74 91	97.54 293	99.25 266	98.84 147	99.06 190	98.76 290	96.76 239	99.93 53	98.57 121	99.77 170	99.50 168

UniMVSNet (Re)		98.87 111	98.71 134	99.35 80	99.24 227	98.73 94	97.73 262	99.38 202	98.93 131	99.12 182	98.73 293	96.77 237	99.86 144	98.63 116	99.80 151	99.46 197

CP-MVSNet		99.21 47	99.09 81	99.56 26	99.65 71	98.96 77	99.13 59	99.34 222	99.42 55	99.33 136	99.26 137	97.01 220	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	99.55 120	99.46 49	99.50 93	99.34 115	97.30 199	99.93 53	98.90 94	99.93 57	99.77 53

WR-MVS		98.40 204	98.19 231	99.03 145	99.00 296	97.65 215	96.85 359	98.94 331	98.57 172	98.89 236	98.50 342	95.60 301	99.85 158	97.54 216	99.85 109	99.59 109

NR-MVSNet		98.95 101	98.82 120	99.36 74	99.16 254	98.72 96	99.22 46	99.20 277	99.10 106	99.72 47	98.76 290	96.38 262	99.86 144	98.00 168	99.82 133	99.50 168

Baseline_NR-MVSNet		98.98 97	98.86 114	99.36 74	99.82 19	98.55 108	97.47 305	99.57 107	99.37 60	99.21 171	99.61 43	96.76 239	99.83 195	98.06 160	99.83 126	99.71 65

TranMVSNet+NR-MVSNet		99.17 52	99.07 84	99.46 63	99.37 190	98.87 84	98.39 157	99.42 190	99.42 55	99.36 128	99.06 197	98.38 88	99.95 25	98.34 140	99.90 88	99.57 124

TSAR-MVS + GP.		98.18 243	97.98 256	98.77 205	98.71 353	97.88 187	96.32 397	98.66 381	96.33 364	99.23 167	98.51 338	97.48 189	99.40 447	97.16 248	99.46 315	99.02 345

n2								0.00 546

nn								0.00 546

mPP-MVS		98.64 163	98.34 203	99.54 31	99.54 123	99.17 43	98.63 116	99.24 271	97.47 277	98.09 344	98.68 307	97.62 169	99.89 97	96.22 340	99.62 261	99.57 124

door-mid								99.57 107

XVG-OURS-SEG-HR		98.49 193	98.28 214	99.14 122	99.49 147	98.83 86	96.54 380	99.48 151	97.32 297	99.11 183	98.61 325	99.33 15	99.30 462	96.23 339	98.38 440	99.28 281

mvsmamba		97.57 302	97.26 314	98.51 262	98.69 362	96.73 295	98.74 99	97.25 444	97.03 326	97.88 362	99.23 150	90.95 413	99.87 135	96.61 307	99.00 397	98.91 368

MVSFormer		98.26 230	98.43 186	97.77 345	98.88 321	93.89 430	99.39 20	99.56 116	99.11 99	98.16 336	98.13 384	93.81 362	99.97 6	99.26 65	99.57 281	99.43 211

jason		97.45 311	97.35 309	97.76 348	99.24 227	93.93 426	95.86 428	98.42 401	94.24 448	98.50 306	98.13 384	94.82 327	99.91 74	97.22 243	99.73 196	99.43 211
jason: jason.
lupinMVS		97.06 347	96.86 343	97.65 363	98.88 321	93.89 430	95.48 444	97.97 420	93.53 464	98.16 336	97.58 426	93.81 362	99.91 74	96.77 287	99.57 281	99.17 320

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 91	99.39 20	99.56 116	99.11 99	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 89	98.82 120	99.57 21	99.71 49	99.35 16	99.00 73	99.50 141	97.33 295	98.94 228	98.86 262	98.75 47	99.82 207	97.53 217	99.71 214	99.56 130

K. test v3		98.00 261	97.66 289	99.03 145	99.79 23	97.56 221	99.19 53	92.47 516	99.62 32	99.52 87	99.66 32	89.61 427	99.96 13	99.25 67	99.81 140	99.56 130

lessismore_v0				98.97 159	99.73 38	97.53 224		86.71 534		99.37 125	99.52 67	89.93 422	99.92 65	98.99 88	99.72 205	99.44 207

SixPastTwentyTwo		98.75 137	98.62 152	99.16 118	99.83 18	97.96 177	99.28 40	98.20 412	99.37 60	99.70 51	99.65 36	92.65 388	99.93 53	99.04 84	99.84 114	99.60 102

OurMVSNet-221017-0		99.37 28	99.31 41	99.53 38	99.91 3	98.98 71	99.63 7	99.58 99	99.44 52	99.78 39	99.76 15	96.39 260	99.92 65	99.44 54	99.92 71	99.68 73

HPM-MVS		98.79 130	98.53 167	99.59 20	99.65 71	99.29 23	99.16 55	99.43 184	96.74 346	98.61 287	98.38 356	98.62 64	99.87 135	96.47 323	99.67 239	99.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 186	98.34 203	99.11 126	99.50 139	98.82 88	95.97 419	99.50 141	97.30 300	99.05 198	98.98 231	99.35 14	99.32 459	95.72 366	99.68 233	99.18 316

XVG-ACMP-BASELINE		98.56 177	98.34 203	99.22 109	99.54 123	98.59 105	97.71 263	99.46 166	97.25 306	98.98 210	98.99 226	97.54 179	99.84 177	95.88 356	99.74 192	99.23 296

casdiffmvs_mvg		99.12 69	99.16 62	98.99 153	99.43 173	97.73 208	98.00 213	99.62 85	99.22 79	99.55 77	99.22 151	98.93 33	99.75 282	98.66 113	99.81 140	99.50 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		98.71 141	98.46 182	99.47 61	99.57 103	98.97 73	98.23 172	99.48 151	96.60 351	99.10 186	99.06 197	98.71 51	99.83 195	95.58 375	99.78 163	99.62 92

LGP-MVS_train				99.47 61	99.57 103	98.97 73		99.48 151	96.60 351	99.10 186	99.06 197	98.71 51	99.83 195	95.58 375	99.78 163	99.62 92

baseline		98.96 100	99.02 89	98.76 207	99.38 184	97.26 250	98.49 140	99.50 141	98.86 141	99.19 173	99.06 197	98.23 109	99.69 323	98.71 110	99.76 186	99.33 263

test11								98.87 347

door								99.41 194

EPNet_dtu		94.93 439	94.78 435	95.38 480	93.58 529	87.68 512	96.78 363	95.69 489	97.35 294	89.14 529	98.09 390	88.15 441	99.49 425	94.95 389	99.30 351	98.98 352
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 307	97.14 324	98.54 257	99.68 64	96.09 324	96.50 384	99.62 85	91.58 491	98.84 249	98.97 233	92.36 390	99.88 115	96.76 288	99.95 39	99.67 78

EPNet		96.14 396	95.44 411	98.25 295	90.76 538	95.50 352	97.92 230	94.65 497	98.97 126	92.98 514	98.85 265	89.12 431	99.87 135	95.99 352	99.68 233	99.39 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 290

HQP-NCC					98.67 367		96.29 399		96.05 378	95.55 478

ACMP_Plane					98.67 367		96.29 399		96.05 378	95.55 478

APD-MVS		98.10 250	97.67 286	99.42 67	99.11 265	98.93 79	97.76 255	99.28 255	94.97 427	98.72 269	98.77 285	97.04 216	99.85 158	93.79 424	99.54 291	99.49 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 453

HQP4-MVS										95.56 477			99.54 408			99.32 267

HQP3-MVS								99.04 316							99.26 359

HQP2-MVS												93.84 360

CNVR-MVS		98.17 246	97.87 271	99.07 135	98.67 367	98.24 137	97.01 347	98.93 334	97.25 306	97.62 381	98.34 361	97.27 202	99.57 395	96.42 327	99.33 342	99.39 229

NCCC		97.86 276	97.47 303	99.05 142	98.61 377	98.07 161	96.98 350	98.90 341	97.63 257	97.04 420	97.93 404	95.99 285	99.66 351	95.31 380	98.82 413	99.43 211

114514_t		96.50 375	95.77 394	98.69 221	99.48 155	97.43 234	97.84 242	99.55 120	81.42 529	96.51 455	98.58 329	95.53 303	99.67 338	93.41 438	99.58 277	98.98 352

CP-MVS		98.70 146	98.42 188	99.52 44	99.36 191	99.12 62	98.72 104	99.36 210	97.54 271	98.30 324	98.40 353	97.86 149	99.89 97	96.53 320	99.72 205	99.56 130

DSMNet-mixed		97.42 314	97.60 294	96.87 419	99.15 258	91.46 474	98.54 128	99.12 300	92.87 478	97.58 385	99.63 39	96.21 271	99.90 81	95.74 365	99.54 291	99.27 284

tpm2		93.09 470	92.58 469	94.62 490	97.56 469	86.53 516	97.66 271	95.79 486	86.15 522	94.07 505	98.23 377	75.95 506	99.53 410	90.91 492	96.86 496	97.81 462

NP-MVS					98.84 328	97.39 236					96.84 456

EG-PatchMatch MVS		98.99 93	99.01 91	98.94 164	99.50 139	97.47 229	98.04 204	99.59 96	98.15 218	99.40 117	99.36 110	98.58 72	99.76 270	98.78 102	99.68 233	99.59 109

tpm cat1		93.29 467	93.13 463	93.75 501	97.39 482	84.74 522	97.39 312	97.65 430	83.39 527	94.16 502	98.41 352	82.86 484	99.39 449	91.56 480	95.35 517	97.14 487

SteuartSystems-ACMMP		98.79 130	98.54 165	99.54 31	99.73 38	99.16 48	98.23 172	99.31 235	97.92 234	98.90 233	98.90 251	98.00 133	99.88 115	96.15 345	99.72 205	99.58 117
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 455	93.78 452	94.51 491	97.53 473	85.83 519	97.98 221	95.96 481	89.29 511	94.99 491	98.63 320	78.63 501	99.62 369	94.54 398	96.50 499	98.09 447

CR-MVSNet		96.28 389	95.95 391	97.28 395	97.71 460	94.22 405	98.11 190	98.92 338	92.31 484	96.91 427	99.37 104	85.44 463	99.81 224	97.39 230	97.36 485	97.81 462

JIA-IIPM		95.52 421	95.03 429	97.00 410	96.85 499	94.03 417	96.93 355	95.82 484	99.20 83	94.63 498	99.71 22	83.09 482	99.60 381	94.42 404	94.64 519	97.36 483

Patchmtry		97.35 321	96.97 334	98.50 266	97.31 485	96.47 310	98.18 178	98.92 338	98.95 130	98.78 260	99.37 104	85.44 463	99.85 158	95.96 354	99.83 126	99.17 320

PatchT		96.65 367	96.35 379	97.54 379	97.40 481	95.32 365	97.98 221	96.64 467	99.33 65	96.89 431	99.42 89	84.32 473	99.81 224	97.69 203	97.49 476	97.48 478

tpmrst		95.07 435	95.46 409	93.91 499	97.11 489	84.36 526	97.62 278	96.96 456	94.98 426	96.35 460	98.80 279	85.46 462	99.59 386	95.60 373	96.23 503	97.79 465

BH-w/o		95.13 434	94.89 434	95.86 463	98.20 425	91.31 479	95.65 437	97.37 436	93.64 462	96.52 454	95.70 483	93.04 380	99.02 481	88.10 507	95.82 513	97.24 486

tpm		94.67 441	94.34 446	95.66 471	97.68 465	88.42 507	97.88 235	94.90 495	94.46 441	96.03 470	98.56 332	78.66 500	99.79 246	95.88 356	95.01 518	98.78 390

DELS-MVS		98.27 228	98.20 227	98.48 268	98.86 324	96.70 296	95.60 439	99.20 277	97.73 249	98.45 312	98.71 296	97.50 185	99.82 207	98.21 148	99.59 272	98.93 364
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		96.83 360	96.75 353	97.08 406	98.74 346	93.33 443	96.71 368	98.26 408	96.72 347	98.44 313	97.37 443	95.20 314	99.47 432	91.89 472	97.43 480	98.44 425

RPMNet		97.02 350	96.93 336	97.30 394	97.71 460	94.22 405	98.11 190	99.30 243	99.37 60	96.91 427	99.34 115	86.72 447	99.87 135	97.53 217	97.36 485	97.81 462

MVSTER		96.86 359	96.55 370	97.79 343	97.91 447	94.21 407	97.56 289	98.87 347	97.49 276	99.06 190	99.05 204	80.72 489	99.80 233	98.44 130	99.82 133	99.37 241

CPTT-MVS		97.84 282	97.36 308	99.27 99	99.31 204	98.46 116	98.29 165	99.27 258	94.90 429	97.83 368	98.37 357	94.90 323	99.84 177	93.85 423	99.54 291	99.51 164

GBi-Net		98.65 161	98.47 180	99.17 115	98.90 315	98.24 137	99.20 49	99.44 178	98.59 167	98.95 220	99.55 56	94.14 353	99.86 144	97.77 191	99.69 227	99.41 219

PVSNet_Blended_VisFu		98.17 246	98.15 238	98.22 301	99.73 38	95.15 374	97.36 319	99.68 62	94.45 444	98.99 209	99.27 131	96.87 228	99.94 41	97.13 253	99.91 80	99.57 124

PVSNet_BlendedMVS		97.55 303	97.53 297	97.60 370	98.92 311	93.77 434	96.64 374	99.43 184	94.49 439	97.62 381	99.18 162	96.82 232	99.67 338	94.73 393	99.93 57	99.36 248

UnsupCasMVSNet_eth		97.89 271	97.60 294	98.75 209	99.31 204	97.17 264	97.62 278	99.35 216	98.72 156	98.76 265	98.68 307	92.57 389	99.74 289	97.76 195	95.60 515	99.34 257

UnsupCasMVSNet_bld		97.30 326	96.92 338	98.45 271	99.28 213	96.78 293	96.20 405	99.27 258	95.42 412	98.28 328	98.30 368	93.16 374	99.71 307	94.99 386	97.37 483	98.87 374

PVSNet_Blended		96.88 357	96.68 357	97.47 387	98.92 311	93.77 434	94.71 468	99.43 184	90.98 500	97.62 381	97.36 444	96.82 232	99.67 338	94.73 393	99.56 285	98.98 352

FMVSNet5		96.01 400	95.20 426	98.41 276	97.53 473	96.10 321	98.74 99	99.50 141	97.22 315	98.03 351	99.04 206	69.80 515	99.88 115	97.27 239	99.71 214	99.25 291

test1		98.65 161	98.47 180	99.17 115	98.90 315	98.24 137	99.20 49	99.44 178	98.59 167	98.95 220	99.55 56	94.14 353	99.86 144	97.77 191	99.69 227	99.41 219

new_pmnet		96.99 354	96.76 351	97.67 359	98.72 349	94.89 384	95.95 423	98.20 412	92.62 481	98.55 300	98.54 333	94.88 326	99.52 414	93.96 418	99.44 324	98.59 414

FMVSNet3		97.50 304	97.24 316	98.29 291	98.08 438	95.83 337	97.86 239	98.91 340	97.89 237	98.95 220	98.95 241	87.06 445	99.81 224	97.77 191	99.69 227	99.23 296

dp		93.47 463	93.59 455	93.13 510	96.64 505	81.62 537	97.66 271	96.42 472	92.80 479	96.11 464	98.64 318	78.55 503	99.59 386	93.31 439	92.18 527	98.16 443

FMVSNet2		98.49 193	98.40 190	98.75 209	98.90 315	97.14 267	98.61 120	99.13 299	98.59 167	99.19 173	99.28 129	94.14 353	99.82 207	97.97 172	99.80 151	99.29 278

FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 117	98.24 137	99.20 49	99.44 178	99.21 81	99.43 108	99.55 56	97.82 153	99.86 144	98.42 136	99.89 94	99.41 219

N_pmnet		97.63 297	97.17 320	98.99 153	99.27 216	97.86 189	95.98 418	93.41 513	95.25 419	99.47 100	98.90 251	95.63 299	99.85 158	96.91 271	99.73 196	99.27 284

cascas		94.79 440	94.33 447	96.15 453	96.02 520	92.36 463	92.34 518	99.26 264	85.34 524	95.08 490	94.96 500	92.96 381	98.53 498	94.41 407	98.59 432	97.56 476

BH-RMVSNet		96.83 360	96.58 369	97.58 372	98.47 395	94.05 414	96.67 372	97.36 437	96.70 349	97.87 363	97.98 399	95.14 318	99.44 441	90.47 497	98.58 433	99.25 291

UGNet		98.53 186	98.45 183	98.79 197	97.94 445	96.96 278	99.08 62	98.54 393	99.10 106	96.82 436	99.47 78	96.55 252	99.84 177	98.56 124	99.94 51	99.55 137
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
WTY-MVS		96.67 366	96.27 385	97.87 338	98.81 336	94.61 397	96.77 364	97.92 422	94.94 428	97.12 413	97.74 417	91.11 412	99.82 207	93.89 420	98.15 454	99.18 316

XXY-MVS		99.14 62	99.15 67	99.10 128	99.76 30	97.74 206	98.85 93	99.62 85	98.48 179	99.37 125	99.49 74	98.75 47	99.86 144	98.20 149	99.80 151	99.71 65

EC-MVSNet		99.09 72	99.05 85	99.20 110	99.28 213	98.93 79	99.24 44	99.84 23	99.08 113	98.12 341	98.37 357	98.72 50	99.90 81	99.05 83	99.77 170	98.77 391

sss		97.21 335	96.93 336	98.06 320	98.83 330	95.22 372	96.75 366	98.48 397	94.49 439	97.27 408	97.90 405	92.77 385	99.80 233	96.57 311	99.32 345	99.16 326

Test_1112_low_res		96.99 354	96.55 370	98.31 289	99.35 196	95.47 356	95.84 431	99.53 130	91.51 493	96.80 437	98.48 345	91.36 409	99.83 195	96.58 309	99.53 295	99.62 92

1112_ss		97.29 328	96.86 343	98.58 242	99.34 201	96.32 316	96.75 366	99.58 99	93.14 470	96.89 431	97.48 435	92.11 398	99.86 144	96.91 271	99.54 291	99.57 124

ab-mvs-re		8.12 506	10.83 509	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	97.48 435	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

ab-mvs		98.41 201	98.36 199	98.59 241	99.19 242	97.23 252	99.32 26	98.81 361	97.66 255	98.62 285	99.40 97	96.82 232	99.80 233	95.88 356	99.51 301	98.75 394

TR-MVS		95.55 420	95.12 428	96.86 422	97.54 471	93.94 425	96.49 385	96.53 470	94.36 447	97.03 422	96.61 462	94.26 350	99.16 474	86.91 512	96.31 502	97.47 479

MDTV_nov1_ep13_2view						74.92 540	97.69 266		90.06 507	97.75 374		85.78 459		93.52 433		98.69 402

MDTV_nov1_ep13			95.22 424		97.06 492	83.20 531	97.74 260	96.16 475	94.37 446	96.99 423	98.83 272	83.95 477	99.53 410	93.90 419	97.95 466

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 41	99.41 17	99.59 96	99.59 36	99.71 49	99.57 49	97.12 212	99.90 81	99.21 70	99.87 100	99.54 143

MIMVSNet		96.62 369	96.25 386	97.71 355	99.04 283	94.66 395	99.16 55	96.92 460	97.23 312	97.87 363	99.10 188	86.11 454	99.65 358	91.65 477	99.21 368	98.82 378

IterMVS-LS		98.55 181	98.70 137	98.09 314	99.48 155	94.73 392	97.22 336	99.39 200	98.97 126	99.38 121	99.31 124	96.00 281	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		97.69 292	97.35 309	98.69 221	98.73 347	97.02 274	96.92 357	98.75 373	95.89 388	98.59 292	98.67 309	92.08 399	99.74 289	96.72 293	99.81 140	99.32 267
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 170

IterMVS		97.73 288	98.11 242	96.57 430	99.24 227	90.28 496	95.52 443	99.21 275	98.86 141	99.33 136	99.33 118	93.11 376	99.94 41	98.49 128	99.94 51	99.48 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 323	96.92 338	98.57 245	99.09 270	97.99 170	96.79 361	99.35 216	93.18 469	97.71 375	98.07 392	95.00 322	99.31 460	93.97 417	99.13 381	98.42 429

MVS_111021_LR		98.30 223	98.12 241	98.83 186	99.16 254	98.03 166	96.09 414	99.30 243	97.58 264	98.10 343	98.24 375	98.25 106	99.34 456	96.69 298	99.65 249	99.12 332

DP-MVS		98.93 103	98.81 122	99.28 96	99.21 235	98.45 117	98.46 145	99.33 228	99.63 28	99.48 96	99.15 174	97.23 205	99.75 282	97.17 247	99.66 247	99.63 91

ACMMP++															99.68 233

HQP-MVS		97.00 353	96.49 373	98.55 252	98.67 367	96.79 290	96.29 399	99.04 316	96.05 378	95.55 478	96.84 456	93.84 360	99.54 408	92.82 453	99.26 359	99.32 267

QAPM		97.31 324	96.81 349	98.82 188	98.80 339	97.49 225	99.06 66	99.19 281	90.22 504	97.69 377	99.16 168	96.91 226	99.90 81	90.89 493	99.41 329	99.07 336

Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 38	98.26 135	99.17 54	99.78 36	99.11 99	99.27 151	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		94.32 446	95.62 400	90.42 515	98.46 397	75.36 539	96.29 399	89.13 530	95.25 419	95.38 484	99.75 16	92.88 382	99.19 472	94.07 416	99.39 331	96.72 496

IS-MVSNet		98.19 241	97.90 269	99.08 133	99.57 103	97.97 174	99.31 30	98.32 405	99.01 122	98.98 210	99.03 208	91.59 403	99.79 246	95.49 377	99.80 151	99.48 187

HyFIR lowres test		97.19 337	96.60 368	98.96 161	99.62 87	97.28 248	95.17 456	99.50 141	94.21 449	99.01 204	98.32 366	86.61 448	99.99 2	97.10 255	99.84 114	99.60 102

EPMVS		93.72 460	93.27 459	95.09 486	96.04 519	87.76 511	98.13 185	85.01 536	94.69 435	96.92 425	98.64 318	78.47 504	99.31 460	95.04 385	96.46 500	98.20 440

PAPM_NR		96.82 362	96.32 381	98.30 290	99.07 274	96.69 297	97.48 301	98.76 369	95.81 395	96.61 447	96.47 466	94.12 356	99.17 473	90.82 495	97.78 468	99.06 337

TAMVS		98.24 234	98.05 249	98.80 193	99.07 274	97.18 262	97.88 235	98.81 361	96.66 350	99.17 181	99.21 153	94.81 329	99.77 264	96.96 269	99.88 95	99.44 207

PAPR		95.29 429	94.47 441	97.75 349	97.50 479	95.14 375	94.89 465	98.71 378	91.39 495	95.35 485	95.48 489	94.57 337	99.14 476	84.95 517	97.37 483	98.97 356

RPSCF		98.62 168	98.36 199	99.42 67	99.65 71	99.42 10	98.55 126	99.57 107	97.72 251	98.90 233	99.26 137	96.12 276	99.52 414	95.72 366	99.71 214	99.32 267

Vis-MVSNet (Re-imp)		97.46 309	97.16 321	98.34 286	99.55 117	96.10 321	98.94 81	98.44 398	98.32 190	98.16 336	98.62 323	88.76 432	99.73 296	93.88 421	99.79 158	99.18 316

test_0402		98.76 136	98.71 134	98.93 167	99.56 111	98.14 148	98.45 147	99.34 222	99.28 72	98.95 220	98.91 248	98.34 94	99.79 246	95.63 371	99.91 80	98.86 375

MVS_111021_HR		98.25 233	98.08 246	98.75 209	99.09 270	97.46 231	95.97 419	99.27 258	97.60 263	97.99 354	98.25 373	98.15 123	99.38 451	96.87 279	99.57 281	99.42 216

CSCG		98.68 156	98.50 172	99.20 110	99.45 166	98.63 100	98.56 125	99.57 107	97.87 238	98.85 247	98.04 394	97.66 163	99.84 177	96.72 293	99.81 140	99.13 331

PatchMatch-RL		97.24 332	96.78 350	98.61 238	99.03 286	97.83 192	96.36 394	99.06 309	93.49 466	97.36 406	97.78 414	95.75 295	99.49 425	93.44 437	98.77 414	98.52 417

API-MVS		97.04 349	96.91 341	97.42 390	97.88 448	98.23 141	98.18 178	98.50 396	97.57 265	97.39 404	96.75 459	96.77 237	99.15 475	90.16 498	99.02 394	94.88 512

Test By Simon												96.52 253

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 47	99.38 59	99.53 83	99.61 43	98.64 61	99.80 233	98.24 144	99.84 114	99.52 160

USDC		97.41 315	97.40 304	97.44 389	98.94 305	93.67 437	95.17 456	99.53 130	94.03 457	98.97 214	99.10 188	95.29 312	99.34 456	95.84 362	99.73 196	99.30 276

EPP-MVSNet		98.30 223	98.04 250	99.07 135	99.56 111	97.83 192	99.29 36	98.07 418	99.03 120	98.59 292	99.13 180	92.16 395	99.90 81	96.87 279	99.68 233	99.49 176

PMMVS		96.51 373	95.98 389	98.09 314	97.53 473	95.84 336	94.92 463	98.84 356	91.58 491	96.05 468	95.58 484	95.68 298	99.66 351	95.59 374	98.09 457	98.76 393

PAPM		91.88 489	90.34 491	96.51 431	98.06 440	92.56 457	92.44 517	97.17 448	86.35 521	90.38 526	96.01 474	86.61 448	99.21 471	70.65 533	95.43 516	97.75 467

ACMMP		98.75 137	98.50 172	99.52 44	99.56 111	99.16 48	98.87 89	99.37 206	97.16 318	98.82 254	99.01 220	97.71 160	99.87 135	96.29 337	99.69 227	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		97.17 339	96.71 355	98.55 252	98.56 387	98.05 165	96.33 396	98.93 334	96.91 334	97.06 418	97.39 441	94.38 344	99.45 439	91.66 476	99.18 375	98.14 444

PatchmatchNet		95.58 419	95.67 399	95.30 483	97.34 483	87.32 514	97.65 273	96.65 466	95.30 416	97.07 417	98.69 305	84.77 468	99.75 282	94.97 388	98.64 427	98.83 377
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 226	97.95 260	99.34 83	98.44 400	99.16 48	98.12 189	99.38 202	96.01 382	98.06 347	98.43 350	97.80 154	99.67 338	95.69 368	99.58 277	99.20 306

F-COLMAP		97.30 326	96.68 357	99.14 122	99.19 242	98.39 121	97.27 331	99.30 243	92.93 475	96.62 446	98.00 397	95.73 296	99.68 333	92.62 460	98.46 438	99.35 254

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 155	99.14 58	99.93 6	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 61	100.00 1	99.82 36

wuyk23d		96.06 397	97.62 293	91.38 512	98.65 376	98.57 107	98.85 93	96.95 457	96.86 340	99.90 14	99.16 168	99.18 19	98.40 499	89.23 504	99.77 170	77.18 534

OMC-MVS		97.88 273	97.49 300	99.04 144	98.89 320	98.63 100	96.94 353	99.25 266	95.02 425	98.53 303	98.51 338	97.27 202	99.47 432	93.50 435	99.51 301	99.01 347

MG-MVS		96.77 363	96.61 366	97.26 397	98.31 412	93.06 446	95.93 424	98.12 417	96.45 361	97.92 358	98.73 293	93.77 364	99.39 449	91.19 487	99.04 390	99.33 263

AdaColmap		97.14 341	96.71 355	98.46 270	98.34 410	97.80 201	96.95 352	98.93 334	95.58 404	96.92 425	97.66 421	95.87 292	99.53 410	90.97 490	99.14 379	98.04 449

uanet		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

ITE_SJBPF				98.87 176	99.22 233	98.48 115		99.35 216	97.50 274	98.28 328	98.60 327	97.64 167	99.35 455	93.86 422	99.27 355	98.79 389

DeepMVS_CX				93.44 506	98.24 421	94.21 407		94.34 502	64.28 534	91.34 523	94.87 503	89.45 430	92.77 533	77.54 529	93.14 524	93.35 520

TinyColmap		97.89 271	97.98 256	97.60 370	98.86 324	94.35 403	96.21 404	99.44 178	97.45 284	99.06 190	98.88 259	97.99 136	99.28 466	94.38 408	99.58 277	99.18 316

MAR-MVS		96.47 379	95.70 397	98.79 197	97.92 446	99.12 62	98.28 166	98.60 386	92.16 486	95.54 481	96.17 472	94.77 332	99.52 414	89.62 501	98.23 447	97.72 470
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		97.90 269	97.69 285	98.52 261	99.17 252	97.66 213	97.19 341	99.47 161	96.31 366	97.85 367	98.20 379	96.71 244	99.52 414	94.62 396	99.72 205	98.38 432

MSDG		97.71 290	97.52 298	98.28 292	98.91 314	96.82 288	94.42 482	99.37 206	97.65 256	98.37 321	98.29 371	97.40 193	99.33 458	94.09 415	99.22 365	98.68 405

LS3D		98.63 165	98.38 196	99.36 74	97.25 486	99.38 12	99.12 61	99.32 230	99.21 81	98.44 313	98.88 259	97.31 198	99.80 233	96.58 309	99.34 340	98.92 365

CLD-MVS		97.49 307	97.16 321	98.48 268	99.07 274	97.03 273	94.71 468	99.21 275	94.46 441	98.06 347	97.16 450	97.57 175	99.48 429	94.46 401	99.78 163	98.95 359
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 464	92.23 473	97.08 406	99.25 226	97.86 189	95.61 438	97.16 449	92.90 477	93.76 511	98.65 314	75.94 507	95.66 528	79.30 528	97.49 476	97.73 469

Gipuma		99.03 87	99.16 62	98.64 228	99.94 2	98.51 113	99.32 26	99.75 42	99.58 38	98.60 290	99.62 40	98.22 112	99.51 420	97.70 201	99.73 196	97.89 457
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark