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 35	99.63 29	99.78 38	99.67 30	99.48 10	99.81 210	99.30 60	99.97 20	99.77 47
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	98.81 108	98.73 108	99.05 138	98.76 296	97.81 182	99.25 43	99.30 196	98.57 151	98.55 250	99.33 106	97.95 117	99.90 78	97.16 200	99.67 200	99.44 185

3Dnovator+	97.89 3	98.69 128	98.51 142	99.24 102	98.81 291	98.40 113	99.02 69	99.19 232	98.99 113	98.07 291	99.28 116	97.11 181	99.84 167	96.84 232	99.32 285	99.47 175

DeepC-MVS	97.60 4	98.97 86	98.93 87	99.10 124	99.35 171	97.98 158	98.01 195	99.46 124	97.56 232	99.54 74	99.50 67	98.97 27	99.84 167	98.06 146	99.92 66	99.49 156
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	98.32 186	98.01 213	99.23 104	98.39 359	98.97 73	95.03 404	99.18 236	96.88 293	99.33 121	98.78 246	98.16 100	99.28 408	96.74 240	99.62 216	99.44 185

DeepC-MVS_fast	96.85 6	98.30 189	98.15 198	98.75 188	98.61 330	97.23 218	97.76 234	99.09 255	97.31 260	98.75 222	98.66 268	97.56 148	99.64 318	96.10 292	99.55 243	99.39 205
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	97.09 295	96.68 306	98.32 254	98.32 362	97.16 227	98.86 91	99.37 159	89.48 428	96.29 390	99.15 154	96.56 214	99.90 78	92.90 380	99.20 307	97.89 400

ACMH	96.65 7	99.25 40	99.24 51	99.26 97	99.72 43	98.38 115	99.07 64	99.55 89	98.30 169	99.65 61	99.45 83	99.22 16	99.76 254	98.44 123	99.77 147	99.64 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 73	99.00 81	99.33 85	99.71 47	98.83 83	98.60 114	99.58 71	99.11 92	99.53 78	99.18 144	98.81 37	99.67 299	96.71 245	99.77 147	99.50 151

COLMAP_ROB	96.50 10	98.99 82	98.85 97	99.41 66	99.58 86	99.10 65	98.74 97	99.56 85	99.09 102	99.33 121	99.19 140	98.40 74	99.72 278	95.98 295	99.76 159	99.42 192
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	96.63 317	95.95 328	98.65 200	98.93 262	98.09 142	96.93 314	99.28 208	83.58 441	98.13 286	97.78 351	96.13 232	99.40 389	93.52 369	99.29 292	98.45 366
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 93	98.73 108	99.48 56	99.55 103	99.14 57	98.07 183	99.37 159	97.62 223	99.04 168	98.96 206	98.84 35	99.79 231	97.43 187	99.65 208	99.49 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 341	95.35 351	97.55 320	97.95 382	94.79 317	98.81 96	96.94 387	92.28 407	95.17 412	98.57 284	89.90 354	99.75 261	91.20 409	97.33 413	98.10 389

OpenMVS_ROB	95.38 14	95.84 344	95.18 357	97.81 291	98.41 358	97.15 228	97.37 283	98.62 328	83.86 440	98.65 233	98.37 308	94.29 295	99.68 296	88.41 424	98.62 365	96.60 431

ACMP	95.32 15	98.41 172	98.09 203	99.36 70	99.51 115	98.79 86	97.68 243	99.38 155	95.76 339	98.81 213	98.82 239	98.36 76	99.82 195	94.75 331	99.77 147	99.48 167
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 320	95.73 333	98.85 168	98.75 298	97.91 167	96.42 342	99.06 258	90.94 421	95.59 401	97.38 375	94.41 290	99.59 336	90.93 413	98.04 392	99.05 285
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 348	95.70 334	95.57 395	98.83 285	88.57 422	92.50 438	97.72 360	92.69 402	96.49 387	96.44 396	93.72 308	99.43 385	93.61 366	99.28 293	98.71 343

PCF-MVS	92.86 18	94.36 370	93.00 388	98.42 242	98.70 310	97.56 198	93.16 436	99.11 252	79.59 445	97.55 328	97.43 372	92.19 331	99.73 271	79.85 443	99.45 268	97.97 397
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 406	90.90 410	96.27 376	97.22 420	91.24 404	94.36 423	93.33 431	92.37 405	92.24 440	94.58 431	66.20 444	99.89 93	93.16 377	94.63 438	97.66 413
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	97.86 234	97.94 222	97.65 307	99.71 47	97.94 164	98.52 123	98.68 323	98.99 113	97.52 331	99.35 100	97.41 162	98.18 439	91.59 402	99.67 200	96.82 428
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 411	90.30 414	93.70 419	97.72 392	84.34 443	90.24 442	97.42 369	90.20 425	93.79 431	93.09 440	90.90 347	98.89 428	86.57 432	72.76 449	97.87 402

MVE	83.40 22	92.50 401	91.92 403	94.25 411	98.83 285	91.64 393	92.71 437	83.52 451	95.92 335	86.46 449	95.46 417	95.20 268	95.40 447	80.51 442	98.64 362	95.73 440
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 328	95.44 346	98.84 169	96.25 440	98.69 94	97.02 307	99.12 250	88.90 431	97.83 309	98.86 228	89.51 358	98.90 427	91.92 394	99.51 254	98.92 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SD_0403		96.28 329	95.83 330	97.64 309	98.72 302	94.30 333	98.87 88	98.77 312	97.80 211	96.53 381	98.02 337	97.34 166	99.47 377	76.93 446	99.48 264	99.16 275

fmvsm_s_conf0.5_n_9		99.17 51	99.38 28	98.53 228	99.51 115	95.82 283	97.62 254	99.78 35	99.72 15	99.90 13	99.48 74	98.66 51	99.89 93	99.85 5	99.93 53	99.89 16

NormalMVS		98.26 195	97.97 219	99.15 117	99.64 74	97.83 174	98.28 154	99.43 138	99.24 73	98.80 214	98.85 231	89.76 355	99.94 41	98.04 148	99.67 200	99.68 67

lecture		99.25 40	99.12 67	99.62 9	99.64 74	99.40 12	98.89 87	99.51 100	99.19 84	99.37 112	99.25 128	98.36 76	99.88 109	98.23 134	99.67 200	99.59 103

SymmetryMVS		98.05 215	97.71 240	99.09 128	99.29 183	97.83 174	98.28 154	97.64 367	99.24 73	98.80 214	98.85 231	89.76 355	99.94 41	98.04 148	99.50 261	99.49 156

Elysia		99.15 56	99.14 65	99.18 109	99.63 80	97.92 165	98.50 130	99.43 138	99.67 21	99.70 49	99.13 159	96.66 209	99.98 4	99.54 41	99.96 27	99.64 80

StellarMVS		99.15 56	99.14 65	99.18 109	99.63 80	97.92 165	98.50 130	99.43 138	99.67 21	99.70 49	99.13 159	96.66 209	99.98 4	99.54 41	99.96 27	99.64 80

KinetiMVS		99.03 77	99.02 78	99.03 141	99.70 55	97.48 203	98.43 141	99.29 204	99.70 16	99.60 68	99.07 171	96.13 232	99.94 41	99.42 53	99.87 94	99.68 67

LuminaMVS		98.39 180	98.20 189	98.98 151	99.50 121	97.49 201	97.78 228	97.69 362	98.75 133	99.49 87	99.25 128	92.30 330	99.94 41	99.14 73	99.88 90	99.50 151

VortexMVS		97.98 224	98.31 176	97.02 348	98.88 276	91.45 396	98.03 189	99.47 120	98.65 138	99.55 72	99.47 77	91.49 340	99.81 210	99.32 58	99.91 75	99.80 39

AstraMVS		98.16 209	98.07 208	98.41 243	99.51 115	95.86 280	98.00 196	95.14 416	98.97 116	99.43 98	99.24 130	93.25 310	99.84 167	99.21 68	99.87 94	99.54 133

guyue		98.01 219	97.93 224	98.26 260	99.45 146	95.48 294	98.08 180	96.24 399	98.89 125	99.34 119	99.14 157	91.32 342	99.82 195	99.07 78	99.83 112	99.48 167

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 62	99.88 4	99.86 23	99.80 11	99.03 23	99.89 93	99.48 50	99.93 53	99.60 96

tt0320-xc		99.64 5	99.68 5	99.50 53	99.72 43	98.98 71	99.51 10	99.85 18	99.86 6	99.88 20	99.82 5	99.02 25	99.90 78	99.54 41	99.95 37	99.61 94

tt0320		99.61 8	99.65 9	99.48 56	99.71 47	98.94 78	99.54 8	99.83 25	99.87 5	99.89 17	99.82 5	98.75 43	99.90 78	99.54 41	99.95 37	99.59 103

fmvsm_s_conf0.5_n_8		99.13 63	99.26 48	98.74 192	99.51 115	96.44 260	97.65 249	99.65 59	99.66 24	99.78 38	99.48 74	97.92 119	99.93 52	99.72 27	99.95 37	99.87 21

fmvsm_s_conf0.5_n_7		98.83 103	99.04 77	98.20 265	99.30 180	94.83 316	97.23 294	99.36 163	98.64 139	99.84 29	99.43 86	98.10 105	99.91 71	99.56 38	99.96 27	99.87 21

fmvsm_s_conf0.5_n_6		99.08 73	99.21 54	98.69 196	99.36 166	96.51 258	97.62 254	99.68 54	98.43 160	99.85 26	99.10 166	99.12 22	99.88 109	99.77 20	99.92 66	99.67 72

fmvsm_s_conf0.5_n_5		99.07 75	99.10 70	98.99 147	99.47 139	97.22 220	97.40 279	99.83 25	97.61 226	99.85 26	99.30 112	98.80 39	99.95 26	99.71 29	99.90 82	99.78 44

fmvsm_s_conf0.5_n_4		99.01 79	99.22 52	98.38 247	99.31 176	95.48 294	97.56 264	99.73 42	98.87 126	99.75 43	99.27 118	98.80 39	99.86 136	99.80 15	99.90 82	99.81 37

SSC-MVS3.2		98.53 159	98.79 102	97.74 300	99.46 141	93.62 363	96.45 338	99.34 175	99.33 63	98.93 191	98.70 259	97.90 120	99.90 78	99.12 74	99.92 66	99.69 66

testing3-2		93.78 382	93.91 374	93.39 423	98.82 288	81.72 450	97.76 234	95.28 414	98.60 146	96.54 380	96.66 390	65.85 446	99.62 324	96.65 249	98.99 335	98.82 324

myMVS_eth3d28		92.92 397	92.31 393	94.77 406	97.84 387	87.59 429	96.19 356	96.11 402	97.08 282	94.27 422	93.49 438	66.07 445	98.78 430	91.78 397	97.93 395	97.92 399

UWE-MVS-28		90.22 412	89.28 415	93.02 427	94.50 448	82.87 446	96.52 335	87.51 446	95.21 356	92.36 439	96.04 401	71.57 432	98.25 438	72.04 448	97.77 397	97.94 398

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 79	99.59 85	98.21 132	97.82 222	99.84 22	99.41 55	99.92 8	99.41 91	99.51 8	99.95 26	99.84 8	99.97 20	99.87 21

fmvsm_s_conf0.5_n_3		99.22 46	99.37 31	98.78 181	99.46 141	96.58 256	97.65 249	99.72 43	99.47 45	99.86 23	99.50 67	98.94 29	99.89 93	99.75 23	99.97 20	99.86 27

fmvsm_s_conf0.5_n_2		99.14 59	99.31 40	98.63 206	99.49 129	96.08 273	97.38 281	99.81 30	99.48 42	99.84 29	99.57 49	98.46 70	99.89 93	99.82 10	99.97 20	99.91 13

fmvsm_s_conf0.1_n_2		99.20 49	99.38 28	98.65 200	99.69 58	96.08 273	97.49 273	99.90 11	99.53 39	99.88 20	99.64 37	98.51 66	99.90 78	99.83 9	99.98 12	99.97 4

GDP-MVS		97.50 260	97.11 279	98.67 199	99.02 249	96.85 242	98.16 169	99.71 45	98.32 167	98.52 255	98.54 286	83.39 402	99.95 26	98.79 98	99.56 239	99.19 265

BP-MVS1		97.40 272	96.97 285	98.71 195	99.07 236	96.81 244	98.34 152	97.18 377	98.58 150	98.17 279	98.61 279	84.01 398	99.94 41	98.97 87	99.78 141	99.37 214

reproduce_monomvs		95.00 364	95.25 353	94.22 412	97.51 412	83.34 444	97.86 218	98.44 336	98.51 156	99.29 130	99.30 112	67.68 439	99.56 347	98.89 93	99.81 120	99.77 47

mmtdpeth		99.30 33	99.42 24	98.92 161	99.58 86	96.89 241	99.48 13	99.92 7	99.92 2	98.26 276	99.80 11	98.33 82	99.91 71	99.56 38	99.95 37	99.97 4

reproduce_model		99.15 56	98.97 85	99.67 4	99.33 174	99.44 10	98.15 170	99.47 120	99.12 91	99.52 80	99.32 110	98.31 83	99.90 78	97.78 167	99.73 166	99.66 74

reproduce-ours		99.09 69	98.90 90	99.67 4	99.27 187	99.49 6	98.00 196	99.42 144	99.05 107	99.48 88	99.27 118	98.29 85	99.89 93	97.61 177	99.71 179	99.62 86

our_new_method		99.09 69	98.90 90	99.67 4	99.27 187	99.49 6	98.00 196	99.42 144	99.05 107	99.48 88	99.27 118	98.29 85	99.89 93	97.61 177	99.71 179	99.62 86

mmdepth		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

monomultidepth		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

mvs5depth		99.30 33	99.59 12	98.44 240	99.65 68	95.35 300	99.82 3	99.94 2	99.83 7	99.42 102	99.94 2	98.13 103	99.96 14	99.63 33	99.96 27	100.00 1

MVStest1		95.86 342	95.60 338	96.63 366	95.87 444	91.70 392	97.93 206	98.94 277	98.03 192	99.56 69	99.66 32	71.83 431	98.26 437	99.35 56	99.24 299	99.91 13

ttmdpeth		97.91 226	98.02 212	97.58 315	98.69 315	94.10 340	98.13 172	98.90 286	97.95 198	97.32 346	99.58 47	95.95 247	98.75 431	96.41 272	99.22 303	99.87 21

WBMVS		95.18 359	94.78 365	96.37 372	97.68 400	89.74 419	95.80 380	98.73 320	97.54 235	98.30 270	98.44 301	70.06 433	99.82 195	96.62 251	99.87 94	99.54 133

dongtai		76.24 416	75.95 419	77.12 432	92.39 450	67.91 456	90.16 443	59.44 457	82.04 443	89.42 445	94.67 430	49.68 455	81.74 450	48.06 450	77.66 448	81.72 446

kuosan		69.30 417	68.95 420	70.34 433	87.68 454	65.00 457	91.11 441	59.90 456	69.02 446	74.46 451	88.89 448	48.58 456	68.03 452	28.61 451	72.33 450	77.99 447

MVSMamba_PlusPlus		98.83 103	98.98 84	98.36 251	99.32 175	96.58 256	98.90 83	99.41 148	99.75 11	98.72 225	99.50 67	96.17 230	99.94 41	99.27 62	99.78 141	98.57 359

MGCFI-Net		98.34 182	98.28 179	98.51 230	98.47 348	97.59 197	98.96 77	99.48 112	99.18 87	97.40 341	95.50 414	98.66 51	99.50 368	98.18 137	98.71 355	98.44 369

testing91		93.32 389	92.27 394	96.47 370	97.54 405	91.25 403	96.17 360	96.76 391	97.18 276	93.65 433	93.50 437	65.11 448	99.63 321	93.04 378	97.45 404	98.53 360

testing11		93.08 394	92.02 399	96.26 377	97.56 403	90.83 411	96.32 348	95.70 410	96.47 313	92.66 437	93.73 434	64.36 449	99.59 336	93.77 364	97.57 400	98.37 378

testing99		93.04 395	91.98 402	96.23 379	97.53 407	90.70 413	96.35 346	95.94 406	96.87 294	93.41 434	93.43 439	63.84 450	99.59 336	93.24 376	97.19 414	98.40 374

UBG		93.25 391	92.32 392	96.04 386	97.72 392	90.16 416	95.92 374	95.91 407	96.03 330	93.95 430	93.04 441	69.60 435	99.52 362	90.72 417	97.98 393	98.45 366

UWE-MVS		92.38 403	91.76 406	94.21 413	97.16 421	84.65 439	95.42 394	88.45 445	95.96 333	96.17 391	95.84 409	66.36 442	99.71 279	91.87 396	98.64 362	98.28 381

ETVMVS		92.60 400	91.08 409	97.18 340	97.70 397	93.65 362	96.54 332	95.70 410	96.51 309	94.68 418	92.39 444	61.80 451	99.50 368	86.97 429	97.41 407	98.40 374

sasdasda		98.34 182	98.26 183	98.58 215	98.46 350	97.82 179	98.96 77	99.46 124	99.19 84	97.46 336	95.46 417	98.59 59	99.46 380	98.08 144	98.71 355	98.46 363

testing222		91.96 408	90.37 412	96.72 365	97.47 414	92.59 378	96.11 362	94.76 418	96.83 296	92.90 436	92.87 442	57.92 452	99.55 351	86.93 430	97.52 401	98.00 396

WB-MVSnew		95.73 347	95.57 341	96.23 379	96.70 431	90.70 413	96.07 364	93.86 428	95.60 343	97.04 355	95.45 420	96.00 239	99.55 351	91.04 411	98.31 374	98.43 371

fmvsm_l_conf0.5_n_a		99.19 50	99.27 46	98.94 156	99.65 68	97.05 230	97.80 226	99.76 38	98.70 137	99.78 38	99.11 163	98.79 41	99.95 26	99.85 5	99.96 27	99.83 31

fmvsm_l_conf0.5_n		99.21 47	99.28 45	99.02 144	99.64 74	97.28 215	97.82 222	99.76 38	98.73 134	99.82 32	99.09 170	98.81 37	99.95 26	99.86 4	99.96 27	99.83 31

fmvsm_s_conf0.1_n_a		99.17 51	99.30 43	98.80 175	99.75 34	96.59 254	97.97 205	99.86 16	98.22 177	99.88 20	99.71 22	98.59 59	99.84 167	99.73 25	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 55	99.33 36	98.64 202	99.71 47	96.10 268	97.87 217	99.85 18	98.56 154	99.90 13	99.68 25	98.69 49	99.85 149	99.72 27	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 68	99.20 55	98.78 181	99.55 103	96.59 254	97.79 227	99.82 29	98.21 178	99.81 35	99.53 63	98.46 70	99.84 167	99.70 30	99.97 20	99.90 15

fmvsm_s_conf0.5_n		99.09 69	99.26 48	98.61 211	99.55 103	96.09 271	97.74 237	99.81 30	98.55 155	99.85 26	99.55 57	98.60 58	99.84 167	99.69 32	99.98 12	99.89 16

MM		98.22 200	97.99 215	98.91 162	98.66 325	96.97 234	97.89 213	94.44 421	99.54 38	98.95 183	99.14 157	93.50 309	99.92 62	99.80 15	99.96 27	99.85 29

WAC-MVS						90.90 409								91.37 406

Syy-MVS		96.04 336	95.56 342	97.49 326	97.10 423	94.48 328	96.18 358	96.58 394	95.65 341	94.77 416	92.29 445	91.27 343	99.36 394	98.17 139	98.05 390	98.63 353

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 79	99.78 24	98.11 139	97.77 231	99.90 11	99.33 63	99.97 3	99.66 32	99.71 3	99.96 14	99.79 17	99.99 5	99.96 8

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 70	99.87 12	98.13 138	98.08 180	99.95 1	99.45 48	99.98 2	99.75 16	99.80 1	99.97 7	99.82 10	99.99 5	99.99 2

myMVS_eth3d		91.92 409	90.45 411	96.30 374	97.10 423	90.90 409	96.18 358	96.58 394	95.65 341	94.77 416	92.29 445	53.88 453	99.36 394	89.59 422	98.05 390	98.63 353

testing3		93.51 386	92.09 397	97.75 298	98.60 332	94.40 330	97.32 287	95.26 415	97.56 232	96.79 372	95.50 414	53.57 454	99.77 248	95.26 321	98.97 339	99.08 281

SSC-MVS		98.71 121	98.74 106	98.62 208	99.72 43	96.08 273	98.74 97	98.64 327	99.74 13	99.67 57	99.24 130	94.57 287	99.95 26	99.11 75	99.24 299	99.82 34

test_fmvsmconf_n		99.44 19	99.48 18	99.31 90	99.64 74	98.10 141	97.68 243	99.84 22	99.29 69	99.92 8	99.57 49	99.60 5	99.96 14	99.74 24	99.98 12	99.89 16

WB-MVS		98.52 163	98.55 137	98.43 241	99.65 68	95.59 287	98.52 123	98.77 312	99.65 26	99.52 80	99.00 196	94.34 293	99.93 52	98.65 111	98.83 347	99.76 52

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 226	99.66 67	96.97 234	98.00 196	99.85 18	99.24 73	99.92 8	99.50 67	99.39 12	99.95 26	99.89 3	99.98 12	98.71 343

dmvs_re		95.98 339	95.39 349	97.74 300	98.86 279	97.45 206	98.37 148	95.69 412	97.95 198	96.56 379	95.95 404	90.70 348	97.68 442	88.32 425	96.13 429	98.11 388

SDMVSNet		99.23 45	99.32 38	98.96 153	99.68 61	97.35 211	98.84 94	99.48 112	99.69 18	99.63 64	99.68 25	99.03 23	99.96 14	97.97 155	99.92 66	99.57 116

dmvs_testset		92.94 396	92.21 396	95.13 403	98.59 335	90.99 408	97.65 249	92.09 436	96.95 289	94.00 428	93.55 436	92.34 329	96.97 445	72.20 447	92.52 443	97.43 420

sd_testset		99.28 36	99.31 40	99.19 108	99.68 61	98.06 151	99.41 17	99.30 196	99.69 18	99.63 64	99.68 25	99.25 15	99.96 14	97.25 196	99.92 66	99.57 116

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 147	99.57 91	97.73 189	97.93 206	99.83 25	99.22 76	99.93 6	99.30 112	99.42 11	99.96 14	99.85 5	99.99 5	99.29 243

test_cas_vis1_n_1920		98.33 185	98.68 119	97.27 337	99.69 58	92.29 386	98.03 189	99.85 18	97.62 223	99.96 4	99.62 40	93.98 302	99.74 266	99.52 47	99.86 100	99.79 41

test_vis1_n_1920		98.40 174	98.92 88	96.81 361	99.74 36	90.76 412	98.15 170	99.91 9	98.33 165	99.89 17	99.55 57	95.07 272	99.88 109	99.76 21	99.93 53	99.79 41

test_vis1_n		98.31 188	98.50 144	97.73 303	99.76 30	94.17 338	98.68 107	99.91 9	96.31 319	99.79 37	99.57 49	92.85 322	99.42 387	99.79 17	99.84 105	99.60 96

test_fmvs1_n		98.09 212	98.28 179	97.52 323	99.68 61	93.47 365	98.63 110	99.93 5	95.41 352	99.68 55	99.64 37	91.88 336	99.48 374	99.82 10	99.87 94	99.62 86

mvsany_test1		97.60 254	97.54 252	97.77 294	97.72 392	95.35 300	95.36 396	97.13 380	94.13 381	99.71 47	99.33 106	97.93 118	99.30 404	97.60 179	98.94 342	98.67 351

APD_test1		98.83 103	98.66 122	99.34 79	99.78 24	99.47 9	98.42 144	99.45 128	98.28 174	98.98 175	99.19 140	97.76 131	99.58 342	96.57 256	99.55 243	98.97 302

test_vis1_rt		97.75 244	97.72 239	97.83 289	98.81 291	96.35 263	97.30 289	99.69 49	94.61 368	97.87 305	98.05 335	96.26 228	98.32 436	98.74 104	98.18 379	98.82 324

test_vis3_rt		99.14 59	99.17 57	99.07 131	99.78 24	98.38 115	98.92 82	99.94 2	97.80 211	99.91 12	99.67 30	97.15 178	98.91 426	99.76 21	99.56 239	99.92 12

test_fmvs2		98.70 125	98.97 85	97.89 286	99.54 108	94.05 341	98.55 119	99.92 7	96.78 299	99.72 45	99.78 13	96.60 213	99.67 299	99.91 2	99.90 82	99.94 10

test_fmvs1		97.72 246	97.94 222	97.07 347	98.66 325	92.39 383	97.68 243	99.81 30	95.20 357	99.54 74	99.44 84	91.56 339	99.41 388	99.78 19	99.77 147	99.40 204

test_fmvs3		99.12 66	99.41 25	98.25 261	99.76 30	95.07 312	99.05 67	99.94 2	97.78 214	99.82 32	99.84 3	98.56 63	99.71 279	99.96 1	99.96 27	99.97 4

mvsany_test3		98.87 98	98.92 88	98.74 192	99.38 159	96.94 238	98.58 116	99.10 253	96.49 311	99.96 4	99.81 8	98.18 96	99.45 382	98.97 87	99.79 136	99.83 31

testf1		99.25 40	99.16 59	99.51 48	99.89 6	99.63 4	98.71 104	99.69 49	98.90 123	99.43 98	99.35 100	98.86 33	99.67 299	97.81 164	99.81 120	99.24 253

APD_test2		99.25 40	99.16 59	99.51 48	99.89 6	99.63 4	98.71 104	99.69 49	98.90 123	99.43 98	99.35 100	98.86 33	99.67 299	97.81 164	99.81 120	99.24 253

test_f		98.67 136	98.87 93	98.05 279	99.72 43	95.59 287	98.51 128	99.81 30	96.30 321	99.78 38	99.82 5	96.14 231	98.63 433	99.82 10	99.93 53	99.95 9

FE-MVS		95.66 349	94.95 362	97.77 294	98.53 344	95.28 303	99.40 19	96.09 403	93.11 396	97.96 299	99.26 123	79.10 420	99.77 248	92.40 392	98.71 355	98.27 382

FA-MVS(test-final)		96.99 304	96.82 297	97.50 325	98.70 310	94.78 318	99.34 23	96.99 383	95.07 358	98.48 258	99.33 106	88.41 369	99.65 315	96.13 291	98.92 344	98.07 391

balanced_conf03		98.63 142	98.72 110	98.38 247	98.66 325	96.68 253	98.90 83	99.42 144	98.99 113	98.97 179	99.19 140	95.81 252	99.85 149	98.77 102	99.77 147	98.60 355

MonoMVSNet		96.25 331	96.53 317	95.39 400	96.57 433	91.01 407	98.82 95	97.68 364	98.57 151	98.03 296	99.37 95	90.92 346	97.78 441	94.99 325	93.88 441	97.38 421

patch_mono-2		98.51 164	98.63 126	98.17 268	99.38 159	94.78 318	97.36 284	99.69 49	98.16 188	98.49 257	99.29 115	97.06 182	99.97 7	98.29 131	99.91 75	99.76 52

EGC-MVSNET		85.24 413	80.54 416	99.34 79	99.77 27	99.20 39	99.08 61	99.29 204	12.08 451	20.84 452	99.42 87	97.55 149	99.85 149	97.08 208	99.72 174	98.96 304

test2506		92.39 402	91.89 404	93.89 417	99.38 159	82.28 448	99.32 26	66.03 455	99.08 104	98.77 219	99.57 49	66.26 443	99.84 167	98.71 107	99.95 37	99.54 133

test1111		96.49 323	96.82 297	95.52 396	99.42 154	87.08 431	99.22 45	87.14 447	99.11 92	99.46 93	99.58 47	88.69 363	99.86 136	98.80 97	99.95 37	99.62 86

ECVR-MVS		96.42 325	96.61 311	95.85 388	99.38 159	88.18 426	99.22 45	86.00 449	99.08 104	99.36 115	99.57 49	88.47 368	99.82 195	98.52 120	99.95 37	99.54 133

test_blank		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

tt0805		98.69 128	98.62 128	98.90 165	99.75 34	99.30 22	99.15 56	96.97 384	98.86 128	98.87 204	97.62 362	98.63 55	98.96 423	99.41 54	98.29 375	98.45 366

DVP-MVS++		98.90 95	98.70 116	99.51 48	98.43 354	99.15 52	99.43 15	99.32 183	98.17 185	99.26 137	99.02 184	98.18 96	99.88 109	97.07 209	99.45 268	99.49 156

FOURS1					99.73 37	99.67 3	99.43 15	99.54 93	99.43 52	99.26 137

MSC_two_6792asdad				99.32 87	98.43 354	98.37 117		98.86 297					99.89 93	97.14 203	99.60 223	99.71 59

PC_three_1452									93.27 393	99.40 107	98.54 286	98.22 92	97.00 444	95.17 322	99.45 268	99.49 156

No_MVS				99.32 87	98.43 354	98.37 117		98.86 297					99.89 93	97.14 203	99.60 223	99.71 59

test_one_0601					99.39 158	99.20 39		99.31 188	98.49 157	98.66 232	99.02 184	97.64 141

eth-test2					0.00 459

eth-test					0.00 459

GeoE		99.05 76	98.99 83	99.25 100	99.44 148	98.35 121	98.73 101	99.56 85	98.42 161	98.91 194	98.81 241	98.94 29	99.91 71	98.35 127	99.73 166	99.49 156

test_method		79.78 414	79.50 417	80.62 430	80.21 455	45.76 458	70.82 446	98.41 340	31.08 450	80.89 450	97.71 355	84.85 389	97.37 443	91.51 404	80.03 447	98.75 340

Anonymous20240521		98.69 128	98.87 93	98.16 270	99.77 27	95.11 311	99.08 61	99.44 132	99.34 62	99.33 121	99.55 57	94.10 301	99.94 41	99.25 65	99.96 27	99.42 192

h-mvs33		97.77 243	97.33 267	99.10 124	99.21 201	97.84 173	98.35 150	98.57 330	99.11 92	98.58 245	99.02 184	88.65 366	99.96 14	98.11 141	96.34 425	99.49 156

hse-mvs2		97.46 265	97.07 280	98.64 202	98.73 300	97.33 212	97.45 277	97.64 367	99.11 92	98.58 245	97.98 340	88.65 366	99.79 231	98.11 141	97.39 408	98.81 329

CL-MVSNet_self_test		97.44 268	97.22 272	98.08 275	98.57 339	95.78 285	94.30 424	98.79 309	96.58 308	98.60 241	98.19 324	94.74 285	99.64 318	96.41 272	98.84 346	98.82 324

KD-MVS_2432*1600		92.87 398	91.99 400	95.51 397	91.37 451	89.27 420	94.07 426	98.14 350	95.42 349	97.25 348	96.44 396	67.86 437	99.24 410	91.28 407	96.08 430	98.02 393

KD-MVS_self_test		99.25 40	99.18 56	99.44 63	99.63 80	99.06 70	98.69 106	99.54 93	99.31 66	99.62 67	99.53 63	97.36 165	99.86 136	99.24 67	99.71 179	99.39 205

AUN-MVS		96.24 333	95.45 345	98.60 213	98.70 310	97.22 220	97.38 281	97.65 365	95.95 334	95.53 408	97.96 344	82.11 410	99.79 231	96.31 278	97.44 405	98.80 334

ZD-MVS					99.01 250	98.84 82		99.07 257	94.10 382	98.05 294	98.12 328	96.36 225	99.86 136	92.70 388	99.19 310

SR-MVS-dyc-post		98.81 108	98.55 137	99.57 21	99.20 205	99.38 13	98.48 136	99.30 196	98.64 139	98.95 183	98.96 206	97.49 159	99.86 136	96.56 260	99.39 275	99.45 181

RE-MVS-def			98.58 135		99.20 205	99.38 13	98.48 136	99.30 196	98.64 139	98.95 183	98.96 206	97.75 132		96.56 260	99.39 275	99.45 181

SED-MVS		98.91 93	98.72 110	99.49 54	99.49 129	99.17 44	98.10 178	99.31 188	98.03 192	99.66 58	99.02 184	98.36 76	99.88 109	96.91 221	99.62 216	99.41 195

IU-MVS					99.49 129	99.15 52		98.87 292	92.97 397	99.41 104				96.76 238	99.62 216	99.66 74

OPU-MVS				98.82 171	98.59 335	98.30 122	98.10 178				98.52 290	98.18 96	98.75 431	94.62 335	99.48 264	99.41 195

test_241102_TWO								99.30 196	98.03 192	99.26 137	99.02 184	97.51 155	99.88 109	96.91 221	99.60 223	99.66 74

test_241102_ONE					99.49 129	99.17 44		99.31 188	97.98 195	99.66 58	98.90 218	98.36 76	99.48 374

SF-MVS		98.53 159	98.27 182	99.32 87	99.31 176	98.75 87	98.19 164	99.41 148	96.77 300	98.83 208	98.90 218	97.80 129	99.82 195	95.68 311	99.52 252	99.38 212

cl22		95.79 345	95.39 349	96.98 351	96.77 430	92.79 375	94.40 422	98.53 332	94.59 369	97.89 303	98.17 325	82.82 407	99.24 410	96.37 274	99.03 328	98.92 311

miper_ehance_all_eth		97.06 297	97.03 282	97.16 344	97.83 388	93.06 369	94.66 414	99.09 255	95.99 332	98.69 227	98.45 300	92.73 325	99.61 331	96.79 234	99.03 328	98.82 324

miper_enhance_ethall		96.01 337	95.74 332	96.81 361	96.41 438	92.27 387	93.69 433	98.89 289	91.14 419	98.30 270	97.35 378	90.58 349	99.58 342	96.31 278	99.03 328	98.60 355

ZNCC-MVS		98.68 133	98.40 161	99.54 31	99.57 91	99.21 33	98.46 138	99.29 204	97.28 263	98.11 288	98.39 305	98.00 112	99.87 128	96.86 231	99.64 210	99.55 129

dcpmvs_2		98.78 112	99.11 68	97.78 293	99.56 99	93.67 360	99.06 65	99.86 16	99.50 41	99.66 58	99.26 123	97.21 176	99.99 2	98.00 153	99.91 75	99.68 67

cl____		97.02 300	96.83 296	97.58 315	97.82 389	94.04 343	94.66 414	99.16 243	97.04 284	98.63 235	98.71 256	88.68 365	99.69 287	97.00 213	99.81 120	99.00 297

DIV-MVS_self_test		97.02 300	96.84 295	97.58 315	97.82 389	94.03 344	94.66 414	99.16 243	97.04 284	98.63 235	98.71 256	88.69 363	99.69 287	97.00 213	99.81 120	99.01 293

eth_miper_zixun_eth		97.23 286	97.25 270	97.17 342	98.00 381	92.77 376	94.71 411	99.18 236	97.27 264	98.56 248	98.74 252	91.89 335	99.69 287	97.06 211	99.81 120	99.05 285

9.14			97.78 233		99.07 236		97.53 268	99.32 183	95.53 346	98.54 252	98.70 259	97.58 146	99.76 254	94.32 348	99.46 266

uanet_test		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

DCPMVS		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

save fliter					99.11 227	97.97 159	96.53 334	99.02 269	98.24 175

ET-MVSNet_ETH3D		94.30 373	93.21 384	97.58 315	98.14 374	94.47 329	94.78 410	93.24 432	94.72 366	89.56 444	95.87 407	78.57 423	99.81 210	96.91 221	97.11 417	98.46 363

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 20	99.69 5	99.58 71	99.90 3	99.86 23	99.78 13	99.58 6	99.95 26	99.00 85	99.95 37	99.78 44

EIA-MVS		98.00 220	97.74 236	98.80 175	98.72 302	98.09 142	98.05 186	99.60 68	97.39 252	96.63 376	95.55 412	97.68 135	99.80 218	96.73 242	99.27 294	98.52 361

miper_refine_blended		92.87 398	91.99 400	95.51 397	91.37 451	89.27 420	94.07 426	98.14 350	95.42 349	97.25 348	96.44 396	67.86 437	99.24 410	91.28 407	96.08 430	98.02 393

miper_lstm_enhance		97.18 290	97.16 275	97.25 339	98.16 372	92.85 374	95.15 402	99.31 188	97.25 266	98.74 224	98.78 246	90.07 352	99.78 242	97.19 198	99.80 131	99.11 280

ETV-MVS		98.03 216	97.86 230	98.56 222	98.69 315	98.07 148	97.51 271	99.50 103	98.10 190	97.50 333	95.51 413	98.41 73	99.88 109	96.27 281	99.24 299	97.71 412

CS-MVS		99.13 63	99.10 70	99.24 102	99.06 241	99.15 52	99.36 22	99.88 14	99.36 61	98.21 278	98.46 299	98.68 50	99.93 52	99.03 83	99.85 101	98.64 352

D2MVS		97.84 240	97.84 231	97.83 289	99.14 223	94.74 320	96.94 312	98.88 290	95.84 337	98.89 197	98.96 206	94.40 291	99.69 287	97.55 180	99.95 37	99.05 285

DVP-MVS		98.77 115	98.52 141	99.52 44	99.50 121	99.21 33	98.02 192	98.84 301	97.97 196	99.08 159	99.02 184	97.61 144	99.88 109	96.99 215	99.63 213	99.48 167
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									98.17 185	99.08 159	99.02 184	97.89 121	99.88 109	97.07 209	99.71 179	99.70 64

test_0728_SECOND				99.60 15	99.50 121	99.23 31	98.02 192	99.32 183					99.88 109	96.99 215	99.63 213	99.68 67

test0726					99.50 121	99.21 33	98.17 168	99.35 169	97.97 196	99.26 137	99.06 172	97.61 144

SR-MVS		98.71 121	98.43 157	99.57 21	99.18 215	99.35 17	98.36 149	99.29 204	98.29 172	98.88 200	98.85 231	97.53 152	99.87 128	96.14 289	99.31 287	99.48 167

DPM-MVS		96.32 327	95.59 340	98.51 230	98.76 296	97.21 222	94.54 420	98.26 344	91.94 409	96.37 388	97.25 379	93.06 317	99.43 385	91.42 405	98.74 351	98.89 316

GST-MVS		98.61 146	98.30 177	99.52 44	99.51 115	99.20 39	98.26 158	99.25 217	97.44 249	98.67 230	98.39 305	97.68 135	99.85 149	96.00 293	99.51 254	99.52 145

test_yl		96.69 313	96.29 323	97.90 284	98.28 364	95.24 304	97.29 290	97.36 371	98.21 178	98.17 279	97.86 347	86.27 377	99.55 351	94.87 329	98.32 372	98.89 316

thisisatest0530		95.27 357	94.45 368	97.74 300	99.19 208	94.37 331	97.86 218	90.20 442	97.17 277	98.22 277	97.65 359	73.53 430	99.90 78	96.90 226	99.35 281	98.95 305

Anonymous20240529		98.93 91	98.87 93	99.12 120	99.19 208	98.22 131	99.01 70	98.99 275	99.25 72	99.54 74	99.37 95	97.04 183	99.80 218	97.89 158	99.52 252	99.35 225

Anonymous202405211		97.90 227	97.50 255	99.08 129	98.90 270	98.25 125	98.53 122	96.16 400	98.87 126	99.11 154	98.86 228	90.40 351	99.78 242	97.36 190	99.31 287	99.19 265

DCV-MVSNet		96.69 313	96.29 323	97.90 284	98.28 364	95.24 304	97.29 290	97.36 371	98.21 178	98.17 279	97.86 347	86.27 377	99.55 351	94.87 329	98.32 372	98.89 316

tttt0517		95.64 350	94.98 360	97.64 309	99.36 166	93.81 355	98.72 102	90.47 441	98.08 191	98.67 230	98.34 312	73.88 429	99.92 62	97.77 168	99.51 254	99.20 260

our_test_3		97.39 273	97.73 238	96.34 373	98.70 310	89.78 418	94.61 417	98.97 276	96.50 310	99.04 168	98.85 231	95.98 244	99.84 167	97.26 195	99.67 200	99.41 195

thisisatest0515		94.12 377	93.16 385	96.97 352	98.60 332	92.90 373	93.77 432	90.61 440	94.10 382	96.91 362	95.87 407	74.99 428	99.80 218	94.52 338	99.12 321	98.20 384

ppachtmachnet_test		97.50 260	97.74 236	96.78 363	98.70 310	91.23 405	94.55 419	99.05 261	96.36 316	99.21 145	98.79 244	96.39 221	99.78 242	96.74 240	99.82 116	99.34 227

SMA-MVS		98.40 174	98.03 211	99.51 48	99.16 218	99.21 33	98.05 186	99.22 225	94.16 380	98.98 175	99.10 166	97.52 154	99.79 231	96.45 270	99.64 210	99.53 142
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																98.81 329

DPE-MVS		98.59 149	98.26 183	99.57 21	99.27 187	99.15 52	97.01 308	99.39 153	97.67 219	99.44 97	98.99 197	97.53 152	99.89 93	95.40 319	99.68 194	99.66 74
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.36 166	99.10 65				99.05 166

thres100view900		94.19 374	93.67 379	95.75 391	99.06 241	91.35 399	98.03 189	94.24 425	98.33 165	97.40 341	94.98 425	79.84 414	99.62 324	83.05 437	98.08 387	96.29 432

tfpnnormal		98.90 95	98.90 90	98.91 162	99.67 65	97.82 179	99.00 72	99.44 132	99.45 48	99.51 85	99.24 130	98.20 95	99.86 136	95.92 297	99.69 189	99.04 289

tfpn200view9		94.03 378	93.44 381	95.78 390	98.93 262	91.44 397	97.60 259	94.29 423	97.94 200	97.10 351	94.31 432	79.67 416	99.62 324	83.05 437	98.08 387	96.29 432

c3_l		97.36 274	97.37 263	97.31 334	98.09 377	93.25 367	95.01 405	99.16 243	97.05 283	98.77 219	98.72 255	92.88 320	99.64 318	96.93 220	99.76 159	99.05 285

CHOSEN 280x420		95.51 354	95.47 343	95.65 394	98.25 366	88.27 425	93.25 435	98.88 290	93.53 390	94.65 419	97.15 382	86.17 379	99.93 52	97.41 188	99.93 53	98.73 342

CANet		97.87 233	97.76 234	98.19 267	97.75 391	95.51 292	96.76 323	99.05 261	97.74 215	96.93 359	98.21 322	95.59 258	99.89 93	97.86 163	99.93 53	99.19 265

Fast-Effi-MVS+-dtu		98.27 193	98.09 203	98.81 173	98.43 354	98.11 139	97.61 258	99.50 103	98.64 139	97.39 343	97.52 367	98.12 104	99.95 26	96.90 226	98.71 355	98.38 376

Effi-MVS+-dtu		98.26 195	97.90 227	99.35 76	98.02 380	99.49 6	98.02 192	99.16 243	98.29 172	97.64 320	97.99 339	96.44 220	99.95 26	96.66 248	98.93 343	98.60 355

CANet_DTU		97.26 282	97.06 281	97.84 288	97.57 402	94.65 325	96.19 356	98.79 309	97.23 272	95.14 413	98.24 319	93.22 312	99.84 167	97.34 191	99.84 105	99.04 289

MVS_0304		97.44 268	97.01 284	98.72 194	96.42 437	96.74 249	97.20 299	91.97 437	98.46 159	98.30 270	98.79 244	92.74 324	99.91 71	99.30 60	99.94 48	99.52 145

MP-MVS-pluss		98.57 150	98.23 187	99.60 15	99.69 58	99.35 17	97.16 303	99.38 155	94.87 364	98.97 179	98.99 197	98.01 111	99.88 109	97.29 193	99.70 186	99.58 111
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 174	98.00 214	99.61 13	99.57 91	99.25 29	98.57 117	99.35 169	97.55 234	99.31 129	97.71 355	94.61 286	99.88 109	96.14 289	99.19 310	99.70 64
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												84.74 391				98.81 329

sam_mvs												84.29 397

IterMVS-SCA-FT		97.85 239	98.18 193	96.87 357	99.27 187	91.16 406	95.53 388	99.25 217	99.10 99	99.41 104	99.35 100	93.10 315	99.96 14	98.65 111	99.94 48	99.49 156

TSAR-MVS + MP.		98.63 142	98.49 148	99.06 137	99.64 74	97.90 168	98.51 128	98.94 277	96.96 288	99.24 142	98.89 224	97.83 124	99.81 210	96.88 228	99.49 263	99.48 167
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		97.86 234	98.17 194	96.92 354	98.98 255	93.91 350	96.45 338	99.17 240	97.85 208	98.41 264	97.14 383	98.47 67	99.92 62	98.02 150	99.05 324	96.92 425

OPM-MVS		98.56 151	98.32 175	99.25 100	99.41 156	98.73 91	97.13 305	99.18 236	97.10 281	98.75 222	98.92 214	98.18 96	99.65 315	96.68 247	99.56 239	99.37 214
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 117	98.48 149	99.57 21	99.58 86	99.29 24	97.82 222	99.25 217	96.94 290	98.78 216	99.12 162	98.02 110	99.84 167	97.13 205	99.67 200	99.59 103

ambc				98.24 263	98.82 288	95.97 277	98.62 112	99.00 274		99.27 133	99.21 137	96.99 188	99.50 368	96.55 263	99.50 261	99.26 249

MTGPA								99.20 228

SPE-MVS-test		99.13 63	99.09 72	99.26 97	99.13 225	98.97 73	99.31 30	99.88 14	99.44 50	98.16 282	98.51 291	98.64 53	99.93 52	98.91 90	99.85 101	98.88 319

Effi-MVS+		98.02 217	97.82 232	98.62 208	98.53 344	97.19 224	97.33 286	99.68 54	97.30 261	96.68 374	97.46 371	98.56 63	99.80 218	96.63 250	98.20 378	98.86 321

xiu_mvs_v2_base		97.16 292	97.49 256	96.17 382	98.54 342	92.46 381	95.45 392	98.84 301	97.25 266	97.48 335	96.49 393	98.31 83	99.90 78	96.34 277	98.68 360	96.15 436

xiu_mvs_v1_base		97.86 234	98.17 194	96.92 354	98.98 255	93.91 350	96.45 338	99.17 240	97.85 208	98.41 264	97.14 383	98.47 67	99.92 62	98.02 150	99.05 324	96.92 425

new-patchmatchnet		98.35 181	98.74 106	97.18 340	99.24 194	92.23 388	96.42 342	99.48 112	98.30 169	99.69 53	99.53 63	97.44 161	99.82 195	98.84 96	99.77 147	99.49 156

pmmvs6		99.67 3	99.70 3	99.60 15	99.90 4	99.27 27	99.53 9	99.76 38	99.64 27	99.84 29	99.83 4	99.50 9	99.87 128	99.36 55	99.92 66	99.64 80

pmmvs5		97.64 252	97.49 256	98.08 275	99.14 223	95.12 310	96.70 327	99.05 261	93.77 387	98.62 237	98.83 236	93.23 311	99.75 261	98.33 130	99.76 159	99.36 221

test_post1							97.59 261				20.48 453	83.07 405	99.66 310	94.16 349

test_post											21.25 452	83.86 400	99.70 283

Fast-Effi-MVS+		97.67 250	97.38 262	98.57 218	98.71 306	97.43 208	97.23 294	99.45 128	94.82 365	96.13 392	96.51 392	98.52 65	99.91 71	96.19 285	98.83 347	98.37 378

patchmatchnet-post											98.77 248	84.37 394	99.85 149

Anonymous20231211		99.27 37	99.27 46	99.26 97	99.29 183	98.18 133	99.49 12	99.51 100	99.70 16	99.80 36	99.68 25	96.84 194	99.83 185	99.21 68	99.91 75	99.77 47

pmmvs-eth3d		98.47 167	98.34 171	98.86 167	99.30 180	97.76 185	97.16 303	99.28 208	95.54 345	99.42 102	99.19 140	97.27 171	99.63 321	97.89 158	99.97 20	99.20 260

GG-mvs-BLEND				94.76 407	94.54 447	92.13 389	99.31 30	80.47 453		88.73 447	91.01 447	67.59 440	98.16 440	82.30 441	94.53 439	93.98 443

xiu_mvs_v1_base_debi		97.86 234	98.17 194	96.92 354	98.98 255	93.91 350	96.45 338	99.17 240	97.85 208	98.41 264	97.14 383	98.47 67	99.92 62	98.02 150	99.05 324	96.92 425

Anonymous20231206		98.21 202	98.21 188	98.20 265	99.51 115	95.43 298	98.13 172	99.32 183	96.16 324	98.93 191	98.82 239	96.00 239	99.83 185	97.32 192	99.73 166	99.36 221

MTAPA		98.88 97	98.64 125	99.61 13	99.67 65	99.36 16	98.43 141	99.20 228	98.83 132	98.89 197	98.90 218	96.98 189	99.92 62	97.16 200	99.70 186	99.56 122

MTMP							97.93 206	91.91 438

gm-plane-assit					94.83 446	81.97 449			88.07 434		94.99 424		99.60 332	91.76 398

test9_res														93.28 375	99.15 315	99.38 212

MVP-Stereo		98.08 213	97.92 225	98.57 218	98.96 258	96.79 245	97.90 212	99.18 236	96.41 315	98.46 259	98.95 210	95.93 248	99.60 332	96.51 266	98.98 338	99.31 238
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 306	98.08 146	95.96 369	99.03 266	91.40 415	95.85 398	97.53 365	96.52 216	99.76 254

train_agg		97.10 294	96.45 319	99.07 131	98.71 306	98.08 146	95.96 369	99.03 266	91.64 410	95.85 398	97.53 365	96.47 218	99.76 254	93.67 365	99.16 313	99.36 221

gg-mvs-nofinetune		92.37 404	91.20 408	95.85 388	95.80 445	92.38 384	99.31 30	81.84 452	99.75 11	91.83 441	99.74 18	68.29 436	99.02 420	87.15 428	97.12 416	96.16 435

SCA		96.41 326	96.66 309	95.67 392	98.24 367	88.35 424	95.85 378	96.88 389	96.11 325	97.67 319	98.67 265	93.10 315	99.85 149	94.16 349	99.22 303	98.81 329

Patchmatch-test		96.55 319	96.34 321	97.17 342	98.35 360	93.06 369	98.40 145	97.79 358	97.33 257	98.41 264	98.67 265	83.68 401	99.69 287	95.16 323	99.31 287	98.77 337

test_8					98.67 320	98.01 154	95.91 375	99.02 269	91.64 410	95.79 400	97.50 368	96.47 218	99.76 254

MS-PatchMatch		97.68 249	97.75 235	97.45 329	98.23 369	93.78 356	97.29 290	98.84 301	96.10 326	98.64 234	98.65 270	96.04 236	99.36 394	96.84 232	99.14 316	99.20 260

Patchmatch-RL test		97.26 282	97.02 283	97.99 283	99.52 113	95.53 291	96.13 361	99.71 45	97.47 241	99.27 133	99.16 150	84.30 396	99.62 324	97.89 158	99.77 147	98.81 329

cdsmvs_eth3d_5k		24.66 418	32.88 421	0.00 436	0.00 459	0.00 461	0.00 447	99.10 253	0.00 454	0.00 455	97.58 363	99.21 17	0.00 455	0.00 454	0.00 453	0.00 451

pcd_1.5k_mvsjas		8.17 421	10.90 424	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	98.07 106	0.00 455	0.00 454	0.00 453	0.00 451

agg_prior2														92.50 391	99.16 313	99.37 214

agg_prior					98.68 319	97.99 155		99.01 272		95.59 401			99.77 248

tmp_tt		78.77 415	78.73 418	78.90 431	58.45 456	74.76 455	94.20 425	78.26 454	39.16 449	86.71 448	92.82 443	80.50 412	75.19 451	86.16 433	92.29 444	86.74 445

canonicalmvs		98.34 182	98.26 183	98.58 215	98.46 350	97.82 179	98.96 77	99.46 124	99.19 84	97.46 336	95.46 417	98.59 59	99.46 380	98.08 144	98.71 355	98.46 363

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 49	98.93 121	99.65 61	99.72 21	98.93 31	99.95 26	99.11 75	100.00 1	99.82 34

alignmvs		97.35 275	96.88 292	98.78 181	98.54 342	98.09 142	97.71 240	97.69 362	99.20 80	97.59 324	95.90 406	88.12 371	99.55 351	98.18 137	98.96 340	98.70 346

nrg030		99.40 26	99.35 33	99.54 31	99.58 86	99.13 60	98.98 75	99.48 112	99.68 20	99.46 93	99.26 123	98.62 56	99.73 271	99.17 72	99.92 66	99.76 52

v144192		98.54 157	98.57 136	98.45 238	99.21 201	95.98 276	97.63 253	99.36 163	97.15 280	99.32 127	99.18 144	95.84 251	99.84 167	99.50 48	99.91 75	99.54 133

FIs		99.14 59	99.09 72	99.29 91	99.70 55	98.28 123	99.13 58	99.52 99	99.48 42	99.24 142	99.41 91	96.79 200	99.82 195	98.69 109	99.88 90	99.76 52

v1921920		98.54 157	98.60 133	98.38 247	99.20 205	95.76 286	97.56 264	99.36 163	97.23 272	99.38 110	99.17 148	96.02 237	99.84 167	99.57 36	99.90 82	99.54 133

UA-Net		99.47 16	99.40 26	99.70 2	99.49 129	99.29 24	99.80 4	99.72 43	99.82 8	99.04 168	99.81 8	98.05 109	99.96 14	98.85 95	99.99 5	99.86 27

v1192		98.60 147	98.66 122	98.41 243	99.27 187	95.88 279	97.52 269	99.36 163	97.41 250	99.33 121	99.20 139	96.37 224	99.82 195	99.57 36	99.92 66	99.55 129

FC-MVSNet-test		99.27 37	99.25 50	99.34 79	99.77 27	98.37 117	99.30 35	99.57 78	99.61 34	99.40 107	99.50 67	97.12 179	99.85 149	99.02 84	99.94 48	99.80 39

v1144		98.60 147	98.66 122	98.41 243	99.36 166	95.90 278	97.58 262	99.34 175	97.51 237	99.27 133	99.15 154	96.34 226	99.80 218	99.47 51	99.93 53	99.51 148

sosnet-low-res		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

HFP-MVS		98.71 121	98.44 156	99.51 48	99.49 129	99.16 48	98.52 123	99.31 188	97.47 241	98.58 245	98.50 295	97.97 116	99.85 149	96.57 256	99.59 227	99.53 142

v148		98.45 169	98.60 133	98.00 282	99.44 148	94.98 313	97.44 278	99.06 258	98.30 169	99.32 127	98.97 203	96.65 211	99.62 324	98.37 126	99.85 101	99.39 205

sosnet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

uncertanet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

AllTest		98.44 170	98.20 189	99.16 114	99.50 121	98.55 103	98.25 159	99.58 71	96.80 297	98.88 200	99.06 172	97.65 138	99.57 344	94.45 341	99.61 221	99.37 214

TestCases				99.16 114	99.50 121	98.55 103		99.58 71	96.80 297	98.88 200	99.06 172	97.65 138	99.57 344	94.45 341	99.61 221	99.37 214

v7n		99.53 12	99.57 13	99.41 66	99.88 9	98.54 106	99.45 14	99.61 67	99.66 24	99.68 55	99.66 32	98.44 72	99.95 26	99.73 25	99.96 27	99.75 56

region2R		98.69 128	98.40 161	99.54 31	99.53 111	99.17 44	98.52 123	99.31 188	97.46 246	98.44 261	98.51 291	97.83 124	99.88 109	96.46 269	99.58 232	99.58 111

RRT-MVS		97.88 231	97.98 216	97.61 312	98.15 373	93.77 357	98.97 76	99.64 61	99.16 89	98.69 227	99.42 87	91.60 337	99.89 93	97.63 176	98.52 369	99.16 275

mamv4		99.44 19	99.39 27	99.58 20	99.30 180	99.74 2	99.04 68	99.81 30	99.77 10	99.82 32	99.57 49	97.82 127	99.98 4	99.53 45	99.89 88	99.01 293

PS-MVSNAJss		99.46 17	99.49 16	99.35 76	99.90 4	98.15 135	99.20 48	99.65 59	99.48 42	99.92 8	99.71 22	98.07 106	99.96 14	99.53 45	100.00 1	99.93 11

PS-MVSNAJ		97.08 296	97.39 261	96.16 384	98.56 340	92.46 381	95.24 399	98.85 300	97.25 266	97.49 334	95.99 403	98.07 106	99.90 78	96.37 274	98.67 361	96.12 437

jajsoiax		99.58 9	99.61 11	99.48 56	99.87 12	98.61 98	99.28 40	99.66 58	99.09 102	99.89 17	99.68 25	99.53 7	99.97 7	99.50 48	99.99 5	99.87 21

mvs_tets		99.63 6	99.67 6	99.49 54	99.88 9	98.61 98	99.34 23	99.71 45	99.27 71	99.90 13	99.74 18	99.68 4	99.97 7	99.55 40	99.99 5	99.88 20

EI-MVSNet-UG-set		98.69 128	98.71 113	98.62 208	99.10 229	96.37 262	97.23 294	98.87 292	99.20 80	99.19 147	98.99 197	97.30 168	99.85 149	98.77 102	99.79 136	99.65 79

EI-MVSNet-Vis-set		98.68 133	98.70 116	98.63 206	99.09 232	96.40 261	97.23 294	98.86 297	99.20 80	99.18 151	98.97 203	97.29 170	99.85 149	98.72 106	99.78 141	99.64 80

HPM-MVS++		98.10 210	97.64 247	99.48 56	99.09 232	99.13 60	97.52 269	98.75 317	97.46 246	96.90 365	97.83 350	96.01 238	99.84 167	95.82 305	99.35 281	99.46 177

test_prior4						97.97 159	95.86 376

XVS		98.72 120	98.45 154	99.53 38	99.46 141	99.21 33	98.65 108	99.34 175	98.62 144	97.54 329	98.63 275	97.50 156	99.83 185	96.79 234	99.53 249	99.56 122

v1240		98.55 155	98.62 128	98.32 254	99.22 199	95.58 289	97.51 271	99.45 128	97.16 278	99.45 96	99.24 130	96.12 234	99.85 149	99.60 34	99.88 90	99.55 129

pm-mvs1		99.44 19	99.48 18	99.33 85	99.80 21	98.63 95	99.29 36	99.63 62	99.30 68	99.65 61	99.60 45	99.16 21	99.82 195	99.07 78	99.83 112	99.56 122

test_prior2							95.74 382		96.48 312	96.11 393	97.63 361	95.92 249		94.16 349	99.20 307

X-MVStestdata		94.32 371	92.59 390	99.53 38	99.46 141	99.21 33	98.65 108	99.34 175	98.62 144	97.54 329	45.85 449	97.50 156	99.83 185	96.79 234	99.53 249	99.56 122

test_prior				98.95 155	98.69 315	97.95 163		99.03 266					99.59 336			99.30 241

旧先验2							95.76 381		88.56 433	97.52 331			99.66 310	94.48 339

新几何2							95.93 372

新几何1				98.91 162	98.94 260	97.76 185		98.76 314	87.58 435	96.75 373	98.10 330	94.80 282	99.78 242	92.73 387	99.00 333	99.20 260

旧先验1					98.82 288	97.45 206		98.76 314			98.34 312	95.50 262			99.01 332	99.23 255

无先验							95.74 382	98.74 319	89.38 429				99.73 271	92.38 393		99.22 259

原ACMM2							95.53 388

原ACMM1				98.35 252	98.90 270	96.25 266		98.83 305	92.48 404	96.07 395	98.10 330	95.39 265	99.71 279	92.61 390	98.99 335	99.08 281

test222					98.92 266	96.93 239	95.54 387	98.78 311	85.72 438	96.86 368	98.11 329	94.43 289			99.10 323	99.23 255

testdata2													99.79 231	92.80 385

segment_acmp												97.02 186

testdata				98.09 272	98.93 262	95.40 299		98.80 308	90.08 426	97.45 338	98.37 308	95.26 267	99.70 283	93.58 368	98.95 341	99.17 272

testdata1							95.44 393		96.32 318

v8		99.01 79	99.16 59	98.57 218	99.47 139	96.31 265	98.90 83	99.47 120	99.03 110	99.52 80	99.57 49	96.93 190	99.81 210	99.60 34	99.98 12	99.60 96

1314		95.74 346	95.60 338	96.17 382	97.53 407	92.75 377	98.07 183	98.31 343	91.22 417	94.25 423	96.68 389	95.53 259	99.03 419	91.64 401	97.18 415	96.74 429

LFMVS		97.20 288	96.72 303	98.64 202	98.72 302	96.95 237	98.93 81	94.14 427	99.74 13	98.78 216	99.01 193	84.45 393	99.73 271	97.44 186	99.27 294	99.25 250

VDD-MVS		98.56 151	98.39 164	99.07 131	99.13 225	98.07 148	98.59 115	97.01 382	99.59 35	99.11 154	99.27 118	94.82 279	99.79 231	98.34 128	99.63 213	99.34 227

VDDNet		98.21 202	97.95 220	99.01 145	99.58 86	97.74 187	99.01 70	97.29 375	99.67 21	98.97 179	99.50 67	90.45 350	99.80 218	97.88 161	99.20 307	99.48 167

v10		98.97 86	99.11 68	98.55 223	99.44 148	96.21 267	98.90 83	99.55 89	98.73 134	99.48 88	99.60 45	96.63 212	99.83 185	99.70 30	99.99 5	99.61 94

VPNet		98.87 98	98.83 98	99.01 145	99.70 55	97.62 196	98.43 141	99.35 169	99.47 45	99.28 131	99.05 179	96.72 206	99.82 195	98.09 143	99.36 279	99.59 103

MVS		93.19 392	92.09 397	96.50 369	96.91 426	94.03 344	98.07 183	98.06 354	68.01 447	94.56 421	96.48 394	95.96 246	99.30 404	83.84 436	96.89 420	96.17 434

v2v482		98.56 151	98.62 128	98.37 250	99.42 154	95.81 284	97.58 262	99.16 243	97.90 204	99.28 131	99.01 193	95.98 244	99.79 231	99.33 57	99.90 82	99.51 148

V42		98.78 112	98.78 104	98.76 186	99.44 148	97.04 231	98.27 157	99.19 232	97.87 206	99.25 141	99.16 150	96.84 194	99.78 242	99.21 68	99.84 105	99.46 177

SD-MVS		98.40 174	98.68 119	97.54 321	98.96 258	97.99 155	97.88 214	99.36 163	98.20 182	99.63 64	99.04 181	98.76 42	95.33 448	96.56 260	99.74 163	99.31 238
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		95.86 342	95.32 352	97.49 326	98.60 332	94.15 339	93.83 431	97.93 356	95.49 347	96.68 374	97.42 373	83.21 403	99.30 404	96.22 283	98.55 368	99.01 293

MSLP-MVS++		98.02 217	98.14 200	97.64 309	98.58 337	95.19 307	97.48 274	99.23 224	97.47 241	97.90 302	98.62 277	97.04 183	98.81 429	97.55 180	99.41 273	98.94 309

APDe-MVS		98.99 82	98.79 102	99.60 15	99.21 201	99.15 52	98.87 88	99.48 112	97.57 230	99.35 117	99.24 130	97.83 124	99.89 93	97.88 161	99.70 186	99.75 56
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.84 102	98.61 132	99.53 38	99.19 208	99.27 27	98.49 133	99.33 181	98.64 139	99.03 171	98.98 201	97.89 121	99.85 149	96.54 264	99.42 272	99.46 177

ADS-MVSNet2		95.43 355	94.98 360	96.76 364	98.14 374	91.74 391	97.92 209	97.76 359	90.23 422	96.51 384	98.91 215	85.61 384	99.85 149	92.88 381	96.90 418	98.69 347

EI-MVSNet		98.40 174	98.51 142	98.04 280	99.10 229	94.73 321	97.20 299	98.87 292	98.97 116	99.06 161	99.02 184	96.00 239	99.80 218	98.58 114	99.82 116	99.60 96

Regformer		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

CVMVSNet		96.25 331	97.21 273	93.38 424	99.10 229	80.56 452	97.20 299	98.19 349	96.94 290	99.00 173	99.02 184	89.50 359	99.80 218	96.36 276	99.59 227	99.78 44

pmmvs4		97.58 257	97.28 268	98.51 230	98.84 283	96.93 239	95.40 395	98.52 333	93.60 389	98.61 239	98.65 270	95.10 271	99.60 332	96.97 218	99.79 136	98.99 298

EU-MVSNet		97.66 251	98.50 144	95.13 403	99.63 80	85.84 434	98.35 150	98.21 346	98.23 176	99.54 74	99.46 79	95.02 273	99.68 296	98.24 132	99.87 94	99.87 21

VNet		98.42 171	98.30 177	98.79 178	98.79 295	97.29 214	98.23 160	98.66 324	99.31 66	98.85 205	98.80 242	94.80 282	99.78 242	98.13 140	99.13 318	99.31 238

test-LLR		93.90 380	93.85 375	94.04 414	96.53 434	84.62 440	94.05 428	92.39 434	96.17 322	94.12 425	95.07 421	82.30 408	99.67 299	95.87 301	98.18 379	97.82 403

TESTMET0.1,1		92.19 407	91.77 405	93.46 421	96.48 436	82.80 447	94.05 428	91.52 439	94.45 374	94.00 428	94.88 427	66.65 441	99.56 347	95.78 306	98.11 385	98.02 393

test-mter		92.33 405	91.76 406	94.04 414	96.53 434	84.62 440	94.05 428	92.39 434	94.00 385	94.12 425	95.07 421	65.63 447	99.67 299	95.87 301	98.18 379	97.82 403

VPA-MVSNet		99.30 33	99.30 43	99.28 92	99.49 129	98.36 120	99.00 72	99.45 128	99.63 29	99.52 80	99.44 84	98.25 87	99.88 109	99.09 77	99.84 105	99.62 86

ACMMPR		98.70 125	98.42 159	99.54 31	99.52 113	99.14 57	98.52 123	99.31 188	97.47 241	98.56 248	98.54 286	97.75 132	99.88 109	96.57 256	99.59 227	99.58 111

testgi		98.32 186	98.39 164	98.13 271	99.57 91	95.54 290	97.78 228	99.49 110	97.37 254	99.19 147	97.65 359	98.96 28	99.49 371	96.50 267	98.99 335	99.34 227

test20.03		98.78 112	98.77 105	98.78 181	99.46 141	97.20 223	97.78 228	99.24 222	99.04 109	99.41 104	98.90 218	97.65 138	99.76 254	97.70 173	99.79 136	99.39 205

thres600view7		94.45 369	93.83 376	96.29 375	99.06 241	91.53 394	97.99 201	94.24 425	98.34 164	97.44 339	95.01 423	79.84 414	99.67 299	84.33 435	98.23 376	97.66 413

ADS-MVSNet		95.24 358	94.93 363	96.18 381	98.14 374	90.10 417	97.92 209	97.32 374	90.23 422	96.51 384	98.91 215	85.61 384	99.74 266	92.88 381	96.90 418	98.69 347

MP-MVS		98.46 168	98.09 203	99.54 31	99.57 91	99.22 32	98.50 130	99.19 232	97.61 226	97.58 325	98.66 268	97.40 163	99.88 109	94.72 334	99.60 223	99.54 133
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 419	20.53 422	6.87 435	12.05 457	4.20 460	93.62 434	6.73 458	4.62 453	10.41 453	24.33 450	8.28 458	3.56 454	9.69 453	15.07 451	12.86 450

thres400		94.14 376	93.44 381	96.24 378	98.93 262	91.44 397	97.60 259	94.29 423	97.94 200	97.10 351	94.31 432	79.67 416	99.62 324	83.05 437	98.08 387	97.66 413

test123		17.04 420	20.11 423	7.82 434	10.25 458	4.91 459	94.80 409	4.47 459	4.93 452	10.00 454	24.28 451	9.69 457	3.64 453	10.14 452	12.43 452	14.92 449

thres200		93.72 384	93.14 386	95.46 399	98.66 325	91.29 401	96.61 331	94.63 420	97.39 252	96.83 369	93.71 435	79.88 413	99.56 347	82.40 440	98.13 384	95.54 441

test0.0.03 1		94.51 368	93.69 378	96.99 350	96.05 441	93.61 364	94.97 406	93.49 429	96.17 322	97.57 327	94.88 427	82.30 408	99.01 422	93.60 367	94.17 440	98.37 378

pmmvs3		95.03 362	94.40 369	96.93 353	97.70 397	92.53 380	95.08 403	97.71 361	88.57 432	97.71 316	98.08 333	79.39 418	99.82 195	96.19 285	99.11 322	98.43 371

EMVS		93.83 381	94.02 373	93.23 425	96.83 429	84.96 437	89.77 445	96.32 398	97.92 202	97.43 340	96.36 399	86.17 379	98.93 425	87.68 427	97.73 398	95.81 439

E-PMN		94.17 375	94.37 370	93.58 420	96.86 427	85.71 436	90.11 444	97.07 381	98.17 185	97.82 311	97.19 380	84.62 392	98.94 424	89.77 420	97.68 399	96.09 438

PGM-MVS		98.66 137	98.37 167	99.55 28	99.53 111	99.18 43	98.23 160	99.49 110	97.01 287	98.69 227	98.88 225	98.00 112	99.89 93	95.87 301	99.59 227	99.58 111

LCM-MVSNet-Re		98.64 140	98.48 149	99.11 122	98.85 282	98.51 108	98.49 133	99.83 25	98.37 162	99.69 53	99.46 79	98.21 94	99.92 62	94.13 353	99.30 290	98.91 314

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 14	100.00 1	99.85 29

MCST-MVS		98.00 220	97.63 248	99.10 124	99.24 194	98.17 134	96.89 317	98.73 320	95.66 340	97.92 300	97.70 357	97.17 177	99.66 310	96.18 287	99.23 302	99.47 175

mvs_anonymous		97.83 242	98.16 197	96.87 357	98.18 371	91.89 390	97.31 288	98.90 286	97.37 254	98.83 208	99.46 79	96.28 227	99.79 231	98.90 91	98.16 382	98.95 305

MVS_Test		98.18 205	98.36 168	97.67 305	98.48 347	94.73 321	98.18 165	99.02 269	97.69 218	98.04 295	99.11 163	97.22 175	99.56 347	98.57 116	98.90 345	98.71 343

MDA-MVSNet-bldmvs		97.94 225	97.91 226	98.06 277	99.44 148	94.96 314	96.63 330	99.15 248	98.35 163	98.83 208	99.11 163	94.31 294	99.85 149	96.60 253	98.72 353	99.37 214

CDPH-MVS		97.26 282	96.66 309	99.07 131	99.00 251	98.15 135	96.03 365	99.01 272	91.21 418	97.79 312	97.85 349	96.89 192	99.69 287	92.75 386	99.38 278	99.39 205

test12				98.93 158	98.58 337	97.83 174		98.66 324		96.53 381		95.51 261	99.69 287		99.13 318	99.27 246

casdiffmvs		98.95 89	99.00 81	98.81 173	99.38 159	97.33 212	97.82 222	99.57 78	99.17 88	99.35 117	99.17 148	98.35 80	99.69 287	98.46 122	99.73 166	99.41 195
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		98.22 200	98.24 186	98.17 268	99.00 251	95.44 297	96.38 344	99.58 71	97.79 213	98.53 253	98.50 295	96.76 203	99.74 266	97.95 157	99.64 210	99.34 227
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		93.73 383	92.83 389	96.42 371	97.70 397	91.28 402	96.84 319	89.77 443	93.96 386	92.44 438	95.93 405	79.14 419	99.77 248	92.94 379	96.76 422	98.21 383

baseline1		95.96 340	95.44 346	97.52 323	98.51 346	93.99 347	98.39 146	96.09 403	98.21 178	98.40 268	97.76 353	86.88 373	99.63 321	95.42 318	89.27 446	98.95 305

YYNet1		97.60 254	97.67 242	97.39 333	99.04 245	93.04 372	95.27 397	98.38 341	97.25 266	98.92 193	98.95 210	95.48 263	99.73 271	96.99 215	98.74 351	99.41 195

PMMVS2		98.07 214	98.08 206	98.04 280	99.41 156	94.59 327	94.59 418	99.40 151	97.50 238	98.82 211	98.83 236	96.83 196	99.84 167	97.50 185	99.81 120	99.71 59

MDA-MVSNet_test_wron		97.60 254	97.66 245	97.41 332	99.04 245	93.09 368	95.27 397	98.42 338	97.26 265	98.88 200	98.95 210	95.43 264	99.73 271	97.02 212	98.72 353	99.41 195

tpmvs		95.02 363	95.25 353	94.33 410	96.39 439	85.87 433	98.08 180	96.83 390	95.46 348	95.51 409	98.69 261	85.91 382	99.53 358	94.16 349	96.23 427	97.58 416

PM-MVS		98.82 106	98.72 110	99.12 120	99.64 74	98.54 106	97.98 202	99.68 54	97.62 223	99.34 119	99.18 144	97.54 150	99.77 248	97.79 166	99.74 163	99.04 289

HQP_MVS		97.99 223	97.67 242	98.93 158	99.19 208	97.65 193	97.77 231	99.27 211	98.20 182	97.79 312	97.98 340	94.90 275	99.70 283	94.42 343	99.51 254	99.45 181

plane_prior7					99.19 208	97.87 170

plane_prior6					98.99 254	97.70 191						94.90 275

plane_prior5								99.27 211					99.70 283	94.42 343	99.51 254	99.45 181

plane_prior4											97.98 340

plane_prior3						97.78 184			97.41 250	97.79 312

plane_prior2							97.77 231		98.20 182

plane_prior1					99.05 244

plane_prior						97.65 193	97.07 306		96.72 302						99.36 279

PS-CasMVS		99.40 26	99.33 36	99.62 9	99.71 47	99.10 65	99.29 36	99.53 96	99.53 39	99.46 93	99.41 91	98.23 89	99.95 26	98.89 93	99.95 37	99.81 37

UniMVSNet_NR-MVSNet		98.86 101	98.68 119	99.40 68	99.17 216	98.74 88	97.68 243	99.40 151	99.14 90	99.06 161	98.59 282	96.71 207	99.93 52	98.57 116	99.77 147	99.53 142

PEN-MVS		99.41 25	99.34 35	99.62 9	99.73 37	99.14 57	99.29 36	99.54 93	99.62 32	99.56 69	99.42 87	98.16 100	99.96 14	98.78 99	99.93 53	99.77 47

TransMVSNet (Re)		99.44 19	99.47 21	99.36 70	99.80 21	98.58 101	99.27 42	99.57 78	99.39 56	99.75 43	99.62 40	99.17 19	99.83 185	99.06 80	99.62 216	99.66 74

DTE-MVSNet		99.43 23	99.35 33	99.66 7	99.71 47	99.30 22	99.31 30	99.51 100	99.64 27	99.56 69	99.46 79	98.23 89	99.97 7	98.78 99	99.93 53	99.72 58

DU-MVS		98.82 106	98.63 126	99.39 69	99.16 218	98.74 88	97.54 267	99.25 217	98.84 131	99.06 161	98.76 250	96.76 203	99.93 52	98.57 116	99.77 147	99.50 151

UniMVSNet (Re)		98.87 98	98.71 113	99.35 76	99.24 194	98.73 91	97.73 239	99.38 155	98.93 121	99.12 153	98.73 253	96.77 201	99.86 136	98.63 113	99.80 131	99.46 177

CP-MVSNet		99.21 47	99.09 72	99.56 26	99.65 68	98.96 77	99.13 58	99.34 175	99.42 53	99.33 121	99.26 123	97.01 187	99.94 41	98.74 104	99.93 53	99.79 41

WR-MVS_H		99.33 31	99.22 52	99.65 8	99.71 47	99.24 30	99.32 26	99.55 89	99.46 47	99.50 86	99.34 104	97.30 168	99.93 52	98.90 91	99.93 53	99.77 47

WR-MVS		98.40 174	98.19 192	99.03 141	99.00 251	97.65 193	96.85 318	98.94 277	98.57 151	98.89 197	98.50 295	95.60 257	99.85 149	97.54 182	99.85 101	99.59 103

NR-MVSNet		98.95 89	98.82 99	99.36 70	99.16 218	98.72 93	99.22 45	99.20 228	99.10 99	99.72 45	98.76 250	96.38 223	99.86 136	98.00 153	99.82 116	99.50 151

Baseline_NR-MVSNet		98.98 85	98.86 96	99.36 70	99.82 19	98.55 103	97.47 276	99.57 78	99.37 58	99.21 145	99.61 43	96.76 203	99.83 185	98.06 146	99.83 112	99.71 59

TranMVSNet+NR-MVSNet		99.17 51	99.07 75	99.46 62	99.37 165	98.87 81	98.39 146	99.42 144	99.42 53	99.36 115	99.06 172	98.38 75	99.95 26	98.34 128	99.90 82	99.57 116

TSAR-MVS + GP.		98.18 205	97.98 216	98.77 185	98.71 306	97.88 169	96.32 348	98.66 324	96.33 317	99.23 144	98.51 291	97.48 160	99.40 389	97.16 200	99.46 266	99.02 292

n2								0.00 460

nn								0.00 460

mPP-MVS		98.64 140	98.34 171	99.54 31	99.54 108	99.17 44	98.63 110	99.24 222	97.47 241	98.09 290	98.68 263	97.62 143	99.89 93	96.22 283	99.62 216	99.57 116

door-mid								99.57 78

XVG-OURS-SEG-HR		98.49 165	98.28 179	99.14 118	99.49 129	98.83 83	96.54 332	99.48 112	97.32 259	99.11 154	98.61 279	99.33 14	99.30 404	96.23 282	98.38 371	99.28 245

mvsmamba		97.57 258	97.26 269	98.51 230	98.69 315	96.73 250	98.74 97	97.25 376	97.03 286	97.88 304	99.23 135	90.95 345	99.87 128	96.61 252	99.00 333	98.91 314

MVSFormer		98.26 195	98.43 157	97.77 294	98.88 276	93.89 353	99.39 20	99.56 85	99.11 92	98.16 282	98.13 326	93.81 305	99.97 7	99.26 63	99.57 236	99.43 189

jason		97.45 267	97.35 265	97.76 297	99.24 194	93.93 349	95.86 376	98.42 338	94.24 378	98.50 256	98.13 326	94.82 279	99.91 71	97.22 197	99.73 166	99.43 189
jason: jason.
lupinMVS		97.06 297	96.86 293	97.65 307	98.88 276	93.89 353	95.48 391	97.97 355	93.53 390	98.16 282	97.58 363	93.81 305	99.91 71	96.77 237	99.57 236	99.17 272

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 88	99.39 20	99.56 85	99.11 92	99.70 49	99.73 20	99.00 26	99.97 7	99.26 63	99.98 12	99.89 16

HPM-MVS_fast		99.01 79	98.82 99	99.57 21	99.71 47	99.35 17	99.00 72	99.50 103	97.33 257	98.94 190	98.86 228	98.75 43	99.82 195	97.53 183	99.71 179	99.56 122

K. test v3		98.00 220	97.66 245	99.03 141	99.79 23	97.56 198	99.19 52	92.47 433	99.62 32	99.52 80	99.66 32	89.61 357	99.96 14	99.25 65	99.81 120	99.56 122

lessismore_v0				98.97 152	99.73 37	97.53 200		86.71 448		99.37 112	99.52 66	89.93 353	99.92 62	98.99 86	99.72 174	99.44 185

SixPastTwentyTwo		98.75 117	98.62 128	99.16 114	99.83 18	97.96 162	99.28 40	98.20 347	99.37 58	99.70 49	99.65 36	92.65 326	99.93 52	99.04 82	99.84 105	99.60 96

OurMVSNet-221017-0		99.37 29	99.31 40	99.53 38	99.91 3	98.98 71	99.63 7	99.58 71	99.44 50	99.78 38	99.76 15	96.39 221	99.92 62	99.44 52	99.92 66	99.68 67

HPM-MVS		98.79 110	98.53 140	99.59 19	99.65 68	99.29 24	99.16 54	99.43 138	96.74 301	98.61 239	98.38 307	98.62 56	99.87 128	96.47 268	99.67 200	99.59 103
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 159	98.34 171	99.11 122	99.50 121	98.82 85	95.97 367	99.50 103	97.30 261	99.05 166	98.98 201	99.35 13	99.32 401	95.72 308	99.68 194	99.18 268

XVG-ACMP-BASELINE		98.56 151	98.34 171	99.22 105	99.54 108	98.59 100	97.71 240	99.46 124	97.25 266	98.98 175	98.99 197	97.54 150	99.84 167	95.88 298	99.74 163	99.23 255

casdiffmvs_mvg		99.12 66	99.16 59	98.99 147	99.43 153	97.73 189	98.00 196	99.62 64	99.22 76	99.55 72	99.22 136	98.93 31	99.75 261	98.66 110	99.81 120	99.50 151
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		98.71 121	98.46 153	99.47 60	99.57 91	98.97 73	98.23 160	99.48 112	96.60 306	99.10 157	99.06 172	98.71 47	99.83 185	95.58 315	99.78 141	99.62 86

LGP-MVS_train				99.47 60	99.57 91	98.97 73		99.48 112	96.60 306	99.10 157	99.06 172	98.71 47	99.83 185	95.58 315	99.78 141	99.62 86

baseline		98.96 88	99.02 78	98.76 186	99.38 159	97.26 217	98.49 133	99.50 103	98.86 128	99.19 147	99.06 172	98.23 89	99.69 287	98.71 107	99.76 159	99.33 232

test11								98.87 292

door								99.41 148

EPNet_dtu		94.93 365	94.78 365	95.38 401	93.58 449	87.68 428	96.78 321	95.69 412	97.35 256	89.14 446	98.09 332	88.15 370	99.49 371	94.95 328	99.30 290	98.98 299
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 263	97.14 278	98.54 226	99.68 61	96.09 271	96.50 336	99.62 64	91.58 412	98.84 207	98.97 203	92.36 328	99.88 109	96.76 238	99.95 37	99.67 72

EPNet		96.14 334	95.44 346	98.25 261	90.76 453	95.50 293	97.92 209	94.65 419	98.97 116	92.98 435	98.85 231	89.12 361	99.87 128	95.99 294	99.68 194	99.39 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 245

HQP-NCC					98.67 320		96.29 350		96.05 327	95.55 404

ACMP_Plane					98.67 320		96.29 350		96.05 327	95.55 404

APD-MVS		98.10 210	97.67 242	99.42 64	99.11 227	98.93 79	97.76 234	99.28 208	94.97 361	98.72 225	98.77 248	97.04 183	99.85 149	93.79 363	99.54 245	99.49 156
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 383

HQP4-MVS										95.56 403			99.54 356			99.32 234

HQP3-MVS								99.04 264							99.26 297

HQP2-MVS												93.84 303

CNVR-MVS		98.17 207	97.87 229	99.07 131	98.67 320	98.24 126	97.01 308	98.93 280	97.25 266	97.62 321	98.34 312	97.27 171	99.57 344	96.42 271	99.33 284	99.39 205

NCCC		97.86 234	97.47 259	99.05 138	98.61 330	98.07 148	96.98 310	98.90 286	97.63 222	97.04 355	97.93 345	95.99 243	99.66 310	95.31 320	98.82 349	99.43 189

114514_t		96.50 322	95.77 331	98.69 196	99.48 137	97.43 208	97.84 221	99.55 89	81.42 444	96.51 384	98.58 283	95.53 259	99.67 299	93.41 373	99.58 232	98.98 299

CP-MVS		98.70 125	98.42 159	99.52 44	99.36 166	99.12 62	98.72 102	99.36 163	97.54 235	98.30 270	98.40 304	97.86 123	99.89 93	96.53 265	99.72 174	99.56 122

DSMNet-mixed		97.42 270	97.60 250	96.87 357	99.15 222	91.46 395	98.54 121	99.12 250	92.87 400	97.58 325	99.63 39	96.21 229	99.90 78	95.74 307	99.54 245	99.27 246

tpm2		93.09 393	92.58 391	94.62 408	97.56 403	86.53 432	97.66 247	95.79 409	86.15 437	94.07 427	98.23 321	75.95 426	99.53 358	90.91 414	96.86 421	97.81 405

NP-MVS					98.84 283	97.39 210					96.84 386

EG-PatchMatch MVS		98.99 82	99.01 80	98.94 156	99.50 121	97.47 204	98.04 188	99.59 69	98.15 189	99.40 107	99.36 99	98.58 62	99.76 254	98.78 99	99.68 194	99.59 103

tpm cat1		93.29 390	93.13 387	93.75 418	97.39 416	84.74 438	97.39 280	97.65 365	83.39 442	94.16 424	98.41 303	82.86 406	99.39 391	91.56 403	95.35 435	97.14 424

SteuartSystems-ACMMP		98.79 110	98.54 139	99.54 31	99.73 37	99.16 48	98.23 160	99.31 188	97.92 202	98.90 195	98.90 218	98.00 112	99.88 109	96.15 288	99.72 174	99.58 111
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 379	93.78 377	94.51 409	97.53 407	85.83 435	97.98 202	95.96 405	89.29 430	94.99 415	98.63 275	78.63 422	99.62 324	94.54 337	96.50 423	98.09 390

CR-MVSNet		96.28 329	95.95 328	97.28 336	97.71 395	94.22 334	98.11 176	98.92 283	92.31 406	96.91 362	99.37 95	85.44 387	99.81 210	97.39 189	97.36 411	97.81 405

JIA-IIPM		95.52 353	95.03 359	97.00 349	96.85 428	94.03 344	96.93 314	95.82 408	99.20 80	94.63 420	99.71 22	83.09 404	99.60 332	94.42 343	94.64 437	97.36 422

Patchmtry		97.35 275	96.97 285	98.50 234	97.31 418	96.47 259	98.18 165	98.92 283	98.95 120	98.78 216	99.37 95	85.44 387	99.85 149	95.96 296	99.83 112	99.17 272

PatchT		96.65 316	96.35 320	97.54 321	97.40 415	95.32 302	97.98 202	96.64 393	99.33 63	96.89 366	99.42 87	84.32 395	99.81 210	97.69 175	97.49 402	97.48 418

tpmrst		95.07 361	95.46 344	93.91 416	97.11 422	84.36 442	97.62 254	96.96 385	94.98 360	96.35 389	98.80 242	85.46 386	99.59 336	95.60 313	96.23 427	97.79 408

BH-w/o		95.13 360	94.89 364	95.86 387	98.20 370	91.31 400	95.65 384	97.37 370	93.64 388	96.52 383	95.70 410	93.04 318	99.02 420	88.10 426	95.82 432	97.24 423

tpm		94.67 367	94.34 371	95.66 393	97.68 400	88.42 423	97.88 214	94.90 417	94.46 372	96.03 397	98.56 285	78.66 421	99.79 231	95.88 298	95.01 436	98.78 336

DELS-MVS		98.27 193	98.20 189	98.48 235	98.86 279	96.70 251	95.60 386	99.20 228	97.73 216	98.45 260	98.71 256	97.50 156	99.82 195	98.21 135	99.59 227	98.93 310
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		96.83 309	96.75 302	97.08 345	98.74 299	93.33 366	96.71 326	98.26 344	96.72 302	98.44 261	97.37 376	95.20 268	99.47 377	91.89 395	97.43 406	98.44 369

RPMNet		97.02 300	96.93 287	97.30 335	97.71 395	94.22 334	98.11 176	99.30 196	99.37 58	96.91 362	99.34 104	86.72 374	99.87 128	97.53 183	97.36 411	97.81 405

MVSTER		96.86 308	96.55 315	97.79 292	97.91 385	94.21 336	97.56 264	98.87 292	97.49 240	99.06 161	99.05 179	80.72 411	99.80 218	98.44 123	99.82 116	99.37 214

CPTT-MVS		97.84 240	97.36 264	99.27 95	99.31 176	98.46 111	98.29 153	99.27 211	94.90 363	97.83 309	98.37 308	94.90 275	99.84 167	93.85 362	99.54 245	99.51 148

GBi-Net		98.65 138	98.47 151	99.17 111	98.90 270	98.24 126	99.20 48	99.44 132	98.59 147	98.95 183	99.55 57	94.14 297	99.86 136	97.77 168	99.69 189	99.41 195

PVSNet_Blended_VisFu		98.17 207	98.15 198	98.22 264	99.73 37	95.15 308	97.36 284	99.68 54	94.45 374	98.99 174	99.27 118	96.87 193	99.94 41	97.13 205	99.91 75	99.57 116

PVSNet_BlendedMVS		97.55 259	97.53 253	97.60 313	98.92 266	93.77 357	96.64 329	99.43 138	94.49 370	97.62 321	99.18 144	96.82 197	99.67 299	94.73 332	99.93 53	99.36 221

UnsupCasMVSNet_eth		97.89 229	97.60 250	98.75 188	99.31 176	97.17 226	97.62 254	99.35 169	98.72 136	98.76 221	98.68 263	92.57 327	99.74 266	97.76 172	95.60 433	99.34 227

UnsupCasMVSNet_bld		97.30 279	96.92 289	98.45 238	99.28 185	96.78 248	96.20 355	99.27 211	95.42 349	98.28 274	98.30 316	93.16 313	99.71 279	94.99 325	97.37 409	98.87 320

PVSNet_Blended		96.88 307	96.68 306	97.47 328	98.92 266	93.77 357	94.71 411	99.43 138	90.98 420	97.62 321	97.36 377	96.82 197	99.67 299	94.73 332	99.56 239	98.98 299

FMVSNet5		96.01 337	95.20 356	98.41 243	97.53 407	96.10 268	98.74 97	99.50 103	97.22 275	98.03 296	99.04 181	69.80 434	99.88 109	97.27 194	99.71 179	99.25 250

test1		98.65 138	98.47 151	99.17 111	98.90 270	98.24 126	99.20 48	99.44 132	98.59 147	98.95 183	99.55 57	94.14 297	99.86 136	97.77 168	99.69 189	99.41 195

new_pmnet		96.99 304	96.76 301	97.67 305	98.72 302	94.89 315	95.95 371	98.20 347	92.62 403	98.55 250	98.54 286	94.88 278	99.52 362	93.96 357	99.44 271	98.59 358

FMVSNet3		97.50 260	97.24 271	98.29 258	98.08 378	95.83 282	97.86 218	98.91 285	97.89 205	98.95 183	98.95 210	87.06 372	99.81 210	97.77 168	99.69 189	99.23 255

dp		93.47 387	93.59 380	93.13 426	96.64 432	81.62 451	97.66 247	96.42 397	92.80 401	96.11 393	98.64 273	78.55 424	99.59 336	93.31 374	92.18 445	98.16 386

FMVSNet2		98.49 165	98.40 161	98.75 188	98.90 270	97.14 229	98.61 113	99.13 249	98.59 147	99.19 147	99.28 116	94.14 297	99.82 195	97.97 155	99.80 131	99.29 243

FMVSNet1		99.17 51	99.17 57	99.17 111	99.55 103	98.24 126	99.20 48	99.44 132	99.21 78	99.43 98	99.55 57	97.82 127	99.86 136	98.42 125	99.89 88	99.41 195

N_pmnet		97.63 253	97.17 274	98.99 147	99.27 187	97.86 171	95.98 366	93.41 430	95.25 354	99.47 92	98.90 218	95.63 256	99.85 149	96.91 221	99.73 166	99.27 246

cascas		94.79 366	94.33 372	96.15 385	96.02 443	92.36 385	92.34 440	99.26 216	85.34 439	95.08 414	94.96 426	92.96 319	98.53 434	94.41 346	98.59 366	97.56 417

BH-RMVSNet		96.83 309	96.58 314	97.58 315	98.47 348	94.05 341	96.67 328	97.36 371	96.70 304	97.87 305	97.98 340	95.14 270	99.44 384	90.47 418	98.58 367	99.25 250

UGNet		98.53 159	98.45 154	98.79 178	97.94 383	96.96 236	99.08 61	98.54 331	99.10 99	96.82 370	99.47 77	96.55 215	99.84 167	98.56 119	99.94 48	99.55 129
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		96.67 315	96.27 325	97.87 287	98.81 291	94.61 326	96.77 322	97.92 357	94.94 362	97.12 350	97.74 354	91.11 344	99.82 195	93.89 359	98.15 383	99.18 268

XXY-MVS		99.14 59	99.15 64	99.10 124	99.76 30	97.74 187	98.85 92	99.62 64	98.48 158	99.37 112	99.49 73	98.75 43	99.86 136	98.20 136	99.80 131	99.71 59

EC-MVSNet		99.09 69	99.05 76	99.20 106	99.28 185	98.93 79	99.24 44	99.84 22	99.08 104	98.12 287	98.37 308	98.72 46	99.90 78	99.05 81	99.77 147	98.77 337

sss		97.21 287	96.93 287	98.06 277	98.83 285	95.22 306	96.75 324	98.48 335	94.49 370	97.27 347	97.90 346	92.77 323	99.80 218	96.57 256	99.32 285	99.16 275

Test_1112_low_res		96.99 304	96.55 315	98.31 256	99.35 171	95.47 296	95.84 379	99.53 96	91.51 414	96.80 371	98.48 298	91.36 341	99.83 185	96.58 254	99.53 249	99.62 86

1112_ss		97.29 281	96.86 293	98.58 215	99.34 173	96.32 264	96.75 324	99.58 71	93.14 395	96.89 366	97.48 369	92.11 333	99.86 136	96.91 221	99.54 245	99.57 116

ab-mvs-re		8.12 422	10.83 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	97.48 369	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

ab-mvs		98.41 172	98.36 168	98.59 214	99.19 208	97.23 218	99.32 26	98.81 306	97.66 220	98.62 237	99.40 94	96.82 197	99.80 218	95.88 298	99.51 254	98.75 340

TR-MVS		95.55 352	95.12 358	96.86 360	97.54 405	93.94 348	96.49 337	96.53 396	94.36 377	97.03 357	96.61 391	94.26 296	99.16 416	86.91 431	96.31 426	97.47 419

MDTV_nov1_ep13_2view						74.92 454	97.69 242		90.06 427	97.75 315		85.78 383		93.52 369		98.69 347

MDTV_nov1_ep13			95.22 355		97.06 425	83.20 445	97.74 237	96.16 400	94.37 376	96.99 358	98.83 236	83.95 399	99.53 358	93.90 358	97.95 394

MIMVSNet1		99.38 28	99.32 38	99.55 28	99.86 14	99.19 42	99.41 17	99.59 69	99.59 35	99.71 47	99.57 49	97.12 179	99.90 78	99.21 68	99.87 94	99.54 133

MIMVSNet		96.62 318	96.25 326	97.71 304	99.04 245	94.66 324	99.16 54	96.92 388	97.23 272	97.87 305	99.10 166	86.11 381	99.65 315	91.65 400	99.21 306	98.82 324

IterMVS-LS		98.55 155	98.70 116	98.09 272	99.48 137	94.73 321	97.22 298	99.39 153	98.97 116	99.38 110	99.31 111	96.00 239	99.93 52	98.58 114	99.97 20	99.60 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		97.69 248	97.35 265	98.69 196	98.73 300	97.02 233	96.92 316	98.75 317	95.89 336	98.59 243	98.67 265	92.08 334	99.74 266	96.72 243	99.81 120	99.32 234
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 147

IterMVS		97.73 245	98.11 202	96.57 367	99.24 194	90.28 415	95.52 390	99.21 226	98.86 128	99.33 121	99.33 106	93.11 314	99.94 41	98.49 121	99.94 48	99.48 167
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 277	96.92 289	98.57 218	99.09 232	97.99 155	96.79 320	99.35 169	93.18 394	97.71 316	98.07 334	95.00 274	99.31 402	93.97 356	99.13 318	98.42 373

MVS_111021_LR		98.30 189	98.12 201	98.83 170	99.16 218	98.03 153	96.09 363	99.30 196	97.58 229	98.10 289	98.24 319	98.25 87	99.34 398	96.69 246	99.65 208	99.12 279

DP-MVS		98.93 91	98.81 101	99.28 92	99.21 201	98.45 112	98.46 138	99.33 181	99.63 29	99.48 88	99.15 154	97.23 174	99.75 261	97.17 199	99.66 207	99.63 85

ACMMP++															99.68 194

HQP-MVS		97.00 303	96.49 318	98.55 223	98.67 320	96.79 245	96.29 350	99.04 264	96.05 327	95.55 404	96.84 386	93.84 303	99.54 356	92.82 383	99.26 297	99.32 234

QAPM		97.31 278	96.81 299	98.82 171	98.80 294	97.49 201	99.06 65	99.19 232	90.22 424	97.69 318	99.16 150	96.91 191	99.90 78	90.89 415	99.41 273	99.07 283

Vis-MVSNet		99.34 30	99.36 32	99.27 95	99.73 37	98.26 124	99.17 53	99.78 35	99.11 92	99.27 133	99.48 74	98.82 36	99.95 26	98.94 89	99.93 53	99.59 103
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		94.32 371	95.62 337	90.42 429	98.46 350	75.36 453	96.29 350	89.13 444	95.25 354	95.38 410	99.75 16	92.88 320	99.19 414	94.07 355	99.39 275	96.72 430

IS-MVSNet		98.19 204	97.90 227	99.08 129	99.57 91	97.97 159	99.31 30	98.32 342	99.01 112	98.98 175	99.03 183	91.59 338	99.79 231	95.49 317	99.80 131	99.48 167

HyFIR lowres test		97.19 289	96.60 313	98.96 153	99.62 84	97.28 215	95.17 400	99.50 103	94.21 379	99.01 172	98.32 315	86.61 375	99.99 2	97.10 207	99.84 105	99.60 96

EPMVS		93.72 384	93.27 383	95.09 405	96.04 442	87.76 427	98.13 172	85.01 450	94.69 367	96.92 360	98.64 273	78.47 425	99.31 402	95.04 324	96.46 424	98.20 384

PAPM_NR		96.82 311	96.32 322	98.30 257	99.07 236	96.69 252	97.48 274	98.76 314	95.81 338	96.61 378	96.47 395	94.12 300	99.17 415	90.82 416	97.78 396	99.06 284

TAMVS		98.24 199	98.05 209	98.80 175	99.07 236	97.18 225	97.88 214	98.81 306	96.66 305	99.17 152	99.21 137	94.81 281	99.77 248	96.96 219	99.88 90	99.44 185

PAPR		95.29 356	94.47 367	97.75 298	97.50 413	95.14 309	94.89 408	98.71 322	91.39 416	95.35 411	95.48 416	94.57 287	99.14 418	84.95 434	97.37 409	98.97 302

RPSCF		98.62 145	98.36 168	99.42 64	99.65 68	99.42 11	98.55 119	99.57 78	97.72 217	98.90 195	99.26 123	96.12 234	99.52 362	95.72 308	99.71 179	99.32 234

Vis-MVSNet (Re-imp)		97.46 265	97.16 275	98.34 253	99.55 103	96.10 268	98.94 80	98.44 336	98.32 167	98.16 282	98.62 277	88.76 362	99.73 271	93.88 360	99.79 136	99.18 268

test_0402		98.76 116	98.71 113	98.93 158	99.56 99	98.14 137	98.45 140	99.34 175	99.28 70	98.95 183	98.91 215	98.34 81	99.79 231	95.63 312	99.91 75	98.86 321

MVS_111021_HR		98.25 198	98.08 206	98.75 188	99.09 232	97.46 205	95.97 367	99.27 211	97.60 228	97.99 298	98.25 318	98.15 102	99.38 393	96.87 229	99.57 236	99.42 192

CSCG		98.68 133	98.50 144	99.20 106	99.45 146	98.63 95	98.56 118	99.57 78	97.87 206	98.85 205	98.04 336	97.66 137	99.84 167	96.72 243	99.81 120	99.13 278

PatchMatch-RL		97.24 285	96.78 300	98.61 211	99.03 248	97.83 174	96.36 345	99.06 258	93.49 392	97.36 345	97.78 351	95.75 253	99.49 371	93.44 372	98.77 350	98.52 361

API-MVS		97.04 299	96.91 291	97.42 331	97.88 386	98.23 130	98.18 165	98.50 334	97.57 230	97.39 343	96.75 388	96.77 201	99.15 417	90.16 419	99.02 331	94.88 442

Test By Simon												96.52 216

TDRefinement		99.42 24	99.38 28	99.55 28	99.76 30	99.33 21	99.68 6	99.71 45	99.38 57	99.53 78	99.61 43	98.64 53	99.80 218	98.24 132	99.84 105	99.52 145

USDC		97.41 271	97.40 260	97.44 330	98.94 260	93.67 360	95.17 400	99.53 96	94.03 384	98.97 179	99.10 166	95.29 266	99.34 398	95.84 304	99.73 166	99.30 241

EPP-MVSNet		98.30 189	98.04 210	99.07 131	99.56 99	97.83 174	99.29 36	98.07 353	99.03 110	98.59 243	99.13 159	92.16 332	99.90 78	96.87 229	99.68 194	99.49 156

PMMVS		96.51 320	95.98 327	98.09 272	97.53 407	95.84 281	94.92 407	98.84 301	91.58 412	96.05 396	95.58 411	95.68 255	99.66 310	95.59 314	98.09 386	98.76 339

PAPM		91.88 410	90.34 413	96.51 368	98.06 379	92.56 379	92.44 439	97.17 378	86.35 436	90.38 443	96.01 402	86.61 375	99.21 413	70.65 449	95.43 434	97.75 409

ACMMP		98.75 117	98.50 144	99.52 44	99.56 99	99.16 48	98.87 88	99.37 159	97.16 278	98.82 211	99.01 193	97.71 134	99.87 128	96.29 280	99.69 189	99.54 133
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		97.17 291	96.71 304	98.55 223	98.56 340	98.05 152	96.33 347	98.93 280	96.91 292	97.06 354	97.39 374	94.38 292	99.45 382	91.66 399	99.18 312	98.14 387

PatchmatchNet		95.58 351	95.67 336	95.30 402	97.34 417	87.32 430	97.65 249	96.65 392	95.30 353	97.07 353	98.69 261	84.77 390	99.75 261	94.97 327	98.64 362	98.83 323
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 192	97.95 220	99.34 79	98.44 353	99.16 48	98.12 175	99.38 155	96.01 331	98.06 292	98.43 302	97.80 129	99.67 299	95.69 310	99.58 232	99.20 260

F-COLMAP		97.30 279	96.68 306	99.14 118	99.19 208	98.39 114	97.27 293	99.30 196	92.93 398	96.62 377	98.00 338	95.73 254	99.68 296	92.62 389	98.46 370	99.35 225

ANet_high		99.57 10	99.67 6	99.28 92	99.89 6	98.09 142	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 19	99.94 41	99.31 59	100.00 1	99.82 34

wuyk23d		96.06 335	97.62 249	91.38 428	98.65 329	98.57 102	98.85 92	96.95 386	96.86 295	99.90 13	99.16 150	99.18 18	98.40 435	89.23 423	99.77 147	77.18 448

OMC-MVS		97.88 231	97.49 256	99.04 140	98.89 275	98.63 95	96.94 312	99.25 217	95.02 359	98.53 253	98.51 291	97.27 171	99.47 377	93.50 371	99.51 254	99.01 293

MG-MVS		96.77 312	96.61 311	97.26 338	98.31 363	93.06 369	95.93 372	98.12 352	96.45 314	97.92 300	98.73 253	93.77 307	99.39 391	91.19 410	99.04 327	99.33 232

AdaColmap		97.14 293	96.71 304	98.46 237	98.34 361	97.80 183	96.95 311	98.93 280	95.58 344	96.92 360	97.66 358	95.87 250	99.53 358	90.97 412	99.14 316	98.04 392

uanet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

ITE_SJBPF				98.87 166	99.22 199	98.48 110		99.35 169	97.50 238	98.28 274	98.60 281	97.64 141	99.35 397	93.86 361	99.27 294	98.79 335

DeepMVS_CX				93.44 422	98.24 367	94.21 336		94.34 422	64.28 448	91.34 442	94.87 429	89.45 360	92.77 449	77.54 445	93.14 442	93.35 444

TinyColmap		97.89 229	97.98 216	97.60 313	98.86 279	94.35 332	96.21 354	99.44 132	97.45 248	99.06 161	98.88 225	97.99 115	99.28 408	94.38 347	99.58 232	99.18 268

MAR-MVS		96.47 324	95.70 334	98.79 178	97.92 384	99.12 62	98.28 154	98.60 329	92.16 408	95.54 407	96.17 400	94.77 284	99.52 362	89.62 421	98.23 376	97.72 411
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		97.90 227	97.69 241	98.52 229	99.17 216	97.66 192	97.19 302	99.47 120	96.31 319	97.85 308	98.20 323	96.71 207	99.52 362	94.62 335	99.72 174	98.38 376

MSDG		97.71 247	97.52 254	98.28 259	98.91 269	96.82 243	94.42 421	99.37 159	97.65 221	98.37 269	98.29 317	97.40 163	99.33 400	94.09 354	99.22 303	98.68 350

LS3D		98.63 142	98.38 166	99.36 70	97.25 419	99.38 13	99.12 60	99.32 183	99.21 78	98.44 261	98.88 225	97.31 167	99.80 218	96.58 254	99.34 283	98.92 311

CLD-MVS		97.49 263	97.16 275	98.48 235	99.07 236	97.03 232	94.71 411	99.21 226	94.46 372	98.06 292	97.16 381	97.57 147	99.48 374	94.46 340	99.78 141	98.95 305
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 388	92.23 395	97.08 345	99.25 193	97.86 171	95.61 385	97.16 379	92.90 399	93.76 432	98.65 270	75.94 427	95.66 446	79.30 444	97.49 402	97.73 410

Gipuma		99.03 77	99.16 59	98.64 202	99.94 2	98.51 108	99.32 26	99.75 41	99.58 37	98.60 241	99.62 40	98.22 92	99.51 367	97.70 173	99.73 166	97.89 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark