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 25	99.85 16	99.11 63	99.90 1	99.78 35	99.63 25	99.78 37	99.67 30	99.48 10	99.81 202	99.30 55	99.97 20	99.77 45
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	98.81 103	98.73 103	99.05 133	98.76 287	97.81 177	99.25 43	99.30 190	98.57 142	98.55 241	99.33 104	97.95 115	99.90 72	97.16 191	99.67 194	99.44 176

3Dnovator+	97.89 3	98.69 123	98.51 137	99.24 101	98.81 282	98.40 112	99.02 69	99.19 225	98.99 106	98.07 282	99.28 114	97.11 178	99.84 159	96.84 223	99.32 275	99.47 166

DeepC-MVS	97.60 4	98.97 81	98.93 82	99.10 120	99.35 164	97.98 157	98.01 187	99.46 121	97.56 222	99.54 69	99.50 67	98.97 27	99.84 159	98.06 139	99.92 63	99.49 148
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	98.32 180	98.01 206	99.23 103	98.39 349	98.97 72	95.03 394	99.18 229	96.88 283	99.33 114	98.78 237	98.16 98	99.28 398	96.74 231	99.62 208	99.44 176

DeepC-MVS_fast	96.85 6	98.30 183	98.15 191	98.75 181	98.61 320	97.23 211	97.76 225	99.09 248	97.31 250	98.75 213	98.66 259	97.56 146	99.64 309	96.10 283	99.55 235	99.39 196
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	97.09 286	96.68 297	98.32 246	98.32 352	97.16 220	98.86 89	99.37 153	89.48 418	96.29 380	99.15 150	96.56 209	99.90 72	92.90 371	99.20 297	97.89 390

ACMH	96.65 7	99.25 40	99.24 50	99.26 96	99.72 43	98.38 114	99.07 64	99.55 88	98.30 160	99.65 58	99.45 81	99.22 16	99.76 245	98.44 117	99.77 141	99.64 76
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 69	99.00 76	99.33 84	99.71 47	98.83 82	98.60 112	99.58 70	99.11 85	99.53 73	99.18 140	98.81 37	99.67 290	96.71 236	99.77 141	99.50 144

COLMAP_ROB	96.50 10	98.99 77	98.85 92	99.41 65	99.58 81	99.10 64	98.74 95	99.56 84	99.09 95	99.33 114	99.19 136	98.40 73	99.72 269	95.98 286	99.76 153	99.42 183
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	96.63 308	95.95 319	98.65 193	98.93 254	98.09 141	96.93 304	99.28 201	83.58 431	98.13 277	97.78 341	96.13 227	99.40 379	93.52 360	99.29 282	98.45 356
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 88	98.73 103	99.48 55	99.55 98	99.14 56	98.07 176	99.37 153	97.62 213	99.04 161	98.96 199	98.84 35	99.79 222	97.43 178	99.65 200	99.49 148
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 331	95.35 341	97.55 311	97.95 372	94.79 309	98.81 94	96.94 377	92.28 397	95.17 402	98.57 275	89.90 346	99.75 252	91.20 400	97.33 403	98.10 379

OpenMVS_ROB	95.38 14	95.84 334	95.18 347	97.81 283	98.41 348	97.15 221	97.37 273	98.62 320	83.86 430	98.65 224	98.37 299	94.29 289	99.68 287	88.41 415	98.62 355	96.60 421

ACMP	95.32 15	98.41 167	98.09 196	99.36 69	99.51 110	98.79 85	97.68 234	99.38 149	95.76 329	98.81 206	98.82 230	98.36 75	99.82 187	94.75 322	99.77 141	99.48 158
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 311	95.73 323	98.85 161	98.75 289	97.91 164	96.42 332	99.06 251	90.94 411	95.59 391	97.38 365	94.41 284	99.59 327	90.93 404	98.04 382	99.05 275
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 338	95.70 324	95.57 385	98.83 276	88.57 412	92.50 428	97.72 352	92.69 392	96.49 377	96.44 386	93.72 302	99.43 375	93.61 357	99.28 283	98.71 333

PCF-MVS	92.86 18	94.36 360	93.00 378	98.42 234	98.70 300	97.56 193	93.16 426	99.11 245	79.59 435	97.55 319	97.43 362	92.19 324	99.73 262	79.85 434	99.45 258	97.97 387
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 396	90.90 400	96.27 366	97.22 410	91.24 394	94.36 413	93.33 421	92.37 395	92.24 430	94.58 421	66.20 434	99.89 87	93.16 368	94.63 428	97.66 403
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	97.86 225	97.94 214	97.65 299	99.71 47	97.94 163	98.52 121	98.68 315	98.99 106	97.52 322	99.35 98	97.41 160	98.18 429	91.59 393	99.67 194	96.82 418
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 401	90.30 404	93.70 409	97.72 382	84.34 433	90.24 432	97.42 359	90.20 415	93.79 421	93.09 430	90.90 339	98.89 418	86.57 423	72.76 439	97.87 392

MVE	83.40 22	92.50 391	91.92 393	94.25 401	98.83 276	91.64 384	92.71 427	83.52 441	95.92 325	86.46 439	95.46 407	95.20 262	95.40 437	80.51 433	98.64 352	95.73 430
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 319	95.44 336	98.84 162	96.25 430	98.69 93	97.02 297	99.12 243	88.90 421	97.83 300	98.86 221	89.51 348	98.90 417	91.92 385	99.51 246	98.92 301
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AstraMVS		98.16 202	98.07 201	98.41 235	99.51 110	95.86 273	98.00 188	95.14 406	98.97 109	99.43 92	99.24 126	93.25 304	99.84 159	99.21 63	99.87 89	99.54 126

guyue		98.01 211	97.93 216	98.26 252	99.45 139	95.48 286	98.08 173	96.24 389	98.89 118	99.34 112	99.14 153	91.32 334	99.82 187	99.07 72	99.83 106	99.48 158

sc_t1		99.62 7	99.66 8	99.53 37	99.82 19	99.09 67	99.50 11	99.63 61	99.88 4	99.86 22	99.80 11	99.03 23	99.89 87	99.48 47	99.93 51	99.60 90

tt0320-xc		99.64 5	99.68 5	99.50 52	99.72 43	98.98 70	99.51 10	99.85 18	99.86 6	99.88 19	99.82 5	99.02 25	99.90 72	99.54 40	99.95 35	99.61 88

tt0320		99.61 8	99.65 9	99.48 55	99.71 47	98.94 77	99.54 8	99.83 25	99.87 5	99.89 16	99.82 5	98.75 43	99.90 72	99.54 40	99.95 35	99.59 97

fmvsm_s_conf0.5_n_8		99.13 59	99.26 47	98.74 185	99.51 110	96.44 253	97.65 240	99.65 58	99.66 20	99.78 37	99.48 74	97.92 117	99.93 46	99.72 26	99.95 35	99.87 20

fmvsm_s_conf0.5_n_7		98.83 98	99.04 73	98.20 257	99.30 173	94.83 308	97.23 284	99.36 157	98.64 130	99.84 28	99.43 84	98.10 103	99.91 65	99.56 37	99.96 27	99.87 20

fmvsm_s_conf0.5_n_6		99.08 69	99.21 53	98.69 189	99.36 159	96.51 251	97.62 245	99.68 53	98.43 151	99.85 25	99.10 160	99.12 22	99.88 102	99.77 19	99.92 63	99.67 68

fmvsm_s_conf0.5_n_5		99.07 71	99.10 66	98.99 141	99.47 132	97.22 213	97.40 269	99.83 25	97.61 216	99.85 25	99.30 110	98.80 39	99.95 24	99.71 28	99.90 78	99.78 42

fmvsm_s_conf0.5_n_4		99.01 74	99.22 51	98.38 239	99.31 169	95.48 286	97.56 254	99.73 41	98.87 119	99.75 42	99.27 116	98.80 39	99.86 128	99.80 14	99.90 78	99.81 36

SSC-MVS3.2		98.53 154	98.79 97	97.74 292	99.46 134	93.62 354	96.45 328	99.34 169	99.33 59	98.93 184	98.70 250	97.90 118	99.90 72	99.12 68	99.92 63	99.69 64

testing3-2		93.78 372	93.91 364	93.39 413	98.82 279	81.72 440	97.76 225	95.28 404	98.60 137	96.54 371	96.66 380	65.85 436	99.62 315	96.65 240	98.99 325	98.82 314

myMVS_eth3d28		92.92 387	92.31 383	94.77 396	97.84 377	87.59 419	96.19 346	96.11 392	97.08 272	94.27 412	93.49 428	66.07 435	98.78 420	91.78 388	97.93 385	97.92 389

UWE-MVS-28		90.22 402	89.28 405	93.02 417	94.50 438	82.87 436	96.52 325	87.51 436	95.21 346	92.36 429	96.04 391	71.57 422	98.25 428	72.04 438	97.77 387	97.94 388

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 78	99.59 80	98.21 131	97.82 214	99.84 22	99.41 51	99.92 8	99.41 89	99.51 8	99.95 24	99.84 7	99.97 20	99.87 20

fmvsm_s_conf0.5_n_3		99.22 45	99.37 30	98.78 174	99.46 134	96.58 249	97.65 240	99.72 42	99.47 41	99.86 22	99.50 67	98.94 29	99.89 87	99.75 22	99.97 20	99.86 26

fmvsm_s_conf0.5_n_2		99.14 55	99.31 39	98.63 199	99.49 122	96.08 266	97.38 271	99.81 30	99.48 38	99.84 28	99.57 49	98.46 69	99.89 87	99.82 9	99.97 20	99.91 13

fmvsm_s_conf0.1_n_2		99.20 48	99.38 28	98.65 193	99.69 57	96.08 266	97.49 263	99.90 11	99.53 35	99.88 19	99.64 37	98.51 65	99.90 72	99.83 8	99.98 12	99.97 4

GDP-MVS		97.50 251	97.11 270	98.67 192	99.02 241	96.85 235	98.16 162	99.71 44	98.32 158	98.52 246	98.54 277	83.39 392	99.95 24	98.79 92	99.56 231	99.19 256

BP-MVS1		97.40 263	96.97 276	98.71 188	99.07 228	96.81 237	98.34 147	97.18 367	98.58 141	98.17 270	98.61 270	84.01 388	99.94 39	98.97 81	99.78 135	99.37 205

reproduce_monomvs		95.00 354	95.25 343	94.22 402	97.51 402	83.34 434	97.86 210	98.44 328	98.51 147	99.29 123	99.30 110	67.68 429	99.56 338	98.89 87	99.81 114	99.77 45

mmtdpeth		99.30 33	99.42 24	98.92 154	99.58 81	96.89 234	99.48 13	99.92 7	99.92 2	98.26 267	99.80 11	98.33 80	99.91 65	99.56 37	99.95 35	99.97 4

reproduce_model		99.15 54	98.97 80	99.67 4	99.33 167	99.44 10	98.15 163	99.47 118	99.12 84	99.52 75	99.32 108	98.31 81	99.90 72	97.78 158	99.73 160	99.66 70

reproduce-ours		99.09 65	98.90 85	99.67 4	99.27 179	99.49 6	98.00 188	99.42 138	99.05 100	99.48 82	99.27 116	98.29 83	99.89 87	97.61 168	99.71 173	99.62 80

our_new_method		99.09 65	98.90 85	99.67 4	99.27 179	99.49 6	98.00 188	99.42 138	99.05 100	99.48 82	99.27 116	98.29 83	99.89 87	97.61 168	99.71 173	99.62 80

mmdepth		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

monomultidepth		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

mvs5depth		99.30 33	99.59 12	98.44 232	99.65 67	95.35 292	99.82 3	99.94 2	99.83 7	99.42 96	99.94 2	98.13 101	99.96 12	99.63 32	99.96 27	100.00 1

MVStest1		95.86 332	95.60 328	96.63 356	95.87 434	91.70 383	97.93 198	98.94 270	98.03 183	99.56 65	99.66 32	71.83 421	98.26 427	99.35 52	99.24 289	99.91 13

ttmdpeth		97.91 217	98.02 205	97.58 306	98.69 305	94.10 331	98.13 165	98.90 279	97.95 189	97.32 337	99.58 47	95.95 241	98.75 421	96.41 263	99.22 293	99.87 20

WBMVS		95.18 349	94.78 355	96.37 362	97.68 390	89.74 409	95.80 370	98.73 312	97.54 225	98.30 261	98.44 292	70.06 423	99.82 187	96.62 242	99.87 89	99.54 126

dongtai		76.24 406	75.95 409	77.12 422	92.39 440	67.91 446	90.16 433	59.44 447	82.04 433	89.42 435	94.67 420	49.68 445	81.74 440	48.06 440	77.66 438	81.72 436

kuosan		69.30 407	68.95 410	70.34 423	87.68 444	65.00 447	91.11 431	59.90 446	69.02 436	74.46 441	88.89 438	48.58 446	68.03 442	28.61 441	72.33 440	77.99 437

MVSMamba_PlusPlus		98.83 98	98.98 79	98.36 243	99.32 168	96.58 249	98.90 83	99.41 142	99.75 11	98.72 216	99.50 67	96.17 225	99.94 39	99.27 57	99.78 135	98.57 349

MGCFI-Net		98.34 176	98.28 173	98.51 222	98.47 338	97.59 192	98.96 77	99.48 110	99.18 80	97.40 332	95.50 404	98.66 51	99.50 359	98.18 130	98.71 345	98.44 359

testing91		93.32 379	92.27 384	96.47 360	97.54 395	91.25 393	96.17 350	96.76 381	97.18 266	93.65 423	93.50 427	65.11 438	99.63 312	93.04 369	97.45 394	98.53 350

testing11		93.08 384	92.02 389	96.26 367	97.56 393	90.83 401	96.32 338	95.70 400	96.47 303	92.66 427	93.73 424	64.36 439	99.59 327	93.77 355	97.57 390	98.37 368

testing99		93.04 385	91.98 392	96.23 369	97.53 397	90.70 403	96.35 336	95.94 396	96.87 284	93.41 424	93.43 429	63.84 440	99.59 327	93.24 367	97.19 404	98.40 364

UBG		93.25 381	92.32 382	96.04 376	97.72 382	90.16 406	95.92 364	95.91 397	96.03 320	93.95 420	93.04 431	69.60 425	99.52 353	90.72 408	97.98 383	98.45 356

UWE-MVS		92.38 393	91.76 396	94.21 403	97.16 411	84.65 429	95.42 384	88.45 435	95.96 323	96.17 381	95.84 399	66.36 432	99.71 270	91.87 387	98.64 352	98.28 371

ETVMVS		92.60 390	91.08 399	97.18 331	97.70 387	93.65 353	96.54 322	95.70 400	96.51 299	94.68 408	92.39 434	61.80 441	99.50 359	86.97 420	97.41 397	98.40 364

sasdasda		98.34 176	98.26 177	98.58 208	98.46 340	97.82 174	98.96 77	99.46 121	99.19 78	97.46 327	95.46 407	98.59 58	99.46 370	98.08 137	98.71 345	98.46 353

testing222		91.96 398	90.37 402	96.72 355	97.47 404	92.59 369	96.11 352	94.76 408	96.83 286	92.90 426	92.87 432	57.92 442	99.55 342	86.93 421	97.52 391	98.00 386

WB-MVSnew		95.73 337	95.57 331	96.23 369	96.70 421	90.70 403	96.07 354	93.86 418	95.60 333	97.04 346	95.45 410	96.00 233	99.55 342	91.04 402	98.31 364	98.43 361

fmvsm_l_conf0.5_n_a		99.19 49	99.27 45	98.94 149	99.65 67	97.05 223	97.80 218	99.76 37	98.70 129	99.78 37	99.11 157	98.79 41	99.95 24	99.85 5	99.96 27	99.83 30

fmvsm_l_conf0.5_n		99.21 46	99.28 44	99.02 138	99.64 73	97.28 208	97.82 214	99.76 37	98.73 126	99.82 31	99.09 164	98.81 37	99.95 24	99.86 4	99.96 27	99.83 30

fmvsm_s_conf0.1_n_a		99.17 50	99.30 42	98.80 168	99.75 34	96.59 247	97.97 197	99.86 16	98.22 168	99.88 19	99.71 22	98.59 58	99.84 159	99.73 24	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 53	99.33 35	98.64 195	99.71 47	96.10 261	97.87 209	99.85 18	98.56 145	99.90 13	99.68 25	98.69 49	99.85 141	99.72 26	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 64	99.20 54	98.78 174	99.55 98	96.59 247	97.79 219	99.82 29	98.21 169	99.81 34	99.53 63	98.46 69	99.84 159	99.70 29	99.97 20	99.90 15

fmvsm_s_conf0.5_n		99.09 65	99.26 47	98.61 204	99.55 98	96.09 264	97.74 228	99.81 30	98.55 146	99.85 25	99.55 57	98.60 57	99.84 159	99.69 31	99.98 12	99.89 16

MM		98.22 193	97.99 208	98.91 155	98.66 315	96.97 227	97.89 205	94.44 411	99.54 34	98.95 176	99.14 153	93.50 303	99.92 56	99.80 14	99.96 27	99.85 28

WAC-MVS						90.90 399								91.37 397

Syy-MVS		96.04 326	95.56 332	97.49 317	97.10 413	94.48 320	96.18 348	96.58 384	95.65 331	94.77 406	92.29 435	91.27 335	99.36 384	98.17 132	98.05 380	98.63 343

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 78	99.78 24	98.11 138	97.77 222	99.90 11	99.33 59	99.97 3	99.66 32	99.71 3	99.96 12	99.79 16	99.99 5	99.96 8

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 69	99.87 12	98.13 137	98.08 173	99.95 1	99.45 44	99.98 2	99.75 16	99.80 1	99.97 5	99.82 9	99.99 5	99.99 2

myMVS_eth3d		91.92 399	90.45 401	96.30 364	97.10 413	90.90 399	96.18 348	96.58 384	95.65 331	94.77 406	92.29 435	53.88 443	99.36 384	89.59 413	98.05 380	98.63 343

testing3		93.51 376	92.09 387	97.75 290	98.60 322	94.40 322	97.32 277	95.26 405	97.56 222	96.79 363	95.50 404	53.57 444	99.77 239	95.26 312	98.97 329	99.08 271

SSC-MVS		98.71 116	98.74 101	98.62 201	99.72 43	96.08 266	98.74 95	98.64 319	99.74 13	99.67 54	99.24 126	94.57 281	99.95 24	99.11 69	99.24 289	99.82 33

test_fmvsmconf_n		99.44 19	99.48 18	99.31 89	99.64 73	98.10 140	97.68 234	99.84 22	99.29 65	99.92 8	99.57 49	99.60 5	99.96 12	99.74 23	99.98 12	99.89 16

WB-MVS		98.52 158	98.55 132	98.43 233	99.65 67	95.59 279	98.52 121	98.77 305	99.65 22	99.52 75	99.00 189	94.34 287	99.93 46	98.65 105	98.83 337	99.76 50

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 219	99.66 66	96.97 227	98.00 188	99.85 18	99.24 69	99.92 8	99.50 67	99.39 12	99.95 24	99.89 3	99.98 12	98.71 333

dmvs_re		95.98 329	95.39 339	97.74 292	98.86 270	97.45 199	98.37 143	95.69 402	97.95 189	96.56 370	95.95 394	90.70 340	97.68 432	88.32 416	96.13 419	98.11 378

SDMVSNet		99.23 44	99.32 37	98.96 146	99.68 60	97.35 204	98.84 92	99.48 110	99.69 16	99.63 61	99.68 25	99.03 23	99.96 12	97.97 146	99.92 63	99.57 109

dmvs_testset		92.94 386	92.21 386	95.13 393	98.59 325	90.99 398	97.65 240	92.09 426	96.95 279	94.00 418	93.55 426	92.34 323	96.97 435	72.20 437	92.52 433	97.43 410

sd_testset		99.28 36	99.31 39	99.19 107	99.68 60	98.06 150	99.41 17	99.30 190	99.69 16	99.63 61	99.68 25	99.25 15	99.96 12	97.25 187	99.92 63	99.57 109

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 141	99.57 86	97.73 184	97.93 198	99.83 25	99.22 70	99.93 6	99.30 110	99.42 11	99.96 12	99.85 5	99.99 5	99.29 234

test_cas_vis1_n_1920		98.33 179	98.68 114	97.27 328	99.69 57	92.29 377	98.03 182	99.85 18	97.62 213	99.96 4	99.62 40	93.98 296	99.74 257	99.52 44	99.86 94	99.79 39

test_vis1_n_1920		98.40 169	98.92 83	96.81 351	99.74 36	90.76 402	98.15 163	99.91 9	98.33 156	99.89 16	99.55 57	95.07 266	99.88 102	99.76 20	99.93 51	99.79 39

test_vis1_n		98.31 182	98.50 139	97.73 295	99.76 30	94.17 329	98.68 105	99.91 9	96.31 309	99.79 36	99.57 49	92.85 316	99.42 377	99.79 16	99.84 99	99.60 90

test_fmvs1_n		98.09 205	98.28 173	97.52 314	99.68 60	93.47 356	98.63 108	99.93 5	95.41 342	99.68 52	99.64 37	91.88 329	99.48 365	99.82 9	99.87 89	99.62 80

mvsany_test1		97.60 245	97.54 243	97.77 286	97.72 382	95.35 292	95.36 386	97.13 370	94.13 371	99.71 46	99.33 104	97.93 116	99.30 394	97.60 170	98.94 332	98.67 341

APD_test1		98.83 98	98.66 117	99.34 78	99.78 24	99.47 9	98.42 139	99.45 125	98.28 165	98.98 168	99.19 136	97.76 129	99.58 333	96.57 247	99.55 235	98.97 292

test_vis1_rt		97.75 235	97.72 231	97.83 281	98.81 282	96.35 256	97.30 279	99.69 48	94.61 358	97.87 296	98.05 326	96.26 223	98.32 426	98.74 98	98.18 369	98.82 314

test_vis3_rt		99.14 55	99.17 56	99.07 126	99.78 24	98.38 114	98.92 82	99.94 2	97.80 202	99.91 12	99.67 30	97.15 175	98.91 416	99.76 20	99.56 231	99.92 12

test_fmvs2		98.70 120	98.97 80	97.89 278	99.54 103	94.05 332	98.55 117	99.92 7	96.78 289	99.72 44	99.78 13	96.60 208	99.67 290	99.91 2	99.90 78	99.94 10

test_fmvs1		97.72 237	97.94 214	97.07 338	98.66 315	92.39 374	97.68 234	99.81 30	95.20 347	99.54 69	99.44 82	91.56 332	99.41 378	99.78 18	99.77 141	99.40 195

test_fmvs3		99.12 62	99.41 25	98.25 253	99.76 30	95.07 304	99.05 67	99.94 2	97.78 204	99.82 31	99.84 3	98.56 62	99.71 270	99.96 1	99.96 27	99.97 4

mvsany_test3		98.87 93	98.92 83	98.74 185	99.38 152	96.94 231	98.58 114	99.10 246	96.49 301	99.96 4	99.81 8	98.18 94	99.45 372	98.97 81	99.79 130	99.83 30

testf1		99.25 40	99.16 58	99.51 47	99.89 6	99.63 4	98.71 102	99.69 48	98.90 116	99.43 92	99.35 98	98.86 33	99.67 290	97.81 155	99.81 114	99.24 244

APD_test2		99.25 40	99.16 58	99.51 47	99.89 6	99.63 4	98.71 102	99.69 48	98.90 116	99.43 92	99.35 98	98.86 33	99.67 290	97.81 155	99.81 114	99.24 244

test_f		98.67 131	98.87 88	98.05 271	99.72 43	95.59 279	98.51 126	99.81 30	96.30 311	99.78 37	99.82 5	96.14 226	98.63 423	99.82 9	99.93 51	99.95 9

FE-MVS		95.66 339	94.95 352	97.77 286	98.53 334	95.28 295	99.40 19	96.09 393	93.11 386	97.96 290	99.26 121	79.10 410	99.77 239	92.40 383	98.71 345	98.27 372

FA-MVS(test-final)		96.99 295	96.82 288	97.50 316	98.70 300	94.78 310	99.34 23	96.99 373	95.07 348	98.48 249	99.33 104	88.41 359	99.65 306	96.13 282	98.92 334	98.07 381

balanced_conf03		98.63 137	98.72 105	98.38 239	98.66 315	96.68 246	98.90 83	99.42 138	98.99 106	98.97 172	99.19 136	95.81 246	99.85 141	98.77 96	99.77 141	98.60 345

MonoMVSNet		96.25 321	96.53 308	95.39 390	96.57 423	91.01 397	98.82 93	97.68 355	98.57 142	98.03 287	99.37 93	90.92 338	97.78 431	94.99 316	93.88 431	97.38 411

patch_mono-2		98.51 159	98.63 121	98.17 260	99.38 152	94.78 310	97.36 274	99.69 48	98.16 179	98.49 248	99.29 113	97.06 179	99.97 5	98.29 125	99.91 72	99.76 50

EGC-MVSNET		85.24 403	80.54 406	99.34 78	99.77 27	99.20 38	99.08 61	99.29 198	12.08 441	20.84 442	99.42 85	97.55 147	99.85 141	97.08 199	99.72 168	98.96 294

test2506		92.39 392	91.89 394	93.89 407	99.38 152	82.28 438	99.32 26	66.03 445	99.08 97	98.77 210	99.57 49	66.26 433	99.84 159	98.71 101	99.95 35	99.54 126

test1111		96.49 314	96.82 288	95.52 386	99.42 147	87.08 421	99.22 45	87.14 437	99.11 85	99.46 87	99.58 47	88.69 353	99.86 128	98.80 91	99.95 35	99.62 80

ECVR-MVS		96.42 316	96.61 302	95.85 378	99.38 152	88.18 416	99.22 45	86.00 439	99.08 97	99.36 108	99.57 49	88.47 358	99.82 187	98.52 114	99.95 35	99.54 126

test_blank		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

tt0805		98.69 123	98.62 123	98.90 158	99.75 34	99.30 21	99.15 56	96.97 374	98.86 121	98.87 197	97.62 352	98.63 54	98.96 413	99.41 50	98.29 365	98.45 356

DVP-MVS++		98.90 90	98.70 111	99.51 47	98.43 344	99.15 51	99.43 15	99.32 177	98.17 176	99.26 130	99.02 177	98.18 94	99.88 102	97.07 200	99.45 258	99.49 148

FOURS1					99.73 37	99.67 3	99.43 15	99.54 92	99.43 48	99.26 130

MSC_two_6792asdad				99.32 86	98.43 344	98.37 116		98.86 290					99.89 87	97.14 194	99.60 215	99.71 57

PC_three_1452									93.27 383	99.40 101	98.54 277	98.22 90	97.00 434	95.17 313	99.45 258	99.49 148

No_MVS				99.32 86	98.43 344	98.37 116		98.86 290					99.89 87	97.14 194	99.60 215	99.71 57

test_one_0601					99.39 151	99.20 38		99.31 182	98.49 148	98.66 223	99.02 177	97.64 139

eth-test2					0.00 449

eth-test					0.00 449

GeoE		99.05 72	98.99 78	99.25 99	99.44 141	98.35 120	98.73 99	99.56 84	98.42 152	98.91 187	98.81 232	98.94 29	99.91 65	98.35 121	99.73 160	99.49 148

test_method		79.78 404	79.50 407	80.62 420	80.21 445	45.76 448	70.82 436	98.41 332	31.08 440	80.89 440	97.71 345	84.85 379	97.37 433	91.51 395	80.03 437	98.75 330

Anonymous20240521		98.69 123	98.87 88	98.16 262	99.77 27	95.11 303	99.08 61	99.44 129	99.34 58	99.33 114	99.55 57	94.10 295	99.94 39	99.25 60	99.96 27	99.42 183

h-mvs33		97.77 234	97.33 258	99.10 120	99.21 193	97.84 170	98.35 145	98.57 322	99.11 85	98.58 236	99.02 177	88.65 356	99.96 12	98.11 134	96.34 415	99.49 148

hse-mvs2		97.46 256	97.07 271	98.64 195	98.73 291	97.33 205	97.45 267	97.64 358	99.11 85	98.58 236	97.98 330	88.65 356	99.79 222	98.11 134	97.39 398	98.81 319

CL-MVSNet_self_test		97.44 259	97.22 263	98.08 267	98.57 329	95.78 277	94.30 414	98.79 302	96.58 298	98.60 232	98.19 315	94.74 279	99.64 309	96.41 263	98.84 336	98.82 314

KD-MVS_2432*1600		92.87 388	91.99 390	95.51 387	91.37 441	89.27 410	94.07 416	98.14 342	95.42 339	97.25 339	96.44 386	67.86 427	99.24 400	91.28 398	96.08 420	98.02 383

KD-MVS_self_test		99.25 40	99.18 55	99.44 62	99.63 77	99.06 69	98.69 104	99.54 92	99.31 62	99.62 64	99.53 63	97.36 163	99.86 128	99.24 62	99.71 173	99.39 196

AUN-MVS		96.24 323	95.45 335	98.60 206	98.70 300	97.22 213	97.38 271	97.65 356	95.95 324	95.53 398	97.96 334	82.11 400	99.79 222	96.31 269	97.44 395	98.80 324

ZD-MVS					99.01 242	98.84 81		99.07 250	94.10 372	98.05 285	98.12 319	96.36 220	99.86 128	92.70 379	99.19 300

SR-MVS-dyc-post		98.81 103	98.55 132	99.57 20	99.20 197	99.38 12	98.48 132	99.30 190	98.64 130	98.95 176	98.96 199	97.49 157	99.86 128	96.56 251	99.39 265	99.45 172

RE-MVS-def			98.58 130		99.20 197	99.38 12	98.48 132	99.30 190	98.64 130	98.95 176	98.96 199	97.75 130		96.56 251	99.39 265	99.45 172

SED-MVS		98.91 88	98.72 105	99.49 53	99.49 122	99.17 43	98.10 171	99.31 182	98.03 183	99.66 55	99.02 177	98.36 75	99.88 102	96.91 212	99.62 208	99.41 186

IU-MVS					99.49 122	99.15 51		98.87 285	92.97 387	99.41 98				96.76 229	99.62 208	99.66 70

OPU-MVS				98.82 164	98.59 325	98.30 121	98.10 171				98.52 281	98.18 94	98.75 421	94.62 326	99.48 255	99.41 186

test_241102_TWO								99.30 190	98.03 183	99.26 130	99.02 177	97.51 153	99.88 102	96.91 212	99.60 215	99.66 70

test_241102_ONE					99.49 122	99.17 43		99.31 182	97.98 186	99.66 55	98.90 211	98.36 75	99.48 365

SF-MVS		98.53 154	98.27 176	99.32 86	99.31 169	98.75 86	98.19 157	99.41 142	96.77 290	98.83 201	98.90 211	97.80 127	99.82 187	95.68 302	99.52 244	99.38 203

cl22		95.79 335	95.39 339	96.98 341	96.77 420	92.79 366	94.40 412	98.53 324	94.59 359	97.89 294	98.17 316	82.82 397	99.24 400	96.37 265	99.03 318	98.92 301

miper_ehance_all_eth		97.06 288	97.03 273	97.16 335	97.83 378	93.06 360	94.66 404	99.09 248	95.99 322	98.69 218	98.45 291	92.73 319	99.61 322	96.79 225	99.03 318	98.82 314

miper_enhance_ethall		96.01 327	95.74 322	96.81 351	96.41 428	92.27 378	93.69 423	98.89 282	91.14 409	98.30 261	97.35 368	90.58 341	99.58 333	96.31 269	99.03 318	98.60 345

ZNCC-MVS		98.68 128	98.40 156	99.54 30	99.57 86	99.21 32	98.46 134	99.29 198	97.28 253	98.11 279	98.39 296	98.00 110	99.87 120	96.86 222	99.64 202	99.55 122

dcpmvs_2		98.78 107	99.11 64	97.78 285	99.56 94	93.67 351	99.06 65	99.86 16	99.50 37	99.66 55	99.26 121	97.21 173	99.99 2	98.00 144	99.91 72	99.68 65

cl____		97.02 291	96.83 287	97.58 306	97.82 379	94.04 334	94.66 404	99.16 236	97.04 274	98.63 226	98.71 247	88.68 355	99.69 278	97.00 204	99.81 114	99.00 287

DIV-MVS_self_test		97.02 291	96.84 286	97.58 306	97.82 379	94.03 335	94.66 404	99.16 236	97.04 274	98.63 226	98.71 247	88.69 353	99.69 278	97.00 204	99.81 114	99.01 283

eth_miper_zixun_eth		97.23 277	97.25 261	97.17 333	98.00 371	92.77 367	94.71 401	99.18 229	97.27 254	98.56 239	98.74 243	91.89 328	99.69 278	97.06 202	99.81 114	99.05 275

9.14			97.78 225		99.07 228		97.53 258	99.32 177	95.53 336	98.54 243	98.70 250	97.58 144	99.76 245	94.32 339	99.46 256

uanet_test		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

DCPMVS		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

save fliter					99.11 219	97.97 158	96.53 324	99.02 262	98.24 166

ET-MVSNet_ETH3D		94.30 363	93.21 374	97.58 306	98.14 364	94.47 321	94.78 400	93.24 422	94.72 356	89.56 434	95.87 397	78.57 413	99.81 202	96.91 212	97.11 407	98.46 353

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 70	99.90 3	99.86 22	99.78 13	99.58 6	99.95 24	99.00 79	99.95 35	99.78 42

EIA-MVS		98.00 212	97.74 228	98.80 168	98.72 293	98.09 141	98.05 179	99.60 67	97.39 242	96.63 367	95.55 402	97.68 133	99.80 209	96.73 233	99.27 284	98.52 351

miper_refine_blended		92.87 388	91.99 390	95.51 387	91.37 441	89.27 410	94.07 416	98.14 342	95.42 339	97.25 339	96.44 386	67.86 427	99.24 400	91.28 398	96.08 420	98.02 383

miper_lstm_enhance		97.18 281	97.16 266	97.25 330	98.16 362	92.85 365	95.15 392	99.31 182	97.25 256	98.74 215	98.78 237	90.07 344	99.78 233	97.19 189	99.80 125	99.11 270

ETV-MVS		98.03 208	97.86 222	98.56 215	98.69 305	98.07 147	97.51 261	99.50 101	98.10 181	97.50 324	95.51 403	98.41 72	99.88 102	96.27 272	99.24 289	97.71 402

CS-MVS		99.13 59	99.10 66	99.24 101	99.06 233	99.15 51	99.36 22	99.88 14	99.36 57	98.21 269	98.46 290	98.68 50	99.93 46	99.03 77	99.85 95	98.64 342

D2MVS		97.84 231	97.84 223	97.83 281	99.14 215	94.74 312	96.94 302	98.88 283	95.84 327	98.89 190	98.96 199	94.40 285	99.69 278	97.55 171	99.95 35	99.05 275

DVP-MVS		98.77 110	98.52 136	99.52 43	99.50 115	99.21 32	98.02 184	98.84 294	97.97 187	99.08 152	99.02 177	97.61 142	99.88 102	96.99 206	99.63 205	99.48 158
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									98.17 176	99.08 152	99.02 177	97.89 119	99.88 102	97.07 200	99.71 173	99.70 62

test_0728_SECOND				99.60 14	99.50 115	99.23 30	98.02 184	99.32 177					99.88 102	96.99 206	99.63 205	99.68 65

test0726					99.50 115	99.21 32	98.17 161	99.35 163	97.97 187	99.26 130	99.06 165	97.61 142

SR-MVS		98.71 116	98.43 152	99.57 20	99.18 207	99.35 16	98.36 144	99.29 198	98.29 163	98.88 193	98.85 224	97.53 150	99.87 120	96.14 280	99.31 277	99.48 158

DPM-MVS		96.32 318	95.59 330	98.51 222	98.76 287	97.21 215	94.54 410	98.26 336	91.94 399	96.37 378	97.25 369	93.06 311	99.43 375	91.42 396	98.74 341	98.89 306

GST-MVS		98.61 141	98.30 171	99.52 43	99.51 110	99.20 38	98.26 151	99.25 210	97.44 239	98.67 221	98.39 296	97.68 133	99.85 141	96.00 284	99.51 246	99.52 138

test_yl		96.69 304	96.29 314	97.90 276	98.28 354	95.24 296	97.29 280	97.36 361	98.21 169	98.17 270	97.86 337	86.27 367	99.55 342	94.87 320	98.32 362	98.89 306

thisisatest0530		95.27 347	94.45 358	97.74 292	99.19 200	94.37 323	97.86 210	90.20 432	97.17 267	98.22 268	97.65 349	73.53 420	99.90 72	96.90 217	99.35 271	98.95 295

Anonymous20240529		98.93 86	98.87 88	99.12 116	99.19 200	98.22 130	99.01 70	98.99 268	99.25 68	99.54 69	99.37 93	97.04 180	99.80 209	97.89 149	99.52 244	99.35 216

Anonymous202405211		97.90 218	97.50 246	99.08 124	98.90 262	98.25 124	98.53 120	96.16 390	98.87 119	99.11 147	98.86 221	90.40 343	99.78 233	97.36 181	99.31 277	99.19 256

DCV-MVSNet		96.69 304	96.29 314	97.90 276	98.28 354	95.24 296	97.29 280	97.36 361	98.21 169	98.17 270	97.86 337	86.27 367	99.55 342	94.87 320	98.32 362	98.89 306

tttt0517		95.64 340	94.98 350	97.64 301	99.36 159	93.81 346	98.72 100	90.47 431	98.08 182	98.67 221	98.34 303	73.88 419	99.92 56	97.77 159	99.51 246	99.20 251

our_test_3		97.39 264	97.73 230	96.34 363	98.70 300	89.78 408	94.61 407	98.97 269	96.50 300	99.04 161	98.85 224	95.98 238	99.84 159	97.26 186	99.67 194	99.41 186

thisisatest0515		94.12 367	93.16 375	96.97 342	98.60 322	92.90 364	93.77 422	90.61 430	94.10 372	96.91 353	95.87 397	74.99 418	99.80 209	94.52 329	99.12 311	98.20 374

ppachtmachnet_test		97.50 251	97.74 228	96.78 353	98.70 300	91.23 395	94.55 409	99.05 254	96.36 306	99.21 138	98.79 235	96.39 216	99.78 233	96.74 231	99.82 110	99.34 218

SMA-MVS		98.40 169	98.03 204	99.51 47	99.16 210	99.21 32	98.05 179	99.22 218	94.16 370	98.98 168	99.10 160	97.52 152	99.79 222	96.45 261	99.64 202	99.53 135
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																98.81 319

DPE-MVS		98.59 144	98.26 177	99.57 20	99.27 179	99.15 51	97.01 298	99.39 147	97.67 209	99.44 91	98.99 190	97.53 150	99.89 87	95.40 310	99.68 188	99.66 70
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.36 159	99.10 64				99.05 159

thres100view900		94.19 364	93.67 369	95.75 381	99.06 233	91.35 389	98.03 182	94.24 415	98.33 156	97.40 332	94.98 415	79.84 404	99.62 315	83.05 428	98.08 377	96.29 422

tfpnnormal		98.90 90	98.90 85	98.91 155	99.67 64	97.82 174	99.00 72	99.44 129	99.45 44	99.51 80	99.24 126	98.20 93	99.86 128	95.92 288	99.69 183	99.04 279

tfpn200view9		94.03 368	93.44 371	95.78 380	98.93 254	91.44 387	97.60 249	94.29 413	97.94 191	97.10 342	94.31 422	79.67 406	99.62 315	83.05 428	98.08 377	96.29 422

c3_l		97.36 265	97.37 254	97.31 325	98.09 367	93.25 358	95.01 395	99.16 236	97.05 273	98.77 210	98.72 246	92.88 314	99.64 309	96.93 211	99.76 153	99.05 275

CHOSEN 280x420		95.51 344	95.47 333	95.65 384	98.25 356	88.27 415	93.25 425	98.88 283	93.53 380	94.65 409	97.15 372	86.17 369	99.93 46	97.41 179	99.93 51	98.73 332

CANet		97.87 224	97.76 226	98.19 259	97.75 381	95.51 284	96.76 313	99.05 254	97.74 205	96.93 350	98.21 313	95.59 252	99.89 87	97.86 154	99.93 51	99.19 256

Fast-Effi-MVS+-dtu		98.27 187	98.09 196	98.81 166	98.43 344	98.11 138	97.61 248	99.50 101	98.64 130	97.39 334	97.52 357	98.12 102	99.95 24	96.90 217	98.71 345	98.38 366

Effi-MVS+-dtu		98.26 189	97.90 219	99.35 75	98.02 370	99.49 6	98.02 184	99.16 236	98.29 163	97.64 311	97.99 329	96.44 215	99.95 24	96.66 239	98.93 333	98.60 345

CANet_DTU		97.26 273	97.06 272	97.84 280	97.57 392	94.65 317	96.19 346	98.79 302	97.23 262	95.14 403	98.24 310	93.22 306	99.84 159	97.34 182	99.84 99	99.04 279

MVS_0304		97.44 259	97.01 275	98.72 187	96.42 427	96.74 242	97.20 289	91.97 427	98.46 150	98.30 261	98.79 235	92.74 318	99.91 65	99.30 55	99.94 46	99.52 138

MP-MVS-pluss		98.57 145	98.23 181	99.60 14	99.69 57	99.35 16	97.16 293	99.38 149	94.87 354	98.97 172	98.99 190	98.01 109	99.88 102	97.29 184	99.70 180	99.58 104
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 169	98.00 207	99.61 12	99.57 86	99.25 28	98.57 115	99.35 163	97.55 224	99.31 122	97.71 345	94.61 280	99.88 102	96.14 280	99.19 300	99.70 62
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												84.74 381				98.81 319

sam_mvs												84.29 387

IterMVS-SCA-FT		97.85 230	98.18 186	96.87 347	99.27 179	91.16 396	95.53 378	99.25 210	99.10 92	99.41 98	99.35 98	93.10 309	99.96 12	98.65 105	99.94 46	99.49 148

TSAR-MVS + MP.		98.63 137	98.49 143	99.06 132	99.64 73	97.90 165	98.51 126	98.94 270	96.96 278	99.24 135	98.89 217	97.83 122	99.81 202	96.88 219	99.49 254	99.48 158
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		97.86 225	98.17 187	96.92 344	98.98 247	93.91 341	96.45 328	99.17 233	97.85 199	98.41 255	97.14 373	98.47 66	99.92 56	98.02 141	99.05 314	96.92 415

OPM-MVS		98.56 146	98.32 170	99.25 99	99.41 149	98.73 90	97.13 295	99.18 229	97.10 271	98.75 213	98.92 207	98.18 94	99.65 306	96.68 238	99.56 231	99.37 205
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 112	98.48 144	99.57 20	99.58 81	99.29 23	97.82 214	99.25 210	96.94 280	98.78 207	99.12 156	98.02 108	99.84 159	97.13 196	99.67 194	99.59 97

ambc				98.24 255	98.82 279	95.97 270	98.62 110	99.00 267		99.27 126	99.21 133	96.99 185	99.50 359	96.55 254	99.50 253	99.26 240

MTGPA								99.20 221

SPE-MVS-test		99.13 59	99.09 68	99.26 96	99.13 217	98.97 72	99.31 30	99.88 14	99.44 46	98.16 273	98.51 282	98.64 52	99.93 46	98.91 84	99.85 95	98.88 309

Effi-MVS+		98.02 209	97.82 224	98.62 201	98.53 334	97.19 217	97.33 276	99.68 53	97.30 251	96.68 365	97.46 361	98.56 62	99.80 209	96.63 241	98.20 368	98.86 311

xiu_mvs_v2_base		97.16 283	97.49 247	96.17 372	98.54 332	92.46 372	95.45 382	98.84 294	97.25 256	97.48 326	96.49 383	98.31 81	99.90 72	96.34 268	98.68 350	96.15 426

xiu_mvs_v1_base		97.86 225	98.17 187	96.92 344	98.98 247	93.91 341	96.45 328	99.17 233	97.85 199	98.41 255	97.14 373	98.47 66	99.92 56	98.02 141	99.05 314	96.92 415

new-patchmatchnet		98.35 175	98.74 101	97.18 331	99.24 186	92.23 379	96.42 332	99.48 110	98.30 160	99.69 50	99.53 63	97.44 159	99.82 187	98.84 90	99.77 141	99.49 148

pmmvs6		99.67 3	99.70 3	99.60 14	99.90 4	99.27 26	99.53 9	99.76 37	99.64 23	99.84 28	99.83 4	99.50 9	99.87 120	99.36 51	99.92 63	99.64 76

pmmvs5		97.64 243	97.49 247	98.08 267	99.14 215	95.12 302	96.70 317	99.05 254	93.77 377	98.62 228	98.83 227	93.23 305	99.75 252	98.33 124	99.76 153	99.36 212

test_post1							97.59 251				20.48 443	83.07 395	99.66 301	94.16 340

test_post											21.25 442	83.86 390	99.70 274

Fast-Effi-MVS+		97.67 241	97.38 253	98.57 211	98.71 296	97.43 201	97.23 284	99.45 125	94.82 355	96.13 382	96.51 382	98.52 64	99.91 65	96.19 276	98.83 337	98.37 368

patchmatchnet-post											98.77 239	84.37 384	99.85 141

Anonymous20231211		99.27 37	99.27 45	99.26 96	99.29 176	98.18 132	99.49 12	99.51 99	99.70 15	99.80 35	99.68 25	96.84 191	99.83 177	99.21 63	99.91 72	99.77 45

pmmvs-eth3d		98.47 162	98.34 166	98.86 160	99.30 173	97.76 180	97.16 293	99.28 201	95.54 335	99.42 96	99.19 136	97.27 168	99.63 312	97.89 149	99.97 20	99.20 251

GG-mvs-BLEND				94.76 397	94.54 437	92.13 380	99.31 30	80.47 443		88.73 437	91.01 437	67.59 430	98.16 430	82.30 432	94.53 429	93.98 433

xiu_mvs_v1_base_debi		97.86 225	98.17 187	96.92 344	98.98 247	93.91 341	96.45 328	99.17 233	97.85 199	98.41 255	97.14 373	98.47 66	99.92 56	98.02 141	99.05 314	96.92 415

Anonymous20231206		98.21 195	98.21 182	98.20 257	99.51 110	95.43 290	98.13 165	99.32 177	96.16 314	98.93 184	98.82 230	96.00 233	99.83 177	97.32 183	99.73 160	99.36 212

MTAPA		98.88 92	98.64 120	99.61 12	99.67 64	99.36 15	98.43 137	99.20 221	98.83 125	98.89 190	98.90 211	96.98 186	99.92 56	97.16 191	99.70 180	99.56 115

MTMP							97.93 198	91.91 428

gm-plane-assit					94.83 436	81.97 439			88.07 424		94.99 414		99.60 323	91.76 389

test9_res														93.28 366	99.15 305	99.38 203

MVP-Stereo		98.08 206	97.92 217	98.57 211	98.96 250	96.79 238	97.90 204	99.18 229	96.41 305	98.46 250	98.95 203	95.93 242	99.60 323	96.51 257	98.98 328	99.31 229
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 296	98.08 145	95.96 359	99.03 259	91.40 405	95.85 388	97.53 355	96.52 211	99.76 245

train_agg		97.10 285	96.45 310	99.07 126	98.71 296	98.08 145	95.96 359	99.03 259	91.64 400	95.85 388	97.53 355	96.47 213	99.76 245	93.67 356	99.16 303	99.36 212

gg-mvs-nofinetune		92.37 394	91.20 398	95.85 378	95.80 435	92.38 375	99.31 30	81.84 442	99.75 11	91.83 431	99.74 18	68.29 426	99.02 410	87.15 419	97.12 406	96.16 425

SCA		96.41 317	96.66 300	95.67 382	98.24 357	88.35 414	95.85 368	96.88 379	96.11 315	97.67 310	98.67 256	93.10 309	99.85 141	94.16 340	99.22 293	98.81 319

Patchmatch-test		96.55 310	96.34 312	97.17 333	98.35 350	93.06 360	98.40 140	97.79 350	97.33 247	98.41 255	98.67 256	83.68 391	99.69 278	95.16 314	99.31 277	98.77 327

test_8					98.67 310	98.01 153	95.91 365	99.02 262	91.64 400	95.79 390	97.50 358	96.47 213	99.76 245

MS-PatchMatch		97.68 240	97.75 227	97.45 320	98.23 359	93.78 347	97.29 280	98.84 294	96.10 316	98.64 225	98.65 261	96.04 230	99.36 384	96.84 223	99.14 306	99.20 251

Patchmatch-RL test		97.26 273	97.02 274	97.99 275	99.52 108	95.53 283	96.13 351	99.71 44	97.47 231	99.27 126	99.16 146	84.30 386	99.62 315	97.89 149	99.77 141	98.81 319

cdsmvs_eth3d_5k		24.66 408	32.88 411	0.00 426	0.00 449	0.00 451	0.00 437	99.10 246	0.00 444	0.00 445	97.58 353	99.21 17	0.00 445	0.00 444	0.00 443	0.00 441

pcd_1.5k_mvsjas		8.17 411	10.90 414	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	98.07 104	0.00 445	0.00 444	0.00 443	0.00 441

agg_prior2														92.50 382	99.16 303	99.37 205

agg_prior					98.68 309	97.99 154		99.01 265		95.59 391			99.77 239

tmp_tt		78.77 405	78.73 408	78.90 421	58.45 446	74.76 445	94.20 415	78.26 444	39.16 439	86.71 438	92.82 433	80.50 402	75.19 441	86.16 424	92.29 434	86.74 435

canonicalmvs		98.34 176	98.26 177	98.58 208	98.46 340	97.82 174	98.96 77	99.46 121	99.19 78	97.46 327	95.46 407	98.59 58	99.46 370	98.08 137	98.71 345	98.46 353

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 68	99.34 23	99.69 48	98.93 114	99.65 58	99.72 21	98.93 31	99.95 24	99.11 69	100.00 1	99.82 33

alignmvs		97.35 266	96.88 283	98.78 174	98.54 332	98.09 141	97.71 231	97.69 354	99.20 74	97.59 315	95.90 396	88.12 361	99.55 342	98.18 130	98.96 330	98.70 336

nrg030		99.40 26	99.35 32	99.54 30	99.58 81	99.13 59	98.98 75	99.48 110	99.68 18	99.46 87	99.26 121	98.62 55	99.73 262	99.17 67	99.92 63	99.76 50

v144192		98.54 152	98.57 131	98.45 230	99.21 193	95.98 269	97.63 244	99.36 157	97.15 270	99.32 120	99.18 140	95.84 245	99.84 159	99.50 45	99.91 72	99.54 126

FIs		99.14 55	99.09 68	99.29 90	99.70 55	98.28 122	99.13 58	99.52 98	99.48 38	99.24 135	99.41 89	96.79 197	99.82 187	98.69 103	99.88 86	99.76 50

v1921920		98.54 152	98.60 128	98.38 239	99.20 197	95.76 278	97.56 254	99.36 157	97.23 262	99.38 104	99.17 144	96.02 231	99.84 159	99.57 35	99.90 78	99.54 126

UA-Net		99.47 16	99.40 26	99.70 2	99.49 122	99.29 23	99.80 4	99.72 42	99.82 8	99.04 161	99.81 8	98.05 107	99.96 12	98.85 89	99.99 5	99.86 26

v1192		98.60 142	98.66 117	98.41 235	99.27 179	95.88 272	97.52 259	99.36 157	97.41 240	99.33 114	99.20 135	96.37 219	99.82 187	99.57 35	99.92 63	99.55 122

FC-MVSNet-test		99.27 37	99.25 49	99.34 78	99.77 27	98.37 116	99.30 35	99.57 77	99.61 30	99.40 101	99.50 67	97.12 176	99.85 141	99.02 78	99.94 46	99.80 38

v1144		98.60 142	98.66 117	98.41 235	99.36 159	95.90 271	97.58 252	99.34 169	97.51 227	99.27 126	99.15 150	96.34 221	99.80 209	99.47 48	99.93 51	99.51 141

sosnet-low-res		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

HFP-MVS		98.71 116	98.44 151	99.51 47	99.49 122	99.16 47	98.52 121	99.31 182	97.47 231	98.58 236	98.50 286	97.97 114	99.85 141	96.57 247	99.59 219	99.53 135

v148		98.45 164	98.60 128	98.00 274	99.44 141	94.98 305	97.44 268	99.06 251	98.30 160	99.32 120	98.97 196	96.65 206	99.62 315	98.37 120	99.85 95	99.39 196

sosnet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

uncertanet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

AllTest		98.44 165	98.20 183	99.16 111	99.50 115	98.55 102	98.25 152	99.58 70	96.80 287	98.88 193	99.06 165	97.65 136	99.57 335	94.45 332	99.61 213	99.37 205

TestCases				99.16 111	99.50 115	98.55 102		99.58 70	96.80 287	98.88 193	99.06 165	97.65 136	99.57 335	94.45 332	99.61 213	99.37 205

v7n		99.53 12	99.57 13	99.41 65	99.88 9	98.54 105	99.45 14	99.61 66	99.66 20	99.68 52	99.66 32	98.44 71	99.95 24	99.73 24	99.96 27	99.75 54

region2R		98.69 123	98.40 156	99.54 30	99.53 106	99.17 43	98.52 121	99.31 182	97.46 236	98.44 252	98.51 282	97.83 122	99.88 102	96.46 260	99.58 224	99.58 104

RRT-MVS		97.88 222	97.98 209	97.61 303	98.15 363	93.77 348	98.97 76	99.64 60	99.16 82	98.69 218	99.42 85	91.60 330	99.89 87	97.63 167	98.52 359	99.16 266

mamv4		99.44 19	99.39 27	99.58 19	99.30 173	99.74 2	99.04 68	99.81 30	99.77 10	99.82 31	99.57 49	97.82 125	99.98 4	99.53 42	99.89 84	99.01 283

PS-MVSNAJss		99.46 17	99.49 16	99.35 75	99.90 4	98.15 134	99.20 48	99.65 58	99.48 38	99.92 8	99.71 22	98.07 104	99.96 12	99.53 42	100.00 1	99.93 11

PS-MVSNAJ		97.08 287	97.39 252	96.16 374	98.56 330	92.46 372	95.24 389	98.85 293	97.25 256	97.49 325	95.99 393	98.07 104	99.90 72	96.37 265	98.67 351	96.12 427

jajsoiax		99.58 9	99.61 11	99.48 55	99.87 12	98.61 97	99.28 40	99.66 57	99.09 95	99.89 16	99.68 25	99.53 7	99.97 5	99.50 45	99.99 5	99.87 20

mvs_tets		99.63 6	99.67 6	99.49 53	99.88 9	98.61 97	99.34 23	99.71 44	99.27 67	99.90 13	99.74 18	99.68 4	99.97 5	99.55 39	99.99 5	99.88 19

EI-MVSNet-UG-set		98.69 123	98.71 108	98.62 201	99.10 221	96.37 255	97.23 284	98.87 285	99.20 74	99.19 140	98.99 190	97.30 165	99.85 141	98.77 96	99.79 130	99.65 75

EI-MVSNet-Vis-set		98.68 128	98.70 111	98.63 199	99.09 224	96.40 254	97.23 284	98.86 290	99.20 74	99.18 144	98.97 196	97.29 167	99.85 141	98.72 100	99.78 135	99.64 76

HPM-MVS++		98.10 203	97.64 238	99.48 55	99.09 224	99.13 59	97.52 259	98.75 309	97.46 236	96.90 356	97.83 340	96.01 232	99.84 159	95.82 296	99.35 271	99.46 168

test_prior4						97.97 158	95.86 366

XVS		98.72 115	98.45 149	99.53 37	99.46 134	99.21 32	98.65 106	99.34 169	98.62 135	97.54 320	98.63 266	97.50 154	99.83 177	96.79 225	99.53 241	99.56 115

v1240		98.55 150	98.62 123	98.32 246	99.22 191	95.58 281	97.51 261	99.45 125	97.16 268	99.45 90	99.24 126	96.12 228	99.85 141	99.60 33	99.88 86	99.55 122

pm-mvs1		99.44 19	99.48 18	99.33 84	99.80 21	98.63 94	99.29 36	99.63 61	99.30 64	99.65 58	99.60 45	99.16 21	99.82 187	99.07 72	99.83 106	99.56 115

test_prior2							95.74 372		96.48 302	96.11 383	97.63 351	95.92 243		94.16 340	99.20 297

X-MVStestdata		94.32 361	92.59 380	99.53 37	99.46 134	99.21 32	98.65 106	99.34 169	98.62 135	97.54 320	45.85 439	97.50 154	99.83 177	96.79 225	99.53 241	99.56 115

test_prior				98.95 148	98.69 305	97.95 162		99.03 259					99.59 327			99.30 232

旧先验2							95.76 371		88.56 423	97.52 322			99.66 301	94.48 330

新几何2							95.93 362

新几何1				98.91 155	98.94 252	97.76 180		98.76 306	87.58 425	96.75 364	98.10 321	94.80 276	99.78 233	92.73 378	99.00 323	99.20 251

旧先验1					98.82 279	97.45 199		98.76 306			98.34 303	95.50 256			99.01 322	99.23 246

无先验							95.74 372	98.74 311	89.38 419				99.73 262	92.38 384		99.22 250

原ACMM2							95.53 378

原ACMM1				98.35 244	98.90 262	96.25 259		98.83 298	92.48 394	96.07 385	98.10 321	95.39 259	99.71 270	92.61 381	98.99 325	99.08 271

test222					98.92 258	96.93 232	95.54 377	98.78 304	85.72 428	96.86 359	98.11 320	94.43 283			99.10 313	99.23 246

testdata2													99.79 222	92.80 376

segment_acmp												97.02 183

testdata				98.09 264	98.93 254	95.40 291		98.80 301	90.08 416	97.45 329	98.37 299	95.26 261	99.70 274	93.58 359	98.95 331	99.17 263

testdata1							95.44 383		96.32 308

v8		99.01 74	99.16 58	98.57 211	99.47 132	96.31 258	98.90 83	99.47 118	99.03 103	99.52 75	99.57 49	96.93 187	99.81 202	99.60 33	99.98 12	99.60 90

1314		95.74 336	95.60 328	96.17 372	97.53 397	92.75 368	98.07 176	98.31 335	91.22 407	94.25 413	96.68 379	95.53 253	99.03 409	91.64 392	97.18 405	96.74 419

LFMVS		97.20 279	96.72 294	98.64 195	98.72 293	96.95 230	98.93 81	94.14 417	99.74 13	98.78 207	99.01 186	84.45 383	99.73 262	97.44 177	99.27 284	99.25 241

VDD-MVS		98.56 146	98.39 159	99.07 126	99.13 217	98.07 147	98.59 113	97.01 372	99.59 31	99.11 147	99.27 116	94.82 273	99.79 222	98.34 122	99.63 205	99.34 218

VDDNet		98.21 195	97.95 212	99.01 139	99.58 81	97.74 182	99.01 70	97.29 365	99.67 19	98.97 172	99.50 67	90.45 342	99.80 209	97.88 152	99.20 297	99.48 158

v10		98.97 81	99.11 64	98.55 216	99.44 141	96.21 260	98.90 83	99.55 88	98.73 126	99.48 82	99.60 45	96.63 207	99.83 177	99.70 29	99.99 5	99.61 88

VPNet		98.87 93	98.83 93	99.01 139	99.70 55	97.62 191	98.43 137	99.35 163	99.47 41	99.28 124	99.05 172	96.72 203	99.82 187	98.09 136	99.36 269	99.59 97

MVS		93.19 382	92.09 387	96.50 359	96.91 416	94.03 335	98.07 176	98.06 346	68.01 437	94.56 411	96.48 384	95.96 240	99.30 394	83.84 427	96.89 410	96.17 424

v2v482		98.56 146	98.62 123	98.37 242	99.42 147	95.81 276	97.58 252	99.16 236	97.90 195	99.28 124	99.01 186	95.98 238	99.79 222	99.33 53	99.90 78	99.51 141

V42		98.78 107	98.78 99	98.76 179	99.44 141	97.04 224	98.27 150	99.19 225	97.87 197	99.25 134	99.16 146	96.84 191	99.78 233	99.21 63	99.84 99	99.46 168

SD-MVS		98.40 169	98.68 114	97.54 312	98.96 250	97.99 154	97.88 206	99.36 157	98.20 173	99.63 61	99.04 174	98.76 42	95.33 438	96.56 251	99.74 157	99.31 229
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		95.86 332	95.32 342	97.49 317	98.60 322	94.15 330	93.83 421	97.93 348	95.49 337	96.68 365	97.42 363	83.21 393	99.30 394	96.22 274	98.55 358	99.01 283

MSLP-MVS++		98.02 209	98.14 193	97.64 301	98.58 327	95.19 299	97.48 264	99.23 217	97.47 231	97.90 293	98.62 268	97.04 180	98.81 419	97.55 171	99.41 263	98.94 299

APDe-MVS		98.99 77	98.79 97	99.60 14	99.21 193	99.15 51	98.87 87	99.48 110	97.57 220	99.35 110	99.24 126	97.83 122	99.89 87	97.88 152	99.70 180	99.75 54
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.84 97	98.61 127	99.53 37	99.19 200	99.27 26	98.49 129	99.33 175	98.64 130	99.03 164	98.98 194	97.89 119	99.85 141	96.54 255	99.42 262	99.46 168

ADS-MVSNet2		95.43 345	94.98 350	96.76 354	98.14 364	91.74 382	97.92 201	97.76 351	90.23 412	96.51 374	98.91 208	85.61 374	99.85 141	92.88 372	96.90 408	98.69 337

EI-MVSNet		98.40 169	98.51 137	98.04 272	99.10 221	94.73 313	97.20 289	98.87 285	98.97 109	99.06 154	99.02 177	96.00 233	99.80 209	98.58 108	99.82 110	99.60 90

Regformer		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

CVMVSNet		96.25 321	97.21 264	93.38 414	99.10 221	80.56 442	97.20 289	98.19 341	96.94 280	99.00 166	99.02 177	89.50 349	99.80 209	96.36 267	99.59 219	99.78 42

pmmvs4		97.58 248	97.28 259	98.51 222	98.84 274	96.93 232	95.40 385	98.52 325	93.60 379	98.61 230	98.65 261	95.10 265	99.60 323	96.97 209	99.79 130	98.99 288

EU-MVSNet		97.66 242	98.50 139	95.13 393	99.63 77	85.84 424	98.35 145	98.21 338	98.23 167	99.54 69	99.46 77	95.02 267	99.68 287	98.24 126	99.87 89	99.87 20

VNet		98.42 166	98.30 171	98.79 171	98.79 286	97.29 207	98.23 153	98.66 316	99.31 62	98.85 198	98.80 233	94.80 276	99.78 233	98.13 133	99.13 308	99.31 229

test-LLR		93.90 370	93.85 365	94.04 404	96.53 424	84.62 430	94.05 418	92.39 424	96.17 312	94.12 415	95.07 411	82.30 398	99.67 290	95.87 292	98.18 369	97.82 393

TESTMET0.1,1		92.19 397	91.77 395	93.46 411	96.48 426	82.80 437	94.05 418	91.52 429	94.45 364	94.00 418	94.88 417	66.65 431	99.56 338	95.78 297	98.11 375	98.02 383

test-mter		92.33 395	91.76 396	94.04 404	96.53 424	84.62 430	94.05 418	92.39 424	94.00 375	94.12 415	95.07 411	65.63 437	99.67 290	95.87 292	98.18 369	97.82 393

VPA-MVSNet		99.30 33	99.30 42	99.28 91	99.49 122	98.36 119	99.00 72	99.45 125	99.63 25	99.52 75	99.44 82	98.25 85	99.88 102	99.09 71	99.84 99	99.62 80

ACMMPR		98.70 120	98.42 154	99.54 30	99.52 108	99.14 56	98.52 121	99.31 182	97.47 231	98.56 239	98.54 277	97.75 130	99.88 102	96.57 247	99.59 219	99.58 104

testgi		98.32 180	98.39 159	98.13 263	99.57 86	95.54 282	97.78 220	99.49 108	97.37 244	99.19 140	97.65 349	98.96 28	99.49 362	96.50 258	98.99 325	99.34 218

test20.03		98.78 107	98.77 100	98.78 174	99.46 134	97.20 216	97.78 220	99.24 215	99.04 102	99.41 98	98.90 211	97.65 136	99.76 245	97.70 164	99.79 130	99.39 196

thres600view7		94.45 359	93.83 366	96.29 365	99.06 233	91.53 385	97.99 193	94.24 415	98.34 155	97.44 330	95.01 413	79.84 404	99.67 290	84.33 426	98.23 366	97.66 403

ADS-MVSNet		95.24 348	94.93 353	96.18 371	98.14 364	90.10 407	97.92 201	97.32 364	90.23 412	96.51 374	98.91 208	85.61 374	99.74 257	92.88 372	96.90 408	98.69 337

MP-MVS		98.46 163	98.09 196	99.54 30	99.57 86	99.22 31	98.50 128	99.19 225	97.61 216	97.58 316	98.66 259	97.40 161	99.88 102	94.72 325	99.60 215	99.54 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 409	20.53 412	6.87 425	12.05 447	4.20 450	93.62 424	6.73 448	4.62 443	10.41 443	24.33 440	8.28 448	3.56 444	9.69 443	15.07 441	12.86 440

thres400		94.14 366	93.44 371	96.24 368	98.93 254	91.44 387	97.60 249	94.29 413	97.94 191	97.10 342	94.31 422	79.67 406	99.62 315	83.05 428	98.08 377	97.66 403

test123		17.04 410	20.11 413	7.82 424	10.25 448	4.91 449	94.80 399	4.47 449	4.93 442	10.00 444	24.28 441	9.69 447	3.64 443	10.14 442	12.43 442	14.92 439

thres200		93.72 374	93.14 376	95.46 389	98.66 315	91.29 391	96.61 321	94.63 410	97.39 242	96.83 360	93.71 425	79.88 403	99.56 338	82.40 431	98.13 374	95.54 431

test0.0.03 1		94.51 358	93.69 368	96.99 340	96.05 431	93.61 355	94.97 396	93.49 419	96.17 312	97.57 318	94.88 417	82.30 398	99.01 412	93.60 358	94.17 430	98.37 368

pmmvs3		95.03 352	94.40 359	96.93 343	97.70 387	92.53 371	95.08 393	97.71 353	88.57 422	97.71 307	98.08 324	79.39 408	99.82 187	96.19 276	99.11 312	98.43 361

EMVS		93.83 371	94.02 363	93.23 415	96.83 419	84.96 427	89.77 435	96.32 388	97.92 193	97.43 331	96.36 389	86.17 369	98.93 415	87.68 418	97.73 388	95.81 429

E-PMN		94.17 365	94.37 360	93.58 410	96.86 417	85.71 426	90.11 434	97.07 371	98.17 176	97.82 302	97.19 370	84.62 382	98.94 414	89.77 411	97.68 389	96.09 428

PGM-MVS		98.66 132	98.37 162	99.55 27	99.53 106	99.18 42	98.23 153	99.49 108	97.01 277	98.69 218	98.88 218	98.00 110	99.89 87	95.87 292	99.59 219	99.58 104

LCM-MVSNet-Re		98.64 135	98.48 144	99.11 118	98.85 273	98.51 107	98.49 129	99.83 25	98.37 153	99.69 50	99.46 77	98.21 92	99.92 56	94.13 344	99.30 280	98.91 304

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 13	100.00 1	99.85 28

MCST-MVS		98.00 212	97.63 239	99.10 120	99.24 186	98.17 133	96.89 307	98.73 312	95.66 330	97.92 291	97.70 347	97.17 174	99.66 301	96.18 278	99.23 292	99.47 166

mvs_anonymous		97.83 233	98.16 190	96.87 347	98.18 361	91.89 381	97.31 278	98.90 279	97.37 244	98.83 201	99.46 77	96.28 222	99.79 222	98.90 85	98.16 372	98.95 295

MVS_Test		98.18 198	98.36 163	97.67 297	98.48 337	94.73 313	98.18 158	99.02 262	97.69 208	98.04 286	99.11 157	97.22 172	99.56 338	98.57 110	98.90 335	98.71 333

MDA-MVSNet-bldmvs		97.94 216	97.91 218	98.06 269	99.44 141	94.96 306	96.63 320	99.15 241	98.35 154	98.83 201	99.11 157	94.31 288	99.85 141	96.60 244	98.72 343	99.37 205

CDPH-MVS		97.26 273	96.66 300	99.07 126	99.00 243	98.15 134	96.03 355	99.01 265	91.21 408	97.79 303	97.85 339	96.89 189	99.69 278	92.75 377	99.38 268	99.39 196

test12				98.93 151	98.58 327	97.83 171		98.66 316		96.53 372		95.51 255	99.69 278		99.13 308	99.27 237

casdiffmvs		98.95 84	99.00 76	98.81 166	99.38 152	97.33 205	97.82 214	99.57 77	99.17 81	99.35 110	99.17 144	98.35 78	99.69 278	98.46 116	99.73 160	99.41 186
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		98.22 193	98.24 180	98.17 260	99.00 243	95.44 289	96.38 334	99.58 70	97.79 203	98.53 244	98.50 286	96.76 200	99.74 257	97.95 148	99.64 202	99.34 218
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		93.73 373	92.83 379	96.42 361	97.70 387	91.28 392	96.84 309	89.77 433	93.96 376	92.44 428	95.93 395	79.14 409	99.77 239	92.94 370	96.76 412	98.21 373

baseline1		95.96 330	95.44 336	97.52 314	98.51 336	93.99 338	98.39 141	96.09 393	98.21 169	98.40 259	97.76 343	86.88 363	99.63 312	95.42 309	89.27 436	98.95 295

YYNet1		97.60 245	97.67 233	97.39 324	99.04 237	93.04 363	95.27 387	98.38 333	97.25 256	98.92 186	98.95 203	95.48 257	99.73 262	96.99 206	98.74 341	99.41 186

PMMVS2		98.07 207	98.08 199	98.04 272	99.41 149	94.59 319	94.59 408	99.40 145	97.50 228	98.82 204	98.83 227	96.83 193	99.84 159	97.50 176	99.81 114	99.71 57

MDA-MVSNet_test_wron		97.60 245	97.66 236	97.41 323	99.04 237	93.09 359	95.27 387	98.42 330	97.26 255	98.88 193	98.95 203	95.43 258	99.73 262	97.02 203	98.72 343	99.41 186

tpmvs		95.02 353	95.25 343	94.33 400	96.39 429	85.87 423	98.08 173	96.83 380	95.46 338	95.51 399	98.69 252	85.91 372	99.53 349	94.16 340	96.23 417	97.58 406

PM-MVS		98.82 101	98.72 105	99.12 116	99.64 73	98.54 105	97.98 194	99.68 53	97.62 213	99.34 112	99.18 140	97.54 148	99.77 239	97.79 157	99.74 157	99.04 279

HQP_MVS		97.99 215	97.67 233	98.93 151	99.19 200	97.65 188	97.77 222	99.27 204	98.20 173	97.79 303	97.98 330	94.90 269	99.70 274	94.42 334	99.51 246	99.45 172

plane_prior7					99.19 200	97.87 167

plane_prior6					98.99 246	97.70 186						94.90 269

plane_prior5								99.27 204					99.70 274	94.42 334	99.51 246	99.45 172

plane_prior4											97.98 330

plane_prior3						97.78 179			97.41 240	97.79 303

plane_prior2							97.77 222		98.20 173

plane_prior1					99.05 236

plane_prior						97.65 188	97.07 296		96.72 292						99.36 269

PS-CasMVS		99.40 26	99.33 35	99.62 9	99.71 47	99.10 64	99.29 36	99.53 95	99.53 35	99.46 87	99.41 89	98.23 87	99.95 24	98.89 87	99.95 35	99.81 36

UniMVSNet_NR-MVSNet		98.86 96	98.68 114	99.40 67	99.17 208	98.74 87	97.68 234	99.40 145	99.14 83	99.06 154	98.59 273	96.71 204	99.93 46	98.57 110	99.77 141	99.53 135

PEN-MVS		99.41 25	99.34 34	99.62 9	99.73 37	99.14 56	99.29 36	99.54 92	99.62 28	99.56 65	99.42 85	98.16 98	99.96 12	98.78 93	99.93 51	99.77 45

TransMVSNet (Re)		99.44 19	99.47 21	99.36 69	99.80 21	98.58 100	99.27 42	99.57 77	99.39 52	99.75 42	99.62 40	99.17 19	99.83 177	99.06 74	99.62 208	99.66 70

DTE-MVSNet		99.43 23	99.35 32	99.66 7	99.71 47	99.30 21	99.31 30	99.51 99	99.64 23	99.56 65	99.46 77	98.23 87	99.97 5	98.78 93	99.93 51	99.72 56

DU-MVS		98.82 101	98.63 121	99.39 68	99.16 210	98.74 87	97.54 257	99.25 210	98.84 124	99.06 154	98.76 241	96.76 200	99.93 46	98.57 110	99.77 141	99.50 144

UniMVSNet (Re)		98.87 93	98.71 108	99.35 75	99.24 186	98.73 90	97.73 230	99.38 149	98.93 114	99.12 146	98.73 244	96.77 198	99.86 128	98.63 107	99.80 125	99.46 168

CP-MVSNet		99.21 46	99.09 68	99.56 25	99.65 67	98.96 76	99.13 58	99.34 169	99.42 49	99.33 114	99.26 121	97.01 184	99.94 39	98.74 98	99.93 51	99.79 39

WR-MVS_H		99.33 31	99.22 51	99.65 8	99.71 47	99.24 29	99.32 26	99.55 88	99.46 43	99.50 81	99.34 102	97.30 165	99.93 46	98.90 85	99.93 51	99.77 45

WR-MVS		98.40 169	98.19 185	99.03 136	99.00 243	97.65 188	96.85 308	98.94 270	98.57 142	98.89 190	98.50 286	95.60 251	99.85 141	97.54 173	99.85 95	99.59 97

NR-MVSNet		98.95 84	98.82 94	99.36 69	99.16 210	98.72 92	99.22 45	99.20 221	99.10 92	99.72 44	98.76 241	96.38 218	99.86 128	98.00 144	99.82 110	99.50 144

Baseline_NR-MVSNet		98.98 80	98.86 91	99.36 69	99.82 19	98.55 102	97.47 266	99.57 77	99.37 54	99.21 138	99.61 43	96.76 200	99.83 177	98.06 139	99.83 106	99.71 57

TranMVSNet+NR-MVSNet		99.17 50	99.07 71	99.46 61	99.37 158	98.87 80	98.39 141	99.42 138	99.42 49	99.36 108	99.06 165	98.38 74	99.95 24	98.34 122	99.90 78	99.57 109

TSAR-MVS + GP.		98.18 198	97.98 209	98.77 178	98.71 296	97.88 166	96.32 338	98.66 316	96.33 307	99.23 137	98.51 282	97.48 158	99.40 379	97.16 191	99.46 256	99.02 282

n2								0.00 450

nn								0.00 450

mPP-MVS		98.64 135	98.34 166	99.54 30	99.54 103	99.17 43	98.63 108	99.24 215	97.47 231	98.09 281	98.68 254	97.62 141	99.89 87	96.22 274	99.62 208	99.57 109

door-mid								99.57 77

XVG-OURS-SEG-HR		98.49 160	98.28 173	99.14 114	99.49 122	98.83 82	96.54 322	99.48 110	97.32 249	99.11 147	98.61 270	99.33 14	99.30 394	96.23 273	98.38 361	99.28 236

mvsmamba		97.57 249	97.26 260	98.51 222	98.69 305	96.73 243	98.74 95	97.25 366	97.03 276	97.88 295	99.23 131	90.95 337	99.87 120	96.61 243	99.00 323	98.91 304

MVSFormer		98.26 189	98.43 152	97.77 286	98.88 268	93.89 344	99.39 20	99.56 84	99.11 85	98.16 273	98.13 317	93.81 299	99.97 5	99.26 58	99.57 228	99.43 180

jason		97.45 258	97.35 256	97.76 289	99.24 186	93.93 340	95.86 366	98.42 330	94.24 368	98.50 247	98.13 317	94.82 273	99.91 65	97.22 188	99.73 160	99.43 180
jason: jason.
lupinMVS		97.06 288	96.86 284	97.65 299	98.88 268	93.89 344	95.48 381	97.97 347	93.53 380	98.16 273	97.58 353	93.81 299	99.91 65	96.77 228	99.57 228	99.17 263

test_djsdf		99.52 13	99.51 15	99.53 37	99.86 14	98.74 87	99.39 20	99.56 84	99.11 85	99.70 48	99.73 20	99.00 26	99.97 5	99.26 58	99.98 12	99.89 16

HPM-MVS_fast		99.01 74	98.82 94	99.57 20	99.71 47	99.35 16	99.00 72	99.50 101	97.33 247	98.94 183	98.86 221	98.75 43	99.82 187	97.53 174	99.71 173	99.56 115

K. test v3		98.00 212	97.66 236	99.03 136	99.79 23	97.56 193	99.19 52	92.47 423	99.62 28	99.52 75	99.66 32	89.61 347	99.96 12	99.25 60	99.81 114	99.56 115

lessismore_v0				98.97 145	99.73 37	97.53 195		86.71 438		99.37 106	99.52 66	89.93 345	99.92 56	98.99 80	99.72 168	99.44 176

SixPastTwentyTwo		98.75 112	98.62 123	99.16 111	99.83 18	97.96 161	99.28 40	98.20 339	99.37 54	99.70 48	99.65 36	92.65 320	99.93 46	99.04 76	99.84 99	99.60 90

OurMVSNet-221017-0		99.37 29	99.31 39	99.53 37	99.91 3	98.98 70	99.63 7	99.58 70	99.44 46	99.78 37	99.76 15	96.39 216	99.92 56	99.44 49	99.92 63	99.68 65

HPM-MVS		98.79 105	98.53 135	99.59 18	99.65 67	99.29 23	99.16 54	99.43 135	96.74 291	98.61 230	98.38 298	98.62 55	99.87 120	96.47 259	99.67 194	99.59 97
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 154	98.34 166	99.11 118	99.50 115	98.82 84	95.97 357	99.50 101	97.30 251	99.05 159	98.98 194	99.35 13	99.32 391	95.72 299	99.68 188	99.18 259

XVG-ACMP-BASELINE		98.56 146	98.34 166	99.22 104	99.54 103	98.59 99	97.71 231	99.46 121	97.25 256	98.98 168	98.99 190	97.54 148	99.84 159	95.88 289	99.74 157	99.23 246

casdiffmvs_mvg		99.12 62	99.16 58	98.99 141	99.43 146	97.73 184	98.00 188	99.62 63	99.22 70	99.55 68	99.22 132	98.93 31	99.75 252	98.66 104	99.81 114	99.50 144
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		98.71 116	98.46 148	99.47 59	99.57 86	98.97 72	98.23 153	99.48 110	96.60 296	99.10 150	99.06 165	98.71 47	99.83 177	95.58 306	99.78 135	99.62 80

LGP-MVS_train				99.47 59	99.57 86	98.97 72		99.48 110	96.60 296	99.10 150	99.06 165	98.71 47	99.83 177	95.58 306	99.78 135	99.62 80

baseline		98.96 83	99.02 74	98.76 179	99.38 152	97.26 210	98.49 129	99.50 101	98.86 121	99.19 140	99.06 165	98.23 87	99.69 278	98.71 101	99.76 153	99.33 223

test11								98.87 285

door								99.41 142

EPNet_dtu		94.93 355	94.78 355	95.38 391	93.58 439	87.68 418	96.78 311	95.69 402	97.35 246	89.14 436	98.09 323	88.15 360	99.49 362	94.95 319	99.30 280	98.98 289
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 254	97.14 269	98.54 219	99.68 60	96.09 264	96.50 326	99.62 63	91.58 402	98.84 200	98.97 196	92.36 322	99.88 102	96.76 229	99.95 35	99.67 68

EPNet		96.14 324	95.44 336	98.25 253	90.76 443	95.50 285	97.92 201	94.65 409	98.97 109	92.98 425	98.85 224	89.12 351	99.87 120	95.99 285	99.68 188	99.39 196
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 238

HQP-NCC					98.67 310		96.29 340		96.05 317	95.55 394

ACMP_Plane					98.67 310		96.29 340		96.05 317	95.55 394

APD-MVS		98.10 203	97.67 233	99.42 63	99.11 219	98.93 78	97.76 225	99.28 201	94.97 351	98.72 216	98.77 239	97.04 180	99.85 141	93.79 354	99.54 237	99.49 148
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 374

HQP4-MVS										95.56 393			99.54 347			99.32 225

HQP3-MVS								99.04 257							99.26 287

HQP2-MVS												93.84 297

CNVR-MVS		98.17 200	97.87 221	99.07 126	98.67 310	98.24 125	97.01 298	98.93 273	97.25 256	97.62 312	98.34 303	97.27 168	99.57 335	96.42 262	99.33 274	99.39 196

NCCC		97.86 225	97.47 250	99.05 133	98.61 320	98.07 147	96.98 300	98.90 279	97.63 212	97.04 346	97.93 335	95.99 237	99.66 301	95.31 311	98.82 339	99.43 180

114514_t		96.50 313	95.77 321	98.69 189	99.48 130	97.43 201	97.84 213	99.55 88	81.42 434	96.51 374	98.58 274	95.53 253	99.67 290	93.41 364	99.58 224	98.98 289

CP-MVS		98.70 120	98.42 154	99.52 43	99.36 159	99.12 61	98.72 100	99.36 157	97.54 225	98.30 261	98.40 295	97.86 121	99.89 87	96.53 256	99.72 168	99.56 115

DSMNet-mixed		97.42 261	97.60 241	96.87 347	99.15 214	91.46 386	98.54 119	99.12 243	92.87 390	97.58 316	99.63 39	96.21 224	99.90 72	95.74 298	99.54 237	99.27 237

tpm2		93.09 383	92.58 381	94.62 398	97.56 393	86.53 422	97.66 238	95.79 399	86.15 427	94.07 417	98.23 312	75.95 416	99.53 349	90.91 405	96.86 411	97.81 395

NP-MVS					98.84 274	97.39 203					96.84 376

EG-PatchMatch MVS		98.99 77	99.01 75	98.94 149	99.50 115	97.47 197	98.04 181	99.59 68	98.15 180	99.40 101	99.36 97	98.58 61	99.76 245	98.78 93	99.68 188	99.59 97

tpm cat1		93.29 380	93.13 377	93.75 408	97.39 406	84.74 428	97.39 270	97.65 356	83.39 432	94.16 414	98.41 294	82.86 396	99.39 381	91.56 394	95.35 425	97.14 414

SteuartSystems-ACMMP		98.79 105	98.54 134	99.54 30	99.73 37	99.16 47	98.23 153	99.31 182	97.92 193	98.90 188	98.90 211	98.00 110	99.88 102	96.15 279	99.72 168	99.58 104
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 369	93.78 367	94.51 399	97.53 397	85.83 425	97.98 194	95.96 395	89.29 420	94.99 405	98.63 266	78.63 412	99.62 315	94.54 328	96.50 413	98.09 380

CR-MVSNet		96.28 320	95.95 319	97.28 327	97.71 385	94.22 325	98.11 169	98.92 276	92.31 396	96.91 353	99.37 93	85.44 377	99.81 202	97.39 180	97.36 401	97.81 395

JIA-IIPM		95.52 343	95.03 349	97.00 339	96.85 418	94.03 335	96.93 304	95.82 398	99.20 74	94.63 410	99.71 22	83.09 394	99.60 323	94.42 334	94.64 427	97.36 412

Patchmtry		97.35 266	96.97 276	98.50 226	97.31 408	96.47 252	98.18 158	98.92 276	98.95 113	98.78 207	99.37 93	85.44 377	99.85 141	95.96 287	99.83 106	99.17 263

PatchT		96.65 307	96.35 311	97.54 312	97.40 405	95.32 294	97.98 194	96.64 383	99.33 59	96.89 357	99.42 85	84.32 385	99.81 202	97.69 166	97.49 392	97.48 408

tpmrst		95.07 351	95.46 334	93.91 406	97.11 412	84.36 432	97.62 245	96.96 375	94.98 350	96.35 379	98.80 233	85.46 376	99.59 327	95.60 304	96.23 417	97.79 398

BH-w/o		95.13 350	94.89 354	95.86 377	98.20 360	91.31 390	95.65 374	97.37 360	93.64 378	96.52 373	95.70 400	93.04 312	99.02 410	88.10 417	95.82 422	97.24 413

tpm		94.67 357	94.34 361	95.66 383	97.68 390	88.42 413	97.88 206	94.90 407	94.46 362	96.03 387	98.56 276	78.66 411	99.79 222	95.88 289	95.01 426	98.78 326

DELS-MVS		98.27 187	98.20 183	98.48 227	98.86 270	96.70 244	95.60 376	99.20 221	97.73 206	98.45 251	98.71 247	97.50 154	99.82 187	98.21 128	99.59 219	98.93 300
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		96.83 300	96.75 293	97.08 336	98.74 290	93.33 357	96.71 316	98.26 336	96.72 292	98.44 252	97.37 366	95.20 262	99.47 368	91.89 386	97.43 396	98.44 359

RPMNet		97.02 291	96.93 278	97.30 326	97.71 385	94.22 325	98.11 169	99.30 190	99.37 54	96.91 353	99.34 102	86.72 364	99.87 120	97.53 174	97.36 401	97.81 395

MVSTER		96.86 299	96.55 306	97.79 284	97.91 375	94.21 327	97.56 254	98.87 285	97.49 230	99.06 154	99.05 172	80.72 401	99.80 209	98.44 117	99.82 110	99.37 205

CPTT-MVS		97.84 231	97.36 255	99.27 94	99.31 169	98.46 110	98.29 148	99.27 204	94.90 353	97.83 300	98.37 299	94.90 269	99.84 159	93.85 353	99.54 237	99.51 141

GBi-Net		98.65 133	98.47 146	99.17 108	98.90 262	98.24 125	99.20 48	99.44 129	98.59 138	98.95 176	99.55 57	94.14 291	99.86 128	97.77 159	99.69 183	99.41 186

PVSNet_Blended_VisFu		98.17 200	98.15 191	98.22 256	99.73 37	95.15 300	97.36 274	99.68 53	94.45 364	98.99 167	99.27 116	96.87 190	99.94 39	97.13 196	99.91 72	99.57 109

PVSNet_BlendedMVS		97.55 250	97.53 244	97.60 304	98.92 258	93.77 348	96.64 319	99.43 135	94.49 360	97.62 312	99.18 140	96.82 194	99.67 290	94.73 323	99.93 51	99.36 212

UnsupCasMVSNet_eth		97.89 220	97.60 241	98.75 181	99.31 169	97.17 219	97.62 245	99.35 163	98.72 128	98.76 212	98.68 254	92.57 321	99.74 257	97.76 163	95.60 423	99.34 218

UnsupCasMVSNet_bld		97.30 270	96.92 280	98.45 230	99.28 177	96.78 241	96.20 345	99.27 204	95.42 339	98.28 265	98.30 307	93.16 307	99.71 270	94.99 316	97.37 399	98.87 310

PVSNet_Blended		96.88 298	96.68 297	97.47 319	98.92 258	93.77 348	94.71 401	99.43 135	90.98 410	97.62 312	97.36 367	96.82 194	99.67 290	94.73 323	99.56 231	98.98 289

FMVSNet5		96.01 327	95.20 346	98.41 235	97.53 397	96.10 261	98.74 95	99.50 101	97.22 265	98.03 287	99.04 174	69.80 424	99.88 102	97.27 185	99.71 173	99.25 241

test1		98.65 133	98.47 146	99.17 108	98.90 262	98.24 125	99.20 48	99.44 129	98.59 138	98.95 176	99.55 57	94.14 291	99.86 128	97.77 159	99.69 183	99.41 186

new_pmnet		96.99 295	96.76 292	97.67 297	98.72 293	94.89 307	95.95 361	98.20 339	92.62 393	98.55 241	98.54 277	94.88 272	99.52 353	93.96 348	99.44 261	98.59 348

FMVSNet3		97.50 251	97.24 262	98.29 250	98.08 368	95.83 275	97.86 210	98.91 278	97.89 196	98.95 176	98.95 203	87.06 362	99.81 202	97.77 159	99.69 183	99.23 246

dp		93.47 377	93.59 370	93.13 416	96.64 422	81.62 441	97.66 238	96.42 387	92.80 391	96.11 383	98.64 264	78.55 414	99.59 327	93.31 365	92.18 435	98.16 376

FMVSNet2		98.49 160	98.40 156	98.75 181	98.90 262	97.14 222	98.61 111	99.13 242	98.59 138	99.19 140	99.28 114	94.14 291	99.82 187	97.97 146	99.80 125	99.29 234

FMVSNet1		99.17 50	99.17 56	99.17 108	99.55 98	98.24 125	99.20 48	99.44 129	99.21 72	99.43 92	99.55 57	97.82 125	99.86 128	98.42 119	99.89 84	99.41 186

N_pmnet		97.63 244	97.17 265	98.99 141	99.27 179	97.86 168	95.98 356	93.41 420	95.25 344	99.47 86	98.90 211	95.63 250	99.85 141	96.91 212	99.73 160	99.27 237

cascas		94.79 356	94.33 362	96.15 375	96.02 433	92.36 376	92.34 430	99.26 209	85.34 429	95.08 404	94.96 416	92.96 313	98.53 424	94.41 337	98.59 356	97.56 407

BH-RMVSNet		96.83 300	96.58 305	97.58 306	98.47 338	94.05 332	96.67 318	97.36 361	96.70 294	97.87 296	97.98 330	95.14 264	99.44 374	90.47 409	98.58 357	99.25 241

UGNet		98.53 154	98.45 149	98.79 171	97.94 373	96.96 229	99.08 61	98.54 323	99.10 92	96.82 361	99.47 76	96.55 210	99.84 159	98.56 113	99.94 46	99.55 122
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		96.67 306	96.27 316	97.87 279	98.81 282	94.61 318	96.77 312	97.92 349	94.94 352	97.12 341	97.74 344	91.11 336	99.82 187	93.89 350	98.15 373	99.18 259

XXY-MVS		99.14 55	99.15 63	99.10 120	99.76 30	97.74 182	98.85 90	99.62 63	98.48 149	99.37 106	99.49 73	98.75 43	99.86 128	98.20 129	99.80 125	99.71 57

EC-MVSNet		99.09 65	99.05 72	99.20 105	99.28 177	98.93 78	99.24 44	99.84 22	99.08 97	98.12 278	98.37 299	98.72 46	99.90 72	99.05 75	99.77 141	98.77 327

sss		97.21 278	96.93 278	98.06 269	98.83 276	95.22 298	96.75 314	98.48 327	94.49 360	97.27 338	97.90 336	92.77 317	99.80 209	96.57 247	99.32 275	99.16 266

Test_1112_low_res		96.99 295	96.55 306	98.31 248	99.35 164	95.47 288	95.84 369	99.53 95	91.51 404	96.80 362	98.48 289	91.36 333	99.83 177	96.58 245	99.53 241	99.62 80

1112_ss		97.29 272	96.86 284	98.58 208	99.34 166	96.32 257	96.75 314	99.58 70	93.14 385	96.89 357	97.48 359	92.11 326	99.86 128	96.91 212	99.54 237	99.57 109

ab-mvs-re		8.12 412	10.83 415	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	97.48 359	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

ab-mvs		98.41 167	98.36 163	98.59 207	99.19 200	97.23 211	99.32 26	98.81 299	97.66 210	98.62 228	99.40 92	96.82 194	99.80 209	95.88 289	99.51 246	98.75 330

TR-MVS		95.55 342	95.12 348	96.86 350	97.54 395	93.94 339	96.49 327	96.53 386	94.36 367	97.03 348	96.61 381	94.26 290	99.16 406	86.91 422	96.31 416	97.47 409

MDTV_nov1_ep13_2view						74.92 444	97.69 233		90.06 417	97.75 306		85.78 373		93.52 360		98.69 337

MDTV_nov1_ep13			95.22 345		97.06 415	83.20 435	97.74 228	96.16 390	94.37 366	96.99 349	98.83 227	83.95 389	99.53 349	93.90 349	97.95 384

MIMVSNet1		99.38 28	99.32 37	99.55 27	99.86 14	99.19 41	99.41 17	99.59 68	99.59 31	99.71 46	99.57 49	97.12 176	99.90 72	99.21 63	99.87 89	99.54 126

MIMVSNet		96.62 309	96.25 317	97.71 296	99.04 237	94.66 316	99.16 54	96.92 378	97.23 262	97.87 296	99.10 160	86.11 371	99.65 306	91.65 391	99.21 296	98.82 314

IterMVS-LS		98.55 150	98.70 111	98.09 264	99.48 130	94.73 313	97.22 288	99.39 147	98.97 109	99.38 104	99.31 109	96.00 233	99.93 46	98.58 108	99.97 20	99.60 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		97.69 239	97.35 256	98.69 189	98.73 291	97.02 226	96.92 306	98.75 309	95.89 326	98.59 234	98.67 256	92.08 327	99.74 257	96.72 234	99.81 114	99.32 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 141

IterMVS		97.73 236	98.11 195	96.57 357	99.24 186	90.28 405	95.52 380	99.21 219	98.86 121	99.33 114	99.33 104	93.11 308	99.94 39	98.49 115	99.94 46	99.48 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 268	96.92 280	98.57 211	99.09 224	97.99 154	96.79 310	99.35 163	93.18 384	97.71 307	98.07 325	95.00 268	99.31 392	93.97 347	99.13 308	98.42 363

MVS_111021_LR		98.30 183	98.12 194	98.83 163	99.16 210	98.03 152	96.09 353	99.30 190	97.58 219	98.10 280	98.24 310	98.25 85	99.34 388	96.69 237	99.65 200	99.12 269

DP-MVS		98.93 86	98.81 96	99.28 91	99.21 193	98.45 111	98.46 134	99.33 175	99.63 25	99.48 82	99.15 150	97.23 171	99.75 252	97.17 190	99.66 199	99.63 79

ACMMP++															99.68 188

HQP-MVS		97.00 294	96.49 309	98.55 216	98.67 310	96.79 238	96.29 340	99.04 257	96.05 317	95.55 394	96.84 376	93.84 297	99.54 347	92.82 374	99.26 287	99.32 225

QAPM		97.31 269	96.81 290	98.82 164	98.80 285	97.49 196	99.06 65	99.19 225	90.22 414	97.69 309	99.16 146	96.91 188	99.90 72	90.89 406	99.41 263	99.07 273

Vis-MVSNet		99.34 30	99.36 31	99.27 94	99.73 37	98.26 123	99.17 53	99.78 35	99.11 85	99.27 126	99.48 74	98.82 36	99.95 24	98.94 83	99.93 51	99.59 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		94.32 361	95.62 327	90.42 419	98.46 340	75.36 443	96.29 340	89.13 434	95.25 344	95.38 400	99.75 16	92.88 314	99.19 404	94.07 346	99.39 265	96.72 420

IS-MVSNet		98.19 197	97.90 219	99.08 124	99.57 86	97.97 158	99.31 30	98.32 334	99.01 105	98.98 168	99.03 176	91.59 331	99.79 222	95.49 308	99.80 125	99.48 158

HyFIR lowres test		97.19 280	96.60 304	98.96 146	99.62 79	97.28 208	95.17 390	99.50 101	94.21 369	99.01 165	98.32 306	86.61 365	99.99 2	97.10 198	99.84 99	99.60 90

EPMVS		93.72 374	93.27 373	95.09 395	96.04 432	87.76 417	98.13 165	85.01 440	94.69 357	96.92 351	98.64 264	78.47 415	99.31 392	95.04 315	96.46 414	98.20 374

PAPM_NR		96.82 302	96.32 313	98.30 249	99.07 228	96.69 245	97.48 264	98.76 306	95.81 328	96.61 369	96.47 385	94.12 294	99.17 405	90.82 407	97.78 386	99.06 274

TAMVS		98.24 192	98.05 202	98.80 168	99.07 228	97.18 218	97.88 206	98.81 299	96.66 295	99.17 145	99.21 133	94.81 275	99.77 239	96.96 210	99.88 86	99.44 176

PAPR		95.29 346	94.47 357	97.75 290	97.50 403	95.14 301	94.89 398	98.71 314	91.39 406	95.35 401	95.48 406	94.57 281	99.14 408	84.95 425	97.37 399	98.97 292

RPSCF		98.62 140	98.36 163	99.42 63	99.65 67	99.42 11	98.55 117	99.57 77	97.72 207	98.90 188	99.26 121	96.12 228	99.52 353	95.72 299	99.71 173	99.32 225

Vis-MVSNet (Re-imp)		97.46 256	97.16 266	98.34 245	99.55 98	96.10 261	98.94 80	98.44 328	98.32 158	98.16 273	98.62 268	88.76 352	99.73 262	93.88 351	99.79 130	99.18 259

test_0402		98.76 111	98.71 108	98.93 151	99.56 94	98.14 136	98.45 136	99.34 169	99.28 66	98.95 176	98.91 208	98.34 79	99.79 222	95.63 303	99.91 72	98.86 311

MVS_111021_HR		98.25 191	98.08 199	98.75 181	99.09 224	97.46 198	95.97 357	99.27 204	97.60 218	97.99 289	98.25 309	98.15 100	99.38 383	96.87 220	99.57 228	99.42 183

CSCG		98.68 128	98.50 139	99.20 105	99.45 139	98.63 94	98.56 116	99.57 77	97.87 197	98.85 198	98.04 327	97.66 135	99.84 159	96.72 234	99.81 114	99.13 268

PatchMatch-RL		97.24 276	96.78 291	98.61 204	99.03 240	97.83 171	96.36 335	99.06 251	93.49 382	97.36 336	97.78 341	95.75 247	99.49 362	93.44 363	98.77 340	98.52 351

API-MVS		97.04 290	96.91 282	97.42 322	97.88 376	98.23 129	98.18 158	98.50 326	97.57 220	97.39 334	96.75 378	96.77 198	99.15 407	90.16 410	99.02 321	94.88 432

Test By Simon												96.52 211

TDRefinement		99.42 24	99.38 28	99.55 27	99.76 30	99.33 20	99.68 6	99.71 44	99.38 53	99.53 73	99.61 43	98.64 52	99.80 209	98.24 126	99.84 99	99.52 138

USDC		97.41 262	97.40 251	97.44 321	98.94 252	93.67 351	95.17 390	99.53 95	94.03 374	98.97 172	99.10 160	95.29 260	99.34 388	95.84 295	99.73 160	99.30 232

EPP-MVSNet		98.30 183	98.04 203	99.07 126	99.56 94	97.83 171	99.29 36	98.07 345	99.03 103	98.59 234	99.13 155	92.16 325	99.90 72	96.87 220	99.68 188	99.49 148

PMMVS		96.51 311	95.98 318	98.09 264	97.53 397	95.84 274	94.92 397	98.84 294	91.58 402	96.05 386	95.58 401	95.68 249	99.66 301	95.59 305	98.09 376	98.76 329

PAPM		91.88 400	90.34 403	96.51 358	98.06 369	92.56 370	92.44 429	97.17 368	86.35 426	90.38 433	96.01 392	86.61 365	99.21 403	70.65 439	95.43 424	97.75 399

ACMMP		98.75 112	98.50 139	99.52 43	99.56 94	99.16 47	98.87 87	99.37 153	97.16 268	98.82 204	99.01 186	97.71 132	99.87 120	96.29 271	99.69 183	99.54 126
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		97.17 282	96.71 295	98.55 216	98.56 330	98.05 151	96.33 337	98.93 273	96.91 282	97.06 345	97.39 364	94.38 286	99.45 372	91.66 390	99.18 302	98.14 377

PatchmatchNet		95.58 341	95.67 326	95.30 392	97.34 407	87.32 420	97.65 240	96.65 382	95.30 343	97.07 344	98.69 252	84.77 380	99.75 252	94.97 318	98.64 352	98.83 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 186	97.95 212	99.34 78	98.44 343	99.16 47	98.12 168	99.38 149	96.01 321	98.06 283	98.43 293	97.80 127	99.67 290	95.69 301	99.58 224	99.20 251

F-COLMAP		97.30 270	96.68 297	99.14 114	99.19 200	98.39 113	97.27 283	99.30 190	92.93 388	96.62 368	98.00 328	95.73 248	99.68 287	92.62 380	98.46 360	99.35 216

ANet_high		99.57 10	99.67 6	99.28 91	99.89 6	98.09 141	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 19	99.94 39	99.31 54	100.00 1	99.82 33

wuyk23d		96.06 325	97.62 240	91.38 418	98.65 319	98.57 101	98.85 90	96.95 376	96.86 285	99.90 13	99.16 146	99.18 18	98.40 425	89.23 414	99.77 141	77.18 438

OMC-MVS		97.88 222	97.49 247	99.04 135	98.89 267	98.63 94	96.94 302	99.25 210	95.02 349	98.53 244	98.51 282	97.27 168	99.47 368	93.50 362	99.51 246	99.01 283

MG-MVS		96.77 303	96.61 302	97.26 329	98.31 353	93.06 360	95.93 362	98.12 344	96.45 304	97.92 291	98.73 244	93.77 301	99.39 381	91.19 401	99.04 317	99.33 223

AdaColmap		97.14 284	96.71 295	98.46 229	98.34 351	97.80 178	96.95 301	98.93 273	95.58 334	96.92 351	97.66 348	95.87 244	99.53 349	90.97 403	99.14 306	98.04 382

uanet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

ITE_SJBPF				98.87 159	99.22 191	98.48 109		99.35 163	97.50 228	98.28 265	98.60 272	97.64 139	99.35 387	93.86 352	99.27 284	98.79 325

DeepMVS_CX				93.44 412	98.24 357	94.21 327		94.34 412	64.28 438	91.34 432	94.87 419	89.45 350	92.77 439	77.54 436	93.14 432	93.35 434

TinyColmap		97.89 220	97.98 209	97.60 304	98.86 270	94.35 324	96.21 344	99.44 129	97.45 238	99.06 154	98.88 218	97.99 113	99.28 398	94.38 338	99.58 224	99.18 259

MAR-MVS		96.47 315	95.70 324	98.79 171	97.92 374	99.12 61	98.28 149	98.60 321	92.16 398	95.54 397	96.17 390	94.77 278	99.52 353	89.62 412	98.23 366	97.72 401
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		97.90 218	97.69 232	98.52 221	99.17 208	97.66 187	97.19 292	99.47 118	96.31 309	97.85 299	98.20 314	96.71 204	99.52 353	94.62 326	99.72 168	98.38 366

MSDG		97.71 238	97.52 245	98.28 251	98.91 261	96.82 236	94.42 411	99.37 153	97.65 211	98.37 260	98.29 308	97.40 161	99.33 390	94.09 345	99.22 293	98.68 340

LS3D		98.63 137	98.38 161	99.36 69	97.25 409	99.38 12	99.12 60	99.32 177	99.21 72	98.44 252	98.88 218	97.31 164	99.80 209	96.58 245	99.34 273	98.92 301

CLD-MVS		97.49 254	97.16 266	98.48 227	99.07 228	97.03 225	94.71 401	99.21 219	94.46 362	98.06 283	97.16 371	97.57 145	99.48 365	94.46 331	99.78 135	98.95 295
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 378	92.23 385	97.08 336	99.25 185	97.86 168	95.61 375	97.16 369	92.90 389	93.76 422	98.65 261	75.94 417	95.66 436	79.30 435	97.49 392	97.73 400

Gipuma		99.03 73	99.16 58	98.64 195	99.94 2	98.51 107	99.32 26	99.75 40	99.58 33	98.60 232	99.62 40	98.22 90	99.51 358	97.70 164	99.73 160	97.89 390
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark