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 29	99.63 21	99.78 27	99.67 27	99.48 9	99.81 185	99.30 43	99.97 19	99.77 37
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 91	98.73 90	99.05 129	98.76 269	97.81 173	99.25 40	99.30 176	98.57 127	98.55 225	99.33 95	97.95 104	99.90 66	97.16 173	99.67 177	99.44 160

3Dnovator+	97.89 3	98.69 111	98.51 124	99.24 97	98.81 264	98.40 109	99.02 66	99.19 211	98.99 97	98.07 264	99.28 103	97.11 165	99.84 144	96.84 205	99.32 257	99.47 150

DeepC-MVS	97.60 4	98.97 70	98.93 70	99.10 116	99.35 151	97.98 153	98.01 181	99.46 109	97.56 203	99.54 57	99.50 62	98.97 23	99.84 144	98.06 121	99.92 54	99.49 133
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	98.32 167	98.01 192	99.23 99	98.39 331	98.97 70	95.03 373	99.18 215	96.88 263	99.33 100	98.78 222	98.16 88	99.28 379	96.74 213	99.62 191	99.44 160

DeepC-MVS_fast	96.85 6	98.30 170	98.15 178	98.75 175	98.61 302	97.23 207	97.76 216	99.09 234	97.31 231	98.75 197	98.66 243	97.56 133	99.64 292	96.10 264	99.55 217	99.39 180
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	97.09 269	96.68 280	98.32 232	98.32 334	97.16 215	98.86 86	99.37 141	89.48 397	96.29 361	99.15 137	96.56 196	99.90 66	92.90 352	99.20 279	97.89 369

ACMH	96.65 7	99.25 36	99.24 42	99.26 92	99.72 42	98.38 111	99.07 61	99.55 76	98.30 142	99.65 46	99.45 74	99.22 15	99.76 228	98.44 99	99.77 124	99.64 65
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 61	99.00 64	99.33 80	99.71 45	98.83 79	98.60 109	99.58 58	99.11 76	99.53 61	99.18 127	98.81 32	99.67 273	96.71 218	99.77 124	99.50 129

COLMAP_ROB	96.50 10	98.99 66	98.85 80	99.41 62	99.58 76	99.10 64	98.74 92	99.56 72	99.09 86	99.33 100	99.19 123	98.40 63	99.72 252	95.98 267	99.76 136	99.42 167
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	96.63 291	95.95 302	98.65 183	98.93 237	98.09 137	96.93 286	99.28 187	83.58 410	98.13 259	97.78 323	96.13 214	99.40 360	93.52 341	99.29 264	98.45 337
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 77	98.73 90	99.48 53	99.55 93	99.14 56	98.07 170	99.37 141	97.62 195	99.04 146	98.96 184	98.84 30	99.79 205	97.43 160	99.65 183	99.49 133
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 314	95.35 324	97.55 294	97.95 354	94.79 293	98.81 91	96.94 361	92.28 376	95.17 383	98.57 258	89.90 331	99.75 235	91.20 380	97.33 382	98.10 360

OpenMVS_ROB	95.38 14	95.84 317	95.18 329	97.81 267	98.41 330	97.15 216	97.37 256	98.62 306	83.86 409	98.65 208	98.37 281	94.29 276	99.68 270	88.41 395	98.62 336	96.60 400

ACMP	95.32 15	98.41 154	98.09 183	99.36 66	99.51 105	98.79 82	97.68 224	99.38 137	95.76 309	98.81 190	98.82 215	98.36 65	99.82 171	94.75 303	99.77 124	99.48 143
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 294	95.73 306	98.85 156	98.75 271	97.91 160	96.42 312	99.06 237	90.94 390	95.59 372	97.38 347	94.41 271	99.59 309	90.93 384	98.04 363	99.05 257
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 321	95.70 307	95.57 368	98.83 259	88.57 395	92.50 407	97.72 337	92.69 371	96.49 358	96.44 367	93.72 289	99.43 356	93.61 338	99.28 265	98.71 314

PCF-MVS	92.86 18	94.36 342	93.00 359	98.42 222	98.70 282	97.56 189	93.16 405	99.11 231	79.59 414	97.55 301	97.43 344	92.19 310	99.73 245	79.85 414	99.45 240	97.97 368
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 376	90.90 380	96.27 349	97.22 390	91.24 377	94.36 392	93.33 401	92.37 374	92.24 409	94.58 401	66.20 415	99.89 77	93.16 349	94.63 407	97.66 382
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	97.86 210	97.94 200	97.65 282	99.71 45	97.94 159	98.52 118	98.68 301	98.99 97	97.52 304	99.35 89	97.41 147	98.18 408	91.59 373	99.67 177	96.82 397
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 381	90.30 384	93.70 390	97.72 363	84.34 415	90.24 411	97.42 344	90.20 394	93.79 401	93.09 409	90.90 324	98.89 399	86.57 403	72.76 418	97.87 371

MVE	83.40 22	92.50 371	91.92 373	94.25 383	98.83 259	91.64 367	92.71 406	83.52 420	95.92 305	86.46 418	95.46 387	95.20 249	95.40 416	80.51 413	98.64 333	95.73 409
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 302	95.44 319	98.84 157	96.25 410	98.69 90	97.02 279	99.12 229	88.90 400	97.83 282	98.86 206	89.51 333	98.90 398	91.92 366	99.51 228	98.92 283
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mmtdpeth		99.30 29	99.42 20	98.92 149	99.58 76	96.89 229	99.48 10	99.92 7	99.92 2	98.26 250	99.80 9	98.33 70	99.91 60	99.56 29	99.95 30	99.97 4

reproduce_model		99.15 48	98.97 68	99.67 4	99.33 154	99.44 10	98.15 158	99.47 106	99.12 75	99.52 63	99.32 99	98.31 71	99.90 66	97.78 140	99.73 143	99.66 59

reproduce-ours		99.09 57	98.90 73	99.67 4	99.27 164	99.49 6	98.00 182	99.42 126	99.05 91	99.48 70	99.27 105	98.29 73	99.89 77	97.61 150	99.71 156	99.62 69

our_new_method		99.09 57	98.90 73	99.67 4	99.27 164	99.49 6	98.00 182	99.42 126	99.05 91	99.48 70	99.27 105	98.29 73	99.89 77	97.61 150	99.71 156	99.62 69

mmdepth		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

monomultidepth		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

mvs5depth		99.30 29	99.59 9	98.44 220	99.65 63	95.35 277	99.82 3	99.94 2	99.83 4	99.42 83	99.94 2	98.13 91	99.96 12	99.63 24	99.96 23	100.00 1

MVStest1		95.86 315	95.60 311	96.63 339	95.87 414	91.70 366	97.93 191	98.94 256	98.03 165	99.56 53	99.66 29	71.83 404	98.26 407	99.35 40	99.24 271	99.91 12

ttmdpeth		97.91 202	98.02 191	97.58 289	98.69 287	94.10 315	98.13 160	98.90 265	97.95 171	97.32 319	99.58 43	95.95 228	98.75 401	96.41 244	99.22 275	99.87 18

WBMVS		95.18 332	94.78 337	96.37 345	97.68 371	89.74 392	95.80 349	98.73 298	97.54 206	98.30 244	98.44 274	70.06 405	99.82 171	96.62 223	99.87 76	99.54 112

dongtai		76.24 385	75.95 388	77.12 401	92.39 419	67.91 425	90.16 412	59.44 426	82.04 412	89.42 414	94.67 400	49.68 424	81.74 419	48.06 419	77.66 417	81.72 415

kuosan		69.30 386	68.95 389	70.34 402	87.68 423	65.00 426	91.11 410	59.90 425	69.02 415	74.46 420	88.89 417	48.58 425	68.03 421	28.61 420	72.33 419	77.99 416

MVSMamba_PlusPlus		98.83 87	98.98 67	98.36 229	99.32 155	96.58 242	98.90 80	99.41 130	99.75 8	98.72 200	99.50 62	96.17 212	99.94 36	99.27 45	99.78 119	98.57 330

MGCFI-Net		98.34 163	98.28 160	98.51 210	98.47 320	97.59 188	98.96 74	99.48 98	99.18 71	97.40 314	95.50 384	98.66 43	99.50 340	98.18 112	98.71 326	98.44 340

testing91		93.32 360	92.27 364	96.47 343	97.54 376	91.25 376	96.17 329	96.76 365	97.18 247	93.65 403	93.50 407	65.11 417	99.63 295	93.04 350	97.45 373	98.53 331

testing11		93.08 365	92.02 369	96.26 350	97.56 374	90.83 384	96.32 318	95.70 382	96.47 283	92.66 407	93.73 404	64.36 418	99.59 309	93.77 336	97.57 369	98.37 349

testing99		93.04 366	91.98 372	96.23 352	97.53 378	90.70 386	96.35 316	95.94 378	96.87 264	93.41 404	93.43 408	63.84 419	99.59 309	93.24 348	97.19 383	98.40 345

UBG		93.25 362	92.32 363	96.04 359	97.72 363	90.16 389	95.92 343	95.91 379	96.03 300	93.95 400	93.04 410	69.60 407	99.52 334	90.72 388	97.98 364	98.45 337

UWE-MVS		92.38 373	91.76 376	94.21 384	97.16 391	84.65 411	95.42 363	88.45 415	95.96 303	96.17 362	95.84 379	66.36 413	99.71 253	91.87 368	98.64 333	98.28 352

ETVMVS		92.60 370	91.08 379	97.18 314	97.70 368	93.65 337	96.54 304	95.70 382	96.51 279	94.68 389	92.39 413	61.80 420	99.50 340	86.97 400	97.41 376	98.40 345

sasdasda		98.34 163	98.26 164	98.58 196	98.46 322	97.82 170	98.96 74	99.46 109	99.19 69	97.46 309	95.46 387	98.59 50	99.46 351	98.08 119	98.71 326	98.46 334

testing222		91.96 378	90.37 382	96.72 338	97.47 384	92.59 352	96.11 331	94.76 388	96.83 266	92.90 406	92.87 411	57.92 421	99.55 323	86.93 401	97.52 370	98.00 367

WB-MVSnew		95.73 320	95.57 314	96.23 352	96.70 401	90.70 386	96.07 333	93.86 398	95.60 313	97.04 328	95.45 390	96.00 220	99.55 323	91.04 382	98.31 345	98.43 342

fmvsm_l_conf0.5_n_a		99.19 43	99.27 38	98.94 144	99.65 63	97.05 218	97.80 209	99.76 31	98.70 117	99.78 27	99.11 143	98.79 34	99.95 24	99.85 5	99.96 23	99.83 24

fmvsm_l_conf0.5_n		99.21 41	99.28 37	99.02 134	99.64 69	97.28 204	97.82 206	99.76 31	98.73 114	99.82 21	99.09 149	98.81 32	99.95 24	99.86 4	99.96 23	99.83 24

fmvsm_s_conf0.1_n_a		99.17 44	99.30 35	98.80 163	99.75 33	96.59 240	97.97 190	99.86 15	98.22 150	99.88 17	99.71 19	98.59 50	99.84 144	99.73 18	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 47	99.33 29	98.64 184	99.71 45	96.10 251	97.87 202	99.85 17	98.56 130	99.90 12	99.68 22	98.69 41	99.85 126	99.72 20	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 56	99.20 44	98.78 169	99.55 93	96.59 240	97.79 210	99.82 24	98.21 151	99.81 24	99.53 58	98.46 60	99.84 144	99.70 21	99.97 19	99.90 13

fmvsm_s_conf0.5_n		99.09 57	99.26 40	98.61 192	99.55 93	96.09 254	97.74 218	99.81 25	98.55 131	99.85 19	99.55 52	98.60 49	99.84 144	99.69 23	99.98 12	99.89 14

MM		98.22 180	97.99 194	98.91 150	98.66 297	96.97 222	97.89 198	94.44 391	99.54 30	98.95 161	99.14 140	93.50 290	99.92 51	99.80 11	99.96 23	99.85 22

WAC-MVS						90.90 382								91.37 377

Syy-MVS		96.04 309	95.56 315	97.49 300	97.10 393	94.48 304	96.18 327	96.58 368	95.65 311	94.77 387	92.29 414	91.27 320	99.36 365	98.17 114	98.05 361	98.63 324

test_fmvsmconf0.1_n		99.49 12	99.54 11	99.34 75	99.78 23	98.11 134	97.77 213	99.90 11	99.33 51	99.97 3	99.66 29	99.71 3	99.96 12	99.79 12	99.99 5	99.96 7

test_fmvsmconf0.01_n		99.57 7	99.63 7	99.36 66	99.87 12	98.13 133	98.08 168	99.95 1	99.45 37	99.98 2	99.75 13	99.80 1	99.97 5	99.82 7	99.99 5	99.99 2

myMVS_eth3d		91.92 379	90.45 381	96.30 347	97.10 393	90.90 382	96.18 327	96.58 368	95.65 311	94.77 387	92.29 414	53.88 422	99.36 365	89.59 393	98.05 361	98.63 324

testing3		93.51 357	92.09 367	97.75 274	98.60 304	94.40 306	97.32 260	95.26 386	97.56 203	96.79 345	95.50 384	53.57 423	99.77 222	95.26 293	98.97 310	99.08 253

SSC-MVS		98.71 104	98.74 88	98.62 189	99.72 42	96.08 256	98.74 92	98.64 305	99.74 10	99.67 42	99.24 114	94.57 268	99.95 24	99.11 55	99.24 271	99.82 27

test_fmvsmconf_n		99.44 15	99.48 15	99.31 85	99.64 69	98.10 136	97.68 224	99.84 20	99.29 56	99.92 8	99.57 45	99.60 5	99.96 12	99.74 17	99.98 12	99.89 14

WB-MVS		98.52 145	98.55 119	98.43 221	99.65 63	95.59 266	98.52 118	98.77 291	99.65 18	99.52 63	99.00 174	94.34 274	99.93 42	98.65 87	98.83 318	99.76 41

test_fmvsmvis_n_1920		99.26 35	99.49 13	98.54 207	99.66 62	96.97 222	98.00 182	99.85 17	99.24 60	99.92 8	99.50 62	99.39 11	99.95 24	99.89 3	99.98 12	98.71 314

dmvs_re		95.98 312	95.39 322	97.74 276	98.86 253	97.45 195	98.37 140	95.69 384	97.95 171	96.56 352	95.95 374	90.70 325	97.68 411	88.32 396	96.13 398	98.11 359

SDMVSNet		99.23 40	99.32 31	98.96 141	99.68 56	97.35 200	98.84 89	99.48 98	99.69 13	99.63 49	99.68 22	99.03 21	99.96 12	97.97 128	99.92 54	99.57 95

dmvs_testset		92.94 367	92.21 366	95.13 376	98.59 307	90.99 381	97.65 230	92.09 406	96.95 259	94.00 398	93.55 406	92.34 309	96.97 414	72.20 417	92.52 412	97.43 389

sd_testset		99.28 32	99.31 33	99.19 103	99.68 56	98.06 146	99.41 14	99.30 176	99.69 13	99.63 49	99.68 22	99.25 14	99.96 12	97.25 169	99.92 54	99.57 95

test_fmvsm_n_1920		99.33 27	99.45 19	98.99 137	99.57 81	97.73 180	97.93 191	99.83 22	99.22 61	99.93 6	99.30 101	99.42 10	99.96 12	99.85 5	99.99 5	99.29 217

test_cas_vis1_n_1920		98.33 166	98.68 101	97.27 311	99.69 54	92.29 360	98.03 176	99.85 17	97.62 195	99.96 4	99.62 36	93.98 283	99.74 240	99.52 33	99.86 80	99.79 32

test_vis1_n_1920		98.40 156	98.92 71	96.81 334	99.74 35	90.76 385	98.15 158	99.91 9	98.33 139	99.89 15	99.55 52	95.07 253	99.88 89	99.76 15	99.93 43	99.79 32

test_vis1_n		98.31 169	98.50 126	97.73 278	99.76 29	94.17 313	98.68 102	99.91 9	96.31 289	99.79 26	99.57 45	92.85 302	99.42 358	99.79 12	99.84 85	99.60 78

test_fmvs1_n		98.09 191	98.28 160	97.52 297	99.68 56	93.47 339	98.63 105	99.93 5	95.41 322	99.68 40	99.64 34	91.88 315	99.48 346	99.82 7	99.87 76	99.62 69

mvsany_test1		97.60 230	97.54 228	97.77 270	97.72 363	95.35 277	95.36 365	97.13 354	94.13 350	99.71 34	99.33 95	97.93 105	99.30 375	97.60 152	98.94 313	98.67 322

APD_test1		98.83 87	98.66 104	99.34 75	99.78 23	99.47 9	98.42 136	99.45 113	98.28 147	98.98 153	99.19 123	97.76 116	99.58 315	96.57 228	99.55 217	98.97 274

test_vis1_rt		97.75 220	97.72 216	97.83 265	98.81 264	96.35 246	97.30 262	99.69 39	94.61 337	97.87 278	98.05 308	96.26 210	98.32 406	98.74 80	98.18 350	98.82 296

test_vis3_rt		99.14 49	99.17 46	99.07 122	99.78 23	98.38 111	98.92 79	99.94 2	97.80 184	99.91 11	99.67 27	97.15 162	98.91 397	99.76 15	99.56 214	99.92 11

test_fmvs2		98.70 108	98.97 68	97.89 262	99.54 98	94.05 316	98.55 114	99.92 7	96.78 269	99.72 32	99.78 10	96.60 195	99.67 273	99.91 2	99.90 67	99.94 9

test_fmvs1		97.72 222	97.94 200	97.07 321	98.66 297	92.39 357	97.68 224	99.81 25	95.20 326	99.54 57	99.44 75	91.56 318	99.41 359	99.78 14	99.77 124	99.40 179

test_fmvs3		99.12 54	99.41 21	98.25 238	99.76 29	95.07 289	99.05 64	99.94 2	97.78 186	99.82 21	99.84 3	98.56 54	99.71 253	99.96 1	99.96 23	99.97 4

mvsany_test3		98.87 82	98.92 71	98.74 179	99.38 140	96.94 226	98.58 111	99.10 232	96.49 281	99.96 4	99.81 6	98.18 84	99.45 353	98.97 66	99.79 114	99.83 24

testf1		99.25 36	99.16 48	99.51 46	99.89 6	99.63 4	98.71 99	99.69 39	98.90 106	99.43 80	99.35 89	98.86 28	99.67 273	97.81 137	99.81 99	99.24 227

APD_test2		99.25 36	99.16 48	99.51 46	99.89 6	99.63 4	98.71 99	99.69 39	98.90 106	99.43 80	99.35 89	98.86 28	99.67 273	97.81 137	99.81 99	99.24 227

test_f		98.67 119	98.87 76	98.05 255	99.72 42	95.59 266	98.51 123	99.81 25	96.30 291	99.78 27	99.82 5	96.14 213	98.63 403	99.82 7	99.93 43	99.95 8

FE-MVS		95.66 322	94.95 334	97.77 270	98.53 316	95.28 280	99.40 16	96.09 375	93.11 365	97.96 272	99.26 109	79.10 393	99.77 222	92.40 364	98.71 326	98.27 353

FA-MVS(test-final)		96.99 278	96.82 271	97.50 299	98.70 282	94.78 294	99.34 20	96.99 357	95.07 327	98.48 232	99.33 95	88.41 344	99.65 289	96.13 263	98.92 315	98.07 362

balanced_conf03		98.63 125	98.72 92	98.38 226	98.66 297	96.68 239	98.90 80	99.42 126	98.99 97	98.97 157	99.19 123	95.81 233	99.85 126	98.77 78	99.77 124	98.60 326

MonoMVSNet		96.25 304	96.53 291	95.39 373	96.57 403	91.01 380	98.82 90	97.68 340	98.57 127	98.03 269	99.37 84	90.92 323	97.78 410	94.99 297	93.88 410	97.38 390

patch_mono-2		98.51 146	98.63 108	98.17 244	99.38 140	94.78 294	97.36 257	99.69 39	98.16 161	98.49 231	99.29 102	97.06 166	99.97 5	98.29 107	99.91 61	99.76 41

EGC-MVSNET		85.24 382	80.54 385	99.34 75	99.77 26	99.20 38	99.08 58	99.29 184	12.08 420	20.84 421	99.42 77	97.55 134	99.85 126	97.08 181	99.72 151	98.96 276

test2506		92.39 372	91.89 374	93.89 388	99.38 140	82.28 418	99.32 23	66.03 424	99.08 88	98.77 194	99.57 45	66.26 414	99.84 144	98.71 83	99.95 30	99.54 112

test1111		96.49 297	96.82 271	95.52 369	99.42 135	87.08 403	99.22 42	87.14 416	99.11 76	99.46 75	99.58 43	88.69 338	99.86 114	98.80 74	99.95 30	99.62 69

ECVR-MVS		96.42 299	96.61 285	95.85 361	99.38 140	88.18 399	99.22 42	86.00 418	99.08 88	99.36 95	99.57 45	88.47 343	99.82 171	98.52 96	99.95 30	99.54 112

test_blank		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

tt0805		98.69 111	98.62 110	98.90 153	99.75 33	99.30 21	99.15 53	96.97 358	98.86 109	98.87 181	97.62 334	98.63 46	98.96 394	99.41 38	98.29 346	98.45 337

DVP-MVS++		98.90 79	98.70 98	99.51 46	98.43 326	99.15 51	99.43 12	99.32 163	98.17 158	99.26 115	99.02 162	98.18 84	99.88 89	97.07 182	99.45 240	99.49 133

FOURS1					99.73 36	99.67 3	99.43 12	99.54 80	99.43 41	99.26 115

MSC_two_6792asdad				99.32 82	98.43 326	98.37 113		98.86 276					99.89 77	97.14 176	99.60 198	99.71 48

PC_three_1452									93.27 362	99.40 88	98.54 260	98.22 80	97.00 413	95.17 294	99.45 240	99.49 133

No_MVS				99.32 82	98.43 326	98.37 113		98.86 276					99.89 77	97.14 176	99.60 198	99.71 48

test_one_0601					99.39 139	99.20 38		99.31 168	98.49 132	98.66 207	99.02 162	97.64 126

eth-test2					0.00 428

eth-test					0.00 428

GeoE		99.05 62	98.99 66	99.25 95	99.44 129	98.35 117	98.73 96	99.56 72	98.42 135	98.91 171	98.81 217	98.94 25	99.91 60	98.35 103	99.73 143	99.49 133

test_method		79.78 383	79.50 386	80.62 399	80.21 424	45.76 427	70.82 415	98.41 317	31.08 419	80.89 419	97.71 327	84.85 364	97.37 412	91.51 375	80.03 416	98.75 311

Anonymous20240521		98.69 111	98.87 76	98.16 246	99.77 26	95.11 288	99.08 58	99.44 117	99.34 50	99.33 100	99.55 52	94.10 282	99.94 36	99.25 48	99.96 23	99.42 167

h-mvs33		97.77 219	97.33 243	99.10 116	99.21 178	97.84 166	98.35 142	98.57 308	99.11 76	98.58 220	99.02 162	88.65 341	99.96 12	98.11 116	96.34 394	99.49 133

hse-mvs2		97.46 240	97.07 255	98.64 184	98.73 273	97.33 201	97.45 252	97.64 343	99.11 76	98.58 220	97.98 312	88.65 341	99.79 205	98.11 116	97.39 377	98.81 300

CL-MVSNet_self_test		97.44 243	97.22 248	98.08 251	98.57 311	95.78 264	94.30 393	98.79 288	96.58 278	98.60 216	98.19 297	94.74 266	99.64 292	96.41 244	98.84 317	98.82 296

KD-MVS_2432*1600		92.87 368	91.99 370	95.51 370	91.37 420	89.27 393	94.07 395	98.14 327	95.42 319	97.25 321	96.44 367	67.86 409	99.24 381	91.28 378	96.08 399	98.02 364

KD-MVS_self_test		99.25 36	99.18 45	99.44 59	99.63 73	99.06 68	98.69 101	99.54 80	99.31 53	99.62 52	99.53 58	97.36 150	99.86 114	99.24 50	99.71 156	99.39 180

AUN-MVS		96.24 306	95.45 318	98.60 194	98.70 282	97.22 209	97.38 255	97.65 341	95.95 304	95.53 379	97.96 316	82.11 383	99.79 205	96.31 250	97.44 374	98.80 305

ZD-MVS					99.01 225	98.84 78		99.07 236	94.10 351	98.05 267	98.12 301	96.36 207	99.86 114	92.70 360	99.19 282

SR-MVS-dyc-post		98.81 91	98.55 119	99.57 20	99.20 182	99.38 12	98.48 129	99.30 176	98.64 118	98.95 161	98.96 184	97.49 144	99.86 114	96.56 232	99.39 247	99.45 156

RE-MVS-def			98.58 117		99.20 182	99.38 12	98.48 129	99.30 176	98.64 118	98.95 161	98.96 184	97.75 117		96.56 232	99.39 247	99.45 156

SED-MVS		98.91 77	98.72 92	99.49 51	99.49 115	99.17 43	98.10 166	99.31 168	98.03 165	99.66 43	99.02 162	98.36 65	99.88 89	96.91 194	99.62 191	99.41 170

IU-MVS					99.49 115	99.15 51		98.87 271	92.97 366	99.41 85				96.76 211	99.62 191	99.66 59

OPU-MVS				98.82 159	98.59 307	98.30 118	98.10 166				98.52 263	98.18 84	98.75 401	94.62 307	99.48 237	99.41 170

test_241102_TWO								99.30 176	98.03 165	99.26 115	99.02 162	97.51 140	99.88 89	96.91 194	99.60 198	99.66 59

test_241102_ONE					99.49 115	99.17 43		99.31 168	97.98 168	99.66 43	98.90 196	98.36 65	99.48 346

SF-MVS		98.53 142	98.27 163	99.32 82	99.31 156	98.75 83	98.19 153	99.41 130	96.77 270	98.83 185	98.90 196	97.80 114	99.82 171	95.68 283	99.52 226	99.38 187

cl22		95.79 318	95.39 322	96.98 324	96.77 400	92.79 349	94.40 391	98.53 310	94.59 338	97.89 276	98.17 298	82.82 380	99.24 381	96.37 246	99.03 300	98.92 283

miper_ehance_all_eth		97.06 271	97.03 257	97.16 318	97.83 359	93.06 343	94.66 383	99.09 234	95.99 302	98.69 202	98.45 273	92.73 305	99.61 304	96.79 207	99.03 300	98.82 296

miper_enhance_ethall		96.01 310	95.74 305	96.81 334	96.41 408	92.27 361	93.69 402	98.89 268	91.14 388	98.30 244	97.35 350	90.58 326	99.58 315	96.31 250	99.03 300	98.60 326

ZNCC-MVS		98.68 116	98.40 143	99.54 30	99.57 81	99.21 32	98.46 131	99.29 184	97.28 234	98.11 261	98.39 278	98.00 99	99.87 106	96.86 204	99.64 185	99.55 108

dcpmvs_2		98.78 95	99.11 54	97.78 269	99.56 89	93.67 335	99.06 62	99.86 15	99.50 32	99.66 43	99.26 109	97.21 160	99.99 2	98.00 126	99.91 61	99.68 55

cl____		97.02 274	96.83 270	97.58 289	97.82 360	94.04 318	94.66 383	99.16 222	97.04 254	98.63 210	98.71 232	88.68 340	99.69 261	97.00 186	99.81 99	99.00 269

DIV-MVS_self_test		97.02 274	96.84 269	97.58 289	97.82 360	94.03 319	94.66 383	99.16 222	97.04 254	98.63 210	98.71 232	88.69 338	99.69 261	97.00 186	99.81 99	99.01 265

eth_miper_zixun_eth		97.23 260	97.25 246	97.17 316	98.00 353	92.77 350	94.71 380	99.18 215	97.27 235	98.56 223	98.74 228	91.89 314	99.69 261	97.06 184	99.81 99	99.05 257

9.14			97.78 210		99.07 213		97.53 244	99.32 163	95.53 316	98.54 227	98.70 235	97.58 131	99.76 228	94.32 320	99.46 238

uanet_test		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

DCPMVS		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

save fliter					99.11 204	97.97 154	96.53 306	99.02 248	98.24 148

ET-MVSNet_ETH3D		94.30 345	93.21 355	97.58 289	98.14 346	94.47 305	94.78 379	93.24 402	94.72 335	89.56 413	95.87 377	78.57 396	99.81 185	96.91 194	97.11 386	98.46 334

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 58	99.90 3	99.86 18	99.78 10	99.58 6	99.95 24	99.00 64	99.95 30	99.78 35

EIA-MVS		98.00 197	97.74 213	98.80 163	98.72 275	98.09 137	98.05 173	99.60 55	97.39 223	96.63 349	95.55 382	97.68 120	99.80 192	96.73 215	99.27 266	98.52 332

miper_refine_blended		92.87 368	91.99 370	95.51 370	91.37 420	89.27 393	94.07 395	98.14 327	95.42 319	97.25 321	96.44 367	67.86 409	99.24 381	91.28 378	96.08 399	98.02 364

miper_lstm_enhance		97.18 264	97.16 251	97.25 313	98.16 344	92.85 348	95.15 371	99.31 168	97.25 237	98.74 199	98.78 222	90.07 329	99.78 216	97.19 171	99.80 109	99.11 252

ETV-MVS		98.03 194	97.86 207	98.56 203	98.69 287	98.07 143	97.51 247	99.50 89	98.10 163	97.50 306	95.51 383	98.41 62	99.88 89	96.27 253	99.24 271	97.71 381

CS-MVS		99.13 52	99.10 56	99.24 97	99.06 217	99.15 51	99.36 19	99.88 13	99.36 49	98.21 252	98.46 272	98.68 42	99.93 42	99.03 62	99.85 81	98.64 323

D2MVS		97.84 216	97.84 208	97.83 265	99.14 200	94.74 296	96.94 284	98.88 269	95.84 307	98.89 174	98.96 184	94.40 272	99.69 261	97.55 153	99.95 30	99.05 257

DVP-MVS		98.77 98	98.52 123	99.52 42	99.50 108	99.21 32	98.02 178	98.84 280	97.97 169	99.08 137	99.02 162	97.61 129	99.88 89	96.99 188	99.63 188	99.48 143
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									98.17 158	99.08 137	99.02 162	97.89 106	99.88 89	97.07 182	99.71 156	99.70 53

test_0728_SECOND				99.60 14	99.50 108	99.23 30	98.02 178	99.32 163					99.88 89	96.99 188	99.63 188	99.68 55

test0726					99.50 108	99.21 32	98.17 157	99.35 150	97.97 169	99.26 115	99.06 150	97.61 129

SR-MVS		98.71 104	98.43 139	99.57 20	99.18 192	99.35 16	98.36 141	99.29 184	98.29 145	98.88 177	98.85 209	97.53 137	99.87 106	96.14 261	99.31 259	99.48 143

DPM-MVS		96.32 301	95.59 313	98.51 210	98.76 269	97.21 210	94.54 389	98.26 321	91.94 378	96.37 359	97.25 351	93.06 297	99.43 356	91.42 376	98.74 322	98.89 288

GST-MVS		98.61 129	98.30 158	99.52 42	99.51 105	99.20 38	98.26 147	99.25 196	97.44 220	98.67 205	98.39 278	97.68 120	99.85 126	96.00 265	99.51 228	99.52 123

test_yl		96.69 287	96.29 297	97.90 260	98.28 336	95.24 281	97.29 263	97.36 346	98.21 151	98.17 253	97.86 319	86.27 352	99.55 323	94.87 301	98.32 343	98.89 288

thisisatest0530		95.27 330	94.45 340	97.74 276	99.19 185	94.37 307	97.86 203	90.20 412	97.17 248	98.22 251	97.65 331	73.53 403	99.90 66	96.90 199	99.35 253	98.95 277

Anonymous20240529		98.93 75	98.87 76	99.12 112	99.19 185	98.22 127	99.01 67	98.99 254	99.25 59	99.54 57	99.37 84	97.04 167	99.80 192	97.89 131	99.52 226	99.35 199

Anonymous202405211		97.90 203	97.50 231	99.08 120	98.90 245	98.25 121	98.53 117	96.16 373	98.87 108	99.11 132	98.86 206	90.40 328	99.78 216	97.36 163	99.31 259	99.19 239

DCV-MVSNet		96.69 287	96.29 297	97.90 260	98.28 336	95.24 281	97.29 263	97.36 346	98.21 151	98.17 253	97.86 319	86.27 352	99.55 323	94.87 301	98.32 343	98.89 288

tttt0517		95.64 323	94.98 332	97.64 284	99.36 147	93.81 330	98.72 97	90.47 411	98.08 164	98.67 205	98.34 285	73.88 402	99.92 51	97.77 141	99.51 228	99.20 234

our_test_3		97.39 247	97.73 215	96.34 346	98.70 282	89.78 391	94.61 386	98.97 255	96.50 280	99.04 146	98.85 209	95.98 225	99.84 144	97.26 168	99.67 177	99.41 170

thisisatest0515		94.12 349	93.16 356	96.97 325	98.60 304	92.90 347	93.77 401	90.61 410	94.10 351	96.91 335	95.87 377	74.99 401	99.80 192	94.52 310	99.12 293	98.20 355

ppachtmachnet_test		97.50 236	97.74 213	96.78 336	98.70 282	91.23 378	94.55 388	99.05 240	96.36 286	99.21 123	98.79 220	96.39 203	99.78 216	96.74 213	99.82 95	99.34 201

SMA-MVS		98.40 156	98.03 190	99.51 46	99.16 195	99.21 32	98.05 173	99.22 204	94.16 349	98.98 153	99.10 146	97.52 139	99.79 205	96.45 242	99.64 185	99.53 120
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																98.81 300

DPE-MVS		98.59 132	98.26 164	99.57 20	99.27 164	99.15 51	97.01 280	99.39 135	97.67 191	99.44 79	98.99 175	97.53 137	99.89 77	95.40 291	99.68 171	99.66 59
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.36 147	99.10 64				99.05 144

thres100view900		94.19 346	93.67 350	95.75 364	99.06 217	91.35 372	98.03 176	94.24 395	98.33 139	97.40 314	94.98 395	79.84 387	99.62 298	83.05 408	98.08 358	96.29 401

tfpnnormal		98.90 79	98.90 73	98.91 150	99.67 60	97.82 170	99.00 69	99.44 117	99.45 37	99.51 68	99.24 114	98.20 83	99.86 114	95.92 269	99.69 166	99.04 261

tfpn200view9		94.03 350	93.44 352	95.78 363	98.93 237	91.44 370	97.60 236	94.29 393	97.94 173	97.10 324	94.31 402	79.67 389	99.62 298	83.05 408	98.08 358	96.29 401

c3_l		97.36 248	97.37 239	97.31 308	98.09 349	93.25 341	95.01 374	99.16 222	97.05 253	98.77 194	98.72 231	92.88 300	99.64 292	96.93 193	99.76 136	99.05 257

CHOSEN 280x420		95.51 327	95.47 316	95.65 367	98.25 338	88.27 398	93.25 404	98.88 269	93.53 359	94.65 390	97.15 354	86.17 354	99.93 42	97.41 161	99.93 43	98.73 313

CANet		97.87 209	97.76 211	98.19 243	97.75 362	95.51 271	96.76 295	99.05 240	97.74 187	96.93 332	98.21 295	95.59 239	99.89 77	97.86 136	99.93 43	99.19 239

Fast-Effi-MVS+-dtu		98.27 174	98.09 183	98.81 161	98.43 326	98.11 134	97.61 235	99.50 89	98.64 118	97.39 316	97.52 339	98.12 92	99.95 24	96.90 199	98.71 326	98.38 347

Effi-MVS+-dtu		98.26 176	97.90 204	99.35 72	98.02 352	99.49 6	98.02 178	99.16 222	98.29 145	97.64 293	97.99 311	96.44 202	99.95 24	96.66 221	98.93 314	98.60 326

CANet_DTU		97.26 256	97.06 256	97.84 264	97.57 373	94.65 301	96.19 326	98.79 288	97.23 243	95.14 384	98.24 292	93.22 292	99.84 144	97.34 164	99.84 85	99.04 261

MVS_0304		97.44 243	97.01 259	98.72 180	96.42 407	96.74 235	97.20 271	91.97 407	98.46 134	98.30 244	98.79 220	92.74 304	99.91 60	99.30 43	99.94 38	99.52 123

MP-MVS-pluss		98.57 133	98.23 168	99.60 14	99.69 54	99.35 16	97.16 275	99.38 137	94.87 333	98.97 157	98.99 175	98.01 98	99.88 89	97.29 166	99.70 163	99.58 90
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 156	98.00 193	99.61 12	99.57 81	99.25 28	98.57 112	99.35 150	97.55 205	99.31 108	97.71 327	94.61 267	99.88 89	96.14 261	99.19 282	99.70 53
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												84.74 366				98.81 300

sam_mvs												84.29 372

IterMVS-SCA-FT		97.85 215	98.18 173	96.87 330	99.27 164	91.16 379	95.53 357	99.25 196	99.10 83	99.41 85	99.35 89	93.10 295	99.96 12	98.65 87	99.94 38	99.49 133

TSAR-MVS + MP.		98.63 125	98.49 130	99.06 128	99.64 69	97.90 161	98.51 123	98.94 256	96.96 258	99.24 120	98.89 202	97.83 109	99.81 185	96.88 201	99.49 236	99.48 143
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		97.86 210	98.17 174	96.92 327	98.98 230	93.91 325	96.45 309	99.17 219	97.85 181	98.41 238	97.14 355	98.47 57	99.92 51	98.02 123	99.05 296	96.92 394

OPM-MVS		98.56 134	98.32 157	99.25 95	99.41 137	98.73 87	97.13 277	99.18 215	97.10 252	98.75 197	98.92 192	98.18 84	99.65 289	96.68 220	99.56 214	99.37 189
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 100	98.48 131	99.57 20	99.58 76	99.29 23	97.82 206	99.25 196	96.94 260	98.78 191	99.12 142	98.02 97	99.84 144	97.13 178	99.67 177	99.59 84

ambc				98.24 240	98.82 262	95.97 258	98.62 107	99.00 253		99.27 111	99.21 120	96.99 172	99.50 340	96.55 235	99.50 235	99.26 223

MTGPA								99.20 207

CS-MVS-test		99.13 52	99.09 57	99.26 92	99.13 202	98.97 70	99.31 27	99.88 13	99.44 39	98.16 255	98.51 264	98.64 44	99.93 42	98.91 68	99.85 81	98.88 291

Effi-MVS+		98.02 195	97.82 209	98.62 189	98.53 316	97.19 212	97.33 259	99.68 44	97.30 232	96.68 347	97.46 343	98.56 54	99.80 192	96.63 222	98.20 349	98.86 293

xiu_mvs_v2_base		97.16 266	97.49 232	96.17 355	98.54 314	92.46 355	95.45 361	98.84 280	97.25 237	97.48 308	96.49 364	98.31 71	99.90 66	96.34 249	98.68 331	96.15 405

xiu_mvs_v1_base		97.86 210	98.17 174	96.92 327	98.98 230	93.91 325	96.45 309	99.17 219	97.85 181	98.41 238	97.14 355	98.47 57	99.92 51	98.02 123	99.05 296	96.92 394

new-patchmatchnet		98.35 162	98.74 88	97.18 314	99.24 171	92.23 362	96.42 312	99.48 98	98.30 142	99.69 38	99.53 58	97.44 146	99.82 171	98.84 73	99.77 124	99.49 133

pmmvs6		99.67 3	99.70 3	99.60 14	99.90 4	99.27 26	99.53 8	99.76 31	99.64 19	99.84 20	99.83 4	99.50 8	99.87 106	99.36 39	99.92 54	99.64 65

pmmvs5		97.64 228	97.49 232	98.08 251	99.14 200	95.12 287	96.70 299	99.05 240	93.77 356	98.62 212	98.83 212	93.23 291	99.75 235	98.33 106	99.76 136	99.36 195

test_post1							97.59 238				20.48 422	83.07 378	99.66 284	94.16 321

test_post											21.25 421	83.86 374	99.70 257

Fast-Effi-MVS+		97.67 226	97.38 238	98.57 199	98.71 278	97.43 197	97.23 267	99.45 113	94.82 334	96.13 363	96.51 363	98.52 56	99.91 60	96.19 257	98.83 318	98.37 349

patchmatchnet-post											98.77 224	84.37 369	99.85 126

Anonymous20231211		99.27 33	99.27 38	99.26 92	99.29 161	98.18 128	99.49 9	99.51 87	99.70 12	99.80 25	99.68 22	96.84 178	99.83 161	99.21 51	99.91 61	99.77 37

pmmvs-eth3d		98.47 149	98.34 153	98.86 155	99.30 159	97.76 176	97.16 275	99.28 187	95.54 315	99.42 83	99.19 123	97.27 155	99.63 295	97.89 131	99.97 19	99.20 234

GG-mvs-BLEND				94.76 379	94.54 417	92.13 363	99.31 27	80.47 422		88.73 416	91.01 416	67.59 411	98.16 409	82.30 412	94.53 408	93.98 412

xiu_mvs_v1_base_debi		97.86 210	98.17 174	96.92 327	98.98 230	93.91 325	96.45 309	99.17 219	97.85 181	98.41 238	97.14 355	98.47 57	99.92 51	98.02 123	99.05 296	96.92 394

Anonymous20231206		98.21 182	98.21 169	98.20 242	99.51 105	95.43 275	98.13 160	99.32 163	96.16 294	98.93 169	98.82 215	96.00 220	99.83 161	97.32 165	99.73 143	99.36 195

MTAPA		98.88 81	98.64 107	99.61 12	99.67 60	99.36 15	98.43 134	99.20 207	98.83 113	98.89 174	98.90 196	96.98 173	99.92 51	97.16 173	99.70 163	99.56 101

MTMP							97.93 191	91.91 408

gm-plane-assit					94.83 416	81.97 419			88.07 403		94.99 394		99.60 305	91.76 369

test9_res														93.28 347	99.15 287	99.38 187

MVP-Stereo		98.08 192	97.92 202	98.57 199	98.96 233	96.79 231	97.90 197	99.18 215	96.41 285	98.46 233	98.95 188	95.93 229	99.60 305	96.51 238	98.98 309	99.31 212
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 278	98.08 141	95.96 338	99.03 245	91.40 384	95.85 369	97.53 337	96.52 198	99.76 228

train_agg		97.10 268	96.45 293	99.07 122	98.71 278	98.08 141	95.96 338	99.03 245	91.64 379	95.85 369	97.53 337	96.47 200	99.76 228	93.67 337	99.16 285	99.36 195

gg-mvs-nofinetune		92.37 374	91.20 378	95.85 361	95.80 415	92.38 358	99.31 27	81.84 421	99.75 8	91.83 410	99.74 15	68.29 408	99.02 391	87.15 399	97.12 385	96.16 404

SCA		96.41 300	96.66 283	95.67 365	98.24 339	88.35 397	95.85 347	96.88 363	96.11 295	97.67 292	98.67 240	93.10 295	99.85 126	94.16 321	99.22 275	98.81 300

Patchmatch-test		96.55 293	96.34 295	97.17 316	98.35 332	93.06 343	98.40 137	97.79 335	97.33 228	98.41 238	98.67 240	83.68 375	99.69 261	95.16 295	99.31 259	98.77 308

test_8					98.67 292	98.01 149	95.91 344	99.02 248	91.64 379	95.79 371	97.50 340	96.47 200	99.76 228

MS-PatchMatch		97.68 225	97.75 212	97.45 303	98.23 341	93.78 331	97.29 263	98.84 280	96.10 296	98.64 209	98.65 245	96.04 217	99.36 365	96.84 205	99.14 288	99.20 234

Patchmatch-RL test		97.26 256	97.02 258	97.99 259	99.52 103	95.53 270	96.13 330	99.71 36	97.47 212	99.27 111	99.16 133	84.30 371	99.62 298	97.89 131	99.77 124	98.81 300

cdsmvs_eth3d_5k		24.66 387	32.88 390	0.00 405	0.00 428	0.00 430	0.00 416	99.10 232	0.00 423	0.00 424	97.58 335	99.21 16	0.00 424	0.00 423	0.00 422	0.00 420

pcd_1.5k_mvsjas		8.17 390	10.90 393	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	98.07 93	0.00 424	0.00 423	0.00 422	0.00 420

agg_prior2														92.50 363	99.16 285	99.37 189

agg_prior					98.68 291	97.99 150		99.01 251		95.59 372			99.77 222

tmp_tt		78.77 384	78.73 387	78.90 400	58.45 425	74.76 424	94.20 394	78.26 423	39.16 418	86.71 417	92.82 412	80.50 385	75.19 420	86.16 404	92.29 413	86.74 414

canonicalmvs		98.34 163	98.26 164	98.58 196	98.46 322	97.82 170	98.96 74	99.46 109	99.19 69	97.46 309	95.46 387	98.59 50	99.46 351	98.08 119	98.71 326	98.46 334

anonymousdsp		99.51 11	99.47 17	99.62 9	99.88 9	99.08 67	99.34 20	99.69 39	98.93 104	99.65 46	99.72 18	98.93 26	99.95 24	99.11 55	100.00 1	99.82 27

alignmvs		97.35 249	96.88 266	98.78 169	98.54 314	98.09 137	97.71 221	97.69 339	99.20 65	97.59 297	95.90 376	88.12 346	99.55 323	98.18 112	98.96 311	98.70 317

nrg030		99.40 22	99.35 26	99.54 30	99.58 76	99.13 59	98.98 72	99.48 98	99.68 15	99.46 75	99.26 109	98.62 47	99.73 245	99.17 54	99.92 54	99.76 41

v144192		98.54 140	98.57 118	98.45 218	99.21 178	95.98 257	97.63 232	99.36 145	97.15 251	99.32 106	99.18 127	95.84 232	99.84 144	99.50 34	99.91 61	99.54 112

FIs		99.14 49	99.09 57	99.29 86	99.70 52	98.28 119	99.13 55	99.52 86	99.48 33	99.24 120	99.41 81	96.79 184	99.82 171	98.69 85	99.88 73	99.76 41

v1921920		98.54 140	98.60 115	98.38 226	99.20 182	95.76 265	97.56 241	99.36 145	97.23 243	99.38 91	99.17 131	96.02 218	99.84 144	99.57 27	99.90 67	99.54 112

UA-Net		99.47 13	99.40 22	99.70 2	99.49 115	99.29 23	99.80 4	99.72 35	99.82 5	99.04 146	99.81 6	98.05 96	99.96 12	98.85 72	99.99 5	99.86 21

v1192		98.60 130	98.66 104	98.41 223	99.27 164	95.88 260	97.52 245	99.36 145	97.41 221	99.33 100	99.20 122	96.37 206	99.82 171	99.57 27	99.92 54	99.55 108

FC-MVSNet-test		99.27 33	99.25 41	99.34 75	99.77 26	98.37 113	99.30 32	99.57 65	99.61 26	99.40 88	99.50 62	97.12 163	99.85 126	99.02 63	99.94 38	99.80 31

v1144		98.60 130	98.66 104	98.41 223	99.36 147	95.90 259	97.58 239	99.34 156	97.51 208	99.27 111	99.15 137	96.34 208	99.80 192	99.47 36	99.93 43	99.51 126

sosnet-low-res		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

HFP-MVS		98.71 104	98.44 138	99.51 46	99.49 115	99.16 47	98.52 118	99.31 168	97.47 212	98.58 220	98.50 268	97.97 103	99.85 126	96.57 228	99.59 202	99.53 120

v148		98.45 151	98.60 115	98.00 258	99.44 129	94.98 290	97.44 253	99.06 237	98.30 142	99.32 106	98.97 181	96.65 193	99.62 298	98.37 102	99.85 81	99.39 180

sosnet		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

uncertanet		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

AllTest		98.44 152	98.20 170	99.16 107	99.50 108	98.55 99	98.25 148	99.58 58	96.80 267	98.88 177	99.06 150	97.65 123	99.57 317	94.45 313	99.61 196	99.37 189

TestCases				99.16 107	99.50 108	98.55 99		99.58 58	96.80 267	98.88 177	99.06 150	97.65 123	99.57 317	94.45 313	99.61 196	99.37 189

v7n		99.53 9	99.57 10	99.41 62	99.88 9	98.54 102	99.45 11	99.61 54	99.66 17	99.68 40	99.66 29	98.44 61	99.95 24	99.73 18	99.96 23	99.75 45

region2R		98.69 111	98.40 143	99.54 30	99.53 101	99.17 43	98.52 118	99.31 168	97.46 217	98.44 235	98.51 264	97.83 109	99.88 89	96.46 241	99.58 207	99.58 90

RRT-MVS		97.88 207	97.98 195	97.61 286	98.15 345	93.77 332	98.97 73	99.64 49	99.16 73	98.69 202	99.42 77	91.60 316	99.89 77	97.63 149	98.52 340	99.16 248

mamv4		99.44 15	99.39 23	99.58 19	99.30 159	99.74 2	99.04 65	99.81 25	99.77 7	99.82 21	99.57 45	97.82 112	99.98 4	99.53 31	99.89 71	99.01 265

PS-MVSNAJss		99.46 14	99.49 13	99.35 72	99.90 4	98.15 130	99.20 45	99.65 48	99.48 33	99.92 8	99.71 19	98.07 93	99.96 12	99.53 31	100.00 1	99.93 10

PS-MVSNAJ		97.08 270	97.39 237	96.16 357	98.56 312	92.46 355	95.24 368	98.85 279	97.25 237	97.49 307	95.99 373	98.07 93	99.90 66	96.37 246	98.67 332	96.12 406

jajsoiax		99.58 6	99.61 8	99.48 53	99.87 12	98.61 94	99.28 37	99.66 47	99.09 86	99.89 15	99.68 22	99.53 7	99.97 5	99.50 34	99.99 5	99.87 18

mvs_tets		99.63 5	99.67 5	99.49 51	99.88 9	98.61 94	99.34 20	99.71 36	99.27 58	99.90 12	99.74 15	99.68 4	99.97 5	99.55 30	99.99 5	99.88 17

EI-MVSNet-UG-set		98.69 111	98.71 95	98.62 189	99.10 206	96.37 245	97.23 267	98.87 271	99.20 65	99.19 125	98.99 175	97.30 152	99.85 126	98.77 78	99.79 114	99.65 64

EI-MVSNet-Vis-set		98.68 116	98.70 98	98.63 188	99.09 209	96.40 244	97.23 267	98.86 276	99.20 65	99.18 129	98.97 181	97.29 154	99.85 126	98.72 82	99.78 119	99.64 65

HPM-MVS++		98.10 189	97.64 223	99.48 53	99.09 209	99.13 59	97.52 245	98.75 295	97.46 217	96.90 338	97.83 322	96.01 219	99.84 144	95.82 277	99.35 253	99.46 152

test_prior4						97.97 154	95.86 345

XVS		98.72 103	98.45 136	99.53 37	99.46 125	99.21 32	98.65 103	99.34 156	98.62 122	97.54 302	98.63 250	97.50 141	99.83 161	96.79 207	99.53 223	99.56 101

v1240		98.55 138	98.62 110	98.32 232	99.22 176	95.58 268	97.51 247	99.45 113	97.16 249	99.45 78	99.24 114	96.12 215	99.85 126	99.60 25	99.88 73	99.55 108

pm-mvs1		99.44 15	99.48 15	99.33 80	99.80 20	98.63 91	99.29 33	99.63 50	99.30 55	99.65 46	99.60 41	99.16 20	99.82 171	99.07 58	99.83 92	99.56 101

test_prior2							95.74 351		96.48 282	96.11 364	97.63 333	95.92 230		94.16 321	99.20 279

X-MVStestdata		94.32 343	92.59 361	99.53 37	99.46 125	99.21 32	98.65 103	99.34 156	98.62 122	97.54 302	45.85 418	97.50 141	99.83 161	96.79 207	99.53 223	99.56 101

test_prior				98.95 143	98.69 287	97.95 158		99.03 245					99.59 309			99.30 215

旧先验2							95.76 350		88.56 402	97.52 304			99.66 284	94.48 311

新几何2							95.93 341

新几何1				98.91 150	98.94 235	97.76 176		98.76 292	87.58 404	96.75 346	98.10 303	94.80 263	99.78 216	92.73 359	99.00 305	99.20 234

旧先验1					98.82 262	97.45 195		98.76 292			98.34 285	95.50 243			99.01 304	99.23 229

无先验							95.74 351	98.74 297	89.38 398				99.73 245	92.38 365		99.22 233

原ACMM2							95.53 357

原ACMM1				98.35 230	98.90 245	96.25 249		98.83 284	92.48 373	96.07 366	98.10 303	95.39 246	99.71 253	92.61 362	98.99 307	99.08 253

test222					98.92 241	96.93 227	95.54 356	98.78 290	85.72 407	96.86 341	98.11 302	94.43 270			99.10 295	99.23 229

testdata2													99.79 205	92.80 357

segment_acmp												97.02 170

testdata				98.09 248	98.93 237	95.40 276		98.80 287	90.08 395	97.45 311	98.37 281	95.26 248	99.70 257	93.58 340	98.95 312	99.17 245

testdata1							95.44 362		96.32 288

v8		99.01 64	99.16 48	98.57 199	99.47 124	96.31 248	98.90 80	99.47 106	99.03 94	99.52 63	99.57 45	96.93 174	99.81 185	99.60 25	99.98 12	99.60 78

1314		95.74 319	95.60 311	96.17 355	97.53 378	92.75 351	98.07 170	98.31 320	91.22 386	94.25 393	96.68 361	95.53 240	99.03 390	91.64 372	97.18 384	96.74 398

LFMVS		97.20 262	96.72 277	98.64 184	98.72 275	96.95 225	98.93 78	94.14 397	99.74 10	98.78 191	99.01 171	84.45 368	99.73 245	97.44 159	99.27 266	99.25 224

VDD-MVS		98.56 134	98.39 146	99.07 122	99.13 202	98.07 143	98.59 110	97.01 356	99.59 27	99.11 132	99.27 105	94.82 260	99.79 205	98.34 104	99.63 188	99.34 201

VDDNet		98.21 182	97.95 198	99.01 135	99.58 76	97.74 178	99.01 67	97.29 350	99.67 16	98.97 157	99.50 62	90.45 327	99.80 192	97.88 134	99.20 279	99.48 143

v10		98.97 70	99.11 54	98.55 204	99.44 129	96.21 250	98.90 80	99.55 76	98.73 114	99.48 70	99.60 41	96.63 194	99.83 161	99.70 21	99.99 5	99.61 77

VPNet		98.87 82	98.83 81	99.01 135	99.70 52	97.62 187	98.43 134	99.35 150	99.47 35	99.28 109	99.05 157	96.72 190	99.82 171	98.09 118	99.36 251	99.59 84

MVS		93.19 363	92.09 367	96.50 342	96.91 396	94.03 319	98.07 170	98.06 331	68.01 416	94.56 392	96.48 365	95.96 227	99.30 375	83.84 407	96.89 389	96.17 403

v2v482		98.56 134	98.62 110	98.37 228	99.42 135	95.81 263	97.58 239	99.16 222	97.90 177	99.28 109	99.01 171	95.98 225	99.79 205	99.33 41	99.90 67	99.51 126

V42		98.78 95	98.78 86	98.76 173	99.44 129	97.04 219	98.27 146	99.19 211	97.87 179	99.25 119	99.16 133	96.84 178	99.78 216	99.21 51	99.84 85	99.46 152

SD-MVS		98.40 156	98.68 101	97.54 295	98.96 233	97.99 150	97.88 199	99.36 145	98.20 155	99.63 49	99.04 159	98.76 35	95.33 417	96.56 232	99.74 140	99.31 212
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		95.86 315	95.32 325	97.49 300	98.60 304	94.15 314	93.83 400	97.93 333	95.49 317	96.68 347	97.42 345	83.21 376	99.30 375	96.22 255	98.55 339	99.01 265

MSLP-MVS++		98.02 195	98.14 180	97.64 284	98.58 309	95.19 284	97.48 249	99.23 203	97.47 212	97.90 275	98.62 252	97.04 167	98.81 400	97.55 153	99.41 245	98.94 281

APDe-MVS		98.99 66	98.79 85	99.60 14	99.21 178	99.15 51	98.87 84	99.48 98	97.57 201	99.35 97	99.24 114	97.83 109	99.89 77	97.88 134	99.70 163	99.75 45
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.84 86	98.61 114	99.53 37	99.19 185	99.27 26	98.49 126	99.33 161	98.64 118	99.03 149	98.98 179	97.89 106	99.85 126	96.54 236	99.42 244	99.46 152

ADS-MVSNet2		95.43 328	94.98 332	96.76 337	98.14 346	91.74 365	97.92 194	97.76 336	90.23 391	96.51 355	98.91 193	85.61 359	99.85 126	92.88 353	96.90 387	98.69 318

EI-MVSNet		98.40 156	98.51 124	98.04 256	99.10 206	94.73 297	97.20 271	98.87 271	98.97 100	99.06 139	99.02 162	96.00 220	99.80 192	98.58 90	99.82 95	99.60 78

Regformer		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

CVMVSNet		96.25 304	97.21 249	93.38 394	99.10 206	80.56 421	97.20 271	98.19 326	96.94 260	99.00 151	99.02 162	89.50 334	99.80 192	96.36 248	99.59 202	99.78 35

pmmvs4		97.58 233	97.28 244	98.51 210	98.84 257	96.93 227	95.40 364	98.52 311	93.60 358	98.61 214	98.65 245	95.10 252	99.60 305	96.97 191	99.79 114	98.99 270

EU-MVSNet		97.66 227	98.50 126	95.13 376	99.63 73	85.84 406	98.35 142	98.21 323	98.23 149	99.54 57	99.46 70	95.02 254	99.68 270	98.24 108	99.87 76	99.87 18

VNet		98.42 153	98.30 158	98.79 166	98.79 268	97.29 203	98.23 149	98.66 302	99.31 53	98.85 182	98.80 218	94.80 263	99.78 216	98.13 115	99.13 290	99.31 212

test-LLR		93.90 352	93.85 346	94.04 385	96.53 404	84.62 412	94.05 397	92.39 404	96.17 292	94.12 395	95.07 391	82.30 381	99.67 273	95.87 273	98.18 350	97.82 372

TESTMET0.1,1		92.19 377	91.77 375	93.46 392	96.48 406	82.80 417	94.05 397	91.52 409	94.45 343	94.00 398	94.88 397	66.65 412	99.56 320	95.78 278	98.11 356	98.02 364

test-mter		92.33 375	91.76 376	94.04 385	96.53 404	84.62 412	94.05 397	92.39 404	94.00 354	94.12 395	95.07 391	65.63 416	99.67 273	95.87 273	98.18 350	97.82 372

VPA-MVSNet		99.30 29	99.30 35	99.28 87	99.49 115	98.36 116	99.00 69	99.45 113	99.63 21	99.52 63	99.44 75	98.25 75	99.88 89	99.09 57	99.84 85	99.62 69

ACMMPR		98.70 108	98.42 141	99.54 30	99.52 103	99.14 56	98.52 118	99.31 168	97.47 212	98.56 223	98.54 260	97.75 117	99.88 89	96.57 228	99.59 202	99.58 90

testgi		98.32 167	98.39 146	98.13 247	99.57 81	95.54 269	97.78 211	99.49 96	97.37 225	99.19 125	97.65 331	98.96 24	99.49 343	96.50 239	98.99 307	99.34 201

test20.03		98.78 95	98.77 87	98.78 169	99.46 125	97.20 211	97.78 211	99.24 201	99.04 93	99.41 85	98.90 196	97.65 123	99.76 228	97.70 146	99.79 114	99.39 180

thres600view7		94.45 341	93.83 347	96.29 348	99.06 217	91.53 368	97.99 186	94.24 395	98.34 138	97.44 312	95.01 393	79.84 387	99.67 273	84.33 406	98.23 347	97.66 382

ADS-MVSNet		95.24 331	94.93 335	96.18 354	98.14 346	90.10 390	97.92 194	97.32 349	90.23 391	96.51 355	98.91 193	85.61 359	99.74 240	92.88 353	96.90 387	98.69 318

MP-MVS		98.46 150	98.09 183	99.54 30	99.57 81	99.22 31	98.50 125	99.19 211	97.61 198	97.58 298	98.66 243	97.40 148	99.88 89	94.72 306	99.60 198	99.54 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 388	20.53 391	6.87 404	12.05 426	4.20 429	93.62 403	6.73 427	4.62 422	10.41 422	24.33 419	8.28 427	3.56 423	9.69 422	15.07 420	12.86 419

thres400		94.14 348	93.44 352	96.24 351	98.93 237	91.44 370	97.60 236	94.29 393	97.94 173	97.10 324	94.31 402	79.67 389	99.62 298	83.05 408	98.08 358	97.66 382

test123		17.04 389	20.11 392	7.82 403	10.25 427	4.91 428	94.80 378	4.47 428	4.93 421	10.00 423	24.28 420	9.69 426	3.64 422	10.14 421	12.43 421	14.92 418

thres200		93.72 355	93.14 357	95.46 372	98.66 297	91.29 374	96.61 303	94.63 390	97.39 223	96.83 342	93.71 405	79.88 386	99.56 320	82.40 411	98.13 355	95.54 410

test0.0.03 1		94.51 340	93.69 349	96.99 323	96.05 411	93.61 338	94.97 375	93.49 399	96.17 292	97.57 300	94.88 397	82.30 381	99.01 393	93.60 339	94.17 409	98.37 349

pmmvs3		95.03 335	94.40 341	96.93 326	97.70 368	92.53 354	95.08 372	97.71 338	88.57 401	97.71 289	98.08 306	79.39 391	99.82 171	96.19 257	99.11 294	98.43 342

EMVS		93.83 353	94.02 345	93.23 395	96.83 399	84.96 409	89.77 414	96.32 372	97.92 175	97.43 313	96.36 370	86.17 354	98.93 396	87.68 398	97.73 367	95.81 408

E-PMN		94.17 347	94.37 342	93.58 391	96.86 397	85.71 408	90.11 413	97.07 355	98.17 158	97.82 284	97.19 352	84.62 367	98.94 395	89.77 391	97.68 368	96.09 407

PGM-MVS		98.66 120	98.37 149	99.55 27	99.53 101	99.18 42	98.23 149	99.49 96	97.01 257	98.69 202	98.88 203	98.00 99	99.89 77	95.87 273	99.59 202	99.58 90

LCM-MVSNet-Re		98.64 123	98.48 131	99.11 114	98.85 256	98.51 104	98.49 126	99.83 22	98.37 136	99.69 38	99.46 70	98.21 82	99.92 51	94.13 325	99.30 262	98.91 286

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 12	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 10	100.00 1	99.85 22

MCST-MVS		98.00 197	97.63 224	99.10 116	99.24 171	98.17 129	96.89 289	98.73 298	95.66 310	97.92 273	97.70 329	97.17 161	99.66 284	96.18 259	99.23 274	99.47 150

mvs_anonymous		97.83 218	98.16 177	96.87 330	98.18 343	91.89 364	97.31 261	98.90 265	97.37 225	98.83 185	99.46 70	96.28 209	99.79 205	98.90 69	98.16 353	98.95 277

MVS_Test		98.18 185	98.36 150	97.67 280	98.48 319	94.73 297	98.18 154	99.02 248	97.69 190	98.04 268	99.11 143	97.22 159	99.56 320	98.57 92	98.90 316	98.71 314

MDA-MVSNet-bldmvs		97.94 201	97.91 203	98.06 253	99.44 129	94.96 291	96.63 302	99.15 227	98.35 137	98.83 185	99.11 143	94.31 275	99.85 126	96.60 225	98.72 324	99.37 189

CDPH-MVS		97.26 256	96.66 283	99.07 122	99.00 226	98.15 130	96.03 334	99.01 251	91.21 387	97.79 285	97.85 321	96.89 176	99.69 261	92.75 358	99.38 250	99.39 180

test12				98.93 146	98.58 309	97.83 167		98.66 302		96.53 353		95.51 242	99.69 261		99.13 290	99.27 220

casdiffmvs		98.95 73	99.00 64	98.81 161	99.38 140	97.33 201	97.82 206	99.57 65	99.17 72	99.35 97	99.17 131	98.35 68	99.69 261	98.46 98	99.73 143	99.41 170
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		98.22 180	98.24 167	98.17 244	99.00 226	95.44 274	96.38 314	99.58 58	97.79 185	98.53 228	98.50 268	96.76 187	99.74 240	97.95 130	99.64 185	99.34 201
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		93.73 354	92.83 360	96.42 344	97.70 368	91.28 375	96.84 291	89.77 413	93.96 355	92.44 408	95.93 375	79.14 392	99.77 222	92.94 351	96.76 391	98.21 354

baseline1		95.96 313	95.44 319	97.52 297	98.51 318	93.99 322	98.39 138	96.09 375	98.21 151	98.40 242	97.76 325	86.88 348	99.63 295	95.42 290	89.27 415	98.95 277

YYNet1		97.60 230	97.67 218	97.39 307	99.04 221	93.04 346	95.27 366	98.38 318	97.25 237	98.92 170	98.95 188	95.48 244	99.73 245	96.99 188	98.74 322	99.41 170

PMMVS2		98.07 193	98.08 186	98.04 256	99.41 137	94.59 303	94.59 387	99.40 133	97.50 209	98.82 188	98.83 212	96.83 180	99.84 144	97.50 158	99.81 99	99.71 48

MDA-MVSNet_test_wron		97.60 230	97.66 221	97.41 306	99.04 221	93.09 342	95.27 366	98.42 315	97.26 236	98.88 177	98.95 188	95.43 245	99.73 245	97.02 185	98.72 324	99.41 170

tpmvs		95.02 336	95.25 326	94.33 382	96.39 409	85.87 405	98.08 168	96.83 364	95.46 318	95.51 380	98.69 236	85.91 357	99.53 330	94.16 321	96.23 396	97.58 385

PM-MVS		98.82 89	98.72 92	99.12 112	99.64 69	98.54 102	97.98 187	99.68 44	97.62 195	99.34 99	99.18 127	97.54 135	99.77 222	97.79 139	99.74 140	99.04 261

HQP_MVS		97.99 200	97.67 218	98.93 146	99.19 185	97.65 184	97.77 213	99.27 190	98.20 155	97.79 285	97.98 312	94.90 256	99.70 257	94.42 315	99.51 228	99.45 156

plane_prior7					99.19 185	97.87 163

plane_prior6					98.99 229	97.70 182						94.90 256

plane_prior5								99.27 190					99.70 257	94.42 315	99.51 228	99.45 156

plane_prior4											97.98 312

plane_prior3						97.78 175			97.41 221	97.79 285

plane_prior2							97.77 213		98.20 155

plane_prior1					99.05 220

plane_prior						97.65 184	97.07 278		96.72 272						99.36 251

PS-CasMVS		99.40 22	99.33 29	99.62 9	99.71 45	99.10 64	99.29 33	99.53 83	99.53 31	99.46 75	99.41 81	98.23 77	99.95 24	98.89 71	99.95 30	99.81 30

UniMVSNet_NR-MVSNet		98.86 85	98.68 101	99.40 64	99.17 193	98.74 84	97.68 224	99.40 133	99.14 74	99.06 139	98.59 256	96.71 191	99.93 42	98.57 92	99.77 124	99.53 120

PEN-MVS		99.41 21	99.34 28	99.62 9	99.73 36	99.14 56	99.29 33	99.54 80	99.62 24	99.56 53	99.42 77	98.16 88	99.96 12	98.78 75	99.93 43	99.77 37

TransMVSNet (Re)		99.44 15	99.47 17	99.36 66	99.80 20	98.58 97	99.27 39	99.57 65	99.39 44	99.75 31	99.62 36	99.17 18	99.83 161	99.06 59	99.62 191	99.66 59

DTE-MVSNet		99.43 19	99.35 26	99.66 7	99.71 45	99.30 21	99.31 27	99.51 87	99.64 19	99.56 53	99.46 70	98.23 77	99.97 5	98.78 75	99.93 43	99.72 47

DU-MVS		98.82 89	98.63 108	99.39 65	99.16 195	98.74 84	97.54 243	99.25 196	98.84 112	99.06 139	98.76 226	96.76 187	99.93 42	98.57 92	99.77 124	99.50 129

UniMVSNet (Re)		98.87 82	98.71 95	99.35 72	99.24 171	98.73 87	97.73 220	99.38 137	98.93 104	99.12 131	98.73 229	96.77 185	99.86 114	98.63 89	99.80 109	99.46 152

CP-MVSNet		99.21 41	99.09 57	99.56 25	99.65 63	98.96 74	99.13 55	99.34 156	99.42 42	99.33 100	99.26 109	97.01 171	99.94 36	98.74 80	99.93 43	99.79 32

WR-MVS_H		99.33 27	99.22 43	99.65 8	99.71 45	99.24 29	99.32 23	99.55 76	99.46 36	99.50 69	99.34 93	97.30 152	99.93 42	98.90 69	99.93 43	99.77 37

WR-MVS		98.40 156	98.19 172	99.03 132	99.00 226	97.65 184	96.85 290	98.94 256	98.57 127	98.89 174	98.50 268	95.60 238	99.85 126	97.54 155	99.85 81	99.59 84

NR-MVSNet		98.95 73	98.82 82	99.36 66	99.16 195	98.72 89	99.22 42	99.20 207	99.10 83	99.72 32	98.76 226	96.38 205	99.86 114	98.00 126	99.82 95	99.50 129

Baseline_NR-MVSNet		98.98 69	98.86 79	99.36 66	99.82 19	98.55 99	97.47 251	99.57 65	99.37 46	99.21 123	99.61 39	96.76 187	99.83 161	98.06 121	99.83 92	99.71 48

TranMVSNet+NR-MVSNet		99.17 44	99.07 60	99.46 58	99.37 146	98.87 77	98.39 138	99.42 126	99.42 42	99.36 95	99.06 150	98.38 64	99.95 24	98.34 104	99.90 67	99.57 95

TSAR-MVS + GP.		98.18 185	97.98 195	98.77 172	98.71 278	97.88 162	96.32 318	98.66 302	96.33 287	99.23 122	98.51 264	97.48 145	99.40 360	97.16 173	99.46 238	99.02 264

n2								0.00 429

nn								0.00 429

mPP-MVS		98.64 123	98.34 153	99.54 30	99.54 98	99.17 43	98.63 105	99.24 201	97.47 212	98.09 263	98.68 238	97.62 128	99.89 77	96.22 255	99.62 191	99.57 95

door-mid								99.57 65

XVG-OURS-SEG-HR		98.49 147	98.28 160	99.14 110	99.49 115	98.83 79	96.54 304	99.48 98	97.32 230	99.11 132	98.61 254	99.33 13	99.30 375	96.23 254	98.38 342	99.28 219

mvsmamba		97.57 234	97.26 245	98.51 210	98.69 287	96.73 236	98.74 92	97.25 351	97.03 256	97.88 277	99.23 118	90.95 322	99.87 106	96.61 224	99.00 305	98.91 286

MVSFormer		98.26 176	98.43 139	97.77 270	98.88 251	93.89 328	99.39 17	99.56 72	99.11 76	98.16 255	98.13 299	93.81 286	99.97 5	99.26 46	99.57 211	99.43 164

jason		97.45 242	97.35 241	97.76 273	99.24 171	93.93 324	95.86 345	98.42 315	94.24 347	98.50 230	98.13 299	94.82 260	99.91 60	97.22 170	99.73 143	99.43 164
jason: jason.
lupinMVS		97.06 271	96.86 267	97.65 282	98.88 251	93.89 328	95.48 360	97.97 332	93.53 359	98.16 255	97.58 335	93.81 286	99.91 60	96.77 210	99.57 211	99.17 245

test_djsdf		99.52 10	99.51 12	99.53 37	99.86 14	98.74 84	99.39 17	99.56 72	99.11 76	99.70 36	99.73 17	99.00 22	99.97 5	99.26 46	99.98 12	99.89 14

HPM-MVS_fast		99.01 64	98.82 82	99.57 20	99.71 45	99.35 16	99.00 69	99.50 89	97.33 228	98.94 168	98.86 206	98.75 36	99.82 171	97.53 156	99.71 156	99.56 101

K. test v3		98.00 197	97.66 221	99.03 132	99.79 22	97.56 189	99.19 49	92.47 403	99.62 24	99.52 63	99.66 29	89.61 332	99.96 12	99.25 48	99.81 99	99.56 101

lessismore_v0				98.97 140	99.73 36	97.53 191		86.71 417		99.37 93	99.52 61	89.93 330	99.92 51	98.99 65	99.72 151	99.44 160

SixPastTwentyTwo		98.75 100	98.62 110	99.16 107	99.83 18	97.96 157	99.28 37	98.20 324	99.37 46	99.70 36	99.65 33	92.65 306	99.93 42	99.04 61	99.84 85	99.60 78

OurMVSNet-221017-0		99.37 25	99.31 33	99.53 37	99.91 3	98.98 69	99.63 7	99.58 58	99.44 39	99.78 27	99.76 12	96.39 203	99.92 51	99.44 37	99.92 54	99.68 55

HPM-MVS		98.79 93	98.53 122	99.59 18	99.65 63	99.29 23	99.16 51	99.43 123	96.74 271	98.61 214	98.38 280	98.62 47	99.87 106	96.47 240	99.67 177	99.59 84
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 142	98.34 153	99.11 114	99.50 108	98.82 81	95.97 336	99.50 89	97.30 232	99.05 144	98.98 179	99.35 12	99.32 372	95.72 280	99.68 171	99.18 241

XVG-ACMP-BASELINE		98.56 134	98.34 153	99.22 100	99.54 98	98.59 96	97.71 221	99.46 109	97.25 237	98.98 153	98.99 175	97.54 135	99.84 144	95.88 270	99.74 140	99.23 229

casdiffmvs_mvg		99.12 54	99.16 48	98.99 137	99.43 134	97.73 180	98.00 182	99.62 51	99.22 61	99.55 56	99.22 119	98.93 26	99.75 235	98.66 86	99.81 99	99.50 129
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		98.71 104	98.46 135	99.47 56	99.57 81	98.97 70	98.23 149	99.48 98	96.60 276	99.10 135	99.06 150	98.71 39	99.83 161	95.58 287	99.78 119	99.62 69

LGP-MVS_train				99.47 56	99.57 81	98.97 70		99.48 98	96.60 276	99.10 135	99.06 150	98.71 39	99.83 161	95.58 287	99.78 119	99.62 69

baseline		98.96 72	99.02 62	98.76 173	99.38 140	97.26 206	98.49 126	99.50 89	98.86 109	99.19 125	99.06 150	98.23 77	99.69 261	98.71 83	99.76 136	99.33 206

test11								98.87 271

door								99.41 130

EPNet_dtu		94.93 337	94.78 337	95.38 374	93.58 418	87.68 401	96.78 293	95.69 384	97.35 227	89.14 415	98.09 305	88.15 345	99.49 343	94.95 300	99.30 262	98.98 271
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 238	97.14 254	98.54 207	99.68 56	96.09 254	96.50 307	99.62 51	91.58 381	98.84 184	98.97 181	92.36 308	99.88 89	96.76 211	99.95 30	99.67 58

EPNet		96.14 307	95.44 319	98.25 238	90.76 422	95.50 272	97.92 194	94.65 389	98.97 100	92.98 405	98.85 209	89.12 336	99.87 106	95.99 266	99.68 171	99.39 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 231

HQP-NCC					98.67 292		96.29 320		96.05 297	95.55 375

ACMP_Plane					98.67 292		96.29 320		96.05 297	95.55 375

APD-MVS		98.10 189	97.67 218	99.42 60	99.11 204	98.93 75	97.76 216	99.28 187	94.97 330	98.72 200	98.77 224	97.04 167	99.85 126	93.79 335	99.54 219	99.49 133
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 355

HQP4-MVS										95.56 374			99.54 328			99.32 208

HQP3-MVS								99.04 243							99.26 269

HQP2-MVS												93.84 284

CNVR-MVS		98.17 187	97.87 206	99.07 122	98.67 292	98.24 122	97.01 280	98.93 259	97.25 237	97.62 294	98.34 285	97.27 155	99.57 317	96.42 243	99.33 256	99.39 180

NCCC		97.86 210	97.47 235	99.05 129	98.61 302	98.07 143	96.98 282	98.90 265	97.63 194	97.04 328	97.93 317	95.99 224	99.66 284	95.31 292	98.82 320	99.43 164

114514_t		96.50 296	95.77 304	98.69 181	99.48 122	97.43 197	97.84 205	99.55 76	81.42 413	96.51 355	98.58 257	95.53 240	99.67 273	93.41 345	99.58 207	98.98 271

CP-MVS		98.70 108	98.42 141	99.52 42	99.36 147	99.12 61	98.72 97	99.36 145	97.54 206	98.30 244	98.40 277	97.86 108	99.89 77	96.53 237	99.72 151	99.56 101

DSMNet-mixed		97.42 245	97.60 226	96.87 330	99.15 199	91.46 369	98.54 116	99.12 229	92.87 369	97.58 298	99.63 35	96.21 211	99.90 66	95.74 279	99.54 219	99.27 220

tpm2		93.09 364	92.58 362	94.62 380	97.56 374	86.53 404	97.66 228	95.79 381	86.15 406	94.07 397	98.23 294	75.95 399	99.53 330	90.91 385	96.86 390	97.81 374

NP-MVS					98.84 257	97.39 199					96.84 358

EG-PatchMatch MVS		98.99 66	99.01 63	98.94 144	99.50 108	97.47 193	98.04 175	99.59 56	98.15 162	99.40 88	99.36 88	98.58 53	99.76 228	98.78 75	99.68 171	99.59 84

tpm cat1		93.29 361	93.13 358	93.75 389	97.39 386	84.74 410	97.39 254	97.65 341	83.39 411	94.16 394	98.41 276	82.86 379	99.39 362	91.56 374	95.35 404	97.14 393

SteuartSystems-ACMMP		98.79 93	98.54 121	99.54 30	99.73 36	99.16 47	98.23 149	99.31 168	97.92 175	98.90 172	98.90 196	98.00 99	99.88 89	96.15 260	99.72 151	99.58 90
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 351	93.78 348	94.51 381	97.53 378	85.83 407	97.98 187	95.96 377	89.29 399	94.99 386	98.63 250	78.63 395	99.62 298	94.54 309	96.50 392	98.09 361

CR-MVSNet		96.28 303	95.95 302	97.28 310	97.71 366	94.22 309	98.11 164	98.92 262	92.31 375	96.91 335	99.37 84	85.44 362	99.81 185	97.39 162	97.36 380	97.81 374

JIA-IIPM		95.52 326	95.03 331	97.00 322	96.85 398	94.03 319	96.93 286	95.82 380	99.20 65	94.63 391	99.71 19	83.09 377	99.60 305	94.42 315	94.64 406	97.36 391

Patchmtry		97.35 249	96.97 260	98.50 214	97.31 388	96.47 243	98.18 154	98.92 262	98.95 103	98.78 191	99.37 84	85.44 362	99.85 126	95.96 268	99.83 92	99.17 245

PatchT		96.65 290	96.35 294	97.54 295	97.40 385	95.32 279	97.98 187	96.64 367	99.33 51	96.89 339	99.42 77	84.32 370	99.81 185	97.69 148	97.49 371	97.48 387

tpmrst		95.07 334	95.46 317	93.91 387	97.11 392	84.36 414	97.62 233	96.96 359	94.98 329	96.35 360	98.80 218	85.46 361	99.59 309	95.60 285	96.23 396	97.79 377

BH-w/o		95.13 333	94.89 336	95.86 360	98.20 342	91.31 373	95.65 353	97.37 345	93.64 357	96.52 354	95.70 380	93.04 298	99.02 391	88.10 397	95.82 401	97.24 392

tpm		94.67 339	94.34 343	95.66 366	97.68 371	88.42 396	97.88 199	94.90 387	94.46 341	96.03 368	98.56 259	78.66 394	99.79 205	95.88 270	95.01 405	98.78 307

DELS-MVS		98.27 174	98.20 170	98.48 215	98.86 253	96.70 237	95.60 355	99.20 207	97.73 188	98.45 234	98.71 232	97.50 141	99.82 171	98.21 110	99.59 202	98.93 282
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		96.83 283	96.75 276	97.08 319	98.74 272	93.33 340	96.71 298	98.26 321	96.72 272	98.44 235	97.37 348	95.20 249	99.47 349	91.89 367	97.43 375	98.44 340

RPMNet		97.02 274	96.93 261	97.30 309	97.71 366	94.22 309	98.11 164	99.30 176	99.37 46	96.91 335	99.34 93	86.72 349	99.87 106	97.53 156	97.36 380	97.81 374

MVSTER		96.86 282	96.55 289	97.79 268	97.91 357	94.21 311	97.56 241	98.87 271	97.49 211	99.06 139	99.05 157	80.72 384	99.80 192	98.44 99	99.82 95	99.37 189

CPTT-MVS		97.84 216	97.36 240	99.27 90	99.31 156	98.46 107	98.29 144	99.27 190	94.90 332	97.83 282	98.37 281	94.90 256	99.84 144	93.85 334	99.54 219	99.51 126

GBi-Net		98.65 121	98.47 133	99.17 104	98.90 245	98.24 122	99.20 45	99.44 117	98.59 124	98.95 161	99.55 52	94.14 278	99.86 114	97.77 141	99.69 166	99.41 170

PVSNet_Blended_VisFu		98.17 187	98.15 178	98.22 241	99.73 36	95.15 285	97.36 257	99.68 44	94.45 343	98.99 152	99.27 105	96.87 177	99.94 36	97.13 178	99.91 61	99.57 95

PVSNet_BlendedMVS		97.55 235	97.53 229	97.60 287	98.92 241	93.77 332	96.64 301	99.43 123	94.49 339	97.62 294	99.18 127	96.82 181	99.67 273	94.73 304	99.93 43	99.36 195

UnsupCasMVSNet_eth		97.89 205	97.60 226	98.75 175	99.31 156	97.17 214	97.62 233	99.35 150	98.72 116	98.76 196	98.68 238	92.57 307	99.74 240	97.76 145	95.60 402	99.34 201

UnsupCasMVSNet_bld		97.30 253	96.92 263	98.45 218	99.28 162	96.78 234	96.20 325	99.27 190	95.42 319	98.28 248	98.30 289	93.16 293	99.71 253	94.99 297	97.37 378	98.87 292

PVSNet_Blended		96.88 281	96.68 280	97.47 302	98.92 241	93.77 332	94.71 380	99.43 123	90.98 389	97.62 294	97.36 349	96.82 181	99.67 273	94.73 304	99.56 214	98.98 271

FMVSNet5		96.01 310	95.20 328	98.41 223	97.53 378	96.10 251	98.74 92	99.50 89	97.22 246	98.03 269	99.04 159	69.80 406	99.88 89	97.27 167	99.71 156	99.25 224

test1		98.65 121	98.47 133	99.17 104	98.90 245	98.24 122	99.20 45	99.44 117	98.59 124	98.95 161	99.55 52	94.14 278	99.86 114	97.77 141	99.69 166	99.41 170

new_pmnet		96.99 278	96.76 275	97.67 280	98.72 275	94.89 292	95.95 340	98.20 324	92.62 372	98.55 225	98.54 260	94.88 259	99.52 334	93.96 329	99.44 243	98.59 329

FMVSNet3		97.50 236	97.24 247	98.29 236	98.08 350	95.83 262	97.86 203	98.91 264	97.89 178	98.95 161	98.95 188	87.06 347	99.81 185	97.77 141	99.69 166	99.23 229

dp		93.47 358	93.59 351	93.13 396	96.64 402	81.62 420	97.66 228	96.42 371	92.80 370	96.11 364	98.64 248	78.55 397	99.59 309	93.31 346	92.18 414	98.16 357

FMVSNet2		98.49 147	98.40 143	98.75 175	98.90 245	97.14 217	98.61 108	99.13 228	98.59 124	99.19 125	99.28 103	94.14 278	99.82 171	97.97 128	99.80 109	99.29 217

FMVSNet1		99.17 44	99.17 46	99.17 104	99.55 93	98.24 122	99.20 45	99.44 117	99.21 63	99.43 80	99.55 52	97.82 112	99.86 114	98.42 101	99.89 71	99.41 170

N_pmnet		97.63 229	97.17 250	98.99 137	99.27 164	97.86 164	95.98 335	93.41 400	95.25 324	99.47 74	98.90 196	95.63 237	99.85 126	96.91 194	99.73 143	99.27 220

cascas		94.79 338	94.33 344	96.15 358	96.02 413	92.36 359	92.34 409	99.26 195	85.34 408	95.08 385	94.96 396	92.96 299	98.53 404	94.41 318	98.59 337	97.56 386

BH-RMVSNet		96.83 283	96.58 288	97.58 289	98.47 320	94.05 316	96.67 300	97.36 346	96.70 274	97.87 278	97.98 312	95.14 251	99.44 355	90.47 389	98.58 338	99.25 224

UGNet		98.53 142	98.45 136	98.79 166	97.94 355	96.96 224	99.08 58	98.54 309	99.10 83	96.82 343	99.47 69	96.55 197	99.84 144	98.56 95	99.94 38	99.55 108
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		96.67 289	96.27 299	97.87 263	98.81 264	94.61 302	96.77 294	97.92 334	94.94 331	97.12 323	97.74 326	91.11 321	99.82 171	93.89 331	98.15 354	99.18 241

XXY-MVS		99.14 49	99.15 53	99.10 116	99.76 29	97.74 178	98.85 87	99.62 51	98.48 133	99.37 93	99.49 67	98.75 36	99.86 114	98.20 111	99.80 109	99.71 48

EC-MVSNet		99.09 57	99.05 61	99.20 101	99.28 162	98.93 75	99.24 41	99.84 20	99.08 88	98.12 260	98.37 281	98.72 38	99.90 66	99.05 60	99.77 124	98.77 308

sss		97.21 261	96.93 261	98.06 253	98.83 259	95.22 283	96.75 296	98.48 313	94.49 339	97.27 320	97.90 318	92.77 303	99.80 192	96.57 228	99.32 257	99.16 248

Test_1112_low_res		96.99 278	96.55 289	98.31 234	99.35 151	95.47 273	95.84 348	99.53 83	91.51 383	96.80 344	98.48 271	91.36 319	99.83 161	96.58 226	99.53 223	99.62 69

1112_ss		97.29 255	96.86 267	98.58 196	99.34 153	96.32 247	96.75 296	99.58 58	93.14 364	96.89 339	97.48 341	92.11 312	99.86 114	96.91 194	99.54 219	99.57 95

ab-mvs-re		8.12 391	10.83 394	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	97.48 341	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

ab-mvs		98.41 154	98.36 150	98.59 195	99.19 185	97.23 207	99.32 23	98.81 285	97.66 192	98.62 212	99.40 83	96.82 181	99.80 192	95.88 270	99.51 228	98.75 311

TR-MVS		95.55 325	95.12 330	96.86 333	97.54 376	93.94 323	96.49 308	96.53 370	94.36 346	97.03 330	96.61 362	94.26 277	99.16 387	86.91 402	96.31 395	97.47 388

MDTV_nov1_ep13_2view						74.92 423	97.69 223		90.06 396	97.75 288		85.78 358		93.52 341		98.69 318

MDTV_nov1_ep13			95.22 327		97.06 395	83.20 416	97.74 218	96.16 373	94.37 345	96.99 331	98.83 212	83.95 373	99.53 330	93.90 330	97.95 365

MIMVSNet1		99.38 24	99.32 31	99.55 27	99.86 14	99.19 41	99.41 14	99.59 56	99.59 27	99.71 34	99.57 45	97.12 163	99.90 66	99.21 51	99.87 76	99.54 112

MIMVSNet		96.62 292	96.25 300	97.71 279	99.04 221	94.66 300	99.16 51	96.92 362	97.23 243	97.87 278	99.10 146	86.11 356	99.65 289	91.65 371	99.21 278	98.82 296

IterMVS-LS		98.55 138	98.70 98	98.09 248	99.48 122	94.73 297	97.22 270	99.39 135	98.97 100	99.38 91	99.31 100	96.00 220	99.93 42	98.58 90	99.97 19	99.60 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		97.69 224	97.35 241	98.69 181	98.73 273	97.02 221	96.92 288	98.75 295	95.89 306	98.59 218	98.67 240	92.08 313	99.74 240	96.72 216	99.81 99	99.32 208
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 124

IterMVS		97.73 221	98.11 182	96.57 340	99.24 171	90.28 388	95.52 359	99.21 205	98.86 109	99.33 100	99.33 95	93.11 294	99.94 36	98.49 97	99.94 38	99.48 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 251	96.92 263	98.57 199	99.09 209	97.99 150	96.79 292	99.35 150	93.18 363	97.71 289	98.07 307	95.00 255	99.31 373	93.97 328	99.13 290	98.42 344

MVS_111021_LR		98.30 170	98.12 181	98.83 158	99.16 195	98.03 148	96.09 332	99.30 176	97.58 200	98.10 262	98.24 292	98.25 75	99.34 369	96.69 219	99.65 183	99.12 251

DP-MVS		98.93 75	98.81 84	99.28 87	99.21 178	98.45 108	98.46 131	99.33 161	99.63 21	99.48 70	99.15 137	97.23 158	99.75 235	97.17 172	99.66 182	99.63 68

ACMMP++															99.68 171

HQP-MVS		97.00 277	96.49 292	98.55 204	98.67 292	96.79 231	96.29 320	99.04 243	96.05 297	95.55 375	96.84 358	93.84 284	99.54 328	92.82 355	99.26 269	99.32 208

QAPM		97.31 252	96.81 273	98.82 159	98.80 267	97.49 192	99.06 62	99.19 211	90.22 393	97.69 291	99.16 133	96.91 175	99.90 66	90.89 386	99.41 245	99.07 255

Vis-MVSNet		99.34 26	99.36 25	99.27 90	99.73 36	98.26 120	99.17 50	99.78 29	99.11 76	99.27 111	99.48 68	98.82 31	99.95 24	98.94 67	99.93 43	99.59 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		94.32 343	95.62 310	90.42 398	98.46 322	75.36 422	96.29 320	89.13 414	95.25 324	95.38 381	99.75 13	92.88 300	99.19 385	94.07 327	99.39 247	96.72 399

IS-MVSNet		98.19 184	97.90 204	99.08 120	99.57 81	97.97 154	99.31 27	98.32 319	99.01 96	98.98 153	99.03 161	91.59 317	99.79 205	95.49 289	99.80 109	99.48 143

HyFIR lowres test		97.19 263	96.60 287	98.96 141	99.62 75	97.28 204	95.17 369	99.50 89	94.21 348	99.01 150	98.32 288	86.61 350	99.99 2	97.10 180	99.84 85	99.60 78

EPMVS		93.72 355	93.27 354	95.09 378	96.04 412	87.76 400	98.13 160	85.01 419	94.69 336	96.92 333	98.64 248	78.47 398	99.31 373	95.04 296	96.46 393	98.20 355

PAPM_NR		96.82 285	96.32 296	98.30 235	99.07 213	96.69 238	97.48 249	98.76 292	95.81 308	96.61 351	96.47 366	94.12 281	99.17 386	90.82 387	97.78 366	99.06 256

TAMVS		98.24 179	98.05 188	98.80 163	99.07 213	97.18 213	97.88 199	98.81 285	96.66 275	99.17 130	99.21 120	94.81 262	99.77 222	96.96 192	99.88 73	99.44 160

PAPR		95.29 329	94.47 339	97.75 274	97.50 383	95.14 286	94.89 377	98.71 300	91.39 385	95.35 382	95.48 386	94.57 268	99.14 389	84.95 405	97.37 378	98.97 274

RPSCF		98.62 128	98.36 150	99.42 60	99.65 63	99.42 11	98.55 114	99.57 65	97.72 189	98.90 172	99.26 109	96.12 215	99.52 334	95.72 280	99.71 156	99.32 208

Vis-MVSNet (Re-imp)		97.46 240	97.16 251	98.34 231	99.55 93	96.10 251	98.94 77	98.44 314	98.32 141	98.16 255	98.62 252	88.76 337	99.73 245	93.88 332	99.79 114	99.18 241

test_0402		98.76 99	98.71 95	98.93 146	99.56 89	98.14 132	98.45 133	99.34 156	99.28 57	98.95 161	98.91 193	98.34 69	99.79 205	95.63 284	99.91 61	98.86 293

MVS_111021_HR		98.25 178	98.08 186	98.75 175	99.09 209	97.46 194	95.97 336	99.27 190	97.60 199	97.99 271	98.25 291	98.15 90	99.38 364	96.87 202	99.57 211	99.42 167

CSCG		98.68 116	98.50 126	99.20 101	99.45 128	98.63 91	98.56 113	99.57 65	97.87 179	98.85 182	98.04 309	97.66 122	99.84 144	96.72 216	99.81 99	99.13 250

PatchMatch-RL		97.24 259	96.78 274	98.61 192	99.03 224	97.83 167	96.36 315	99.06 237	93.49 361	97.36 318	97.78 323	95.75 234	99.49 343	93.44 344	98.77 321	98.52 332

API-MVS		97.04 273	96.91 265	97.42 305	97.88 358	98.23 126	98.18 154	98.50 312	97.57 201	97.39 316	96.75 360	96.77 185	99.15 388	90.16 390	99.02 303	94.88 411

Test By Simon												96.52 198

TDRefinement		99.42 20	99.38 24	99.55 27	99.76 29	99.33 20	99.68 6	99.71 36	99.38 45	99.53 61	99.61 39	98.64 44	99.80 192	98.24 108	99.84 85	99.52 123

USDC		97.41 246	97.40 236	97.44 304	98.94 235	93.67 335	95.17 369	99.53 83	94.03 353	98.97 157	99.10 146	95.29 247	99.34 369	95.84 276	99.73 143	99.30 215

EPP-MVSNet		98.30 170	98.04 189	99.07 122	99.56 89	97.83 167	99.29 33	98.07 330	99.03 94	98.59 218	99.13 141	92.16 311	99.90 66	96.87 202	99.68 171	99.49 133

PMMVS		96.51 294	95.98 301	98.09 248	97.53 378	95.84 261	94.92 376	98.84 280	91.58 381	96.05 367	95.58 381	95.68 236	99.66 284	95.59 286	98.09 357	98.76 310

PAPM		91.88 380	90.34 383	96.51 341	98.06 351	92.56 353	92.44 408	97.17 352	86.35 405	90.38 412	96.01 372	86.61 350	99.21 384	70.65 418	95.43 403	97.75 378

ACMMP		98.75 100	98.50 126	99.52 42	99.56 89	99.16 47	98.87 84	99.37 141	97.16 249	98.82 188	99.01 171	97.71 119	99.87 106	96.29 252	99.69 166	99.54 112
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		97.17 265	96.71 278	98.55 204	98.56 312	98.05 147	96.33 317	98.93 259	96.91 262	97.06 327	97.39 346	94.38 273	99.45 353	91.66 370	99.18 284	98.14 358

PatchmatchNet		95.58 324	95.67 309	95.30 375	97.34 387	87.32 402	97.65 230	96.65 366	95.30 323	97.07 326	98.69 236	84.77 365	99.75 235	94.97 299	98.64 333	98.83 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 173	97.95 198	99.34 75	98.44 325	99.16 47	98.12 163	99.38 137	96.01 301	98.06 265	98.43 275	97.80 114	99.67 273	95.69 282	99.58 207	99.20 234

F-COLMAP		97.30 253	96.68 280	99.14 110	99.19 185	98.39 110	97.27 266	99.30 176	92.93 367	96.62 350	98.00 310	95.73 235	99.68 270	92.62 361	98.46 341	99.35 199

ANet_high		99.57 7	99.67 5	99.28 87	99.89 6	98.09 137	99.14 54	99.93 5	99.82 5	99.93 6	99.81 6	99.17 18	99.94 36	99.31 42	100.00 1	99.82 27

wuyk23d		96.06 308	97.62 225	91.38 397	98.65 301	98.57 98	98.85 87	96.95 360	96.86 265	99.90 12	99.16 133	99.18 17	98.40 405	89.23 394	99.77 124	77.18 417

OMC-MVS		97.88 207	97.49 232	99.04 131	98.89 250	98.63 91	96.94 284	99.25 196	95.02 328	98.53 228	98.51 264	97.27 155	99.47 349	93.50 343	99.51 228	99.01 265

MG-MVS		96.77 286	96.61 285	97.26 312	98.31 335	93.06 343	95.93 341	98.12 329	96.45 284	97.92 273	98.73 229	93.77 288	99.39 362	91.19 381	99.04 299	99.33 206

AdaColmap		97.14 267	96.71 278	98.46 217	98.34 333	97.80 174	96.95 283	98.93 259	95.58 314	96.92 333	97.66 330	95.87 231	99.53 330	90.97 383	99.14 288	98.04 363

uanet		0.00 392	0.00 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.00 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

ITE_SJBPF				98.87 154	99.22 176	98.48 106		99.35 150	97.50 209	98.28 248	98.60 255	97.64 126	99.35 368	93.86 333	99.27 266	98.79 306

DeepMVS_CX				93.44 393	98.24 339	94.21 311		94.34 392	64.28 417	91.34 411	94.87 399	89.45 335	92.77 418	77.54 416	93.14 411	93.35 413

TinyColmap		97.89 205	97.98 195	97.60 287	98.86 253	94.35 308	96.21 324	99.44 117	97.45 219	99.06 139	98.88 203	97.99 102	99.28 379	94.38 319	99.58 207	99.18 241

MAR-MVS		96.47 298	95.70 307	98.79 166	97.92 356	99.12 61	98.28 145	98.60 307	92.16 377	95.54 378	96.17 371	94.77 265	99.52 334	89.62 392	98.23 347	97.72 380
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		97.90 203	97.69 217	98.52 209	99.17 193	97.66 183	97.19 274	99.47 106	96.31 289	97.85 281	98.20 296	96.71 191	99.52 334	94.62 307	99.72 151	98.38 347

MSDG		97.71 223	97.52 230	98.28 237	98.91 244	96.82 230	94.42 390	99.37 141	97.65 193	98.37 243	98.29 290	97.40 148	99.33 371	94.09 326	99.22 275	98.68 321

LS3D		98.63 125	98.38 148	99.36 66	97.25 389	99.38 12	99.12 57	99.32 163	99.21 63	98.44 235	98.88 203	97.31 151	99.80 192	96.58 226	99.34 255	98.92 283

CLD-MVS		97.49 238	97.16 251	98.48 215	99.07 213	97.03 220	94.71 380	99.21 205	94.46 341	98.06 265	97.16 353	97.57 132	99.48 346	94.46 312	99.78 119	98.95 277
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 359	92.23 365	97.08 319	99.25 170	97.86 164	95.61 354	97.16 353	92.90 368	93.76 402	98.65 245	75.94 400	95.66 415	79.30 415	97.49 371	97.73 379

Gipuma		99.03 63	99.16 48	98.64 184	99.94 2	98.51 104	99.32 23	99.75 34	99.58 29	98.60 216	99.62 36	98.22 80	99.51 339	97.70 146	99.73 143	97.89 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark