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.

test2506		99.48 82	99.38 83	99.75 139	99.89 122	99.51 150	99.45 436	100.00 1	98.38 105	99.83 217	100.00 1	98.86 132	99.81 254	99.25 262	98.78 209	99.94 154

test1111		98.42 256	98.12 269	99.29 247	99.88 124	98.15 306	99.46 434	100.00 1	98.36 109	99.42 278	100.00 1	87.91 415	99.79 259	99.31 259	98.78 209	99.94 154

ECVR-MVS		98.43 254	98.14 268	99.32 238	99.89 122	98.21 300	99.46 434	100.00 1	98.38 105	99.47 274	100.00 1	87.91 415	99.80 258	99.35 254	98.78 209	99.94 154

PVSNet_BlendedMVS		98.71 213	98.62 203	98.98 275	99.98 94	99.60 132	100.00 1	100.00 1	97.23 224	100.00 1	99.03 431	96.57 229	99.99 107	100.00 1	94.75 372	97.35 459

PVSNet_Blended		99.48 82	99.36 89	99.83 110	99.98 94	99.60 132	100.00 1	100.00 1	97.79 155	100.00 1	100.00 1	96.57 229	99.99 107	100.00 1	99.88 152	99.90 182

UGNet		98.41 258	98.11 270	99.31 240	99.54 251	98.55 256	99.18 468	100.00 1	98.64 91	99.79 234	99.04 428	87.61 420	100.00 1	99.30 260	99.89 149	99.40 332
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		99.54 74	99.40 81	99.95 61	99.81 144	99.93 53	100.00 1	100.00 1	97.98 137	99.84 214	100.00 1	98.94 125	99.98 141	99.86 128	98.21 270	99.94 154

HY-MVS	96.53 9	99.50 78	99.35 91	99.96 52	99.81 144	99.93 53	99.64 412	100.00 1	97.97 139	99.84 214	99.85 320	98.94 125	99.99 107	99.86 128	98.23 269	99.95 149

EPMVS		99.25 126	99.13 126	99.60 168	99.60 228	99.20 195	99.60 419	100.00 1	96.93 252	99.92 198	99.36 407	99.05 107	99.71 286	98.77 293	98.94 206	99.90 182

MVS_111021_HR		99.71 36	99.63 44	99.93 78	99.95 108	99.83 98	100.00 1	100.00 1	98.89 60	100.00 1	100.00 1	97.85 176	99.95 183	100.00 1	100.00 1	100.00 1

PatchMatch-RL		99.02 161	98.78 173	99.74 140	99.99 53	99.29 181	100.00 1	100.00 1	98.38 105	99.89 207	99.81 330	93.14 320	99.99 107	97.85 338	99.98 118	99.95 149

PVSNet_0	93.57 19	96.41 365	95.74 382	98.41 315	99.84 131	95.22 404	100.00 1	100.00 1	98.08 130	97.55 419	99.78 340	84.40 446	100.00 1	100.00 1	81.99 494	100.00 1

CHOSEN 1792x2688		99.00 164	98.91 160	99.25 255	99.90 120	97.79 337	100.00 1	99.99 13	98.79 80	98.28 380	100.00 1	93.63 303	99.95 183	99.66 194	99.95 128	100.00 1

HyFIR lowres test		99.32 110	99.24 108	99.58 174	99.95 108	99.26 186	100.00 1	99.99 13	96.72 285	99.29 292	99.91 307	99.49 46	99.47 330	99.74 159	98.08 284	100.00 1

AdaColmap		99.44 88	99.26 102	99.95 61	100.00 1	99.86 90	99.70 405	99.99 13	98.53 94	99.90 203	100.00 1	95.34 251	100.00 1	99.92 117	100.00 1	100.00 1

CHOSEN 280x420		99.85 5	99.87 1	99.80 123	99.99 53	99.97 27	99.97 312	99.98 16	98.96 39	100.00 1	100.00 1	99.96 4	99.42 340	100.00 1	100.00 1	100.00 1

MM		99.63 58	99.52 68	99.94 74	99.99 53	99.82 99	100.00 1	99.97 17	99.11 10	100.00 1	100.00 1	96.65 227	100.00 1	100.00 1	99.97 122	100.00 1

MVS_111021_LR		99.70 39	99.65 37	99.88 95	99.96 104	99.70 121	100.00 1	99.97 17	98.96 39	100.00 1	100.00 1	97.93 170	99.95 183	99.99 77	100.00 1	100.00 1

aaatest				99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.10 12	99.99 129	100.00 1		100.00 1	100.00 1	100.00 1	100.00 1

MED-MVS		99.89 1	99.86 2	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.18 6	99.99 129	100.00 1	99.58 27	100.00 1	100.00 1	100.00 1	100.00 1

TestfortrainingZip a		99.85 5	99.81 6	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.18 6	100.00 1	100.00 1	99.45 53	99.99 107	99.68 183	99.99 107	100.00 1

TestfortrainingZip				100.00 1	99.99 53	100.00 1	100.00 1	99.95 19	99.03 25	100.00 1	100.00 1	99.59 24	100.00 1		100.00 1	100.00 1

aaEdge-Enhanced		99.87 3	99.83 4	99.99 13	99.99 53	99.98 18	100.00 1	99.95 19	99.05 18	100.00 1	100.00 1	99.50 43	100.00 1	100.00 1	100.00 1	100.00 1

MSLP-MVS++		99.89 1	99.85 3	99.99 13	100.00 1	99.96 30	100.00 1	99.95 19	99.11 10	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 108	99.18 121	99.78 133	99.82 138	99.49 156	100.00 1	99.95 19	97.36 208	99.63 260	100.00 1	96.45 233	99.95 183	99.79 143	99.65 183	99.89 190

MAR-MVS		99.49 80	99.36 89	99.89 90	99.97 98	99.66 126	99.74 394	99.95 19	97.89 146	100.00 1	100.00 1	96.71 226	100.00 1	100.00 1	100.00 1	100.00 1
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
thres100view900		99.25 126	99.01 141	99.95 61	99.81 144	99.87 87	100.00 1	99.94 27	97.13 232	99.83 217	99.96 282	97.01 211	100.00 1	99.59 213	97.85 301	99.98 127

tfpn200view9		99.26 122	99.03 137	99.96 52	99.81 144	99.89 78	100.00 1	99.94 27	97.23 224	99.83 217	99.96 282	97.04 207	100.00 1	99.59 213	97.85 301	99.98 127

MVS		99.22 130	98.96 150	99.98 28	99.00 374	99.95 38	99.24 458	99.94 27	98.14 124	98.88 326	100.00 1	95.63 247	100.00 1	99.85 131	100.00 1	100.00 1

thres600view7		99.24 129	99.00 144	99.95 61	99.81 144	99.87 87	100.00 1	99.94 27	97.13 232	99.83 217	99.96 282	97.01 211	100.00 1	99.54 226	97.77 310	99.97 137

thres400		99.26 122	99.03 137	99.95 61	99.81 144	99.89 78	100.00 1	99.94 27	97.23 224	99.83 217	99.96 282	97.04 207	100.00 1	99.59 213	97.85 301	99.97 137

thres200		99.27 120	99.04 136	99.96 52	99.81 144	99.90 71	100.00 1	99.94 27	97.31 217	99.83 217	99.96 282	97.04 207	100.00 1	99.62 206	97.88 299	99.98 127

API-MVS		99.72 32	99.70 24	99.79 128	99.97 98	99.37 173	99.96 320	99.94 27	98.48 98	100.00 1	100.00 1	98.92 128	100.00 1	100.00 1	100.00 1	100.00 1

PHI-MVS		99.50 78	99.39 82	99.82 112	100.00 1	99.45 162	100.00 1	99.94 27	96.38 328	100.00 1	100.00 1	98.18 163	100.00 1	100.00 1	100.00 1	100.00 1

1314		99.38 96	99.19 118	99.96 52	98.88 388	99.89 78	99.24 458	99.93 35	98.88 61	98.79 336	100.00 1	97.02 210	100.00 1	100.00 1	100.00 1	100.00 1

test0.0.03 1		98.12 278	98.03 279	98.39 316	99.11 355	98.07 313	100.00 1	99.93 35	96.70 291	96.91 434	99.95 290	99.31 76	98.19 448	91.93 453	98.44 225	98.91 341

QAPM		98.99 168	98.66 196	99.96 52	99.01 369	99.87 87	99.88 361	99.93 35	97.99 135	98.68 341	100.00 1	93.17 316	100.00 1	99.32 258	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 149	98.76 177	99.96 52	98.86 393	99.90 71	99.98 302	99.93 35	98.95 42	98.49 364	100.00 1	92.91 325	100.00 1	99.71 169	100.00 1	100.00 1

VDDNet		96.39 369	95.55 391	98.90 280	99.27 345	97.45 348	99.15 476	99.92 39	91.28 455	99.98 139	100.00 1	73.55 487	100.00 1	99.85 131	96.98 327	99.24 335

MGCNet		99.72 32	99.65 37	99.93 78	99.99 53	99.79 103	100.00 1	99.91 40	99.17 8	100.00 1	100.00 1	97.84 178	100.00 1	100.00 1	99.95 128	100.00 1

sss		99.45 86	99.34 93	99.80 123	99.76 170	99.50 152	100.00 1	99.91 40	97.72 160	99.98 139	99.94 296	98.45 155	100.00 1	99.53 229	98.75 212	99.89 190

EPNet		99.62 63	99.69 25	99.42 204	99.99 53	98.37 278	100.00 1	99.89 42	98.83 70	100.00 1	100.00 1	98.97 119	100.00 1	99.90 120	99.61 187	99.89 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	94.91 18	99.30 114	99.25 104	99.44 198	100.00 1	98.32 288	100.00 1	99.86 43	98.04 132	100.00 1	100.00 1	96.10 237	100.00 1	99.55 222	99.73 173	100.00 1

HPM-MVS++		99.82 12	99.76 15	99.99 13	99.99 53	99.98 18	100.00 1	99.83 44	98.88 61	99.96 153	100.00 1	99.21 89	100.00 1	100.00 1	100.00 1	99.99 124

D2MVS		97.63 304	97.83 290	97.05 396	98.83 396	94.60 427	100.00 1	99.82 45	96.89 258	98.28 380	99.03 431	94.05 290	99.47 330	98.58 308	94.97 369	97.09 465

旧先验1					99.99 53	99.88 85		99.82 45			100.00 1	99.27 85			100.00 1	100.00 1

新几何1				99.99 13	100.00 1	99.96 30		99.81 47	97.89 146	100.00 1	100.00 1	99.20 90	100.00 1	97.91 336	100.00 1	100.00 1

ZD-MVS					100.00 1	99.98 18		99.80 48	97.31 217	100.00 1	100.00 1	99.32 74	99.99 107	100.00 1	100.00 1

无先验							100.00 1	99.80 48	97.98 137				100.00 1	99.33 257		100.00 1

ZNCC-MVS		99.71 36	99.62 47	99.97 40	99.99 53	99.90 71	100.00 1	99.79 50	97.97 139	99.97 145	100.00 1	98.97 119	100.00 1	99.94 114	100.00 1	100.00 1

GST-MVS		99.64 54	99.53 65	99.95 61	100.00 1	99.86 90	100.00 1	99.79 50	97.72 160	99.95 185	100.00 1	98.39 159	100.00 1	99.96 106	99.99 107	100.00 1

DELS-MVS		99.62 63	99.56 60	99.82 112	99.92 116	99.45 162	100.00 1	99.78 52	98.92 52	99.73 246	100.00 1	97.70 184	100.00 1	99.93 116	100.00 1	100.00 1
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
CNVR-MVS		99.85 5	99.80 7	100.00 1	100.00 1	99.99 6	100.00 1	99.77 53	99.07 14	100.00 1	100.00 1	99.39 69	100.00 1	100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 28	99.67 33	99.96 52	100.00 1	99.89 78	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.31 76	100.00 1	99.99 77	100.00 1	100.00 1

ACMMPR		99.74 28	99.67 33	99.96 52	100.00 1	99.89 78	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.29 82	100.00 1	99.99 77	100.00 1	100.00 1

OpenMVS	95.20 17	98.76 205	98.41 236	99.78 133	98.89 387	99.81 100	99.99 269	99.76 54	98.02 133	98.02 395	100.00 1	91.44 347	100.00 1	99.63 204	99.97 122	99.55 323

XVS		99.79 17	99.73 20	99.98 28	100.00 1	99.94 47	100.00 1	99.75 57	98.67 88	100.00 1	100.00 1	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 336	96.06 365	99.98 28	100.00 1	99.94 47	100.00 1	99.75 57	98.67 88	100.00 1	66.97 557	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 19	99.76 15	99.85 104	99.01 369	99.95 38	100.00 1	99.75 57	99.37 3	99.99 129	100.00 1	99.76 12	99.60 294	100.00 1	100.00 1	100.00 1

region2R		99.72 32	99.64 40	99.97 40	100.00 1	99.90 71	100.00 1	99.74 60	97.86 149	100.00 1	100.00 1	99.19 91	100.00 1	99.99 77	100.00 1	100.00 1

CANet		99.40 92	99.24 108	99.89 90	99.99 53	99.76 108	100.00 1	99.73 61	98.40 102	99.78 236	100.00 1	95.28 252	99.96 170	100.00 1	99.99 107	99.96 143

test_prior				99.90 87	100.00 1	99.75 109		99.73 61					99.97 150			100.00 1

testdata				99.66 157	99.99 53	98.97 222		99.73 61	97.96 142	100.00 1	100.00 1	99.42 64	100.00 1	99.28 261	100.00 1	100.00 1

MCST-MVS		99.85 5	99.80 7	100.00 1	100.00 1	99.99 6	100.00 1	99.73 61	99.19 5	100.00 1	100.00 1	99.31 76	100.00 1	100.00 1	100.00 1	100.00 1

ACMMP		99.65 52	99.57 55	99.89 90	99.99 53	99.66 126	99.75 393	99.73 61	98.16 121	99.75 240	100.00 1	98.90 130	100.00 1	99.96 106	99.88 152	100.00 1
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
NCCC		99.86 4	99.82 5	100.00 1	100.00 1	99.99 6	100.00 1	99.71 66	99.07 14	100.00 1	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	100.00 1

test-260524					100.00 1	99.98 18		99.69 67		100.00 1		99.45 53	100.00 1	100.00 1	100.00 1

test222					99.99 53	99.90 71	100.00 1	99.69 67	97.66 166	100.00 1	100.00 1	99.30 81			100.00 1	100.00 1

原ACMM1				99.93 78	100.00 1	99.80 102		99.66 69	98.18 120	100.00 1	100.00 1	99.43 60	100.00 1	99.50 233	100.00 1	100.00 1

SR-MVS		99.68 46	99.58 52	99.98 28	100.00 1	99.95 38	100.00 1	99.64 70	97.59 181	100.00 1	100.00 1	98.99 114	99.99 107	100.00 1	100.00 1	100.00 1

ab-mvs		98.42 256	98.02 280	99.61 165	99.71 177	99.00 218	99.10 482	99.64 70	96.70 291	99.04 315	99.81 330	90.64 365	99.98 141	99.64 197	97.93 296	99.84 222

testing3		98.44 253	98.37 249	98.65 297	99.51 276	98.32 288	100.00 1	99.62 72	96.43 321	97.93 399	99.99 244	99.11 98	97.81 474	94.88 421	97.80 307	99.82 232

PGM-MVS		99.69 42	99.61 48	99.95 61	99.99 53	99.85 94	100.00 1	99.58 73	97.69 164	100.00 1	100.00 1	99.44 56	100.00 1	99.79 143	100.00 1	100.00 1

Syy-MVS		96.17 382	96.57 342	95.00 448	99.50 285	87.37 489	100.00 1	99.57 74	96.23 340	98.07 390	100.00 1	92.41 338	97.81 474	85.34 494	97.96 292	99.82 232

myMVS_eth3d		98.52 248	98.51 225	98.53 305	99.50 285	97.98 320	100.00 1	99.57 74	96.23 340	98.07 390	100.00 1	99.09 100	97.81 474	96.17 393	97.96 292	99.82 232

LFMVS		97.42 317	96.62 340	99.81 117	99.80 157	99.50 152	99.16 474	99.56 76	94.48 401	100.00 1	100.00 1	79.35 470	100.00 1	99.89 122	97.37 320	99.94 154

PLC	98.56 2	99.70 39	99.74 19	99.58 174	100.00 1	98.79 236	100.00 1	99.54 77	98.58 93	99.96 153	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 154
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GG-mvs-BLEND				99.59 170	99.54 251	99.49 156	99.17 473	99.52 78		99.96 153	99.68 357	100.00 1	99.33 347	99.71 169	99.99 107	99.96 143

gg-mvs-nofinetune		96.95 341	96.10 363	99.50 184	99.41 322	99.36 176	99.07 487	99.52 78	83.69 499	99.96 153	83.60 542	100.00 1	99.20 354	99.68 183	99.99 107	99.96 143

DP-MVS Recon		99.76 21	99.69 25	99.98 28	100.00 1	99.95 38	100.00 1	99.52 78	97.99 135	99.99 129	100.00 1	99.72 14	100.00 1	99.96 106	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 26	99.67 33	99.99 13	99.99 53	99.96 30	99.73 399	99.52 78	99.06 16	100.00 1	100.00 1	98.80 139	100.00 1	99.95 112	100.00 1	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
alignmvs		99.38 96	99.21 113	99.91 83	99.73 175	99.92 60	100.00 1	99.51 82	97.61 177	100.00 1	100.00 1	99.06 105	99.93 200	99.83 135	97.12 323	99.90 182

3Dnovator+	95.58 15	99.03 155	98.71 188	99.96 52	98.99 377	99.89 78	100.00 1	99.51 82	98.96 39	98.32 377	100.00 1	92.78 327	100.00 1	99.87 127	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 52	99.55 62	99.97 40	99.99 53	99.91 64	100.00 1	99.48 84	97.54 185	100.00 1	100.00 1	98.97 119	99.99 107	99.98 92	100.00 1	100.00 1

lecture		99.64 54	99.53 65	99.98 28	99.99 53	99.93 53	100.00 1	99.47 85	98.53 94	100.00 1	100.00 1	97.88 174	100.00 1	99.98 92	99.92 141	100.00 1

SymmetryMVS		99.30 114	99.25 104	99.45 195	99.79 162	98.55 256	99.94 337	99.47 85	98.39 103	100.00 1	100.00 1	98.44 156	99.98 141	99.36 250	97.83 304	99.83 225

testing3-2		99.45 86	99.31 94	99.86 100	99.70 180	99.73 114	100.00 1	99.47 85	97.46 198	99.97 145	99.97 264	99.48 50	100.00 1	99.78 149	97.99 289	99.85 220

test_yl		99.51 75	99.37 86	99.95 61	99.82 138	99.90 71	100.00 1	99.47 85	97.48 196	100.00 1	100.00 1	99.80 6	100.00 1	99.98 92	97.75 311	99.94 154

Anonymous20240529		96.93 342	96.22 359	99.05 267	99.79 162	97.30 357	99.16 474	99.47 85	88.51 474	98.69 339	100.00 1	83.50 454	100.00 1	99.83 135	97.02 326	99.83 225

DCV-MVSNet		99.51 75	99.37 86	99.95 61	99.82 138	99.90 71	100.00 1	99.47 85	97.48 196	100.00 1	100.00 1	99.80 6	100.00 1	99.98 92	97.75 311	99.94 154

CVMVSNet		98.56 242	98.47 228	98.82 287	99.11 355	97.67 340	99.74 394	99.47 85	97.57 183	99.06 312	100.00 1	95.72 244	98.97 371	98.21 324	97.33 321	99.83 225

EU-MVSNet		96.63 354	96.53 343	96.94 403	97.59 461	96.87 372	99.76 391	99.47 85	96.35 333	96.85 436	99.78 340	92.57 335	96.27 495	95.33 413	91.08 424	97.68 425

VNet		99.04 152	98.75 178	99.90 87	99.81 144	99.75 109	99.50 431	99.47 85	98.36 109	100.00 1	99.99 244	94.66 275	100.00 1	99.90 120	97.09 324	99.96 143

VPA-MVSNet		97.03 337	96.43 349	98.82 287	98.64 402	99.32 178	99.38 444	99.47 85	96.73 283	98.91 325	98.94 441	87.00 427	99.40 341	99.23 265	89.59 439	97.76 351

tpmvs		98.59 235	98.38 247	99.23 256	99.69 183	97.90 328	99.31 452	99.47 85	94.52 399	99.68 251	99.28 411	97.64 187	99.89 221	97.71 346	98.17 276	99.89 190

EPNet_dtu		98.53 247	98.23 265	99.43 201	99.92 116	99.01 215	99.96 320	99.47 85	98.80 77	99.96 153	99.96 282	98.56 151	99.30 348	87.78 486	99.68 178	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM_NR		99.74 28	99.66 36	99.99 13	100.00 1	99.96 30	100.00 1	99.47 85	97.87 148	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 21	99.68 31	99.99 13	100.00 1	99.96 30	100.00 1	99.47 85	98.16 121	100.00 1	100.00 1	99.51 39	100.00 1	100.00 1	100.00 1	100.00 1

IB-MVS	96.24 12	97.54 311	96.95 328	99.33 232	99.67 196	98.10 311	100.00 1	99.47 85	97.42 204	99.26 293	99.69 353	98.83 136	99.89 221	99.43 245	78.77 508	100.00 1
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
CSCG		99.28 119	99.35 91	99.05 267	99.99 53	97.15 364	100.00 1	99.47 85	97.44 202	99.42 278	100.00 1	97.83 180	100.00 1	99.99 77	100.00 1	100.00 1

CNLPA		99.72 32	99.65 37	99.91 83	99.97 98	99.72 116	100.00 1	99.47 85	98.43 101	99.88 210	100.00 1	99.14 97	100.00 1	99.97 104	100.00 1	100.00 1

MG-MVS		99.75 26	99.68 31	99.97 40	100.00 1	99.91 64	99.98 302	99.47 85	99.09 13	100.00 1	100.00 1	98.59 150	100.00 1	99.95 112	100.00 1	100.00 1

dmvs_re		97.54 311	97.88 288	96.54 423	99.55 248	90.35 476	99.86 364	99.46 103	97.00 244	99.41 283	100.00 1	90.78 363	99.30 348	99.60 211	95.24 356	99.96 143

thisisatest0530		99.37 99	99.27 98	99.69 150	99.59 232	99.41 168	100.00 1	99.46 103	96.46 320	99.90 203	100.00 1	99.44 56	99.85 240	98.97 282	99.58 188	99.80 280

Anonymous202405211		97.87 290	97.53 302	98.90 280	99.81 144	96.70 377	99.35 447	99.46 103	92.98 442	98.83 333	99.99 244	90.63 366	100.00 1	99.70 173	97.03 325	100.00 1

tttt0517		99.34 105	99.23 111	99.67 154	99.57 241	99.38 170	100.00 1	99.46 103	96.33 335	99.89 207	100.00 1	99.44 56	99.84 244	98.93 284	99.46 192	99.78 291

thisisatest0515		99.42 90	99.31 94	99.74 140	99.59 232	99.55 140	100.00 1	99.46 103	96.65 298	99.92 198	100.00 1	99.44 56	99.85 240	99.09 277	99.63 186	99.81 249

baseline2		98.99 168	98.93 157	99.18 260	99.26 347	99.15 201	100.00 1	99.46 103	96.71 290	96.79 438	100.00 1	99.42 64	99.25 351	98.75 295	99.94 134	99.15 337

MDTV_nov1_ep13			98.94 155		99.53 255	98.36 281	99.39 443	99.46 103	96.54 310	99.99 129	99.63 371	98.92 128	99.86 233	98.30 321	98.71 213

LS3D		99.31 112	99.13 126	99.87 97	99.99 53	99.71 117	99.55 425	99.46 103	97.32 215	99.82 226	100.00 1	96.85 221	99.97 150	99.14 271	100.00 1	99.92 167

ETVMVS		99.16 138	98.98 147	99.69 150	99.67 196	99.56 138	100.00 1	99.45 111	96.36 331	99.98 139	99.95 290	98.65 146	99.64 292	99.11 275	97.63 318	99.88 203

SDMVSNet		98.49 251	98.08 274	99.73 143	99.82 138	99.53 145	99.99 269	99.45 111	97.62 173	99.38 286	99.86 315	90.06 384	99.88 229	99.92 117	96.61 336	99.79 286

sd_testset		97.81 295	97.48 303	98.79 291	99.82 138	96.80 374	99.32 449	99.45 111	97.62 173	99.38 286	99.86 315	85.56 441	99.77 267	99.72 165	96.61 336	99.79 286

tt0805		96.52 358	96.23 358	97.40 380	99.30 341	93.55 445	99.32 449	99.45 111	96.75 276	97.88 402	99.99 244	79.99 468	99.59 296	97.39 360	95.98 340	99.06 340

h-mvs33		97.03 337	96.53 343	98.51 306	99.79 162	95.90 391	99.45 436	99.45 111	98.21 117	100.00 1	99.78 340	97.49 195	99.99 107	99.72 165	74.92 511	99.65 319

tfpnnormal		96.36 370	95.69 387	98.37 318	98.55 406	98.71 243	99.69 407	99.45 111	93.16 440	96.69 442	99.71 347	88.44 414	98.99 368	94.17 430	91.38 421	97.41 456

SCA		98.30 266	97.98 282	99.23 256	99.41 322	98.25 296	99.99 269	99.45 111	96.91 255	99.76 239	99.58 381	89.65 393	99.54 315	98.31 318	98.79 208	99.91 171

UA-Net		99.06 149	98.83 167	99.74 140	99.52 269	99.40 169	99.08 485	99.45 111	97.64 170	99.83 217	100.00 1	95.80 242	99.94 196	98.35 316	99.80 171	99.88 203

VPNet		96.41 365	95.76 381	98.33 321	98.61 403	98.30 291	99.48 432	99.45 111	96.98 246	98.87 328	99.88 312	81.57 462	98.93 375	99.22 267	87.82 458	97.76 351

test-LLR		99.03 155	98.91 160	99.40 210	99.40 327	99.28 183	100.00 1	99.45 111	96.70 291	99.42 278	99.12 420	99.31 76	99.01 365	96.82 377	99.99 107	99.91 171

TESTMET0.1,1		99.08 145	98.96 150	99.44 198	99.63 216	99.38 170	100.00 1	99.45 111	95.53 366	99.48 271	100.00 1	99.71 15	99.02 363	96.84 376	99.99 107	99.91 171

test-mter		98.96 176	98.82 168	99.40 210	99.40 327	99.28 183	100.00 1	99.45 111	95.44 377	99.42 278	99.12 420	99.70 16	99.01 365	96.82 377	99.99 107	99.91 171

UniMVSNet_NR-MVSNet		97.16 329	96.80 333	98.22 332	98.38 413	98.41 270	100.00 1	99.45 111	96.14 346	97.76 406	99.64 367	95.05 262	98.50 421	97.98 332	86.84 468	97.75 362

PatchmatchNet		99.03 155	98.96 150	99.26 254	99.49 289	98.33 286	99.38 444	99.45 111	96.64 299	99.96 153	99.58 381	99.49 46	99.50 326	97.63 349	99.00 205	99.93 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KinetiMVS		98.61 232	98.26 258	99.65 159	99.46 306	99.24 190	99.96 320	99.44 125	97.54 185	99.99 129	99.99 244	90.83 362	99.95 183	97.18 365	99.92 141	99.75 295

dongtai		98.29 269	98.25 259	98.42 314	99.58 237	95.86 392	100.00 1	99.44 125	93.46 431	99.69 250	99.97 264	97.53 193	99.51 323	96.28 392	98.27 262	99.89 190

kuosan		98.55 243	98.53 219	98.62 299	99.66 205	96.16 387	100.00 1	99.44 125	93.93 418	99.81 232	99.98 252	97.58 188	99.81 254	98.08 327	98.28 259	99.89 190

EI-MVSNet-UG-set		99.69 42	99.63 44	99.87 97	99.99 53	99.64 128	99.95 329	99.44 125	98.35 111	100.00 1	100.00 1	98.98 117	99.97 150	99.98 92	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 40	99.87 97	100.00 1	99.64 128	99.98 302	99.44 125	98.35 111	99.99 129	100.00 1	99.04 110	99.96 170	99.98 92	100.00 1	100.00 1

APDe-MVS		99.84 9	99.78 10	99.99 13	100.00 1	99.98 18	100.00 1	99.44 125	99.06 16	100.00 1	100.00 1	99.56 29	99.99 107	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ADS-MVSNet		98.70 216	98.51 225	99.28 250	99.51 276	98.39 274	99.24 458	99.44 125	95.52 368	99.96 153	99.70 350	97.57 190	99.58 300	97.11 367	98.54 219	99.88 203

baseline1		98.91 186	98.61 204	99.81 117	99.71 177	99.77 107	99.78 384	99.44 125	97.51 192	98.81 334	99.99 244	98.25 161	99.76 272	98.60 306	95.41 347	99.89 190

F-COLMAP		99.64 54	99.64 40	99.67 154	99.99 53	99.07 206	100.00 1	99.44 125	98.30 114	99.90 203	100.00 1	99.18 92	99.99 107	99.91 119	100.00 1	99.94 154

FBQ-MVS		99.13 141	99.11 129	99.21 258	99.64 213	97.94 325	100.00 1	99.43 134	96.78 269	99.97 145	99.92 303	99.03 113	99.84 244	99.18 270	98.01 287	99.86 218

myMVS_eth3d28		99.41 91	99.28 96	99.80 123	99.69 183	99.53 145	100.00 1	99.43 134	97.12 234	99.98 139	99.97 264	99.41 66	100.00 1	99.81 142	98.07 285	99.88 203

UWE-MVS-28		99.29 117	99.23 111	99.48 189	99.73 175	98.86 230	100.00 1	99.43 134	96.97 248	99.99 129	99.83 323	99.43 60	99.77 267	99.35 254	98.31 253	99.80 280

testing91		99.18 134	99.10 130	99.41 205	99.60 228	98.43 268	100.00 1	99.43 134	96.76 273	99.82 226	99.92 303	99.05 107	99.98 141	99.62 206	97.67 315	99.81 249

testing11		99.26 122	99.19 118	99.46 191	99.64 213	98.61 252	100.00 1	99.43 134	96.94 251	99.92 198	99.94 296	99.43 60	99.97 150	99.67 187	97.79 309	99.82 232

testing99		99.18 134	99.10 130	99.41 205	99.60 228	98.43 268	100.00 1	99.43 134	96.76 273	99.84 214	99.92 303	99.06 105	99.98 141	99.62 206	97.67 315	99.81 249

UBG		99.36 100	99.27 98	99.63 161	99.63 216	99.01 215	100.00 1	99.43 134	96.99 245	100.00 1	99.92 303	99.69 17	99.99 107	99.74 159	98.06 286	99.88 203

UWE-MVS		99.18 134	99.06 134	99.51 181	99.67 196	98.80 235	100.00 1	99.43 134	96.80 266	99.93 196	99.86 315	99.79 8	99.94 196	97.78 344	98.33 249	99.80 280

testing222		99.14 140	98.94 155	99.73 143	99.67 196	99.51 150	100.00 1	99.43 134	96.90 257	99.99 129	99.90 309	98.55 152	99.86 233	98.85 288	97.18 322	99.81 249

WB-MVSnew		97.02 339	97.24 318	96.37 428	99.44 316	97.36 352	100.00 1	99.43 134	96.12 347	99.35 288	99.89 310	93.60 305	98.42 428	88.91 483	98.39 231	93.33 516

FE-MVS		99.16 138	98.99 146	99.66 157	99.65 207	99.18 198	99.58 421	99.43 134	95.24 378	99.91 201	99.59 379	99.37 70	99.97 150	98.31 318	99.81 168	99.83 225

FA-MVS(test-final)		99.00 164	98.75 178	99.73 143	99.63 216	99.43 166	99.83 369	99.43 134	95.84 357	99.52 267	99.37 406	97.84 178	99.96 170	97.63 349	99.68 178	99.79 286

DPM-MVS		99.63 58	99.51 70	100.00 1	99.90 120	100.00 1	100.00 1	99.43 134	99.00 32	100.00 1	100.00 1	99.58 27	100.00 1	97.64 348	100.00 1	100.00 1

SMA-MVS		99.69 42	99.59 50	99.98 28	99.99 53	99.93 53	100.00 1	99.43 134	97.50 194	100.00 1	100.00 1	99.43 60	100.00 1	100.00 1	100.00 1	100.00 1
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
TSAR-MVS + MP.		99.82 12	99.77 12	99.99 13	100.00 1	99.96 30	100.00 1	99.43 134	99.05 18	100.00 1	100.00 1	99.45 53	99.99 107	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
FIs		97.95 288	97.73 295	98.62 299	98.53 408	99.24 190	100.00 1	99.43 134	96.74 279	97.87 403	99.82 327	95.27 253	98.89 380	98.78 292	93.07 388	97.74 390

FC-MVSNet-test		97.84 293	97.63 301	98.45 310	98.30 420	99.05 209	100.00 1	99.43 134	96.63 303	97.61 415	99.82 327	95.19 258	98.57 416	98.64 301	93.05 389	97.73 402

WR-MVS_H		96.73 348	96.32 356	97.95 361	98.26 424	97.88 331	99.72 402	99.43 134	95.06 381	96.99 431	98.68 456	93.02 324	98.53 419	97.43 357	88.33 454	97.43 455

JIA-IIPM		97.09 332	96.34 354	99.36 220	98.88 388	98.59 254	99.81 373	99.43 134	84.81 496	99.96 153	90.34 528	98.55 152	99.52 321	97.00 370	98.28 259	99.98 127

EPP-MVSNet		99.10 144	99.00 144	99.40 210	99.51 276	98.68 246	99.92 345	99.43 134	95.47 372	99.65 259	100.00 1	99.51 39	99.76 272	99.53 229	98.00 288	99.75 295

reproduce_model		99.76 21	99.69 25	99.98 28	99.96 104	99.93 53	100.00 1	99.42 154	98.81 76	100.00 1	100.00 1	98.98 117	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 21	99.69 25	99.98 28	99.96 104	99.94 47	100.00 1	99.42 154	98.82 72	100.00 1	100.00 1	98.99 114	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 21	99.69 25	99.98 28	99.96 104	99.94 47	100.00 1	99.42 154	98.82 72	100.00 1	100.00 1	98.99 114	100.00 1	100.00 1	100.00 1	100.00 1

DVP-MVS++		99.81 14	99.75 17	100.00 1	100.00 1	99.99 6	100.00 1	99.42 154	98.79 80	100.00 1	100.00 1	99.54 32	100.00 1	100.00 1	100.00 1	100.00 1

FOURS1					100.00 1	99.97 27	100.00 1	99.42 154	98.52 96	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 154					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 154					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 6		99.42 154	98.72 85	100.00 1	100.00 1	99.60 21

hse-mvs2		96.79 345	96.38 351	98.04 356	99.68 188	95.54 397	99.81 373	99.42 154	98.21 117	100.00 1	99.80 336	97.49 195	99.46 335	99.72 165	73.27 515	99.12 338

AUN-MVS		96.26 376	95.67 388	98.06 350	99.68 188	95.60 396	99.82 372	99.42 154	96.78 269	99.88 210	99.80 336	94.84 268	99.47 330	97.48 355	73.29 514	99.12 338

SR-MVS-dyc-post		99.63 58	99.52 68	99.97 40	99.99 53	99.91 64	100.00 1	99.42 154	97.62 173	100.00 1	100.00 1	98.65 146	99.99 107	99.99 77	100.00 1	100.00 1

RE-MVS-def			99.55 62		99.99 53	99.91 64	100.00 1	99.42 154	97.62 173	100.00 1	100.00 1	98.94 125		99.99 77	100.00 1	100.00 1

SED-MVS		99.83 10	99.77 12	100.00 1	100.00 1	99.99 6	100.00 1	99.42 154	99.03 25	100.00 1	100.00 1	99.50 43	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 6		99.42 154	99.12 9	100.00 1				100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 154	99.03 25	100.00 1	100.00 1	99.56 29	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 6		99.42 154	99.03 25	100.00 1	100.00 1	99.50 43	100.00 1

SF-MVS		99.66 51	99.57 55	99.95 61	99.99 53	99.85 94	100.00 1	99.42 154	97.67 165	100.00 1	100.00 1	99.05 107	99.99 107	100.00 1	100.00 1	100.00 1

9.14			99.57 55		99.99 53		100.00 1	99.42 154	97.54 185	100.00 1	100.00 1	99.15 96	99.99 107	100.00 1	100.00 1

save fliter					99.99 53	99.93 53	100.00 1	99.42 154	98.93 49

DVP-MVS		99.83 10	99.78 10	100.00 1	100.00 1	99.99 6	100.00 1	99.42 154	99.04 20	100.00 1	100.00 1	99.53 35	100.00 1	100.00 1	100.00 1	100.00 1
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_SECOND				100.00 1	99.99 53	99.99 6	100.00 1	99.42 154					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 6	100.00 1	99.42 154	99.04 20	100.00 1	100.00 1	99.53 35

DPE-MVS		99.79 17	99.73 20	99.99 13	99.99 53	99.98 18	100.00 1	99.42 154	98.91 55	100.00 1	100.00 1	99.22 88	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		99.61 65	99.50 71	99.97 40	99.98 94	99.92 60	100.00 1	99.42 154	97.53 188	99.77 237	100.00 1	98.77 141	100.00 1	99.99 77	100.00 1	99.99 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.81 14	99.77 12	99.94 74	100.00 1	99.86 90	100.00 1	99.42 154	98.87 64	100.00 1	100.00 1	99.65 19	99.96 170	100.00 1	100.00 1	100.00 1
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
ACMMP_NAP		99.67 49	99.57 55	99.97 40	99.98 94	99.92 60	100.00 1	99.42 154	97.83 150	100.00 1	100.00 1	98.89 131	100.00 1	99.98 92	100.00 1	100.00 1

MTGPA								99.42 154

MTAPA		99.68 46	99.59 50	99.97 40	99.99 53	99.91 64	100.00 1	99.42 154	98.32 113	99.94 191	100.00 1	98.65 146	100.00 1	99.96 106	100.00 1	100.00 1

TEST9					100.00 1	99.95 38	100.00 1	99.42 154	97.65 168	100.00 1	100.00 1	99.53 35	99.97 150

train_agg		99.71 36	99.63 44	99.97 40	100.00 1	99.95 38	100.00 1	99.42 154	97.70 162	100.00 1	100.00 1	99.51 39	99.97 150	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 64	100.00 1	99.42 154	97.70 162	100.00 1	100.00 1	99.51 39	99.98 141

agg_prior					100.00 1	99.88 85		99.42 154		100.00 1			99.97 150

PS-MVSNAJ		99.64 54	99.57 55	99.85 104	99.78 167	99.81 100	99.95 329	99.42 154	98.38 105	100.00 1	100.00 1	98.75 142	100.00 1	99.88 124	99.99 107	99.74 302

SD-MVS		99.81 14	99.75 17	99.99 13	99.99 53	99.96 30	100.00 1	99.42 154	99.01 31	100.00 1	100.00 1	99.33 71	100.00 1	100.00 1	100.00 1	100.00 1
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
MP-MVS		99.61 65	99.49 73	99.98 28	99.99 53	99.94 47	100.00 1	99.42 154	97.82 152	99.99 129	100.00 1	98.20 162	100.00 1	99.99 77	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		99.73 31	99.64 40	99.99 13	100.00 1	99.97 27	100.00 1	99.42 154	98.02 133	100.00 1	100.00 1	99.32 74	99.99 107	100.00 1	100.00 1	100.00 1

test12				99.95 61	99.99 53	99.89 78		99.42 154		100.00 1		99.24 87	99.97 150		100.00 1	100.00 1

UniMVSNet (Re)		97.29 325	96.85 332	98.59 302	98.49 409	99.13 202	100.00 1	99.42 154	96.52 315	98.24 386	98.90 444	94.93 265	98.89 380	97.54 353	87.61 459	97.75 362

mPP-MVS		99.69 42	99.60 49	99.97 40	100.00 1	99.91 64	100.00 1	99.42 154	97.91 145	100.00 1	100.00 1	99.04 110	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS_fast		99.60 68	99.49 73	99.91 83	99.99 53	99.78 104	100.00 1	99.42 154	97.09 235	100.00 1	100.00 1	98.95 123	99.96 170	99.98 92	100.00 1	100.00 1

HPM-MVS		99.59 69	99.50 71	99.89 90	100.00 1	99.70 121	100.00 1	99.42 154	97.46 198	100.00 1	100.00 1	98.60 149	99.96 170	99.99 77	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test11								99.42 154

APD-MVS		99.68 46	99.58 52	99.97 40	99.99 53	99.96 30	100.00 1	99.42 154	97.53 188	100.00 1	100.00 1	99.27 85	99.97 150	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
114514_t		99.39 93	99.25 104	99.81 117	99.97 98	99.48 160	100.00 1	99.42 154	95.53 366	100.00 1	100.00 1	98.37 160	99.95 183	99.97 104	100.00 1	100.00 1

CP-MVS		99.67 49	99.58 52	99.95 61	100.00 1	99.84 96	100.00 1	99.42 154	97.77 157	100.00 1	100.00 1	99.07 104	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 19	99.71 23	99.98 28	99.76 170	99.95 38	100.00 1	99.42 154	98.69 86	100.00 1	100.00 1	99.52 38	99.99 107	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
RPMNet		95.26 406	93.82 417	99.56 177	99.31 338	98.86 230	99.13 478	99.42 154	79.82 508	99.96 153	95.13 510	95.69 246	99.98 141	77.54 520	98.40 229	99.84 222

CPTT-MVS		99.49 80	99.38 83	99.85 104	100.00 1	99.54 143	100.00 1	99.42 154	97.58 182	99.98 139	100.00 1	97.43 201	100.00 1	99.99 77	100.00 1	100.00 1

DeepPCF-MVS	98.03 4	98.54 246	99.72 22	94.98 450	99.99 53	84.94 495	100.00 1	99.42 154	99.98 1	100.00 1	100.00 1	98.11 165	100.00 1	100.00 1	100.00 1	100.00 1

test_fmvsm_n_1920		99.55 73	99.49 73	99.73 143	99.85 129	99.19 196	100.00 1	99.41 203	98.87 64	100.00 1	100.00 1	97.34 204	100.00 1	99.98 92	99.90 148	100.00 1

patch_mono-2		99.04 152	99.79 9	96.81 417	99.92 116	90.47 475	100.00 1	99.41 203	98.95 42	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 149

ADS-MVSNet2		98.28 271	98.51 225	97.62 375	99.51 276	95.03 408	99.24 458	99.41 203	95.52 368	99.96 153	99.70 350	97.57 190	97.94 471	97.11 367	98.54 219	99.88 203

Vis-MVSNet		98.52 248	98.25 259	99.34 224	99.68 188	98.55 256	99.68 409	99.41 203	97.34 212	99.94 191	100.00 1	90.38 375	99.70 288	99.03 279	98.84 207	99.76 294
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
0.3-1-1-0.015		97.60 305	97.19 321	98.83 286	99.13 353	96.55 382	100.00 1	99.40 207	94.19 411	99.83 217	99.81 330	99.18 92	99.97 150	99.70 173	83.50 487	99.98 127

0.4-1-1-0.1		97.56 308	97.15 325	98.79 291	99.01 369	96.44 385	100.00 1	99.40 207	94.11 414	99.81 232	99.81 330	99.09 100	99.97 150	99.65 196	83.48 489	99.98 127

0.4-1-1-0.2		97.60 305	97.18 322	98.86 284	99.05 366	96.62 380	100.00 1	99.40 207	94.24 406	99.82 226	99.81 330	99.09 100	99.97 150	99.70 173	83.50 487	99.98 127

xiu_mvs_v2_base		99.51 75	99.41 80	99.82 112	99.70 180	99.73 114	99.92 345	99.40 207	98.15 123	100.00 1	100.00 1	98.50 154	100.00 1	99.85 131	99.13 199	99.74 302

Anonymous20231211		96.29 374	95.70 384	98.07 346	99.80 157	97.49 346	99.15 476	99.40 207	89.11 471	97.75 409	99.45 400	88.93 404	98.98 369	98.26 323	89.47 442	97.73 402

AllTest		98.55 243	98.40 242	98.99 273	99.93 113	97.35 353	100.00 1	99.40 207	97.08 237	99.09 308	99.98 252	93.37 310	99.95 183	96.94 371	99.84 163	99.68 314

TestCases				98.99 273	99.93 113	97.35 353		99.40 207	97.08 237	99.09 308	99.98 252	93.37 310	99.95 183	96.94 371	99.84 163	99.68 314

HQP_MVS		97.71 300	97.82 291	97.37 382	99.00 374	94.80 417	100.00 1	99.40 207	99.00 32	99.08 310	99.97 264	88.58 412	99.55 312	99.79 143	95.57 345	97.76 351

plane_prior5								99.40 207					99.55 312	99.79 143	95.57 345	97.76 351

HQP3-MVS								99.40 207							95.58 341

DP-MVS		98.86 193	98.54 217	99.81 117	99.97 98	99.45 162	99.52 429	99.40 207	94.35 405	98.36 372	100.00 1	96.13 236	99.97 150	99.12 274	100.00 1	100.00 1

HQP-MVS		97.73 298	97.85 289	97.39 381	99.07 360	94.82 414	100.00 1	99.40 207	99.04 20	99.17 298	99.97 264	88.61 410	99.57 302	99.79 143	95.58 341	97.77 349

OMC-MVS		99.27 120	99.38 83	98.96 276	99.95 108	97.06 368	100.00 1	99.40 207	98.83 70	99.88 210	100.00 1	97.01 211	99.86 233	99.47 238	99.84 163	99.97 137

DeepC-MVS	97.84 5	99.00 164	98.80 171	99.60 168	99.93 113	99.03 211	100.00 1	99.40 207	98.61 92	99.33 289	100.00 1	92.23 339	99.95 183	99.74 159	99.96 126	99.83 225
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS	96.40 10	97.64 301	97.37 310	98.45 310	99.94 111	95.70 395	100.00 1	99.40 207	97.65 168	99.53 266	100.00 1	99.31 76	99.66 290	80.48 510	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
COLMAP_ROB	97.10 7	98.29 269	98.17 267	98.65 297	99.94 111	97.39 350	99.30 453	99.40 207	95.64 361	97.75 409	100.00 1	92.69 332	99.95 183	98.89 286	99.92 141	98.62 346
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_3		98.99 168	98.69 192	99.89 90	99.70 180	99.69 123	100.00 1	99.39 223	98.93 49	100.00 1	100.00 1	90.20 376	99.99 107	100.00 1	99.95 128	100.00 1

cdsmvs_eth3d_5k		24.41 523	32.55 525	0.00 541	0.00 565	0.00 568	0.00 553	99.39 223	0.00 560	0.00 561	100.00 1	93.55 306	0.00 562	0.00 560	0.00 560	0.00 557

dp		98.72 209	98.61 204	99.03 270	99.53 255	97.39 350	99.45 436	99.39 223	95.62 363	99.94 191	99.52 391	98.83 136	99.82 250	96.77 382	98.42 227	99.89 190

nomal-1		98.99 168	99.02 140	98.88 282	99.47 297	97.25 362	100.00 1	99.38 226	96.38 328	99.90 203	99.94 296	98.78 140	99.56 306	99.40 249	97.94 295	99.83 225

fmvsm_s_conf0.5_n_8		99.34 105	99.14 125	99.91 83	99.83 135	99.74 112	100.00 1	99.38 226	98.94 45	100.00 1	100.00 1	94.25 288	99.99 107	100.00 1	99.91 146	100.00 1

fmvsm_s_conf0.5_n		99.21 131	99.01 141	99.83 110	99.84 131	99.53 145	100.00 1	99.38 226	98.29 115	100.00 1	100.00 1	93.62 304	99.99 107	99.99 77	99.93 138	99.98 127

VDD-MVS		96.58 357	95.99 368	98.34 320	99.52 269	95.33 402	99.18 468	99.38 226	96.64 299	99.77 237	100.00 1	72.51 491	100.00 1	100.00 1	96.94 328	99.70 312

CMPMVS	66.12 22	90.65 454	92.04 442	86.46 492	96.18 485	66.87 531	98.03 514	99.38 226	83.38 500	85.49 508	99.55 387	77.59 475	98.80 387	94.44 427	94.31 377	93.72 514
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DU-MVS		96.93 342	96.49 346	98.22 332	98.31 418	98.41 270	100.00 1	99.37 231	96.41 326	97.76 406	99.65 363	92.14 341	98.50 421	97.98 332	86.84 468	97.75 362

LPG-MVS_test		97.31 323	97.32 312	97.28 389	98.85 394	94.60 427	100.00 1	99.37 231	97.35 209	98.85 329	99.98 252	86.66 429	99.56 306	99.55 222	95.26 353	97.70 418

LGP-MVS_train				97.28 389	98.85 394	94.60 427		99.37 231	97.35 209	98.85 329	99.98 252	86.66 429	99.56 306	99.55 222	95.26 353	97.70 418

tpm2		98.64 222	98.58 211	98.81 290	99.42 320	97.12 365	99.69 407	99.37 231	93.63 425	99.94 191	99.67 358	98.96 122	99.47 330	98.62 305	97.95 294	99.83 225

CDS-MVSNet		98.96 176	98.95 154	99.01 272	99.48 292	98.36 281	99.93 343	99.37 231	96.79 267	99.31 291	99.83 323	99.77 11	98.91 377	98.07 329	97.98 290	99.77 292
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.90 188	98.63 200	99.70 149	99.92 116	99.25 188	100.00 1	99.37 231	95.71 359	99.40 284	100.00 1	96.58 228	99.95 183	96.80 379	99.94 134	99.91 171

fmvsm_l_conf0.5_n_a		99.63 58	99.55 62	99.86 100	99.83 135	99.58 136	100.00 1	99.36 237	98.98 35	100.00 1	100.00 1	97.85 176	99.99 107	100.00 1	99.94 134	100.00 1

fmvsm_l_conf0.5_n		99.63 58	99.56 60	99.86 100	99.81 144	99.59 134	100.00 1	99.36 237	98.98 35	100.00 1	100.00 1	97.92 171	99.99 107	100.00 1	99.95 128	100.00 1

mvsany_test1		99.57 70	99.48 76	99.85 104	99.86 127	99.54 143	100.00 1	99.36 237	98.94 45	100.00 1	100.00 1	97.97 168	100.00 1	99.88 124	99.28 195	100.00 1

xiu_mvs_v1_base_debu		99.35 101	99.21 113	99.79 128	99.67 196	99.71 117	99.78 384	99.36 237	98.13 125	100.00 1	100.00 1	97.00 214	100.00 1	99.83 135	99.07 201	99.66 316

xiu_mvs_v1_base		99.35 101	99.21 113	99.79 128	99.67 196	99.71 117	99.78 384	99.36 237	98.13 125	100.00 1	100.00 1	97.00 214	100.00 1	99.83 135	99.07 201	99.66 316

xiu_mvs_v1_base_debi		99.35 101	99.21 113	99.79 128	99.67 196	99.71 117	99.78 384	99.36 237	98.13 125	100.00 1	100.00 1	97.00 214	100.00 1	99.83 135	99.07 201	99.66 316

TSAR-MVS + GP.		99.61 65	99.69 25	99.35 222	99.99 53	98.06 315	100.00 1	99.36 237	99.83 2	100.00 1	100.00 1	98.95 123	99.99 107	100.00 1	99.11 200	100.00 1

XVG-OURS-SEG-HR		98.27 272	98.31 255	98.14 340	99.59 232	95.92 389	100.00 1	99.36 237	98.48 98	99.21 297	100.00 1	89.27 398	99.94 196	99.76 153	99.17 197	98.56 347

XVG-OURS		98.30 266	98.36 251	98.13 343	99.58 237	95.91 390	100.00 1	99.36 237	98.69 86	99.23 296	100.00 1	91.20 351	99.92 206	99.34 256	97.82 305	98.56 347

tpmrst		98.98 173	98.93 157	99.14 263	99.61 225	97.74 338	99.52 429	99.36 237	96.05 348	99.98 139	99.64 367	99.04 110	99.86 233	98.94 283	98.19 273	99.82 232

UnsupCasMVSNet_eth		94.25 416	93.89 416	95.34 443	97.63 457	92.13 461	99.73 399	99.36 237	94.88 384	92.78 478	98.63 458	82.72 456	96.53 491	94.57 425	84.73 478	97.36 458

fmvsm_l_conf0.5_n_3		99.38 96	99.20 117	99.92 82	99.80 157	99.78 104	100.00 1	99.35 248	98.94 45	100.00 1	100.00 1	94.77 270	99.99 107	99.99 77	99.92 141	100.00 1

fmvsm_s_conf0.5_n_a		99.32 110	99.15 123	99.81 117	99.80 157	99.47 161	100.00 1	99.35 248	98.22 116	100.00 1	100.00 1	95.21 257	99.99 107	99.96 106	99.86 158	99.98 127

UniMVSNet_ETH3D		95.28 405	94.41 412	97.89 366	98.91 385	95.14 405	99.13 478	99.35 248	92.11 450	97.17 429	99.66 360	70.28 496	99.36 343	97.88 337	95.18 360	99.16 336

NR-MVSNet		96.63 354	96.04 366	98.38 317	98.31 418	98.98 220	99.22 467	99.35 248	95.87 352	94.43 470	99.65 363	92.73 330	98.40 429	96.78 380	88.05 455	97.75 362

TranMVSNet+NR-MVSNet		96.45 364	96.01 367	97.79 371	98.00 441	97.62 343	100.00 1	99.35 248	95.98 349	97.31 424	99.64 367	90.09 383	98.00 467	96.89 375	86.80 471	97.75 362

CostFormer		98.84 196	98.77 176	99.04 269	99.41 322	97.58 344	99.67 410	99.35 248	94.66 394	99.96 153	99.36 407	99.28 84	99.74 278	99.41 247	97.81 306	99.81 249

MIMVSNet		97.06 335	96.73 336	98.05 354	99.38 331	96.64 379	98.47 507	99.35 248	93.41 432	99.48 271	98.53 467	89.66 392	97.70 480	94.16 432	98.11 283	99.80 280

SD_0403		97.92 289	98.43 233	96.39 426	99.68 188	89.74 481	99.92 345	99.34 255	96.75 276	99.39 285	99.93 302	93.54 307	99.51 323	99.11 275	98.21 270	99.92 167

TAMVS		98.76 205	98.73 182	98.86 284	99.44 316	97.69 339	99.57 422	99.34 255	96.57 308	99.12 304	99.81 330	98.83 136	99.16 356	97.97 335	97.91 297	99.73 311

CLD-MVS		97.64 301	97.74 293	97.36 383	99.01 369	94.76 422	100.00 1	99.34 255	99.30 4	99.00 316	99.97 264	87.49 421	99.57 302	99.96 106	95.58 341	97.75 362
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
reproduce_monomvs		98.61 232	98.54 217	98.82 287	99.97 98	99.28 183	100.00 1	99.33 258	98.51 97	97.87 403	99.24 414	99.98 3	99.45 336	99.02 280	92.93 391	97.74 390

WR-MVS		97.09 332	96.64 338	98.46 309	98.43 411	99.09 204	99.97 312	99.33 258	95.62 363	97.76 406	99.67 358	91.17 352	98.56 418	98.49 310	89.28 445	97.74 390

ACMH	96.25 11	96.77 346	96.62 340	97.21 392	98.96 381	94.43 434	99.64 412	99.33 258	97.43 203	96.55 443	99.97 264	83.52 453	99.54 315	99.07 278	95.13 363	97.66 430
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_9		99.04 152	98.78 173	99.81 117	99.86 127	99.44 165	100.00 1	99.32 261	98.94 45	100.00 1	100.00 1	91.00 357	99.99 107	100.00 1	99.94 134	100.00 1

guyue		99.21 131	99.07 133	99.62 163	99.55 248	99.29 181	100.00 1	99.32 261	97.66 166	99.96 153	100.00 1	95.84 241	99.84 244	99.63 204	99.67 180	99.75 295

BP-MVS1		99.56 71	99.48 76	99.79 128	99.48 292	99.61 131	100.00 1	99.32 261	97.34 212	99.94 191	100.00 1	99.74 13	99.89 221	99.75 157	99.72 174	99.87 214

WBMVS		98.19 276	98.10 273	98.47 308	99.63 216	99.03 211	100.00 1	99.32 261	95.46 373	98.39 371	99.40 404	99.69 17	98.61 407	98.64 301	92.39 400	97.76 351

PS-CasMVS		96.34 372	95.78 380	98.03 357	98.18 432	98.27 294	99.71 403	99.32 261	94.75 388	96.82 437	99.65 363	86.98 428	98.15 450	97.74 345	88.85 450	97.66 430

CP-MVSNet		96.73 348	96.25 357	98.18 336	98.21 429	98.67 247	99.77 389	99.32 261	95.06 381	97.20 428	99.65 363	90.10 382	98.19 448	98.06 330	88.90 449	97.66 430

XVG-ACMP-BASELINE		96.60 356	96.52 345	96.84 409	98.41 412	93.29 450	99.99 269	99.32 261	97.76 159	98.51 362	99.29 410	81.95 461	99.54 315	98.40 313	95.03 366	97.68 425

PatchT		95.90 394	94.95 411	98.75 294	99.03 367	98.39 274	99.08 485	99.32 261	85.52 493	99.96 153	94.99 513	97.94 169	98.05 466	80.20 512	98.47 224	99.81 249

ACMP	97.00 8	97.19 327	97.16 324	97.27 391	98.97 380	94.58 430	100.00 1	99.32 261	97.97 139	97.45 421	99.98 252	85.79 439	99.56 306	99.70 173	95.24 356	97.67 429
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_7		98.98 173	98.85 166	99.37 218	99.67 196	98.34 285	100.00 1	99.31 270	98.97 37	100.00 1	100.00 1	91.70 345	99.97 150	99.99 77	99.97 122	99.80 280

fmvsm_s_conf0.5_n_6		99.30 114	99.12 128	99.84 109	99.24 348	99.56 138	100.00 1	99.31 270	98.90 59	100.00 1	100.00 1	94.75 272	99.97 150	99.98 92	99.88 152	100.00 1

test_fmvsmconf_n		99.56 71	99.46 79	99.86 100	99.68 188	99.58 136	100.00 1	99.31 270	98.92 52	99.88 210	100.00 1	97.35 203	99.99 107	99.98 92	99.99 107	100.00 1

test_cas_vis1_n_1920		98.63 228	98.25 259	99.77 136	99.69 183	99.32 178	100.00 1	99.31 270	98.84 68	99.96 153	100.00 1	87.42 422	99.99 107	99.14 271	99.86 158	100.00 1

Vis-MVSNet (Re-imp)		98.99 168	98.89 164	99.29 247	99.64 213	98.89 229	99.98 302	99.31 270	96.74 279	99.48 271	100.00 1	98.11 165	99.10 358	98.39 314	98.34 246	99.89 190

ACMH+	96.20 13	96.49 363	96.33 355	97.00 399	99.06 364	93.80 443	99.81 373	99.31 270	97.32 215	95.89 456	99.97 264	82.62 458	99.54 315	98.34 317	94.63 374	97.65 435

fmvsm_l_conf0.5_n_9		99.35 101	99.15 123	99.95 61	99.83 135	99.84 96	100.00 1	99.30 276	98.92 52	100.00 1	100.00 1	94.32 286	100.00 1	100.00 1	99.93 138	100.00 1

SSC-MVS3.2		95.32 403	94.97 410	96.37 428	98.29 422	92.75 455	100.00 1	99.30 276	95.46 373	98.36 372	99.42 402	78.92 472	98.63 404	93.28 443	91.72 413	97.72 409

MVSMamba_PlusPlus		99.39 93	99.25 104	99.80 123	99.68 188	99.59 134	99.99 269	99.30 276	96.66 296	99.96 153	99.97 264	97.89 173	99.92 206	99.76 153	100.00 1	99.90 182

sasdasda		99.03 155	98.73 182	99.94 74	99.75 172	99.95 38	100.00 1	99.30 276	97.64 170	100.00 1	100.00 1	95.22 255	99.97 150	99.76 153	96.90 329	99.91 171

MS-PatchMatch		95.66 399	95.87 374	95.05 446	97.80 449	89.25 483	98.88 493	99.30 276	96.35 333	96.86 435	99.01 433	81.35 464	99.43 338	93.30 441	99.98 118	96.46 483

canonicalmvs		99.03 155	98.73 182	99.94 74	99.75 172	99.95 38	100.00 1	99.30 276	97.64 170	100.00 1	100.00 1	95.22 255	99.97 150	99.76 153	96.90 329	99.91 171

balanced_ft_v1		98.70 216	98.61 204	98.94 277	99.67 196	96.90 370	99.91 352	99.30 276	96.73 283	99.96 153	99.97 264	92.18 340	99.93 200	99.86 128	99.95 128	100.00 1

tpm cat1		98.05 282	97.76 292	98.92 279	99.50 285	97.10 367	99.77 389	99.30 276	90.20 467	99.72 247	98.71 454	97.71 183	99.86 233	96.75 383	98.20 272	99.81 249

PMMVS		99.12 142	98.97 149	99.58 174	99.57 241	98.98 220	100.00 1	99.30 276	97.14 230	99.96 153	100.00 1	96.53 232	99.82 250	99.70 173	98.49 222	99.94 154

usedtu_dtu_shiyan1		97.34 321	96.97 326	98.43 312	97.82 447	98.91 227	100.00 1	99.29 285	94.70 391	98.46 366	98.89 445	93.95 297	98.64 402	95.86 399	93.75 379	97.74 390

FE-MVSNET3		97.34 321	96.97 326	98.43 312	97.82 447	98.91 227	100.00 1	99.29 285	94.70 391	98.46 366	98.89 445	93.95 297	98.64 402	95.88 397	93.75 379	97.74 390

MGCFI-Net		99.01 163	98.70 190	99.93 78	99.74 174	99.94 47	100.00 1	99.29 285	97.60 180	100.00 1	100.00 1	95.10 261	99.96 170	99.74 159	96.85 331	99.91 171

CANet_DTU		99.02 161	98.90 163	99.41 205	99.88 124	98.71 243	100.00 1	99.29 285	98.84 68	100.00 1	100.00 1	94.02 294	100.00 1	98.08 327	99.96 126	99.52 326

EI-MVSNet		97.98 285	97.93 284	98.16 339	99.11 355	97.84 334	99.74 394	99.29 285	94.39 404	98.65 344	100.00 1	97.21 205	98.88 383	97.62 352	95.31 351	97.75 362

PEN-MVS		96.01 391	95.48 396	97.58 377	97.74 452	97.26 359	99.90 354	99.29 285	94.55 397	96.79 438	99.55 387	87.38 423	97.84 473	96.92 374	87.24 465	97.65 435

MVSTER		98.58 237	98.52 220	98.77 293	99.65 207	99.68 124	100.00 1	99.29 285	95.63 362	98.65 344	99.80 336	99.78 9	98.88 383	98.59 307	95.31 351	97.73 402

XXY-MVS		97.14 331	96.63 339	98.67 296	98.65 401	98.92 226	99.54 427	99.29 285	95.57 365	97.63 412	99.83 323	87.79 419	99.35 345	98.39 314	92.95 390	97.75 362

gbinet_0.2-2-1-0.02		93.73 425	92.69 437	96.84 409	94.91 508	94.62 426	100.00 1	99.28 293	87.02 488	98.53 358	98.45 471	89.72 390	98.15 450	96.65 384	69.64 527	97.74 390

fmvsm_s_conf0.5_n_10		99.08 145	98.78 173	99.97 40	99.84 131	99.92 60	100.00 1	99.28 293	98.93 49	100.00 1	100.00 1	91.07 354	99.99 107	100.00 1	99.95 128	100.00 1

NormalMVS		99.47 84	99.48 76	99.43 201	99.99 53	98.55 256	99.94 337	99.28 293	98.39 103	100.00 1	100.00 1	98.44 156	99.98 141	99.36 250	99.92 141	99.75 295

Elysia		98.12 278	97.72 296	99.34 224	99.30 341	98.96 223	99.95 329	99.28 293	96.64 299	99.75 240	99.99 244	88.71 407	99.81 254	95.99 395	99.84 163	99.26 333

StellarMVS		98.12 278	97.72 296	99.34 224	99.30 341	98.96 223	99.95 329	99.28 293	96.64 299	99.75 240	99.99 244	88.71 407	99.81 254	95.99 395	99.84 163	99.26 333

BridgeMVS		99.43 89	99.28 96	99.85 104	99.68 188	99.68 124	99.97 312	99.28 293	97.03 242	99.96 153	99.97 264	97.90 172	99.93 200	99.77 151	100.00 1	99.94 154

cl22		98.23 274	98.11 270	98.58 304	99.82 138	99.01 215	100.00 1	99.28 293	96.92 254	98.33 376	99.21 417	98.09 167	98.97 371	98.72 296	92.61 395	97.76 351

OPM-MVS		97.21 326	97.18 322	97.32 386	98.08 436	94.66 423	100.00 1	99.28 293	98.65 90	98.92 323	99.98 252	86.03 437	99.56 306	98.28 322	95.41 347	97.72 409
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvsmamba		99.05 151	98.98 147	99.27 253	99.57 241	98.10 311	100.00 1	99.28 293	95.92 351	99.96 153	99.97 264	96.73 225	99.89 221	99.72 165	99.65 183	99.81 249

BH-w/o		98.82 198	98.81 170	98.88 282	99.62 223	96.71 376	100.00 1	99.28 293	97.09 235	98.81 334	100.00 1	94.91 266	99.96 170	99.54 226	100.00 1	99.96 143

BH-untuned		98.64 222	98.65 197	98.60 301	99.59 232	96.17 386	100.00 1	99.28 293	96.67 295	98.41 369	100.00 1	94.52 279	99.83 247	99.41 247	100.00 1	99.81 249

UnsupCasMVSNet_bld		89.50 459	88.00 466	93.99 462	95.30 499	88.86 486	98.52 506	99.28 293	85.50 494	87.80 499	94.11 516	61.63 504	96.96 485	90.63 463	79.26 504	96.15 490

FMVSNet3		97.30 324	96.95 328	98.37 318	99.65 207	99.25 188	99.71 403	99.28 293	94.23 407	98.53 358	98.91 443	93.30 312	98.11 456	95.31 414	93.60 382	97.73 402

BH-RMVSNet		98.46 252	98.08 274	99.59 170	99.61 225	99.19 196	100.00 1	99.28 293	97.06 239	98.95 319	100.00 1	88.99 402	99.82 250	98.83 291	100.00 1	99.77 292

LTVRE_ROB	95.29 16	96.32 373	96.10 363	96.99 400	98.55 406	93.88 442	99.45 436	99.28 293	94.50 400	96.46 444	99.52 391	84.86 444	99.48 328	97.26 364	95.03 366	97.59 445
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
fmvsm_s_conf0.1_n		98.77 202	98.42 234	99.82 112	99.47 297	99.52 149	100.00 1	99.27 308	97.53 188	100.00 1	100.00 1	89.73 389	99.96 170	99.84 134	99.93 138	99.97 137

EIA-MVS		99.26 122	99.19 118	99.45 195	99.63 216	98.75 238	100.00 1	99.27 308	96.93 252	99.95 185	100.00 1	97.47 197	99.79 259	99.74 159	99.72 174	99.82 232

GBi-Net		96.07 388	95.80 378	96.89 406	99.53 255	94.87 411	99.18 468	99.27 308	93.71 420	98.53 358	98.81 450	84.23 448	98.07 462	95.31 414	93.60 382	97.72 409

test1		96.07 388	95.80 378	96.89 406	99.53 255	94.87 411	99.18 468	99.27 308	93.71 420	98.53 358	98.81 450	84.23 448	98.07 462	95.31 414	93.60 382	97.72 409

FMVSNet2		96.22 378	95.60 390	98.06 350	99.53 255	98.33 286	99.45 436	99.27 308	93.71 420	98.03 393	98.84 448	84.23 448	98.10 460	93.97 434	93.40 385	97.73 402

FMVSNet1		94.45 413	93.63 420	96.89 406	98.87 391	94.87 411	99.18 468	99.27 308	90.95 459	97.31 424	98.81 450	72.89 490	98.07 462	92.61 447	92.81 392	97.72 409

fmvsm_s_conf0.5_n_5		99.00 164	98.70 190	99.88 95	99.81 144	99.64 128	100.00 1	99.26 314	98.78 83	99.97 145	100.00 1	90.65 364	99.99 107	100.00 1	99.89 149	99.99 124

fmvsm_s_conf0.5_n_4		98.98 173	98.74 180	99.68 153	99.81 144	99.50 152	100.00 1	99.26 314	98.91 55	100.00 1	100.00 1	90.87 361	99.97 150	99.99 77	99.81 168	99.57 322

fmvsm_s_conf0.1_n_a		98.71 213	98.36 251	99.78 133	99.09 358	99.42 167	100.00 1	99.26 314	97.42 204	100.00 1	100.00 1	89.78 387	99.96 170	99.82 140	99.85 161	99.97 137

test_fmvsmvis_n_1920		99.46 85	99.37 86	99.73 143	98.88 388	99.18 198	100.00 1	99.26 314	98.85 66	99.79 234	100.00 1	97.70 184	100.00 1	99.98 92	99.86 158	100.00 1

PRO-TEST		99.18 134	99.03 137	99.61 165	99.71 177	99.37 173	100.00 1	99.25 318	97.51 192	99.96 153	100.00 1	95.41 250	99.66 290	99.75 157	99.69 177	99.82 232

fmvsm_s_conf0.5_n_2		98.90 188	98.57 212	99.90 87	99.79 162	99.78 104	100.00 1	99.25 318	98.97 37	100.00 1	100.00 1	89.22 399	99.99 107	100.00 1	99.88 152	99.92 167

test_vis1_n_1920		97.77 297	97.24 318	99.34 224	99.79 162	98.04 317	100.00 1	99.25 318	98.88 61	100.00 1	100.00 1	77.52 476	100.00 1	99.88 124	99.85 161	100.00 1

CL-MVSNet_self_test		91.07 452	90.35 454	93.24 467	93.27 512	89.16 484	99.55 425	99.25 318	92.34 449	95.23 460	97.05 494	88.86 406	93.59 513	80.67 509	66.95 535	96.96 470

ETV-MVS		99.34 105	99.24 108	99.64 160	99.58 237	99.33 177	100.00 1	99.25 318	97.57 183	99.96 153	100.00 1	97.44 200	99.79 259	99.70 173	99.65 183	99.81 249

DTE-MVSNet		95.52 400	94.99 409	97.08 395	97.49 467	96.45 384	100.00 1	99.25 318	93.82 419	96.17 449	99.57 385	87.81 418	97.18 483	94.57 425	86.26 474	97.62 441

ACMM	97.17 6	97.37 319	97.40 308	97.29 388	99.01 369	94.64 425	100.00 1	99.25 318	98.07 131	98.44 368	99.98 252	87.38 423	99.55 312	99.25 262	95.19 359	97.69 423
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_11		98.92 183	98.63 200	99.80 123	99.85 129	99.86 90	100.00 1	99.24 325	98.91 55	100.00 1	100.00 1	89.69 391	99.99 107	100.00 1	99.98 118	99.54 324

ppachtmachnet_test		96.17 382	95.89 372	97.02 398	97.61 459	95.24 403	99.99 269	99.24 325	93.31 436	96.71 441	99.62 375	94.34 285	98.07 462	89.87 472	92.30 403	97.75 362

GA-MVS		97.72 299	97.27 316	99.06 265	99.24 348	97.93 327	100.00 1	99.24 325	95.80 358	98.99 317	99.64 367	89.77 388	99.36 343	95.12 418	97.62 319	99.89 190

FMVSNet5		95.32 403	95.43 399	94.99 449	99.39 330	92.99 453	99.25 457	99.24 325	90.45 463	97.44 422	98.45 471	95.78 243	94.39 507	87.02 488	91.88 409	97.59 445

IS-MVSNet		99.08 145	98.91 160	99.59 170	99.65 207	99.38 170	99.78 384	99.24 325	96.70 291	99.51 268	100.00 1	98.44 156	99.52 321	98.47 311	98.39 231	99.88 203

AstraMVS		99.03 155	99.01 141	99.09 264	99.46 306	97.66 341	100.00 1	99.23 330	97.83 150	99.95 185	100.00 1	95.52 249	99.86 233	99.74 159	99.39 194	99.74 302

fmvsm_s_conf0.1_n_2		98.95 179	98.69 192	99.73 143	99.61 225	99.74 112	100.00 1	99.23 330	98.95 42	99.97 145	100.00 1	90.92 360	99.97 150	100.00 1	99.58 188	99.47 329

test_fmvsmconf0.1_n		99.25 126	99.05 135	99.82 112	98.92 384	99.55 140	100.00 1	99.23 330	98.91 55	99.75 240	99.97 264	94.79 269	99.94 196	99.94 114	99.99 107	99.97 137

PS-MVSNAJss		98.03 283	98.06 277	97.94 362	97.63 457	97.33 356	99.89 358	99.23 330	96.27 338	98.03 393	99.59 379	98.75 142	98.78 388	98.52 309	94.61 375	97.70 418

K. test v3		95.46 402	95.14 406	96.40 425	97.53 464	93.40 448	99.99 269	99.23 330	95.49 371	92.70 481	99.73 344	84.26 447	98.12 454	93.94 435	93.38 386	97.68 425

wanda-best-256-512		93.76 422	92.74 434	96.84 409	95.22 500	94.54 431	100.00 1	99.22 335	87.22 482	98.54 353	98.56 462	90.48 369	98.22 445	95.67 404	69.73 523	97.75 362

FE-blended-shiyan7		93.76 422	92.74 434	96.84 409	95.22 500	94.54 431	100.00 1	99.22 335	87.22 482	98.54 353	98.56 462	90.48 369	98.22 445	95.67 404	69.73 523	97.75 362

blended_shiyan6		93.70 427	92.67 439	96.78 419	95.17 504	94.38 438	100.00 1	99.22 335	87.03 487	98.54 353	98.56 462	90.14 378	98.22 445	95.62 408	69.73 523	97.75 362

blend_shiyan4		95.76 396	95.40 402	96.82 415	95.50 498	94.40 435	100.00 1	99.22 335	87.12 484	98.67 342	98.59 459	99.09 100	98.31 436	96.31 390	84.14 482	97.75 362

E3new		98.95 179	98.80 171	99.41 205	99.57 241	98.50 265	100.00 1	99.22 335	96.84 262	99.89 207	100.00 1	95.70 245	99.93 200	99.57 219	98.39 231	99.82 232

E2		98.77 202	98.57 212	99.37 218	99.53 255	98.38 277	99.98 302	99.22 335	96.77 272	99.75 240	100.00 1	94.03 292	99.91 208	99.53 229	98.35 242	99.82 232

E3		98.77 202	98.57 212	99.36 220	99.47 297	98.36 281	99.98 302	99.22 335	96.76 273	99.75 240	100.00 1	94.10 289	99.91 208	99.53 229	98.35 242	99.82 232

viewdifsd2359ckpt13		98.72 209	98.52 220	99.34 224	99.55 248	98.46 267	99.99 269	99.22 335	96.50 318	99.05 313	100.00 1	94.54 278	99.73 282	99.46 241	98.35 242	99.81 249

viewcassd2359sk11		98.90 188	98.73 182	99.40 210	99.57 241	98.47 266	99.99 269	99.22 335	96.79 267	99.82 226	100.00 1	95.24 254	99.91 208	99.54 226	98.38 235	99.82 232

viewdifsd2359ckpt11		97.98 285	97.89 285	98.26 328	99.47 297	94.98 410	99.99 269	99.22 335	96.74 279	99.24 294	100.00 1	90.14 378	99.90 219	99.49 235	96.73 332	99.90 182

viewmsd2359difaftdt		97.98 285	97.89 285	98.27 325	99.47 297	94.99 409	99.99 269	99.22 335	96.74 279	99.24 294	100.00 1	90.14 378	99.90 219	99.49 235	96.73 332	99.90 182

viewmanbaseed2359cas		98.86 193	98.68 194	99.40 210	99.51 276	98.51 264	99.98 302	99.22 335	97.05 240	99.72 247	100.00 1	94.77 270	99.89 221	99.58 216	98.31 253	99.81 249

LuminaMVS		99.07 148	98.92 159	99.50 184	98.87 391	99.12 203	99.92 345	99.22 335	97.45 200	99.82 226	99.98 252	96.29 235	99.85 240	99.71 169	99.05 204	99.52 326

miper_enhance_ethall		98.33 264	98.27 257	98.51 306	99.66 205	99.04 210	100.00 1	99.22 335	97.53 188	98.51 362	99.38 405	99.49 46	98.75 393	98.02 331	92.61 395	97.76 351

nrg030		97.64 301	97.27 316	98.75 294	98.34 414	99.53 145	100.00 1	99.22 335	96.21 344	98.27 382	99.95 290	94.40 282	98.98 369	99.23 265	89.78 438	97.75 362

lessismore_v0				96.05 435	97.55 463	91.80 464		99.22 335		91.87 482	99.91 307	83.50 454	98.68 396	92.48 450	90.42 434	97.68 425

casdiffmvs_mvg		98.64 222	98.39 245	99.40 210	99.50 285	98.60 253	100.00 1	99.22 335	96.85 260	99.10 306	100.00 1	92.75 328	99.78 265	99.71 169	98.35 242	99.81 249
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
cascas		98.43 254	98.07 276	99.50 184	99.65 207	99.02 213	100.00 1	99.22 335	94.21 409	99.72 247	99.98 252	92.03 343	99.93 200	99.68 183	98.12 282	99.54 324

MIMVSNet1		91.96 442	91.20 446	94.23 460	94.94 507	91.69 465	99.34 448	99.22 335	88.23 475	94.18 471	98.45 471	75.52 484	93.41 515	79.37 513	91.49 418	97.60 444

E5new		98.63 228	98.41 236	99.31 240	99.51 276	98.21 300	99.79 379	99.21 354	96.62 304	99.67 257	100.00 1	93.15 318	99.91 208	99.46 241	98.26 264	99.81 249

E6new		98.64 222	98.41 236	99.30 244	99.46 306	98.19 303	99.79 379	99.21 354	96.62 304	99.68 251	100.00 1	93.24 314	99.91 208	99.47 238	98.26 264	99.81 249

E6		98.64 222	98.41 236	99.30 244	99.46 306	98.19 303	99.79 379	99.21 354	96.62 304	99.68 251	100.00 1	93.24 314	99.91 208	99.47 238	98.26 264	99.81 249

E5		98.63 228	98.41 236	99.31 240	99.51 276	98.21 300	99.79 379	99.21 354	96.62 304	99.67 257	100.00 1	93.15 318	99.91 208	99.46 241	98.26 264	99.81 249

E4		98.68 220	98.46 230	99.33 232	99.51 276	98.27 294	99.96 320	99.21 354	96.66 296	99.68 251	100.00 1	93.38 309	99.91 208	99.49 235	98.27 262	99.81 249

viewmacassd2359aftdt		98.57 239	98.31 255	99.33 232	99.49 289	98.31 290	99.89 358	99.21 354	96.87 259	99.10 306	100.00 1	92.48 337	99.88 229	99.50 233	98.28 259	99.81 249

Patchmatch-test		97.83 294	97.42 306	99.06 265	99.08 359	97.66 341	98.66 501	99.21 354	93.65 424	98.25 384	99.58 381	99.47 51	99.57 302	90.25 469	98.59 217	99.95 149

mvs_anonymous		98.80 200	98.60 207	99.38 217	99.57 241	99.24 190	100.00 1	99.21 354	95.87 352	98.92 323	99.82 327	96.39 234	99.03 362	99.13 273	98.50 221	99.88 203

EC-MVSNet		99.19 133	99.09 132	99.48 189	99.42 320	99.07 206	100.00 1	99.21 354	96.95 250	99.96 153	100.00 1	96.88 220	99.48 328	99.64 197	99.79 172	99.88 203

TR-MVS		98.14 277	97.74 293	99.33 232	99.59 232	98.28 292	99.27 454	99.21 354	96.42 325	99.15 302	99.94 296	88.87 405	99.79 259	98.88 287	98.29 256	99.93 165

onestephybrid01		98.89 191	98.67 195	99.56 177	99.51 276	99.08 205	100.00 1	99.20 364	97.30 219	99.95 185	100.00 1	94.04 291	99.79 259	99.77 151	98.29 256	99.81 249

blended_shiyan8		93.73 425	92.69 437	96.84 409	95.17 504	94.40 435	100.00 1	99.20 364	87.05 485	98.60 348	98.54 466	90.15 377	98.39 430	95.54 411	69.93 522	97.74 390

viewdifsd2359ckpt07		98.72 209	98.52 220	99.34 224	99.47 297	98.28 292	99.99 269	99.20 364	96.98 246	99.60 262	100.00 1	93.45 308	99.93 200	99.58 216	98.36 240	99.82 232

viewdifsd2359ckpt09		98.78 201	98.60 207	99.31 240	99.53 255	98.37 278	100.00 1	99.20 364	96.85 260	99.32 290	100.00 1	94.68 274	99.74 278	99.46 241	98.36 240	99.81 249

viewmamba		98.92 183	98.74 180	99.46 191	99.46 306	98.83 233	100.00 1	99.19 368	97.18 227	99.95 185	100.00 1	94.97 264	99.74 278	99.64 197	98.29 256	99.81 249

hybridnocas07		98.85 195	98.63 200	99.53 180	99.52 269	98.95 225	100.00 1	99.19 368	97.15 229	99.93 196	100.00 1	93.83 300	99.82 250	99.67 187	98.38 235	99.82 232

hybrid		98.81 199	98.60 207	99.45 195	99.52 269	98.74 241	100.00 1	99.19 368	97.04 241	99.95 185	100.00 1	93.89 299	99.78 265	99.64 197	98.19 273	99.81 249

GeoE		98.06 281	97.65 300	99.29 247	99.47 297	98.41 270	100.00 1	99.19 368	94.85 385	98.88 326	100.00 1	91.21 350	99.59 296	97.02 369	98.19 273	99.88 203

KD-MVS_2432*1600		94.15 417	93.08 427	97.35 384	99.53 255	97.83 335	99.63 414	99.19 368	92.88 444	96.29 446	97.68 488	98.84 134	96.70 487	89.73 473	63.92 536	97.53 449

miper_refine_blended		94.15 417	93.08 427	97.35 384	99.53 255	97.83 335	99.63 414	99.19 368	92.88 444	96.29 446	97.68 488	98.84 134	96.70 487	89.73 473	63.92 536	97.53 449

SPE-MVS-test		99.31 112	99.27 98	99.43 201	99.99 53	98.77 237	100.00 1	99.19 368	97.24 222	99.96 153	100.00 1	97.56 192	99.70 288	99.68 183	99.81 168	99.82 232

jajsoiax		97.07 334	96.79 335	97.89 366	97.28 475	97.12 365	99.95 329	99.19 368	96.55 309	97.31 424	99.69 353	87.35 425	98.91 377	98.70 297	95.12 364	97.66 430

MVS_Test		98.93 182	98.65 197	99.77 136	99.62 223	99.50 152	99.99 269	99.19 368	95.52 368	99.96 153	99.86 315	96.54 231	99.98 141	98.65 300	98.48 223	99.82 232

MVS-HIRNet		94.12 419	92.73 436	98.29 323	99.33 337	95.95 388	99.38 444	99.19 368	74.54 517	98.26 383	86.34 536	86.07 435	99.06 360	91.60 456	99.87 157	99.85 220

hybridcas		98.64 222	98.41 236	99.33 232	99.54 251	98.41 270	100.00 1	99.18 378	96.78 269	99.68 251	100.00 1	92.58 334	99.75 277	99.57 219	98.38 235	99.82 232

icg_test_0407_2		98.30 266	98.45 231	97.85 368	99.38 331	95.36 398	99.99 269	99.18 378	96.72 285	99.58 263	100.00 1	95.17 259	98.45 426	97.84 339	98.15 278	99.74 302

IMVS_0407		98.36 263	98.42 234	98.19 335	99.38 331	95.36 398	99.73 399	99.18 378	96.72 285	99.58 263	100.00 1	95.17 259	99.47 330	97.84 339	98.15 278	99.74 302

IMVS_0404		97.87 290	97.89 285	97.81 370	99.38 331	95.36 398	99.84 367	99.18 378	96.72 285	98.41 369	100.00 1	91.43 348	98.32 435	97.84 339	98.15 278	99.74 302

IMVS_0403		98.37 261	98.39 245	98.29 323	99.38 331	95.36 398	99.97 312	99.18 378	96.72 285	99.68 251	100.00 1	94.61 276	99.77 267	97.84 339	98.15 278	99.74 302

MTMP							100.00 1	99.18 378

mvs_tets		97.00 340	96.69 337	97.94 362	97.41 473	97.27 358	99.60 419	99.18 378	96.51 316	97.35 423	99.69 353	86.53 431	98.91 377	98.84 289	95.09 365	97.65 435

pmmvs4		97.17 328	96.80 333	98.27 325	97.68 456	98.64 251	100.00 1	99.18 378	94.22 408	98.55 352	99.71 347	93.67 302	98.47 424	95.66 406	92.57 398	97.71 417

diffmvs		98.96 176	98.73 182	99.63 161	99.54 251	99.16 200	100.00 1	99.18 378	97.33 214	99.96 153	100.00 1	94.60 277	99.91 208	99.66 194	98.33 249	99.82 232
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepMVS_CX				89.98 480	98.90 386	71.46 518		99.18 378	97.61 177	96.92 432	99.83 323	86.07 435	99.83 247	96.02 394	97.65 317	98.65 345

Casviewmamba		98.71 213	98.47 228	99.46 191	99.47 297	98.70 245	100.00 1	99.17 388	96.97 248	99.45 277	100.00 1	93.04 322	99.87 231	99.67 187	98.41 228	99.81 249

casdiffseed414692147		98.31 265	97.94 283	99.40 210	99.46 306	98.67 247	99.91 352	99.17 388	96.33 335	98.66 343	99.97 264	90.47 373	99.71 286	99.36 250	98.16 277	99.81 249

baseline		98.69 218	98.45 231	99.41 205	99.52 269	98.67 247	100.00 1	99.17 388	97.03 242	99.13 303	100.00 1	93.17 316	99.74 278	99.70 173	98.34 246	99.81 249

PatchmatchNet2					0.00 565	95.13 406	99.92 345	99.16 391	89.91 469
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mamba_0408		98.63 228	98.40 242	99.34 224	99.53 255	98.52 261	99.24 458	99.16 391	96.43 321	98.95 319	99.98 252	94.47 280	99.76 272	99.21 268	98.62 214	99.75 295

SSM_04072		98.59 235	98.40 242	99.15 261	99.53 255	98.52 261	99.24 458	99.16 391	96.43 321	98.95 319	99.98 252	94.47 280	99.19 355	99.21 268	98.62 214	99.75 295

viewmambaseed2359dif		98.57 239	98.34 253	99.28 250	99.46 306	98.23 297	100.00 1	99.16 391	96.26 339	99.11 305	100.00 1	93.12 321	99.79 259	99.61 209	98.33 249	99.80 280

MonoMVSNet		98.55 243	98.64 199	98.26 328	98.21 429	95.76 394	99.94 337	99.16 391	96.23 340	99.47 274	99.24 414	96.75 224	99.22 352	99.61 209	99.17 197	99.81 249

Fast-Effi-MVS+-dtu		98.38 260	98.56 215	97.82 369	99.58 237	94.44 433	100.00 1	99.16 391	96.75 276	99.51 268	99.63 371	95.03 263	99.60 294	97.71 346	99.67 180	99.42 331

SSM_0407		98.72 209	98.52 220	99.33 232	99.53 255	98.52 261	99.88 361	99.15 397	96.53 311	98.95 319	100.00 1	94.38 283	99.72 284	99.64 197	98.62 214	99.75 295

SSM_0404		98.76 205	98.56 215	99.35 222	99.53 255	98.65 250	99.80 378	99.15 397	96.53 311	99.47 274	100.00 1	94.38 283	99.76 272	99.64 197	98.59 217	99.64 320

Fast-Effi-MVS+		98.40 259	98.02 280	99.55 179	99.63 216	99.06 208	100.00 1	99.15 397	95.07 380	99.42 278	99.95 290	93.26 313	99.73 282	97.44 356	98.24 268	99.87 214

anonymousdsp		97.16 329	96.88 330	98.00 358	97.08 477	98.06 315	99.81 373	99.15 397	94.58 396	97.84 405	99.62 375	90.49 368	98.60 410	97.98 332	95.32 350	97.33 460

casdiffmvs		98.65 221	98.38 247	99.46 191	99.52 269	98.74 241	100.00 1	99.15 397	96.91 255	99.05 313	100.00 1	92.75 328	99.83 247	99.70 173	98.38 235	99.81 249
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
lupinMVS		99.29 117	99.16 122	99.69 150	99.45 314	99.49 156	100.00 1	99.15 397	97.45 200	99.97 145	100.00 1	96.76 222	99.76 272	99.67 187	100.00 1	99.81 249

1112_ss		98.91 186	98.71 188	99.51 181	99.69 183	98.75 238	99.99 269	99.15 397	96.82 264	98.84 331	100.00 1	97.45 198	99.89 221	98.66 298	97.75 311	99.89 190

dtuplus		98.57 239	98.32 254	99.30 244	99.44 316	98.35 284	100.00 1	99.14 404	96.36 331	98.97 318	100.00 1	93.04 322	99.77 267	99.55 222	98.39 231	99.79 286

diffmvs_AUTHOR		98.92 183	98.73 182	99.49 188	99.48 292	98.81 234	99.94 337	99.14 404	97.24 222	99.96 153	100.00 1	94.85 267	99.87 231	99.67 187	98.31 253	99.79 286

tt0320		92.36 441	91.28 445	95.58 441	98.30 420	90.65 473	98.69 500	99.14 404	76.73 510	96.07 452	99.50 394	72.28 492	98.39 430	93.29 442	87.56 460	97.70 418

IterMVS-SCA-FT		96.72 350	96.42 350	97.62 375	99.40 327	96.83 373	99.99 269	99.14 404	94.65 395	97.55 419	99.72 345	89.65 393	98.31 436	95.62 408	92.05 405	97.73 402

YYNet1		92.44 440	90.92 450	97.03 397	96.20 484	97.06 368	99.99 269	99.14 404	88.21 477	67.93 537	98.43 474	88.63 409	96.28 494	90.64 462	89.08 447	97.74 390

MDA-MVSNet_test_wron		92.61 438	91.09 449	97.19 393	96.71 480	97.26 359	100.00 1	99.14 404	88.61 473	67.90 538	98.32 478	89.03 401	96.57 490	90.47 467	89.59 439	97.74 390

Test_1112_low_res		98.83 197	98.60 207	99.51 181	99.69 183	98.75 238	99.99 269	99.14 404	96.81 265	98.84 331	99.06 425	97.45 198	99.89 221	98.66 298	97.75 311	99.89 190

CS-MVS		99.33 108	99.27 98	99.50 184	99.99 53	99.00 218	100.00 1	99.13 411	97.26 221	99.96 153	100.00 1	97.79 181	99.64 292	99.64 197	99.67 180	99.87 214

v2v482		96.70 351	96.18 360	98.27 325	98.04 437	98.39 274	100.00 1	99.13 411	94.19 411	98.58 350	99.08 424	90.48 369	98.67 397	95.69 403	90.44 432	97.75 362

MVSFormer		98.94 181	98.82 168	99.28 250	99.45 314	99.49 156	100.00 1	99.13 411	95.46 373	99.97 145	100.00 1	96.76 222	98.59 412	98.63 303	100.00 1	99.74 302

jason		99.11 143	98.96 150	99.59 170	99.17 351	99.31 180	100.00 1	99.13 411	97.38 207	99.83 217	100.00 1	95.54 248	99.72 284	99.57 219	99.97 122	99.74 302
jason: jason.
test_djsdf		97.55 310	97.38 309	98.07 346	97.50 465	97.99 319	100.00 1	99.13 411	95.46 373	98.47 365	99.85 320	92.01 344	98.59 412	98.63 303	95.36 349	97.62 441

IterMVS		96.76 347	96.46 348	97.63 373	99.41 322	96.89 371	99.99 269	99.13 411	94.74 390	97.59 418	99.66 360	89.63 395	98.28 441	95.71 402	92.31 402	97.72 409
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
sc_t1		92.52 439	91.34 444	96.09 434	97.80 449	89.86 480	98.61 503	99.12 417	77.73 509	96.09 451	99.79 339	68.64 498	98.94 374	96.94 371	87.31 463	99.46 330

tpm		98.24 273	98.22 266	98.32 322	99.13 353	95.79 393	99.53 428	99.12 417	95.20 379	99.96 153	99.36 407	97.58 188	99.28 350	97.41 358	96.67 334	99.88 203

GDP-MVS		99.39 93	99.26 102	99.77 136	99.53 255	99.55 140	100.00 1	99.11 419	97.14 230	99.96 153	100.00 1	99.83 5	99.89 221	98.47 311	99.26 196	99.87 214

miper_ehance_all_eth		97.81 295	97.66 299	98.23 331	99.49 289	98.37 278	99.99 269	99.11 419	94.78 387	98.25 384	99.21 417	98.18 163	98.57 416	97.35 362	92.61 395	97.76 351

v7n		96.06 390	95.42 401	97.99 360	97.58 462	97.35 353	99.86 364	99.11 419	92.81 447	97.91 401	99.49 395	90.99 358	98.92 376	92.51 449	88.49 453	97.70 418

cl____		97.54 311	97.32 312	98.18 336	99.47 297	98.14 308	100.00 1	99.10 422	94.16 413	97.60 416	99.63 371	97.52 194	98.65 400	96.47 385	91.97 408	97.76 351

DIV-MVS_self_test		97.52 314	97.35 311	98.05 354	99.46 306	98.11 309	100.00 1	99.10 422	94.21 409	97.62 414	99.63 371	97.65 186	98.29 440	96.47 385	91.98 407	97.76 351

c3_l		97.58 307	97.42 306	98.06 350	99.48 292	98.16 305	99.96 320	99.10 422	94.54 398	98.13 388	99.20 419	97.87 175	98.25 443	97.28 363	91.20 423	97.75 362

Effi-MVS+		98.58 237	98.24 262	99.61 165	99.60 228	99.26 186	97.85 516	99.10 422	96.22 343	99.97 145	99.89 310	93.75 301	99.77 267	99.43 245	98.34 246	99.81 249

IterMVS-LS		97.56 308	97.44 305	97.92 365	99.38 331	97.90 328	99.89 358	99.10 422	94.41 403	98.32 377	99.54 390	97.21 205	98.11 456	97.50 354	91.62 415	97.75 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		96.65 353	96.16 362	98.11 345	98.17 433	98.23 297	99.99 269	99.09 427	93.97 416	98.74 338	99.05 427	91.09 353	98.82 386	95.46 412	89.90 436	97.27 461

KD-MVS_self_test		91.16 450	90.09 455	94.35 457	94.44 509	91.27 468	99.74 394	99.08 428	90.82 460	94.53 468	94.91 514	86.11 434	94.78 506	82.67 503	68.52 528	96.99 469

eth_miper_zixun_eth		97.47 315	97.28 314	98.06 350	99.41 322	97.94 325	99.62 417	99.08 428	94.46 402	98.19 387	99.56 386	96.91 219	98.50 421	96.78 380	91.49 418	97.74 390

v1192		96.18 380	95.49 394	98.26 328	98.01 440	98.15 306	99.99 269	99.08 428	93.36 434	98.54 353	98.97 439	89.47 396	98.89 380	91.15 460	90.82 426	97.75 362

v1144		96.51 360	95.97 370	98.13 343	97.98 442	98.04 317	99.99 269	99.08 428	93.51 429	98.62 347	98.98 435	90.98 359	98.62 406	93.79 436	90.79 427	97.74 390

RRT-MVS		98.75 208	98.52 220	99.44 198	99.65 207	98.57 255	99.90 354	99.08 428	96.51 316	99.96 153	99.95 290	92.59 333	99.96 170	99.60 211	99.45 193	99.81 249

dcpmvs_2		98.87 192	99.53 65	96.90 405	99.87 126	90.88 471	99.94 337	99.07 433	98.20 119	100.00 1	100.00 1	98.69 145	99.86 233	100.00 1	100.00 1	99.95 149

miper_lstm_enhance		97.40 318	97.28 314	97.75 372	99.48 292	97.52 345	100.00 1	99.07 433	94.08 415	98.01 396	99.61 377	97.38 202	97.98 469	96.44 388	91.47 420	97.76 351

v1921920		96.16 384	95.50 392	98.14 340	97.88 446	97.96 323	99.99 269	99.07 433	93.33 435	98.60 348	99.24 414	89.37 397	98.71 395	91.28 457	90.74 428	97.75 362

v148		96.29 374	95.84 375	97.63 373	97.74 452	96.53 383	100.00 1	99.07 433	93.52 428	98.01 396	99.42 402	91.22 349	98.60 410	96.37 389	87.22 466	97.75 362

v1240		95.96 392	95.25 403	98.07 346	97.91 444	97.87 333	99.96 320	99.07 433	93.24 438	98.64 346	98.96 440	88.98 403	98.61 407	89.58 477	90.92 425	97.75 362

v8		96.35 371	95.73 383	98.21 334	98.11 435	98.23 297	99.94 337	99.07 433	92.66 448	98.29 379	99.00 434	91.46 346	98.77 391	94.17 430	88.83 451	97.62 441

v10		96.14 386	95.50 392	98.07 346	98.19 431	97.96 323	99.83 369	99.07 433	92.10 451	98.07 390	98.94 441	91.07 354	98.61 407	92.41 452	89.82 437	97.63 439

testgi		96.18 380	95.93 371	96.93 404	98.98 378	94.20 441	100.00 1	99.07 433	97.16 228	96.06 453	99.86 315	84.08 451	97.79 477	90.38 468	97.80 307	98.81 342

v144192		96.40 368	95.81 376	98.17 338	97.89 445	98.11 309	99.99 269	99.06 441	93.39 433	98.75 337	99.09 423	90.43 374	98.66 398	93.10 444	90.55 430	97.75 362

PCF-MVS	98.23 3	98.69 218	98.37 249	99.62 163	99.78 167	99.02 213	99.23 465	99.06 441	96.43 321	98.08 389	100.00 1	94.72 273	99.95 183	98.16 325	99.91 146	99.90 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RPSCF		97.37 319	98.24 262	94.76 453	99.80 157	84.57 496	99.99 269	99.05 443	94.95 383	99.82 226	100.00 1	94.03 292	100.00 1	98.15 326	98.38 235	99.70 312

FPMVS		77.92 493	79.45 489	73.34 518	76.87 554	46.81 547	98.24 509	99.05 443	59.89 526	73.55 529	98.34 477	36.81 537	86.55 530	80.96 508	91.35 422	86.65 531

Gipuma		84.73 474	83.50 478	88.40 487	97.50 465	82.21 503	88.87 539	99.05 443	65.81 520	85.71 507	90.49 525	53.70 521	96.31 493	78.64 515	91.74 411	86.67 530
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		98.60 234	98.24 262	99.67 154	96.90 478	99.21 194	99.99 269	99.04 446	98.80 77	99.57 265	99.96 282	90.12 381	99.91 208	99.89 122	99.89 149	99.90 182

EGC-MVSNET		79.46 488	74.04 498	95.72 439	96.00 489	92.73 456	99.09 484	99.04 446	5.08 559	16.72 559	98.71 454	73.03 489	98.74 394	82.05 505	96.64 335	95.69 499

pm-mvs1		95.76 396	95.01 408	98.00 358	98.23 428	97.45 348	99.24 458	99.04 446	93.13 441	95.93 455	99.72 345	86.28 433	98.84 385	95.62 408	87.92 456	97.72 409

VortexMVS		98.23 274	98.11 270	98.59 302	99.56 247	99.37 173	99.95 329	99.03 449	96.47 319	98.69 339	99.55 387	95.91 238	98.66 398	99.01 281	94.80 371	97.73 402

ET-MVSNet_ETH3D		96.41 365	95.48 396	99.20 259	99.81 144	99.75 109	100.00 1	99.02 450	97.30 219	78.33 522	100.00 1	97.73 182	97.94 471	99.70 173	87.41 461	99.92 167

pmmvs6		93.64 428	92.87 431	95.94 437	97.47 469	91.41 467	98.92 491	99.02 450	87.84 480	95.01 462	99.61 377	77.24 478	98.77 391	94.33 428	86.41 473	97.63 439

our_test_3		96.51 360	96.35 353	96.98 401	97.61 459	95.05 407	99.98 302	99.01 452	94.68 393	96.77 440	99.06 425	95.87 240	98.14 452	91.81 454	92.37 401	97.75 362

ArgMatch-Sym		94.50 412	94.12 415	95.63 440	98.16 434	90.84 472	100.00 1	99.00 453	97.42 204	97.22 427	99.76 343	73.91 486	99.05 361	91.22 458	90.43 433	97.01 468

test_fmvs1		98.37 261	98.04 278	99.34 224	99.84 131	98.07 313	100.00 1	99.00 453	98.85 66	100.00 1	100.00 1	85.11 443	99.96 170	99.69 182	99.88 152	100.00 1

CR-MVSNet		98.02 284	97.71 298	98.93 278	99.31 338	98.86 230	99.13 478	99.00 453	96.53 311	99.96 153	98.98 435	96.94 217	98.10 460	91.18 459	98.40 229	99.84 222

Patchmtry		96.81 344	96.37 352	98.14 340	99.31 338	98.55 256	98.91 492	99.00 453	90.45 463	97.92 400	98.98 435	96.94 217	98.12 454	94.27 429	91.53 417	97.75 362

test_vis1_n		96.69 352	95.81 376	99.32 238	99.14 352	97.98 320	99.97 312	98.98 457	98.45 100	100.00 1	100.00 1	66.44 502	99.99 107	99.78 149	99.57 190	100.00 1

test_fmvs1_n		97.43 316	96.86 331	99.15 261	99.68 188	97.48 347	99.99 269	98.98 457	98.82 72	100.00 1	100.00 1	74.85 485	99.96 170	99.67 187	99.70 176	100.00 1

Effi-MVS+-dtu		98.51 250	98.86 165	97.47 379	99.77 169	94.21 440	100.00 1	98.94 459	97.61 177	99.91 201	98.75 453	95.89 239	99.51 323	99.36 250	99.48 191	98.68 344

MDA-MVSNet-bldmvs		91.65 449	89.94 458	96.79 418	96.72 479	96.70 377	99.42 441	98.94 459	88.89 472	66.97 540	98.37 476	81.43 463	95.91 498	89.24 480	89.46 443	97.75 362

test_method		91.04 453	91.10 448	90.85 477	98.34 414	77.63 508	100.00 1	98.93 461	76.69 511	96.25 448	98.52 468	70.44 495	97.98 469	89.02 482	91.74 411	96.92 471

dtuonlycased		95.07 409	95.43 399	93.98 463	98.26 424	85.63 493	99.98 302	98.92 462	94.83 386	94.13 473	99.47 397	82.60 459	97.61 481	94.66 423	96.01 339	98.70 343

dmvs_testset		93.27 432	95.48 396	86.65 491	98.74 399	68.42 526	99.92 345	98.91 463	96.19 345	93.28 477	100.00 1	91.06 356	91.67 522	89.64 475	91.54 416	99.86 218

new_pmnet		94.11 420	93.47 422	96.04 436	96.60 483	92.82 454	99.97 312	98.91 463	90.21 466	95.26 459	98.05 486	85.89 438	98.14 452	84.28 499	92.01 406	97.16 463

APD_test1		93.07 435	94.14 414	89.85 481	99.18 350	72.49 516	99.76 391	98.90 465	92.86 446	96.35 445	99.94 296	75.56 483	99.91 208	86.73 489	97.98 290	97.15 464

FE-MVSNET2		91.15 451	90.00 457	94.58 454	90.74 525	92.52 460	99.56 423	98.87 466	90.82 460	88.96 494	95.40 508	76.26 482	95.56 502	87.84 485	81.59 498	95.66 501

test20.03		93.11 433	92.85 432	93.88 464	95.19 503	91.83 463	100.00 1	98.87 466	93.68 423	92.76 479	98.88 447	89.20 400	92.71 517	77.88 519	89.19 446	97.09 465

ArgMatch-SfM		93.74 424	93.14 426	95.54 442	98.57 405	90.54 474	99.97 312	98.86 468	97.35 209	97.60 416	99.66 360	71.88 493	99.02 363	90.18 470	84.16 481	97.07 467

test_0402		94.35 414	93.70 419	96.32 430	97.92 443	93.60 444	99.61 418	98.85 469	88.19 478	94.68 465	99.48 396	80.01 467	98.58 415	89.39 478	95.15 362	96.77 473

Anonymous20240521		93.29 431	92.76 433	94.90 452	95.64 496	91.27 468	99.97 312	98.82 470	87.04 486	94.71 464	98.19 481	83.86 452	96.80 486	84.04 500	92.56 399	96.64 478

new-patchmatchnet		90.30 458	89.46 461	92.84 471	90.77 524	88.55 487	99.83 369	98.80 471	90.07 468	87.86 498	95.00 512	78.77 473	94.30 508	84.86 497	79.15 505	95.68 500

MVStest1		94.27 415	93.30 424	97.19 393	98.83 396	97.18 363	99.93 343	98.79 472	86.80 489	84.88 511	99.04 428	94.32 286	98.25 443	90.55 465	86.57 472	96.12 492

ttmdpeth		96.24 377	95.88 373	97.32 386	97.80 449	96.61 381	99.95 329	98.77 473	97.80 154	93.42 476	99.28 411	86.42 432	99.01 365	97.63 349	91.84 410	96.33 486

WB-MVS		88.24 465	90.09 455	82.68 507	91.56 522	69.51 521	100.00 1	98.73 474	90.72 462	87.29 500	98.12 482	92.87 326	85.01 536	62.19 536	89.34 444	93.54 515

tt0320-xc		91.69 448	90.50 452	95.26 444	98.04 437	90.12 479	98.60 504	98.70 475	76.63 512	94.66 466	99.52 391	68.57 499	97.99 468	94.61 424	85.18 476	97.66 430

SSC-MVS		87.61 466	89.47 460	82.04 508	90.63 526	68.77 525	99.99 269	98.66 476	90.34 465	86.70 502	98.08 483	92.72 331	84.12 537	59.41 539	88.71 452	93.22 519

usedtu_blend_shiyan5		92.75 437	91.39 443	96.82 415	95.22 500	94.40 435	99.05 489	98.64 477	75.98 516	98.54 353	98.56 462	90.48 369	98.31 436	96.31 390	69.73 523	97.75 362

mmtdpeth		94.58 411	94.18 413	95.81 438	98.82 398	91.09 470	99.99 269	98.61 478	96.38 328	100.00 1	97.23 492	76.52 480	99.85 240	99.82 140	80.22 502	96.48 482

pmmvs-eth3d		91.73 447	90.67 451	94.92 451	91.63 521	92.71 457	99.90 354	98.54 479	91.19 456	88.08 497	95.50 505	79.31 471	96.13 496	90.55 465	81.32 500	95.91 496

Anonymous20231206		93.45 430	93.17 425	94.30 458	95.00 506	89.69 482	99.98 302	98.43 480	93.30 437	94.50 469	98.59 459	90.52 367	95.73 501	77.46 521	90.73 429	97.48 454

MASt3R-SfM		91.92 444	92.47 441	90.28 479	96.64 482	75.61 512	99.63 414	98.31 481	95.70 360	95.42 458	98.84 448	67.34 500	99.22 352	89.92 471	90.47 431	96.01 494

USDC		95.90 394	95.70 384	96.50 424	98.60 404	92.56 459	100.00 1	98.30 482	97.77 157	96.92 432	99.94 296	81.25 465	99.45 336	93.54 439	94.96 370	97.49 451

SixPastTwentyTwo		95.71 398	95.49 394	96.38 427	97.42 471	93.01 451	99.84 367	98.23 483	94.75 388	95.98 454	99.97 264	85.35 442	98.43 427	94.71 422	93.17 387	97.69 423

RoMa-SfM		90.39 457	89.63 459	92.66 472	97.47 469	83.18 500	98.81 495	98.21 484	85.44 495	89.21 492	99.46 399	63.72 503	98.30 439	87.11 487	87.25 464	96.51 481

MVP-Stereo		96.51 360	96.48 347	96.60 422	95.65 495	94.25 439	98.84 494	98.16 485	95.85 356	95.23 460	99.04 428	92.54 336	99.13 357	92.98 445	99.98 118	96.43 484
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		96.14 386	95.98 369	96.62 421	97.49 467	93.44 447	99.92 345	98.16 485	95.86 354	97.65 411	99.95 290	85.71 440	98.78 388	94.93 420	94.18 378	97.64 438

ITE_SJBPF				96.84 409	98.96 381	93.49 446		98.12 487	98.12 128	98.35 374	99.97 264	84.45 445	99.56 306	95.63 407	95.25 355	97.49 451

EG-PatchMatch MVS		92.94 436	92.49 440	94.29 459	95.87 491	87.07 490	99.07 487	98.11 488	93.19 439	88.98 493	98.66 457	70.89 494	99.08 359	92.43 451	95.21 358	96.72 475

DenseAffine		90.43 456	89.28 463	93.87 465	97.71 455	86.21 491	99.13 478	98.10 489	87.86 479	90.15 490	98.43 474	60.76 506	98.65 400	84.48 498	86.90 467	96.74 474

pmmvs5		95.94 393	95.61 389	96.95 402	97.42 471	94.66 423	100.00 1	98.08 490	93.60 426	97.05 430	99.43 401	87.02 426	98.46 425	95.76 400	92.12 404	97.72 409

mvs5depth		93.81 421	93.00 429	96.23 432	94.25 510	93.33 449	97.43 522	98.07 491	93.47 430	94.15 472	99.58 381	77.52 476	98.97 371	93.64 437	88.92 448	96.39 485

LCM-MVSNet		79.01 492	76.93 495	85.27 493	78.28 553	68.01 528	96.57 526	98.03 492	55.10 532	82.03 518	93.27 518	31.99 547	93.95 510	82.72 502	74.37 512	93.84 513

OpenMVS_ROB	88.34 20	91.89 445	91.12 447	94.19 461	95.55 497	87.63 488	99.26 456	98.03 492	86.61 491	90.65 489	96.82 495	70.14 497	98.78 388	86.54 490	96.50 338	96.15 490

ambc				88.45 486	86.84 539	70.76 519	97.79 518	98.02 494		90.91 486	95.14 509	38.69 534	98.51 420	94.97 419	84.23 480	96.09 493

tmp_tt		75.80 495	74.26 497	80.43 510	52.91 563	53.67 544	87.42 544	97.98 495	61.80 524	67.04 539	100.00 1	76.43 481	96.40 492	96.47 385	28.26 552	91.23 523

TransMVSNet (Re)		94.78 410	93.72 418	97.93 364	98.34 414	97.88 331	99.23 465	97.98 495	91.60 453	94.55 467	99.71 347	87.89 417	98.36 432	89.30 479	84.92 477	97.56 447

DKM		88.67 462	87.74 467	91.44 475	97.38 474	82.60 501	98.95 490	97.94 497	87.54 481	87.00 501	98.48 469	55.08 518	95.81 500	86.05 492	81.29 501	95.91 496

LF4IMVS		96.19 379	96.18 360	96.23 432	98.26 424	92.09 462	100.00 1	97.89 498	97.82 152	97.94 398	99.87 313	82.71 457	99.38 342	97.41 358	93.71 381	97.20 462

MatchFormer		86.71 471	84.75 477	92.57 473	96.14 487	82.52 502	99.27 454	97.86 499	80.17 506	78.74 521	96.16 501	54.81 519	98.63 404	75.87 524	83.75 486	96.56 480

Baseline_NR-MVSNet		96.16 384	95.70 384	97.56 378	98.28 423	96.79 375	100.00 1	97.86 499	91.93 452	97.63 412	99.47 397	92.14 341	98.35 433	97.13 366	86.83 470	97.54 448

test_fmvs2		95.17 408	95.23 404	95.01 447	98.95 383	88.99 485	99.99 269	97.77 501	97.79 155	98.58 350	99.70 350	73.36 488	99.34 346	95.88 397	95.03 366	96.70 477

testf1		84.40 475	84.79 475	83.23 505	95.71 493	58.71 542	98.79 496	97.75 502	81.58 503	84.94 509	98.07 484	45.33 530	97.73 478	77.09 522	83.85 483	93.24 517

APD_test2		84.40 475	84.79 475	83.23 505	95.71 493	58.71 542	98.79 496	97.75 502	81.58 503	84.94 509	98.07 484	45.33 530	97.73 478	77.09 522	83.85 483	93.24 517

TinyColmap		95.50 401	95.12 407	96.64 420	98.69 400	93.00 452	99.40 442	97.75 502	96.40 327	96.14 450	99.87 313	79.47 469	99.50 326	93.62 438	94.72 373	97.40 457

dtuonly		97.85 292	97.46 304	99.02 271	98.44 410	97.89 330	99.99 269	97.62 505	96.53 311	99.49 270	99.96 282	94.01 295	99.58 300	92.75 446	98.32 252	99.59 321

TDRefinement		91.93 443	90.48 453	96.27 431	81.60 550	92.65 458	99.10 482	97.61 506	93.96 417	93.77 474	99.85 320	80.03 466	99.53 320	97.82 343	70.59 521	96.63 479

usedtu_dtu_shiyan2		85.34 472	83.22 479	91.71 474	88.10 536	83.34 499	98.75 498	97.59 507	76.21 514	91.11 483	96.80 496	58.14 511	94.30 508	75.00 526	67.24 533	97.49 451

DKM-HiRes		87.00 469	86.38 472	88.84 485	96.71 480	79.05 505	98.73 499	97.57 508	84.56 497	84.00 513	98.23 480	52.90 523	92.48 518	84.95 496	79.77 503	95.00 505

test_fmvs3		87.19 468	87.02 470	87.71 488	92.69 514	76.64 509	99.96 320	97.27 509	93.55 427	90.82 487	94.03 517	38.00 536	92.19 519	93.49 440	83.35 491	94.32 511

LoFTR		88.61 463	87.13 469	93.06 468	96.18 485	83.87 497	99.48 432	97.21 510	86.37 492	82.32 517	96.66 497	58.07 512	98.59 412	81.76 506	86.15 475	96.72 475

test_f		86.87 470	86.06 473	89.28 484	91.45 523	76.37 510	99.87 363	97.11 511	91.10 457	88.46 495	93.05 519	38.31 535	96.66 489	91.77 455	83.46 490	94.82 508

RoMa-HiRes		87.37 467	86.72 471	89.32 483	95.81 492	78.25 507	98.63 502	97.01 512	82.18 502	86.32 504	99.25 413	56.48 515	94.79 505	83.17 501	81.62 497	94.91 507

PMVS	60.66 23	65.98 507	65.05 509	68.75 521	55.06 562	38.40 559	88.19 542	96.98 513	48.30 537	44.82 551	88.52 533	12.22 561	86.49 531	67.58 532	83.79 485	81.35 538
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Patchmatch-RL test		93.49 429	93.63 420	93.05 469	91.78 519	83.41 498	98.21 510	96.95 514	91.58 454	91.05 484	97.64 490	99.40 68	95.83 499	94.11 433	81.95 495	99.91 171

FE-MVSNET		89.50 459	88.33 465	93.00 470	88.89 532	90.24 477	99.96 320	96.86 515	88.23 475	88.46 495	95.47 506	77.03 479	93.37 516	78.54 516	81.56 499	95.39 504

ELoFTR		83.63 477	81.67 484	89.53 482	92.30 516	75.98 511	98.27 508	96.74 516	83.38 500	74.05 528	95.78 503	43.66 532	98.11 456	78.01 517	72.80 517	94.48 510

pmmvs3		90.62 455	89.36 462	94.40 456	90.53 528	91.49 466	100.00 1	96.73 517	84.21 498	93.65 475	96.65 498	82.56 460	94.83 504	82.28 504	77.62 509	96.89 472

PM-MVS		88.39 464	87.41 468	91.31 476	91.73 520	82.02 504	99.79 379	96.62 518	91.06 458	90.71 488	95.73 504	48.60 527	95.96 497	90.56 464	81.91 496	95.97 495

LCM-MVSNet-Re		96.52 358	97.21 320	94.44 455	99.27 345	85.80 492	99.85 366	96.61 519	95.98 349	92.75 480	98.48 469	93.97 296	97.55 482	99.58 216	98.43 226	99.98 127

mvsany_test3		89.36 461	88.96 464	90.56 478	91.95 518	78.97 506	99.74 394	96.59 520	96.84 262	89.25 491	96.07 502	52.59 524	97.11 484	95.17 417	82.44 493	95.58 503

door-mid								96.32 521

door								96.13 522

SP-DiffGlue		85.17 473	85.16 474	85.22 494	93.54 511	69.16 523	97.83 517	95.33 523	60.61 525	86.04 505	92.86 520	61.04 505	90.90 526	89.62 476	89.57 441	95.59 502

SP-SuperGlue		82.71 480	81.92 482	85.07 497	98.02 439	67.96 529	98.10 512	95.26 524	57.79 527	82.47 516	90.37 527	57.02 513	91.04 524	70.34 530	87.92 456	96.23 488

SP-NN		83.33 478	82.73 480	85.13 496	98.98 378	65.96 534	97.92 515	95.13 525	56.43 530	83.71 514	90.52 524	58.27 509	91.69 521	71.99 527	91.66 414	97.74 390

PMMVS2		79.15 491	77.28 494	84.76 498	82.34 547	72.66 515	99.70 405	95.11 526	71.68 519	84.78 512	90.87 522	32.05 546	89.99 528	75.53 525	63.45 538	91.64 521

SP-LightGlue		82.73 479	81.92 482	85.19 495	97.73 454	68.40 527	98.05 513	94.51 527	56.95 529	82.72 515	90.14 530	58.20 510	90.97 525	71.57 528	87.38 462	96.20 489

test_vis1_rt		93.10 434	92.93 430	93.58 466	99.63 216	85.07 494	99.99 269	93.71 528	97.49 195	90.96 485	97.10 493	60.40 507	99.95 183	99.24 264	97.90 298	95.72 498

DSMNet-mixed		95.18 407	95.21 405	95.08 445	96.03 488	90.21 478	99.65 411	93.64 529	92.91 443	98.34 375	97.40 491	90.05 385	95.51 503	91.02 461	97.86 300	99.51 328

SP-MNN		81.80 482	81.08 486	83.94 502	98.26 424	64.81 537	98.20 511	93.56 530	55.15 531	77.43 523	90.43 526	56.33 516	90.69 527	70.11 531	90.27 435	96.32 487

E-PMN		70.72 498	70.06 504	72.69 519	83.92 545	65.48 536	99.95 329	92.72 531	49.88 535	72.30 531	86.26 537	47.17 529	77.43 545	53.83 542	44.49 543	75.17 540

PMatch-SfM		81.57 483	79.80 487	86.88 490	92.36 515	73.86 514	97.50 521	92.66 532	80.39 505	73.10 530	96.35 499	33.54 543	91.86 520	81.28 507	71.01 520	94.92 506

N_pmnet		91.88 446	93.37 423	87.40 489	97.24 476	66.33 533	99.90 354	91.05 533	89.77 470	95.65 457	98.58 461	90.05 385	98.11 456	85.39 493	92.72 394	97.75 362

MVE	68.59 21	67.22 505	64.68 511	74.84 513	74.67 557	62.32 539	95.84 529	90.87 534	50.98 534	58.72 545	81.05 551	12.20 562	78.95 542	61.06 538	56.75 539	83.24 537
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMatch-Up-SfM		79.27 490	77.62 493	84.22 501	90.58 527	69.08 524	96.98 524	90.47 535	76.44 513	71.47 533	96.27 500	30.15 548	88.77 529	78.74 514	67.46 530	94.81 509

ALIKED-NN		82.28 481	81.49 485	84.63 499	99.44 316	67.26 530	97.36 523	90.47 535	62.09 523	81.26 520	95.45 507	59.17 508	93.89 511	63.93 535	84.26 479	92.75 520

ALIKED-MNN		79.54 487	78.11 492	83.80 504	99.29 344	66.55 532	97.70 519	90.37 537	57.60 528	74.96 527	92.30 521	53.12 522	93.57 514	58.80 540	78.89 507	91.27 522

ALIKED-LG		80.86 484	79.70 488	84.33 500	98.33 417	69.33 522	97.59 520	90.14 538	65.38 521	76.03 525	94.87 515	54.78 520	93.65 512	57.59 541	82.61 492	90.01 526

VLMVS_CLIP		69.45 502	71.86 503	62.23 525	66.80 559	30.24 566	87.12 546	87.67 539	33.62 556	82.03 518	98.28 479	28.75 549	67.69 554	88.35 484	74.12 513	88.74 527

ANet_high		66.05 506	63.44 512	73.88 517	61.14 560	63.45 538	95.68 530	87.18 540	79.93 507	47.35 548	80.68 553	22.35 557	72.33 551	61.24 537	35.42 548	85.88 532

testmvs		80.17 485	81.95 481	74.80 514	58.54 561	59.58 541	100.00 1	87.14 541	76.09 515	99.61 261	100.00 1	67.06 501	74.19 549	98.84 289	50.30 540	90.64 524

EMVS		69.88 500	69.09 505	72.24 520	84.70 542	65.82 535	99.96 320	87.08 542	49.82 536	71.51 532	84.74 539	49.30 526	75.32 547	50.97 543	43.71 544	75.59 539

XFeat-MNN		73.39 497	73.10 500	74.25 515	89.63 530	53.35 545	96.25 527	84.01 543	43.66 539	69.74 534	89.91 531	52.56 525	85.32 533	64.72 534	67.44 531	84.08 535

VLMVS		69.79 501	73.02 501	60.12 531	72.70 558	33.43 563	87.87 543	83.71 544	40.13 547	86.04 505	98.98 435	34.57 539	58.39 557	85.00 495	68.17 529	88.54 528

GLUNet-SfM		70.22 499	66.87 507	80.24 511	84.13 544	61.64 540	96.72 525	82.62 545	51.83 533	60.24 544	88.02 535	36.12 538	91.44 523	67.32 533	34.86 550	87.65 529

test_vis3_rt		79.61 486	78.19 491	83.86 503	88.68 535	69.56 520	99.81 373	82.19 546	86.78 490	68.57 536	84.51 540	25.06 554	98.26 442	89.18 481	78.94 506	83.75 536

XFeat-NN		75.54 496	76.00 496	74.19 516	93.25 513	52.63 546	95.93 528	81.98 547	46.32 538	75.32 526	90.27 529	56.80 514	85.05 535	71.26 529	72.85 516	84.87 533

SIFT-NN		67.52 504	68.28 506	65.25 522	96.00 489	45.92 548	93.38 531	80.01 548	43.05 540	69.06 535	85.13 538	39.13 533	85.13 534	32.15 546	76.58 510	64.70 543

SIFT-MNN		64.77 508	65.11 508	63.77 523	92.18 517	44.02 550	91.93 533	78.84 549	41.80 542	61.69 542	84.03 541	33.92 542	81.69 539	29.20 551	72.39 518	65.59 542

SIFT-NN-NCMNet		64.49 509	64.92 510	63.20 524	88.84 533	44.41 549	92.37 532	78.67 550	41.90 541	62.62 541	83.27 543	34.31 540	81.88 538	30.88 547	71.40 519	63.31 545

test123		79.44 489	79.23 490	80.05 512	80.03 552	71.72 517	100.00 1	77.93 551	62.52 522	94.81 463	99.69 353	78.21 474	74.53 548	92.57 448	27.33 553	93.90 512

SIFT-NN-CMatch		60.63 510	60.17 513	62.02 526	86.89 538	43.32 552	90.70 536	71.03 552	41.60 544	61.16 543	83.16 544	33.45 544	78.31 543	30.28 548	43.26 545	64.44 544

SIFT-NN-UMatch		59.27 512	58.65 515	61.13 528	83.27 546	43.66 551	91.00 535	70.69 553	41.78 543	44.38 552	82.21 548	34.17 541	79.10 541	30.07 549	50.25 541	60.64 548

SIFT-NCM-Cal		59.75 511	59.15 514	61.53 527	90.12 529	43.18 553	91.26 534	70.04 554	40.34 546	38.39 554	81.51 550	27.19 550	79.90 540	26.25 556	67.30 532	61.50 547

SIFT-ConvMatch		56.83 514	55.72 517	60.16 529	88.80 534	43.02 554	88.55 540	64.15 555	40.75 545	45.84 549	83.12 545	27.00 551	77.01 546	28.36 552	34.89 549	60.45 549

PDCNetPlus		75.87 494	73.92 499	81.72 509	89.55 531	74.48 513	98.59 505	62.34 556	72.19 518	76.04 524	95.03 511	47.66 528	86.31 532	77.97 518	45.88 542	84.35 534

SIFT-NN-PointCN		57.34 513	56.95 516	58.53 533	82.11 548	41.35 557	90.36 537	61.72 557	40.01 548	54.78 546	80.99 552	32.74 545	72.39 550	29.64 550	40.16 546	61.83 546

SIFT-UMatch		55.48 515	53.92 518	60.16 529	85.84 541	42.45 555	89.09 538	61.68 558	39.97 549	41.34 553	82.92 546	26.90 552	77.66 544	27.36 553	30.17 551	60.37 550

MVS_clip		68.81 503	72.22 502	58.58 532	84.27 543	34.51 560	80.78 549	61.23 559	34.94 555	86.68 503	99.12 420	55.61 517	50.86 559	80.33 511	66.99 534	90.36 525

SIFT-PointCN		49.44 518	48.89 521	51.12 536	81.24 551	34.25 561	87.16 545	56.78 560	36.95 552	33.84 555	76.32 555	20.17 558	61.65 556	21.99 558	25.53 555	57.46 552

SIFT-CM-Cal		53.99 516	52.89 519	57.28 534	87.31 537	41.77 556	86.71 547	54.86 561	39.82 551	45.09 550	82.10 549	25.89 553	71.72 552	27.27 554	26.97 554	58.36 551

SIFT-UM-Cal		51.73 517	50.25 520	56.15 535	85.87 540	41.10 558	88.21 541	50.44 562	39.83 550	33.54 556	82.23 547	23.59 555	71.25 553	27.05 555	21.52 556	56.10 553

SIFT-PCN-Cal		47.97 519	47.56 522	49.20 537	81.85 549	33.99 562	86.00 548	49.11 563	36.44 553	32.13 557	77.60 554	22.63 556	62.04 555	23.11 557	19.17 557	51.55 554

SIFT-NCMNet		41.74 520	41.17 523	43.45 538	76.48 555	31.10 565	80.74 550	30.14 564	35.07 554	28.33 558	71.87 556	16.32 559	52.56 558	19.72 559	11.82 559	46.67 555

wuyk23d		28.28 522	29.73 526	23.92 540	75.89 556	32.61 564	66.50 551	12.88 565	16.09 557	14.59 560	16.59 558	12.35 560	32.36 560	39.36 545	13.36 558	6.79 556

MVS_baseline		35.10 521	36.24 524	31.67 539	45.91 564	12.01 567	34.47 552	7.88 566	5.62 558	47.50 547	90.75 523	11.45 563	7.89 561	46.41 544	36.20 547	75.11 541

mmdepth		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

monomultidepth		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

test_blank		0.07 526	0.09 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.79 559	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uanet_test		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

DCPMVS		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		8.24 525	10.99 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	98.75 142	0.00 562	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

sosnet		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uncertanet		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

Regformer		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

n2								0.00 567

nn								0.00 567

ab-mvs-re		8.33 524	11.11 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uanet		0.01 527	0.02 530	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.14 560	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

PatchmatchNet1														86.42 491	92.76 393	97.75 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													98.34 434
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						97.98 320								95.74 401

PC_three_1452									98.80 77	100.00 1	100.00 1	99.54 32	100.00 1	100.00 1	100.00 1	100.00 1

eth-test2					0.00 565

eth-test					0.00 565

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 32	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									98.79 80	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 171

test_part2					100.00 1	99.99 6				100.00 1

sam_mvs1												99.29 82				99.91 171

sam_mvs												99.33 71

test_post1							99.32 449				88.24 534	99.33 71	99.59 296	98.31 318

test_post											89.05 532	99.49 46	99.59 296

patchmatchnet-post											97.79 487	99.41 66	99.54 315

gm-plane-assit					99.52 269	97.26 359			95.86 354		100.00 1		99.43 338	98.76 294

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

test_prior4						99.93 53	100.00 1

test_prior2							100.00 1		98.82 72	100.00 1	100.00 1	99.47 51		100.00 1	100.00 1

旧先验2							100.00 1		98.11 129	100.00 1			100.00 1	99.67 187

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 361

segment_acmp												99.55 31

testdata1							100.00 1		98.77 84

plane_prior7					99.00 374	94.78 421

plane_prior6					99.06 364	94.80 417						88.58 412

plane_prior4											99.97 264

plane_prior3						94.79 420			99.03 25	99.08 310

plane_prior2							100.00 1		99.00 32

plane_prior1					99.02 368

plane_prior						94.80 417	100.00 1		99.03 25						95.58 341

HQP5-MVS						94.82 414

HQP-NCC					99.07 360		100.00 1		99.04 20	99.17 298

ACMP_Plane					99.07 360		100.00 1		99.04 20	99.17 298

BP-MVS														99.79 143

HQP4-MVS										99.17 298			99.57 302			97.77 349

HQP2-MVS												88.61 410

NP-MVS					99.07 360	94.81 416					99.97 264

MDTV_nov1_ep13_2view						99.24 190	99.56 423		96.31 337	99.96 153		98.86 132		98.92 285		99.89 190

ACMMP++_ref															94.58 376

ACMMP++															95.17 361

Test By Simon												99.10 99

High-resolution multi-view benchmark