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	99.89 121	99.51 149	99.45 419	100.00 1	98.38 105	99.83 209	100.00 1	98.86 130	99.81 250	99.25 252	98.78 207	99.94 154

test1111		98.42 246	98.12 259	99.29 239	99.88 123	98.15 297	99.46 417	100.00 1	98.36 109	99.42 267	100.00 1	87.91 403	99.79 255	99.31 249	98.78 207	99.94 154

ECVR-MVS		98.43 244	98.14 258	99.32 231	99.89 121	98.21 291	99.46 417	100.00 1	98.38 105	99.47 264	100.00 1	87.91 403	99.80 254	99.35 244	98.78 207	99.94 154

PVSNet_BlendedMVS		98.71 206	98.62 197	98.98 265	99.98 93	99.60 131	100.00 1	100.00 1	97.23 220	100.00 1	99.03 414	96.57 226	99.99 106	100.00 1	94.75 359	97.35 445

PVSNet_Blended		99.48 82	99.36 89	99.83 110	99.98 93	99.60 131	100.00 1	100.00 1	97.79 155	100.00 1	100.00 1	96.57 226	99.99 106	100.00 1	99.88 151	99.90 182

UGNet		98.41 248	98.11 260	99.31 233	99.54 248	98.55 249	99.18 450	100.00 1	98.64 91	99.79 226	99.04 411	87.61 408	100.00 1	99.30 250	99.89 148	99.40 321
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	99.81 143	99.93 52	100.00 1	100.00 1	97.98 137	99.84 206	100.00 1	98.94 123	99.98 140	99.86 127	98.21 261	99.94 154

HY-MVS	96.53 9	99.50 78	99.35 91	99.96 52	99.81 143	99.93 52	99.64 397	100.00 1	97.97 139	99.84 206	99.85 309	98.94 123	99.99 106	99.86 127	98.23 260	99.95 149

EPMVS		99.25 126	99.13 126	99.60 167	99.60 225	99.20 193	99.60 403	100.00 1	96.93 244	99.92 191	99.36 392	99.05 106	99.71 277	98.77 282	98.94 204	99.90 182

MVS_111021_HR		99.71 36	99.63 44	99.93 78	99.95 107	99.83 97	100.00 1	100.00 1	98.89 60	100.00 1	100.00 1	97.85 173	99.95 182	100.00 1	100.00 1	100.00 1

PatchMatch-RL		99.02 159	98.78 170	99.74 140	99.99 52	99.29 179	100.00 1	100.00 1	98.38 105	99.89 199	99.81 319	93.14 311	99.99 106	97.85 327	99.98 117	99.95 149

PVSNet_0	93.57 19	96.41 354	95.74 371	98.41 304	99.84 130	95.22 392	100.00 1	100.00 1	98.08 130	97.55 406	99.78 329	84.40 434	100.00 1	100.00 1	81.99 466	100.00 1

CHOSEN 1792x2688		99.00 162	98.91 157	99.25 247	99.90 119	97.79 326	100.00 1	99.99 13	98.79 80	98.28 368	100.00 1	93.63 294	99.95 182	99.66 189	99.95 127	100.00 1

HyFIR lowres test		99.32 110	99.24 108	99.58 173	99.95 107	99.26 184	100.00 1	99.99 13	96.72 275	99.29 281	99.91 296	99.49 46	99.47 318	99.74 156	98.08 274	100.00 1

AdaColmap		99.44 88	99.26 102	99.95 61	100.00 1	99.86 89	99.70 390	99.99 13	98.53 94	99.90 196	100.00 1	95.34 247	100.00 1	99.92 116	100.00 1	100.00 1

CHOSEN 280x420		99.85 5	99.87 1	99.80 123	99.99 52	99.97 26	99.97 299	99.98 16	98.96 39	100.00 1	100.00 1	99.96 4	99.42 328	100.00 1	100.00 1	100.00 1

MM		99.63 58	99.52 68	99.94 74	99.99 52	99.82 98	100.00 1	99.97 17	99.11 10	100.00 1	100.00 1	96.65 224	100.00 1	100.00 1	99.97 121	100.00 1

MVS_111021_LR		99.70 39	99.65 37	99.88 95	99.96 103	99.70 120	100.00 1	99.97 17	98.96 39	100.00 1	100.00 1	97.93 167	99.95 182	99.99 76	100.00 1	100.00 1

MED-MVS test				99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.10 12	99.99 128	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	99.99 128	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	99.99 106	99.68 180	99.99 106	100.00 1

TestfortrainingZip				100.00 1	99.99 52	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

ME-MVS		99.87 3	99.83 4	99.99 13	99.99 52	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 29	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	99.82 137	99.49 155	100.00 1	99.95 19	97.36 206	99.63 251	100.00 1	96.45 230	99.95 182	99.79 142	99.65 181	99.89 190

MAR-MVS		99.49 80	99.36 89	99.89 90	99.97 97	99.66 125	99.74 379	99.95 19	97.89 146	100.00 1	100.00 1	96.71 223	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	99.01 138	99.95 61	99.81 143	99.87 86	100.00 1	99.94 27	97.13 226	99.83 209	99.96 274	97.01 208	100.00 1	99.59 206	97.85 289	99.98 127

tfpn200view9		99.26 122	99.03 136	99.96 52	99.81 143	99.89 77	100.00 1	99.94 27	97.23 220	99.83 209	99.96 274	97.04 204	100.00 1	99.59 206	97.85 289	99.98 127

MVS		99.22 130	98.96 147	99.98 28	99.00 361	99.95 37	99.24 440	99.94 27	98.14 124	98.88 314	100.00 1	95.63 244	100.00 1	99.85 130	100.00 1	100.00 1

thres600view7		99.24 129	99.00 141	99.95 61	99.81 143	99.87 86	100.00 1	99.94 27	97.13 226	99.83 209	99.96 274	97.01 208	100.00 1	99.54 217	97.77 298	99.97 137

thres400		99.26 122	99.03 136	99.95 61	99.81 143	99.89 77	100.00 1	99.94 27	97.23 220	99.83 209	99.96 274	97.04 204	100.00 1	99.59 206	97.85 289	99.97 137

thres200		99.27 120	99.04 135	99.96 52	99.81 143	99.90 70	100.00 1	99.94 27	97.31 214	99.83 209	99.96 274	97.04 204	100.00 1	99.62 199	97.88 287	99.98 127

API-MVS		99.72 32	99.70 24	99.79 128	99.97 97	99.37 172	99.96 306	99.94 27	98.48 98	100.00 1	100.00 1	98.92 126	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	99.45 161	100.00 1	99.94 27	96.38 317	100.00 1	100.00 1	98.18 160	100.00 1	100.00 1	100.00 1	100.00 1

1314		99.38 96	99.19 118	99.96 52	98.88 374	99.89 77	99.24 440	99.93 35	98.88 61	98.79 324	100.00 1	97.02 207	100.00 1	100.00 1	100.00 1	100.00 1

test0.0.03 1		98.12 268	98.03 269	98.39 305	99.11 342	98.07 304	100.00 1	99.93 35	96.70 281	96.91 420	99.95 281	99.31 75	98.19 428	91.93 440	98.44 223	98.91 330

QAPM		98.99 166	98.66 191	99.96 52	99.01 356	99.87 86	99.88 346	99.93 35	97.99 135	98.68 329	100.00 1	93.17 307	100.00 1	99.32 248	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 147	98.76 174	99.96 52	98.86 379	99.90 70	99.98 290	99.93 35	98.95 42	98.49 352	100.00 1	92.91 314	100.00 1	99.71 166	100.00 1	100.00 1

VDDNet		96.39 358	95.55 380	98.90 270	99.27 332	97.45 337	99.15 458	99.92 39	91.28 440	99.98 138	100.00 1	73.55 473	100.00 1	99.85 130	96.98 315	99.24 324

MGCNet		99.72 32	99.65 37	99.93 78	99.99 52	99.79 102	100.00 1	99.91 40	99.17 8	100.00 1	100.00 1	97.84 175	100.00 1	100.00 1	99.95 127	100.00 1

sss		99.45 86	99.34 93	99.80 123	99.76 169	99.50 151	100.00 1	99.91 40	97.72 160	99.98 138	99.94 287	98.45 152	100.00 1	99.53 220	98.75 210	99.89 190

EPNet		99.62 63	99.69 25	99.42 198	99.99 52	98.37 270	100.00 1	99.89 42	98.83 70	100.00 1	100.00 1	98.97 117	100.00 1	99.90 119	99.61 185	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	99.44 192	100.00 1	98.32 279	100.00 1	99.86 43	98.04 132	100.00 1	100.00 1	96.10 234	100.00 1	99.55 214	99.73 172	100.00 1

HPM-MVS++		99.82 12	99.76 15	99.99 13	99.99 52	99.98 18	100.00 1	99.83 44	98.88 61	99.96 151	100.00 1	99.21 88	100.00 1	100.00 1	100.00 1	99.99 124

D2MVS		97.63 293	97.83 280	97.05 385	98.83 382	94.60 414	100.00 1	99.82 45	96.89 250	98.28 368	99.03 414	94.05 285	99.47 318	98.58 297	94.97 356	97.09 451

旧先验1					99.99 52	99.88 84		99.82 45			100.00 1	99.27 84			100.00 1	100.00 1

新几何1				99.99 13	100.00 1	99.96 29		99.81 47	97.89 146	100.00 1	100.00 1	99.20 89	100.00 1	97.91 325	100.00 1	100.00 1

ZD-MVS					100.00 1	99.98 18		99.80 48	97.31 214	100.00 1	100.00 1	99.32 73	99.99 106	100.00 1	100.00 1

无先验							100.00 1	99.80 48	97.98 137				100.00 1	99.33 247		100.00 1

ZNCC-MVS		99.71 36	99.62 47	99.97 40	99.99 52	99.90 70	100.00 1	99.79 50	97.97 139	99.97 144	100.00 1	98.97 117	100.00 1	99.94 113	100.00 1	100.00 1

GST-MVS		99.64 54	99.53 65	99.95 61	100.00 1	99.86 89	100.00 1	99.79 50	97.72 160	99.95 182	100.00 1	98.39 156	100.00 1	99.96 105	99.99 106	100.00 1

DELS-MVS		99.62 63	99.56 60	99.82 112	99.92 115	99.45 161	100.00 1	99.78 52	98.92 52	99.73 238	100.00 1	97.70 181	100.00 1	99.93 115	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 68	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 77	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.31 75	100.00 1	99.99 76	100.00 1	100.00 1

ACMMPR		99.74 28	99.67 33	99.96 52	100.00 1	99.89 77	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.29 81	100.00 1	99.99 76	100.00 1	100.00 1

OpenMVS	95.20 17	98.76 198	98.41 228	99.78 133	98.89 373	99.81 99	99.99 258	99.76 54	98.02 133	98.02 383	100.00 1	91.44 335	100.00 1	99.63 197	99.97 121	99.55 312

XVS		99.79 17	99.73 20	99.98 28	100.00 1	99.94 46	100.00 1	99.75 57	98.67 88	100.00 1	100.00 1	99.16 93	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 325	96.06 354	99.98 28	100.00 1	99.94 46	100.00 1	99.75 57	98.67 88	100.00 1	66.97 502	99.16 93	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 19	99.76 15	99.85 104	99.01 356	99.95 37	100.00 1	99.75 57	99.37 3	99.99 128	100.00 1	99.76 12	99.60 284	100.00 1	100.00 1	100.00 1

region2R		99.72 32	99.64 40	99.97 40	100.00 1	99.90 70	100.00 1	99.74 60	97.86 149	100.00 1	100.00 1	99.19 90	100.00 1	99.99 76	100.00 1	100.00 1

CANet		99.40 92	99.24 108	99.89 90	99.99 52	99.76 107	100.00 1	99.73 61	98.40 102	99.78 228	100.00 1	95.28 248	99.96 169	100.00 1	99.99 106	99.96 143

test_prior				99.90 87	100.00 1	99.75 108		99.73 61					99.97 149			100.00 1

testdata				99.66 157	99.99 52	98.97 219		99.73 61	97.96 142	100.00 1	100.00 1	99.42 63	100.00 1	99.28 251	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 75	100.00 1	100.00 1	100.00 1	100.00 1

ACMMP		99.65 52	99.57 55	99.89 90	99.99 52	99.66 125	99.75 378	99.73 61	98.16 121	99.75 232	100.00 1	98.90 128	100.00 1	99.96 105	99.88 151	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

test222					99.99 52	99.90 70	100.00 1	99.69 67	97.66 166	100.00 1	100.00 1	99.30 80			100.00 1	100.00 1

原ACMM1				99.93 78	100.00 1	99.80 101		99.66 68	98.18 120	100.00 1	100.00 1	99.43 59	100.00 1	99.50 224	100.00 1	100.00 1

SR-MVS		99.68 46	99.58 52	99.98 28	100.00 1	99.95 37	100.00 1	99.64 69	97.59 181	100.00 1	100.00 1	98.99 112	99.99 106	100.00 1	100.00 1	100.00 1

ab-mvs		98.42 246	98.02 270	99.61 165	99.71 176	99.00 215	99.10 463	99.64 69	96.70 281	99.04 304	99.81 319	90.64 353	99.98 140	99.64 192	97.93 284	99.84 221

testing3		98.44 243	98.37 240	98.65 286	99.51 270	98.32 279	100.00 1	99.62 71	96.43 310	97.93 387	99.99 236	99.11 97	97.81 453	94.88 410	97.80 295	99.82 230

PGM-MVS		99.69 42	99.61 48	99.95 61	99.99 52	99.85 93	100.00 1	99.58 72	97.69 164	100.00 1	100.00 1	99.44 55	100.00 1	99.79 142	100.00 1	100.00 1

Syy-MVS		96.17 371	96.57 331	95.00 435	99.50 278	87.37 474	100.00 1	99.57 73	96.23 327	98.07 378	100.00 1	92.41 326	97.81 453	85.34 473	97.96 281	99.82 230

myMVS_eth3d		98.52 238	98.51 218	98.53 294	99.50 278	97.98 311	100.00 1	99.57 73	96.23 327	98.07 378	100.00 1	99.09 99	97.81 453	96.17 382	97.96 281	99.82 230

LFMVS		97.42 306	96.62 329	99.81 117	99.80 156	99.50 151	99.16 456	99.56 75	94.48 386	100.00 1	100.00 1	79.35 457	100.00 1	99.89 121	97.37 308	99.94 154

PLC	98.56 2	99.70 39	99.74 19	99.58 173	100.00 1	98.79 231	100.00 1	99.54 76	98.58 93	99.96 151	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				99.59 169	99.54 248	99.49 155	99.17 455	99.52 77		99.96 151	99.68 345	100.00 1	99.33 335	99.71 166	99.99 106	99.96 143

gg-mvs-nofinetune		96.95 330	96.10 352	99.50 181	99.41 310	99.36 174	99.07 468	99.52 77	83.69 478	99.96 151	83.60 499	100.00 1	99.20 341	99.68 180	99.99 106	99.96 143

DP-MVS Recon		99.76 21	99.69 25	99.98 28	100.00 1	99.95 37	100.00 1	99.52 77	97.99 135	99.99 128	100.00 1	99.72 14	100.00 1	99.96 105	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 26	99.67 33	99.99 13	99.99 52	99.96 29	99.73 384	99.52 77	99.06 16	100.00 1	100.00 1	98.80 137	100.00 1	99.95 111	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	99.73 174	99.92 59	100.00 1	99.51 81	97.61 177	100.00 1	100.00 1	99.06 104	99.93 199	99.83 134	97.12 311	99.90 182

3Dnovator+	95.58 15	99.03 153	98.71 184	99.96 52	98.99 364	99.89 77	100.00 1	99.51 81	98.96 39	98.32 365	100.00 1	92.78 316	100.00 1	99.87 126	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 52	99.55 62	99.97 40	99.99 52	99.91 63	100.00 1	99.48 83	97.54 185	100.00 1	100.00 1	98.97 117	99.99 106	99.98 91	100.00 1	100.00 1

lecture		99.64 54	99.53 65	99.98 28	99.99 52	99.93 52	100.00 1	99.47 84	98.53 94	100.00 1	100.00 1	97.88 171	100.00 1	99.98 91	99.92 140	100.00 1

SymmetryMVS		99.30 114	99.25 104	99.45 190	99.79 161	98.55 249	99.94 323	99.47 84	98.39 103	100.00 1	100.00 1	98.44 153	99.98 140	99.36 240	97.83 292	99.83 224

testing3-2		99.45 86	99.31 94	99.86 100	99.70 178	99.73 113	100.00 1	99.47 84	97.46 197	99.97 144	99.97 256	99.48 50	100.00 1	99.78 148	97.99 278	99.85 219

test_yl		99.51 75	99.37 86	99.95 61	99.82 137	99.90 70	100.00 1	99.47 84	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 91	97.75 299	99.94 154

Anonymous20240529		96.93 331	96.22 348	99.05 258	99.79 161	97.30 346	99.16 456	99.47 84	88.51 458	98.69 327	100.00 1	83.50 442	100.00 1	99.83 134	97.02 314	99.83 224

DCV-MVSNet		99.51 75	99.37 86	99.95 61	99.82 137	99.90 70	100.00 1	99.47 84	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 91	97.75 299	99.94 154

CVMVSNet		98.56 232	98.47 221	98.82 276	99.11 342	97.67 329	99.74 379	99.47 84	97.57 183	99.06 301	100.00 1	95.72 241	98.97 356	98.21 313	97.33 309	99.83 224

EU-MVSNet		96.63 343	96.53 332	96.94 392	97.59 438	96.87 360	99.76 376	99.47 84	96.35 320	96.85 422	99.78 329	92.57 323	96.27 473	95.33 402	91.08 409	97.68 411

VNet		99.04 150	98.75 175	99.90 87	99.81 143	99.75 108	99.50 415	99.47 84	98.36 109	100.00 1	99.99 236	94.66 270	100.00 1	99.90 119	97.09 312	99.96 143

VPA-MVSNet		97.03 326	96.43 338	98.82 276	98.64 388	99.32 176	99.38 427	99.47 84	96.73 273	98.91 313	98.94 423	87.00 415	99.40 329	99.23 255	89.59 421	97.76 339

tpmvs		98.59 226	98.38 238	99.23 248	99.69 181	97.90 318	99.31 435	99.47 84	94.52 384	99.68 243	99.28 396	97.64 184	99.89 220	97.71 335	98.17 266	99.89 190

EPNet_dtu		98.53 237	98.23 255	99.43 195	99.92 115	99.01 212	99.96 306	99.47 84	98.80 77	99.96 151	99.96 274	98.56 148	99.30 336	87.78 468	99.68 176	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 29	100.00 1	99.47 84	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 29	100.00 1	99.47 84	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	97.54 300	96.95 317	99.33 226	99.67 194	98.10 302	100.00 1	99.47 84	97.42 203	99.26 282	99.69 341	98.83 134	99.89 220	99.43 236	78.77 476	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	99.05 258	99.99 52	97.15 352	100.00 1	99.47 84	97.44 201	99.42 267	100.00 1	97.83 177	100.00 1	99.99 76	100.00 1	100.00 1

CNLPA		99.72 32	99.65 37	99.91 83	99.97 97	99.72 115	100.00 1	99.47 84	98.43 101	99.88 202	100.00 1	99.14 96	100.00 1	99.97 103	100.00 1	100.00 1

MG-MVS		99.75 26	99.68 31	99.97 40	100.00 1	99.91 63	99.98 290	99.47 84	99.09 13	100.00 1	100.00 1	98.59 147	100.00 1	99.95 111	100.00 1	100.00 1

dmvs_re		97.54 300	97.88 278	96.54 412	99.55 245	90.35 461	99.86 349	99.46 102	97.00 237	99.41 272	100.00 1	90.78 351	99.30 336	99.60 204	95.24 343	99.96 143

thisisatest0530		99.37 99	99.27 98	99.69 150	99.59 229	99.41 167	100.00 1	99.46 102	96.46 309	99.90 196	100.00 1	99.44 55	99.85 238	98.97 271	99.58 186	99.80 271

Anonymous202405211		97.87 280	97.53 292	98.90 270	99.81 143	96.70 365	99.35 430	99.46 102	92.98 427	98.83 321	99.99 236	90.63 354	100.00 1	99.70 170	97.03 313	100.00 1

tttt0517		99.34 105	99.23 111	99.67 154	99.57 238	99.38 169	100.00 1	99.46 102	96.33 322	99.89 199	100.00 1	99.44 55	99.84 242	98.93 273	99.46 190	99.78 281

thisisatest0515		99.42 90	99.31 94	99.74 140	99.59 229	99.55 139	100.00 1	99.46 102	96.65 288	99.92 191	100.00 1	99.44 55	99.85 238	99.09 266	99.63 184	99.81 244

baseline2		98.99 166	98.93 154	99.18 251	99.26 334	99.15 199	100.00 1	99.46 102	96.71 280	96.79 424	100.00 1	99.42 63	99.25 339	98.75 284	99.94 133	99.15 326

MDTV_nov1_ep13			98.94 152		99.53 251	98.36 273	99.39 426	99.46 102	96.54 300	99.99 128	99.63 358	98.92 126	99.86 231	98.30 310	98.71 211

LS3D		99.31 112	99.13 126	99.87 97	99.99 52	99.71 116	99.55 409	99.46 102	97.32 212	99.82 218	100.00 1	96.85 218	99.97 149	99.14 260	100.00 1	99.92 167

ETVMVS		99.16 137	98.98 144	99.69 150	99.67 194	99.56 137	100.00 1	99.45 110	96.36 319	99.98 138	99.95 281	98.65 143	99.64 282	99.11 264	97.63 306	99.88 203

SDMVSNet		98.49 241	98.08 264	99.73 143	99.82 137	99.53 144	99.99 258	99.45 110	97.62 173	99.38 275	99.86 304	90.06 372	99.88 228	99.92 116	96.61 324	99.79 277

sd_testset		97.81 284	97.48 293	98.79 280	99.82 137	96.80 362	99.32 432	99.45 110	97.62 173	99.38 275	99.86 304	85.56 429	99.77 261	99.72 162	96.61 324	99.79 277

tt0805		96.52 347	96.23 347	97.40 369	99.30 329	93.55 432	99.32 432	99.45 110	96.75 266	97.88 390	99.99 236	79.99 455	99.59 286	97.39 349	95.98 327	99.06 329

h-mvs33		97.03 326	96.53 332	98.51 295	99.79 161	95.90 379	99.45 419	99.45 110	98.21 117	100.00 1	99.78 329	97.49 192	99.99 106	99.72 162	74.92 478	99.65 309

tfpnnormal		96.36 359	95.69 376	98.37 307	98.55 391	98.71 237	99.69 392	99.45 110	93.16 425	96.69 428	99.71 335	88.44 402	98.99 353	94.17 418	91.38 406	97.41 442

SCA		98.30 256	97.98 272	99.23 248	99.41 310	98.25 287	99.99 258	99.45 110	96.91 247	99.76 231	99.58 368	89.65 381	99.54 303	98.31 307	98.79 206	99.91 171

UA-Net		99.06 147	98.83 164	99.74 140	99.52 265	99.40 168	99.08 466	99.45 110	97.64 170	99.83 209	100.00 1	95.80 239	99.94 195	98.35 305	99.80 170	99.88 203

VPNet		96.41 354	95.76 370	98.33 310	98.61 389	98.30 282	99.48 416	99.45 110	96.98 239	98.87 316	99.88 301	81.57 449	98.93 360	99.22 257	87.82 438	97.76 339

test-LLR		99.03 153	98.91 157	99.40 204	99.40 315	99.28 181	100.00 1	99.45 110	96.70 281	99.42 267	99.12 404	99.31 75	99.01 350	96.82 366	99.99 106	99.91 171

TESTMET0.1,1		99.08 143	98.96 147	99.44 192	99.63 213	99.38 169	100.00 1	99.45 110	95.53 352	99.48 261	100.00 1	99.71 15	99.02 349	96.84 365	99.99 106	99.91 171

test-mter		98.96 173	98.82 165	99.40 204	99.40 315	99.28 181	100.00 1	99.45 110	95.44 363	99.42 267	99.12 404	99.70 16	99.01 350	96.82 366	99.99 106	99.91 171

UniMVSNet_NR-MVSNet		97.16 318	96.80 322	98.22 321	98.38 397	98.41 263	100.00 1	99.45 110	96.14 333	97.76 394	99.64 354	95.05 258	98.50 403	97.98 321	86.84 445	97.75 350

PatchmatchNet		99.03 153	98.96 147	99.26 246	99.49 282	98.33 277	99.38 427	99.45 110	96.64 289	99.96 151	99.58 368	99.49 46	99.50 314	97.63 338	99.00 203	99.93 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KinetiMVS		98.61 223	98.26 248	99.65 159	99.46 297	99.24 188	99.96 306	99.44 124	97.54 185	99.99 128	99.99 236	90.83 350	99.95 182	97.18 354	99.92 140	99.75 285

dongtai		98.29 259	98.25 249	98.42 303	99.58 234	95.86 380	100.00 1	99.44 124	93.46 416	99.69 242	99.97 256	97.53 190	99.51 311	96.28 381	98.27 253	99.89 190

kuosan		98.55 233	98.53 212	98.62 288	99.66 203	96.16 375	100.00 1	99.44 124	93.93 403	99.81 224	99.98 244	97.58 185	99.81 250	98.08 316	98.28 250	99.89 190

EI-MVSNet-UG-set		99.69 42	99.63 44	99.87 97	99.99 52	99.64 127	99.95 315	99.44 124	98.35 111	100.00 1	100.00 1	98.98 115	99.97 149	99.98 91	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 40	99.87 97	100.00 1	99.64 127	99.98 290	99.44 124	98.35 111	99.99 128	100.00 1	99.04 109	99.96 169	99.98 91	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	99.44 124	99.06 16	100.00 1	100.00 1	99.56 29	99.99 106	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		98.70 208	98.51 218	99.28 242	99.51 270	98.39 266	99.24 440	99.44 124	95.52 354	99.96 151	99.70 338	97.57 187	99.58 290	97.11 356	98.54 217	99.88 203

baseline1		98.91 182	98.61 198	99.81 117	99.71 176	99.77 106	99.78 369	99.44 124	97.51 192	98.81 322	99.99 236	98.25 158	99.76 265	98.60 295	95.41 334	99.89 190

F-COLMAP		99.64 54	99.64 40	99.67 154	99.99 52	99.07 203	100.00 1	99.44 124	98.30 114	99.90 196	100.00 1	99.18 91	99.99 106	99.91 118	100.00 1	99.94 154

myMVS_eth3d28		99.41 91	99.28 96	99.80 123	99.69 181	99.53 144	100.00 1	99.43 133	97.12 228	99.98 138	99.97 256	99.41 65	100.00 1	99.81 141	98.07 275	99.88 203

UWE-MVS-28		99.29 117	99.23 111	99.48 186	99.73 174	98.86 226	100.00 1	99.43 133	96.97 241	99.99 128	99.83 312	99.43 59	99.77 261	99.35 244	98.31 246	99.80 271

testing91		99.18 134	99.10 129	99.41 199	99.60 225	98.43 261	100.00 1	99.43 133	96.76 263	99.82 218	99.92 293	99.05 106	99.98 140	99.62 199	97.67 303	99.81 244

testing11		99.26 122	99.19 118	99.46 188	99.64 211	98.61 245	100.00 1	99.43 133	96.94 243	99.92 191	99.94 287	99.43 59	99.97 149	99.67 184	97.79 297	99.82 230

testing99		99.18 134	99.10 129	99.41 199	99.60 225	98.43 261	100.00 1	99.43 133	96.76 263	99.84 206	99.92 293	99.06 104	99.98 140	99.62 199	97.67 303	99.81 244

UBG		99.36 100	99.27 98	99.63 161	99.63 213	99.01 212	100.00 1	99.43 133	96.99 238	100.00 1	99.92 293	99.69 17	99.99 106	99.74 156	98.06 276	99.88 203

UWE-MVS		99.18 134	99.06 133	99.51 178	99.67 194	98.80 230	100.00 1	99.43 133	96.80 258	99.93 190	99.86 304	99.79 8	99.94 195	97.78 333	98.33 243	99.80 271

testing222		99.14 139	98.94 152	99.73 143	99.67 194	99.51 149	100.00 1	99.43 133	96.90 249	99.99 128	99.90 298	98.55 149	99.86 231	98.85 277	97.18 310	99.81 244

WB-MVSnew		97.02 328	97.24 307	96.37 417	99.44 306	97.36 341	100.00 1	99.43 133	96.12 334	99.35 277	99.89 299	93.60 296	98.42 410	88.91 466	98.39 228	93.33 485

FE-MVS		99.16 137	98.99 143	99.66 157	99.65 205	99.18 196	99.58 405	99.43 133	95.24 364	99.91 194	99.59 366	99.37 69	99.97 149	98.31 307	99.81 167	99.83 224

FA-MVS(test-final)		99.00 162	98.75 175	99.73 143	99.63 213	99.43 165	99.83 354	99.43 133	95.84 344	99.52 258	99.37 391	97.84 175	99.96 169	97.63 338	99.68 176	99.79 277

DPM-MVS		99.63 58	99.51 70	100.00 1	99.90 119	100.00 1	100.00 1	99.43 133	99.00 32	100.00 1	100.00 1	99.58 27	100.00 1	97.64 337	100.00 1	100.00 1

SMA-MVS		99.69 42	99.59 50	99.98 28	99.99 52	99.93 52	100.00 1	99.43 133	97.50 193	100.00 1	100.00 1	99.43 59	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 29	100.00 1	99.43 133	99.05 18	100.00 1	100.00 1	99.45 53	99.99 106	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		97.95 278	97.73 285	98.62 288	98.53 393	99.24 188	100.00 1	99.43 133	96.74 269	97.87 391	99.82 316	95.27 249	98.89 365	98.78 281	93.07 375	97.74 377

FC-MVSNet-test		97.84 282	97.63 291	98.45 299	98.30 403	99.05 206	100.00 1	99.43 133	96.63 293	97.61 403	99.82 316	95.19 254	98.57 398	98.64 290	93.05 376	97.73 388

WR-MVS_H		96.73 337	96.32 345	97.95 350	98.26 407	97.88 320	99.72 387	99.43 133	95.06 367	96.99 417	98.68 437	93.02 313	98.53 401	97.43 346	88.33 435	97.43 441

JIA-IIPM		97.09 321	96.34 343	99.36 214	98.88 374	98.59 247	99.81 358	99.43 133	84.81 476	99.96 151	90.34 491	98.55 149	99.52 309	97.00 359	98.28 250	99.98 127

EPP-MVSNet		99.10 142	99.00 141	99.40 204	99.51 270	98.68 239	99.92 331	99.43 133	95.47 358	99.65 250	100.00 1	99.51 39	99.76 265	99.53 220	98.00 277	99.75 285

reproduce_model		99.76 21	99.69 25	99.98 28	99.96 103	99.93 52	100.00 1	99.42 152	98.81 76	100.00 1	100.00 1	98.98 115	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 21	99.69 25	99.98 28	99.96 103	99.94 46	100.00 1	99.42 152	98.82 72	100.00 1	100.00 1	98.99 112	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 21	99.69 25	99.98 28	99.96 103	99.94 46	100.00 1	99.42 152	98.82 72	100.00 1	100.00 1	98.99 112	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	99.42 152	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 26	100.00 1	99.42 152	98.52 96	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 152					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 152					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 6		99.42 152	98.72 85	100.00 1	100.00 1	99.60 21

hse-mvs2		96.79 334	96.38 340	98.04 345	99.68 186	95.54 385	99.81 358	99.42 152	98.21 117	100.00 1	99.80 325	97.49 192	99.46 323	99.72 162	73.27 481	99.12 327

AUN-MVS		96.26 365	95.67 377	98.06 339	99.68 186	95.60 384	99.82 357	99.42 152	96.78 261	99.88 202	99.80 325	94.84 263	99.47 318	97.48 344	73.29 480	99.12 327

SR-MVS-dyc-post		99.63 58	99.52 68	99.97 40	99.99 52	99.91 63	100.00 1	99.42 152	97.62 173	100.00 1	100.00 1	98.65 143	99.99 106	99.99 76	100.00 1	100.00 1

RE-MVS-def			99.55 62		99.99 52	99.91 63	100.00 1	99.42 152	97.62 173	100.00 1	100.00 1	98.94 123		99.99 76	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	99.42 152	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		99.42 152	99.12 9	100.00 1				100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 152	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		99.42 152	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 52	99.85 93	100.00 1	99.42 152	97.67 165	100.00 1	100.00 1	99.05 106	99.99 106	100.00 1	100.00 1	100.00 1

9.14			99.57 55		99.99 52		100.00 1	99.42 152	97.54 185	100.00 1	100.00 1	99.15 95	99.99 106	100.00 1	100.00 1

save fliter					99.99 52	99.93 52	100.00 1	99.42 152	98.93 49

DVP-MVS		99.83 10	99.78 10	100.00 1	100.00 1	99.99 6	100.00 1	99.42 152	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 52	99.99 6	100.00 1	99.42 152					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 6	100.00 1	99.42 152	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 52	99.98 18	100.00 1	99.42 152	98.91 55	100.00 1	100.00 1	99.22 87	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 93	99.92 59	100.00 1	99.42 152	97.53 188	99.77 229	100.00 1	98.77 138	100.00 1	99.99 76	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 89	100.00 1	99.42 152	98.87 64	100.00 1	100.00 1	99.65 19	99.96 169	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 93	99.92 59	100.00 1	99.42 152	97.83 150	100.00 1	100.00 1	98.89 129	100.00 1	99.98 91	100.00 1	100.00 1

MTGPA								99.42 152

MTAPA		99.68 46	99.59 50	99.97 40	99.99 52	99.91 63	100.00 1	99.42 152	98.32 113	99.94 185	100.00 1	98.65 143	100.00 1	99.96 105	100.00 1	100.00 1

TEST9					100.00 1	99.95 37	100.00 1	99.42 152	97.65 168	100.00 1	100.00 1	99.53 35	99.97 149

train_agg		99.71 36	99.63 44	99.97 40	100.00 1	99.95 37	100.00 1	99.42 152	97.70 162	100.00 1	100.00 1	99.51 39	99.97 149	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 63	100.00 1	99.42 152	97.70 162	100.00 1	100.00 1	99.51 39	99.98 140

agg_prior					100.00 1	99.88 84		99.42 152		100.00 1			99.97 149

PS-MVSNAJ		99.64 54	99.57 55	99.85 104	99.78 166	99.81 99	99.95 315	99.42 152	98.38 105	100.00 1	100.00 1	98.75 139	100.00 1	99.88 123	99.99 106	99.74 292

SD-MVS		99.81 14	99.75 17	99.99 13	99.99 52	99.96 29	100.00 1	99.42 152	99.01 31	100.00 1	100.00 1	99.33 70	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 52	99.94 46	100.00 1	99.42 152	97.82 152	99.99 128	100.00 1	98.20 159	100.00 1	99.99 76	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 26	100.00 1	99.42 152	98.02 133	100.00 1	100.00 1	99.32 73	99.99 106	100.00 1	100.00 1	100.00 1

test12				99.95 61	99.99 52	99.89 77		99.42 152		100.00 1		99.24 86	99.97 149		100.00 1	100.00 1

UniMVSNet (Re)		97.29 314	96.85 321	98.59 291	98.49 394	99.13 200	100.00 1	99.42 152	96.52 304	98.24 374	98.90 426	94.93 260	98.89 365	97.54 342	87.61 439	97.75 350

mPP-MVS		99.69 42	99.60 49	99.97 40	100.00 1	99.91 63	100.00 1	99.42 152	97.91 145	100.00 1	100.00 1	99.04 109	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 52	99.78 103	100.00 1	99.42 152	97.09 229	100.00 1	100.00 1	98.95 121	99.96 169	99.98 91	100.00 1	100.00 1

HPM-MVS		99.59 69	99.50 71	99.89 90	100.00 1	99.70 120	100.00 1	99.42 152	97.46 197	100.00 1	100.00 1	98.60 146	99.96 169	99.99 76	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								99.42 152

APD-MVS		99.68 46	99.58 52	99.97 40	99.99 52	99.96 29	100.00 1	99.42 152	97.53 188	100.00 1	100.00 1	99.27 84	99.97 149	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 97	99.48 159	100.00 1	99.42 152	95.53 352	100.00 1	100.00 1	98.37 157	99.95 182	99.97 103	100.00 1	100.00 1

CP-MVS		99.67 49	99.58 52	99.95 61	100.00 1	99.84 95	100.00 1	99.42 152	97.77 157	100.00 1	100.00 1	99.07 103	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 19	99.71 23	99.98 28	99.76 169	99.95 37	100.00 1	99.42 152	98.69 86	100.00 1	100.00 1	99.52 38	99.99 106	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
RPMNet		95.26 395	93.82 404	99.56 176	99.31 326	98.86 226	99.13 460	99.42 152	79.82 483	99.96 151	95.13 481	95.69 243	99.98 140	77.54 489	98.40 226	99.84 221

CPTT-MVS		99.49 80	99.38 83	99.85 104	100.00 1	99.54 142	100.00 1	99.42 152	97.58 182	99.98 138	100.00 1	97.43 198	100.00 1	99.99 76	100.00 1	100.00 1

DeepPCF-MVS	98.03 4	98.54 236	99.72 22	94.98 437	99.99 52	84.94 478	100.00 1	99.42 152	99.98 1	100.00 1	100.00 1	98.11 162	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	99.85 128	99.19 194	100.00 1	99.41 201	98.87 64	100.00 1	100.00 1	97.34 201	100.00 1	99.98 91	99.90 147	100.00 1

patch_mono-2		99.04 150	99.79 9	96.81 406	99.92 115	90.47 460	100.00 1	99.41 201	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		98.28 261	98.51 218	97.62 364	99.51 270	95.03 395	99.24 440	99.41 201	95.52 354	99.96 151	99.70 338	97.57 187	97.94 450	97.11 356	98.54 217	99.88 203

Vis-MVSNet		98.52 238	98.25 249	99.34 218	99.68 186	98.55 249	99.68 394	99.41 201	97.34 209	99.94 185	100.00 1	90.38 363	99.70 279	99.03 268	98.84 205	99.76 284
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
0.3-1-1-0.015		97.60 294	97.19 310	98.83 275	99.13 340	96.55 370	100.00 1	99.40 205	94.19 396	99.83 209	99.81 319	99.18 91	99.97 149	99.70 170	83.50 460	99.98 127

0.4-1-1-0.1		97.56 297	97.15 314	98.79 280	99.01 356	96.44 373	100.00 1	99.40 205	94.11 399	99.81 224	99.81 319	99.09 99	99.97 149	99.65 191	83.48 462	99.98 127

0.4-1-1-0.2		97.60 294	97.18 311	98.86 273	99.05 353	96.62 368	100.00 1	99.40 205	94.24 391	99.82 218	99.81 319	99.09 99	99.97 149	99.70 170	83.50 460	99.98 127

xiu_mvs_v2_base		99.51 75	99.41 80	99.82 112	99.70 178	99.73 113	99.92 331	99.40 205	98.15 123	100.00 1	100.00 1	98.50 151	100.00 1	99.85 130	99.13 197	99.74 292

Anonymous20231211		96.29 363	95.70 373	98.07 335	99.80 156	97.49 335	99.15 458	99.40 205	89.11 455	97.75 397	99.45 385	88.93 392	98.98 354	98.26 312	89.47 423	97.73 388

AllTest		98.55 233	98.40 233	98.99 263	99.93 112	97.35 342	100.00 1	99.40 205	97.08 231	99.09 297	99.98 244	93.37 301	99.95 182	96.94 360	99.84 162	99.68 304

TestCases				98.99 263	99.93 112	97.35 342		99.40 205	97.08 231	99.09 297	99.98 244	93.37 301	99.95 182	96.94 360	99.84 162	99.68 304

HQP_MVS		97.71 289	97.82 281	97.37 371	99.00 361	94.80 404	100.00 1	99.40 205	99.00 32	99.08 299	99.97 256	88.58 400	99.55 300	99.79 142	95.57 332	97.76 339

plane_prior5								99.40 205					99.55 300	99.79 142	95.57 332	97.76 339

HQP3-MVS								99.40 205							95.58 328

DP-MVS		98.86 188	98.54 210	99.81 117	99.97 97	99.45 161	99.52 413	99.40 205	94.35 390	98.36 360	100.00 1	96.13 233	99.97 149	99.12 263	100.00 1	100.00 1

HQP-MVS		97.73 287	97.85 279	97.39 370	99.07 347	94.82 401	100.00 1	99.40 205	99.04 20	99.17 287	99.97 256	88.61 398	99.57 291	99.79 142	95.58 328	97.77 337

OMC-MVS		99.27 120	99.38 83	98.96 266	99.95 107	97.06 356	100.00 1	99.40 205	98.83 70	99.88 202	100.00 1	97.01 208	99.86 231	99.47 229	99.84 162	99.97 137

DeepC-MVS	97.84 5	99.00 162	98.80 168	99.60 167	99.93 112	99.03 208	100.00 1	99.40 205	98.61 92	99.33 278	100.00 1	92.23 327	99.95 182	99.74 156	99.96 125	99.83 224
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	97.64 290	97.37 299	98.45 299	99.94 110	95.70 383	100.00 1	99.40 205	97.65 168	99.53 257	100.00 1	99.31 75	99.66 281	80.48 483	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	98.29 259	98.17 257	98.65 286	99.94 110	97.39 339	99.30 436	99.40 205	95.64 347	97.75 397	100.00 1	92.69 321	99.95 182	98.89 275	99.92 140	98.62 334
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		98.99 166	98.69 188	99.89 90	99.70 178	99.69 122	100.00 1	99.39 221	98.93 49	100.00 1	100.00 1	90.20 364	99.99 106	100.00 1	99.95 127	100.00 1

cdsmvs_eth3d_5k		24.41 468	32.55 470	0.00 486	0.00 509	0.00 511	0.00 497	99.39 221	0.00 504	0.00 505	100.00 1	93.55 297	0.00 505	0.00 503	0.00 503	0.00 501

dp		98.72 202	98.61 198	99.03 261	99.53 251	97.39 339	99.45 419	99.39 221	95.62 349	99.94 185	99.52 378	98.83 134	99.82 247	96.77 371	98.42 225	99.89 190

fmvsm_s_conf0.5_n_8		99.34 105	99.14 125	99.91 83	99.83 134	99.74 111	100.00 1	99.38 224	98.94 45	100.00 1	100.00 1	94.25 283	99.99 106	100.00 1	99.91 145	100.00 1

fmvsm_s_conf0.5_n		99.21 131	99.01 138	99.83 110	99.84 130	99.53 144	100.00 1	99.38 224	98.29 115	100.00 1	100.00 1	93.62 295	99.99 106	99.99 76	99.93 137	99.98 127

VDD-MVS		96.58 346	95.99 357	98.34 309	99.52 265	95.33 390	99.18 450	99.38 224	96.64 289	99.77 229	100.00 1	72.51 477	100.00 1	100.00 1	96.94 316	99.70 302

CMPMVS	66.12 22	90.65 439	92.04 427	86.46 468	96.18 458	66.87 498	98.03 486	99.38 224	83.38 479	85.49 485	99.55 374	77.59 462	98.80 372	94.44 415	94.31 364	93.72 483
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DU-MVS		96.93 331	96.49 335	98.22 321	98.31 401	98.41 263	100.00 1	99.37 228	96.41 315	97.76 394	99.65 350	92.14 329	98.50 403	97.98 321	86.84 445	97.75 350

LPG-MVS_test		97.31 312	97.32 301	97.28 378	98.85 380	94.60 414	100.00 1	99.37 228	97.35 207	98.85 317	99.98 244	86.66 417	99.56 295	99.55 214	95.26 340	97.70 404

LGP-MVS_train				97.28 378	98.85 380	94.60 414		99.37 228	97.35 207	98.85 317	99.98 244	86.66 417	99.56 295	99.55 214	95.26 340	97.70 404

tpm2		98.64 214	98.58 204	98.81 279	99.42 308	97.12 353	99.69 392	99.37 228	93.63 410	99.94 185	99.67 346	98.96 120	99.47 318	98.62 294	97.95 283	99.83 224

CDS-MVSNet		98.96 173	98.95 151	99.01 262	99.48 285	98.36 273	99.93 329	99.37 228	96.79 259	99.31 280	99.83 312	99.77 11	98.91 362	98.07 318	97.98 279	99.77 282
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.90 184	98.63 195	99.70 149	99.92 115	99.25 186	100.00 1	99.37 228	95.71 346	99.40 273	100.00 1	96.58 225	99.95 182	96.80 368	99.94 133	99.91 171

fmvsm_l_conf0.5_n_a		99.63 58	99.55 62	99.86 100	99.83 134	99.58 135	100.00 1	99.36 234	98.98 35	100.00 1	100.00 1	97.85 173	99.99 106	100.00 1	99.94 133	100.00 1

fmvsm_l_conf0.5_n		99.63 58	99.56 60	99.86 100	99.81 143	99.59 133	100.00 1	99.36 234	98.98 35	100.00 1	100.00 1	97.92 168	99.99 106	100.00 1	99.95 127	100.00 1

mvsany_test1		99.57 70	99.48 76	99.85 104	99.86 126	99.54 142	100.00 1	99.36 234	98.94 45	100.00 1	100.00 1	97.97 165	100.00 1	99.88 123	99.28 193	100.00 1

xiu_mvs_v1_base_debu		99.35 101	99.21 113	99.79 128	99.67 194	99.71 116	99.78 369	99.36 234	98.13 125	100.00 1	100.00 1	97.00 211	100.00 1	99.83 134	99.07 199	99.66 306

xiu_mvs_v1_base		99.35 101	99.21 113	99.79 128	99.67 194	99.71 116	99.78 369	99.36 234	98.13 125	100.00 1	100.00 1	97.00 211	100.00 1	99.83 134	99.07 199	99.66 306

xiu_mvs_v1_base_debi		99.35 101	99.21 113	99.79 128	99.67 194	99.71 116	99.78 369	99.36 234	98.13 125	100.00 1	100.00 1	97.00 211	100.00 1	99.83 134	99.07 199	99.66 306

TSAR-MVS + GP.		99.61 65	99.69 25	99.35 216	99.99 52	98.06 306	100.00 1	99.36 234	99.83 2	100.00 1	100.00 1	98.95 121	99.99 106	100.00 1	99.11 198	100.00 1

XVG-OURS-SEG-HR		98.27 262	98.31 245	98.14 329	99.59 229	95.92 377	100.00 1	99.36 234	98.48 98	99.21 286	100.00 1	89.27 386	99.94 195	99.76 151	99.17 195	98.56 335

XVG-OURS		98.30 256	98.36 242	98.13 332	99.58 234	95.91 378	100.00 1	99.36 234	98.69 86	99.23 285	100.00 1	91.20 339	99.92 205	99.34 246	97.82 293	98.56 335

tpmrst		98.98 170	98.93 154	99.14 254	99.61 222	97.74 327	99.52 413	99.36 234	96.05 335	99.98 138	99.64 354	99.04 109	99.86 231	98.94 272	98.19 264	99.82 230

UnsupCasMVSNet_eth		94.25 403	93.89 403	95.34 430	97.63 434	92.13 448	99.73 384	99.36 234	94.88 370	92.78 462	98.63 439	82.72 444	96.53 469	94.57 413	84.73 454	97.36 444

fmvsm_l_conf0.5_n_3		99.38 96	99.20 117	99.92 82	99.80 156	99.78 103	100.00 1	99.35 245	98.94 45	100.00 1	100.00 1	94.77 265	99.99 106	99.99 76	99.92 140	100.00 1

fmvsm_s_conf0.5_n_a		99.32 110	99.15 123	99.81 117	99.80 156	99.47 160	100.00 1	99.35 245	98.22 116	100.00 1	100.00 1	95.21 253	99.99 106	99.96 105	99.86 157	99.98 127

UniMVSNet_ETH3D		95.28 394	94.41 400	97.89 355	98.91 371	95.14 393	99.13 460	99.35 245	92.11 435	97.17 415	99.66 348	70.28 481	99.36 331	97.88 326	95.18 347	99.16 325

NR-MVSNet		96.63 343	96.04 355	98.38 306	98.31 401	98.98 217	99.22 449	99.35 245	95.87 339	94.43 455	99.65 350	92.73 319	98.40 411	96.78 369	88.05 436	97.75 350

TranMVSNet+NR-MVSNet		96.45 353	96.01 356	97.79 360	98.00 420	97.62 332	100.00 1	99.35 245	95.98 336	97.31 411	99.64 354	90.09 371	98.00 446	96.89 364	86.80 448	97.75 350

CostFormer		98.84 190	98.77 173	99.04 260	99.41 310	97.58 333	99.67 395	99.35 245	94.66 379	99.96 151	99.36 392	99.28 83	99.74 270	99.41 238	97.81 294	99.81 244

MIMVSNet		97.06 324	96.73 325	98.05 343	99.38 319	96.64 367	98.47 483	99.35 245	93.41 417	99.48 261	98.53 448	89.66 380	97.70 459	94.16 420	98.11 273	99.80 271

SD_0403		97.92 279	98.43 225	96.39 415	99.68 186	89.74 466	99.92 331	99.34 252	96.75 266	99.39 274	99.93 292	93.54 298	99.51 311	99.11 264	98.21 261	99.92 167

TAMVS		98.76 198	98.73 178	98.86 273	99.44 306	97.69 328	99.57 406	99.34 252	96.57 298	99.12 293	99.81 319	98.83 134	99.16 343	97.97 324	97.91 285	99.73 301

CLD-MVS		97.64 290	97.74 283	97.36 372	99.01 356	94.76 409	100.00 1	99.34 252	99.30 4	99.00 305	99.97 256	87.49 409	99.57 291	99.96 105	95.58 328	97.75 350
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
reproduce_monomvs		98.61 223	98.54 210	98.82 276	99.97 97	99.28 181	100.00 1	99.33 255	98.51 97	97.87 391	99.24 398	99.98 3	99.45 324	99.02 269	92.93 378	97.74 377

WR-MVS		97.09 321	96.64 327	98.46 298	98.43 395	99.09 202	99.97 299	99.33 255	95.62 349	97.76 394	99.67 346	91.17 340	98.56 400	98.49 299	89.28 426	97.74 377

ACMH	96.25 11	96.77 335	96.62 329	97.21 381	98.96 367	94.43 421	99.64 397	99.33 255	97.43 202	96.55 429	99.97 256	83.52 441	99.54 303	99.07 267	95.13 350	97.66 416
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_9		99.04 150	98.78 170	99.81 117	99.86 126	99.44 164	100.00 1	99.32 258	98.94 45	100.00 1	100.00 1	91.00 345	99.99 106	100.00 1	99.94 133	100.00 1

guyue		99.21 131	99.07 132	99.62 163	99.55 245	99.29 179	100.00 1	99.32 258	97.66 166	99.96 151	100.00 1	95.84 238	99.84 242	99.63 197	99.67 178	99.75 285

BP-MVS1		99.56 71	99.48 76	99.79 128	99.48 285	99.61 130	100.00 1	99.32 258	97.34 209	99.94 185	100.00 1	99.74 13	99.89 220	99.75 155	99.72 173	99.87 214

WBMVS		98.19 266	98.10 263	98.47 297	99.63 213	99.03 208	100.00 1	99.32 258	95.46 359	98.39 359	99.40 389	99.69 17	98.61 390	98.64 290	92.39 386	97.76 339

PS-CasMVS		96.34 361	95.78 369	98.03 346	98.18 413	98.27 285	99.71 388	99.32 258	94.75 373	96.82 423	99.65 350	86.98 416	98.15 430	97.74 334	88.85 431	97.66 416

CP-MVSNet		96.73 337	96.25 346	98.18 325	98.21 410	98.67 240	99.77 374	99.32 258	95.06 367	97.20 414	99.65 350	90.10 370	98.19 428	98.06 319	88.90 430	97.66 416

XVG-ACMP-BASELINE		96.60 345	96.52 334	96.84 398	98.41 396	93.29 437	99.99 258	99.32 258	97.76 159	98.51 350	99.29 395	81.95 448	99.54 303	98.40 302	95.03 353	97.68 411

PatchT		95.90 383	94.95 399	98.75 283	99.03 354	98.39 266	99.08 466	99.32 258	85.52 474	99.96 151	94.99 483	97.94 166	98.05 445	80.20 484	98.47 222	99.81 244

ACMP	97.00 8	97.19 316	97.16 313	97.27 380	98.97 366	94.58 417	100.00 1	99.32 258	97.97 139	97.45 408	99.98 244	85.79 427	99.56 295	99.70 170	95.24 343	97.67 415
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_7		98.98 170	98.85 163	99.37 212	99.67 194	98.34 276	100.00 1	99.31 267	98.97 37	100.00 1	100.00 1	91.70 333	99.97 149	99.99 76	99.97 121	99.80 271

fmvsm_s_conf0.5_n_6		99.30 114	99.12 128	99.84 109	99.24 335	99.56 137	100.00 1	99.31 267	98.90 59	100.00 1	100.00 1	94.75 267	99.97 149	99.98 91	99.88 151	100.00 1

test_fmvsmconf_n		99.56 71	99.46 79	99.86 100	99.68 186	99.58 135	100.00 1	99.31 267	98.92 52	99.88 202	100.00 1	97.35 200	99.99 106	99.98 91	99.99 106	100.00 1

test_cas_vis1_n_1920		98.63 219	98.25 249	99.77 136	99.69 181	99.32 176	100.00 1	99.31 267	98.84 68	99.96 151	100.00 1	87.42 410	99.99 106	99.14 260	99.86 157	100.00 1

Vis-MVSNet (Re-imp)		98.99 166	98.89 161	99.29 239	99.64 211	98.89 225	99.98 290	99.31 267	96.74 269	99.48 261	100.00 1	98.11 162	99.10 345	98.39 303	98.34 240	99.89 190

ACMH+	96.20 13	96.49 352	96.33 344	97.00 388	99.06 351	93.80 430	99.81 358	99.31 267	97.32 212	95.89 442	99.97 256	82.62 446	99.54 303	98.34 306	94.63 361	97.65 421

fmvsm_l_conf0.5_n_9		99.35 101	99.15 123	99.95 61	99.83 134	99.84 95	100.00 1	99.30 273	98.92 52	100.00 1	100.00 1	94.32 281	100.00 1	100.00 1	99.93 137	100.00 1

SSC-MVS3.2		95.32 392	94.97 398	96.37 417	98.29 405	92.75 442	100.00 1	99.30 273	95.46 359	98.36 360	99.42 387	78.92 459	98.63 388	93.28 431	91.72 399	97.72 395

MVSMamba_PlusPlus		99.39 93	99.25 104	99.80 123	99.68 186	99.59 133	99.99 258	99.30 273	96.66 286	99.96 151	99.97 256	97.89 170	99.92 205	99.76 151	100.00 1	99.90 182

sasdasda		99.03 153	98.73 178	99.94 74	99.75 171	99.95 37	100.00 1	99.30 273	97.64 170	100.00 1	100.00 1	95.22 251	99.97 149	99.76 151	96.90 317	99.91 171

MS-PatchMatch		95.66 388	95.87 363	95.05 433	97.80 428	89.25 468	98.88 473	99.30 273	96.35 320	96.86 421	99.01 416	81.35 451	99.43 326	93.30 429	99.98 117	96.46 463

canonicalmvs		99.03 153	98.73 178	99.94 74	99.75 171	99.95 37	100.00 1	99.30 273	97.64 170	100.00 1	100.00 1	95.22 251	99.97 149	99.76 151	96.90 317	99.91 171

balanced_ft_v1		98.70 208	98.61 198	98.94 267	99.67 194	96.90 358	99.91 337	99.30 273	96.73 273	99.96 151	99.97 256	92.18 328	99.93 199	99.86 127	99.95 127	100.00 1

tpm cat1		98.05 272	97.76 282	98.92 269	99.50 278	97.10 355	99.77 374	99.30 273	90.20 452	99.72 239	98.71 435	97.71 180	99.86 231	96.75 372	98.20 263	99.81 244

PMMVS		99.12 140	98.97 146	99.58 173	99.57 238	98.98 217	100.00 1	99.30 273	97.14 224	99.96 151	100.00 1	96.53 229	99.82 247	99.70 170	98.49 220	99.94 154

usedtu_dtu_shiyan1		97.34 310	96.97 315	98.43 301	97.82 426	98.91 223	100.00 1	99.29 282	94.70 376	98.46 354	98.89 427	93.95 290	98.64 386	95.86 388	93.75 366	97.74 377

FE-MVSNET3		97.34 310	96.97 315	98.43 301	97.82 426	98.91 223	100.00 1	99.29 282	94.70 376	98.46 354	98.89 427	93.95 290	98.64 386	95.88 386	93.75 366	97.74 377

MGCFI-Net		99.01 161	98.70 186	99.93 78	99.74 173	99.94 46	100.00 1	99.29 282	97.60 180	100.00 1	100.00 1	95.10 257	99.96 169	99.74 156	96.85 319	99.91 171

CANet_DTU		99.02 159	98.90 160	99.41 199	99.88 123	98.71 237	100.00 1	99.29 282	98.84 68	100.00 1	100.00 1	94.02 288	100.00 1	98.08 316	99.96 125	99.52 315

EI-MVSNet		97.98 275	97.93 274	98.16 328	99.11 342	97.84 323	99.74 379	99.29 282	94.39 389	98.65 332	100.00 1	97.21 202	98.88 368	97.62 341	95.31 338	97.75 350

PEN-MVS		96.01 380	95.48 385	97.58 366	97.74 431	97.26 348	99.90 339	99.29 282	94.55 382	96.79 424	99.55 374	87.38 411	97.84 452	96.92 363	87.24 443	97.65 421

MVSTER		98.58 228	98.52 213	98.77 282	99.65 205	99.68 123	100.00 1	99.29 282	95.63 348	98.65 332	99.80 325	99.78 9	98.88 368	98.59 296	95.31 338	97.73 388

XXY-MVS		97.14 320	96.63 328	98.67 285	98.65 387	98.92 222	99.54 411	99.29 282	95.57 351	97.63 400	99.83 312	87.79 407	99.35 333	98.39 303	92.95 377	97.75 350

gbinet_0.2-2-1-0.02		93.73 411	92.69 423	96.84 398	94.91 477	94.62 413	100.00 1	99.28 290	87.02 470	98.53 346	98.45 451	89.72 378	98.15 430	96.65 373	69.64 488	97.74 377

fmvsm_s_conf0.5_n_10		99.08 143	98.78 170	99.97 40	99.84 130	99.92 59	100.00 1	99.28 290	98.93 49	100.00 1	100.00 1	91.07 342	99.99 106	100.00 1	99.95 127	100.00 1

NormalMVS		99.47 84	99.48 76	99.43 195	99.99 52	98.55 249	99.94 323	99.28 290	98.39 103	100.00 1	100.00 1	98.44 153	99.98 140	99.36 240	99.92 140	99.75 285

Elysia		98.12 268	97.72 286	99.34 218	99.30 329	98.96 220	99.95 315	99.28 290	96.64 289	99.75 232	99.99 236	88.71 395	99.81 250	95.99 384	99.84 162	99.26 322

StellarMVS		98.12 268	97.72 286	99.34 218	99.30 329	98.96 220	99.95 315	99.28 290	96.64 289	99.75 232	99.99 236	88.71 395	99.81 250	95.99 384	99.84 162	99.26 322

BridgeMVS		99.43 89	99.28 96	99.85 104	99.68 186	99.68 123	99.97 299	99.28 290	97.03 235	99.96 151	99.97 256	97.90 169	99.93 199	99.77 150	100.00 1	99.94 154

cl22		98.23 264	98.11 260	98.58 293	99.82 137	99.01 212	100.00 1	99.28 290	96.92 246	98.33 364	99.21 401	98.09 164	98.97 356	98.72 285	92.61 381	97.76 339

OPM-MVS		97.21 315	97.18 311	97.32 375	98.08 416	94.66 410	100.00 1	99.28 290	98.65 90	98.92 311	99.98 244	86.03 425	99.56 295	98.28 311	95.41 334	97.72 395
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvsmamba		99.05 149	98.98 144	99.27 245	99.57 238	98.10 302	100.00 1	99.28 290	95.92 338	99.96 151	99.97 256	96.73 222	99.89 220	99.72 162	99.65 181	99.81 244

BH-w/o		98.82 192	98.81 167	98.88 272	99.62 220	96.71 364	100.00 1	99.28 290	97.09 229	98.81 322	100.00 1	94.91 261	99.96 169	99.54 217	100.00 1	99.96 143

BH-untuned		98.64 214	98.65 192	98.60 290	99.59 229	96.17 374	100.00 1	99.28 290	96.67 285	98.41 357	100.00 1	94.52 274	99.83 244	99.41 238	100.00 1	99.81 244

UnsupCasMVSNet_bld		89.50 442	88.00 449	93.99 449	95.30 468	88.86 471	98.52 482	99.28 290	85.50 475	87.80 481	94.11 485	61.63 487	96.96 463	90.63 449	79.26 473	96.15 467

FMVSNet3		97.30 313	96.95 317	98.37 307	99.65 205	99.25 186	99.71 388	99.28 290	94.23 392	98.53 346	98.91 425	93.30 303	98.11 436	95.31 403	93.60 369	97.73 388

BH-RMVSNet		98.46 242	98.08 264	99.59 169	99.61 222	99.19 194	100.00 1	99.28 290	97.06 233	98.95 307	100.00 1	88.99 390	99.82 247	98.83 280	100.00 1	99.77 282

LTVRE_ROB	95.29 16	96.32 362	96.10 352	96.99 389	98.55 391	93.88 429	99.45 419	99.28 290	94.50 385	96.46 430	99.52 378	84.86 432	99.48 316	97.26 353	95.03 353	97.59 431
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		98.77 195	98.42 226	99.82 112	99.47 290	99.52 148	100.00 1	99.27 305	97.53 188	100.00 1	100.00 1	89.73 377	99.96 169	99.84 133	99.93 137	99.97 137

EIA-MVS		99.26 122	99.19 118	99.45 190	99.63 213	98.75 233	100.00 1	99.27 305	96.93 244	99.95 182	100.00 1	97.47 194	99.79 255	99.74 156	99.72 173	99.82 230

GBi-Net		96.07 377	95.80 367	96.89 395	99.53 251	94.87 398	99.18 450	99.27 305	93.71 405	98.53 346	98.81 431	84.23 436	98.07 441	95.31 403	93.60 369	97.72 395

test1		96.07 377	95.80 367	96.89 395	99.53 251	94.87 398	99.18 450	99.27 305	93.71 405	98.53 346	98.81 431	84.23 436	98.07 441	95.31 403	93.60 369	97.72 395

FMVSNet2		96.22 367	95.60 379	98.06 339	99.53 251	98.33 277	99.45 419	99.27 305	93.71 405	98.03 381	98.84 430	84.23 436	98.10 439	93.97 422	93.40 372	97.73 388

FMVSNet1		94.45 400	93.63 407	96.89 395	98.87 377	94.87 398	99.18 450	99.27 305	90.95 444	97.31 411	98.81 431	72.89 476	98.07 441	92.61 434	92.81 379	97.72 395

fmvsm_s_conf0.5_n_5		99.00 162	98.70 186	99.88 95	99.81 143	99.64 127	100.00 1	99.26 311	98.78 83	99.97 144	100.00 1	90.65 352	99.99 106	100.00 1	99.89 148	99.99 124

fmvsm_s_conf0.5_n_4		98.98 170	98.74 177	99.68 153	99.81 143	99.50 151	100.00 1	99.26 311	98.91 55	100.00 1	100.00 1	90.87 349	99.97 149	99.99 76	99.81 167	99.57 311

fmvsm_s_conf0.1_n_a		98.71 206	98.36 242	99.78 133	99.09 345	99.42 166	100.00 1	99.26 311	97.42 203	100.00 1	100.00 1	89.78 375	99.96 169	99.82 139	99.85 160	99.97 137

test_fmvsmvis_n_1920		99.46 85	99.37 86	99.73 143	98.88 374	99.18 196	100.00 1	99.26 311	98.85 66	99.79 226	100.00 1	97.70 181	100.00 1	99.98 91	99.86 157	100.00 1

fmvsm_s_conf0.5_n_2		98.90 184	98.57 205	99.90 87	99.79 161	99.78 103	100.00 1	99.25 315	98.97 37	100.00 1	100.00 1	89.22 387	99.99 106	100.00 1	99.88 151	99.92 167

test_vis1_n_1920		97.77 286	97.24 307	99.34 218	99.79 161	98.04 308	100.00 1	99.25 315	98.88 61	100.00 1	100.00 1	77.52 463	100.00 1	99.88 123	99.85 160	100.00 1

CL-MVSNet_self_test		91.07 437	90.35 439	93.24 452	93.27 480	89.16 469	99.55 409	99.25 315	92.34 434	95.23 445	97.05 471	88.86 394	93.59 487	80.67 482	66.95 491	96.96 454

ETV-MVS		99.34 105	99.24 108	99.64 160	99.58 234	99.33 175	100.00 1	99.25 315	97.57 183	99.96 151	100.00 1	97.44 197	99.79 255	99.70 170	99.65 181	99.81 244

DTE-MVSNet		95.52 389	94.99 397	97.08 384	97.49 444	96.45 372	100.00 1	99.25 315	93.82 404	96.17 435	99.57 372	87.81 406	97.18 461	94.57 413	86.26 451	97.62 427

ACMM	97.17 6	97.37 308	97.40 297	97.29 377	99.01 356	94.64 412	100.00 1	99.25 315	98.07 131	98.44 356	99.98 244	87.38 411	99.55 300	99.25 252	95.19 346	97.69 409
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_11		98.92 180	98.63 195	99.80 123	99.85 128	99.86 89	100.00 1	99.24 321	98.91 55	100.00 1	100.00 1	89.69 379	99.99 106	100.00 1	99.98 117	99.54 313

ppachtmachnet_test		96.17 371	95.89 361	97.02 387	97.61 436	95.24 391	99.99 258	99.24 321	93.31 421	96.71 427	99.62 362	94.34 280	98.07 441	89.87 456	92.30 389	97.75 350

GA-MVS		97.72 288	97.27 305	99.06 256	99.24 335	97.93 317	100.00 1	99.24 321	95.80 345	98.99 306	99.64 354	89.77 376	99.36 331	95.12 407	97.62 307	99.89 190

FMVSNet5		95.32 392	95.43 388	94.99 436	99.39 318	92.99 440	99.25 439	99.24 321	90.45 448	97.44 409	98.45 451	95.78 240	94.39 483	87.02 469	91.88 395	97.59 431

IS-MVSNet		99.08 143	98.91 157	99.59 169	99.65 205	99.38 169	99.78 369	99.24 321	96.70 281	99.51 259	100.00 1	98.44 153	99.52 309	98.47 300	98.39 228	99.88 203

AstraMVS		99.03 153	99.01 138	99.09 255	99.46 297	97.66 330	100.00 1	99.23 326	97.83 150	99.95 182	100.00 1	95.52 246	99.86 231	99.74 156	99.39 192	99.74 292

fmvsm_s_conf0.1_n_2		98.95 176	98.69 188	99.73 143	99.61 222	99.74 111	100.00 1	99.23 326	98.95 42	99.97 144	100.00 1	90.92 348	99.97 149	100.00 1	99.58 186	99.47 318

test_fmvsmconf0.1_n		99.25 126	99.05 134	99.82 112	98.92 370	99.55 139	100.00 1	99.23 326	98.91 55	99.75 232	99.97 256	94.79 264	99.94 195	99.94 113	99.99 106	99.97 137

PS-MVSNAJss		98.03 273	98.06 267	97.94 351	97.63 434	97.33 345	99.89 343	99.23 326	96.27 325	98.03 381	99.59 366	98.75 139	98.78 373	98.52 298	94.61 362	97.70 404

K. test v3		95.46 391	95.14 394	96.40 414	97.53 441	93.40 435	99.99 258	99.23 326	95.49 357	92.70 465	99.73 332	84.26 435	98.12 434	93.94 423	93.38 373	97.68 411

wanda-best-256-512		93.76 409	92.74 420	96.84 398	95.22 469	94.54 418	100.00 1	99.22 331	87.22 464	98.54 341	98.56 443	90.48 357	98.22 425	95.67 393	69.73 484	97.75 350

FE-blended-shiyan7		93.76 409	92.74 420	96.84 398	95.22 469	94.54 418	100.00 1	99.22 331	87.22 464	98.54 341	98.56 443	90.48 357	98.22 425	95.67 393	69.73 484	97.75 350

blended_shiyan6		93.70 413	92.67 425	96.78 408	95.17 473	94.38 425	100.00 1	99.22 331	87.03 469	98.54 341	98.56 443	90.14 366	98.22 425	95.62 397	69.73 484	97.75 350

blend_shiyan4		95.76 385	95.40 390	96.82 404	95.50 467	94.40 422	100.00 1	99.22 331	87.12 466	98.67 330	98.59 440	99.09 99	98.31 417	96.31 379	84.14 456	97.75 350

E3new		98.95 176	98.80 168	99.41 199	99.57 238	98.50 258	100.00 1	99.22 331	96.84 254	99.89 199	100.00 1	95.70 242	99.93 199	99.57 212	98.39 228	99.82 230

E2		98.77 195	98.57 205	99.37 212	99.53 251	98.38 269	99.98 290	99.22 331	96.77 262	99.75 232	100.00 1	94.03 286	99.91 207	99.53 220	98.35 236	99.82 230

E3		98.77 195	98.57 205	99.36 214	99.47 290	98.36 273	99.98 290	99.22 331	96.76 263	99.75 232	100.00 1	94.10 284	99.91 207	99.53 220	98.35 236	99.82 230

viewdifsd2359ckpt13		98.72 202	98.52 213	99.34 218	99.55 245	98.46 260	99.99 258	99.22 331	96.50 307	99.05 302	100.00 1	94.54 273	99.73 273	99.46 232	98.35 236	99.81 244

viewcassd2359sk11		98.90 184	98.73 178	99.40 204	99.57 238	98.47 259	99.99 258	99.22 331	96.79 259	99.82 218	100.00 1	95.24 250	99.91 207	99.54 217	98.38 231	99.82 230

viewdifsd2359ckpt11		97.98 275	97.89 275	98.26 317	99.47 290	94.98 397	99.99 258	99.22 331	96.74 269	99.24 283	100.00 1	90.14 366	99.90 218	99.49 226	96.73 320	99.90 182

viewmsd2359difaftdt		97.98 275	97.89 275	98.27 314	99.47 290	94.99 396	99.99 258	99.22 331	96.74 269	99.24 283	100.00 1	90.14 366	99.90 218	99.49 226	96.73 320	99.90 182

viewmanbaseed2359cas		98.86 188	98.68 190	99.40 204	99.51 270	98.51 257	99.98 290	99.22 331	97.05 234	99.72 239	100.00 1	94.77 265	99.89 220	99.58 209	98.31 246	99.81 244

LuminaMVS		99.07 146	98.92 156	99.50 181	98.87 377	99.12 201	99.92 331	99.22 331	97.45 199	99.82 218	99.98 244	96.29 232	99.85 238	99.71 166	99.05 202	99.52 315

miper_enhance_ethall		98.33 254	98.27 247	98.51 295	99.66 203	99.04 207	100.00 1	99.22 331	97.53 188	98.51 350	99.38 390	99.49 46	98.75 378	98.02 320	92.61 381	97.76 339

nrg030		97.64 290	97.27 305	98.75 283	98.34 398	99.53 144	100.00 1	99.22 331	96.21 331	98.27 370	99.95 281	94.40 277	98.98 354	99.23 255	89.78 420	97.75 350

lessismore_v0				96.05 424	97.55 440	91.80 451		99.22 331		91.87 466	99.91 296	83.50 442	98.68 381	92.48 437	90.42 417	97.68 411

casdiffmvs_mvg		98.64 214	98.39 236	99.40 204	99.50 278	98.60 246	100.00 1	99.22 331	96.85 252	99.10 295	100.00 1	92.75 317	99.78 260	99.71 166	98.35 236	99.81 244
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		98.43 244	98.07 266	99.50 181	99.65 205	99.02 210	100.00 1	99.22 331	94.21 394	99.72 239	99.98 244	92.03 331	99.93 199	99.68 180	98.12 272	99.54 313

MIMVSNet1		91.96 428	91.20 431	94.23 447	94.94 476	91.69 452	99.34 431	99.22 331	88.23 459	94.18 456	98.45 451	75.52 471	93.41 488	79.37 485	91.49 403	97.60 430

E5new		98.63 219	98.41 228	99.31 233	99.51 270	98.21 291	99.79 364	99.21 350	96.62 294	99.67 248	100.00 1	93.15 309	99.91 207	99.46 232	98.26 255	99.81 244

E6new		98.64 214	98.41 228	99.30 237	99.46 297	98.19 294	99.79 364	99.21 350	96.62 294	99.68 243	100.00 1	93.24 305	99.91 207	99.47 229	98.26 255	99.81 244

E6		98.64 214	98.41 228	99.30 237	99.46 297	98.19 294	99.79 364	99.21 350	96.62 294	99.68 243	100.00 1	93.24 305	99.91 207	99.47 229	98.26 255	99.81 244

E5		98.63 219	98.41 228	99.31 233	99.51 270	98.21 291	99.79 364	99.21 350	96.62 294	99.67 248	100.00 1	93.15 309	99.91 207	99.46 232	98.26 255	99.81 244

E4		98.68 212	98.46 222	99.33 226	99.51 270	98.27 285	99.96 306	99.21 350	96.66 286	99.68 243	100.00 1	93.38 300	99.91 207	99.49 226	98.27 253	99.81 244

viewmacassd2359aftdt		98.57 230	98.31 245	99.33 226	99.49 282	98.31 281	99.89 343	99.21 350	96.87 251	99.10 295	100.00 1	92.48 325	99.88 228	99.50 224	98.28 250	99.81 244

Patchmatch-test		97.83 283	97.42 295	99.06 256	99.08 346	97.66 330	98.66 479	99.21 350	93.65 409	98.25 372	99.58 368	99.47 51	99.57 291	90.25 455	98.59 215	99.95 149

mvs_anonymous		98.80 193	98.60 201	99.38 211	99.57 238	99.24 188	100.00 1	99.21 350	95.87 339	98.92 311	99.82 316	96.39 231	99.03 348	99.13 262	98.50 219	99.88 203

EC-MVSNet		99.19 133	99.09 131	99.48 186	99.42 308	99.07 203	100.00 1	99.21 350	96.95 242	99.96 151	100.00 1	96.88 217	99.48 316	99.64 192	99.79 171	99.88 203

TR-MVS		98.14 267	97.74 283	99.33 226	99.59 229	98.28 283	99.27 437	99.21 350	96.42 314	99.15 291	99.94 287	88.87 393	99.79 255	98.88 276	98.29 249	99.93 165

blended_shiyan8		93.73 411	92.69 423	96.84 398	95.17 473	94.40 422	100.00 1	99.20 360	87.05 467	98.60 336	98.54 447	90.15 365	98.39 412	95.54 400	69.93 483	97.74 377

viewdifsd2359ckpt07		98.72 202	98.52 213	99.34 218	99.47 290	98.28 283	99.99 258	99.20 360	96.98 239	99.60 253	100.00 1	93.45 299	99.93 199	99.58 209	98.36 234	99.82 230

viewdifsd2359ckpt09		98.78 194	98.60 201	99.31 233	99.53 251	98.37 270	100.00 1	99.20 360	96.85 252	99.32 279	100.00 1	94.68 269	99.74 270	99.46 232	98.36 234	99.81 244

GeoE		98.06 271	97.65 290	99.29 239	99.47 290	98.41 263	100.00 1	99.19 363	94.85 371	98.88 314	100.00 1	91.21 338	99.59 286	97.02 358	98.19 264	99.88 203

KD-MVS_2432*1600		94.15 404	93.08 413	97.35 373	99.53 251	97.83 324	99.63 399	99.19 363	92.88 429	96.29 432	97.68 465	98.84 132	96.70 465	89.73 457	63.92 492	97.53 435

miper_refine_blended		94.15 404	93.08 413	97.35 373	99.53 251	97.83 324	99.63 399	99.19 363	92.88 429	96.29 432	97.68 465	98.84 132	96.70 465	89.73 457	63.92 492	97.53 435

SPE-MVS-test		99.31 112	99.27 98	99.43 195	99.99 52	98.77 232	100.00 1	99.19 363	97.24 218	99.96 151	100.00 1	97.56 189	99.70 279	99.68 180	99.81 167	99.82 230

jajsoiax		97.07 323	96.79 324	97.89 355	97.28 450	97.12 353	99.95 315	99.19 363	96.55 299	97.31 411	99.69 341	87.35 413	98.91 362	98.70 286	95.12 351	97.66 416

MVS_Test		98.93 179	98.65 192	99.77 136	99.62 220	99.50 151	99.99 258	99.19 363	95.52 354	99.96 151	99.86 304	96.54 228	99.98 140	98.65 289	98.48 221	99.82 230

MVS-HIRNet		94.12 406	92.73 422	98.29 312	99.33 325	95.95 376	99.38 427	99.19 363	74.54 491	98.26 371	86.34 495	86.07 423	99.06 347	91.60 443	99.87 156	99.85 219

icg_test_0407_2		98.30 256	98.45 223	97.85 357	99.38 319	95.36 386	99.99 258	99.18 370	96.72 275	99.58 254	100.00 1	95.17 255	98.45 408	97.84 328	98.15 268	99.74 292

IMVS_0407		98.36 253	98.42 226	98.19 324	99.38 319	95.36 386	99.73 384	99.18 370	96.72 275	99.58 254	100.00 1	95.17 255	99.47 318	97.84 328	98.15 268	99.74 292

IMVS_0404		97.87 280	97.89 275	97.81 359	99.38 319	95.36 386	99.84 352	99.18 370	96.72 275	98.41 357	100.00 1	91.43 336	98.32 416	97.84 328	98.15 268	99.74 292

IMVS_0403		98.37 251	98.39 236	98.29 312	99.38 319	95.36 386	99.97 299	99.18 370	96.72 275	99.68 243	100.00 1	94.61 271	99.77 261	97.84 328	98.15 268	99.74 292

MTMP							100.00 1	99.18 370

mvs_tets		97.00 329	96.69 326	97.94 351	97.41 449	97.27 347	99.60 403	99.18 370	96.51 305	97.35 410	99.69 341	86.53 419	98.91 362	98.84 278	95.09 352	97.65 421

pmmvs4		97.17 317	96.80 322	98.27 314	97.68 433	98.64 244	100.00 1	99.18 370	94.22 393	98.55 340	99.71 335	93.67 293	98.47 406	95.66 395	92.57 384	97.71 403

diffmvs		98.96 173	98.73 178	99.63 161	99.54 248	99.16 198	100.00 1	99.18 370	97.33 211	99.96 151	100.00 1	94.60 272	99.91 207	99.66 189	98.33 243	99.82 230
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				89.98 460	98.90 372	71.46 491		99.18 370	97.61 177	96.92 418	99.83 312	86.07 423	99.83 244	96.02 383	97.65 305	98.65 333

casdiffseed414692147		98.31 255	97.94 273	99.40 204	99.46 297	98.67 240	99.91 337	99.17 379	96.33 322	98.66 331	99.97 256	90.47 361	99.71 277	99.36 240	98.16 267	99.81 244

baseline		98.69 210	98.45 223	99.41 199	99.52 265	98.67 240	100.00 1	99.17 379	97.03 235	99.13 292	100.00 1	93.17 307	99.74 270	99.70 170	98.34 240	99.81 244

mamba_0408		98.63 219	98.40 233	99.34 218	99.53 251	98.52 254	99.24 440	99.16 381	96.43 310	98.95 307	99.98 244	94.47 275	99.76 265	99.21 258	98.62 212	99.75 285

SSM_04072		98.59 226	98.40 233	99.15 252	99.53 251	98.52 254	99.24 440	99.16 381	96.43 310	98.95 307	99.98 244	94.47 275	99.19 342	99.21 258	98.62 212	99.75 285

viewmambaseed2359dif		98.57 230	98.34 244	99.28 242	99.46 297	98.23 288	100.00 1	99.16 381	96.26 326	99.11 294	100.00 1	93.12 312	99.79 255	99.61 202	98.33 243	99.80 271

MonoMVSNet		98.55 233	98.64 194	98.26 317	98.21 410	95.76 382	99.94 323	99.16 381	96.23 327	99.47 264	99.24 398	96.75 221	99.22 340	99.61 202	99.17 195	99.81 244

Fast-Effi-MVS+-dtu		98.38 250	98.56 208	97.82 358	99.58 234	94.44 420	100.00 1	99.16 381	96.75 266	99.51 259	99.63 358	95.03 259	99.60 284	97.71 335	99.67 178	99.42 320

SSM_0407		98.72 202	98.52 213	99.33 226	99.53 251	98.52 254	99.88 346	99.15 386	96.53 301	98.95 307	100.00 1	94.38 278	99.72 275	99.64 192	98.62 212	99.75 285

SSM_0404		98.76 198	98.56 208	99.35 216	99.53 251	98.65 243	99.80 363	99.15 386	96.53 301	99.47 264	100.00 1	94.38 278	99.76 265	99.64 192	98.59 215	99.64 310

Fast-Effi-MVS+		98.40 249	98.02 270	99.55 177	99.63 213	99.06 205	100.00 1	99.15 386	95.07 366	99.42 267	99.95 281	93.26 304	99.73 273	97.44 345	98.24 259	99.87 214

anonymousdsp		97.16 318	96.88 319	98.00 347	97.08 452	98.06 306	99.81 358	99.15 386	94.58 381	97.84 393	99.62 362	90.49 356	98.60 393	97.98 321	95.32 337	97.33 446

casdiffmvs		98.65 213	98.38 238	99.46 188	99.52 265	98.74 236	100.00 1	99.15 386	96.91 247	99.05 302	100.00 1	92.75 317	99.83 244	99.70 170	98.38 231	99.81 244
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	99.45 304	99.49 155	100.00 1	99.15 386	97.45 199	99.97 144	100.00 1	96.76 219	99.76 265	99.67 184	100.00 1	99.81 244

1112_ss		98.91 182	98.71 184	99.51 178	99.69 181	98.75 233	99.99 258	99.15 386	96.82 256	98.84 319	100.00 1	97.45 195	99.89 220	98.66 287	97.75 299	99.89 190

diffmvs_AUTHOR		98.92 180	98.73 178	99.49 185	99.48 285	98.81 229	99.94 323	99.14 393	97.24 218	99.96 151	100.00 1	94.85 262	99.87 230	99.67 184	98.31 246	99.79 277

tt0320		92.36 427	91.28 430	95.58 429	98.30 403	90.65 459	98.69 478	99.14 393	76.73 485	96.07 438	99.50 381	72.28 478	98.39 412	93.29 430	87.56 440	97.70 404

IterMVS-SCA-FT		96.72 339	96.42 339	97.62 364	99.40 315	96.83 361	99.99 258	99.14 393	94.65 380	97.55 406	99.72 333	89.65 381	98.31 417	95.62 397	92.05 391	97.73 388

YYNet1		92.44 426	90.92 435	97.03 386	96.20 457	97.06 356	99.99 258	99.14 393	88.21 461	67.93 496	98.43 454	88.63 397	96.28 472	90.64 448	89.08 428	97.74 377

MDA-MVSNet_test_wron		92.61 424	91.09 434	97.19 382	96.71 455	97.26 348	100.00 1	99.14 393	88.61 457	67.90 497	98.32 457	89.03 389	96.57 468	90.47 453	89.59 421	97.74 377

Test_1112_low_res		98.83 191	98.60 201	99.51 178	99.69 181	98.75 233	99.99 258	99.14 393	96.81 257	98.84 319	99.06 408	97.45 195	99.89 220	98.66 287	97.75 299	99.89 190

CS-MVS		99.33 108	99.27 98	99.50 181	99.99 52	99.00 215	100.00 1	99.13 399	97.26 217	99.96 151	100.00 1	97.79 178	99.64 282	99.64 192	99.67 178	99.87 214

v2v482		96.70 340	96.18 349	98.27 314	98.04 417	98.39 266	100.00 1	99.13 399	94.19 396	98.58 338	99.08 407	90.48 357	98.67 382	95.69 392	90.44 416	97.75 350

MVSFormer		98.94 178	98.82 165	99.28 242	99.45 304	99.49 155	100.00 1	99.13 399	95.46 359	99.97 144	100.00 1	96.76 219	98.59 395	98.63 292	100.00 1	99.74 292

jason		99.11 141	98.96 147	99.59 169	99.17 338	99.31 178	100.00 1	99.13 399	97.38 205	99.83 209	100.00 1	95.54 245	99.72 275	99.57 212	99.97 121	99.74 292
jason: jason.
test_djsdf		97.55 299	97.38 298	98.07 335	97.50 442	97.99 310	100.00 1	99.13 399	95.46 359	98.47 353	99.85 309	92.01 332	98.59 395	98.63 292	95.36 336	97.62 427

IterMVS		96.76 336	96.46 337	97.63 362	99.41 310	96.89 359	99.99 258	99.13 399	94.74 375	97.59 405	99.66 348	89.63 383	98.28 421	95.71 391	92.31 388	97.72 395
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
sc_t1		92.52 425	91.34 429	96.09 423	97.80 428	89.86 465	98.61 480	99.12 405	77.73 484	96.09 437	99.79 328	68.64 483	98.94 359	96.94 360	87.31 442	99.46 319

tpm		98.24 263	98.22 256	98.32 311	99.13 340	95.79 381	99.53 412	99.12 405	95.20 365	99.96 151	99.36 392	97.58 185	99.28 338	97.41 347	96.67 322	99.88 203

GDP-MVS		99.39 93	99.26 102	99.77 136	99.53 251	99.55 139	100.00 1	99.11 407	97.14 224	99.96 151	100.00 1	99.83 5	99.89 220	98.47 300	99.26 194	99.87 214

miper_ehance_all_eth		97.81 284	97.66 289	98.23 320	99.49 282	98.37 270	99.99 258	99.11 407	94.78 372	98.25 372	99.21 401	98.18 160	98.57 398	97.35 351	92.61 381	97.76 339

v7n		96.06 379	95.42 389	97.99 349	97.58 439	97.35 342	99.86 349	99.11 407	92.81 432	97.91 389	99.49 382	90.99 346	98.92 361	92.51 436	88.49 434	97.70 404

cl____		97.54 300	97.32 301	98.18 325	99.47 290	98.14 299	100.00 1	99.10 410	94.16 398	97.60 404	99.63 358	97.52 191	98.65 385	96.47 374	91.97 394	97.76 339

DIV-MVS_self_test		97.52 303	97.35 300	98.05 343	99.46 297	98.11 300	100.00 1	99.10 410	94.21 394	97.62 402	99.63 358	97.65 183	98.29 420	96.47 374	91.98 393	97.76 339

c3_l		97.58 296	97.42 295	98.06 339	99.48 285	98.16 296	99.96 306	99.10 410	94.54 383	98.13 376	99.20 403	97.87 172	98.25 423	97.28 352	91.20 408	97.75 350

Effi-MVS+		98.58 228	98.24 252	99.61 165	99.60 225	99.26 184	97.85 487	99.10 410	96.22 330	99.97 144	99.89 299	93.75 292	99.77 261	99.43 236	98.34 240	99.81 244

IterMVS-LS		97.56 297	97.44 294	97.92 354	99.38 319	97.90 318	99.89 343	99.10 410	94.41 388	98.32 365	99.54 377	97.21 202	98.11 436	97.50 343	91.62 400	97.75 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		96.65 342	96.16 351	98.11 334	98.17 414	98.23 288	99.99 258	99.09 415	93.97 401	98.74 326	99.05 410	91.09 341	98.82 371	95.46 401	89.90 418	97.27 447

KD-MVS_self_test		91.16 435	90.09 440	94.35 444	94.44 478	91.27 455	99.74 379	99.08 416	90.82 445	94.53 453	94.91 484	86.11 422	94.78 482	82.67 478	68.52 489	96.99 453

eth_miper_zixun_eth		97.47 304	97.28 303	98.06 339	99.41 310	97.94 316	99.62 401	99.08 416	94.46 387	98.19 375	99.56 373	96.91 216	98.50 403	96.78 369	91.49 403	97.74 377

v1192		96.18 369	95.49 383	98.26 317	98.01 419	98.15 297	99.99 258	99.08 416	93.36 419	98.54 341	98.97 421	89.47 384	98.89 365	91.15 446	90.82 411	97.75 350

v1144		96.51 349	95.97 359	98.13 332	97.98 421	98.04 308	99.99 258	99.08 416	93.51 414	98.62 335	98.98 418	90.98 347	98.62 389	93.79 424	90.79 412	97.74 377

RRT-MVS		98.75 201	98.52 213	99.44 192	99.65 205	98.57 248	99.90 339	99.08 416	96.51 305	99.96 151	99.95 281	92.59 322	99.96 169	99.60 204	99.45 191	99.81 244

dcpmvs_2		98.87 187	99.53 65	96.90 394	99.87 125	90.88 458	99.94 323	99.07 421	98.20 119	100.00 1	100.00 1	98.69 142	99.86 231	100.00 1	100.00 1	99.95 149

miper_lstm_enhance		97.40 307	97.28 303	97.75 361	99.48 285	97.52 334	100.00 1	99.07 421	94.08 400	98.01 384	99.61 364	97.38 199	97.98 448	96.44 377	91.47 405	97.76 339

v1921920		96.16 373	95.50 381	98.14 329	97.88 425	97.96 314	99.99 258	99.07 421	93.33 420	98.60 336	99.24 398	89.37 385	98.71 380	91.28 444	90.74 413	97.75 350

v148		96.29 363	95.84 364	97.63 362	97.74 431	96.53 371	100.00 1	99.07 421	93.52 413	98.01 384	99.42 387	91.22 337	98.60 393	96.37 378	87.22 444	97.75 350

v1240		95.96 381	95.25 391	98.07 335	97.91 423	97.87 322	99.96 306	99.07 421	93.24 423	98.64 334	98.96 422	88.98 391	98.61 390	89.58 460	90.92 410	97.75 350

v8		96.35 360	95.73 372	98.21 323	98.11 415	98.23 288	99.94 323	99.07 421	92.66 433	98.29 367	99.00 417	91.46 334	98.77 376	94.17 418	88.83 432	97.62 427

v10		96.14 375	95.50 381	98.07 335	98.19 412	97.96 314	99.83 354	99.07 421	92.10 436	98.07 378	98.94 423	91.07 342	98.61 390	92.41 439	89.82 419	97.63 425

testgi		96.18 369	95.93 360	96.93 393	98.98 365	94.20 428	100.00 1	99.07 421	97.16 223	96.06 439	99.86 304	84.08 439	97.79 456	90.38 454	97.80 295	98.81 331

v144192		96.40 357	95.81 365	98.17 327	97.89 424	98.11 300	99.99 258	99.06 429	93.39 418	98.75 325	99.09 406	90.43 362	98.66 383	93.10 432	90.55 415	97.75 350

PCF-MVS	98.23 3	98.69 210	98.37 240	99.62 163	99.78 166	99.02 210	99.23 447	99.06 429	96.43 310	98.08 377	100.00 1	94.72 268	99.95 182	98.16 314	99.91 145	99.90 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RPSCF		97.37 308	98.24 252	94.76 440	99.80 156	84.57 479	99.99 258	99.05 431	94.95 369	99.82 218	100.00 1	94.03 286	100.00 1	98.15 315	98.38 231	99.70 302

FPMVS		77.92 460	79.45 458	73.34 481	76.87 502	46.81 508	98.24 484	99.05 431	59.89 496	73.55 492	98.34 456	36.81 500	86.55 494	80.96 481	91.35 407	86.65 493

Gipuma		84.73 451	83.50 455	88.40 464	97.50 442	82.21 482	88.87 493	99.05 431	65.81 493	85.71 484	90.49 490	53.70 490	96.31 471	78.64 486	91.74 397	86.67 492
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		98.60 225	98.24 252	99.67 154	96.90 453	99.21 192	99.99 258	99.04 434	98.80 77	99.57 256	99.96 274	90.12 369	99.91 207	99.89 121	99.89 148	99.90 182

EGC-MVSNET		79.46 456	74.04 464	95.72 428	96.00 460	92.73 443	99.09 465	99.04 434	5.08 503	16.72 503	98.71 435	73.03 475	98.74 379	82.05 480	96.64 323	95.69 474

pm-mvs1		95.76 385	95.01 396	98.00 347	98.23 409	97.45 337	99.24 440	99.04 434	93.13 426	95.93 441	99.72 333	86.28 421	98.84 370	95.62 397	87.92 437	97.72 395

VortexMVS		98.23 264	98.11 260	98.59 291	99.56 244	99.37 172	99.95 315	99.03 437	96.47 308	98.69 327	99.55 374	95.91 235	98.66 383	99.01 270	94.80 358	97.73 388

ET-MVSNet_ETH3D		96.41 354	95.48 385	99.20 250	99.81 143	99.75 108	100.00 1	99.02 438	97.30 216	78.33 491	100.00 1	97.73 179	97.94 450	99.70 170	87.41 441	99.92 167

pmmvs6		93.64 414	92.87 417	95.94 426	97.47 446	91.41 454	98.92 471	99.02 438	87.84 463	95.01 447	99.61 364	77.24 465	98.77 376	94.33 416	86.41 450	97.63 425

our_test_3		96.51 349	96.35 342	96.98 390	97.61 436	95.05 394	99.98 290	99.01 440	94.68 378	96.77 426	99.06 408	95.87 237	98.14 432	91.81 441	92.37 387	97.75 350

test_fmvs1		98.37 251	98.04 268	99.34 218	99.84 130	98.07 304	100.00 1	99.00 441	98.85 66	100.00 1	100.00 1	85.11 431	99.96 169	99.69 179	99.88 151	100.00 1

CR-MVSNet		98.02 274	97.71 288	98.93 268	99.31 326	98.86 226	99.13 460	99.00 441	96.53 301	99.96 151	98.98 418	96.94 214	98.10 439	91.18 445	98.40 226	99.84 221

Patchmtry		96.81 333	96.37 341	98.14 329	99.31 326	98.55 249	98.91 472	99.00 441	90.45 448	97.92 388	98.98 418	96.94 214	98.12 434	94.27 417	91.53 402	97.75 350

test_vis1_n		96.69 341	95.81 365	99.32 231	99.14 339	97.98 311	99.97 299	98.98 444	98.45 100	100.00 1	100.00 1	66.44 486	99.99 106	99.78 148	99.57 188	100.00 1

test_fmvs1_n		97.43 305	96.86 320	99.15 252	99.68 186	97.48 336	99.99 258	98.98 444	98.82 72	100.00 1	100.00 1	74.85 472	99.96 169	99.67 184	99.70 175	100.00 1

Effi-MVS+-dtu		98.51 240	98.86 162	97.47 368	99.77 168	94.21 427	100.00 1	98.94 446	97.61 177	99.91 194	98.75 434	95.89 236	99.51 311	99.36 240	99.48 189	98.68 332

MDA-MVSNet-bldmvs		91.65 434	89.94 443	96.79 407	96.72 454	96.70 365	99.42 424	98.94 446	88.89 456	66.97 499	98.37 455	81.43 450	95.91 476	89.24 463	89.46 424	97.75 350

test_method		91.04 438	91.10 433	90.85 458	98.34 398	77.63 485	100.00 1	98.93 448	76.69 486	96.25 434	98.52 449	70.44 480	97.98 448	89.02 465	91.74 397	96.92 455

dmvs_testset		93.27 418	95.48 385	86.65 467	98.74 385	68.42 496	99.92 331	98.91 449	96.19 332	93.28 461	100.00 1	91.06 344	91.67 492	89.64 459	91.54 401	99.86 218

new_pmnet		94.11 407	93.47 409	96.04 425	96.60 456	92.82 441	99.97 299	98.91 449	90.21 451	95.26 444	98.05 463	85.89 426	98.14 432	84.28 475	92.01 392	97.16 449

APD_test1		93.07 421	94.14 402	89.85 461	99.18 337	72.49 489	99.76 376	98.90 451	92.86 431	96.35 431	99.94 287	75.56 470	99.91 207	86.73 470	97.98 279	97.15 450

FE-MVSNET2		91.15 436	90.00 442	94.58 441	90.74 489	92.52 447	99.56 407	98.87 452	90.82 445	88.96 476	95.40 479	76.26 469	95.56 479	87.84 467	81.59 469	95.66 476

test20.03		93.11 419	92.85 418	93.88 450	95.19 472	91.83 450	100.00 1	98.87 452	93.68 408	92.76 463	98.88 429	89.20 388	92.71 490	77.88 488	89.19 427	97.09 451

test_0402		94.35 401	93.70 406	96.32 419	97.92 422	93.60 431	99.61 402	98.85 454	88.19 462	94.68 450	99.48 383	80.01 454	98.58 397	89.39 461	95.15 349	96.77 457

Anonymous20240521		93.29 417	92.76 419	94.90 439	95.64 465	91.27 455	99.97 299	98.82 455	87.04 468	94.71 449	98.19 458	83.86 440	96.80 464	84.04 476	92.56 385	96.64 460

new-patchmatchnet		90.30 441	89.46 445	92.84 455	90.77 488	88.55 472	99.83 354	98.80 456	90.07 453	87.86 480	95.00 482	78.77 460	94.30 484	84.86 474	79.15 474	95.68 475

MVStest1		94.27 402	93.30 411	97.19 382	98.83 382	97.18 351	99.93 329	98.79 457	86.80 471	84.88 488	99.04 411	94.32 281	98.25 423	90.55 451	86.57 449	96.12 469

ttmdpeth		96.24 366	95.88 362	97.32 375	97.80 428	96.61 369	99.95 315	98.77 458	97.80 154	93.42 460	99.28 396	86.42 420	99.01 350	97.63 338	91.84 396	96.33 466

WB-MVS		88.24 446	90.09 440	82.68 474	91.56 486	69.51 494	100.00 1	98.73 459	90.72 447	87.29 482	98.12 459	92.87 315	85.01 496	62.19 496	89.34 425	93.54 484

tt0320-xc		91.69 433	90.50 437	95.26 431	98.04 417	90.12 464	98.60 481	98.70 460	76.63 487	94.66 451	99.52 378	68.57 484	97.99 447	94.61 412	85.18 452	97.66 416

SSC-MVS		87.61 447	89.47 444	82.04 475	90.63 490	68.77 495	99.99 258	98.66 461	90.34 450	86.70 483	98.08 460	92.72 320	84.12 497	59.41 499	88.71 433	93.22 488

usedtu_blend_shiyan5		92.75 423	91.39 428	96.82 404	95.22 469	94.40 422	99.05 470	98.64 462	75.98 490	98.54 341	98.56 443	90.48 357	98.31 417	96.31 379	69.73 484	97.75 350

mmtdpeth		94.58 399	94.18 401	95.81 427	98.82 384	91.09 457	99.99 258	98.61 463	96.38 317	100.00 1	97.23 469	76.52 467	99.85 238	99.82 139	80.22 472	96.48 462

pmmvs-eth3d		91.73 432	90.67 436	94.92 438	91.63 485	92.71 444	99.90 339	98.54 464	91.19 441	88.08 479	95.50 477	79.31 458	96.13 474	90.55 451	81.32 471	95.91 472

Anonymous20231206		93.45 416	93.17 412	94.30 445	95.00 475	89.69 467	99.98 290	98.43 465	93.30 422	94.50 454	98.59 440	90.52 355	95.73 478	77.46 490	90.73 414	97.48 440

USDC		95.90 383	95.70 373	96.50 413	98.60 390	92.56 446	100.00 1	98.30 466	97.77 157	96.92 418	99.94 287	81.25 452	99.45 324	93.54 427	94.96 357	97.49 437

SixPastTwentyTwo		95.71 387	95.49 383	96.38 416	97.42 447	93.01 438	99.84 352	98.23 467	94.75 373	95.98 440	99.97 256	85.35 430	98.43 409	94.71 411	93.17 374	97.69 409

MVP-Stereo		96.51 349	96.48 336	96.60 411	95.65 464	94.25 426	98.84 474	98.16 468	95.85 343	95.23 445	99.04 411	92.54 324	99.13 344	92.98 433	99.98 117	96.43 464
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		96.14 375	95.98 358	96.62 410	97.49 444	93.44 434	99.92 331	98.16 468	95.86 341	97.65 399	99.95 281	85.71 428	98.78 373	94.93 409	94.18 365	97.64 424

ITE_SJBPF				96.84 398	98.96 367	93.49 433		98.12 470	98.12 128	98.35 362	99.97 256	84.45 433	99.56 295	95.63 396	95.25 342	97.49 437

EG-PatchMatch MVS		92.94 422	92.49 426	94.29 446	95.87 461	87.07 475	99.07 468	98.11 471	93.19 424	88.98 475	98.66 438	70.89 479	99.08 346	92.43 438	95.21 345	96.72 458

pmmvs5		95.94 382	95.61 378	96.95 391	97.42 447	94.66 410	100.00 1	98.08 472	93.60 411	97.05 416	99.43 386	87.02 414	98.46 407	95.76 389	92.12 390	97.72 395

mvs5depth		93.81 408	93.00 415	96.23 421	94.25 479	93.33 436	97.43 489	98.07 473	93.47 415	94.15 457	99.58 368	77.52 463	98.97 356	93.64 425	88.92 429	96.39 465

LCM-MVSNet		79.01 459	76.93 462	85.27 469	78.28 501	68.01 497	96.57 490	98.03 474	55.10 497	82.03 490	93.27 487	31.99 502	93.95 486	82.72 477	74.37 479	93.84 482

OpenMVS_ROB	88.34 20	91.89 430	91.12 432	94.19 448	95.55 466	87.63 473	99.26 438	98.03 474	86.61 473	90.65 473	96.82 472	70.14 482	98.78 373	86.54 471	96.50 326	96.15 467

ambc				88.45 463	86.84 495	70.76 492	97.79 488	98.02 476		90.91 470	95.14 480	38.69 497	98.51 402	94.97 408	84.23 455	96.09 470

tmp_tt		75.80 461	74.26 463	80.43 476	52.91 508	53.67 507	87.42 495	97.98 477	61.80 495	67.04 498	100.00 1	76.43 468	96.40 470	96.47 374	28.26 500	91.23 490

TransMVSNet (Re)		94.78 398	93.72 405	97.93 353	98.34 398	97.88 320	99.23 447	97.98 477	91.60 438	94.55 452	99.71 335	87.89 405	98.36 414	89.30 462	84.92 453	97.56 433

LF4IMVS		96.19 368	96.18 349	96.23 421	98.26 407	92.09 449	100.00 1	97.89 479	97.82 152	97.94 386	99.87 302	82.71 445	99.38 330	97.41 347	93.71 368	97.20 448

Baseline_NR-MVSNet		96.16 373	95.70 373	97.56 367	98.28 406	96.79 363	100.00 1	97.86 480	91.93 437	97.63 400	99.47 384	92.14 329	98.35 415	97.13 355	86.83 447	97.54 434

test_fmvs2		95.17 397	95.23 392	95.01 434	98.95 369	88.99 470	99.99 258	97.77 481	97.79 155	98.58 338	99.70 338	73.36 474	99.34 334	95.88 386	95.03 353	96.70 459

testf1		84.40 452	84.79 453	83.23 472	95.71 462	58.71 505	98.79 475	97.75 482	81.58 480	84.94 486	98.07 461	45.33 495	97.73 457	77.09 491	83.85 457	93.24 486

APD_test2		84.40 452	84.79 453	83.23 472	95.71 462	58.71 505	98.79 475	97.75 482	81.58 480	84.94 486	98.07 461	45.33 495	97.73 457	77.09 491	83.85 457	93.24 486

TinyColmap		95.50 390	95.12 395	96.64 409	98.69 386	93.00 439	99.40 425	97.75 482	96.40 316	96.14 436	99.87 302	79.47 456	99.50 314	93.62 426	94.72 360	97.40 443

TDRefinement		91.93 429	90.48 438	96.27 420	81.60 499	92.65 445	99.10 463	97.61 485	93.96 402	93.77 458	99.85 309	80.03 453	99.53 308	97.82 332	70.59 482	96.63 461

usedtu_dtu_shiyan2		85.34 450	83.22 456	91.71 456	88.10 494	83.34 481	98.75 477	97.59 486	76.21 488	91.11 467	96.80 473	58.14 489	94.30 484	75.00 494	67.24 490	97.49 437

test_fmvs3		87.19 448	87.02 451	87.71 465	92.69 481	76.64 486	99.96 306	97.27 487	93.55 412	90.82 471	94.03 486	38.00 499	92.19 491	93.49 428	83.35 464	94.32 480

test_f		86.87 449	86.06 452	89.28 462	91.45 487	76.37 487	99.87 348	97.11 488	91.10 442	88.46 477	93.05 488	38.31 498	96.66 467	91.77 442	83.46 463	94.82 479

PMVS	60.66 23	65.98 466	65.05 467	68.75 484	55.06 507	38.40 509	88.19 494	96.98 489	48.30 501	44.82 502	88.52 493	12.22 506	86.49 495	67.58 495	83.79 459	81.35 497
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Patchmatch-RL test		93.49 415	93.63 407	93.05 453	91.78 483	83.41 480	98.21 485	96.95 490	91.58 439	91.05 468	97.64 467	99.40 67	95.83 477	94.11 421	81.95 467	99.91 171

FE-MVSNET		89.50 442	88.33 448	93.00 454	88.89 492	90.24 462	99.96 306	96.86 491	88.23 459	88.46 477	95.47 478	77.03 466	93.37 489	78.54 487	81.56 470	95.39 478

pmmvs3		90.62 440	89.36 446	94.40 443	90.53 491	91.49 453	100.00 1	96.73 492	84.21 477	93.65 459	96.65 474	82.56 447	94.83 481	82.28 479	77.62 477	96.89 456

PM-MVS		88.39 445	87.41 450	91.31 457	91.73 484	82.02 483	99.79 364	96.62 493	91.06 443	90.71 472	95.73 476	48.60 493	95.96 475	90.56 450	81.91 468	95.97 471

LCM-MVSNet-Re		96.52 347	97.21 309	94.44 442	99.27 332	85.80 476	99.85 351	96.61 494	95.98 336	92.75 464	98.48 450	93.97 289	97.55 460	99.58 209	98.43 224	99.98 127

mvsany_test3		89.36 444	88.96 447	90.56 459	91.95 482	78.97 484	99.74 379	96.59 495	96.84 254	89.25 474	96.07 475	52.59 491	97.11 462	95.17 406	82.44 465	95.58 477

door-mid								96.32 496

door								96.13 497

PMMVS2		79.15 458	77.28 461	84.76 470	82.34 498	72.66 488	99.70 390	95.11 498	71.68 492	84.78 489	90.87 489	32.05 501	89.99 493	75.53 493	63.45 494	91.64 489

test_vis1_rt		93.10 420	92.93 416	93.58 451	99.63 213	85.07 477	99.99 258	93.71 499	97.49 194	90.96 469	97.10 470	60.40 488	99.95 182	99.24 254	97.90 286	95.72 473

DSMNet-mixed		95.18 396	95.21 393	95.08 432	96.03 459	90.21 463	99.65 396	93.64 500	92.91 428	98.34 363	97.40 468	90.05 373	95.51 480	91.02 447	97.86 288	99.51 317

E-PMN		70.72 462	70.06 465	72.69 482	83.92 497	65.48 501	99.95 315	92.72 501	49.88 499	72.30 493	86.26 496	47.17 494	77.43 499	53.83 500	44.49 497	75.17 499

N_pmnet		91.88 431	93.37 410	87.40 466	97.24 451	66.33 499	99.90 339	91.05 502	89.77 454	95.65 443	98.58 442	90.05 373	98.11 436	85.39 472	92.72 380	97.75 350

MVE	68.59 21	67.22 464	64.68 468	74.84 478	74.67 504	62.32 503	95.84 491	90.87 503	50.98 498	58.72 500	81.05 500	12.20 507	78.95 498	61.06 498	56.75 495	83.24 496
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 465	63.44 469	73.88 480	61.14 505	63.45 502	95.68 492	87.18 504	79.93 482	47.35 501	80.68 501	22.35 504	72.33 503	61.24 497	35.42 499	85.88 494

testmvs		80.17 454	81.95 457	74.80 479	58.54 506	59.58 504	100.00 1	87.14 505	76.09 489	99.61 252	100.00 1	67.06 485	74.19 502	98.84 278	50.30 496	90.64 491

EMVS		69.88 463	69.09 466	72.24 483	84.70 496	65.82 500	99.96 306	87.08 506	49.82 500	71.51 494	84.74 497	49.30 492	75.32 500	50.97 501	43.71 498	75.59 498

test_vis3_rt		79.61 455	78.19 460	83.86 471	88.68 493	69.56 493	99.81 358	82.19 507	86.78 472	68.57 495	84.51 498	25.06 503	98.26 422	89.18 464	78.94 475	83.75 495

test123		79.44 457	79.23 459	80.05 477	80.03 500	71.72 490	100.00 1	77.93 508	62.52 494	94.81 448	99.69 341	78.21 461	74.53 501	92.57 435	27.33 501	93.90 481

wuyk23d		28.28 467	29.73 471	23.92 485	75.89 503	32.61 510	66.50 496	12.88 509	16.09 502	14.59 504	16.59 503	12.35 505	32.36 504	39.36 502	13.36 502	6.79 500

mmdepth		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

monomultidepth		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

test_blank		0.07 471	0.09 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.79 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet_test		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

DCPMVS		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

pcd_1.5k_mvsjas		8.24 470	10.99 473	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	98.75 139	0.00 505	0.00 503	0.00 503	0.00 501

sosnet-low-res		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

sosnet		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uncertanet		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

Regformer		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

n2								0.00 510

nn								0.00 510

ab-mvs-re		8.33 469	11.11 472	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	100.00 1	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet		0.01 472	0.02 475	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.14 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

WAC-MVS						97.98 311								95.74 390

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					0.00 509

eth-test					0.00 509

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 81				99.91 171

sam_mvs												99.33 70

test_post1							99.32 432				88.24 494	99.33 70	99.59 286	98.31 307

test_post											89.05 492	99.49 46	99.59 286

patchmatchnet-post											97.79 464	99.41 65	99.54 303

gm-plane-assit					99.52 265	97.26 348			95.86 341		100.00 1		99.43 326	98.76 283

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 52	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	99.67 184

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 350

segment_acmp												99.55 31

testdata1							100.00 1		98.77 84

plane_prior7					99.00 361	94.78 408

plane_prior6					99.06 351	94.80 404						88.58 400

plane_prior4											99.97 256

plane_prior3						94.79 407			99.03 25	99.08 299

plane_prior2							100.00 1		99.00 32

plane_prior1					99.02 355

plane_prior						94.80 404	100.00 1		99.03 25						95.58 328

HQP5-MVS						94.82 401

HQP-NCC					99.07 347		100.00 1		99.04 20	99.17 287

ACMP_Plane					99.07 347		100.00 1		99.04 20	99.17 287

BP-MVS														99.79 142

HQP4-MVS										99.17 287			99.57 291			97.77 337

HQP2-MVS												88.61 398

NP-MVS					99.07 347	94.81 403					99.97 256

MDTV_nov1_ep13_2view						99.24 188	99.56 407		96.31 324	99.96 151		98.86 130		98.92 274		99.89 190

ACMMP++_ref															94.58 363

ACMMP++															95.17 348

Test By Simon												99.10 98

High-resolution multi-view benchmark