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 83	99.38 84	99.75 140	99.89 122	99.51 151	99.45 437	100.00 1	98.38 106	99.83 218	100.00 1	98.86 132	99.81 255	99.25 263	98.78 210	99.94 155

test1111		98.42 257	98.12 270	99.29 248	99.88 124	98.15 307	99.46 435	100.00 1	98.36 110	99.42 279	100.00 1	87.91 416	99.79 260	99.31 260	98.78 210	99.94 155

ECVR-MVS		98.43 255	98.14 269	99.32 239	99.89 122	98.21 301	99.46 435	100.00 1	98.38 106	99.47 275	100.00 1	87.91 416	99.80 259	99.35 255	98.78 210	99.94 155

PVSNet_BlendedMVS		98.71 214	98.62 204	98.98 276	99.98 94	99.60 133	100.00 1	100.00 1	97.23 225	100.00 1	99.03 432	96.57 230	99.99 107	100.00 1	94.75 373	97.35 460

PVSNet_Blended		99.48 83	99.36 90	99.83 111	99.98 94	99.60 133	100.00 1	100.00 1	97.79 156	100.00 1	100.00 1	96.57 230	99.99 107	100.00 1	99.88 153	99.90 183

UGNet		98.41 259	98.11 271	99.31 241	99.54 252	98.55 257	99.18 469	100.00 1	98.64 92	99.79 235	99.04 429	87.61 421	100.00 1	99.30 261	99.89 150	99.40 333
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 75	99.40 82	99.95 62	99.81 145	99.93 54	100.00 1	100.00 1	97.98 138	99.84 215	100.00 1	98.94 125	99.98 142	99.86 129	98.21 271	99.94 155

HY-MVS	96.53 9	99.50 79	99.35 92	99.96 53	99.81 145	99.93 54	99.64 413	100.00 1	97.97 140	99.84 215	99.85 321	98.94 125	99.99 107	99.86 129	98.23 270	99.95 150

EPMVS		99.25 127	99.13 127	99.60 169	99.60 229	99.20 196	99.60 420	100.00 1	96.93 253	99.92 199	99.36 408	99.05 107	99.71 287	98.77 294	98.94 207	99.90 183

MVS_111021_HR		99.71 36	99.63 44	99.93 79	99.95 108	99.83 99	100.00 1	100.00 1	98.89 61	100.00 1	100.00 1	97.85 177	99.95 184	100.00 1	100.00 1	100.00 1

PatchMatch-RL		99.02 162	98.78 174	99.74 141	99.99 53	99.29 182	100.00 1	100.00 1	98.38 106	99.89 208	99.81 331	93.14 321	99.99 107	97.85 339	99.98 119	99.95 150

PVSNet_0	93.57 19	96.41 366	95.74 383	98.41 316	99.84 131	95.22 405	100.00 1	100.00 1	98.08 131	97.55 420	99.78 341	84.40 447	100.00 1	100.00 1	81.99 495	100.00 1

CHOSEN 1792x2688		99.00 165	98.91 161	99.25 256	99.90 120	97.79 338	100.00 1	99.99 13	98.79 81	98.28 381	100.00 1	93.63 304	99.95 184	99.66 195	99.95 129	100.00 1

HyFIR lowres test		99.32 111	99.24 109	99.58 175	99.95 108	99.26 187	100.00 1	99.99 13	96.72 286	99.29 293	99.91 308	99.49 46	99.47 331	99.74 160	98.08 285	100.00 1

AdaColmap		99.44 89	99.26 103	99.95 62	100.00 1	99.86 91	99.70 406	99.99 13	98.53 95	99.90 204	100.00 1	95.34 252	100.00 1	99.92 118	100.00 1	100.00 1

CHOSEN 280x420		99.85 5	99.87 1	99.80 124	99.99 53	99.97 28	99.97 313	99.98 16	98.96 40	100.00 1	100.00 1	99.96 4	99.42 341	100.00 1	100.00 1	100.00 1

MM		99.63 59	99.52 69	99.94 75	99.99 53	99.82 100	100.00 1	99.97 17	99.11 10	100.00 1	100.00 1	96.65 228	100.00 1	100.00 1	99.97 123	100.00 1

MVS_111021_LR		99.70 39	99.65 37	99.88 96	99.96 104	99.70 122	100.00 1	99.97 17	98.96 40	100.00 1	100.00 1	97.93 171	99.95 184	99.99 78	100.00 1	100.00 1

aaatest				99.99 13	100.00 1	99.98 19	100.00 1	99.95 19	99.10 12	99.99 130	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 19	100.00 1	99.95 19	99.18 6	99.99 130	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 19	100.00 1	99.95 19	99.18 6	100.00 1	100.00 1	99.45 53	99.99 107	99.68 184	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 19	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 31	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		99.33 109	99.18 122	99.78 134	99.82 139	99.49 157	100.00 1	99.95 19	97.36 209	99.63 261	100.00 1	96.45 234	99.95 184	99.79 144	99.65 184	99.89 191

MAR-MVS		99.49 81	99.36 90	99.89 91	99.97 98	99.66 127	99.74 395	99.95 19	97.89 147	100.00 1	100.00 1	96.71 227	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		99.25 127	99.01 142	99.95 62	99.81 145	99.87 88	100.00 1	99.94 27	97.13 233	99.83 218	99.96 283	97.01 212	100.00 1	99.59 214	97.85 302	99.98 128

tfpn200view9		99.26 123	99.03 138	99.96 53	99.81 145	99.89 79	100.00 1	99.94 27	97.23 225	99.83 218	99.96 283	97.04 208	100.00 1	99.59 214	97.85 302	99.98 128

MVS		99.22 131	98.96 151	99.98 29	99.00 375	99.95 39	99.24 459	99.94 27	98.14 125	98.88 327	100.00 1	95.63 248	100.00 1	99.85 132	100.00 1	100.00 1

thres600view7		99.24 130	99.00 145	99.95 62	99.81 145	99.87 88	100.00 1	99.94 27	97.13 233	99.83 218	99.96 283	97.01 212	100.00 1	99.54 227	97.77 311	99.97 138

thres400		99.26 123	99.03 138	99.95 62	99.81 145	99.89 79	100.00 1	99.94 27	97.23 225	99.83 218	99.96 283	97.04 208	100.00 1	99.59 214	97.85 302	99.97 138

thres200		99.27 121	99.04 137	99.96 53	99.81 145	99.90 72	100.00 1	99.94 27	97.31 218	99.83 218	99.96 283	97.04 208	100.00 1	99.62 207	97.88 300	99.98 128

API-MVS		99.72 32	99.70 24	99.79 129	99.97 98	99.37 174	99.96 321	99.94 27	98.48 99	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 79	99.39 83	99.82 113	100.00 1	99.45 163	100.00 1	99.94 27	96.38 329	100.00 1	100.00 1	98.18 163	100.00 1	100.00 1	100.00 1	100.00 1

1314		99.38 97	99.19 119	99.96 53	98.88 389	99.89 79	99.24 459	99.93 35	98.88 62	98.79 337	100.00 1	97.02 211	100.00 1	100.00 1	100.00 1	100.00 1

test0.0.03 1		98.12 279	98.03 280	98.39 317	99.11 356	98.07 314	100.00 1	99.93 35	96.70 292	96.91 435	99.95 291	99.31 76	98.19 449	91.93 454	98.44 226	98.91 342

QAPM		98.99 169	98.66 197	99.96 53	99.01 370	99.87 88	99.88 362	99.93 35	97.99 136	98.68 342	100.00 1	93.17 317	100.00 1	99.32 259	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 150	98.76 178	99.96 53	98.86 394	99.90 72	99.98 303	99.93 35	98.95 43	98.49 365	100.00 1	92.91 326	100.00 1	99.71 170	100.00 1	100.00 1

VDDNet		96.39 370	95.55 392	98.90 281	99.27 346	97.45 349	99.15 477	99.92 39	91.28 456	99.98 140	100.00 1	73.55 488	100.00 1	99.85 132	96.98 328	99.24 336

MGCNet		99.72 32	99.65 37	99.93 79	99.99 53	99.79 104	100.00 1	99.91 40	99.17 8	100.00 1	100.00 1	97.84 179	100.00 1	100.00 1	99.95 129	100.00 1

sss		99.45 87	99.34 94	99.80 124	99.76 171	99.50 153	100.00 1	99.91 40	97.72 161	99.98 140	99.94 297	98.45 155	100.00 1	99.53 230	98.75 213	99.89 191

EPNet		99.62 64	99.69 25	99.42 205	99.99 53	98.37 279	100.00 1	99.89 42	98.83 71	100.00 1	100.00 1	98.97 119	100.00 1	99.90 121	99.61 188	99.89 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	94.91 18	99.30 115	99.25 105	99.44 199	100.00 1	98.32 289	100.00 1	99.86 43	98.04 133	100.00 1	100.00 1	96.10 238	100.00 1	99.55 223	99.73 174	100.00 1

HPM-MVS++		99.82 12	99.76 15	99.99 13	99.99 53	99.98 19	100.00 1	99.83 44	98.88 62	99.96 154	100.00 1	99.21 89	100.00 1	100.00 1	100.00 1	99.99 125

D2MVS		97.63 305	97.83 291	97.05 397	98.83 397	94.60 428	100.00 1	99.82 45	96.89 259	98.28 381	99.03 432	94.05 291	99.47 331	98.58 309	94.97 370	97.09 466

旧先验1					99.99 53	99.88 86		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 31		99.81 47	97.89 147	100.00 1	100.00 1	99.20 90	100.00 1	97.91 337	100.00 1	100.00 1

ZD-MVS					100.00 1	99.98 19		99.80 48	97.31 218	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	97.98 138				100.00 1	99.33 258		100.00 1

ZNCC-MVS		99.71 36	99.62 47	99.97 41	99.99 53	99.90 72	100.00 1	99.79 50	97.97 140	99.97 146	100.00 1	98.97 119	100.00 1	99.94 115	100.00 1	100.00 1

GST-MVS		99.64 55	99.53 66	99.95 62	100.00 1	99.86 91	100.00 1	99.79 50	97.72 161	99.95 186	100.00 1	98.39 159	100.00 1	99.96 107	99.99 107	100.00 1

DELS-MVS		99.62 64	99.56 61	99.82 113	99.92 116	99.45 163	100.00 1	99.78 52	98.92 53	99.73 247	100.00 1	97.70 185	100.00 1	99.93 117	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 53	100.00 1	99.89 79	100.00 1	99.76 54	97.95 144	100.00 1	100.00 1	99.31 76	100.00 1	99.99 78	100.00 1	100.00 1

ACMMPR		99.74 28	99.67 33	99.96 53	100.00 1	99.89 79	100.00 1	99.76 54	97.95 144	100.00 1	100.00 1	99.29 82	100.00 1	99.99 78	100.00 1	100.00 1

OpenMVS	95.20 17	98.76 206	98.41 237	99.78 134	98.89 388	99.81 101	99.99 270	99.76 54	98.02 134	98.02 396	100.00 1	91.44 348	100.00 1	99.63 205	99.97 123	99.55 324

XVS		99.79 17	99.73 20	99.98 29	100.00 1	99.94 48	100.00 1	99.75 57	98.67 89	100.00 1	100.00 1	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 337	96.06 366	99.98 29	100.00 1	99.94 48	100.00 1	99.75 57	98.67 89	100.00 1	66.97 558	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 19	99.76 15	99.85 105	99.01 370	99.95 39	100.00 1	99.75 57	99.37 3	99.99 130	100.00 1	99.76 12	99.60 295	100.00 1	100.00 1	100.00 1

region2R		99.72 32	99.64 40	99.97 41	100.00 1	99.90 72	100.00 1	99.74 60	97.86 150	100.00 1	100.00 1	99.19 91	100.00 1	99.99 78	100.00 1	100.00 1

CANet		99.40 93	99.24 109	99.89 91	99.99 53	99.76 109	100.00 1	99.73 61	98.40 103	99.78 237	100.00 1	95.28 253	99.96 171	100.00 1	99.99 107	99.96 144

test_prior				99.90 88	100.00 1	99.75 110		99.73 61					99.97 151			100.00 1

testdata				99.66 158	99.99 53	98.97 223		99.73 61	97.96 143	100.00 1	100.00 1	99.42 64	100.00 1	99.28 262	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 53	99.57 56	99.89 91	99.99 53	99.66 127	99.75 394	99.73 61	98.16 122	99.75 241	100.00 1	98.90 130	100.00 1	99.96 107	99.88 153	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 19		99.69 67		100.00 1		99.45 53	100.00 1	100.00 1	100.00 1

test222					99.99 53	99.90 72	100.00 1	99.69 67	97.66 167	100.00 1	100.00 1	99.30 81			100.00 1	100.00 1

原ACMM1				99.93 79	100.00 1	99.80 103		99.66 69	98.18 121	100.00 1	100.00 1	99.43 60	100.00 1	99.50 234	100.00 1	100.00 1

SR-MVS		99.68 47	99.58 53	99.98 29	100.00 1	99.95 39	100.00 1	99.64 70	97.59 182	100.00 1	100.00 1	98.99 114	99.99 107	100.00 1	100.00 1	100.00 1

ab-mvs		98.42 257	98.02 281	99.61 166	99.71 178	99.00 219	99.10 483	99.64 70	96.70 292	99.04 316	99.81 331	90.64 366	99.98 142	99.64 198	97.93 297	99.84 223

testing3		98.44 254	98.37 250	98.65 298	99.51 277	98.32 289	100.00 1	99.62 72	96.43 322	97.93 400	99.99 245	99.11 98	97.81 475	94.88 422	97.80 308	99.82 233

PGM-MVS		99.69 42	99.61 48	99.95 62	99.99 53	99.85 95	100.00 1	99.58 73	97.69 165	100.00 1	100.00 1	99.44 56	100.00 1	99.79 144	100.00 1	100.00 1

Syy-MVS		96.17 383	96.57 343	95.00 449	99.50 286	87.37 490	100.00 1	99.57 74	96.23 341	98.07 391	100.00 1	92.41 339	97.81 475	85.34 495	97.96 293	99.82 233

myMVS_eth3d		98.52 249	98.51 226	98.53 306	99.50 286	97.98 321	100.00 1	99.57 74	96.23 341	98.07 391	100.00 1	99.09 100	97.81 475	96.17 394	97.96 293	99.82 233

LFMVS		97.42 318	96.62 341	99.81 118	99.80 158	99.50 153	99.16 475	99.56 76	94.48 402	100.00 1	100.00 1	79.35 471	100.00 1	99.89 123	97.37 321	99.94 155

PLC	98.56 2	99.70 39	99.74 19	99.58 175	100.00 1	98.79 237	100.00 1	99.54 77	98.58 94	99.96 154	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 155
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GG-mvs-BLEND				99.59 171	99.54 252	99.49 157	99.17 474	99.52 78		99.96 154	99.68 358	100.00 1	99.33 348	99.71 170	99.99 107	99.96 144

gg-mvs-nofinetune		96.95 342	96.10 364	99.50 185	99.41 323	99.36 177	99.07 488	99.52 78	83.69 500	99.96 154	83.60 543	100.00 1	99.20 355	99.68 184	99.99 107	99.96 144

DP-MVS Recon		99.76 21	99.69 25	99.98 29	100.00 1	99.95 39	100.00 1	99.52 78	97.99 136	99.99 130	100.00 1	99.72 14	100.00 1	99.96 107	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 31	99.73 400	99.52 78	99.06 16	100.00 1	100.00 1	98.80 139	100.00 1	99.95 113	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 97	99.21 114	99.91 84	99.73 176	99.92 61	100.00 1	99.51 82	97.61 178	100.00 1	100.00 1	99.06 105	99.93 201	99.83 136	97.12 324	99.90 183

3Dnovator+	95.58 15	99.03 156	98.71 189	99.96 53	98.99 378	99.89 79	100.00 1	99.51 82	98.96 40	98.32 378	100.00 1	92.78 328	100.00 1	99.87 128	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 53	99.55 63	99.97 41	99.99 53	99.91 65	100.00 1	99.48 84	97.54 186	100.00 1	100.00 1	98.97 119	99.99 107	99.98 93	100.00 1	100.00 1

lecture		99.64 55	99.53 66	99.98 29	99.99 53	99.93 54	100.00 1	99.47 85	98.53 95	100.00 1	100.00 1	97.88 175	100.00 1	99.98 93	99.92 142	100.00 1

SymmetryMVS		99.30 115	99.25 105	99.45 196	99.79 163	98.55 257	99.94 338	99.47 85	98.39 104	100.00 1	100.00 1	98.44 156	99.98 142	99.36 251	97.83 305	99.83 226

testing3-2		99.45 87	99.31 95	99.86 101	99.70 181	99.73 115	100.00 1	99.47 85	97.46 199	99.97 146	99.97 265	99.48 50	100.00 1	99.78 150	97.99 290	99.85 221

test_yl		99.51 76	99.37 87	99.95 62	99.82 139	99.90 72	100.00 1	99.47 85	97.48 197	100.00 1	100.00 1	99.80 6	100.00 1	99.98 93	97.75 312	99.94 155

Anonymous20240529		96.93 343	96.22 360	99.05 268	99.79 163	97.30 358	99.16 475	99.47 85	88.51 475	98.69 340	100.00 1	83.50 455	100.00 1	99.83 136	97.02 327	99.83 226

DCV-MVSNet		99.51 76	99.37 87	99.95 62	99.82 139	99.90 72	100.00 1	99.47 85	97.48 197	100.00 1	100.00 1	99.80 6	100.00 1	99.98 93	97.75 312	99.94 155

CVMVSNet		98.56 243	98.47 229	98.82 288	99.11 356	97.67 341	99.74 395	99.47 85	97.57 184	99.06 313	100.00 1	95.72 245	98.97 372	98.21 325	97.33 322	99.83 226

EU-MVSNet		96.63 355	96.53 344	96.94 404	97.59 462	96.87 373	99.76 392	99.47 85	96.35 334	96.85 437	99.78 341	92.57 336	96.27 496	95.33 414	91.08 425	97.68 426

VNet		99.04 153	98.75 179	99.90 88	99.81 145	99.75 110	99.50 432	99.47 85	98.36 110	100.00 1	99.99 245	94.66 276	100.00 1	99.90 121	97.09 325	99.96 144

VPA-MVSNet		97.03 338	96.43 350	98.82 288	98.64 403	99.32 179	99.38 445	99.47 85	96.73 284	98.91 326	98.94 442	87.00 428	99.40 342	99.23 266	89.59 440	97.76 352

tpmvs		98.59 236	98.38 248	99.23 257	99.69 184	97.90 329	99.31 453	99.47 85	94.52 400	99.68 252	99.28 412	97.64 188	99.89 222	97.71 347	98.17 277	99.89 191

EPNet_dtu		98.53 248	98.23 266	99.43 202	99.92 116	99.01 216	99.96 321	99.47 85	98.80 78	99.96 154	99.96 283	98.56 151	99.30 349	87.78 487	99.68 179	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 31	100.00 1	99.47 85	97.87 149	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 31	100.00 1	99.47 85	98.16 122	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	97.54 312	96.95 329	99.33 233	99.67 197	98.10 312	100.00 1	99.47 85	97.42 205	99.26 294	99.69 354	98.83 136	99.89 222	99.43 246	78.77 509	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		99.28 120	99.35 92	99.05 268	99.99 53	97.15 365	100.00 1	99.47 85	97.44 203	99.42 279	100.00 1	97.83 181	100.00 1	99.99 78	100.00 1	100.00 1

CNLPA		99.72 32	99.65 37	99.91 84	99.97 98	99.72 117	100.00 1	99.47 85	98.43 102	99.88 211	100.00 1	99.14 97	100.00 1	99.97 105	100.00 1	100.00 1

MG-MVS		99.75 26	99.68 31	99.97 41	100.00 1	99.91 65	99.98 303	99.47 85	99.09 13	100.00 1	100.00 1	98.59 150	100.00 1	99.95 113	100.00 1	100.00 1

dmvs_re		97.54 312	97.88 289	96.54 424	99.55 249	90.35 477	99.86 365	99.46 103	97.00 245	99.41 284	100.00 1	90.78 364	99.30 349	99.60 212	95.24 357	99.96 144

thisisatest0530		99.37 100	99.27 99	99.69 151	99.59 233	99.41 169	100.00 1	99.46 103	96.46 321	99.90 204	100.00 1	99.44 56	99.85 241	98.97 283	99.58 189	99.80 281

Anonymous202405211		97.87 291	97.53 303	98.90 281	99.81 145	96.70 378	99.35 448	99.46 103	92.98 443	98.83 334	99.99 245	90.63 367	100.00 1	99.70 174	97.03 326	100.00 1

tttt0517		99.34 106	99.23 112	99.67 155	99.57 242	99.38 171	100.00 1	99.46 103	96.33 336	99.89 208	100.00 1	99.44 56	99.84 245	98.93 285	99.46 193	99.78 292

thisisatest0515		99.42 91	99.31 95	99.74 141	99.59 233	99.55 141	100.00 1	99.46 103	96.65 299	99.92 199	100.00 1	99.44 56	99.85 241	99.09 278	99.63 187	99.81 250

baseline2		98.99 169	98.93 158	99.18 261	99.26 348	99.15 202	100.00 1	99.46 103	96.71 291	96.79 439	100.00 1	99.42 64	99.25 352	98.75 296	99.94 135	99.15 338

MDTV_nov1_ep13			98.94 156		99.53 256	98.36 282	99.39 444	99.46 103	96.54 311	99.99 130	99.63 372	98.92 128	99.86 234	98.30 322	98.71 214

LS3D		99.31 113	99.13 127	99.87 98	99.99 53	99.71 118	99.55 426	99.46 103	97.32 216	99.82 227	100.00 1	96.85 222	99.97 151	99.14 272	100.00 1	99.92 168

ETVMVS		99.16 139	98.98 148	99.69 151	99.67 197	99.56 139	100.00 1	99.45 111	96.36 332	99.98 140	99.95 291	98.65 146	99.64 293	99.11 276	97.63 319	99.88 204

SDMVSNet		98.49 252	98.08 275	99.73 144	99.82 139	99.53 146	99.99 270	99.45 111	97.62 174	99.38 287	99.86 316	90.06 385	99.88 230	99.92 118	96.61 337	99.79 287

sd_testset		97.81 296	97.48 304	98.79 292	99.82 139	96.80 375	99.32 450	99.45 111	97.62 174	99.38 287	99.86 316	85.56 442	99.77 268	99.72 166	96.61 337	99.79 287

tt0805		96.52 359	96.23 359	97.40 381	99.30 342	93.55 446	99.32 450	99.45 111	96.75 277	97.88 403	99.99 245	79.99 469	99.59 297	97.39 361	95.98 341	99.06 341

h-mvs33		97.03 338	96.53 344	98.51 307	99.79 163	95.90 392	99.45 437	99.45 111	98.21 118	100.00 1	99.78 341	97.49 196	99.99 107	99.72 166	74.92 512	99.65 320

tfpnnormal		96.36 371	95.69 388	98.37 319	98.55 407	98.71 244	99.69 408	99.45 111	93.16 441	96.69 443	99.71 348	88.44 415	98.99 369	94.17 431	91.38 422	97.41 457

SCA		98.30 267	97.98 283	99.23 257	99.41 323	98.25 297	99.99 270	99.45 111	96.91 256	99.76 240	99.58 382	89.65 394	99.54 316	98.31 319	98.79 209	99.91 172

UA-Net		99.06 150	98.83 168	99.74 141	99.52 270	99.40 170	99.08 486	99.45 111	97.64 171	99.83 218	100.00 1	95.80 243	99.94 197	98.35 317	99.80 172	99.88 204

VPNet		96.41 366	95.76 382	98.33 322	98.61 404	98.30 292	99.48 433	99.45 111	96.98 247	98.87 329	99.88 313	81.57 463	98.93 376	99.22 268	87.82 459	97.76 352

test-LLR		99.03 156	98.91 161	99.40 211	99.40 328	99.28 184	100.00 1	99.45 111	96.70 292	99.42 279	99.12 421	99.31 76	99.01 366	96.82 378	99.99 107	99.91 172

TESTMET0.1,1		99.08 146	98.96 151	99.44 199	99.63 217	99.38 171	100.00 1	99.45 111	95.53 367	99.48 272	100.00 1	99.71 15	99.02 364	96.84 377	99.99 107	99.91 172

test-mter		98.96 177	98.82 169	99.40 211	99.40 328	99.28 184	100.00 1	99.45 111	95.44 378	99.42 279	99.12 421	99.70 16	99.01 366	96.82 378	99.99 107	99.91 172

UniMVSNet_NR-MVSNet		97.16 330	96.80 334	98.22 333	98.38 414	98.41 271	100.00 1	99.45 111	96.14 347	97.76 407	99.64 368	95.05 263	98.50 422	97.98 333	86.84 469	97.75 363

PatchmatchNet		99.03 156	98.96 151	99.26 255	99.49 290	98.33 287	99.38 445	99.45 111	96.64 300	99.96 154	99.58 382	99.49 46	99.50 327	97.63 350	99.00 206	99.93 166
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KinetiMVS		98.61 233	98.26 259	99.65 160	99.46 307	99.24 191	99.96 321	99.44 125	97.54 186	99.99 130	99.99 245	90.83 363	99.95 184	97.18 366	99.92 142	99.75 296

dongtai		98.29 270	98.25 260	98.42 315	99.58 238	95.86 393	100.00 1	99.44 125	93.46 432	99.69 251	99.97 265	97.53 194	99.51 324	96.28 393	98.27 263	99.89 191

kuosan		98.55 244	98.53 220	98.62 300	99.66 206	96.16 388	100.00 1	99.44 125	93.93 419	99.81 233	99.98 253	97.58 189	99.81 255	98.08 328	98.28 260	99.89 191

EI-MVSNet-UG-set		99.69 42	99.63 44	99.87 98	99.99 53	99.64 129	99.95 330	99.44 125	98.35 112	100.00 1	100.00 1	98.98 117	99.97 151	99.98 93	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 40	99.87 98	100.00 1	99.64 129	99.98 303	99.44 125	98.35 112	99.99 130	100.00 1	99.04 110	99.96 171	99.98 93	100.00 1	100.00 1

APDe-MVS		99.84 9	99.78 10	99.99 13	100.00 1	99.98 19	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		98.70 217	98.51 226	99.28 251	99.51 277	98.39 275	99.24 459	99.44 125	95.52 369	99.96 154	99.70 351	97.57 191	99.58 301	97.11 368	98.54 220	99.88 204

baseline1		98.91 187	98.61 205	99.81 118	99.71 178	99.77 108	99.78 385	99.44 125	97.51 193	98.81 335	99.99 245	98.25 161	99.76 273	98.60 307	95.41 348	99.89 191

F-COLMAP		99.64 55	99.64 40	99.67 155	99.99 53	99.07 207	100.00 1	99.44 125	98.30 115	99.90 204	100.00 1	99.18 92	99.99 107	99.91 120	100.00 1	99.94 155

FBQ-MVS		99.13 142	99.11 130	99.21 259	99.64 214	97.94 326	100.00 1	99.43 134	96.78 270	99.97 146	99.92 304	99.03 113	99.84 245	99.18 271	98.01 288	99.86 219

myMVS_eth3d28		99.41 92	99.28 97	99.80 124	99.69 184	99.53 146	100.00 1	99.43 134	97.12 235	99.98 140	99.97 265	99.41 66	100.00 1	99.81 143	98.07 286	99.88 204

UWE-MVS-28		99.29 118	99.23 112	99.48 190	99.73 176	98.86 231	100.00 1	99.43 134	96.97 249	99.99 130	99.83 324	99.43 60	99.77 268	99.35 255	98.31 254	99.80 281

testing91		99.18 135	99.10 131	99.41 206	99.60 229	98.43 269	100.00 1	99.43 134	96.76 274	99.82 227	99.92 304	99.05 107	99.98 142	99.62 207	97.67 316	99.81 250

testing11		99.26 123	99.19 119	99.46 192	99.64 214	98.61 253	100.00 1	99.43 134	96.94 252	99.92 199	99.94 297	99.43 60	99.97 151	99.67 188	97.79 310	99.82 233

testing99		99.18 135	99.10 131	99.41 206	99.60 229	98.43 269	100.00 1	99.43 134	96.76 274	99.84 215	99.92 304	99.06 105	99.98 142	99.62 207	97.67 316	99.81 250

UBG		99.36 101	99.27 99	99.63 162	99.63 217	99.01 216	100.00 1	99.43 134	96.99 246	100.00 1	99.92 304	99.69 17	99.99 107	99.74 160	98.06 287	99.88 204

UWE-MVS		99.18 135	99.06 135	99.51 182	99.67 197	98.80 236	100.00 1	99.43 134	96.80 267	99.93 197	99.86 316	99.79 8	99.94 197	97.78 345	98.33 250	99.80 281

testing222		99.14 141	98.94 156	99.73 144	99.67 197	99.51 151	100.00 1	99.43 134	96.90 258	99.99 130	99.90 310	98.55 152	99.86 234	98.85 289	97.18 323	99.81 250

WB-MVSnew		97.02 340	97.24 319	96.37 429	99.44 317	97.36 353	100.00 1	99.43 134	96.12 348	99.35 289	99.89 311	93.60 306	98.42 429	88.91 484	98.39 232	93.33 517

FE-MVS		99.16 139	98.99 147	99.66 158	99.65 208	99.18 199	99.58 422	99.43 134	95.24 379	99.91 202	99.59 380	99.37 70	99.97 151	98.31 319	99.81 169	99.83 226

FA-MVS(test-final)		99.00 165	98.75 179	99.73 144	99.63 217	99.43 167	99.83 370	99.43 134	95.84 358	99.52 268	99.37 407	97.84 179	99.96 171	97.63 350	99.68 179	99.79 287

DPM-MVS		99.63 59	99.51 71	100.00 1	99.90 120	100.00 1	100.00 1	99.43 134	99.00 33	100.00 1	100.00 1	99.58 27	100.00 1	97.64 349	100.00 1	100.00 1

SMA-MVS		99.69 42	99.59 50	99.98 29	99.99 53	99.93 54	100.00 1	99.43 134	97.50 195	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 31	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		97.95 289	97.73 296	98.62 300	98.53 409	99.24 191	100.00 1	99.43 134	96.74 280	97.87 404	99.82 328	95.27 254	98.89 381	98.78 293	93.07 389	97.74 391

FC-MVSNet-test		97.84 294	97.63 302	98.45 311	98.30 421	99.05 210	100.00 1	99.43 134	96.63 304	97.61 416	99.82 328	95.19 259	98.57 417	98.64 302	93.05 390	97.73 403

WR-MVS_H		96.73 349	96.32 357	97.95 362	98.26 425	97.88 332	99.72 403	99.43 134	95.06 382	96.99 432	98.68 457	93.02 325	98.53 420	97.43 358	88.33 455	97.43 456

JIA-IIPM		97.09 333	96.34 355	99.36 221	98.88 389	98.59 255	99.81 374	99.43 134	84.81 497	99.96 154	90.34 529	98.55 152	99.52 322	97.00 371	98.28 260	99.98 128

EPP-MVSNet		99.10 145	99.00 145	99.40 211	99.51 277	98.68 247	99.92 346	99.43 134	95.47 373	99.65 260	100.00 1	99.51 39	99.76 273	99.53 230	98.00 289	99.75 296

reproduce_model		99.76 21	99.69 25	99.98 29	99.96 104	99.93 54	100.00 1	99.42 154	98.81 77	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 29	99.96 104	99.94 48	100.00 1	99.42 154	98.82 73	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 29	99.96 104	99.94 48	100.00 1	99.42 154	98.82 73	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 81	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 28	100.00 1	99.42 154	98.52 97	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 86	100.00 1	100.00 1	99.60 21

hse-mvs2		96.79 346	96.38 352	98.04 357	99.68 189	95.54 398	99.81 374	99.42 154	98.21 118	100.00 1	99.80 337	97.49 196	99.46 336	99.72 166	73.27 516	99.12 339

AUN-MVS		96.26 377	95.67 389	98.06 351	99.68 189	95.60 397	99.82 373	99.42 154	96.78 270	99.88 211	99.80 337	94.84 269	99.47 331	97.48 356	73.29 515	99.12 339

SR-MVS-dyc-post		99.63 59	99.52 69	99.97 41	99.99 53	99.91 65	100.00 1	99.42 154	97.62 174	100.00 1	100.00 1	98.65 146	99.99 107	99.99 78	100.00 1	100.00 1

RE-MVS-def			99.55 63		99.99 53	99.91 65	100.00 1	99.42 154	97.62 174	100.00 1	100.00 1	98.94 125		99.99 78	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 52	99.57 56	99.95 62	99.99 53	99.85 95	100.00 1	99.42 154	97.67 166	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 56		99.99 53		100.00 1	99.42 154	97.54 186	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 54	100.00 1	99.42 154	98.93 50

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 19	100.00 1	99.42 154	98.91 56	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 66	99.50 72	99.97 41	99.98 94	99.92 61	100.00 1	99.42 154	97.53 189	99.77 238	100.00 1	98.77 141	100.00 1	99.99 78	100.00 1	99.99 125
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.81 14	99.77 12	99.94 75	100.00 1	99.86 91	100.00 1	99.42 154	98.87 65	100.00 1	100.00 1	99.65 19	99.96 171	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 50	99.57 56	99.97 41	99.98 94	99.92 61	100.00 1	99.42 154	97.83 151	100.00 1	100.00 1	98.89 131	100.00 1	99.98 93	100.00 1	100.00 1

MTGPA								99.42 154

MTAPA		99.68 47	99.59 50	99.97 41	99.99 53	99.91 65	100.00 1	99.42 154	98.32 114	99.94 192	100.00 1	98.65 146	100.00 1	99.96 107	100.00 1	100.00 1

TEST9					100.00 1	99.95 39	100.00 1	99.42 154	97.65 169	100.00 1	100.00 1	99.53 35	99.97 151

train_agg		99.71 36	99.63 44	99.97 41	100.00 1	99.95 39	100.00 1	99.42 154	97.70 163	100.00 1	100.00 1	99.51 39	99.97 151	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 65	100.00 1	99.42 154	97.70 163	100.00 1	100.00 1	99.51 39	99.98 142

agg_prior					100.00 1	99.88 86		99.42 154		100.00 1			99.97 151

PS-MVSNAJ		99.64 55	99.57 56	99.85 105	99.78 168	99.81 101	99.95 330	99.42 154	98.38 106	100.00 1	100.00 1	98.75 142	100.00 1	99.88 125	99.99 107	99.74 303

SD-MVS		99.81 14	99.75 17	99.99 13	99.99 53	99.96 31	100.00 1	99.42 154	99.01 32	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 66	99.49 74	99.98 29	99.99 53	99.94 48	100.00 1	99.42 154	97.82 153	99.99 130	100.00 1	98.20 162	100.00 1	99.99 78	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 28	100.00 1	99.42 154	98.02 134	100.00 1	100.00 1	99.32 74	99.99 107	100.00 1	100.00 1	100.00 1

test12				99.95 62	99.99 53	99.89 79		99.42 154		100.00 1		99.24 87	99.97 151		100.00 1	100.00 1

UniMVSNet (Re)		97.29 326	96.85 333	98.59 303	98.49 410	99.13 203	100.00 1	99.42 154	96.52 316	98.24 387	98.90 445	94.93 266	98.89 381	97.54 354	87.61 460	97.75 363

mPP-MVS		99.69 42	99.60 49	99.97 41	100.00 1	99.91 65	100.00 1	99.42 154	97.91 146	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 69	99.49 74	99.91 84	99.99 53	99.78 105	100.00 1	99.42 154	97.09 236	100.00 1	100.00 1	98.95 123	99.96 171	99.98 93	100.00 1	100.00 1

HPM-MVS		99.59 70	99.50 72	99.89 91	100.00 1	99.70 122	100.00 1	99.42 154	97.46 199	100.00 1	100.00 1	98.60 149	99.96 171	99.99 78	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 47	99.58 53	99.97 41	99.99 53	99.96 31	100.00 1	99.42 154	97.53 189	100.00 1	100.00 1	99.27 85	99.97 151	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 94	99.25 105	99.81 118	99.97 98	99.48 161	100.00 1	99.42 154	95.53 367	100.00 1	100.00 1	98.37 160	99.95 184	99.97 105	100.00 1	100.00 1

CP-MVS		99.67 50	99.58 53	99.95 62	100.00 1	99.84 97	100.00 1	99.42 154	97.77 158	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 29	99.76 171	99.95 39	100.00 1	99.42 154	98.69 87	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		95.26 407	93.82 418	99.56 178	99.31 339	98.86 231	99.13 479	99.42 154	79.82 509	99.96 154	95.13 511	95.69 247	99.98 142	77.54 521	98.40 230	99.84 223

CPTT-MVS		99.49 81	99.38 84	99.85 105	100.00 1	99.54 144	100.00 1	99.42 154	97.58 183	99.98 140	100.00 1	97.43 202	100.00 1	99.99 78	100.00 1	100.00 1

DeepPCF-MVS	98.03 4	98.54 247	99.72 22	94.98 451	99.99 53	84.94 496	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 74	99.49 74	99.73 144	99.85 129	99.19 197	100.00 1	99.41 203	98.87 65	100.00 1	100.00 1	97.34 205	100.00 1	99.98 93	99.90 149	100.00 1

patch_mono-2		99.04 153	99.79 9	96.81 418	99.92 116	90.47 476	100.00 1	99.41 203	98.95 43	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 150

ADS-MVSNet2		98.28 272	98.51 226	97.62 376	99.51 277	95.03 409	99.24 459	99.41 203	95.52 369	99.96 154	99.70 351	97.57 191	97.94 472	97.11 368	98.54 220	99.88 204

Vis-MVSNet		98.52 249	98.25 260	99.34 225	99.68 189	98.55 257	99.68 410	99.41 203	97.34 213	99.94 192	100.00 1	90.38 376	99.70 289	99.03 280	98.84 208	99.76 295
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
0.3-1-1-0.015		97.60 306	97.19 322	98.83 287	99.13 354	96.55 383	100.00 1	99.40 207	94.19 412	99.83 218	99.81 331	99.18 92	99.97 151	99.70 174	83.50 488	99.98 128

0.4-1-1-0.1		97.56 309	97.15 326	98.79 292	99.01 370	96.44 386	100.00 1	99.40 207	94.11 415	99.81 233	99.81 331	99.09 100	99.97 151	99.65 197	83.48 490	99.98 128

0.4-1-1-0.2		97.60 306	97.18 323	98.86 285	99.05 367	96.62 381	100.00 1	99.40 207	94.24 407	99.82 227	99.81 331	99.09 100	99.97 151	99.70 174	83.50 488	99.98 128

xiu_mvs_v2_base		99.51 76	99.41 81	99.82 113	99.70 181	99.73 115	99.92 346	99.40 207	98.15 124	100.00 1	100.00 1	98.50 154	100.00 1	99.85 132	99.13 200	99.74 303

Anonymous20231211		96.29 375	95.70 385	98.07 347	99.80 158	97.49 347	99.15 477	99.40 207	89.11 472	97.75 410	99.45 401	88.93 405	98.98 370	98.26 324	89.47 443	97.73 403

AllTest		98.55 244	98.40 243	98.99 274	99.93 113	97.35 354	100.00 1	99.40 207	97.08 238	99.09 309	99.98 253	93.37 311	99.95 184	96.94 372	99.84 164	99.68 315

TestCases				98.99 274	99.93 113	97.35 354		99.40 207	97.08 238	99.09 309	99.98 253	93.37 311	99.95 184	96.94 372	99.84 164	99.68 315

HQP_MVS		97.71 301	97.82 292	97.37 383	99.00 375	94.80 418	100.00 1	99.40 207	99.00 33	99.08 311	99.97 265	88.58 413	99.55 313	99.79 144	95.57 346	97.76 352

plane_prior5								99.40 207					99.55 313	99.79 144	95.57 346	97.76 352

HQP3-MVS								99.40 207							95.58 342

DP-MVS		98.86 194	98.54 218	99.81 118	99.97 98	99.45 163	99.52 430	99.40 207	94.35 406	98.36 373	100.00 1	96.13 237	99.97 151	99.12 275	100.00 1	100.00 1

HQP-MVS		97.73 299	97.85 290	97.39 382	99.07 361	94.82 415	100.00 1	99.40 207	99.04 20	99.17 299	99.97 265	88.61 411	99.57 303	99.79 144	95.58 342	97.77 350

OMC-MVS		99.27 121	99.38 84	98.96 277	99.95 108	97.06 369	100.00 1	99.40 207	98.83 71	99.88 211	100.00 1	97.01 212	99.86 234	99.47 239	99.84 164	99.97 138

DeepC-MVS	97.84 5	99.00 165	98.80 172	99.60 169	99.93 113	99.03 212	100.00 1	99.40 207	98.61 93	99.33 290	100.00 1	92.23 340	99.95 184	99.74 160	99.96 127	99.83 226
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	97.64 302	97.37 311	98.45 311	99.94 111	95.70 396	100.00 1	99.40 207	97.65 169	99.53 267	100.00 1	99.31 76	99.66 291	80.48 511	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	98.29 270	98.17 268	98.65 298	99.94 111	97.39 351	99.30 454	99.40 207	95.64 362	97.75 410	100.00 1	92.69 333	99.95 184	98.89 287	99.92 142	98.62 347
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		98.99 169	98.69 193	99.89 91	99.70 181	99.69 124	100.00 1	99.39 223	98.93 50	100.00 1	100.00 1	90.20 377	99.99 107	100.00 1	99.95 129	100.00 1

cdsmvs_eth3d_5k		24.41 524	32.55 526	0.00 542	0.00 566	0.00 569	0.00 554	99.39 223	0.00 561	0.00 562	100.00 1	93.55 307	0.00 563	0.00 561	0.00 561	0.00 558

dp		98.72 210	98.61 205	99.03 271	99.53 256	97.39 351	99.45 437	99.39 223	95.62 364	99.94 192	99.52 392	98.83 136	99.82 251	96.77 383	98.42 228	99.89 191

nomal-1		98.99 169	99.02 141	98.88 283	99.47 298	97.25 363	100.00 1	99.38 226	96.38 329	99.90 204	99.94 297	98.78 140	99.56 307	99.40 250	97.94 296	99.83 226

fmvsm_s_conf0.5_n_8		99.34 106	99.14 126	99.91 84	99.83 136	99.74 113	100.00 1	99.38 226	98.94 46	100.00 1	100.00 1	94.25 289	99.99 107	100.00 1	99.91 147	100.00 1

fmvsm_s_conf0.5_n		99.21 132	99.01 142	99.83 111	99.84 131	99.53 146	100.00 1	99.38 226	98.29 116	100.00 1	100.00 1	93.62 305	99.99 107	99.99 78	99.93 139	99.98 128

VDD-MVS		96.58 358	95.99 369	98.34 321	99.52 270	95.33 403	99.18 469	99.38 226	96.64 300	99.77 238	100.00 1	72.51 492	100.00 1	100.00 1	96.94 329	99.70 313

CMPMVS	66.12 22	90.65 455	92.04 443	86.46 493	96.18 486	66.87 532	98.03 515	99.38 226	83.38 501	85.49 509	99.55 388	77.59 476	98.80 388	94.44 428	94.31 378	93.72 515
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DU-MVS		96.93 343	96.49 347	98.22 333	98.31 419	98.41 271	100.00 1	99.37 231	96.41 327	97.76 407	99.65 364	92.14 342	98.50 422	97.98 333	86.84 469	97.75 363

LPG-MVS_test		97.31 324	97.32 313	97.28 390	98.85 395	94.60 428	100.00 1	99.37 231	97.35 210	98.85 330	99.98 253	86.66 430	99.56 307	99.55 223	95.26 354	97.70 419

LGP-MVS_train				97.28 390	98.85 395	94.60 428		99.37 231	97.35 210	98.85 330	99.98 253	86.66 430	99.56 307	99.55 223	95.26 354	97.70 419

tpm2		98.64 223	98.58 212	98.81 291	99.42 321	97.12 366	99.69 408	99.37 231	93.63 426	99.94 192	99.67 359	98.96 122	99.47 331	98.62 306	97.95 295	99.83 226

CDS-MVSNet		98.96 177	98.95 155	99.01 273	99.48 293	98.36 282	99.93 344	99.37 231	96.79 268	99.31 292	99.83 324	99.77 11	98.91 378	98.07 330	97.98 291	99.77 293
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.90 189	98.63 201	99.70 150	99.92 116	99.25 189	100.00 1	99.37 231	95.71 360	99.40 285	100.00 1	96.58 229	99.95 184	96.80 380	99.94 135	99.91 172

fmvsm_l_conf0.5_n_a		99.63 59	99.55 63	99.86 101	99.83 136	99.58 137	100.00 1	99.36 237	98.98 36	100.00 1	100.00 1	97.85 177	99.99 107	100.00 1	99.94 135	100.00 1

fmvsm_l_conf0.5_n		99.63 59	99.56 61	99.86 101	99.81 145	99.59 135	100.00 1	99.36 237	98.98 36	100.00 1	100.00 1	97.92 172	99.99 107	100.00 1	99.95 129	100.00 1

mvsany_test1		99.57 71	99.48 77	99.85 105	99.86 127	99.54 144	100.00 1	99.36 237	98.94 46	100.00 1	100.00 1	97.97 169	100.00 1	99.88 125	99.28 196	100.00 1

xiu_mvs_v1_base_debu		99.35 102	99.21 114	99.79 129	99.67 197	99.71 118	99.78 385	99.36 237	98.13 126	100.00 1	100.00 1	97.00 215	100.00 1	99.83 136	99.07 202	99.66 317

xiu_mvs_v1_base		99.35 102	99.21 114	99.79 129	99.67 197	99.71 118	99.78 385	99.36 237	98.13 126	100.00 1	100.00 1	97.00 215	100.00 1	99.83 136	99.07 202	99.66 317

xiu_mvs_v1_base_debi		99.35 102	99.21 114	99.79 129	99.67 197	99.71 118	99.78 385	99.36 237	98.13 126	100.00 1	100.00 1	97.00 215	100.00 1	99.83 136	99.07 202	99.66 317

TSAR-MVS + GP.		99.61 66	99.69 25	99.35 223	99.99 53	98.06 316	100.00 1	99.36 237	99.83 2	100.00 1	100.00 1	98.95 123	99.99 107	100.00 1	99.11 201	100.00 1

XVG-OURS-SEG-HR		98.27 273	98.31 256	98.14 341	99.59 233	95.92 390	100.00 1	99.36 237	98.48 99	99.21 298	100.00 1	89.27 399	99.94 197	99.76 154	99.17 198	98.56 348

XVG-OURS		98.30 267	98.36 252	98.13 344	99.58 238	95.91 391	100.00 1	99.36 237	98.69 87	99.23 297	100.00 1	91.20 352	99.92 207	99.34 257	97.82 306	98.56 348

tpmrst		98.98 174	98.93 158	99.14 264	99.61 226	97.74 339	99.52 430	99.36 237	96.05 349	99.98 140	99.64 368	99.04 110	99.86 234	98.94 284	98.19 274	99.82 233

UnsupCasMVSNet_eth		94.25 417	93.89 417	95.34 444	97.63 458	92.13 462	99.73 400	99.36 237	94.88 385	92.78 479	98.63 459	82.72 457	96.53 492	94.57 426	84.73 479	97.36 459

fmvsm_l_mol_unc0.5_1		99.69 42	99.59 50	99.99 13	99.84 131	99.99 6	100.00 1	99.35 248	99.02 31	100.00 1	100.00 1	98.09 167	99.99 107	100.00 1	99.99 107	100.00 1

fmvsm_l_conf0.5_n_3		99.38 97	99.20 118	99.92 83	99.80 158	99.78 105	100.00 1	99.35 248	98.94 46	100.00 1	100.00 1	94.77 271	99.99 107	99.99 78	99.92 142	100.00 1

fmvsm_s_conf0.5_n_a		99.32 111	99.15 124	99.81 118	99.80 158	99.47 162	100.00 1	99.35 248	98.22 117	100.00 1	100.00 1	95.21 258	99.99 107	99.96 107	99.86 159	99.98 128

UniMVSNet_ETH3D		95.28 406	94.41 413	97.89 367	98.91 386	95.14 406	99.13 479	99.35 248	92.11 451	97.17 430	99.66 361	70.28 497	99.36 344	97.88 338	95.18 361	99.16 337

NR-MVSNet		96.63 355	96.04 367	98.38 318	98.31 419	98.98 221	99.22 468	99.35 248	95.87 353	94.43 471	99.65 364	92.73 331	98.40 430	96.78 381	88.05 456	97.75 363

TranMVSNet+NR-MVSNet		96.45 365	96.01 368	97.79 372	98.00 442	97.62 344	100.00 1	99.35 248	95.98 350	97.31 425	99.64 368	90.09 384	98.00 468	96.89 376	86.80 472	97.75 363

CostFormer		98.84 197	98.77 177	99.04 270	99.41 323	97.58 345	99.67 411	99.35 248	94.66 395	99.96 154	99.36 408	99.28 84	99.74 279	99.41 248	97.81 307	99.81 250

MIMVSNet		97.06 336	96.73 337	98.05 355	99.38 332	96.64 380	98.47 508	99.35 248	93.41 433	99.48 272	98.53 468	89.66 393	97.70 481	94.16 433	98.11 284	99.80 281

SD_0403		97.92 290	98.43 234	96.39 427	99.68 189	89.74 482	99.92 346	99.34 256	96.75 277	99.39 286	99.93 303	93.54 308	99.51 324	99.11 276	98.21 271	99.92 168

TAMVS		98.76 206	98.73 183	98.86 285	99.44 317	97.69 340	99.57 423	99.34 256	96.57 309	99.12 305	99.81 331	98.83 136	99.16 357	97.97 336	97.91 298	99.73 312

CLD-MVS		97.64 302	97.74 294	97.36 384	99.01 370	94.76 423	100.00 1	99.34 256	99.30 4	99.00 317	99.97 265	87.49 422	99.57 303	99.96 107	95.58 342	97.75 363
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
reproduce_monomvs		98.61 233	98.54 218	98.82 288	99.97 98	99.28 184	100.00 1	99.33 259	98.51 98	97.87 404	99.24 415	99.98 3	99.45 337	99.02 281	92.93 392	97.74 391

WR-MVS		97.09 333	96.64 339	98.46 310	98.43 412	99.09 205	99.97 313	99.33 259	95.62 364	97.76 407	99.67 359	91.17 353	98.56 419	98.49 311	89.28 446	97.74 391

ACMH	96.25 11	96.77 347	96.62 341	97.21 393	98.96 382	94.43 435	99.64 413	99.33 259	97.43 204	96.55 444	99.97 265	83.52 454	99.54 316	99.07 279	95.13 364	97.66 431
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_9		99.04 153	98.78 174	99.81 118	99.86 127	99.44 166	100.00 1	99.32 262	98.94 46	100.00 1	100.00 1	91.00 358	99.99 107	100.00 1	99.94 135	100.00 1

guyue		99.21 132	99.07 134	99.62 164	99.55 249	99.29 182	100.00 1	99.32 262	97.66 167	99.96 154	100.00 1	95.84 242	99.84 245	99.63 205	99.67 181	99.75 296

BP-MVS1		99.56 72	99.48 77	99.79 129	99.48 293	99.61 132	100.00 1	99.32 262	97.34 213	99.94 192	100.00 1	99.74 13	99.89 222	99.75 158	99.72 175	99.87 215

WBMVS		98.19 277	98.10 274	98.47 309	99.63 217	99.03 212	100.00 1	99.32 262	95.46 374	98.39 372	99.40 405	99.69 17	98.61 408	98.64 302	92.39 401	97.76 352

PS-CasMVS		96.34 373	95.78 381	98.03 358	98.18 433	98.27 295	99.71 404	99.32 262	94.75 389	96.82 438	99.65 364	86.98 429	98.15 451	97.74 346	88.85 451	97.66 431

CP-MVSNet		96.73 349	96.25 358	98.18 337	98.21 430	98.67 248	99.77 390	99.32 262	95.06 382	97.20 429	99.65 364	90.10 383	98.19 449	98.06 331	88.90 450	97.66 431

XVG-ACMP-BASELINE		96.60 357	96.52 346	96.84 410	98.41 413	93.29 451	99.99 270	99.32 262	97.76 160	98.51 363	99.29 411	81.95 462	99.54 316	98.40 314	95.03 367	97.68 426

PatchT		95.90 395	94.95 412	98.75 295	99.03 368	98.39 275	99.08 486	99.32 262	85.52 494	99.96 154	94.99 514	97.94 170	98.05 467	80.20 513	98.47 225	99.81 250

ACMP	97.00 8	97.19 328	97.16 325	97.27 392	98.97 381	94.58 431	100.00 1	99.32 262	97.97 140	97.45 422	99.98 253	85.79 440	99.56 307	99.70 174	95.24 357	97.67 430
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_7		98.98 174	98.85 167	99.37 219	99.67 197	98.34 286	100.00 1	99.31 271	98.97 38	100.00 1	100.00 1	91.70 346	99.97 151	99.99 78	99.97 123	99.80 281

fmvsm_s_conf0.5_n_6		99.30 115	99.12 129	99.84 110	99.24 349	99.56 139	100.00 1	99.31 271	98.90 60	100.00 1	100.00 1	94.75 273	99.97 151	99.98 93	99.88 153	100.00 1

test_fmvsmconf_n		99.56 72	99.46 80	99.86 101	99.68 189	99.58 137	100.00 1	99.31 271	98.92 53	99.88 211	100.00 1	97.35 204	99.99 107	99.98 93	99.99 107	100.00 1

test_cas_vis1_n_1920		98.63 229	98.25 260	99.77 137	99.69 184	99.32 179	100.00 1	99.31 271	98.84 69	99.96 154	100.00 1	87.42 423	99.99 107	99.14 272	99.86 159	100.00 1

Vis-MVSNet (Re-imp)		98.99 169	98.89 165	99.29 248	99.64 214	98.89 230	99.98 303	99.31 271	96.74 280	99.48 272	100.00 1	98.11 165	99.10 359	98.39 315	98.34 247	99.89 191

ACMH+	96.20 13	96.49 364	96.33 356	97.00 400	99.06 365	93.80 444	99.81 374	99.31 271	97.32 216	95.89 457	99.97 265	82.62 459	99.54 316	98.34 318	94.63 375	97.65 436

fmvsm_l_conf0.5_n_9		99.35 102	99.15 124	99.95 62	99.83 136	99.84 97	100.00 1	99.30 277	98.92 53	100.00 1	100.00 1	94.32 287	100.00 1	100.00 1	99.93 139	100.00 1

SSC-MVS3.2		95.32 404	94.97 411	96.37 429	98.29 423	92.75 456	100.00 1	99.30 277	95.46 374	98.36 373	99.42 403	78.92 473	98.63 405	93.28 444	91.72 414	97.72 410

MVSMamba_PlusPlus		99.39 94	99.25 105	99.80 124	99.68 189	99.59 135	99.99 270	99.30 277	96.66 297	99.96 154	99.97 265	97.89 174	99.92 207	99.76 154	100.00 1	99.90 183

sasdasda		99.03 156	98.73 183	99.94 75	99.75 173	99.95 39	100.00 1	99.30 277	97.64 171	100.00 1	100.00 1	95.22 256	99.97 151	99.76 154	96.90 330	99.91 172

MS-PatchMatch		95.66 400	95.87 375	95.05 447	97.80 450	89.25 484	98.88 494	99.30 277	96.35 334	96.86 436	99.01 434	81.35 465	99.43 339	93.30 442	99.98 119	96.46 484

canonicalmvs		99.03 156	98.73 183	99.94 75	99.75 173	99.95 39	100.00 1	99.30 277	97.64 171	100.00 1	100.00 1	95.22 256	99.97 151	99.76 154	96.90 330	99.91 172

balanced_ft_v1		98.70 217	98.61 205	98.94 278	99.67 197	96.90 371	99.91 353	99.30 277	96.73 284	99.96 154	99.97 265	92.18 341	99.93 201	99.86 129	99.95 129	100.00 1

tpm cat1		98.05 283	97.76 293	98.92 280	99.50 286	97.10 368	99.77 390	99.30 277	90.20 468	99.72 248	98.71 455	97.71 184	99.86 234	96.75 384	98.20 273	99.81 250

PMMVS		99.12 143	98.97 150	99.58 175	99.57 242	98.98 221	100.00 1	99.30 277	97.14 231	99.96 154	100.00 1	96.53 233	99.82 251	99.70 174	98.49 223	99.94 155

usedtu_dtu_shiyan1		97.34 322	96.97 327	98.43 313	97.82 448	98.91 228	100.00 1	99.29 286	94.70 392	98.46 367	98.89 446	93.95 298	98.64 403	95.86 400	93.75 380	97.74 391

FE-MVSNET3		97.34 322	96.97 327	98.43 313	97.82 448	98.91 228	100.00 1	99.29 286	94.70 392	98.46 367	98.89 446	93.95 298	98.64 403	95.88 398	93.75 380	97.74 391

MGCFI-Net		99.01 164	98.70 191	99.93 79	99.74 175	99.94 48	100.00 1	99.29 286	97.60 181	100.00 1	100.00 1	95.10 262	99.96 171	99.74 160	96.85 332	99.91 172

CANet_DTU		99.02 162	98.90 164	99.41 206	99.88 124	98.71 244	100.00 1	99.29 286	98.84 69	100.00 1	100.00 1	94.02 295	100.00 1	98.08 328	99.96 127	99.52 327

EI-MVSNet		97.98 286	97.93 285	98.16 340	99.11 356	97.84 335	99.74 395	99.29 286	94.39 405	98.65 345	100.00 1	97.21 206	98.88 384	97.62 353	95.31 352	97.75 363

PEN-MVS		96.01 392	95.48 397	97.58 378	97.74 453	97.26 360	99.90 355	99.29 286	94.55 398	96.79 439	99.55 388	87.38 424	97.84 474	96.92 375	87.24 466	97.65 436

MVSTER		98.58 238	98.52 221	98.77 294	99.65 208	99.68 125	100.00 1	99.29 286	95.63 363	98.65 345	99.80 337	99.78 9	98.88 384	98.59 308	95.31 352	97.73 403

XXY-MVS		97.14 332	96.63 340	98.67 297	98.65 402	98.92 227	99.54 428	99.29 286	95.57 366	97.63 413	99.83 324	87.79 420	99.35 346	98.39 315	92.95 391	97.75 363

gbinet_0.2-2-1-0.02		93.73 426	92.69 438	96.84 410	94.91 509	94.62 427	100.00 1	99.28 294	87.02 489	98.53 359	98.45 472	89.72 391	98.15 451	96.65 385	69.64 528	97.74 391

fmvsm_s_conf0.5_n_10		99.08 146	98.78 174	99.97 41	99.84 131	99.92 61	100.00 1	99.28 294	98.93 50	100.00 1	100.00 1	91.07 355	99.99 107	100.00 1	99.95 129	100.00 1

NormalMVS		99.47 85	99.48 77	99.43 202	99.99 53	98.55 257	99.94 338	99.28 294	98.39 104	100.00 1	100.00 1	98.44 156	99.98 142	99.36 251	99.92 142	99.75 296

Elysia		98.12 279	97.72 297	99.34 225	99.30 342	98.96 224	99.95 330	99.28 294	96.64 300	99.75 241	99.99 245	88.71 408	99.81 255	95.99 396	99.84 164	99.26 334

StellarMVS		98.12 279	97.72 297	99.34 225	99.30 342	98.96 224	99.95 330	99.28 294	96.64 300	99.75 241	99.99 245	88.71 408	99.81 255	95.99 396	99.84 164	99.26 334

BridgeMVS		99.43 90	99.28 97	99.85 105	99.68 189	99.68 125	99.97 313	99.28 294	97.03 243	99.96 154	99.97 265	97.90 173	99.93 201	99.77 152	100.00 1	99.94 155

cl22		98.23 275	98.11 271	98.58 305	99.82 139	99.01 216	100.00 1	99.28 294	96.92 255	98.33 377	99.21 418	98.09 167	98.97 372	98.72 297	92.61 396	97.76 352

OPM-MVS		97.21 327	97.18 323	97.32 387	98.08 437	94.66 424	100.00 1	99.28 294	98.65 91	98.92 324	99.98 253	86.03 438	99.56 307	98.28 323	95.41 348	97.72 410
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvsmamba		99.05 152	98.98 148	99.27 254	99.57 242	98.10 312	100.00 1	99.28 294	95.92 352	99.96 154	99.97 265	96.73 226	99.89 222	99.72 166	99.65 184	99.81 250

BH-w/o		98.82 199	98.81 171	98.88 283	99.62 224	96.71 377	100.00 1	99.28 294	97.09 236	98.81 335	100.00 1	94.91 267	99.96 171	99.54 227	100.00 1	99.96 144

BH-untuned		98.64 223	98.65 198	98.60 302	99.59 233	96.17 387	100.00 1	99.28 294	96.67 296	98.41 370	100.00 1	94.52 280	99.83 248	99.41 248	100.00 1	99.81 250

UnsupCasMVSNet_bld		89.50 460	88.00 467	93.99 463	95.30 500	88.86 487	98.52 507	99.28 294	85.50 495	87.80 500	94.11 517	61.63 505	96.96 486	90.63 464	79.26 505	96.15 491

FMVSNet3		97.30 325	96.95 329	98.37 319	99.65 208	99.25 189	99.71 404	99.28 294	94.23 408	98.53 359	98.91 444	93.30 313	98.11 457	95.31 415	93.60 383	97.73 403

BH-RMVSNet		98.46 253	98.08 275	99.59 171	99.61 226	99.19 197	100.00 1	99.28 294	97.06 240	98.95 320	100.00 1	88.99 403	99.82 251	98.83 292	100.00 1	99.77 293

LTVRE_ROB	95.29 16	96.32 374	96.10 364	96.99 401	98.55 407	93.88 443	99.45 437	99.28 294	94.50 401	96.46 445	99.52 392	84.86 445	99.48 329	97.26 365	95.03 367	97.59 446
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		98.77 203	98.42 235	99.82 113	99.47 298	99.52 150	100.00 1	99.27 309	97.53 189	100.00 1	100.00 1	89.73 390	99.96 171	99.84 135	99.93 139	99.97 138

EIA-MVS		99.26 123	99.19 119	99.45 196	99.63 217	98.75 239	100.00 1	99.27 309	96.93 253	99.95 186	100.00 1	97.47 198	99.79 260	99.74 160	99.72 175	99.82 233

GBi-Net		96.07 389	95.80 379	96.89 407	99.53 256	94.87 412	99.18 469	99.27 309	93.71 421	98.53 359	98.81 451	84.23 449	98.07 463	95.31 415	93.60 383	97.72 410

test1		96.07 389	95.80 379	96.89 407	99.53 256	94.87 412	99.18 469	99.27 309	93.71 421	98.53 359	98.81 451	84.23 449	98.07 463	95.31 415	93.60 383	97.72 410

FMVSNet2		96.22 379	95.60 391	98.06 351	99.53 256	98.33 287	99.45 437	99.27 309	93.71 421	98.03 394	98.84 449	84.23 449	98.10 461	93.97 435	93.40 386	97.73 403

FMVSNet1		94.45 414	93.63 421	96.89 407	98.87 392	94.87 412	99.18 469	99.27 309	90.95 460	97.31 425	98.81 451	72.89 491	98.07 463	92.61 448	92.81 393	97.72 410

fmvsm_s_conf0.5_n_5		99.00 165	98.70 191	99.88 96	99.81 145	99.64 129	100.00 1	99.26 315	98.78 84	99.97 146	100.00 1	90.65 365	99.99 107	100.00 1	99.89 150	99.99 125

fmvsm_s_conf0.5_n_4		98.98 174	98.74 181	99.68 154	99.81 145	99.50 153	100.00 1	99.26 315	98.91 56	100.00 1	100.00 1	90.87 362	99.97 151	99.99 78	99.81 169	99.57 323

fmvsm_s_conf0.1_n_a		98.71 214	98.36 252	99.78 134	99.09 359	99.42 168	100.00 1	99.26 315	97.42 205	100.00 1	100.00 1	89.78 388	99.96 171	99.82 141	99.85 162	99.97 138

test_fmvsmvis_n_1920		99.46 86	99.37 87	99.73 144	98.88 389	99.18 199	100.00 1	99.26 315	98.85 67	99.79 235	100.00 1	97.70 185	100.00 1	99.98 93	99.86 159	100.00 1

PRO-TEST		99.18 135	99.03 138	99.61 166	99.71 178	99.37 174	100.00 1	99.25 319	97.51 193	99.96 154	100.00 1	95.41 251	99.66 291	99.75 158	99.69 178	99.82 233

fmvsm_s_conf0.5_n_2		98.90 189	98.57 213	99.90 88	99.79 163	99.78 105	100.00 1	99.25 319	98.97 38	100.00 1	100.00 1	89.22 400	99.99 107	100.00 1	99.88 153	99.92 168

test_vis1_n_1920		97.77 298	97.24 319	99.34 225	99.79 163	98.04 318	100.00 1	99.25 319	98.88 62	100.00 1	100.00 1	77.52 477	100.00 1	99.88 125	99.85 162	100.00 1

CL-MVSNet_self_test		91.07 453	90.35 455	93.24 468	93.27 513	89.16 485	99.55 426	99.25 319	92.34 450	95.23 461	97.05 495	88.86 407	93.59 514	80.67 510	66.95 536	96.96 471

ETV-MVS		99.34 106	99.24 109	99.64 161	99.58 238	99.33 178	100.00 1	99.25 319	97.57 184	99.96 154	100.00 1	97.44 201	99.79 260	99.70 174	99.65 184	99.81 250

DTE-MVSNet		95.52 401	94.99 410	97.08 396	97.49 468	96.45 385	100.00 1	99.25 319	93.82 420	96.17 450	99.57 386	87.81 419	97.18 484	94.57 426	86.26 475	97.62 442

ACMM	97.17 6	97.37 320	97.40 309	97.29 389	99.01 370	94.64 426	100.00 1	99.25 319	98.07 132	98.44 369	99.98 253	87.38 424	99.55 313	99.25 263	95.19 360	97.69 424
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_11		98.92 184	98.63 201	99.80 124	99.85 129	99.86 91	100.00 1	99.24 326	98.91 56	100.00 1	100.00 1	89.69 392	99.99 107	100.00 1	99.98 119	99.54 325

ppachtmachnet_test		96.17 383	95.89 373	97.02 399	97.61 460	95.24 404	99.99 270	99.24 326	93.31 437	96.71 442	99.62 376	94.34 286	98.07 463	89.87 473	92.30 404	97.75 363

GA-MVS		97.72 300	97.27 317	99.06 266	99.24 349	97.93 328	100.00 1	99.24 326	95.80 359	98.99 318	99.64 368	89.77 389	99.36 344	95.12 419	97.62 320	99.89 191

FMVSNet5		95.32 404	95.43 400	94.99 450	99.39 331	92.99 454	99.25 458	99.24 326	90.45 464	97.44 423	98.45 472	95.78 244	94.39 508	87.02 489	91.88 410	97.59 446

IS-MVSNet		99.08 146	98.91 161	99.59 171	99.65 208	99.38 171	99.78 385	99.24 326	96.70 292	99.51 269	100.00 1	98.44 156	99.52 322	98.47 312	98.39 232	99.88 204

AstraMVS		99.03 156	99.01 142	99.09 265	99.46 307	97.66 342	100.00 1	99.23 331	97.83 151	99.95 186	100.00 1	95.52 250	99.86 234	99.74 160	99.39 195	99.74 303

fmvsm_s_conf0.1_n_2		98.95 180	98.69 193	99.73 144	99.61 226	99.74 113	100.00 1	99.23 331	98.95 43	99.97 146	100.00 1	90.92 361	99.97 151	100.00 1	99.58 189	99.47 330

test_fmvsmconf0.1_n		99.25 127	99.05 136	99.82 113	98.92 385	99.55 141	100.00 1	99.23 331	98.91 56	99.75 241	99.97 265	94.79 270	99.94 197	99.94 115	99.99 107	99.97 138

PS-MVSNAJss		98.03 284	98.06 278	97.94 363	97.63 458	97.33 357	99.89 359	99.23 331	96.27 339	98.03 394	99.59 380	98.75 142	98.78 389	98.52 310	94.61 376	97.70 419

K. test v3		95.46 403	95.14 407	96.40 426	97.53 465	93.40 449	99.99 270	99.23 331	95.49 372	92.70 482	99.73 345	84.26 448	98.12 455	93.94 436	93.38 387	97.68 426

wanda-best-256-512		93.76 423	92.74 435	96.84 410	95.22 501	94.54 432	100.00 1	99.22 336	87.22 483	98.54 354	98.56 463	90.48 370	98.22 446	95.67 405	69.73 524	97.75 363

FE-blended-shiyan7		93.76 423	92.74 435	96.84 410	95.22 501	94.54 432	100.00 1	99.22 336	87.22 483	98.54 354	98.56 463	90.48 370	98.22 446	95.67 405	69.73 524	97.75 363

blended_shiyan6		93.70 428	92.67 440	96.78 420	95.17 505	94.38 439	100.00 1	99.22 336	87.03 488	98.54 354	98.56 463	90.14 379	98.22 446	95.62 409	69.73 524	97.75 363

blend_shiyan4		95.76 397	95.40 403	96.82 416	95.50 499	94.40 436	100.00 1	99.22 336	87.12 485	98.67 343	98.59 460	99.09 100	98.31 437	96.31 391	84.14 483	97.75 363

E3new		98.95 180	98.80 172	99.41 206	99.57 242	98.50 266	100.00 1	99.22 336	96.84 263	99.89 208	100.00 1	95.70 246	99.93 201	99.57 220	98.39 232	99.82 233

E2		98.77 203	98.57 213	99.37 219	99.53 256	98.38 278	99.98 303	99.22 336	96.77 273	99.75 241	100.00 1	94.03 293	99.91 209	99.53 230	98.35 243	99.82 233

E3		98.77 203	98.57 213	99.36 221	99.47 298	98.36 282	99.98 303	99.22 336	96.76 274	99.75 241	100.00 1	94.10 290	99.91 209	99.53 230	98.35 243	99.82 233

viewdifsd2359ckpt13		98.72 210	98.52 221	99.34 225	99.55 249	98.46 268	99.99 270	99.22 336	96.50 319	99.05 314	100.00 1	94.54 279	99.73 283	99.46 242	98.35 243	99.81 250

viewcassd2359sk11		98.90 189	98.73 183	99.40 211	99.57 242	98.47 267	99.99 270	99.22 336	96.79 268	99.82 227	100.00 1	95.24 255	99.91 209	99.54 227	98.38 236	99.82 233

viewdifsd2359ckpt11		97.98 286	97.89 286	98.26 329	99.47 298	94.98 411	99.99 270	99.22 336	96.74 280	99.24 295	100.00 1	90.14 379	99.90 220	99.49 236	96.73 333	99.90 183

viewmsd2359difaftdt		97.98 286	97.89 286	98.27 326	99.47 298	94.99 410	99.99 270	99.22 336	96.74 280	99.24 295	100.00 1	90.14 379	99.90 220	99.49 236	96.73 333	99.90 183

viewmanbaseed2359cas		98.86 194	98.68 195	99.40 211	99.51 277	98.51 265	99.98 303	99.22 336	97.05 241	99.72 248	100.00 1	94.77 271	99.89 222	99.58 217	98.31 254	99.81 250

LuminaMVS		99.07 149	98.92 160	99.50 185	98.87 392	99.12 204	99.92 346	99.22 336	97.45 201	99.82 227	99.98 253	96.29 236	99.85 241	99.71 170	99.05 205	99.52 327

miper_enhance_ethall		98.33 265	98.27 258	98.51 307	99.66 206	99.04 211	100.00 1	99.22 336	97.53 189	98.51 363	99.38 406	99.49 46	98.75 394	98.02 332	92.61 396	97.76 352

nrg030		97.64 302	97.27 317	98.75 295	98.34 415	99.53 146	100.00 1	99.22 336	96.21 345	98.27 383	99.95 291	94.40 283	98.98 370	99.23 266	89.78 439	97.75 363

lessismore_v0				96.05 436	97.55 464	91.80 465		99.22 336		91.87 483	99.91 308	83.50 455	98.68 397	92.48 451	90.42 435	97.68 426

casdiffmvs_mvg		98.64 223	98.39 246	99.40 211	99.50 286	98.60 254	100.00 1	99.22 336	96.85 261	99.10 307	100.00 1	92.75 329	99.78 266	99.71 170	98.35 243	99.81 250
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		98.43 255	98.07 277	99.50 185	99.65 208	99.02 214	100.00 1	99.22 336	94.21 410	99.72 248	99.98 253	92.03 344	99.93 201	99.68 184	98.12 283	99.54 325

MIMVSNet1		91.96 443	91.20 447	94.23 461	94.94 508	91.69 466	99.34 449	99.22 336	88.23 476	94.18 472	98.45 472	75.52 485	93.41 516	79.37 514	91.49 419	97.60 445

E5new		98.63 229	98.41 237	99.31 241	99.51 277	98.21 301	99.79 380	99.21 355	96.62 305	99.67 258	100.00 1	93.15 319	99.91 209	99.46 242	98.26 265	99.81 250

E6new		98.64 223	98.41 237	99.30 245	99.46 307	98.19 304	99.79 380	99.21 355	96.62 305	99.68 252	100.00 1	93.24 315	99.91 209	99.47 239	98.26 265	99.81 250

E6		98.64 223	98.41 237	99.30 245	99.46 307	98.19 304	99.79 380	99.21 355	96.62 305	99.68 252	100.00 1	93.24 315	99.91 209	99.47 239	98.26 265	99.81 250

E5		98.63 229	98.41 237	99.31 241	99.51 277	98.21 301	99.79 380	99.21 355	96.62 305	99.67 258	100.00 1	93.15 319	99.91 209	99.46 242	98.26 265	99.81 250

E4		98.68 221	98.46 231	99.33 233	99.51 277	98.27 295	99.96 321	99.21 355	96.66 297	99.68 252	100.00 1	93.38 310	99.91 209	99.49 236	98.27 263	99.81 250

viewmacassd2359aftdt		98.57 240	98.31 256	99.33 233	99.49 290	98.31 291	99.89 359	99.21 355	96.87 260	99.10 307	100.00 1	92.48 338	99.88 230	99.50 234	98.28 260	99.81 250

Patchmatch-test		97.83 295	97.42 307	99.06 266	99.08 360	97.66 342	98.66 502	99.21 355	93.65 425	98.25 385	99.58 382	99.47 51	99.57 303	90.25 470	98.59 218	99.95 150

mvs_anonymous		98.80 201	98.60 208	99.38 218	99.57 242	99.24 191	100.00 1	99.21 355	95.87 353	98.92 324	99.82 328	96.39 235	99.03 363	99.13 274	98.50 222	99.88 204

EC-MVSNet		99.19 134	99.09 133	99.48 190	99.42 321	99.07 207	100.00 1	99.21 355	96.95 251	99.96 154	100.00 1	96.88 221	99.48 329	99.64 198	99.79 173	99.88 204

TR-MVS		98.14 278	97.74 294	99.33 233	99.59 233	98.28 293	99.27 455	99.21 355	96.42 326	99.15 303	99.94 297	88.87 406	99.79 260	98.88 288	98.29 257	99.93 166

onestephybrid01		98.89 192	98.67 196	99.56 178	99.51 277	99.08 206	100.00 1	99.20 365	97.30 220	99.95 186	100.00 1	94.04 292	99.79 260	99.77 152	98.29 257	99.81 250

blended_shiyan8		93.73 426	92.69 438	96.84 410	95.17 505	94.40 436	100.00 1	99.20 365	87.05 486	98.60 349	98.54 467	90.15 378	98.39 431	95.54 412	69.93 523	97.74 391

viewdifsd2359ckpt07		98.72 210	98.52 221	99.34 225	99.47 298	98.28 293	99.99 270	99.20 365	96.98 247	99.60 263	100.00 1	93.45 309	99.93 201	99.58 217	98.36 241	99.82 233

viewdifsd2359ckpt09		98.78 202	98.60 208	99.31 241	99.53 256	98.37 279	100.00 1	99.20 365	96.85 261	99.32 291	100.00 1	94.68 275	99.74 279	99.46 242	98.36 241	99.81 250

viewmamba		98.92 184	98.74 181	99.46 192	99.46 307	98.83 234	100.00 1	99.19 369	97.18 228	99.95 186	100.00 1	94.97 265	99.74 279	99.64 198	98.29 257	99.81 250

hybridnocas07		98.85 196	98.63 201	99.53 181	99.52 270	98.95 226	100.00 1	99.19 369	97.15 230	99.93 197	100.00 1	93.83 301	99.82 251	99.67 188	98.38 236	99.82 233

hybrid		98.81 200	98.60 208	99.45 196	99.52 270	98.74 242	100.00 1	99.19 369	97.04 242	99.95 186	100.00 1	93.89 300	99.78 266	99.64 198	98.19 274	99.81 250

GeoE		98.06 282	97.65 301	99.29 248	99.47 298	98.41 271	100.00 1	99.19 369	94.85 386	98.88 327	100.00 1	91.21 351	99.59 297	97.02 370	98.19 274	99.88 204

KD-MVS_2432*1600		94.15 418	93.08 428	97.35 385	99.53 256	97.83 336	99.63 415	99.19 369	92.88 445	96.29 447	97.68 489	98.84 134	96.70 488	89.73 474	63.92 537	97.53 450

miper_refine_blended		94.15 418	93.08 428	97.35 385	99.53 256	97.83 336	99.63 415	99.19 369	92.88 445	96.29 447	97.68 489	98.84 134	96.70 488	89.73 474	63.92 537	97.53 450

SPE-MVS-test		99.31 113	99.27 99	99.43 202	99.99 53	98.77 238	100.00 1	99.19 369	97.24 223	99.96 154	100.00 1	97.56 193	99.70 289	99.68 184	99.81 169	99.82 233

jajsoiax		97.07 335	96.79 336	97.89 367	97.28 476	97.12 366	99.95 330	99.19 369	96.55 310	97.31 425	99.69 354	87.35 426	98.91 378	98.70 298	95.12 365	97.66 431

MVS_Test		98.93 183	98.65 198	99.77 137	99.62 224	99.50 153	99.99 270	99.19 369	95.52 369	99.96 154	99.86 316	96.54 232	99.98 142	98.65 301	98.48 224	99.82 233

MVS-HIRNet		94.12 420	92.73 437	98.29 324	99.33 338	95.95 389	99.38 445	99.19 369	74.54 518	98.26 384	86.34 537	86.07 436	99.06 361	91.60 457	99.87 158	99.85 221

hybridcas		98.64 223	98.41 237	99.33 233	99.54 252	98.41 271	100.00 1	99.18 379	96.78 270	99.68 252	100.00 1	92.58 335	99.75 278	99.57 220	98.38 236	99.82 233

icg_test_0407_2		98.30 267	98.45 232	97.85 369	99.38 332	95.36 399	99.99 270	99.18 379	96.72 286	99.58 264	100.00 1	95.17 260	98.45 427	97.84 340	98.15 279	99.74 303

IMVS_0407		98.36 264	98.42 235	98.19 336	99.38 332	95.36 399	99.73 400	99.18 379	96.72 286	99.58 264	100.00 1	95.17 260	99.47 331	97.84 340	98.15 279	99.74 303

IMVS_0404		97.87 291	97.89 286	97.81 371	99.38 332	95.36 399	99.84 368	99.18 379	96.72 286	98.41 370	100.00 1	91.43 349	98.32 436	97.84 340	98.15 279	99.74 303

IMVS_0403		98.37 262	98.39 246	98.29 324	99.38 332	95.36 399	99.97 313	99.18 379	96.72 286	99.68 252	100.00 1	94.61 277	99.77 268	97.84 340	98.15 279	99.74 303

MTMP							100.00 1	99.18 379

mvs_tets		97.00 341	96.69 338	97.94 363	97.41 474	97.27 359	99.60 420	99.18 379	96.51 317	97.35 424	99.69 354	86.53 432	98.91 378	98.84 290	95.09 366	97.65 436

pmmvs4		97.17 329	96.80 334	98.27 326	97.68 457	98.64 252	100.00 1	99.18 379	94.22 409	98.55 353	99.71 348	93.67 303	98.47 425	95.66 407	92.57 399	97.71 418

diffmvs		98.96 177	98.73 183	99.63 162	99.54 252	99.16 201	100.00 1	99.18 379	97.33 215	99.96 154	100.00 1	94.60 278	99.91 209	99.66 195	98.33 250	99.82 233
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				89.98 481	98.90 387	71.46 519		99.18 379	97.61 178	96.92 433	99.83 324	86.07 436	99.83 248	96.02 395	97.65 318	98.65 346

Casviewmamba		98.71 214	98.47 229	99.46 192	99.47 298	98.70 246	100.00 1	99.17 389	96.97 249	99.45 278	100.00 1	93.04 323	99.87 232	99.67 188	98.41 229	99.81 250

casdiffseed414692147		98.31 266	97.94 284	99.40 211	99.46 307	98.67 248	99.91 353	99.17 389	96.33 336	98.66 344	99.97 265	90.47 374	99.71 287	99.36 251	98.16 278	99.81 250

baseline		98.69 219	98.45 232	99.41 206	99.52 270	98.67 248	100.00 1	99.17 389	97.03 243	99.13 304	100.00 1	93.17 317	99.74 279	99.70 174	98.34 247	99.81 250

PatchmatchNet2					0.00 566	95.13 407	99.92 346	99.16 392	89.91 470
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mamba_0408		98.63 229	98.40 243	99.34 225	99.53 256	98.52 262	99.24 459	99.16 392	96.43 322	98.95 320	99.98 253	94.47 281	99.76 273	99.21 269	98.62 215	99.75 296

SSM_04072		98.59 236	98.40 243	99.15 262	99.53 256	98.52 262	99.24 459	99.16 392	96.43 322	98.95 320	99.98 253	94.47 281	99.19 356	99.21 269	98.62 215	99.75 296

viewmambaseed2359dif		98.57 240	98.34 254	99.28 251	99.46 307	98.23 298	100.00 1	99.16 392	96.26 340	99.11 306	100.00 1	93.12 322	99.79 260	99.61 210	98.33 250	99.80 281

MonoMVSNet		98.55 244	98.64 200	98.26 329	98.21 430	95.76 395	99.94 338	99.16 392	96.23 341	99.47 275	99.24 415	96.75 225	99.22 353	99.61 210	99.17 198	99.81 250

Fast-Effi-MVS+-dtu		98.38 261	98.56 216	97.82 370	99.58 238	94.44 434	100.00 1	99.16 392	96.75 277	99.51 269	99.63 372	95.03 264	99.60 295	97.71 347	99.67 181	99.42 332

SSM_0407		98.72 210	98.52 221	99.33 233	99.53 256	98.52 262	99.88 362	99.15 398	96.53 312	98.95 320	100.00 1	94.38 284	99.72 285	99.64 198	98.62 215	99.75 296

SSM_0404		98.76 206	98.56 216	99.35 223	99.53 256	98.65 251	99.80 379	99.15 398	96.53 312	99.47 275	100.00 1	94.38 284	99.76 273	99.64 198	98.59 218	99.64 321

Fast-Effi-MVS+		98.40 260	98.02 281	99.55 180	99.63 217	99.06 209	100.00 1	99.15 398	95.07 381	99.42 279	99.95 291	93.26 314	99.73 283	97.44 357	98.24 269	99.87 215

anonymousdsp		97.16 330	96.88 331	98.00 359	97.08 478	98.06 316	99.81 374	99.15 398	94.58 397	97.84 406	99.62 376	90.49 369	98.60 411	97.98 333	95.32 351	97.33 461

casdiffmvs		98.65 222	98.38 248	99.46 192	99.52 270	98.74 242	100.00 1	99.15 398	96.91 256	99.05 314	100.00 1	92.75 329	99.83 248	99.70 174	98.38 236	99.81 250
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		99.29 118	99.16 123	99.69 151	99.45 315	99.49 157	100.00 1	99.15 398	97.45 201	99.97 146	100.00 1	96.76 223	99.76 273	99.67 188	100.00 1	99.81 250

1112_ss		98.91 187	98.71 189	99.51 182	99.69 184	98.75 239	99.99 270	99.15 398	96.82 265	98.84 332	100.00 1	97.45 199	99.89 222	98.66 299	97.75 312	99.89 191

dtuplus		98.57 240	98.32 255	99.30 245	99.44 317	98.35 285	100.00 1	99.14 405	96.36 332	98.97 319	100.00 1	93.04 323	99.77 268	99.55 223	98.39 232	99.79 287

diffmvs_AUTHOR		98.92 184	98.73 183	99.49 189	99.48 293	98.81 235	99.94 338	99.14 405	97.24 223	99.96 154	100.00 1	94.85 268	99.87 232	99.67 188	98.31 254	99.79 287

tt0320		92.36 442	91.28 446	95.58 442	98.30 421	90.65 474	98.69 501	99.14 405	76.73 511	96.07 453	99.50 395	72.28 493	98.39 431	93.29 443	87.56 461	97.70 419

IterMVS-SCA-FT		96.72 351	96.42 351	97.62 376	99.40 328	96.83 374	99.99 270	99.14 405	94.65 396	97.55 420	99.72 346	89.65 394	98.31 437	95.62 409	92.05 406	97.73 403

YYNet1		92.44 441	90.92 451	97.03 398	96.20 485	97.06 369	99.99 270	99.14 405	88.21 478	67.93 538	98.43 475	88.63 410	96.28 495	90.64 463	89.08 448	97.74 391

MDA-MVSNet_test_wron		92.61 439	91.09 450	97.19 394	96.71 481	97.26 360	100.00 1	99.14 405	88.61 474	67.90 539	98.32 479	89.03 402	96.57 491	90.47 468	89.59 440	97.74 391

Test_1112_low_res		98.83 198	98.60 208	99.51 182	99.69 184	98.75 239	99.99 270	99.14 405	96.81 266	98.84 332	99.06 426	97.45 199	99.89 222	98.66 299	97.75 312	99.89 191

CS-MVS		99.33 109	99.27 99	99.50 185	99.99 53	99.00 219	100.00 1	99.13 412	97.26 222	99.96 154	100.00 1	97.79 182	99.64 293	99.64 198	99.67 181	99.87 215

v2v482		96.70 352	96.18 361	98.27 326	98.04 438	98.39 275	100.00 1	99.13 412	94.19 412	98.58 351	99.08 425	90.48 370	98.67 398	95.69 404	90.44 433	97.75 363

MVSFormer		98.94 182	98.82 169	99.28 251	99.45 315	99.49 157	100.00 1	99.13 412	95.46 374	99.97 146	100.00 1	96.76 223	98.59 413	98.63 304	100.00 1	99.74 303

jason		99.11 144	98.96 151	99.59 171	99.17 352	99.31 181	100.00 1	99.13 412	97.38 208	99.83 218	100.00 1	95.54 249	99.72 285	99.57 220	99.97 123	99.74 303
jason: jason.
test_djsdf		97.55 311	97.38 310	98.07 347	97.50 466	97.99 320	100.00 1	99.13 412	95.46 374	98.47 366	99.85 321	92.01 345	98.59 413	98.63 304	95.36 350	97.62 442

IterMVS		96.76 348	96.46 349	97.63 374	99.41 323	96.89 372	99.99 270	99.13 412	94.74 391	97.59 419	99.66 361	89.63 396	98.28 442	95.71 403	92.31 403	97.72 410
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
sc_t1		92.52 440	91.34 445	96.09 435	97.80 450	89.86 481	98.61 504	99.12 418	77.73 510	96.09 452	99.79 340	68.64 499	98.94 375	96.94 372	87.31 464	99.46 331

tpm		98.24 274	98.22 267	98.32 323	99.13 354	95.79 394	99.53 429	99.12 418	95.20 380	99.96 154	99.36 408	97.58 189	99.28 351	97.41 359	96.67 335	99.88 204

GDP-MVS		99.39 94	99.26 103	99.77 137	99.53 256	99.55 141	100.00 1	99.11 420	97.14 231	99.96 154	100.00 1	99.83 5	99.89 222	98.47 312	99.26 197	99.87 215

miper_ehance_all_eth		97.81 296	97.66 300	98.23 332	99.49 290	98.37 279	99.99 270	99.11 420	94.78 388	98.25 385	99.21 418	98.18 163	98.57 417	97.35 363	92.61 396	97.76 352

v7n		96.06 391	95.42 402	97.99 361	97.58 463	97.35 354	99.86 365	99.11 420	92.81 448	97.91 402	99.49 396	90.99 359	98.92 377	92.51 450	88.49 454	97.70 419

cl____		97.54 312	97.32 313	98.18 337	99.47 298	98.14 309	100.00 1	99.10 423	94.16 414	97.60 417	99.63 372	97.52 195	98.65 401	96.47 386	91.97 409	97.76 352

DIV-MVS_self_test		97.52 315	97.35 312	98.05 355	99.46 307	98.11 310	100.00 1	99.10 423	94.21 410	97.62 415	99.63 372	97.65 187	98.29 441	96.47 386	91.98 408	97.76 352

c3_l		97.58 308	97.42 307	98.06 351	99.48 293	98.16 306	99.96 321	99.10 423	94.54 399	98.13 389	99.20 420	97.87 176	98.25 444	97.28 364	91.20 424	97.75 363

Effi-MVS+		98.58 238	98.24 263	99.61 166	99.60 229	99.26 187	97.85 517	99.10 423	96.22 344	99.97 146	99.89 311	93.75 302	99.77 268	99.43 246	98.34 247	99.81 250

IterMVS-LS		97.56 309	97.44 306	97.92 366	99.38 332	97.90 329	99.89 359	99.10 423	94.41 404	98.32 378	99.54 391	97.21 206	98.11 457	97.50 355	91.62 416	97.75 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		96.65 354	96.16 363	98.11 346	98.17 434	98.23 298	99.99 270	99.09 428	93.97 417	98.74 339	99.05 428	91.09 354	98.82 387	95.46 413	89.90 437	97.27 462

KD-MVS_self_test		91.16 451	90.09 456	94.35 458	94.44 510	91.27 469	99.74 395	99.08 429	90.82 461	94.53 469	94.91 515	86.11 435	94.78 507	82.67 504	68.52 529	96.99 470

eth_miper_zixun_eth		97.47 316	97.28 315	98.06 351	99.41 323	97.94 326	99.62 418	99.08 429	94.46 403	98.19 388	99.56 387	96.91 220	98.50 422	96.78 381	91.49 419	97.74 391

v1192		96.18 381	95.49 395	98.26 329	98.01 441	98.15 307	99.99 270	99.08 429	93.36 435	98.54 354	98.97 440	89.47 397	98.89 381	91.15 461	90.82 427	97.75 363

v1144		96.51 361	95.97 371	98.13 344	97.98 443	98.04 318	99.99 270	99.08 429	93.51 430	98.62 348	98.98 436	90.98 360	98.62 407	93.79 437	90.79 428	97.74 391

RRT-MVS		98.75 209	98.52 221	99.44 199	99.65 208	98.57 256	99.90 355	99.08 429	96.51 317	99.96 154	99.95 291	92.59 334	99.96 171	99.60 212	99.45 194	99.81 250

dcpmvs_2		98.87 193	99.53 66	96.90 406	99.87 126	90.88 472	99.94 338	99.07 434	98.20 120	100.00 1	100.00 1	98.69 145	99.86 234	100.00 1	100.00 1	99.95 150

miper_lstm_enhance		97.40 319	97.28 315	97.75 373	99.48 293	97.52 346	100.00 1	99.07 434	94.08 416	98.01 397	99.61 378	97.38 203	97.98 470	96.44 389	91.47 421	97.76 352

v1921920		96.16 385	95.50 393	98.14 341	97.88 447	97.96 324	99.99 270	99.07 434	93.33 436	98.60 349	99.24 415	89.37 398	98.71 396	91.28 458	90.74 429	97.75 363

v148		96.29 375	95.84 376	97.63 374	97.74 453	96.53 384	100.00 1	99.07 434	93.52 429	98.01 397	99.42 403	91.22 350	98.60 411	96.37 390	87.22 467	97.75 363

v1240		95.96 393	95.25 404	98.07 347	97.91 445	97.87 334	99.96 321	99.07 434	93.24 439	98.64 347	98.96 441	88.98 404	98.61 408	89.58 478	90.92 426	97.75 363

v8		96.35 372	95.73 384	98.21 335	98.11 436	98.23 298	99.94 338	99.07 434	92.66 449	98.29 380	99.00 435	91.46 347	98.77 392	94.17 431	88.83 452	97.62 442

v10		96.14 387	95.50 393	98.07 347	98.19 432	97.96 324	99.83 370	99.07 434	92.10 452	98.07 391	98.94 442	91.07 355	98.61 408	92.41 453	89.82 438	97.63 440

testgi		96.18 381	95.93 372	96.93 405	98.98 379	94.20 442	100.00 1	99.07 434	97.16 229	96.06 454	99.86 316	84.08 452	97.79 478	90.38 469	97.80 308	98.81 343

v144192		96.40 369	95.81 377	98.17 339	97.89 446	98.11 310	99.99 270	99.06 442	93.39 434	98.75 338	99.09 424	90.43 375	98.66 399	93.10 445	90.55 431	97.75 363

PCF-MVS	98.23 3	98.69 219	98.37 250	99.62 164	99.78 168	99.02 214	99.23 466	99.06 442	96.43 322	98.08 390	100.00 1	94.72 274	99.95 184	98.16 326	99.91 147	99.90 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RPSCF		97.37 320	98.24 263	94.76 454	99.80 158	84.57 497	99.99 270	99.05 444	94.95 384	99.82 227	100.00 1	94.03 293	100.00 1	98.15 327	98.38 236	99.70 313

FPMVS		77.92 494	79.45 490	73.34 519	76.87 555	46.81 548	98.24 510	99.05 444	59.89 527	73.55 530	98.34 478	36.81 538	86.55 531	80.96 509	91.35 423	86.65 532

Gipuma		84.73 475	83.50 479	88.40 488	97.50 466	82.21 504	88.87 540	99.05 444	65.81 521	85.71 508	90.49 526	53.70 522	96.31 494	78.64 516	91.74 412	86.67 531
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		98.60 235	98.24 263	99.67 155	96.90 479	99.21 195	99.99 270	99.04 447	98.80 78	99.57 266	99.96 283	90.12 382	99.91 209	99.89 123	99.89 150	99.90 183

EGC-MVSNET		79.46 489	74.04 499	95.72 440	96.00 490	92.73 457	99.09 485	99.04 447	5.08 560	16.72 560	98.71 455	73.03 490	98.74 395	82.05 506	96.64 336	95.69 500

pm-mvs1		95.76 397	95.01 409	98.00 359	98.23 429	97.45 349	99.24 459	99.04 447	93.13 442	95.93 456	99.72 346	86.28 434	98.84 386	95.62 409	87.92 457	97.72 410

VortexMVS		98.23 275	98.11 271	98.59 303	99.56 248	99.37 174	99.95 330	99.03 450	96.47 320	98.69 340	99.55 388	95.91 239	98.66 399	99.01 282	94.80 372	97.73 403

ET-MVSNet_ETH3D		96.41 366	95.48 397	99.20 260	99.81 145	99.75 110	100.00 1	99.02 451	97.30 220	78.33 523	100.00 1	97.73 183	97.94 472	99.70 174	87.41 462	99.92 168

pmmvs6		93.64 429	92.87 432	95.94 438	97.47 470	91.41 468	98.92 492	99.02 451	87.84 481	95.01 463	99.61 378	77.24 479	98.77 392	94.33 429	86.41 474	97.63 440

our_test_3		96.51 361	96.35 354	96.98 402	97.61 460	95.05 408	99.98 303	99.01 453	94.68 394	96.77 441	99.06 426	95.87 241	98.14 453	91.81 455	92.37 402	97.75 363

ArgMatch-Sym		94.50 413	94.12 416	95.63 441	98.16 435	90.84 473	100.00 1	99.00 454	97.42 205	97.22 428	99.76 344	73.91 487	99.05 362	91.22 459	90.43 434	97.01 469

test_fmvs1		98.37 262	98.04 279	99.34 225	99.84 131	98.07 314	100.00 1	99.00 454	98.85 67	100.00 1	100.00 1	85.11 444	99.96 171	99.69 183	99.88 153	100.00 1

CR-MVSNet		98.02 285	97.71 299	98.93 279	99.31 339	98.86 231	99.13 479	99.00 454	96.53 312	99.96 154	98.98 436	96.94 218	98.10 461	91.18 460	98.40 230	99.84 223

Patchmtry		96.81 345	96.37 353	98.14 341	99.31 339	98.55 257	98.91 493	99.00 454	90.45 464	97.92 401	98.98 436	96.94 218	98.12 455	94.27 430	91.53 418	97.75 363

test_vis1_n		96.69 353	95.81 377	99.32 239	99.14 353	97.98 321	99.97 313	98.98 458	98.45 101	100.00 1	100.00 1	66.44 503	99.99 107	99.78 150	99.57 191	100.00 1

test_fmvs1_n		97.43 317	96.86 332	99.15 262	99.68 189	97.48 348	99.99 270	98.98 458	98.82 73	100.00 1	100.00 1	74.85 486	99.96 171	99.67 188	99.70 177	100.00 1

Effi-MVS+-dtu		98.51 251	98.86 166	97.47 380	99.77 170	94.21 441	100.00 1	98.94 460	97.61 178	99.91 202	98.75 454	95.89 240	99.51 324	99.36 251	99.48 192	98.68 345

MDA-MVSNet-bldmvs		91.65 450	89.94 459	96.79 419	96.72 480	96.70 378	99.42 442	98.94 460	88.89 473	66.97 541	98.37 477	81.43 464	95.91 499	89.24 481	89.46 444	97.75 363

test_method		91.04 454	91.10 449	90.85 478	98.34 415	77.63 509	100.00 1	98.93 462	76.69 512	96.25 449	98.52 469	70.44 496	97.98 470	89.02 483	91.74 412	96.92 472

dtuonlycased		95.07 410	95.43 400	93.98 464	98.26 425	85.63 494	99.98 303	98.92 463	94.83 387	94.13 474	99.47 398	82.60 460	97.61 482	94.66 424	96.01 340	98.70 344

dmvs_testset		93.27 433	95.48 397	86.65 492	98.74 400	68.42 527	99.92 346	98.91 464	96.19 346	93.28 478	100.00 1	91.06 357	91.67 523	89.64 476	91.54 417	99.86 219

new_pmnet		94.11 421	93.47 423	96.04 437	96.60 484	92.82 455	99.97 313	98.91 464	90.21 467	95.26 460	98.05 487	85.89 439	98.14 453	84.28 500	92.01 407	97.16 464

APD_test1		93.07 436	94.14 415	89.85 482	99.18 351	72.49 517	99.76 392	98.90 466	92.86 447	96.35 446	99.94 297	75.56 484	99.91 209	86.73 490	97.98 291	97.15 465

FE-MVSNET2		91.15 452	90.00 458	94.58 455	90.74 526	92.52 461	99.56 424	98.87 467	90.82 461	88.96 495	95.40 509	76.26 483	95.56 503	87.84 486	81.59 499	95.66 502

test20.03		93.11 434	92.85 433	93.88 465	95.19 504	91.83 464	100.00 1	98.87 467	93.68 424	92.76 480	98.88 448	89.20 401	92.71 518	77.88 520	89.19 447	97.09 466

ArgMatch-SfM		93.74 425	93.14 427	95.54 443	98.57 406	90.54 475	99.97 313	98.86 469	97.35 210	97.60 417	99.66 361	71.88 494	99.02 364	90.18 471	84.16 482	97.07 468

test_0402		94.35 415	93.70 420	96.32 431	97.92 444	93.60 445	99.61 419	98.85 470	88.19 479	94.68 466	99.48 397	80.01 468	98.58 416	89.39 479	95.15 363	96.77 474

Anonymous20240521		93.29 432	92.76 434	94.90 453	95.64 497	91.27 469	99.97 313	98.82 471	87.04 487	94.71 465	98.19 482	83.86 453	96.80 487	84.04 501	92.56 400	96.64 479

new-patchmatchnet		90.30 459	89.46 462	92.84 472	90.77 525	88.55 488	99.83 370	98.80 472	90.07 469	87.86 499	95.00 513	78.77 474	94.30 509	84.86 498	79.15 506	95.68 501

MVStest1		94.27 416	93.30 425	97.19 394	98.83 397	97.18 364	99.93 344	98.79 473	86.80 490	84.88 512	99.04 429	94.32 287	98.25 444	90.55 466	86.57 473	96.12 493

ttmdpeth		96.24 378	95.88 374	97.32 387	97.80 450	96.61 382	99.95 330	98.77 474	97.80 155	93.42 477	99.28 412	86.42 433	99.01 366	97.63 350	91.84 411	96.33 487

WB-MVS		88.24 466	90.09 456	82.68 508	91.56 523	69.51 522	100.00 1	98.73 475	90.72 463	87.29 501	98.12 483	92.87 327	85.01 537	62.19 537	89.34 445	93.54 516

tt0320-xc		91.69 449	90.50 453	95.26 445	98.04 438	90.12 480	98.60 505	98.70 476	76.63 513	94.66 467	99.52 392	68.57 500	97.99 469	94.61 425	85.18 477	97.66 431

SSC-MVS		87.61 467	89.47 461	82.04 509	90.63 527	68.77 526	99.99 270	98.66 477	90.34 466	86.70 503	98.08 484	92.72 332	84.12 538	59.41 540	88.71 453	93.22 520

usedtu_blend_shiyan5		92.75 438	91.39 444	96.82 416	95.22 501	94.40 436	99.05 490	98.64 478	75.98 517	98.54 354	98.56 463	90.48 370	98.31 437	96.31 391	69.73 524	97.75 363

mmtdpeth		94.58 412	94.18 414	95.81 439	98.82 399	91.09 471	99.99 270	98.61 479	96.38 329	100.00 1	97.23 493	76.52 481	99.85 241	99.82 141	80.22 503	96.48 483

pmmvs-eth3d		91.73 448	90.67 452	94.92 452	91.63 522	92.71 458	99.90 355	98.54 480	91.19 457	88.08 498	95.50 506	79.31 472	96.13 497	90.55 466	81.32 501	95.91 497

Anonymous20231206		93.45 431	93.17 426	94.30 459	95.00 507	89.69 483	99.98 303	98.43 481	93.30 438	94.50 470	98.59 460	90.52 368	95.73 502	77.46 522	90.73 430	97.48 455

MASt3R-SfM		91.92 445	92.47 442	90.28 480	96.64 483	75.61 513	99.63 415	98.31 482	95.70 361	95.42 459	98.84 449	67.34 501	99.22 353	89.92 472	90.47 432	96.01 495

USDC		95.90 395	95.70 385	96.50 425	98.60 405	92.56 460	100.00 1	98.30 483	97.77 158	96.92 433	99.94 297	81.25 466	99.45 337	93.54 440	94.96 371	97.49 452

SixPastTwentyTwo		95.71 399	95.49 395	96.38 428	97.42 472	93.01 452	99.84 368	98.23 484	94.75 389	95.98 455	99.97 265	85.35 443	98.43 428	94.71 423	93.17 388	97.69 424

RoMa-SfM		90.39 458	89.63 460	92.66 473	97.47 470	83.18 501	98.81 496	98.21 485	85.44 496	89.21 493	99.46 400	63.72 504	98.30 440	87.11 488	87.25 465	96.51 482

MVP-Stereo		96.51 361	96.48 348	96.60 423	95.65 496	94.25 440	98.84 495	98.16 486	95.85 357	95.23 461	99.04 429	92.54 337	99.13 358	92.98 446	99.98 119	96.43 485
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		96.14 387	95.98 370	96.62 422	97.49 468	93.44 448	99.92 346	98.16 486	95.86 355	97.65 412	99.95 291	85.71 441	98.78 389	94.93 421	94.18 379	97.64 439

ITE_SJBPF				96.84 410	98.96 382	93.49 447		98.12 488	98.12 129	98.35 375	99.97 265	84.45 446	99.56 307	95.63 408	95.25 356	97.49 452

EG-PatchMatch MVS		92.94 437	92.49 441	94.29 460	95.87 492	87.07 491	99.07 488	98.11 489	93.19 440	88.98 494	98.66 458	70.89 495	99.08 360	92.43 452	95.21 359	96.72 476

DenseAffine		90.43 457	89.28 464	93.87 466	97.71 456	86.21 492	99.13 479	98.10 490	87.86 480	90.15 491	98.43 475	60.76 507	98.65 401	84.48 499	86.90 468	96.74 475

pmmvs5		95.94 394	95.61 390	96.95 403	97.42 472	94.66 424	100.00 1	98.08 491	93.60 427	97.05 431	99.43 402	87.02 427	98.46 426	95.76 401	92.12 405	97.72 410

mvs5depth		93.81 422	93.00 430	96.23 433	94.25 511	93.33 450	97.43 523	98.07 492	93.47 431	94.15 473	99.58 382	77.52 477	98.97 372	93.64 438	88.92 449	96.39 486

LCM-MVSNet		79.01 493	76.93 496	85.27 494	78.28 554	68.01 529	96.57 527	98.03 493	55.10 533	82.03 519	93.27 519	31.99 548	93.95 511	82.72 503	74.37 513	93.84 514

OpenMVS_ROB	88.34 20	91.89 446	91.12 448	94.19 462	95.55 498	87.63 489	99.26 457	98.03 493	86.61 492	90.65 490	96.82 496	70.14 498	98.78 389	86.54 491	96.50 339	96.15 491

ambc				88.45 487	86.84 540	70.76 520	97.79 519	98.02 495		90.91 487	95.14 510	38.69 535	98.51 421	94.97 420	84.23 481	96.09 494

tmp_tt		75.80 496	74.26 498	80.43 511	52.91 564	53.67 545	87.42 545	97.98 496	61.80 525	67.04 540	100.00 1	76.43 482	96.40 493	96.47 386	28.26 553	91.23 524

TransMVSNet (Re)		94.78 411	93.72 419	97.93 365	98.34 415	97.88 332	99.23 466	97.98 496	91.60 454	94.55 468	99.71 348	87.89 418	98.36 433	89.30 480	84.92 478	97.56 448

DKM		88.67 463	87.74 468	91.44 476	97.38 475	82.60 502	98.95 491	97.94 498	87.54 482	87.00 502	98.48 470	55.08 519	95.81 501	86.05 493	81.29 502	95.91 497

LF4IMVS		96.19 380	96.18 361	96.23 433	98.26 425	92.09 463	100.00 1	97.89 499	97.82 153	97.94 399	99.87 314	82.71 458	99.38 343	97.41 359	93.71 382	97.20 463

MatchFormer		86.71 472	84.75 478	92.57 474	96.14 488	82.52 503	99.27 455	97.86 500	80.17 507	78.74 522	96.16 502	54.81 520	98.63 405	75.87 525	83.75 487	96.56 481

Baseline_NR-MVSNet		96.16 385	95.70 385	97.56 379	98.28 424	96.79 376	100.00 1	97.86 500	91.93 453	97.63 413	99.47 398	92.14 342	98.35 434	97.13 367	86.83 471	97.54 449

test_fmvs2		95.17 409	95.23 405	95.01 448	98.95 384	88.99 486	99.99 270	97.77 502	97.79 156	98.58 351	99.70 351	73.36 489	99.34 347	95.88 398	95.03 367	96.70 478

testf1		84.40 476	84.79 476	83.23 506	95.71 494	58.71 543	98.79 497	97.75 503	81.58 504	84.94 510	98.07 485	45.33 531	97.73 479	77.09 523	83.85 484	93.24 518

APD_test2		84.40 476	84.79 476	83.23 506	95.71 494	58.71 543	98.79 497	97.75 503	81.58 504	84.94 510	98.07 485	45.33 531	97.73 479	77.09 523	83.85 484	93.24 518

TinyColmap		95.50 402	95.12 408	96.64 421	98.69 401	93.00 453	99.40 443	97.75 503	96.40 328	96.14 451	99.87 314	79.47 470	99.50 327	93.62 439	94.72 374	97.40 458

dtuonly		97.85 293	97.46 305	99.02 272	98.44 411	97.89 331	99.99 270	97.62 506	96.53 312	99.49 271	99.96 283	94.01 296	99.58 301	92.75 447	98.32 253	99.59 322

TDRefinement		91.93 444	90.48 454	96.27 432	81.60 551	92.65 459	99.10 483	97.61 507	93.96 418	93.77 475	99.85 321	80.03 467	99.53 321	97.82 344	70.59 522	96.63 480

usedtu_dtu_shiyan2		85.34 473	83.22 480	91.71 475	88.10 537	83.34 500	98.75 499	97.59 508	76.21 515	91.11 484	96.80 497	58.14 512	94.30 509	75.00 527	67.24 534	97.49 452

DKM-HiRes		87.00 470	86.38 473	88.84 486	96.71 481	79.05 506	98.73 500	97.57 509	84.56 498	84.00 514	98.23 481	52.90 524	92.48 519	84.95 497	79.77 504	95.00 506

test_fmvs3		87.19 469	87.02 471	87.71 489	92.69 515	76.64 510	99.96 321	97.27 510	93.55 428	90.82 488	94.03 518	38.00 537	92.19 520	93.49 441	83.35 492	94.32 512

LoFTR		88.61 464	87.13 470	93.06 469	96.18 486	83.87 498	99.48 433	97.21 511	86.37 493	82.32 518	96.66 498	58.07 513	98.59 413	81.76 507	86.15 476	96.72 476

test_f		86.87 471	86.06 474	89.28 485	91.45 524	76.37 511	99.87 364	97.11 512	91.10 458	88.46 496	93.05 520	38.31 536	96.66 490	91.77 456	83.46 491	94.82 509

RoMa-HiRes		87.37 468	86.72 472	89.32 484	95.81 493	78.25 508	98.63 503	97.01 513	82.18 503	86.32 505	99.25 414	56.48 516	94.79 506	83.17 502	81.62 498	94.91 508

PMVS	60.66 23	65.98 508	65.05 510	68.75 522	55.06 563	38.40 560	88.19 543	96.98 514	48.30 538	44.82 552	88.52 534	12.22 562	86.49 532	67.58 533	83.79 486	81.35 539
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Patchmatch-RL test		93.49 430	93.63 421	93.05 470	91.78 520	83.41 499	98.21 511	96.95 515	91.58 455	91.05 485	97.64 491	99.40 68	95.83 500	94.11 434	81.95 496	99.91 172

FE-MVSNET		89.50 460	88.33 466	93.00 471	88.89 533	90.24 478	99.96 321	96.86 516	88.23 476	88.46 496	95.47 507	77.03 480	93.37 517	78.54 517	81.56 500	95.39 505

ELoFTR		83.63 478	81.67 485	89.53 483	92.30 517	75.98 512	98.27 509	96.74 517	83.38 501	74.05 529	95.78 504	43.66 533	98.11 457	78.01 518	72.80 518	94.48 511

pmmvs3		90.62 456	89.36 463	94.40 457	90.53 529	91.49 467	100.00 1	96.73 518	84.21 499	93.65 476	96.65 499	82.56 461	94.83 505	82.28 505	77.62 510	96.89 473

PM-MVS		88.39 465	87.41 469	91.31 477	91.73 521	82.02 505	99.79 380	96.62 519	91.06 459	90.71 489	95.73 505	48.60 528	95.96 498	90.56 465	81.91 497	95.97 496

LCM-MVSNet-Re		96.52 359	97.21 321	94.44 456	99.27 346	85.80 493	99.85 367	96.61 520	95.98 350	92.75 481	98.48 470	93.97 297	97.55 483	99.58 217	98.43 227	99.98 128

mvsany_test3		89.36 462	88.96 465	90.56 479	91.95 519	78.97 507	99.74 395	96.59 521	96.84 263	89.25 492	96.07 503	52.59 525	97.11 485	95.17 418	82.44 494	95.58 504

door-mid								96.32 522

door								96.13 523

SP-DiffGlue		85.17 474	85.16 475	85.22 495	93.54 512	69.16 524	97.83 518	95.33 524	60.61 526	86.04 506	92.86 521	61.04 506	90.90 527	89.62 477	89.57 442	95.59 503

SP-SuperGlue		82.71 481	81.92 483	85.07 498	98.02 440	67.96 530	98.10 513	95.26 525	57.79 528	82.47 517	90.37 528	57.02 514	91.04 525	70.34 531	87.92 457	96.23 489

SP-NN		83.33 479	82.73 481	85.13 497	98.98 379	65.96 535	97.92 516	95.13 526	56.43 531	83.71 515	90.52 525	58.27 510	91.69 522	71.99 528	91.66 415	97.74 391

PMMVS2		79.15 492	77.28 495	84.76 499	82.34 548	72.66 516	99.70 406	95.11 527	71.68 520	84.78 513	90.87 523	32.05 547	89.99 529	75.53 526	63.45 539	91.64 522

SP-LightGlue		82.73 480	81.92 483	85.19 496	97.73 455	68.40 528	98.05 514	94.51 528	56.95 530	82.72 516	90.14 531	58.20 511	90.97 526	71.57 529	87.38 463	96.20 490

test_vis1_rt		93.10 435	92.93 431	93.58 467	99.63 217	85.07 495	99.99 270	93.71 529	97.49 196	90.96 486	97.10 494	60.40 508	99.95 184	99.24 265	97.90 299	95.72 499

DSMNet-mixed		95.18 408	95.21 406	95.08 446	96.03 489	90.21 479	99.65 412	93.64 530	92.91 444	98.34 376	97.40 492	90.05 386	95.51 504	91.02 462	97.86 301	99.51 329

SP-MNN		81.80 483	81.08 487	83.94 503	98.26 425	64.81 538	98.20 512	93.56 531	55.15 532	77.43 524	90.43 527	56.33 517	90.69 528	70.11 532	90.27 436	96.32 488

E-PMN		70.72 499	70.06 505	72.69 520	83.92 546	65.48 537	99.95 330	92.72 532	49.88 536	72.30 532	86.26 538	47.17 530	77.43 546	53.83 543	44.49 544	75.17 541

PMatch-SfM		81.57 484	79.80 488	86.88 491	92.36 516	73.86 515	97.50 522	92.66 533	80.39 506	73.10 531	96.35 500	33.54 544	91.86 521	81.28 508	71.01 521	94.92 507

N_pmnet		91.88 447	93.37 424	87.40 490	97.24 477	66.33 534	99.90 355	91.05 534	89.77 471	95.65 458	98.58 462	90.05 386	98.11 457	85.39 494	92.72 395	97.75 363

MVE	68.59 21	67.22 506	64.68 512	74.84 514	74.67 558	62.32 540	95.84 530	90.87 535	50.98 535	58.72 546	81.05 552	12.20 563	78.95 543	61.06 539	56.75 540	83.24 538
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMatch-Up-SfM		79.27 491	77.62 494	84.22 502	90.58 528	69.08 525	96.98 525	90.47 536	76.44 514	71.47 534	96.27 501	30.15 549	88.77 530	78.74 515	67.46 531	94.81 510

ALIKED-NN		82.28 482	81.49 486	84.63 500	99.44 317	67.26 531	97.36 524	90.47 536	62.09 524	81.26 521	95.45 508	59.17 509	93.89 512	63.93 536	84.26 480	92.75 521

ALIKED-MNN		79.54 488	78.11 493	83.80 505	99.29 345	66.55 533	97.70 520	90.37 538	57.60 529	74.96 528	92.30 522	53.12 523	93.57 515	58.80 541	78.89 508	91.27 523

ALIKED-LG		80.86 485	79.70 489	84.33 501	98.33 418	69.33 523	97.59 521	90.14 539	65.38 522	76.03 526	94.87 516	54.78 521	93.65 513	57.59 542	82.61 493	90.01 527

VLMVS_CLIP		69.45 503	71.86 504	62.23 526	66.80 560	30.24 567	87.12 547	87.67 540	33.62 557	82.03 519	98.28 480	28.75 550	67.69 555	88.35 485	74.12 514	88.74 528

ANet_high		66.05 507	63.44 513	73.88 518	61.14 561	63.45 539	95.68 531	87.18 541	79.93 508	47.35 549	80.68 554	22.35 558	72.33 552	61.24 538	35.42 549	85.88 533

testmvs		80.17 486	81.95 482	74.80 515	58.54 562	59.58 542	100.00 1	87.14 542	76.09 516	99.61 262	100.00 1	67.06 502	74.19 550	98.84 290	50.30 541	90.64 525

EMVS		69.88 501	69.09 506	72.24 521	84.70 543	65.82 536	99.96 321	87.08 543	49.82 537	71.51 533	84.74 540	49.30 527	75.32 548	50.97 544	43.71 545	75.59 540

XFeat-MNN		73.39 498	73.10 501	74.25 516	89.63 531	53.35 546	96.25 528	84.01 544	43.66 540	69.74 535	89.91 532	52.56 526	85.32 534	64.72 535	67.44 532	84.08 536

VLMVS		69.79 502	73.02 502	60.12 532	72.70 559	33.43 564	87.87 544	83.71 545	40.13 548	86.04 506	98.98 436	34.57 540	58.39 558	85.00 496	68.17 530	88.54 529

GLUNet-SfM		70.22 500	66.87 508	80.24 512	84.13 545	61.64 541	96.72 526	82.62 546	51.83 534	60.24 545	88.02 536	36.12 539	91.44 524	67.32 534	34.86 551	87.65 530

test_vis3_rt		79.61 487	78.19 492	83.86 504	88.68 536	69.56 521	99.81 374	82.19 547	86.78 491	68.57 537	84.51 541	25.06 555	98.26 443	89.18 482	78.94 507	83.75 537

XFeat-NN		75.54 497	76.00 497	74.19 517	93.25 514	52.63 547	95.93 529	81.98 548	46.32 539	75.32 527	90.27 530	56.80 515	85.05 536	71.26 530	72.85 517	84.87 534

SIFT-NN		67.52 505	68.28 507	65.25 523	96.00 490	45.92 549	93.38 532	80.01 549	43.05 541	69.06 536	85.13 539	39.13 534	85.13 535	32.15 547	76.58 511	64.70 544

SIFT-MNN		64.77 509	65.11 509	63.77 524	92.18 518	44.02 551	91.93 534	78.84 550	41.80 543	61.69 543	84.03 542	33.92 543	81.69 540	29.20 552	72.39 519	65.59 543

SIFT-NN-NCMNet		64.49 510	64.92 511	63.20 525	88.84 534	44.41 550	92.37 533	78.67 551	41.90 542	62.62 542	83.27 544	34.31 541	81.88 539	30.88 548	71.40 520	63.31 546

test123		79.44 490	79.23 491	80.05 513	80.03 553	71.72 518	100.00 1	77.93 552	62.52 523	94.81 464	99.69 354	78.21 475	74.53 549	92.57 449	27.33 554	93.90 513

SIFT-NN-CMatch		60.63 511	60.17 514	62.02 527	86.89 539	43.32 553	90.70 537	71.03 553	41.60 545	61.16 544	83.16 545	33.45 545	78.31 544	30.28 549	43.26 546	64.44 545

SIFT-NN-UMatch		59.27 513	58.65 516	61.13 529	83.27 547	43.66 552	91.00 536	70.69 554	41.78 544	44.38 553	82.21 549	34.17 542	79.10 542	30.07 550	50.25 542	60.64 549

SIFT-NCM-Cal		59.75 512	59.15 515	61.53 528	90.12 530	43.18 554	91.26 535	70.04 555	40.34 547	38.39 555	81.51 551	27.19 551	79.90 541	26.25 557	67.30 533	61.50 548

SIFT-ConvMatch		56.83 515	55.72 518	60.16 530	88.80 535	43.02 555	88.55 541	64.15 556	40.75 546	45.84 550	83.12 546	27.00 552	77.01 547	28.36 553	34.89 550	60.45 550

PDCNetPlus		75.87 495	73.92 500	81.72 510	89.55 532	74.48 514	98.59 506	62.34 557	72.19 519	76.04 525	95.03 512	47.66 529	86.31 533	77.97 519	45.88 543	84.35 535

SIFT-NN-PointCN		57.34 514	56.95 517	58.53 534	82.11 549	41.35 558	90.36 538	61.72 558	40.01 549	54.78 547	80.99 553	32.74 546	72.39 551	29.64 551	40.16 547	61.83 547

SIFT-UMatch		55.48 516	53.92 519	60.16 530	85.84 542	42.45 556	89.09 539	61.68 559	39.97 550	41.34 554	82.92 547	26.90 553	77.66 545	27.36 554	30.17 552	60.37 551

MVS_clip		68.81 504	72.22 503	58.58 533	84.27 544	34.51 561	80.78 550	61.23 560	34.94 556	86.68 504	99.12 421	55.61 518	50.86 560	80.33 512	66.99 535	90.36 526

SIFT-PointCN		49.44 519	48.89 522	51.12 537	81.24 552	34.25 562	87.16 546	56.78 561	36.95 553	33.84 556	76.32 556	20.17 559	61.65 557	21.99 559	25.53 556	57.46 553

SIFT-CM-Cal		53.99 517	52.89 520	57.28 535	87.31 538	41.77 557	86.71 548	54.86 562	39.82 552	45.09 551	82.10 550	25.89 554	71.72 553	27.27 555	26.97 555	58.36 552

SIFT-UM-Cal		51.73 518	50.25 521	56.15 536	85.87 541	41.10 559	88.21 542	50.44 563	39.83 551	33.54 557	82.23 548	23.59 556	71.25 554	27.05 556	21.52 557	56.10 554

SIFT-PCN-Cal		47.97 520	47.56 523	49.20 538	81.85 550	33.99 563	86.00 549	49.11 564	36.44 554	32.13 558	77.60 555	22.63 557	62.04 556	23.11 558	19.17 558	51.55 555

SIFT-NCMNet		41.74 521	41.17 524	43.45 539	76.48 556	31.10 566	80.74 551	30.14 565	35.07 555	28.33 559	71.87 557	16.32 560	52.56 559	19.72 560	11.82 560	46.67 556

wuyk23d		28.28 523	29.73 527	23.92 541	75.89 557	32.61 565	66.50 552	12.88 566	16.09 558	14.59 561	16.59 559	12.35 561	32.36 561	39.36 546	13.36 559	6.79 557

MVS_baseline		35.10 522	36.24 525	31.67 540	45.91 565	12.01 568	34.47 553	7.88 567	5.62 559	47.50 548	90.75 524	11.45 564	7.89 562	46.41 545	36.20 548	75.11 542

mmdepth		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

monomultidepth		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

test_blank		0.07 527	0.09 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.79 560	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

uanet_test		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

DCPMVS		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

pcd_1.5k_mvsjas		8.24 526	10.99 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	98.75 142	0.00 563	0.00 561	0.00 561	0.00 558

sosnet-low-res		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

sosnet		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

uncertanet		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

Regformer		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

n2								0.00 568

nn								0.00 568

ab-mvs-re		8.33 525	11.11 528	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	100.00 1	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

uanet		0.01 528	0.02 531	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.14 561	0.00 565	0.00 563	0.00 561	0.00 561	0.00 558

Meshroom													0.00 563
: In preparation.
AliceVision / Meshro													0.00 563
: In preparation.
AliceVision_Meshroom													0.00 563
: In preparation.
PatchmatchNet1														86.42 492	92.76 394	97.75 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													98.34 435
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						97.98 321								95.74 402

PC_three_1452									98.80 78	100.00 1	100.00 1	99.54 32	100.00 1	100.00 1	100.00 1	100.00 1

eth-test2					0.00 566

eth-test					0.00 566

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 81	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 172

test_part2					100.00 1	99.99 6				100.00 1

sam_mvs1												99.29 82				99.91 172

sam_mvs												99.33 71

test_post1							99.32 450				88.24 535	99.33 71	99.59 297	98.31 319

test_post											89.05 533	99.49 46	99.59 297

patchmatchnet-post											97.79 488	99.41 66	99.54 316

gm-plane-assit					99.52 270	97.26 360			95.86 355		100.00 1		99.43 339	98.76 295

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 54	100.00 1

test_prior2							100.00 1		98.82 73	100.00 1	100.00 1	99.47 51		100.00 1	100.00 1

旧先验2							100.00 1		98.11 130	100.00 1			100.00 1	99.67 188

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 362

segment_acmp												99.55 31

testdata1							100.00 1		98.77 85

plane_prior7					99.00 375	94.78 422

plane_prior6					99.06 365	94.80 418						88.58 413

plane_prior4											99.97 265

plane_prior3						94.79 421			99.03 25	99.08 311

plane_prior2							100.00 1		99.00 33

plane_prior1					99.02 369

plane_prior						94.80 418	100.00 1		99.03 25						95.58 342

HQP5-MVS						94.82 415

HQP-NCC					99.07 361		100.00 1		99.04 20	99.17 299

ACMP_Plane					99.07 361		100.00 1		99.04 20	99.17 299

BP-MVS														99.79 144

HQP4-MVS										99.17 299			99.57 303			97.77 350

HQP2-MVS												88.61 411

NP-MVS					99.07 361	94.81 417					99.97 265

MDTV_nov1_ep13_2view						99.24 191	99.56 424		96.31 338	99.96 154		98.86 132		98.92 286		99.89 191

ACMMP++_ref															94.58 377

ACMMP++															95.17 362

Test By Simon												99.10 99

High-resolution multi-view benchmark