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	99.75 141	99.89 122	99.51 152	99.45 438	100.00 1	98.38 106	99.83 219	100.00 1	98.86 132	99.81 256	99.25 264	98.78 210	99.94 156

test1111		98.42 258	98.12 271	99.29 249	99.88 124	98.15 308	99.46 436	100.00 1	98.36 110	99.42 280	100.00 1	87.91 417	99.79 261	99.31 261	98.78 210	99.94 156

ECVR-MVS		98.43 256	98.14 270	99.32 240	99.89 122	98.21 302	99.46 436	100.00 1	98.38 106	99.47 276	100.00 1	87.91 417	99.80 260	99.35 256	98.78 210	99.94 156

PVSNet_BlendedMVS		98.71 215	98.62 205	98.98 277	99.98 94	99.60 133	100.00 1	100.00 1	97.23 225	100.00 1	99.03 433	96.57 231	99.99 107	100.00 1	94.75 374	97.35 461

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	96.57 231	99.99 107	100.00 1	99.88 153	99.90 184

UGNet		98.41 260	98.11 272	99.31 242	99.54 253	98.55 258	99.18 470	100.00 1	98.64 92	99.79 236	99.04 430	87.61 422	100.00 1	99.30 262	99.89 150	99.40 334
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	99.84 216	100.00 1	98.94 125	99.98 142	99.86 129	98.21 271	99.94 156

HY-MVS	96.53 9	99.50 79	99.35 92	99.96 53	99.81 145	99.93 54	99.64 414	100.00 1	97.97 140	99.84 216	99.85 322	98.94 125	99.99 107	99.86 129	98.23 270	99.95 151

EPMVS		99.25 128	99.13 127	99.60 170	99.60 230	99.20 197	99.60 421	100.00 1	96.93 254	99.92 200	99.36 409	99.05 107	99.71 288	98.77 295	98.94 207	99.90 184

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	97.85 178	99.95 184	100.00 1	100.00 1	100.00 1

PatchMatch-RL		99.02 163	98.78 175	99.74 142	99.99 53	99.29 183	100.00 1	100.00 1	98.38 106	99.89 209	99.81 332	93.14 322	99.99 107	97.85 340	99.98 119	99.95 151

PVSNet_0	93.57 19	96.41 367	95.74 384	98.41 317	99.84 131	95.22 406	100.00 1	100.00 1	98.08 131	97.55 421	99.78 342	84.40 448	100.00 1	100.00 1	81.99 496	100.00 1

CHOSEN 1792x2688		99.00 166	98.91 162	99.25 257	99.90 120	97.79 339	100.00 1	99.99 13	98.79 81	98.28 382	100.00 1	93.63 305	99.95 184	99.66 196	99.95 129	100.00 1

HyFIR lowres test		99.32 111	99.24 109	99.58 176	99.95 108	99.26 188	100.00 1	99.99 13	96.72 287	99.29 294	99.91 309	99.49 46	99.47 332	99.74 161	98.08 286	100.00 1

AdaColmap		99.44 89	99.26 103	99.95 62	100.00 1	99.86 91	99.70 407	99.99 13	98.53 95	99.90 205	100.00 1	95.34 253	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	99.97 314	99.98 16	98.96 40	100.00 1	100.00 1	99.96 4	99.42 342	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	96.65 229	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	97.93 172	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	99.68 185	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	99.78 135	99.82 139	99.49 158	100.00 1	99.95 19	97.36 209	99.63 262	100.00 1	96.45 235	99.95 184	99.79 144	99.65 184	99.89 192

MAR-MVS		99.49 81	99.36 90	99.89 91	99.97 98	99.66 127	99.74 396	99.95 19	97.89 147	100.00 1	100.00 1	96.71 228	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		99.25 128	99.01 143	99.95 62	99.81 145	99.87 88	100.00 1	99.94 27	97.13 233	99.83 219	99.96 283	97.01 213	100.00 1	99.59 215	97.85 303	99.98 129

tfpn200view9		99.26 124	99.03 139	99.96 53	99.81 145	99.89 79	100.00 1	99.94 27	97.23 225	99.83 219	99.96 283	97.04 209	100.00 1	99.59 215	97.85 303	99.98 129

MVS		99.22 132	98.96 152	99.98 29	99.00 376	99.95 39	99.24 460	99.94 27	98.14 125	98.88 328	100.00 1	95.63 249	100.00 1	99.85 132	100.00 1	100.00 1

thres600view7		99.24 131	99.00 146	99.95 62	99.81 145	99.87 88	100.00 1	99.94 27	97.13 233	99.83 219	99.96 283	97.01 213	100.00 1	99.54 228	97.77 312	99.97 139

thres400		99.26 124	99.03 139	99.95 62	99.81 145	99.89 79	100.00 1	99.94 27	97.23 225	99.83 219	99.96 283	97.04 209	100.00 1	99.59 215	97.85 303	99.97 139

thres200		99.27 122	99.04 138	99.96 53	99.81 145	99.90 72	100.00 1	99.94 27	97.31 218	99.83 219	99.96 283	97.04 209	100.00 1	99.62 208	97.88 301	99.98 129

API-MVS		99.72 32	99.70 24	99.79 130	99.97 98	99.37 175	99.96 322	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	99.45 164	100.00 1	99.94 27	96.38 330	100.00 1	100.00 1	98.18 164	100.00 1	100.00 1	100.00 1	100.00 1

1314		99.38 97	99.19 119	99.96 53	98.88 390	99.89 79	99.24 460	99.93 35	98.88 62	98.79 338	100.00 1	97.02 212	100.00 1	100.00 1	100.00 1	100.00 1

test0.0.03 1		98.12 280	98.03 281	98.39 318	99.11 357	98.07 315	100.00 1	99.93 35	96.70 293	96.91 436	99.95 291	99.31 76	98.19 450	91.93 455	98.44 226	98.91 343

QAPM		98.99 170	98.66 198	99.96 53	99.01 371	99.87 88	99.88 363	99.93 35	97.99 136	98.68 343	100.00 1	93.17 318	100.00 1	99.32 260	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 151	98.76 179	99.96 53	98.86 395	99.90 72	99.98 304	99.93 35	98.95 43	98.49 366	100.00 1	92.91 327	100.00 1	99.71 171	100.00 1	100.00 1

VDDNet		96.39 371	95.55 393	98.90 282	99.27 347	97.45 350	99.15 478	99.92 39	91.28 457	99.98 140	100.00 1	73.55 489	100.00 1	99.85 132	96.98 329	99.24 337

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	97.84 180	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	99.50 154	100.00 1	99.91 40	97.72 161	99.98 140	99.94 297	98.45 155	100.00 1	99.53 231	98.75 213	99.89 192

EPNet		99.62 64	99.69 25	99.42 206	99.99 53	98.37 280	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	99.89 192
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	99.44 200	100.00 1	98.32 290	100.00 1	99.86 43	98.04 133	100.00 1	100.00 1	96.10 239	100.00 1	99.55 224	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	99.96 155	100.00 1	99.21 89	100.00 1	100.00 1	100.00 1	99.99 126

D2MVS		97.63 306	97.83 292	97.05 398	98.83 398	94.60 429	100.00 1	99.82 45	96.89 260	98.28 382	99.03 433	94.05 292	99.47 332	98.58 310	94.97 371	97.09 467

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	97.91 338	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	99.33 259		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	99.97 147	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	99.95 187	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	99.45 164	100.00 1	99.78 52	98.92 53	99.73 248	100.00 1	97.70 186	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	98.76 207	98.41 238	99.78 135	98.89 389	99.81 101	99.99 271	99.76 54	98.02 134	98.02 397	100.00 1	91.44 349	100.00 1	99.63 206	99.97 123	99.55 325

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		97.04 338	96.06 367	99.98 29	100.00 1	99.94 48	100.00 1	99.75 57	98.67 89	100.00 1	66.97 559	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	99.01 371	99.95 39	100.00 1	99.75 57	99.37 3	99.99 130	100.00 1	99.76 12	99.60 296	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	99.78 238	100.00 1	95.28 254	99.96 171	100.00 1	99.99 107	99.96 145

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

testdata				99.66 159	99.99 53	98.97 224		99.73 61	97.96 143	100.00 1	100.00 1	99.42 64	100.00 1	99.28 263	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	99.75 395	99.73 61	98.16 122	99.75 242	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	99.50 235	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		98.42 258	98.02 282	99.61 167	99.71 178	99.00 220	99.10 484	99.64 70	96.70 293	99.04 317	99.81 332	90.64 367	99.98 142	99.64 199	97.93 298	99.84 224

testing3		98.44 255	98.37 251	98.65 299	99.51 278	98.32 290	100.00 1	99.62 72	96.43 323	97.93 401	99.99 245	99.11 98	97.81 476	94.88 423	97.80 309	99.82 234

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		96.17 384	96.57 344	95.00 450	99.50 287	87.37 491	100.00 1	99.57 74	96.23 342	98.07 392	100.00 1	92.41 340	97.81 476	85.34 496	97.96 294	99.82 234

myMVS_eth3d		98.52 250	98.51 227	98.53 307	99.50 287	97.98 322	100.00 1	99.57 74	96.23 342	98.07 392	100.00 1	99.09 100	97.81 476	96.17 395	97.96 294	99.82 234

LFMVS		97.42 319	96.62 342	99.81 118	99.80 158	99.50 154	99.16 476	99.56 76	94.48 403	100.00 1	100.00 1	79.35 472	100.00 1	99.89 123	97.37 322	99.94 156

PLC	98.56 2	99.70 39	99.74 19	99.58 176	100.00 1	98.79 238	100.00 1	99.54 77	98.58 94	99.96 155	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 156
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GG-mvs-BLEND				99.59 172	99.54 253	99.49 158	99.17 475	99.52 78		99.96 155	99.68 359	100.00 1	99.33 349	99.71 171	99.99 107	99.96 145

gg-mvs-nofinetune		96.95 343	96.10 365	99.50 186	99.41 324	99.36 178	99.07 489	99.52 78	83.69 501	99.96 155	83.60 544	100.00 1	99.20 356	99.68 185	99.99 107	99.96 145

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	99.73 401	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	97.12 325	99.90 184

3Dnovator+	95.58 15	99.03 157	98.71 190	99.96 53	98.99 379	99.89 79	100.00 1	99.51 82	98.96 40	98.32 379	100.00 1	92.78 329	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	97.88 176	100.00 1	99.98 93	99.92 142	100.00 1

SymmetryMVS		99.30 115	99.25 105	99.45 197	99.79 163	98.55 258	99.94 339	99.47 85	98.39 104	100.00 1	100.00 1	98.44 156	99.98 142	99.36 252	97.83 306	99.83 227

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	99.97 147	99.97 265	99.48 50	100.00 1	99.78 150	97.99 291	99.85 222

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	97.75 313	99.94 156

Anonymous20240529		96.93 344	96.22 361	99.05 269	99.79 163	97.30 359	99.16 476	99.47 85	88.51 476	98.69 341	100.00 1	83.50 456	100.00 1	99.83 136	97.02 328	99.83 227

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	97.75 313	99.94 156

CVMVSNet		98.56 244	98.47 230	98.82 289	99.11 357	97.67 342	99.74 396	99.47 85	97.57 184	99.06 314	100.00 1	95.72 246	98.97 373	98.21 326	97.33 323	99.83 227

EU-MVSNet		96.63 356	96.53 345	96.94 405	97.59 463	96.87 374	99.76 393	99.47 85	96.35 335	96.85 438	99.78 342	92.57 337	96.27 497	95.33 415	91.08 426	97.68 427

VNet		99.04 154	98.75 180	99.90 88	99.81 145	99.75 110	99.50 433	99.47 85	98.36 110	100.00 1	99.99 245	94.66 277	100.00 1	99.90 121	97.09 326	99.96 145

VPA-MVSNet		97.03 339	96.43 351	98.82 289	98.64 404	99.32 180	99.38 446	99.47 85	96.73 285	98.91 327	98.94 443	87.00 429	99.40 343	99.23 267	89.59 441	97.76 353

tpmvs		98.59 237	98.38 249	99.23 258	99.69 184	97.90 330	99.31 454	99.47 85	94.52 401	99.68 253	99.28 413	97.64 189	99.89 222	97.71 348	98.17 278	99.89 192

EPNet_dtu		98.53 249	98.23 267	99.43 203	99.92 116	99.01 217	99.96 322	99.47 85	98.80 78	99.96 155	99.96 283	98.56 151	99.30 350	87.78 488	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	97.54 313	96.95 330	99.33 234	99.67 197	98.10 313	100.00 1	99.47 85	97.42 205	99.26 295	99.69 355	98.83 136	99.89 222	99.43 247	78.77 510	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		99.28 121	99.35 92	99.05 269	99.99 53	97.15 366	100.00 1	99.47 85	97.44 203	99.42 280	100.00 1	97.83 182	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	99.88 212	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	99.98 304	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		97.54 313	97.88 290	96.54 425	99.55 250	90.35 478	99.86 366	99.46 103	97.00 245	99.41 285	100.00 1	90.78 365	99.30 350	99.60 213	95.24 358	99.96 145

thisisatest0530		99.37 100	99.27 99	99.69 152	99.59 234	99.41 170	100.00 1	99.46 103	96.46 322	99.90 205	100.00 1	99.44 56	99.85 242	98.97 284	99.58 189	99.80 282

Anonymous202405211		97.87 292	97.53 304	98.90 282	99.81 145	96.70 379	99.35 449	99.46 103	92.98 444	98.83 335	99.99 245	90.63 368	100.00 1	99.70 175	97.03 327	100.00 1

tttt0517		99.34 106	99.23 112	99.67 156	99.57 243	99.38 172	100.00 1	99.46 103	96.33 337	99.89 209	100.00 1	99.44 56	99.84 246	98.93 286	99.46 193	99.78 293

thisisatest0515		99.42 91	99.31 95	99.74 142	99.59 234	99.55 141	100.00 1	99.46 103	96.65 300	99.92 200	100.00 1	99.44 56	99.85 242	99.09 279	99.63 187	99.81 251

baseline2		98.99 170	98.93 159	99.18 262	99.26 349	99.15 203	100.00 1	99.46 103	96.71 292	96.79 440	100.00 1	99.42 64	99.25 353	98.75 297	99.94 135	99.15 339

MDTV_nov1_ep13			98.94 157		99.53 257	98.36 283	99.39 445	99.46 103	96.54 312	99.99 130	99.63 373	98.92 128	99.86 235	98.30 323	98.71 214

LS3D		99.31 113	99.13 127	99.87 98	99.99 53	99.71 118	99.55 427	99.46 103	97.32 216	99.82 228	100.00 1	96.85 223	99.97 151	99.14 273	100.00 1	99.92 169

ETVMVS		99.16 140	98.98 149	99.69 152	99.67 197	99.56 139	100.00 1	99.45 111	96.36 333	99.98 140	99.95 291	98.65 146	99.64 294	99.11 277	97.63 320	99.88 205

SDMVSNet		98.49 253	98.08 276	99.73 145	99.82 139	99.53 146	99.99 271	99.45 111	97.62 174	99.38 288	99.86 317	90.06 386	99.88 231	99.92 118	96.61 338	99.79 288

sd_testset		97.81 297	97.48 305	98.79 293	99.82 139	96.80 376	99.32 451	99.45 111	97.62 174	99.38 288	99.86 317	85.56 443	99.77 269	99.72 167	96.61 338	99.79 288

tt0805		96.52 360	96.23 360	97.40 382	99.30 343	93.55 447	99.32 451	99.45 111	96.75 278	97.88 404	99.99 245	79.99 470	99.59 298	97.39 362	95.98 342	99.06 342

h-mvs33		97.03 339	96.53 345	98.51 308	99.79 163	95.90 393	99.45 438	99.45 111	98.21 118	100.00 1	99.78 342	97.49 197	99.99 107	99.72 167	74.92 513	99.65 321

tfpnnormal		96.36 372	95.69 389	98.37 320	98.55 408	98.71 245	99.69 409	99.45 111	93.16 442	96.69 444	99.71 349	88.44 416	98.99 370	94.17 432	91.38 423	97.41 458

SCA		98.30 268	97.98 284	99.23 258	99.41 324	98.25 298	99.99 271	99.45 111	96.91 257	99.76 241	99.58 383	89.65 395	99.54 317	98.31 320	98.79 209	99.91 173

UA-Net		99.06 151	98.83 169	99.74 142	99.52 271	99.40 171	99.08 487	99.45 111	97.64 171	99.83 219	100.00 1	95.80 244	99.94 197	98.35 318	99.80 172	99.88 205

VPNet		96.41 367	95.76 383	98.33 323	98.61 405	98.30 293	99.48 434	99.45 111	96.98 247	98.87 330	99.88 314	81.57 464	98.93 377	99.22 269	87.82 460	97.76 353

test-LLR		99.03 157	98.91 162	99.40 212	99.40 329	99.28 185	100.00 1	99.45 111	96.70 293	99.42 280	99.12 422	99.31 76	99.01 367	96.82 379	99.99 107	99.91 173

TESTMET0.1,1		99.08 147	98.96 152	99.44 200	99.63 217	99.38 172	100.00 1	99.45 111	95.53 368	99.48 273	100.00 1	99.71 15	99.02 365	96.84 378	99.99 107	99.91 173

test-mter		98.96 178	98.82 170	99.40 212	99.40 329	99.28 185	100.00 1	99.45 111	95.44 379	99.42 280	99.12 422	99.70 16	99.01 367	96.82 379	99.99 107	99.91 173

UniMVSNet_NR-MVSNet		97.16 331	96.80 335	98.22 334	98.38 415	98.41 272	100.00 1	99.45 111	96.14 348	97.76 408	99.64 369	95.05 264	98.50 423	97.98 334	86.84 470	97.75 364

PatchmatchNet		99.03 157	98.96 152	99.26 256	99.49 291	98.33 288	99.38 446	99.45 111	96.64 301	99.96 155	99.58 383	99.49 46	99.50 328	97.63 351	99.00 206	99.93 167
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KinetiMVS		98.61 234	98.26 260	99.65 161	99.46 308	99.24 192	99.96 322	99.44 125	97.54 186	99.99 130	99.99 245	90.83 364	99.95 184	97.18 367	99.92 142	99.75 297

dongtai		98.29 271	98.25 261	98.42 316	99.58 239	95.86 394	100.00 1	99.44 125	93.46 433	99.69 252	99.97 265	97.53 195	99.51 325	96.28 394	98.27 263	99.89 192

kuosan		98.55 245	98.53 221	98.62 301	99.66 206	96.16 389	100.00 1	99.44 125	93.93 420	99.81 234	99.98 253	97.58 190	99.81 256	98.08 329	98.28 260	99.89 192

EI-MVSNet-UG-set		99.69 42	99.63 44	99.87 98	99.99 53	99.64 129	99.95 331	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	99.98 304	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		98.70 218	98.51 227	99.28 252	99.51 278	98.39 276	99.24 460	99.44 125	95.52 370	99.96 155	99.70 352	97.57 192	99.58 302	97.11 369	98.54 220	99.88 205

baseline1		98.91 188	98.61 206	99.81 118	99.71 178	99.77 108	99.78 386	99.44 125	97.51 193	98.81 336	99.99 245	98.25 162	99.76 274	98.60 308	95.41 349	99.89 192

F-COLMAP		99.64 55	99.64 40	99.67 156	99.99 53	99.07 208	100.00 1	99.44 125	98.30 115	99.90 205	100.00 1	99.18 92	99.99 107	99.91 120	100.00 1	99.94 156

testing915		99.30 115	99.13 127	99.80 124	99.62 224	99.52 150	100.00 1	99.43 134	96.98 247	99.98 140	99.93 303	98.29 161	99.89 222	99.76 154	98.21 271	100.00 1

FBQ-MVS		99.13 143	99.11 131	99.21 260	99.64 214	97.94 327	100.00 1	99.43 134	96.78 271	99.97 147	99.92 305	99.03 113	99.84 246	99.18 272	98.01 289	99.86 220

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	98.07 287	99.88 205

UWE-MVS-28		99.29 119	99.23 112	99.48 191	99.73 176	98.86 232	100.00 1	99.43 134	96.97 250	99.99 130	99.83 325	99.43 60	99.77 269	99.35 256	98.31 254	99.80 282

testing91		99.18 136	99.10 132	99.41 207	99.60 230	98.43 270	100.00 1	99.43 134	96.76 275	99.82 228	99.92 305	99.05 107	99.98 142	99.62 208	97.67 317	99.81 251

testing11		99.26 124	99.19 119	99.46 193	99.64 214	98.61 254	100.00 1	99.43 134	96.94 253	99.92 200	99.94 297	99.43 60	99.97 151	99.67 189	97.79 311	99.82 234

testing99		99.18 136	99.10 132	99.41 207	99.60 230	98.43 270	100.00 1	99.43 134	96.76 275	99.84 216	99.92 305	99.06 105	99.98 142	99.62 208	97.67 317	99.81 251

UBG		99.36 101	99.27 99	99.63 163	99.63 217	99.01 217	100.00 1	99.43 134	96.99 246	100.00 1	99.92 305	99.69 17	99.99 107	99.74 161	98.06 288	99.88 205

UWE-MVS		99.18 136	99.06 136	99.51 183	99.67 197	98.80 237	100.00 1	99.43 134	96.80 268	99.93 198	99.86 317	99.79 8	99.94 197	97.78 346	98.33 250	99.80 282

testing222		99.14 142	98.94 157	99.73 145	99.67 197	99.51 152	100.00 1	99.43 134	96.90 259	99.99 130	99.90 311	98.55 152	99.86 235	98.85 290	97.18 324	99.81 251

WB-MVSnew		97.02 341	97.24 320	96.37 430	99.44 318	97.36 354	100.00 1	99.43 134	96.12 349	99.35 290	99.89 312	93.60 307	98.42 430	88.91 485	98.39 232	93.33 518

FE-MVS		99.16 140	98.99 148	99.66 159	99.65 208	99.18 200	99.58 423	99.43 134	95.24 380	99.91 203	99.59 381	99.37 70	99.97 151	98.31 320	99.81 169	99.83 227

FA-MVS(test-final)		99.00 166	98.75 180	99.73 145	99.63 217	99.43 168	99.83 371	99.43 134	95.84 359	99.52 269	99.37 408	97.84 180	99.96 171	97.63 351	99.68 179	99.79 288

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	97.64 350	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		97.95 290	97.73 297	98.62 301	98.53 410	99.24 192	100.00 1	99.43 134	96.74 281	97.87 405	99.82 329	95.27 255	98.89 382	98.78 294	93.07 390	97.74 392

FC-MVSNet-test		97.84 295	97.63 303	98.45 312	98.30 422	99.05 211	100.00 1	99.43 134	96.63 305	97.61 417	99.82 329	95.19 260	98.57 418	98.64 303	93.05 391	97.73 404

WR-MVS_H		96.73 350	96.32 358	97.95 363	98.26 426	97.88 333	99.72 404	99.43 134	95.06 383	96.99 433	98.68 458	93.02 326	98.53 421	97.43 359	88.33 456	97.43 457

JIA-IIPM		97.09 334	96.34 356	99.36 222	98.88 390	98.59 256	99.81 375	99.43 134	84.81 498	99.96 155	90.34 530	98.55 152	99.52 323	97.00 372	98.28 260	99.98 129

EPP-MVSNet		99.10 146	99.00 146	99.40 212	99.51 278	98.68 248	99.92 347	99.43 134	95.47 374	99.65 261	100.00 1	99.51 39	99.76 274	99.53 231	98.00 290	99.75 297

reproduce_model		99.76 21	99.69 25	99.98 29	99.96 104	99.93 54	100.00 1	99.42 155	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	99.42 155	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	99.42 155	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	99.42 155	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	99.42 155	98.52 97	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 155					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 155					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 6		99.42 155	98.72 86	100.00 1	100.00 1	99.60 21

hse-mvs2		96.79 347	96.38 353	98.04 358	99.68 189	95.54 399	99.81 375	99.42 155	98.21 118	100.00 1	99.80 338	97.49 197	99.46 337	99.72 167	73.27 517	99.12 340

AUN-MVS		96.26 378	95.67 390	98.06 352	99.68 189	95.60 398	99.82 374	99.42 155	96.78 271	99.88 212	99.80 338	94.84 270	99.47 332	97.48 357	73.29 516	99.12 340

SR-MVS-dyc-post		99.63 59	99.52 69	99.97 41	99.99 53	99.91 65	100.00 1	99.42 155	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	99.42 155	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	99.42 155	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		99.42 155	99.12 9	100.00 1				100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 155	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		99.42 155	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	99.42 155	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	99.42 155	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	99.42 155	98.93 50

DVP-MVS		99.83 10	99.78 10	100.00 1	100.00 1	99.99 6	100.00 1	99.42 155	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	99.42 155					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 6	100.00 1	99.42 155	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	99.42 155	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	99.42 155	97.53 189	99.77 239	100.00 1	98.77 141	100.00 1	99.99 78	100.00 1	99.99 126
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	99.42 155	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	99.42 155	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								99.42 155

MTAPA		99.68 47	99.59 50	99.97 41	99.99 53	99.91 65	100.00 1	99.42 155	98.32 114	99.94 193	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	99.42 155	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	99.42 155	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	99.42 155	97.70 163	100.00 1	100.00 1	99.51 39	99.98 142

agg_prior					100.00 1	99.88 86		99.42 155		100.00 1			99.97 151

PS-MVSNAJ		99.64 55	99.57 56	99.85 105	99.78 168	99.81 101	99.95 331	99.42 155	98.38 106	100.00 1	100.00 1	98.75 142	100.00 1	99.88 125	99.99 107	99.74 304

SD-MVS		99.81 14	99.75 17	99.99 13	99.99 53	99.96 31	100.00 1	99.42 155	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	99.42 155	97.82 153	99.99 130	100.00 1	98.20 163	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	99.42 155	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		99.42 155		100.00 1		99.24 87	99.97 151		100.00 1	100.00 1

UniMVSNet (Re)		97.29 327	96.85 334	98.59 304	98.49 411	99.13 204	100.00 1	99.42 155	96.52 317	98.24 388	98.90 446	94.93 267	98.89 382	97.54 355	87.61 461	97.75 364

mPP-MVS		99.69 42	99.60 49	99.97 41	100.00 1	99.91 65	100.00 1	99.42 155	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	99.42 155	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	99.42 155	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								99.42 155

APD-MVS		99.68 47	99.58 53	99.97 41	99.99 53	99.96 31	100.00 1	99.42 155	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	99.48 162	100.00 1	99.42 155	95.53 368	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	99.42 155	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	99.42 155	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		95.26 408	93.82 419	99.56 179	99.31 340	98.86 232	99.13 480	99.42 155	79.82 510	99.96 155	95.13 512	95.69 248	99.98 142	77.54 522	98.40 230	99.84 224

CPTT-MVS		99.49 81	99.38 84	99.85 105	100.00 1	99.54 144	100.00 1	99.42 155	97.58 183	99.98 140	100.00 1	97.43 203	100.00 1	99.99 78	100.00 1	100.00 1

DeepPCF-MVS	98.03 4	98.54 248	99.72 22	94.98 452	99.99 53	84.94 497	100.00 1	99.42 155	99.98 1	100.00 1	100.00 1	98.11 166	100.00 1	100.00 1	100.00 1	100.00 1

test_fmvsm_n_1920		99.55 74	99.49 74	99.73 145	99.85 129	99.19 198	100.00 1	99.41 204	98.87 65	100.00 1	100.00 1	97.34 206	100.00 1	99.98 93	99.90 149	100.00 1

patch_mono-2		99.04 154	99.79 9	96.81 419	99.92 116	90.47 477	100.00 1	99.41 204	98.95 43	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 151

ADS-MVSNet2		98.28 273	98.51 227	97.62 377	99.51 278	95.03 410	99.24 460	99.41 204	95.52 370	99.96 155	99.70 352	97.57 192	97.94 473	97.11 369	98.54 220	99.88 205

Vis-MVSNet		98.52 250	98.25 261	99.34 226	99.68 189	98.55 258	99.68 411	99.41 204	97.34 213	99.94 193	100.00 1	90.38 377	99.70 290	99.03 281	98.84 208	99.76 296
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
0.3-1-1-0.015		97.60 307	97.19 323	98.83 288	99.13 355	96.55 384	100.00 1	99.40 208	94.19 413	99.83 219	99.81 332	99.18 92	99.97 151	99.70 175	83.50 489	99.98 129

0.4-1-1-0.1		97.56 310	97.15 327	98.79 293	99.01 371	96.44 387	100.00 1	99.40 208	94.11 416	99.81 234	99.81 332	99.09 100	99.97 151	99.65 198	83.48 491	99.98 129

0.4-1-1-0.2		97.60 307	97.18 324	98.86 286	99.05 368	96.62 382	100.00 1	99.40 208	94.24 408	99.82 228	99.81 332	99.09 100	99.97 151	99.70 175	83.50 489	99.98 129

xiu_mvs_v2_base		99.51 76	99.41 81	99.82 113	99.70 181	99.73 115	99.92 347	99.40 208	98.15 124	100.00 1	100.00 1	98.50 154	100.00 1	99.85 132	99.13 200	99.74 304

Anonymous20231211		96.29 376	95.70 386	98.07 348	99.80 158	97.49 348	99.15 478	99.40 208	89.11 473	97.75 411	99.45 402	88.93 406	98.98 371	98.26 325	89.47 444	97.73 404

AllTest		98.55 245	98.40 244	98.99 275	99.93 113	97.35 355	100.00 1	99.40 208	97.08 238	99.09 310	99.98 253	93.37 312	99.95 184	96.94 373	99.84 164	99.68 316

TestCases				98.99 275	99.93 113	97.35 355		99.40 208	97.08 238	99.09 310	99.98 253	93.37 312	99.95 184	96.94 373	99.84 164	99.68 316

HQP_MVS		97.71 302	97.82 293	97.37 384	99.00 376	94.80 419	100.00 1	99.40 208	99.00 33	99.08 312	99.97 265	88.58 414	99.55 314	99.79 144	95.57 347	97.76 353

plane_prior5								99.40 208					99.55 314	99.79 144	95.57 347	97.76 353

HQP3-MVS								99.40 208							95.58 343

DP-MVS		98.86 195	98.54 219	99.81 118	99.97 98	99.45 164	99.52 431	99.40 208	94.35 407	98.36 374	100.00 1	96.13 238	99.97 151	99.12 276	100.00 1	100.00 1

HQP-MVS		97.73 300	97.85 291	97.39 383	99.07 362	94.82 416	100.00 1	99.40 208	99.04 20	99.17 300	99.97 265	88.61 412	99.57 304	99.79 144	95.58 343	97.77 351

OMC-MVS		99.27 122	99.38 84	98.96 278	99.95 108	97.06 370	100.00 1	99.40 208	98.83 71	99.88 212	100.00 1	97.01 213	99.86 235	99.47 240	99.84 164	99.97 139

DeepC-MVS	97.84 5	99.00 166	98.80 173	99.60 170	99.93 113	99.03 213	100.00 1	99.40 208	98.61 93	99.33 291	100.00 1	92.23 341	99.95 184	99.74 161	99.96 127	99.83 227
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	97.64 303	97.37 312	98.45 312	99.94 111	95.70 397	100.00 1	99.40 208	97.65 169	99.53 268	100.00 1	99.31 76	99.66 292	80.48 512	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	98.29 271	98.17 269	98.65 299	99.94 111	97.39 352	99.30 455	99.40 208	95.64 363	97.75 411	100.00 1	92.69 334	99.95 184	98.89 288	99.92 142	98.62 348
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		98.99 170	98.69 194	99.89 91	99.70 181	99.69 124	100.00 1	99.39 224	98.93 50	100.00 1	100.00 1	90.20 378	99.99 107	100.00 1	99.95 129	100.00 1

cdsmvs_eth3d_5k		24.41 525	32.55 527	0.00 543	0.00 567	0.00 570	0.00 555	99.39 224	0.00 562	0.00 563	100.00 1	93.55 308	0.00 564	0.00 562	0.00 562	0.00 559

dp		98.72 211	98.61 206	99.03 272	99.53 257	97.39 352	99.45 438	99.39 224	95.62 365	99.94 193	99.52 393	98.83 136	99.82 252	96.77 384	98.42 228	99.89 192

nomal-1		98.99 170	99.02 142	98.88 284	99.47 299	97.25 364	100.00 1	99.38 227	96.38 330	99.90 205	99.94 297	98.78 140	99.56 308	99.40 251	97.94 297	99.83 227

fmvsm_s_conf0.5_n_8		99.34 106	99.14 126	99.91 84	99.83 136	99.74 113	100.00 1	99.38 227	98.94 46	100.00 1	100.00 1	94.25 290	99.99 107	100.00 1	99.91 147	100.00 1

fmvsm_s_conf0.5_n		99.21 133	99.01 143	99.83 111	99.84 131	99.53 146	100.00 1	99.38 227	98.29 116	100.00 1	100.00 1	93.62 306	99.99 107	99.99 78	99.93 139	99.98 129

VDD-MVS		96.58 359	95.99 370	98.34 322	99.52 271	95.33 404	99.18 470	99.38 227	96.64 301	99.77 239	100.00 1	72.51 493	100.00 1	100.00 1	96.94 330	99.70 314

CMPMVS	66.12 22	90.65 456	92.04 444	86.46 494	96.18 487	66.87 533	98.03 516	99.38 227	83.38 502	85.49 510	99.55 389	77.59 477	98.80 389	94.44 429	94.31 379	93.72 516
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DU-MVS		96.93 344	96.49 348	98.22 334	98.31 420	98.41 272	100.00 1	99.37 232	96.41 328	97.76 408	99.65 365	92.14 343	98.50 423	97.98 334	86.84 470	97.75 364

LPG-MVS_test		97.31 325	97.32 314	97.28 391	98.85 396	94.60 429	100.00 1	99.37 232	97.35 210	98.85 331	99.98 253	86.66 431	99.56 308	99.55 224	95.26 355	97.70 420

LGP-MVS_train				97.28 391	98.85 396	94.60 429		99.37 232	97.35 210	98.85 331	99.98 253	86.66 431	99.56 308	99.55 224	95.26 355	97.70 420

tpm2		98.64 224	98.58 213	98.81 292	99.42 322	97.12 367	99.69 409	99.37 232	93.63 427	99.94 193	99.67 360	98.96 122	99.47 332	98.62 307	97.95 296	99.83 227

CDS-MVSNet		98.96 178	98.95 156	99.01 274	99.48 294	98.36 283	99.93 345	99.37 232	96.79 269	99.31 293	99.83 325	99.77 11	98.91 379	98.07 331	97.98 292	99.77 294
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.90 190	98.63 202	99.70 151	99.92 116	99.25 190	100.00 1	99.37 232	95.71 361	99.40 286	100.00 1	96.58 230	99.95 184	96.80 381	99.94 135	99.91 173

fmvsm_l_conf0.5_n_a		99.63 59	99.55 63	99.86 101	99.83 136	99.58 137	100.00 1	99.36 238	98.98 36	100.00 1	100.00 1	97.85 178	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	99.36 238	98.98 36	100.00 1	100.00 1	97.92 173	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	99.36 238	98.94 46	100.00 1	100.00 1	97.97 170	100.00 1	99.88 125	99.28 196	100.00 1

xiu_mvs_v1_base_debu		99.35 102	99.21 114	99.79 130	99.67 197	99.71 118	99.78 386	99.36 238	98.13 126	100.00 1	100.00 1	97.00 216	100.00 1	99.83 136	99.07 202	99.66 318

xiu_mvs_v1_base		99.35 102	99.21 114	99.79 130	99.67 197	99.71 118	99.78 386	99.36 238	98.13 126	100.00 1	100.00 1	97.00 216	100.00 1	99.83 136	99.07 202	99.66 318

xiu_mvs_v1_base_debi		99.35 102	99.21 114	99.79 130	99.67 197	99.71 118	99.78 386	99.36 238	98.13 126	100.00 1	100.00 1	97.00 216	100.00 1	99.83 136	99.07 202	99.66 318

TSAR-MVS + GP.		99.61 66	99.69 25	99.35 224	99.99 53	98.06 317	100.00 1	99.36 238	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		98.27 274	98.31 257	98.14 342	99.59 234	95.92 391	100.00 1	99.36 238	98.48 99	99.21 299	100.00 1	89.27 400	99.94 197	99.76 154	99.17 198	98.56 349

XVG-OURS		98.30 268	98.36 253	98.13 345	99.58 239	95.91 392	100.00 1	99.36 238	98.69 87	99.23 298	100.00 1	91.20 353	99.92 207	99.34 258	97.82 307	98.56 349

tpmrst		98.98 175	98.93 159	99.14 265	99.61 227	97.74 340	99.52 431	99.36 238	96.05 350	99.98 140	99.64 369	99.04 110	99.86 235	98.94 285	98.19 275	99.82 234

UnsupCasMVSNet_eth		94.25 418	93.89 418	95.34 445	97.63 459	92.13 463	99.73 401	99.36 238	94.88 386	92.78 480	98.63 460	82.72 458	96.53 493	94.57 427	84.73 480	97.36 460

fmvsm_l_mol_unc0.5_1		99.69 42	99.59 50	99.99 13	99.84 131	99.99 6	100.00 1	99.35 249	99.02 31	100.00 1	100.00 1	98.09 168	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	99.35 249	98.94 46	100.00 1	100.00 1	94.77 272	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	99.47 163	100.00 1	99.35 249	98.22 117	100.00 1	100.00 1	95.21 259	99.99 107	99.96 107	99.86 159	99.98 129

UniMVSNet_ETH3D		95.28 407	94.41 414	97.89 368	98.91 387	95.14 407	99.13 480	99.35 249	92.11 452	97.17 431	99.66 362	70.28 498	99.36 345	97.88 339	95.18 362	99.16 338

NR-MVSNet		96.63 356	96.04 368	98.38 319	98.31 420	98.98 222	99.22 469	99.35 249	95.87 354	94.43 472	99.65 365	92.73 332	98.40 431	96.78 382	88.05 457	97.75 364

TranMVSNet+NR-MVSNet		96.45 366	96.01 369	97.79 373	98.00 443	97.62 345	100.00 1	99.35 249	95.98 351	97.31 426	99.64 369	90.09 385	98.00 469	96.89 377	86.80 473	97.75 364

CostFormer		98.84 198	98.77 178	99.04 271	99.41 324	97.58 346	99.67 412	99.35 249	94.66 396	99.96 155	99.36 409	99.28 84	99.74 280	99.41 249	97.81 308	99.81 251

MIMVSNet		97.06 337	96.73 338	98.05 356	99.38 333	96.64 381	98.47 509	99.35 249	93.41 434	99.48 273	98.53 469	89.66 394	97.70 482	94.16 434	98.11 285	99.80 282

SD_0403		97.92 291	98.43 235	96.39 428	99.68 189	89.74 483	99.92 347	99.34 257	96.75 278	99.39 287	99.93 303	93.54 309	99.51 325	99.11 277	98.21 271	99.92 169

TAMVS		98.76 207	98.73 184	98.86 286	99.44 318	97.69 341	99.57 424	99.34 257	96.57 310	99.12 306	99.81 332	98.83 136	99.16 358	97.97 337	97.91 299	99.73 313

CLD-MVS		97.64 303	97.74 295	97.36 385	99.01 371	94.76 424	100.00 1	99.34 257	99.30 4	99.00 318	99.97 265	87.49 423	99.57 304	99.96 107	95.58 343	97.75 364
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
reproduce_monomvs		98.61 234	98.54 219	98.82 289	99.97 98	99.28 185	100.00 1	99.33 260	98.51 98	97.87 405	99.24 416	99.98 3	99.45 338	99.02 282	92.93 393	97.74 392

WR-MVS		97.09 334	96.64 340	98.46 311	98.43 413	99.09 206	99.97 314	99.33 260	95.62 365	97.76 408	99.67 360	91.17 354	98.56 420	98.49 312	89.28 447	97.74 392

ACMH	96.25 11	96.77 348	96.62 342	97.21 394	98.96 383	94.43 436	99.64 414	99.33 260	97.43 204	96.55 445	99.97 265	83.52 455	99.54 317	99.07 280	95.13 365	97.66 432
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_9		99.04 154	98.78 175	99.81 118	99.86 127	99.44 167	100.00 1	99.32 263	98.94 46	100.00 1	100.00 1	91.00 359	99.99 107	100.00 1	99.94 135	100.00 1

guyue		99.21 133	99.07 135	99.62 165	99.55 250	99.29 183	100.00 1	99.32 263	97.66 167	99.96 155	100.00 1	95.84 243	99.84 246	99.63 206	99.67 181	99.75 297

BP-MVS1		99.56 72	99.48 77	99.79 130	99.48 294	99.61 132	100.00 1	99.32 263	97.34 213	99.94 193	100.00 1	99.74 13	99.89 222	99.75 159	99.72 175	99.87 216

WBMVS		98.19 278	98.10 275	98.47 310	99.63 217	99.03 213	100.00 1	99.32 263	95.46 375	98.39 373	99.40 406	99.69 17	98.61 409	98.64 303	92.39 402	97.76 353

PS-CasMVS		96.34 374	95.78 382	98.03 359	98.18 434	98.27 296	99.71 405	99.32 263	94.75 390	96.82 439	99.65 365	86.98 430	98.15 452	97.74 347	88.85 452	97.66 432

CP-MVSNet		96.73 350	96.25 359	98.18 338	98.21 431	98.67 249	99.77 391	99.32 263	95.06 383	97.20 430	99.65 365	90.10 384	98.19 450	98.06 332	88.90 451	97.66 432

XVG-ACMP-BASELINE		96.60 358	96.52 347	96.84 411	98.41 414	93.29 452	99.99 271	99.32 263	97.76 160	98.51 364	99.29 412	81.95 463	99.54 317	98.40 315	95.03 368	97.68 427

PatchT		95.90 396	94.95 413	98.75 296	99.03 369	98.39 276	99.08 487	99.32 263	85.52 495	99.96 155	94.99 515	97.94 171	98.05 468	80.20 514	98.47 225	99.81 251

ACMP	97.00 8	97.19 329	97.16 326	97.27 393	98.97 382	94.58 432	100.00 1	99.32 263	97.97 140	97.45 423	99.98 253	85.79 441	99.56 308	99.70 175	95.24 358	97.67 431
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_7		98.98 175	98.85 168	99.37 220	99.67 197	98.34 287	100.00 1	99.31 272	98.97 38	100.00 1	100.00 1	91.70 347	99.97 151	99.99 78	99.97 123	99.80 282

fmvsm_s_conf0.5_n_6		99.30 115	99.12 130	99.84 110	99.24 350	99.56 139	100.00 1	99.31 272	98.90 60	100.00 1	100.00 1	94.75 274	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	99.31 272	98.92 53	99.88 212	100.00 1	97.35 205	99.99 107	99.98 93	99.99 107	100.00 1

test_cas_vis1_n_1920		98.63 230	98.25 261	99.77 138	99.69 184	99.32 180	100.00 1	99.31 272	98.84 69	99.96 155	100.00 1	87.42 424	99.99 107	99.14 273	99.86 159	100.00 1

Vis-MVSNet (Re-imp)		98.99 170	98.89 166	99.29 249	99.64 214	98.89 231	99.98 304	99.31 272	96.74 281	99.48 273	100.00 1	98.11 166	99.10 360	98.39 316	98.34 247	99.89 192

ACMH+	96.20 13	96.49 365	96.33 357	97.00 401	99.06 366	93.80 445	99.81 375	99.31 272	97.32 216	95.89 458	99.97 265	82.62 460	99.54 317	98.34 319	94.63 376	97.65 437

fmvsm_l_conf0.5_n_9		99.35 102	99.15 124	99.95 62	99.83 136	99.84 97	100.00 1	99.30 278	98.92 53	100.00 1	100.00 1	94.32 288	100.00 1	100.00 1	99.93 139	100.00 1

SSC-MVS3.2		95.32 405	94.97 412	96.37 430	98.29 424	92.75 457	100.00 1	99.30 278	95.46 375	98.36 374	99.42 404	78.92 474	98.63 406	93.28 445	91.72 415	97.72 411

MVSMamba_PlusPlus		99.39 94	99.25 105	99.80 124	99.68 189	99.59 135	99.99 271	99.30 278	96.66 298	99.96 155	99.97 265	97.89 175	99.92 207	99.76 154	100.00 1	99.90 184

sasdasda		99.03 157	98.73 184	99.94 75	99.75 173	99.95 39	100.00 1	99.30 278	97.64 171	100.00 1	100.00 1	95.22 257	99.97 151	99.76 154	96.90 331	99.91 173

MS-PatchMatch		95.66 401	95.87 376	95.05 448	97.80 451	89.25 485	98.88 495	99.30 278	96.35 335	96.86 437	99.01 435	81.35 466	99.43 340	93.30 443	99.98 119	96.46 485

canonicalmvs		99.03 157	98.73 184	99.94 75	99.75 173	99.95 39	100.00 1	99.30 278	97.64 171	100.00 1	100.00 1	95.22 257	99.97 151	99.76 154	96.90 331	99.91 173

balanced_ft_v1		98.70 218	98.61 206	98.94 279	99.67 197	96.90 372	99.91 354	99.30 278	96.73 285	99.96 155	99.97 265	92.18 342	99.93 201	99.86 129	99.95 129	100.00 1

tpm cat1		98.05 284	97.76 294	98.92 281	99.50 287	97.10 369	99.77 391	99.30 278	90.20 469	99.72 249	98.71 456	97.71 185	99.86 235	96.75 385	98.20 274	99.81 251

PMMVS		99.12 144	98.97 151	99.58 176	99.57 243	98.98 222	100.00 1	99.30 278	97.14 231	99.96 155	100.00 1	96.53 234	99.82 252	99.70 175	98.49 223	99.94 156

usedtu_dtu_shiyan1		97.34 323	96.97 328	98.43 314	97.82 449	98.91 229	100.00 1	99.29 287	94.70 393	98.46 368	98.89 447	93.95 299	98.64 404	95.86 401	93.75 381	97.74 392

FE-MVSNET3		97.34 323	96.97 328	98.43 314	97.82 449	98.91 229	100.00 1	99.29 287	94.70 393	98.46 368	98.89 447	93.95 299	98.64 404	95.88 399	93.75 381	97.74 392

MGCFI-Net		99.01 165	98.70 192	99.93 79	99.74 175	99.94 48	100.00 1	99.29 287	97.60 181	100.00 1	100.00 1	95.10 263	99.96 171	99.74 161	96.85 333	99.91 173

CANet_DTU		99.02 163	98.90 165	99.41 207	99.88 124	98.71 245	100.00 1	99.29 287	98.84 69	100.00 1	100.00 1	94.02 296	100.00 1	98.08 329	99.96 127	99.52 328

EI-MVSNet		97.98 287	97.93 286	98.16 341	99.11 357	97.84 336	99.74 396	99.29 287	94.39 406	98.65 346	100.00 1	97.21 207	98.88 385	97.62 354	95.31 353	97.75 364

PEN-MVS		96.01 393	95.48 398	97.58 379	97.74 454	97.26 361	99.90 356	99.29 287	94.55 399	96.79 440	99.55 389	87.38 425	97.84 475	96.92 376	87.24 467	97.65 437

MVSTER		98.58 239	98.52 222	98.77 295	99.65 208	99.68 125	100.00 1	99.29 287	95.63 364	98.65 346	99.80 338	99.78 9	98.88 385	98.59 309	95.31 353	97.73 404

XXY-MVS		97.14 333	96.63 341	98.67 298	98.65 403	98.92 228	99.54 429	99.29 287	95.57 367	97.63 414	99.83 325	87.79 421	99.35 347	98.39 316	92.95 392	97.75 364

gbinet_0.2-2-1-0.02		93.73 427	92.69 439	96.84 411	94.91 510	94.62 428	100.00 1	99.28 295	87.02 490	98.53 360	98.45 473	89.72 392	98.15 452	96.65 386	69.64 529	97.74 392

fmvsm_s_conf0.5_n_10		99.08 147	98.78 175	99.97 41	99.84 131	99.92 61	100.00 1	99.28 295	98.93 50	100.00 1	100.00 1	91.07 356	99.99 107	100.00 1	99.95 129	100.00 1

NormalMVS		99.47 85	99.48 77	99.43 203	99.99 53	98.55 258	99.94 339	99.28 295	98.39 104	100.00 1	100.00 1	98.44 156	99.98 142	99.36 252	99.92 142	99.75 297

Elysia		98.12 280	97.72 298	99.34 226	99.30 343	98.96 225	99.95 331	99.28 295	96.64 301	99.75 242	99.99 245	88.71 409	99.81 256	95.99 397	99.84 164	99.26 335

StellarMVS		98.12 280	97.72 298	99.34 226	99.30 343	98.96 225	99.95 331	99.28 295	96.64 301	99.75 242	99.99 245	88.71 409	99.81 256	95.99 397	99.84 164	99.26 335

BridgeMVS		99.43 90	99.28 97	99.85 105	99.68 189	99.68 125	99.97 314	99.28 295	97.03 243	99.96 155	99.97 265	97.90 174	99.93 201	99.77 152	100.00 1	99.94 156

cl22		98.23 276	98.11 272	98.58 306	99.82 139	99.01 217	100.00 1	99.28 295	96.92 256	98.33 378	99.21 419	98.09 168	98.97 373	98.72 298	92.61 397	97.76 353

OPM-MVS		97.21 328	97.18 324	97.32 388	98.08 438	94.66 425	100.00 1	99.28 295	98.65 91	98.92 325	99.98 253	86.03 439	99.56 308	98.28 324	95.41 349	97.72 411
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvsmamba		99.05 153	98.98 149	99.27 255	99.57 243	98.10 313	100.00 1	99.28 295	95.92 353	99.96 155	99.97 265	96.73 227	99.89 222	99.72 167	99.65 184	99.81 251

BH-w/o		98.82 200	98.81 172	98.88 284	99.62 224	96.71 378	100.00 1	99.28 295	97.09 236	98.81 336	100.00 1	94.91 268	99.96 171	99.54 228	100.00 1	99.96 145

BH-untuned		98.64 224	98.65 199	98.60 303	99.59 234	96.17 388	100.00 1	99.28 295	96.67 297	98.41 371	100.00 1	94.52 281	99.83 249	99.41 249	100.00 1	99.81 251

UnsupCasMVSNet_bld		89.50 461	88.00 468	93.99 464	95.30 501	88.86 488	98.52 508	99.28 295	85.50 496	87.80 501	94.11 518	61.63 506	96.96 487	90.63 465	79.26 506	96.15 492

FMVSNet3		97.30 326	96.95 330	98.37 320	99.65 208	99.25 190	99.71 405	99.28 295	94.23 409	98.53 360	98.91 445	93.30 314	98.11 458	95.31 416	93.60 384	97.73 404

BH-RMVSNet		98.46 254	98.08 276	99.59 172	99.61 227	99.19 198	100.00 1	99.28 295	97.06 240	98.95 321	100.00 1	88.99 404	99.82 252	98.83 293	100.00 1	99.77 294

LTVRE_ROB	95.29 16	96.32 375	96.10 365	96.99 402	98.55 408	93.88 444	99.45 438	99.28 295	94.50 402	96.46 446	99.52 393	84.86 446	99.48 330	97.26 366	95.03 368	97.59 447
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		98.77 204	98.42 236	99.82 113	99.47 299	99.52 150	100.00 1	99.27 310	97.53 189	100.00 1	100.00 1	89.73 391	99.96 171	99.84 135	99.93 139	99.97 139

EIA-MVS		99.26 124	99.19 119	99.45 197	99.63 217	98.75 240	100.00 1	99.27 310	96.93 254	99.95 187	100.00 1	97.47 199	99.79 261	99.74 161	99.72 175	99.82 234

GBi-Net		96.07 390	95.80 380	96.89 408	99.53 257	94.87 413	99.18 470	99.27 310	93.71 422	98.53 360	98.81 452	84.23 450	98.07 464	95.31 416	93.60 384	97.72 411

test1		96.07 390	95.80 380	96.89 408	99.53 257	94.87 413	99.18 470	99.27 310	93.71 422	98.53 360	98.81 452	84.23 450	98.07 464	95.31 416	93.60 384	97.72 411

FMVSNet2		96.22 380	95.60 392	98.06 352	99.53 257	98.33 288	99.45 438	99.27 310	93.71 422	98.03 395	98.84 450	84.23 450	98.10 462	93.97 436	93.40 387	97.73 404

FMVSNet1		94.45 415	93.63 422	96.89 408	98.87 393	94.87 413	99.18 470	99.27 310	90.95 461	97.31 426	98.81 452	72.89 492	98.07 464	92.61 449	92.81 394	97.72 411

fmvsm_s_conf0.5_n_5		99.00 166	98.70 192	99.88 96	99.81 145	99.64 129	100.00 1	99.26 316	98.78 84	99.97 147	100.00 1	90.65 366	99.99 107	100.00 1	99.89 150	99.99 126

fmvsm_s_conf0.5_n_4		98.98 175	98.74 182	99.68 155	99.81 145	99.50 154	100.00 1	99.26 316	98.91 56	100.00 1	100.00 1	90.87 363	99.97 151	99.99 78	99.81 169	99.57 324

fmvsm_s_conf0.1_n_a		98.71 215	98.36 253	99.78 135	99.09 360	99.42 169	100.00 1	99.26 316	97.42 205	100.00 1	100.00 1	89.78 389	99.96 171	99.82 141	99.85 162	99.97 139

test_fmvsmvis_n_1920		99.46 86	99.37 87	99.73 145	98.88 390	99.18 200	100.00 1	99.26 316	98.85 67	99.79 236	100.00 1	97.70 186	100.00 1	99.98 93	99.86 159	100.00 1

PRO-TEST		99.18 136	99.03 139	99.61 167	99.71 178	99.37 175	100.00 1	99.25 320	97.51 193	99.96 155	100.00 1	95.41 252	99.66 292	99.75 159	99.69 178	99.82 234

fmvsm_s_conf0.5_n_2		98.90 190	98.57 214	99.90 88	99.79 163	99.78 105	100.00 1	99.25 320	98.97 38	100.00 1	100.00 1	89.22 401	99.99 107	100.00 1	99.88 153	99.92 169

test_vis1_n_1920		97.77 299	97.24 320	99.34 226	99.79 163	98.04 319	100.00 1	99.25 320	98.88 62	100.00 1	100.00 1	77.52 478	100.00 1	99.88 125	99.85 162	100.00 1

CL-MVSNet_self_test		91.07 454	90.35 456	93.24 469	93.27 514	89.16 486	99.55 427	99.25 320	92.34 451	95.23 462	97.05 496	88.86 408	93.59 515	80.67 511	66.95 537	96.96 472

ETV-MVS		99.34 106	99.24 109	99.64 162	99.58 239	99.33 179	100.00 1	99.25 320	97.57 184	99.96 155	100.00 1	97.44 202	99.79 261	99.70 175	99.65 184	99.81 251

DTE-MVSNet		95.52 402	94.99 411	97.08 397	97.49 469	96.45 386	100.00 1	99.25 320	93.82 421	96.17 451	99.57 387	87.81 420	97.18 485	94.57 427	86.26 476	97.62 443

ACMM	97.17 6	97.37 321	97.40 310	97.29 390	99.01 371	94.64 427	100.00 1	99.25 320	98.07 132	98.44 370	99.98 253	87.38 425	99.55 314	99.25 264	95.19 361	97.69 425
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_11		98.92 185	98.63 202	99.80 124	99.85 129	99.86 91	100.00 1	99.24 327	98.91 56	100.00 1	100.00 1	89.69 393	99.99 107	100.00 1	99.98 119	99.54 326

ppachtmachnet_test		96.17 384	95.89 374	97.02 400	97.61 461	95.24 405	99.99 271	99.24 327	93.31 438	96.71 443	99.62 377	94.34 287	98.07 464	89.87 474	92.30 405	97.75 364

GA-MVS		97.72 301	97.27 318	99.06 267	99.24 350	97.93 329	100.00 1	99.24 327	95.80 360	98.99 319	99.64 369	89.77 390	99.36 345	95.12 420	97.62 321	99.89 192

FMVSNet5		95.32 405	95.43 401	94.99 451	99.39 332	92.99 455	99.25 459	99.24 327	90.45 465	97.44 424	98.45 473	95.78 245	94.39 509	87.02 490	91.88 411	97.59 447

IS-MVSNet		99.08 147	98.91 162	99.59 172	99.65 208	99.38 172	99.78 386	99.24 327	96.70 293	99.51 270	100.00 1	98.44 156	99.52 323	98.47 313	98.39 232	99.88 205

AstraMVS		99.03 157	99.01 143	99.09 266	99.46 308	97.66 343	100.00 1	99.23 332	97.83 151	99.95 187	100.00 1	95.52 251	99.86 235	99.74 161	99.39 195	99.74 304

fmvsm_s_conf0.1_n_2		98.95 181	98.69 194	99.73 145	99.61 227	99.74 113	100.00 1	99.23 332	98.95 43	99.97 147	100.00 1	90.92 362	99.97 151	100.00 1	99.58 189	99.47 331

test_fmvsmconf0.1_n		99.25 128	99.05 137	99.82 113	98.92 386	99.55 141	100.00 1	99.23 332	98.91 56	99.75 242	99.97 265	94.79 271	99.94 197	99.94 115	99.99 107	99.97 139

PS-MVSNAJss		98.03 285	98.06 279	97.94 364	97.63 459	97.33 358	99.89 360	99.23 332	96.27 340	98.03 395	99.59 381	98.75 142	98.78 390	98.52 311	94.61 377	97.70 420

K. test v3		95.46 404	95.14 408	96.40 427	97.53 466	93.40 450	99.99 271	99.23 332	95.49 373	92.70 483	99.73 346	84.26 449	98.12 456	93.94 437	93.38 388	97.68 427

wanda-best-256-512		93.76 424	92.74 436	96.84 411	95.22 502	94.54 433	100.00 1	99.22 337	87.22 484	98.54 355	98.56 464	90.48 371	98.22 447	95.67 406	69.73 525	97.75 364

FE-blended-shiyan7		93.76 424	92.74 436	96.84 411	95.22 502	94.54 433	100.00 1	99.22 337	87.22 484	98.54 355	98.56 464	90.48 371	98.22 447	95.67 406	69.73 525	97.75 364

blended_shiyan6		93.70 429	92.67 441	96.78 421	95.17 506	94.38 440	100.00 1	99.22 337	87.03 489	98.54 355	98.56 464	90.14 380	98.22 447	95.62 410	69.73 525	97.75 364

blend_shiyan4		95.76 398	95.40 404	96.82 417	95.50 500	94.40 437	100.00 1	99.22 337	87.12 486	98.67 344	98.59 461	99.09 100	98.31 438	96.31 392	84.14 484	97.75 364

E3new		98.95 181	98.80 173	99.41 207	99.57 243	98.50 267	100.00 1	99.22 337	96.84 264	99.89 209	100.00 1	95.70 247	99.93 201	99.57 221	98.39 232	99.82 234

E2		98.77 204	98.57 214	99.37 220	99.53 257	98.38 279	99.98 304	99.22 337	96.77 274	99.75 242	100.00 1	94.03 294	99.91 209	99.53 231	98.35 243	99.82 234

E3		98.77 204	98.57 214	99.36 222	99.47 299	98.36 283	99.98 304	99.22 337	96.76 275	99.75 242	100.00 1	94.10 291	99.91 209	99.53 231	98.35 243	99.82 234

viewdifsd2359ckpt13		98.72 211	98.52 222	99.34 226	99.55 250	98.46 269	99.99 271	99.22 337	96.50 320	99.05 315	100.00 1	94.54 280	99.73 284	99.46 243	98.35 243	99.81 251

viewcassd2359sk11		98.90 190	98.73 184	99.40 212	99.57 243	98.47 268	99.99 271	99.22 337	96.79 269	99.82 228	100.00 1	95.24 256	99.91 209	99.54 228	98.38 236	99.82 234

viewdifsd2359ckpt11		97.98 287	97.89 287	98.26 330	99.47 299	94.98 412	99.99 271	99.22 337	96.74 281	99.24 296	100.00 1	90.14 380	99.90 220	99.49 237	96.73 334	99.90 184

viewmsd2359difaftdt		97.98 287	97.89 287	98.27 327	99.47 299	94.99 411	99.99 271	99.22 337	96.74 281	99.24 296	100.00 1	90.14 380	99.90 220	99.49 237	96.73 334	99.90 184

viewmanbaseed2359cas		98.86 195	98.68 196	99.40 212	99.51 278	98.51 266	99.98 304	99.22 337	97.05 241	99.72 249	100.00 1	94.77 272	99.89 222	99.58 218	98.31 254	99.81 251

LuminaMVS		99.07 150	98.92 161	99.50 186	98.87 393	99.12 205	99.92 347	99.22 337	97.45 201	99.82 228	99.98 253	96.29 237	99.85 242	99.71 171	99.05 205	99.52 328

miper_enhance_ethall		98.33 266	98.27 259	98.51 308	99.66 206	99.04 212	100.00 1	99.22 337	97.53 189	98.51 364	99.38 407	99.49 46	98.75 395	98.02 333	92.61 397	97.76 353

nrg030		97.64 303	97.27 318	98.75 296	98.34 416	99.53 146	100.00 1	99.22 337	96.21 346	98.27 384	99.95 291	94.40 284	98.98 371	99.23 267	89.78 440	97.75 364

lessismore_v0				96.05 437	97.55 465	91.80 466		99.22 337		91.87 484	99.91 309	83.50 456	98.68 398	92.48 452	90.42 436	97.68 427

casdiffmvs_mvg		98.64 224	98.39 247	99.40 212	99.50 287	98.60 255	100.00 1	99.22 337	96.85 262	99.10 308	100.00 1	92.75 330	99.78 267	99.71 171	98.35 243	99.81 251
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		98.43 256	98.07 278	99.50 186	99.65 208	99.02 215	100.00 1	99.22 337	94.21 411	99.72 249	99.98 253	92.03 345	99.93 201	99.68 185	98.12 284	99.54 326

MIMVSNet1		91.96 444	91.20 448	94.23 462	94.94 509	91.69 467	99.34 450	99.22 337	88.23 477	94.18 473	98.45 473	75.52 486	93.41 517	79.37 515	91.49 420	97.60 446

E5new		98.63 230	98.41 238	99.31 242	99.51 278	98.21 302	99.79 381	99.21 356	96.62 306	99.67 259	100.00 1	93.15 320	99.91 209	99.46 243	98.26 265	99.81 251

E6new		98.64 224	98.41 238	99.30 246	99.46 308	98.19 305	99.79 381	99.21 356	96.62 306	99.68 253	100.00 1	93.24 316	99.91 209	99.47 240	98.26 265	99.81 251

E6		98.64 224	98.41 238	99.30 246	99.46 308	98.19 305	99.79 381	99.21 356	96.62 306	99.68 253	100.00 1	93.24 316	99.91 209	99.47 240	98.26 265	99.81 251

E5		98.63 230	98.41 238	99.31 242	99.51 278	98.21 302	99.79 381	99.21 356	96.62 306	99.67 259	100.00 1	93.15 320	99.91 209	99.46 243	98.26 265	99.81 251

E4		98.68 222	98.46 232	99.33 234	99.51 278	98.27 296	99.96 322	99.21 356	96.66 298	99.68 253	100.00 1	93.38 311	99.91 209	99.49 237	98.27 263	99.81 251

viewmacassd2359aftdt		98.57 241	98.31 257	99.33 234	99.49 291	98.31 292	99.89 360	99.21 356	96.87 261	99.10 308	100.00 1	92.48 339	99.88 231	99.50 235	98.28 260	99.81 251

Patchmatch-test		97.83 296	97.42 308	99.06 267	99.08 361	97.66 343	98.66 503	99.21 356	93.65 426	98.25 386	99.58 383	99.47 51	99.57 304	90.25 471	98.59 218	99.95 151

mvs_anonymous		98.80 202	98.60 209	99.38 219	99.57 243	99.24 192	100.00 1	99.21 356	95.87 354	98.92 325	99.82 329	96.39 236	99.03 364	99.13 275	98.50 222	99.88 205

EC-MVSNet		99.19 135	99.09 134	99.48 191	99.42 322	99.07 208	100.00 1	99.21 356	96.95 252	99.96 155	100.00 1	96.88 222	99.48 330	99.64 199	99.79 173	99.88 205

TR-MVS		98.14 279	97.74 295	99.33 234	99.59 234	98.28 294	99.27 456	99.21 356	96.42 327	99.15 304	99.94 297	88.87 407	99.79 261	98.88 289	98.29 257	99.93 167

onestephybrid01		98.89 193	98.67 197	99.56 179	99.51 278	99.08 207	100.00 1	99.20 366	97.30 220	99.95 187	100.00 1	94.04 293	99.79 261	99.77 152	98.29 257	99.81 251

blended_shiyan8		93.73 427	92.69 439	96.84 411	95.17 506	94.40 437	100.00 1	99.20 366	87.05 487	98.60 350	98.54 468	90.15 379	98.39 432	95.54 413	69.93 524	97.74 392

viewdifsd2359ckpt07		98.72 211	98.52 222	99.34 226	99.47 299	98.28 294	99.99 271	99.20 366	96.98 247	99.60 264	100.00 1	93.45 310	99.93 201	99.58 218	98.36 241	99.82 234

viewdifsd2359ckpt09		98.78 203	98.60 209	99.31 242	99.53 257	98.37 280	100.00 1	99.20 366	96.85 262	99.32 292	100.00 1	94.68 276	99.74 280	99.46 243	98.36 241	99.81 251

viewmamba		98.92 185	98.74 182	99.46 193	99.46 308	98.83 235	100.00 1	99.19 370	97.18 228	99.95 187	100.00 1	94.97 266	99.74 280	99.64 199	98.29 257	99.81 251

hybridnocas07		98.85 197	98.63 202	99.53 182	99.52 271	98.95 227	100.00 1	99.19 370	97.15 230	99.93 198	100.00 1	93.83 302	99.82 252	99.67 189	98.38 236	99.82 234

hybrid		98.81 201	98.60 209	99.45 197	99.52 271	98.74 243	100.00 1	99.19 370	97.04 242	99.95 187	100.00 1	93.89 301	99.78 267	99.64 199	98.19 275	99.81 251

GeoE		98.06 283	97.65 302	99.29 249	99.47 299	98.41 272	100.00 1	99.19 370	94.85 387	98.88 328	100.00 1	91.21 352	99.59 298	97.02 371	98.19 275	99.88 205

KD-MVS_2432*1600		94.15 419	93.08 429	97.35 386	99.53 257	97.83 337	99.63 416	99.19 370	92.88 446	96.29 448	97.68 490	98.84 134	96.70 489	89.73 475	63.92 538	97.53 451

miper_refine_blended		94.15 419	93.08 429	97.35 386	99.53 257	97.83 337	99.63 416	99.19 370	92.88 446	96.29 448	97.68 490	98.84 134	96.70 489	89.73 475	63.92 538	97.53 451

SPE-MVS-test		99.31 113	99.27 99	99.43 203	99.99 53	98.77 239	100.00 1	99.19 370	97.24 223	99.96 155	100.00 1	97.56 194	99.70 290	99.68 185	99.81 169	99.82 234

jajsoiax		97.07 336	96.79 337	97.89 368	97.28 477	97.12 367	99.95 331	99.19 370	96.55 311	97.31 426	99.69 355	87.35 427	98.91 379	98.70 299	95.12 366	97.66 432

MVS_Test		98.93 184	98.65 199	99.77 138	99.62 224	99.50 154	99.99 271	99.19 370	95.52 370	99.96 155	99.86 317	96.54 233	99.98 142	98.65 302	98.48 224	99.82 234

MVS-HIRNet		94.12 421	92.73 438	98.29 325	99.33 339	95.95 390	99.38 446	99.19 370	74.54 519	98.26 385	86.34 538	86.07 437	99.06 362	91.60 458	99.87 158	99.85 222

hybridcas		98.64 224	98.41 238	99.33 234	99.54 253	98.41 272	100.00 1	99.18 380	96.78 271	99.68 253	100.00 1	92.58 336	99.75 279	99.57 221	98.38 236	99.82 234

icg_test_0407_2		98.30 268	98.45 233	97.85 370	99.38 333	95.36 400	99.99 271	99.18 380	96.72 287	99.58 265	100.00 1	95.17 261	98.45 428	97.84 341	98.15 280	99.74 304

IMVS_0407		98.36 265	98.42 236	98.19 337	99.38 333	95.36 400	99.73 401	99.18 380	96.72 287	99.58 265	100.00 1	95.17 261	99.47 332	97.84 341	98.15 280	99.74 304

IMVS_0404		97.87 292	97.89 287	97.81 372	99.38 333	95.36 400	99.84 369	99.18 380	96.72 287	98.41 371	100.00 1	91.43 350	98.32 437	97.84 341	98.15 280	99.74 304

IMVS_0403		98.37 263	98.39 247	98.29 325	99.38 333	95.36 400	99.97 314	99.18 380	96.72 287	99.68 253	100.00 1	94.61 278	99.77 269	97.84 341	98.15 280	99.74 304

MTMP							100.00 1	99.18 380

mvs_tets		97.00 342	96.69 339	97.94 364	97.41 475	97.27 360	99.60 421	99.18 380	96.51 318	97.35 425	99.69 355	86.53 433	98.91 379	98.84 291	95.09 367	97.65 437

pmmvs4		97.17 330	96.80 335	98.27 327	97.68 458	98.64 253	100.00 1	99.18 380	94.22 410	98.55 354	99.71 349	93.67 304	98.47 426	95.66 408	92.57 400	97.71 419

diffmvs		98.96 178	98.73 184	99.63 163	99.54 253	99.16 202	100.00 1	99.18 380	97.33 215	99.96 155	100.00 1	94.60 279	99.91 209	99.66 196	98.33 250	99.82 234
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				89.98 482	98.90 388	71.46 520		99.18 380	97.61 178	96.92 434	99.83 325	86.07 437	99.83 249	96.02 396	97.65 319	98.65 347

Casviewmamba		98.71 215	98.47 230	99.46 193	99.47 299	98.70 247	100.00 1	99.17 390	96.97 250	99.45 279	100.00 1	93.04 324	99.87 233	99.67 189	98.41 229	99.81 251

casdiffseed414692147		98.31 267	97.94 285	99.40 212	99.46 308	98.67 249	99.91 354	99.17 390	96.33 337	98.66 345	99.97 265	90.47 375	99.71 288	99.36 252	98.16 279	99.81 251

baseline		98.69 220	98.45 233	99.41 207	99.52 271	98.67 249	100.00 1	99.17 390	97.03 243	99.13 305	100.00 1	93.17 318	99.74 280	99.70 175	98.34 247	99.81 251

PatchmatchNet2					0.00 567	95.13 408	99.92 347	99.16 393	89.91 471
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mamba_0408		98.63 230	98.40 244	99.34 226	99.53 257	98.52 263	99.24 460	99.16 393	96.43 323	98.95 321	99.98 253	94.47 282	99.76 274	99.21 270	98.62 215	99.75 297

SSM_04072		98.59 237	98.40 244	99.15 263	99.53 257	98.52 263	99.24 460	99.16 393	96.43 323	98.95 321	99.98 253	94.47 282	99.19 357	99.21 270	98.62 215	99.75 297

viewmambaseed2359dif		98.57 241	98.34 255	99.28 252	99.46 308	98.23 299	100.00 1	99.16 393	96.26 341	99.11 307	100.00 1	93.12 323	99.79 261	99.61 211	98.33 250	99.80 282

MonoMVSNet		98.55 245	98.64 201	98.26 330	98.21 431	95.76 396	99.94 339	99.16 393	96.23 342	99.47 276	99.24 416	96.75 226	99.22 354	99.61 211	99.17 198	99.81 251

Fast-Effi-MVS+-dtu		98.38 262	98.56 217	97.82 371	99.58 239	94.44 435	100.00 1	99.16 393	96.75 278	99.51 270	99.63 373	95.03 265	99.60 296	97.71 348	99.67 181	99.42 333

SSM_0407		98.72 211	98.52 222	99.33 234	99.53 257	98.52 263	99.88 363	99.15 399	96.53 313	98.95 321	100.00 1	94.38 285	99.72 286	99.64 199	98.62 215	99.75 297

SSM_0404		98.76 207	98.56 217	99.35 224	99.53 257	98.65 252	99.80 380	99.15 399	96.53 313	99.47 276	100.00 1	94.38 285	99.76 274	99.64 199	98.59 218	99.64 322

Fast-Effi-MVS+		98.40 261	98.02 282	99.55 181	99.63 217	99.06 210	100.00 1	99.15 399	95.07 382	99.42 280	99.95 291	93.26 315	99.73 284	97.44 358	98.24 269	99.87 216

anonymousdsp		97.16 331	96.88 332	98.00 360	97.08 479	98.06 317	99.81 375	99.15 399	94.58 398	97.84 407	99.62 377	90.49 370	98.60 412	97.98 334	95.32 352	97.33 462

casdiffmvs		98.65 223	98.38 249	99.46 193	99.52 271	98.74 243	100.00 1	99.15 399	96.91 257	99.05 315	100.00 1	92.75 330	99.83 249	99.70 175	98.38 236	99.81 251
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		99.29 119	99.16 123	99.69 152	99.45 316	99.49 158	100.00 1	99.15 399	97.45 201	99.97 147	100.00 1	96.76 224	99.76 274	99.67 189	100.00 1	99.81 251

1112_ss		98.91 188	98.71 190	99.51 183	99.69 184	98.75 240	99.99 271	99.15 399	96.82 266	98.84 333	100.00 1	97.45 200	99.89 222	98.66 300	97.75 313	99.89 192

dtuplus		98.57 241	98.32 256	99.30 246	99.44 318	98.35 286	100.00 1	99.14 406	96.36 333	98.97 320	100.00 1	93.04 324	99.77 269	99.55 224	98.39 232	99.79 288

diffmvs_AUTHOR		98.92 185	98.73 184	99.49 190	99.48 294	98.81 236	99.94 339	99.14 406	97.24 223	99.96 155	100.00 1	94.85 269	99.87 233	99.67 189	98.31 254	99.79 288

tt0320		92.36 443	91.28 447	95.58 443	98.30 422	90.65 475	98.69 502	99.14 406	76.73 512	96.07 454	99.50 396	72.28 494	98.39 432	93.29 444	87.56 462	97.70 420

IterMVS-SCA-FT		96.72 352	96.42 352	97.62 377	99.40 329	96.83 375	99.99 271	99.14 406	94.65 397	97.55 421	99.72 347	89.65 395	98.31 438	95.62 410	92.05 407	97.73 404

YYNet1		92.44 442	90.92 452	97.03 399	96.20 486	97.06 370	99.99 271	99.14 406	88.21 479	67.93 539	98.43 476	88.63 411	96.28 496	90.64 464	89.08 449	97.74 392

MDA-MVSNet_test_wron		92.61 440	91.09 451	97.19 395	96.71 482	97.26 361	100.00 1	99.14 406	88.61 475	67.90 540	98.32 480	89.03 403	96.57 492	90.47 469	89.59 441	97.74 392

Test_1112_low_res		98.83 199	98.60 209	99.51 183	99.69 184	98.75 240	99.99 271	99.14 406	96.81 267	98.84 333	99.06 427	97.45 200	99.89 222	98.66 300	97.75 313	99.89 192

CS-MVS		99.33 109	99.27 99	99.50 186	99.99 53	99.00 220	100.00 1	99.13 413	97.26 222	99.96 155	100.00 1	97.79 183	99.64 294	99.64 199	99.67 181	99.87 216

v2v482		96.70 353	96.18 362	98.27 327	98.04 439	98.39 276	100.00 1	99.13 413	94.19 413	98.58 352	99.08 426	90.48 371	98.67 399	95.69 405	90.44 434	97.75 364

MVSFormer		98.94 183	98.82 170	99.28 252	99.45 316	99.49 158	100.00 1	99.13 413	95.46 375	99.97 147	100.00 1	96.76 224	98.59 414	98.63 305	100.00 1	99.74 304

jason		99.11 145	98.96 152	99.59 172	99.17 353	99.31 182	100.00 1	99.13 413	97.38 208	99.83 219	100.00 1	95.54 250	99.72 286	99.57 221	99.97 123	99.74 304
jason: jason.
test_djsdf		97.55 312	97.38 311	98.07 348	97.50 467	97.99 321	100.00 1	99.13 413	95.46 375	98.47 367	99.85 322	92.01 346	98.59 414	98.63 305	95.36 351	97.62 443

IterMVS		96.76 349	96.46 350	97.63 375	99.41 324	96.89 373	99.99 271	99.13 413	94.74 392	97.59 420	99.66 362	89.63 397	98.28 443	95.71 404	92.31 404	97.72 411
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
sc_t1		92.52 441	91.34 446	96.09 436	97.80 451	89.86 482	98.61 505	99.12 419	77.73 511	96.09 453	99.79 341	68.64 500	98.94 376	96.94 373	87.31 465	99.46 332

tpm		98.24 275	98.22 268	98.32 324	99.13 355	95.79 395	99.53 430	99.12 419	95.20 381	99.96 155	99.36 409	97.58 190	99.28 352	97.41 360	96.67 336	99.88 205

GDP-MVS		99.39 94	99.26 103	99.77 138	99.53 257	99.55 141	100.00 1	99.11 421	97.14 231	99.96 155	100.00 1	99.83 5	99.89 222	98.47 313	99.26 197	99.87 216

miper_ehance_all_eth		97.81 297	97.66 301	98.23 333	99.49 291	98.37 280	99.99 271	99.11 421	94.78 389	98.25 386	99.21 419	98.18 164	98.57 418	97.35 364	92.61 397	97.76 353

v7n		96.06 392	95.42 403	97.99 362	97.58 464	97.35 355	99.86 366	99.11 421	92.81 449	97.91 403	99.49 397	90.99 360	98.92 378	92.51 451	88.49 455	97.70 420

cl____		97.54 313	97.32 314	98.18 338	99.47 299	98.14 310	100.00 1	99.10 424	94.16 415	97.60 418	99.63 373	97.52 196	98.65 402	96.47 387	91.97 410	97.76 353

DIV-MVS_self_test		97.52 316	97.35 313	98.05 356	99.46 308	98.11 311	100.00 1	99.10 424	94.21 411	97.62 416	99.63 373	97.65 188	98.29 442	96.47 387	91.98 409	97.76 353

c3_l		97.58 309	97.42 308	98.06 352	99.48 294	98.16 307	99.96 322	99.10 424	94.54 400	98.13 390	99.20 421	97.87 177	98.25 445	97.28 365	91.20 425	97.75 364

Effi-MVS+		98.58 239	98.24 264	99.61 167	99.60 230	99.26 188	97.85 518	99.10 424	96.22 345	99.97 147	99.89 312	93.75 303	99.77 269	99.43 247	98.34 247	99.81 251

IterMVS-LS		97.56 310	97.44 307	97.92 367	99.38 333	97.90 330	99.89 360	99.10 424	94.41 405	98.32 379	99.54 392	97.21 207	98.11 458	97.50 356	91.62 417	97.75 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		96.65 355	96.16 364	98.11 347	98.17 435	98.23 299	99.99 271	99.09 429	93.97 418	98.74 340	99.05 429	91.09 355	98.82 388	95.46 414	89.90 438	97.27 463

KD-MVS_self_test		91.16 452	90.09 457	94.35 459	94.44 511	91.27 470	99.74 396	99.08 430	90.82 462	94.53 470	94.91 516	86.11 436	94.78 508	82.67 505	68.52 530	96.99 471

eth_miper_zixun_eth		97.47 317	97.28 316	98.06 352	99.41 324	97.94 327	99.62 419	99.08 430	94.46 404	98.19 389	99.56 388	96.91 221	98.50 423	96.78 382	91.49 420	97.74 392

v1192		96.18 382	95.49 396	98.26 330	98.01 442	98.15 308	99.99 271	99.08 430	93.36 436	98.54 355	98.97 441	89.47 398	98.89 382	91.15 462	90.82 428	97.75 364

v1144		96.51 362	95.97 372	98.13 345	97.98 444	98.04 319	99.99 271	99.08 430	93.51 431	98.62 349	98.98 437	90.98 361	98.62 408	93.79 438	90.79 429	97.74 392

RRT-MVS		98.75 210	98.52 222	99.44 200	99.65 208	98.57 257	99.90 356	99.08 430	96.51 318	99.96 155	99.95 291	92.59 335	99.96 171	99.60 213	99.45 194	99.81 251

dcpmvs_2		98.87 194	99.53 66	96.90 407	99.87 126	90.88 473	99.94 339	99.07 435	98.20 120	100.00 1	100.00 1	98.69 145	99.86 235	100.00 1	100.00 1	99.95 151

miper_lstm_enhance		97.40 320	97.28 316	97.75 374	99.48 294	97.52 347	100.00 1	99.07 435	94.08 417	98.01 398	99.61 379	97.38 204	97.98 471	96.44 390	91.47 422	97.76 353

v1921920		96.16 386	95.50 394	98.14 342	97.88 448	97.96 325	99.99 271	99.07 435	93.33 437	98.60 350	99.24 416	89.37 399	98.71 397	91.28 459	90.74 430	97.75 364

v148		96.29 376	95.84 377	97.63 375	97.74 454	96.53 385	100.00 1	99.07 435	93.52 430	98.01 398	99.42 404	91.22 351	98.60 412	96.37 391	87.22 468	97.75 364

v1240		95.96 394	95.25 405	98.07 348	97.91 446	97.87 335	99.96 322	99.07 435	93.24 440	98.64 348	98.96 442	88.98 405	98.61 409	89.58 479	90.92 427	97.75 364

v8		96.35 373	95.73 385	98.21 336	98.11 437	98.23 299	99.94 339	99.07 435	92.66 450	98.29 381	99.00 436	91.46 348	98.77 393	94.17 432	88.83 453	97.62 443

v10		96.14 388	95.50 394	98.07 348	98.19 433	97.96 325	99.83 371	99.07 435	92.10 453	98.07 392	98.94 443	91.07 356	98.61 409	92.41 454	89.82 439	97.63 441

testgi		96.18 382	95.93 373	96.93 406	98.98 380	94.20 443	100.00 1	99.07 435	97.16 229	96.06 455	99.86 317	84.08 453	97.79 479	90.38 470	97.80 309	98.81 344

v144192		96.40 370	95.81 378	98.17 340	97.89 447	98.11 311	99.99 271	99.06 443	93.39 435	98.75 339	99.09 425	90.43 376	98.66 400	93.10 446	90.55 432	97.75 364

PCF-MVS	98.23 3	98.69 220	98.37 251	99.62 165	99.78 168	99.02 215	99.23 467	99.06 443	96.43 323	98.08 391	100.00 1	94.72 275	99.95 184	98.16 327	99.91 147	99.90 184
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RPSCF		97.37 321	98.24 264	94.76 455	99.80 158	84.57 498	99.99 271	99.05 445	94.95 385	99.82 228	100.00 1	94.03 294	100.00 1	98.15 328	98.38 236	99.70 314

FPMVS		77.92 495	79.45 491	73.34 520	76.87 556	46.81 549	98.24 511	99.05 445	59.89 528	73.55 531	98.34 479	36.81 539	86.55 532	80.96 510	91.35 424	86.65 533

Gipuma		84.73 476	83.50 480	88.40 489	97.50 467	82.21 505	88.87 541	99.05 445	65.81 522	85.71 509	90.49 527	53.70 523	96.31 495	78.64 517	91.74 413	86.67 532
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		98.60 236	98.24 264	99.67 156	96.90 480	99.21 196	99.99 271	99.04 448	98.80 78	99.57 267	99.96 283	90.12 383	99.91 209	99.89 123	99.89 150	99.90 184

EGC-MVSNET		79.46 490	74.04 500	95.72 441	96.00 491	92.73 458	99.09 486	99.04 448	5.08 561	16.72 561	98.71 456	73.03 491	98.74 396	82.05 507	96.64 337	95.69 501

pm-mvs1		95.76 398	95.01 410	98.00 360	98.23 430	97.45 350	99.24 460	99.04 448	93.13 443	95.93 457	99.72 347	86.28 435	98.84 387	95.62 410	87.92 458	97.72 411

VortexMVS		98.23 276	98.11 272	98.59 304	99.56 249	99.37 175	99.95 331	99.03 451	96.47 321	98.69 341	99.55 389	95.91 240	98.66 400	99.01 283	94.80 373	97.73 404

ET-MVSNet_ETH3D		96.41 367	95.48 398	99.20 261	99.81 145	99.75 110	100.00 1	99.02 452	97.30 220	78.33 524	100.00 1	97.73 184	97.94 473	99.70 175	87.41 463	99.92 169

pmmvs6		93.64 430	92.87 433	95.94 439	97.47 471	91.41 469	98.92 493	99.02 452	87.84 482	95.01 464	99.61 379	77.24 480	98.77 393	94.33 430	86.41 475	97.63 441

our_test_3		96.51 362	96.35 355	96.98 403	97.61 461	95.05 409	99.98 304	99.01 454	94.68 395	96.77 442	99.06 427	95.87 242	98.14 454	91.81 456	92.37 403	97.75 364

ArgMatch-Sym		94.50 414	94.12 417	95.63 442	98.16 436	90.84 474	100.00 1	99.00 455	97.42 205	97.22 429	99.76 345	73.91 488	99.05 363	91.22 460	90.43 435	97.01 470

test_fmvs1		98.37 263	98.04 280	99.34 226	99.84 131	98.07 315	100.00 1	99.00 455	98.85 67	100.00 1	100.00 1	85.11 445	99.96 171	99.69 184	99.88 153	100.00 1

CR-MVSNet		98.02 286	97.71 300	98.93 280	99.31 340	98.86 232	99.13 480	99.00 455	96.53 313	99.96 155	98.98 437	96.94 219	98.10 462	91.18 461	98.40 230	99.84 224

Patchmtry		96.81 346	96.37 354	98.14 342	99.31 340	98.55 258	98.91 494	99.00 455	90.45 465	97.92 402	98.98 437	96.94 219	98.12 456	94.27 431	91.53 419	97.75 364

test_vis1_n		96.69 354	95.81 378	99.32 240	99.14 354	97.98 322	99.97 314	98.98 459	98.45 101	100.00 1	100.00 1	66.44 504	99.99 107	99.78 150	99.57 191	100.00 1

test_fmvs1_n		97.43 318	96.86 333	99.15 263	99.68 189	97.48 349	99.99 271	98.98 459	98.82 73	100.00 1	100.00 1	74.85 487	99.96 171	99.67 189	99.70 177	100.00 1

Effi-MVS+-dtu		98.51 252	98.86 167	97.47 381	99.77 170	94.21 442	100.00 1	98.94 461	97.61 178	99.91 203	98.75 455	95.89 241	99.51 325	99.36 252	99.48 192	98.68 346

MDA-MVSNet-bldmvs		91.65 451	89.94 460	96.79 420	96.72 481	96.70 379	99.42 443	98.94 461	88.89 474	66.97 542	98.37 478	81.43 465	95.91 500	89.24 482	89.46 445	97.75 364

test_method		91.04 455	91.10 450	90.85 479	98.34 416	77.63 510	100.00 1	98.93 463	76.69 513	96.25 450	98.52 470	70.44 497	97.98 471	89.02 484	91.74 413	96.92 473

dtuonlycased		95.07 411	95.43 401	93.98 465	98.26 426	85.63 495	99.98 304	98.92 464	94.83 388	94.13 475	99.47 399	82.60 461	97.61 483	94.66 425	96.01 341	98.70 345

dmvs_testset		93.27 434	95.48 398	86.65 493	98.74 401	68.42 528	99.92 347	98.91 465	96.19 347	93.28 479	100.00 1	91.06 358	91.67 524	89.64 477	91.54 418	99.86 220

new_pmnet		94.11 422	93.47 424	96.04 438	96.60 485	92.82 456	99.97 314	98.91 465	90.21 468	95.26 461	98.05 488	85.89 440	98.14 454	84.28 501	92.01 408	97.16 465

APD_test1		93.07 437	94.14 416	89.85 483	99.18 352	72.49 518	99.76 393	98.90 467	92.86 448	96.35 447	99.94 297	75.56 485	99.91 209	86.73 491	97.98 292	97.15 466

FE-MVSNET2		91.15 453	90.00 459	94.58 456	90.74 527	92.52 462	99.56 425	98.87 468	90.82 462	88.96 496	95.40 510	76.26 484	95.56 504	87.84 487	81.59 500	95.66 503

test20.03		93.11 435	92.85 434	93.88 466	95.19 505	91.83 465	100.00 1	98.87 468	93.68 425	92.76 481	98.88 449	89.20 402	92.71 519	77.88 521	89.19 448	97.09 467

ArgMatch-SfM		93.74 426	93.14 428	95.54 444	98.57 407	90.54 476	99.97 314	98.86 470	97.35 210	97.60 418	99.66 362	71.88 495	99.02 365	90.18 472	84.16 483	97.07 469

test_0402		94.35 416	93.70 421	96.32 432	97.92 445	93.60 446	99.61 420	98.85 471	88.19 480	94.68 467	99.48 398	80.01 469	98.58 417	89.39 480	95.15 364	96.77 475

Anonymous20240521		93.29 433	92.76 435	94.90 454	95.64 498	91.27 470	99.97 314	98.82 472	87.04 488	94.71 466	98.19 483	83.86 454	96.80 488	84.04 502	92.56 401	96.64 480

new-patchmatchnet		90.30 460	89.46 463	92.84 473	90.77 526	88.55 489	99.83 371	98.80 473	90.07 470	87.86 500	95.00 514	78.77 475	94.30 510	84.86 499	79.15 507	95.68 502

MVStest1		94.27 417	93.30 426	97.19 395	98.83 398	97.18 365	99.93 345	98.79 474	86.80 491	84.88 513	99.04 430	94.32 288	98.25 445	90.55 467	86.57 474	96.12 494

ttmdpeth		96.24 379	95.88 375	97.32 388	97.80 451	96.61 383	99.95 331	98.77 475	97.80 155	93.42 478	99.28 413	86.42 434	99.01 367	97.63 351	91.84 412	96.33 488

WB-MVS		88.24 467	90.09 457	82.68 509	91.56 524	69.51 523	100.00 1	98.73 476	90.72 464	87.29 502	98.12 484	92.87 328	85.01 538	62.19 538	89.34 446	93.54 517

tt0320-xc		91.69 450	90.50 454	95.26 446	98.04 439	90.12 481	98.60 506	98.70 477	76.63 514	94.66 468	99.52 393	68.57 501	97.99 470	94.61 426	85.18 478	97.66 432

SSC-MVS		87.61 468	89.47 462	82.04 510	90.63 528	68.77 527	99.99 271	98.66 478	90.34 467	86.70 504	98.08 485	92.72 333	84.12 539	59.41 541	88.71 454	93.22 521

usedtu_blend_shiyan5		92.75 439	91.39 445	96.82 417	95.22 502	94.40 437	99.05 491	98.64 479	75.98 518	98.54 355	98.56 464	90.48 371	98.31 438	96.31 392	69.73 525	97.75 364

mmtdpeth		94.58 413	94.18 415	95.81 440	98.82 400	91.09 472	99.99 271	98.61 480	96.38 330	100.00 1	97.23 494	76.52 482	99.85 242	99.82 141	80.22 504	96.48 484

pmmvs-eth3d		91.73 449	90.67 453	94.92 453	91.63 523	92.71 459	99.90 356	98.54 481	91.19 458	88.08 499	95.50 507	79.31 473	96.13 498	90.55 467	81.32 502	95.91 498

Anonymous20231206		93.45 432	93.17 427	94.30 460	95.00 508	89.69 484	99.98 304	98.43 482	93.30 439	94.50 471	98.59 461	90.52 369	95.73 503	77.46 523	90.73 431	97.48 456

MASt3R-SfM		91.92 446	92.47 443	90.28 481	96.64 484	75.61 514	99.63 416	98.31 483	95.70 362	95.42 460	98.84 450	67.34 502	99.22 354	89.92 473	90.47 433	96.01 496

USDC		95.90 396	95.70 386	96.50 426	98.60 406	92.56 461	100.00 1	98.30 484	97.77 158	96.92 434	99.94 297	81.25 467	99.45 338	93.54 441	94.96 372	97.49 453

SixPastTwentyTwo		95.71 400	95.49 396	96.38 429	97.42 473	93.01 453	99.84 369	98.23 485	94.75 390	95.98 456	99.97 265	85.35 444	98.43 429	94.71 424	93.17 389	97.69 425

RoMa-SfM		90.39 459	89.63 461	92.66 474	97.47 471	83.18 502	98.81 497	98.21 486	85.44 497	89.21 494	99.46 401	63.72 505	98.30 441	87.11 489	87.25 466	96.51 483

MVP-Stereo		96.51 362	96.48 349	96.60 424	95.65 497	94.25 441	98.84 496	98.16 487	95.85 358	95.23 462	99.04 430	92.54 338	99.13 359	92.98 447	99.98 119	96.43 486
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		96.14 388	95.98 371	96.62 423	97.49 469	93.44 449	99.92 347	98.16 487	95.86 356	97.65 413	99.95 291	85.71 442	98.78 390	94.93 422	94.18 380	97.64 440

ITE_SJBPF				96.84 411	98.96 383	93.49 448		98.12 489	98.12 129	98.35 376	99.97 265	84.45 447	99.56 308	95.63 409	95.25 357	97.49 453

EG-PatchMatch MVS		92.94 438	92.49 442	94.29 461	95.87 493	87.07 492	99.07 489	98.11 490	93.19 441	88.98 495	98.66 459	70.89 496	99.08 361	92.43 453	95.21 360	96.72 477

DenseAffine		90.43 458	89.28 465	93.87 467	97.71 457	86.21 493	99.13 480	98.10 491	87.86 481	90.15 492	98.43 476	60.76 508	98.65 402	84.48 500	86.90 469	96.74 476

pmmvs5		95.94 395	95.61 391	96.95 404	97.42 473	94.66 425	100.00 1	98.08 492	93.60 428	97.05 432	99.43 403	87.02 428	98.46 427	95.76 402	92.12 406	97.72 411

mvs5depth		93.81 423	93.00 431	96.23 434	94.25 512	93.33 451	97.43 524	98.07 493	93.47 432	94.15 474	99.58 383	77.52 478	98.97 373	93.64 439	88.92 450	96.39 487

LCM-MVSNet		79.01 494	76.93 497	85.27 495	78.28 555	68.01 530	96.57 528	98.03 494	55.10 534	82.03 520	93.27 520	31.99 549	93.95 512	82.72 504	74.37 514	93.84 515

OpenMVS_ROB	88.34 20	91.89 447	91.12 449	94.19 463	95.55 499	87.63 490	99.26 458	98.03 494	86.61 493	90.65 491	96.82 497	70.14 499	98.78 390	86.54 492	96.50 340	96.15 492

ambc				88.45 488	86.84 541	70.76 521	97.79 520	98.02 496		90.91 488	95.14 511	38.69 536	98.51 422	94.97 421	84.23 482	96.09 495

tmp_tt		75.80 497	74.26 499	80.43 512	52.91 565	53.67 546	87.42 546	97.98 497	61.80 526	67.04 541	100.00 1	76.43 483	96.40 494	96.47 387	28.26 554	91.23 525

TransMVSNet (Re)		94.78 412	93.72 420	97.93 366	98.34 416	97.88 333	99.23 467	97.98 497	91.60 455	94.55 469	99.71 349	87.89 419	98.36 434	89.30 481	84.92 479	97.56 449

DKM		88.67 464	87.74 469	91.44 477	97.38 476	82.60 503	98.95 492	97.94 499	87.54 483	87.00 503	98.48 471	55.08 520	95.81 502	86.05 494	81.29 503	95.91 498

LF4IMVS		96.19 381	96.18 362	96.23 434	98.26 426	92.09 464	100.00 1	97.89 500	97.82 153	97.94 400	99.87 315	82.71 459	99.38 344	97.41 360	93.71 383	97.20 464

MatchFormer		86.71 473	84.75 479	92.57 475	96.14 489	82.52 504	99.27 456	97.86 501	80.17 508	78.74 523	96.16 503	54.81 521	98.63 406	75.87 526	83.75 488	96.56 482

Baseline_NR-MVSNet		96.16 386	95.70 386	97.56 380	98.28 425	96.79 377	100.00 1	97.86 501	91.93 454	97.63 414	99.47 399	92.14 343	98.35 435	97.13 368	86.83 472	97.54 450

test_fmvs2		95.17 410	95.23 406	95.01 449	98.95 385	88.99 487	99.99 271	97.77 503	97.79 156	98.58 352	99.70 352	73.36 490	99.34 348	95.88 399	95.03 368	96.70 479

testf1		84.40 477	84.79 477	83.23 507	95.71 495	58.71 544	98.79 498	97.75 504	81.58 505	84.94 511	98.07 486	45.33 532	97.73 480	77.09 524	83.85 485	93.24 519

APD_test2		84.40 477	84.79 477	83.23 507	95.71 495	58.71 544	98.79 498	97.75 504	81.58 505	84.94 511	98.07 486	45.33 532	97.73 480	77.09 524	83.85 485	93.24 519

TinyColmap		95.50 403	95.12 409	96.64 422	98.69 402	93.00 454	99.40 444	97.75 504	96.40 329	96.14 452	99.87 315	79.47 471	99.50 328	93.62 440	94.72 375	97.40 459

dtuonly		97.85 294	97.46 306	99.02 273	98.44 412	97.89 332	99.99 271	97.62 507	96.53 313	99.49 272	99.96 283	94.01 297	99.58 302	92.75 448	98.32 253	99.59 323

TDRefinement		91.93 445	90.48 455	96.27 433	81.60 552	92.65 460	99.10 484	97.61 508	93.96 419	93.77 476	99.85 322	80.03 468	99.53 322	97.82 345	70.59 523	96.63 481

usedtu_dtu_shiyan2		85.34 474	83.22 481	91.71 476	88.10 538	83.34 501	98.75 500	97.59 509	76.21 516	91.11 485	96.80 498	58.14 513	94.30 510	75.00 528	67.24 535	97.49 453

DKM-HiRes		87.00 471	86.38 474	88.84 487	96.71 482	79.05 507	98.73 501	97.57 510	84.56 499	84.00 515	98.23 482	52.90 525	92.48 520	84.95 498	79.77 505	95.00 507

test_fmvs3		87.19 470	87.02 472	87.71 490	92.69 516	76.64 511	99.96 322	97.27 511	93.55 429	90.82 489	94.03 519	38.00 538	92.19 521	93.49 442	83.35 493	94.32 513

LoFTR		88.61 465	87.13 471	93.06 470	96.18 487	83.87 499	99.48 434	97.21 512	86.37 494	82.32 519	96.66 499	58.07 514	98.59 414	81.76 508	86.15 477	96.72 477

test_f		86.87 472	86.06 475	89.28 486	91.45 525	76.37 512	99.87 365	97.11 513	91.10 459	88.46 497	93.05 521	38.31 537	96.66 491	91.77 457	83.46 492	94.82 510

RoMa-HiRes		87.37 469	86.72 473	89.32 485	95.81 494	78.25 509	98.63 504	97.01 514	82.18 504	86.32 506	99.25 415	56.48 517	94.79 507	83.17 503	81.62 499	94.91 509

PMVS	60.66 23	65.98 509	65.05 511	68.75 523	55.06 564	38.40 561	88.19 544	96.98 515	48.30 539	44.82 553	88.52 535	12.22 563	86.49 533	67.58 534	83.79 487	81.35 540
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Patchmatch-RL test		93.49 431	93.63 422	93.05 471	91.78 521	83.41 500	98.21 512	96.95 516	91.58 456	91.05 486	97.64 492	99.40 68	95.83 501	94.11 435	81.95 497	99.91 173

FE-MVSNET		89.50 461	88.33 467	93.00 472	88.89 534	90.24 479	99.96 322	96.86 517	88.23 477	88.46 497	95.47 508	77.03 481	93.37 518	78.54 518	81.56 501	95.39 506

ELoFTR		83.63 479	81.67 486	89.53 484	92.30 518	75.98 513	98.27 510	96.74 518	83.38 502	74.05 530	95.78 505	43.66 534	98.11 458	78.01 519	72.80 519	94.48 512

pmmvs3		90.62 457	89.36 464	94.40 458	90.53 530	91.49 468	100.00 1	96.73 519	84.21 500	93.65 477	96.65 500	82.56 462	94.83 506	82.28 506	77.62 511	96.89 474

PM-MVS		88.39 466	87.41 470	91.31 478	91.73 522	82.02 506	99.79 381	96.62 520	91.06 460	90.71 490	95.73 506	48.60 529	95.96 499	90.56 466	81.91 498	95.97 497

LCM-MVSNet-Re		96.52 360	97.21 322	94.44 457	99.27 347	85.80 494	99.85 368	96.61 521	95.98 351	92.75 482	98.48 471	93.97 298	97.55 484	99.58 218	98.43 227	99.98 129

mvsany_test3		89.36 463	88.96 466	90.56 480	91.95 520	78.97 508	99.74 396	96.59 522	96.84 264	89.25 493	96.07 504	52.59 526	97.11 486	95.17 419	82.44 495	95.58 505

door-mid								96.32 523

door								96.13 524

SP-DiffGlue		85.17 475	85.16 476	85.22 496	93.54 513	69.16 525	97.83 519	95.33 525	60.61 527	86.04 507	92.86 522	61.04 507	90.90 528	89.62 478	89.57 443	95.59 504

SP-SuperGlue		82.71 482	81.92 484	85.07 499	98.02 441	67.96 531	98.10 514	95.26 526	57.79 529	82.47 518	90.37 529	57.02 515	91.04 526	70.34 532	87.92 458	96.23 490

SP-NN		83.33 480	82.73 482	85.13 498	98.98 380	65.96 536	97.92 517	95.13 527	56.43 532	83.71 516	90.52 526	58.27 511	91.69 523	71.99 529	91.66 416	97.74 392

PMMVS2		79.15 493	77.28 496	84.76 500	82.34 549	72.66 517	99.70 407	95.11 528	71.68 521	84.78 514	90.87 524	32.05 548	89.99 530	75.53 527	63.45 540	91.64 523

SP-LightGlue		82.73 481	81.92 484	85.19 497	97.73 456	68.40 529	98.05 515	94.51 529	56.95 531	82.72 517	90.14 532	58.20 512	90.97 527	71.57 530	87.38 464	96.20 491

test_vis1_rt		93.10 436	92.93 432	93.58 468	99.63 217	85.07 496	99.99 271	93.71 530	97.49 196	90.96 487	97.10 495	60.40 509	99.95 184	99.24 266	97.90 300	95.72 500

DSMNet-mixed		95.18 409	95.21 407	95.08 447	96.03 490	90.21 480	99.65 413	93.64 531	92.91 445	98.34 377	97.40 493	90.05 387	95.51 505	91.02 463	97.86 302	99.51 330

SP-MNN		81.80 484	81.08 488	83.94 504	98.26 426	64.81 539	98.20 513	93.56 532	55.15 533	77.43 525	90.43 528	56.33 518	90.69 529	70.11 533	90.27 437	96.32 489

E-PMN		70.72 500	70.06 506	72.69 521	83.92 547	65.48 538	99.95 331	92.72 533	49.88 537	72.30 533	86.26 539	47.17 531	77.43 547	53.83 544	44.49 545	75.17 542

PMatch-SfM		81.57 485	79.80 489	86.88 492	92.36 517	73.86 516	97.50 523	92.66 534	80.39 507	73.10 532	96.35 501	33.54 545	91.86 522	81.28 509	71.01 522	94.92 508

N_pmnet		91.88 448	93.37 425	87.40 491	97.24 478	66.33 535	99.90 356	91.05 535	89.77 472	95.65 459	98.58 463	90.05 387	98.11 458	85.39 495	92.72 396	97.75 364

MVE	68.59 21	67.22 507	64.68 513	74.84 515	74.67 559	62.32 541	95.84 531	90.87 536	50.98 536	58.72 547	81.05 553	12.20 564	78.95 544	61.06 540	56.75 541	83.24 539
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMatch-Up-SfM		79.27 492	77.62 495	84.22 503	90.58 529	69.08 526	96.98 526	90.47 537	76.44 515	71.47 535	96.27 502	30.15 550	88.77 531	78.74 516	67.46 532	94.81 511

ALIKED-NN		82.28 483	81.49 487	84.63 501	99.44 318	67.26 532	97.36 525	90.47 537	62.09 525	81.26 522	95.45 509	59.17 510	93.89 513	63.93 537	84.26 481	92.75 522

ALIKED-MNN		79.54 489	78.11 494	83.80 506	99.29 346	66.55 534	97.70 521	90.37 539	57.60 530	74.96 529	92.30 523	53.12 524	93.57 516	58.80 542	78.89 509	91.27 524

ALIKED-LG		80.86 486	79.70 490	84.33 502	98.33 419	69.33 524	97.59 522	90.14 540	65.38 523	76.03 527	94.87 517	54.78 522	93.65 514	57.59 543	82.61 494	90.01 528

VLMVS_CLIP		69.45 504	71.86 505	62.23 527	66.80 561	30.24 568	87.12 548	87.67 541	33.62 558	82.03 520	98.28 481	28.75 551	67.69 556	88.35 486	74.12 515	88.74 529

ANet_high		66.05 508	63.44 514	73.88 519	61.14 562	63.45 540	95.68 532	87.18 542	79.93 509	47.35 550	80.68 555	22.35 559	72.33 553	61.24 539	35.42 550	85.88 534

testmvs		80.17 487	81.95 483	74.80 516	58.54 563	59.58 543	100.00 1	87.14 543	76.09 517	99.61 263	100.00 1	67.06 503	74.19 551	98.84 291	50.30 542	90.64 526

EMVS		69.88 502	69.09 507	72.24 522	84.70 544	65.82 537	99.96 322	87.08 544	49.82 538	71.51 534	84.74 541	49.30 528	75.32 549	50.97 545	43.71 546	75.59 541

XFeat-MNN		73.39 499	73.10 502	74.25 517	89.63 532	53.35 547	96.25 529	84.01 545	43.66 541	69.74 536	89.91 533	52.56 527	85.32 535	64.72 536	67.44 533	84.08 537

VLMVS		69.79 503	73.02 503	60.12 533	72.70 560	33.43 565	87.87 545	83.71 546	40.13 549	86.04 507	98.98 437	34.57 541	58.39 559	85.00 497	68.17 531	88.54 530

GLUNet-SfM		70.22 501	66.87 509	80.24 513	84.13 546	61.64 542	96.72 527	82.62 547	51.83 535	60.24 546	88.02 537	36.12 540	91.44 525	67.32 535	34.86 552	87.65 531

test_vis3_rt		79.61 488	78.19 493	83.86 505	88.68 537	69.56 522	99.81 375	82.19 548	86.78 492	68.57 538	84.51 542	25.06 556	98.26 444	89.18 483	78.94 508	83.75 538

XFeat-NN		75.54 498	76.00 498	74.19 518	93.25 515	52.63 548	95.93 530	81.98 549	46.32 540	75.32 528	90.27 531	56.80 516	85.05 537	71.26 531	72.85 518	84.87 535

SIFT-NN		67.52 506	68.28 508	65.25 524	96.00 491	45.92 550	93.38 533	80.01 550	43.05 542	69.06 537	85.13 540	39.13 535	85.13 536	32.15 548	76.58 512	64.70 545

SIFT-MNN		64.77 510	65.11 510	63.77 525	92.18 519	44.02 552	91.93 535	78.84 551	41.80 544	61.69 544	84.03 543	33.92 544	81.69 541	29.20 553	72.39 520	65.59 544

SIFT-NN-NCMNet		64.49 511	64.92 512	63.20 526	88.84 535	44.41 551	92.37 534	78.67 552	41.90 543	62.62 543	83.27 545	34.31 542	81.88 540	30.88 549	71.40 521	63.31 547

test123		79.44 491	79.23 492	80.05 514	80.03 554	71.72 519	100.00 1	77.93 553	62.52 524	94.81 465	99.69 355	78.21 476	74.53 550	92.57 450	27.33 555	93.90 514

SIFT-NN-CMatch		60.63 512	60.17 515	62.02 528	86.89 540	43.32 554	90.70 538	71.03 554	41.60 546	61.16 545	83.16 546	33.45 546	78.31 545	30.28 550	43.26 547	64.44 546

SIFT-NN-UMatch		59.27 514	58.65 517	61.13 530	83.27 548	43.66 553	91.00 537	70.69 555	41.78 545	44.38 554	82.21 550	34.17 543	79.10 543	30.07 551	50.25 543	60.64 550

SIFT-NCM-Cal		59.75 513	59.15 516	61.53 529	90.12 531	43.18 555	91.26 536	70.04 556	40.34 548	38.39 556	81.51 552	27.19 552	79.90 542	26.25 558	67.30 534	61.50 549

SIFT-ConvMatch		56.83 516	55.72 519	60.16 531	88.80 536	43.02 556	88.55 542	64.15 557	40.75 547	45.84 551	83.12 547	27.00 553	77.01 548	28.36 554	34.89 551	60.45 551

PDCNetPlus		75.87 496	73.92 501	81.72 511	89.55 533	74.48 515	98.59 507	62.34 558	72.19 520	76.04 526	95.03 513	47.66 530	86.31 534	77.97 520	45.88 544	84.35 536

SIFT-NN-PointCN		57.34 515	56.95 518	58.53 535	82.11 550	41.35 559	90.36 539	61.72 559	40.01 550	54.78 548	80.99 554	32.74 547	72.39 552	29.64 552	40.16 548	61.83 548

SIFT-UMatch		55.48 517	53.92 520	60.16 531	85.84 543	42.45 557	89.09 540	61.68 560	39.97 551	41.34 555	82.92 548	26.90 554	77.66 546	27.36 555	30.17 553	60.37 552

MVS_clip		68.81 505	72.22 504	58.58 534	84.27 545	34.51 562	80.78 551	61.23 561	34.94 557	86.68 505	99.12 422	55.61 519	50.86 561	80.33 513	66.99 536	90.36 527

SIFT-PointCN		49.44 520	48.89 523	51.12 538	81.24 553	34.25 563	87.16 547	56.78 562	36.95 554	33.84 557	76.32 557	20.17 560	61.65 558	21.99 560	25.53 557	57.46 554

SIFT-CM-Cal		53.99 518	52.89 521	57.28 536	87.31 539	41.77 558	86.71 549	54.86 563	39.82 553	45.09 552	82.10 551	25.89 555	71.72 554	27.27 556	26.97 556	58.36 553

SIFT-UM-Cal		51.73 519	50.25 522	56.15 537	85.87 542	41.10 560	88.21 543	50.44 564	39.83 552	33.54 558	82.23 549	23.59 557	71.25 555	27.05 557	21.52 558	56.10 555

SIFT-PCN-Cal		47.97 521	47.56 524	49.20 539	81.85 551	33.99 564	86.00 550	49.11 565	36.44 555	32.13 559	77.60 556	22.63 558	62.04 557	23.11 559	19.17 559	51.55 556

SIFT-NCMNet		41.74 522	41.17 525	43.45 540	76.48 557	31.10 567	80.74 552	30.14 566	35.07 556	28.33 560	71.87 558	16.32 561	52.56 560	19.72 561	11.82 561	46.67 557

wuyk23d		28.28 524	29.73 528	23.92 542	75.89 558	32.61 566	66.50 553	12.88 567	16.09 559	14.59 562	16.59 560	12.35 562	32.36 562	39.36 547	13.36 560	6.79 558

MVS_baseline		35.10 523	36.24 526	31.67 541	45.91 566	12.01 569	34.47 554	7.88 568	5.62 560	47.50 549	90.75 525	11.45 565	7.89 563	46.41 546	36.20 549	75.11 543

mmdepth		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

monomultidepth		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

test_blank		0.07 528	0.09 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.79 561	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

uanet_test		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

DCPMVS		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

pcd_1.5k_mvsjas		8.24 527	10.99 530	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	98.75 142	0.00 564	0.00 562	0.00 562	0.00 559

sosnet-low-res		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

sosnet		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

uncertanet		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

Regformer		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

n2								0.00 569

nn								0.00 569

ab-mvs-re		8.33 526	11.11 529	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	100.00 1	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

uanet		0.01 529	0.02 532	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.14 562	0.00 566	0.00 564	0.00 562	0.00 562	0.00 559

Meshroom													0.00 564
: In preparation.
AliceVision / Meshro													0.00 564
: In preparation.
AliceVision_Meshroom													0.00 564
: In preparation.
PatchmatchNet1														86.42 493	92.76 395	97.75 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													98.34 436
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						97.98 322								95.74 403

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					0.00 567

eth-test					0.00 567

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																99.91 173

test_part2					100.00 1	99.99 6				100.00 1

sam_mvs1												99.29 82				99.91 173

sam_mvs												99.33 71

test_post1							99.32 451				88.24 536	99.33 71	99.59 298	98.31 320

test_post											89.05 534	99.49 46	99.59 298

patchmatchnet-post											97.79 489	99.41 66	99.54 317

gm-plane-assit					99.52 271	97.26 361			95.86 356		100.00 1		99.43 340	98.76 296

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	99.67 189

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 363

segment_acmp												99.55 31

testdata1							100.00 1		98.77 85

plane_prior7					99.00 376	94.78 423

plane_prior6					99.06 366	94.80 419						88.58 414

plane_prior4											99.97 265

plane_prior3						94.79 422			99.03 25	99.08 312

plane_prior2							100.00 1		99.00 33

plane_prior1					99.02 370

plane_prior						94.80 419	100.00 1		99.03 25						95.58 343

HQP5-MVS						94.82 416

HQP-NCC					99.07 362		100.00 1		99.04 20	99.17 300

ACMP_Plane					99.07 362		100.00 1		99.04 20	99.17 300

BP-MVS														99.79 144

HQP4-MVS										99.17 300			99.57 304			97.77 351

HQP2-MVS												88.61 412

NP-MVS					99.07 362	94.81 418					99.97 265

MDTV_nov1_ep13_2view						99.24 192	99.56 425		96.31 339	99.96 155		98.86 132		98.92 287		99.89 192

ACMMP++_ref															94.58 378

ACMMP++															95.17 363

Test By Simon												99.10 99

High-resolution multi-view benchmark