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.

SIFT-PCN-Cal		98.24 394	98.51 350	97.43 490	99.65 254	98.64 374	97.09 509	99.35 391	98.16 416	99.69 201	99.52 338	95.59 411	99.83 338	97.57 376	100.00 1	93.81 544

ELoFTR		99.25 216	99.26 202	99.21 347	99.86 60	98.66 367	99.00 292	99.93 43	98.56 365	99.83 110	99.83 83	97.34 343	99.92 154	99.03 191	100.00 1	99.04 432

SIFT-NCMNet		98.18 401	98.46 357	97.36 495	99.67 247	99.19 280	96.33 534	98.99 455	98.83 327	99.62 248	99.63 265	95.41 420	99.33 521	97.64 369	100.00 1	93.54 548

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 87	99.01 286	99.99 12	99.99 3	99.98 14	99.88 50	99.97 2	99.99 7	99.96 9	100.00 1	99.98 5

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 70	99.12 245	100.00 1	100.00 1	99.99 7	99.91 31	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 41	99.82 99	99.70 109	99.17 220	99.97 21	99.99 3	99.96 34	99.82 91	99.94 4	100.00 1	99.95 14	100.00 1	99.80 67

test_vis3_rt		99.89 3	99.90 4	99.87 26	99.98 3	99.75 79	99.70 38	100.00 1	99.73 112	100.00 1	99.89 41	99.79 22	99.88 241	99.98 1	100.00 1	99.98 5

IterMVS-SCA-FT		99.00 300	99.16 218	98.51 437	99.75 182	95.90 502	98.07 444	99.84 105	99.84 75	99.89 72	99.73 176	96.01 403	99.99 7	99.33 126	100.00 1	99.63 176

new-patchmatchnet		99.35 191	99.57 105	98.71 426	99.82 99	96.62 484	98.55 386	99.75 184	99.50 192	99.88 82	99.87 56	99.31 89	99.88 241	99.43 106	100.00 1	99.62 188

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 95	99.70 129	99.92 59	99.93 22	99.45 63	99.97 44	99.36 119	100.00 1	99.85 50

UA-Net		99.78 37	99.76 49	99.86 30	99.72 202	99.71 101	99.91 4	99.95 38	99.96 28	99.71 193	99.91 31	99.15 115	99.97 44	99.50 95	100.00 1	99.90 30

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 63	99.68 48	99.85 95	99.95 32	99.98 14	99.92 27	99.28 93	99.98 26	99.75 56	100.00 1	99.94 18

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 57	99.70 38	99.86 89	99.89 55	99.98 14	99.90 36	99.94 4	99.98 26	99.75 56	100.00 1	99.90 30

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 54	99.72 33	99.88 74	99.92 45	99.98 14	99.93 22	99.94 4	99.98 26	99.77 55	100.00 1	99.92 25

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 21	99.99 3	100.00 1	99.98 13	99.78 23	100.00 1	99.92 30	100.00 1	99.87 45

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 26	99.77 19	99.80 143	99.73 112	99.97 24	99.92 27	99.77 25	99.98 26	99.43 106	100.00 1	99.90 30

IterMVS		98.97 304	99.16 218	98.42 442	99.74 193	95.64 509	98.06 446	99.83 115	99.83 81	99.85 101	99.74 171	96.10 402	99.99 7	99.27 138	100.00 1	99.63 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ANet_high		99.88 6	99.87 11	99.91 3	99.99 1	99.91 4	99.65 62	100.00 1	99.90 49	100.00 1	99.97 14	99.61 41	99.97 44	99.75 56	100.00 1	99.84 55

PDCNetPlus		98.55 361	98.50 353	98.69 427	99.64 256	96.12 497	97.67 479	100.00 1	98.34 401	99.79 133	99.75 163	92.45 467	99.98 26	98.92 215	99.99 19	99.96 13

fmvsm_s_conf0.5_n_11		99.76 46	99.75 51	99.81 54	99.81 112	99.53 176	99.15 229	99.89 68	99.99 3	99.98 14	99.86 63	99.13 120	99.98 26	99.93 25	99.99 19	99.92 25

fmvsm_s_conf0.5_n_10		99.77 44	99.73 54	99.88 19	99.81 112	99.75 79	99.06 268	99.85 95	99.99 3	99.97 24	99.84 76	99.12 123	99.98 26	99.95 14	99.99 19	99.90 30

fmvsm_l_conf0.5_n_9		99.83 21	99.81 28	99.89 11	99.86 60	99.80 51	98.94 314	99.96 30	99.98 18	99.96 34	99.78 133	99.88 11	99.98 26	99.96 9	99.99 19	99.90 30

fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 46	99.83 90	99.59 160	98.97 305	99.92 47	99.99 3	99.97 24	99.84 76	99.90 9	99.94 98	99.94 20	99.99 19	99.92 25

fmvsm_s_conf0.5_n_8		99.76 46	99.72 55	99.88 19	99.82 99	99.75 79	99.02 281	99.87 80	99.98 18	99.98 14	99.81 98	99.07 134	99.97 44	99.91 33	99.99 19	99.92 25

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 32	99.78 146	99.78 57	99.00 292	99.97 21	99.96 28	99.97 24	99.56 320	99.92 8	99.93 120	99.91 33	99.99 19	99.83 59

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 46	99.86 18	99.08 262	99.97 21	99.98 18	99.96 34	99.79 120	99.90 9	99.99 7	99.96 9	99.99 19	99.90 30

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 112	99.71 101	98.97 305	99.92 47	99.98 18	99.97 24	99.86 63	99.53 58	99.95 81	99.88 41	99.99 19	99.89 38

fmvsm_s_conf0.1_n_2		99.81 28	99.78 39	99.89 11	99.93 24	99.76 70	98.92 318	99.98 13	99.99 3	99.99 7	99.88 50	99.43 67	99.94 98	99.94 20	99.99 19	99.99 2

mmtdpeth		99.78 37	99.83 21	99.66 153	99.85 75	99.05 308	99.79 15	99.97 21	100.00 1	99.43 318	99.94 19	99.64 35	99.94 98	99.83 46	99.99 19	99.98 5

mvs5depth		99.88 6	99.91 3	99.80 64	99.92 29	99.42 212	99.94 3	100.00 1	99.97 25	99.89 72	99.99 12	99.63 37	99.97 44	99.87 44	99.99 19	100.00 1

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 38	99.88 46	99.64 136	99.12 245	99.91 57	99.98 18	99.95 45	99.67 234	99.67 34	99.99 7	99.94 20	99.99 19	99.88 41

fmvsm_l_conf0.5_n		99.80 30	99.78 39	99.85 32	99.88 46	99.66 123	99.11 250	99.91 57	99.98 18	99.96 34	99.64 249	99.60 44	99.99 7	99.95 14	99.99 19	99.88 41

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 41	99.10 254	99.98 13	99.99 3	99.98 14	99.91 31	99.68 33	99.93 120	99.93 25	99.99 19	99.99 2

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 57	99.07 267	99.98 13	99.99 3	99.98 14	99.90 36	99.88 11	99.92 154	99.93 25	99.99 19	99.98 5

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 26	99.85 75	99.78 57	99.03 277	99.96 30	99.99 3	99.97 24	99.84 76	99.78 23	99.92 154	99.92 30	99.99 19	99.92 25

test_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 90	98.97 305	99.98 13	99.99 3	99.96 34	99.85 68	99.93 7	99.99 7	99.94 20	99.99 19	99.93 21

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 54	99.88 46	99.55 173	99.17 220	99.98 13	99.99 3	99.96 34	99.84 76	99.96 3	99.99 7	99.96 9	99.99 19	99.88 41

test_vis1_n_1920		99.72 53	99.88 7	99.27 335	99.93 24	97.84 438	99.34 149	100.00 1	99.99 3	99.99 7	99.82 91	99.87 13	99.99 7	99.97 4	99.99 19	99.97 10

test_fmvs2		99.72 53	99.85 17	99.34 309	99.91 31	98.08 425	99.48 109	100.00 1	99.90 49	99.99 7	99.91 31	99.50 62	99.98 26	99.98 1	99.99 19	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 232	99.96 7	98.62 377	99.67 53	100.00 1	99.95 32	100.00 1	99.95 16	99.85 14	99.99 7	99.98 1	99.99 19	99.98 5

test_f		99.75 49	99.88 7	99.37 295	99.96 7	98.21 411	99.51 101	100.00 1	99.94 36	100.00 1	99.93 22	99.58 50	99.94 98	99.97 4	99.99 19	99.97 10

test2506		94.73 508	94.59 508	95.15 528	99.59 276	85.90 558	99.75 25	74.01 561	99.89 55	99.71 193	99.86 63	79.00 540	99.90 204	99.52 91	99.99 19	99.65 158

test1111		97.74 428	98.16 395	96.49 520	99.60 270	89.86 556	99.71 37	91.21 553	99.89 55	99.88 82	99.87 56	93.73 446	99.90 204	99.56 83	99.99 19	99.70 107

ECVR-MVS		97.73 429	98.04 403	96.78 512	99.59 276	90.81 550	99.72 33	90.43 555	99.89 55	99.86 96	99.86 63	93.60 448	99.89 226	99.46 101	99.99 19	99.65 158

pmmvs-eth3d		99.48 135	99.47 132	99.51 238	99.77 159	99.41 219	98.81 340	99.66 240	99.42 221	99.75 165	99.66 240	99.20 107	99.76 416	98.98 199	99.99 19	99.36 341

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 26	99.82 10	99.82 122	99.84 75	99.94 48	99.91 31	99.13 120	99.96 69	99.83 46	99.99 19	99.83 59

v8		99.68 64	99.69 60	99.65 160	99.80 123	99.40 220	99.66 57	99.76 178	99.64 160	99.93 53	99.85 68	98.66 204	99.84 315	99.88 41	99.99 19	99.71 104

v10		99.69 59	99.69 60	99.66 153	99.81 112	99.39 224	99.66 57	99.75 184	99.60 177	99.92 59	99.87 56	98.75 190	99.86 278	99.90 37	99.99 19	99.73 95

CHOSEN 1792x2688		99.39 176	99.30 188	99.65 160	99.88 46	99.25 259	98.78 347	99.88 74	98.66 353	99.96 34	99.79 120	97.45 337	99.93 120	99.34 123	99.99 19	99.78 77

PVSNet_Blended_VisFu		99.40 172	99.38 161	99.44 263	99.90 37	98.66 367	98.94 314	99.91 57	97.97 432	99.79 133	99.73 176	99.05 143	99.97 44	99.15 164	99.99 19	99.68 126

IterMVS-LS		99.41 170	99.47 132	99.25 342	99.81 112	98.09 422	98.85 329	99.76 178	99.62 165	99.83 110	99.64 249	98.54 225	99.97 44	99.15 164	99.99 19	99.68 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS	98.90 4	99.62 94	99.61 89	99.67 145	99.72 202	99.44 205	99.24 193	99.71 208	99.27 246	99.93 53	99.90 36	99.70 31	99.93 120	98.99 197	99.99 19	99.64 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 19	99.91 31	99.90 7	99.96 1	99.92 47	99.90 49	99.97 24	99.87 56	99.81 20	99.95 81	99.54 87	99.99 19	99.80 67
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
FE-MVSNET2		99.68 64	99.67 65	99.72 122	99.86 60	99.68 117	99.46 116	99.88 74	99.62 165	99.87 92	99.85 68	99.06 141	99.85 297	99.44 104	99.98 54	99.63 176

Elysia		99.69 59	99.65 74	99.81 54	99.86 60	99.72 95	99.34 149	99.77 170	99.94 36	99.91 62	99.76 155	98.55 220	99.99 7	99.70 61	99.98 54	99.72 99

StellarMVS		99.69 59	99.65 74	99.81 54	99.86 60	99.72 95	99.34 149	99.77 170	99.94 36	99.91 62	99.76 155	98.55 220	99.99 7	99.70 61	99.98 54	99.72 99

LuminaMVS		99.39 176	99.28 197	99.73 113	99.83 90	99.49 184	99.00 292	99.05 449	99.81 91	99.89 72	99.79 120	96.54 380	99.97 44	99.64 73	99.98 54	99.73 95

VortexMVS		99.13 262	99.24 208	98.79 415	99.67 247	96.60 486	99.24 193	99.80 143	99.85 71	99.93 53	99.84 76	95.06 424	99.89 226	99.80 52	99.98 54	99.89 38

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 195	99.74 193	98.93 329	98.85 329	99.96 30	99.96 28	99.97 24	99.76 155	99.82 18	99.96 69	99.95 14	99.98 54	99.90 30

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 32	99.79 137	99.72 95	98.84 332	99.96 30	99.96 28	99.96 34	99.72 186	99.71 28	99.99 7	99.93 25	99.98 54	99.85 50

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 72	99.75 182	99.56 169	98.98 303	99.94 41	99.92 45	99.97 24	99.72 186	99.84 16	99.92 154	99.91 33	99.98 54	99.89 38

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 99	99.76 70	98.88 323	99.92 47	99.98 18	99.98 14	99.85 68	99.42 69	99.94 98	99.93 25	99.98 54	99.94 18

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 75	99.82 41	99.03 277	99.96 30	99.99 3	99.97 24	99.84 76	99.58 50	99.93 120	99.92 30	99.98 54	99.93 21

MM		99.18 246	99.05 259	99.55 221	99.35 390	98.81 350	99.05 269	97.79 515	99.99 3	99.48 305	99.59 305	96.29 394	99.95 81	99.94 20	99.98 54	99.88 41

test_fmvs1_n		99.68 64	99.81 28	99.28 329	99.95 15	97.93 434	99.49 107	100.00 1	99.82 85	99.99 7	99.89 41	99.21 105	99.98 26	99.97 4	99.98 54	99.93 21

mvsany_test3		99.85 12	99.88 7	99.75 98	99.95 15	99.37 231	99.53 92	99.98 13	99.77 107	99.99 7	99.95 16	99.85 14	99.94 98	99.95 14	99.98 54	99.94 18

Anonymous20240521		99.44 155	99.42 152	99.49 244	99.89 40	98.96 323	99.62 67	99.76 178	99.85 71	99.82 112	99.88 50	96.39 387	99.97 44	99.59 78	99.98 54	99.55 236

D2MVS		99.22 232	99.19 215	99.29 326	99.69 231	98.74 359	98.81 340	99.41 370	98.55 367	99.68 208	99.69 215	98.13 285	99.87 258	98.82 224	99.98 54	99.24 371

CHOSEN 280x420		98.41 379	98.41 366	98.40 443	99.34 399	95.89 503	96.94 519	99.44 363	98.80 333	99.25 370	99.52 338	93.51 449	99.98 26	98.94 212	99.98 54	99.32 355

MGCNet		98.61 351	98.30 381	99.52 234	97.88 535	98.95 324	98.76 349	94.11 548	99.84 75	99.32 352	99.57 316	95.57 413	99.95 81	99.68 66	99.98 54	99.68 126

v1192		99.57 102	99.57 105	99.57 210	99.77 159	99.22 270	99.04 274	99.60 285	99.18 263	99.87 92	99.72 186	99.08 131	99.85 297	99.89 40	99.98 54	99.66 149

v1144		99.54 116	99.53 120	99.59 198	99.79 137	99.28 250	99.10 254	99.61 273	99.20 260	99.84 104	99.73 176	98.67 202	99.84 315	99.86 45	99.98 54	99.64 170

OurMVSNet-221017-0		99.75 49	99.71 56	99.84 38	99.96 7	99.83 33	99.83 7	99.85 95	99.80 95	99.93 53	99.93 22	98.54 225	99.93 120	99.59 78	99.98 54	99.76 86

UGNet		99.38 179	99.34 175	99.49 244	98.90 478	98.90 335	99.70 38	99.35 391	99.86 65	98.57 458	99.81 98	98.50 237	99.93 120	99.38 115	99.98 54	99.66 149
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
MIMVSNet1		99.66 77	99.62 85	99.80 64	99.94 18	99.87 15	99.69 45	99.77 170	99.78 102	99.93 53	99.89 41	97.94 303	99.92 154	99.65 70	99.98 54	99.62 188

Vis-MVSNet		99.75 49	99.74 53	99.79 72	99.88 46	99.66 123	99.69 45	99.92 47	99.67 144	99.77 151	99.75 163	99.61 41	99.98 26	99.35 122	99.98 54	99.72 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tt0320		99.79 34	99.79 34	99.81 54	99.82 99	99.84 26	99.82 10	99.90 64	99.94 36	99.94 48	99.94 19	99.07 134	99.92 154	99.68 66	99.97 77	99.67 135

test_cas_vis1_n_1920		99.76 46	99.86 13	99.45 259	99.93 24	98.40 399	99.30 167	99.98 13	99.94 36	99.99 7	99.89 41	99.80 21	99.97 44	99.96 9	99.97 77	99.97 10

test_fmvs1		99.48 135	99.65 74	98.97 382	99.54 316	97.16 469	99.11 250	99.98 13	99.78 102	99.96 34	99.81 98	98.72 195	99.97 44	99.95 14	99.97 77	99.79 75

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 40	99.91 4	99.89 5	99.71 208	99.93 43	99.95 45	99.89 41	99.71 28	99.96 69	99.51 93	99.97 77	99.84 55

CANet		99.11 270	99.05 259	99.28 329	98.83 490	98.56 383	98.71 360	99.41 370	99.25 251	99.23 374	99.22 432	97.66 328	99.94 98	99.19 152	99.97 77	99.33 351

pmmvs6		99.86 10	99.86 13	99.83 41	99.94 18	99.90 7	99.83 7	99.91 57	99.85 71	99.94 48	99.95 16	99.73 27	99.90 204	99.65 70	99.97 77	99.69 119

v144192		99.55 111	99.54 116	99.58 202	99.78 146	99.20 277	99.11 250	99.62 265	99.18 263	99.89 72	99.72 186	98.66 204	99.87 258	99.88 41	99.97 77	99.66 149

v1921920		99.56 106	99.57 105	99.55 221	99.75 182	99.11 295	99.05 269	99.61 273	99.15 277	99.88 82	99.71 196	99.08 131	99.87 258	99.90 37	99.97 77	99.66 149

FC-MVSNet-test		99.70 57	99.65 74	99.86 30	99.88 46	99.86 18	99.72 33	99.78 165	99.90 49	99.82 112	99.83 83	98.45 244	99.87 258	99.51 93	99.97 77	99.86 47

v2v482		99.50 127	99.47 132	99.58 202	99.78 146	99.25 259	99.14 233	99.58 300	99.25 251	99.81 119	99.62 275	98.24 271	99.84 315	99.83 46	99.97 77	99.64 170

Patchmtry		98.78 333	98.54 347	99.49 244	98.89 482	99.19 280	99.32 158	99.67 235	99.65 156	99.72 188	99.79 120	91.87 474	99.95 81	98.00 321	99.97 77	99.33 351

PVSNet_BlendedMVS		99.03 288	99.01 274	99.09 366	99.54 316	97.99 428	98.58 378	99.82 122	97.62 458	99.34 347	99.71 196	98.52 234	99.77 409	97.98 322	99.97 77	99.52 268

FMVSNet1		99.66 77	99.63 82	99.73 113	99.78 146	99.77 63	99.68 48	99.70 217	99.67 144	99.82 112	99.83 83	98.98 155	99.90 204	99.24 139	99.97 77	99.53 257

HyFIR lowres test		98.91 315	98.64 332	99.73 113	99.85 75	99.47 189	98.07 444	99.83 115	98.64 356	99.89 72	99.60 295	92.57 461	100.00 1	99.33 126	99.97 77	99.72 99

dtuplus		99.52 122	99.55 112	99.43 267	99.76 164	98.90 335	98.71 360	99.89 68	99.67 144	99.79 133	99.77 145	99.25 101	99.81 378	99.18 155	99.96 91	99.57 228

E5new		99.68 64	99.67 65	99.70 133	99.87 55	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

E6new		99.68 64	99.67 65	99.70 133	99.86 60	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

E6		99.68 64	99.67 65	99.70 133	99.86 60	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

E5		99.68 64	99.67 65	99.70 133	99.87 55	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

viewdifsd2359ckpt11		99.62 94	99.64 79	99.56 214	99.86 60	99.19 280	99.02 281	99.93 43	99.83 81	99.88 82	99.81 98	98.99 151	99.83 338	99.48 97	99.96 91	99.65 158

viewmacassd2359aftdt		99.63 86	99.61 89	99.68 141	99.84 81	99.61 154	99.14 233	99.87 80	99.71 122	99.75 165	99.77 145	99.54 55	99.72 439	98.91 216	99.96 91	99.70 107

viewmsd2359difaftdt		99.62 94	99.64 79	99.56 214	99.86 60	99.19 280	99.02 281	99.93 43	99.83 81	99.88 82	99.81 98	98.99 151	99.83 338	99.48 97	99.96 91	99.65 158

FE-MVSNET		99.45 151	99.36 169	99.71 128	99.84 81	99.64 136	99.16 226	99.91 57	98.65 354	99.73 182	99.73 176	98.54 225	99.82 361	98.71 249	99.96 91	99.67 135

AstraMVS		99.15 258	99.06 252	99.42 270	99.85 75	98.59 380	99.13 240	97.26 525	99.84 75	99.87 92	99.77 145	96.11 400	99.93 120	99.71 60	99.96 91	99.74 91

tt0320-xc		99.82 24	99.82 25	99.82 46	99.82 99	99.84 26	99.82 10	99.92 47	99.94 36	99.94 48	99.93 22	99.34 85	99.92 154	99.70 61	99.96 91	99.70 107

SSC-MVS3.2		99.64 85	99.67 65	99.56 214	99.75 182	98.98 317	98.96 309	99.87 80	99.88 60	99.84 104	99.64 249	99.32 88	99.91 185	99.78 54	99.96 91	99.80 67

SDMVSNet		99.77 44	99.77 45	99.76 87	99.80 123	99.65 129	99.63 64	99.86 89	99.97 25	99.89 72	99.89 41	99.52 60	99.99 7	99.42 111	99.96 91	99.65 158

sd_testset		99.78 37	99.78 39	99.80 64	99.80 123	99.76 70	99.80 14	99.79 152	99.97 25	99.89 72	99.89 41	99.53 58	99.99 7	99.36 119	99.96 91	99.65 158

test_vis1_n		99.68 64	99.79 34	99.36 301	99.94 18	98.18 414	99.52 94	100.00 1	99.86 65	100.00 1	99.88 50	98.99 151	99.96 69	99.97 4	99.96 91	99.95 15

patch_mono-2		99.51 124	99.46 138	99.64 167	99.70 223	99.11 295	99.04 274	99.87 80	99.71 122	99.47 307	99.79 120	98.24 271	99.98 26	99.38 115	99.96 91	99.83 59

dcpmvs_2		99.61 98	99.64 79	99.53 232	99.79 137	98.82 349	99.58 82	99.97 21	99.95 32	99.96 34	99.76 155	98.44 245	99.99 7	99.34 123	99.96 91	99.78 77

ppachtmachnet_test		98.89 321	99.12 230	98.20 458	99.66 250	95.24 518	97.63 481	99.68 230	99.08 285	99.78 139	99.62 275	98.65 206	99.88 241	98.02 317	99.96 91	99.48 286

Anonymous20231211		99.62 94	99.57 105	99.76 87	99.61 267	99.60 158	99.81 13	99.73 195	99.82 85	99.90 67	99.90 36	97.97 302	99.86 278	99.42 111	99.96 91	99.80 67

nrg030		99.70 57	99.66 72	99.82 46	99.76 164	99.84 26	99.61 73	99.70 217	99.93 43	99.78 139	99.68 228	99.10 125	99.78 396	99.45 103	99.96 91	99.83 59

v1240		99.56 106	99.58 100	99.51 238	99.80 123	99.00 313	99.00 292	99.65 250	99.15 277	99.90 67	99.75 163	99.09 127	99.88 241	99.90 37	99.96 91	99.67 135

PS-CasMVS		99.66 77	99.58 100	99.89 11	99.80 123	99.85 21	99.66 57	99.73 195	99.62 165	99.84 104	99.71 196	98.62 208	99.96 69	99.30 131	99.96 91	99.86 47

casdiffmvs_mvg		99.68 64	99.68 63	99.69 139	99.81 112	99.59 160	99.29 175	99.90 64	99.71 122	99.79 133	99.73 176	99.54 55	99.84 315	99.36 119	99.96 91	99.65 158
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TAMVS		99.49 132	99.45 141	99.63 175	99.48 350	99.42 212	99.45 117	99.57 303	99.66 151	99.78 139	99.83 83	97.85 310	99.86 278	99.44 104	99.96 91	99.61 203

test_0402		99.22 232	99.14 223	99.45 259	99.79 137	99.43 209	99.28 177	99.68 230	99.54 185	99.40 333	99.56 320	99.07 134	99.82 361	96.01 480	99.96 91	99.11 405

viewmamba		99.49 132	99.51 122	99.42 270	99.75 182	98.90 335	98.85 329	99.85 95	99.69 132	99.73 182	99.67 234	98.79 182	99.82 361	99.28 136	99.95 116	99.54 248

hybridnocas07		99.43 159	99.44 146	99.39 286	99.75 182	98.85 346	98.76 349	99.85 95	99.71 122	99.70 197	99.68 228	98.47 239	99.77 409	99.13 174	99.95 116	99.55 236

casdiffseed414692147		99.68 64	99.68 63	99.67 145	99.86 60	99.65 129	99.32 158	99.87 80	99.75 110	99.77 151	99.80 108	99.61 41	99.68 467	99.21 146	99.95 116	99.67 135

E4		99.61 98	99.59 96	99.66 153	99.84 81	99.53 176	99.08 262	99.84 105	99.65 156	99.74 176	99.80 108	99.45 63	99.77 409	98.93 213	99.95 116	99.69 119

diffmvs_AUTHOR		99.48 135	99.48 130	99.47 252	99.80 123	98.89 338	98.71 360	99.82 122	99.79 99	99.66 223	99.63 265	98.87 173	99.88 241	99.13 174	99.95 116	99.62 188

SSM_0404		99.57 102	99.58 100	99.54 227	99.76 164	99.28 250	99.19 211	99.84 105	99.80 95	99.78 139	99.70 206	99.44 65	99.93 120	98.74 240	99.95 116	99.41 325

reproduce_monomvs		97.40 447	97.46 435	97.20 501	99.05 461	91.91 541	99.20 205	99.18 436	99.84 75	99.86 96	99.75 163	80.67 529	99.83 338	99.69 64	99.95 116	99.85 50

WBMVS		97.50 443	97.18 449	98.48 439	98.85 487	95.89 503	98.44 406	99.52 337	99.53 187	99.52 290	99.42 368	80.10 532	99.86 278	99.24 139	99.95 116	99.68 126

our_test_3		98.85 327	99.09 244	98.13 460	99.66 250	94.90 523	97.72 474	99.58 300	99.07 287	99.64 233	99.62 275	98.19 280	99.93 120	98.41 282	99.95 116	99.55 236

CANet_DTU		98.91 315	98.85 309	99.09 366	98.79 496	98.13 417	98.18 426	99.31 406	99.48 197	98.86 428	99.51 342	96.56 377	99.95 81	99.05 188	99.95 116	99.19 387

pmmvs5		99.19 242	99.11 233	99.42 270	99.76 164	98.88 340	98.55 386	99.73 195	98.82 329	99.72 188	99.62 275	96.56 377	99.82 361	99.32 128	99.95 116	99.56 232

V42		99.56 106	99.54 116	99.63 175	99.79 137	99.46 197	99.39 129	99.59 291	99.24 253	99.86 96	99.70 206	98.55 220	99.82 361	99.79 53	99.95 116	99.60 208

EU-MVSNet		99.39 176	99.62 85	98.72 422	99.88 46	96.44 488	99.56 87	99.85 95	99.90 49	99.90 67	99.85 68	98.09 290	99.83 338	99.58 81	99.95 116	99.90 30

PMMVS2		99.48 135	99.45 141	99.57 210	99.76 164	98.99 315	98.09 441	99.90 64	98.95 304	99.78 139	99.58 308	99.57 52	99.93 120	99.48 97	99.95 116	99.79 75

DTE-MVSNet		99.68 64	99.61 89	99.88 19	99.80 123	99.87 15	99.67 53	99.71 208	99.72 116	99.84 104	99.78 133	98.67 202	99.97 44	99.30 131	99.95 116	99.80 67

WR-MVS_H		99.61 98	99.53 120	99.87 26	99.80 123	99.83 33	99.67 53	99.75 184	99.58 181	99.85 101	99.69 215	98.18 282	99.94 98	99.28 136	99.95 116	99.83 59

K. test v3		98.87 323	98.60 336	99.69 139	99.93 24	99.46 197	99.74 27	94.97 543	99.78 102	99.88 82	99.88 50	93.66 447	99.97 44	99.61 76	99.95 116	99.64 170

TDRefinement		99.72 53	99.70 57	99.77 80	99.90 37	99.85 21	99.86 6	99.92 47	99.69 132	99.78 139	99.92 27	99.37 78	99.88 241	98.93 213	99.95 116	99.60 208

Gipuma		99.57 102	99.59 96	99.49 244	99.98 3	99.71 101	99.72 33	99.84 105	99.81 91	99.94 48	99.78 133	98.91 167	99.71 444	98.41 282	99.95 116	99.05 429
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
hybridcas		99.65 83	99.63 82	99.70 133	99.85 75	99.67 120	99.30 167	99.87 80	99.67 144	99.81 119	99.77 145	99.21 105	99.81 378	99.24 139	99.94 135	99.61 203

hybrid		99.42 163	99.43 149	99.37 295	99.75 182	98.77 356	98.72 357	99.84 105	99.61 170	99.65 227	99.68 228	98.53 230	99.79 392	99.16 163	99.94 135	99.54 248

viewdifsd2359ckpt07		99.51 124	99.50 125	99.52 234	99.80 123	99.19 280	98.92 318	99.88 74	99.72 116	99.64 233	99.62 275	99.06 141	99.81 378	98.96 204	99.94 135	99.56 232

viewmambaseed2359dif		99.47 145	99.50 125	99.37 295	99.70 223	98.80 353	98.67 364	99.92 47	99.49 194	99.77 151	99.71 196	99.08 131	99.78 396	99.20 150	99.94 135	99.54 248

KinetiMVS		99.66 77	99.63 82	99.76 87	99.89 40	99.57 168	99.37 140	99.82 122	99.95 32	99.90 67	99.63 265	98.57 216	99.97 44	99.65 70	99.94 135	99.74 91

guyue		99.12 265	99.02 268	99.41 280	99.84 81	98.56 383	99.19 211	98.30 497	99.82 85	99.84 104	99.75 163	94.84 428	99.92 154	99.68 66	99.94 135	99.74 91

sc_t1		99.81 28	99.80 32	99.82 46	99.88 46	99.88 12	99.83 7	99.79 152	99.94 36	99.93 53	99.92 27	99.35 84	99.92 154	99.64 73	99.94 135	99.68 126

v148		99.40 172	99.41 156	99.39 286	99.76 164	98.94 326	99.09 259	99.59 291	99.17 270	99.81 119	99.61 285	98.41 249	99.69 455	99.32 128	99.94 135	99.53 257

casdiffmvs		99.63 86	99.61 89	99.67 145	99.79 137	99.59 160	99.13 240	99.85 95	99.79 99	99.76 160	99.72 186	99.33 87	99.82 361	99.21 146	99.94 135	99.59 215
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PEN-MVS		99.66 77	99.59 96	99.89 11	99.83 90	99.87 15	99.66 57	99.73 195	99.70 129	99.84 104	99.73 176	98.56 219	99.96 69	99.29 134	99.94 135	99.83 59

CP-MVSNet		99.54 116	99.43 149	99.87 26	99.76 164	99.82 41	99.57 85	99.61 273	99.54 185	99.80 126	99.64 249	97.79 314	99.95 81	99.21 146	99.94 135	99.84 55

baseline		99.63 86	99.62 85	99.66 153	99.80 123	99.62 144	99.44 119	99.80 143	99.71 122	99.72 188	99.69 215	99.15 115	99.83 338	99.32 128	99.94 135	99.53 257

FMVSNet2		99.35 191	99.28 197	99.55 221	99.49 345	99.35 238	99.45 117	99.57 303	99.44 210	99.70 197	99.74 171	97.21 350	99.87 258	99.03 191	99.94 135	99.44 312

ACMMP++_ref															99.94 135

onestephybrid01		99.45 151	99.46 138	99.42 270	99.69 231	98.88 340	98.76 349	99.81 135	99.78 102	99.67 216	99.73 176	98.61 210	99.84 315	99.17 159	99.93 149	99.52 268

Casviewmamba		99.63 86	99.60 93	99.73 113	99.84 81	99.72 95	99.36 144	99.87 80	99.67 144	99.74 176	99.73 176	99.07 134	99.83 338	99.14 171	99.93 149	99.62 188

DKM		99.12 265	98.98 288	99.54 227	99.71 207	99.48 188	98.53 391	99.88 74	99.18 263	98.99 412	99.64 249	96.25 395	99.75 427	98.66 257	99.93 149	99.40 328

E2		99.54 116	99.51 122	99.62 184	99.78 146	99.47 189	99.01 286	99.82 122	99.55 183	99.69 201	99.77 145	99.26 97	99.76 416	98.82 224	99.93 149	99.62 188

E3		99.54 116	99.51 122	99.62 184	99.78 146	99.47 189	99.01 286	99.82 122	99.55 183	99.69 201	99.77 145	99.25 101	99.76 416	98.82 224	99.93 149	99.62 188

eth_miper_zixun_eth		98.68 346	98.71 325	98.60 431	99.10 453	96.84 481	97.52 490	99.54 321	98.94 305	99.58 263	99.48 354	96.25 395	99.76 416	98.01 320	99.93 149	99.21 379

FIs		99.65 83	99.58 100	99.84 38	99.84 81	99.85 21	99.66 57	99.75 184	99.86 65	99.74 176	99.79 120	98.27 269	99.85 297	99.37 118	99.93 149	99.83 59

pmmvs4		99.13 262	99.06 252	99.36 301	99.57 296	99.10 302	98.01 450	99.25 419	98.78 337	99.58 263	99.44 365	98.24 271	99.76 416	98.74 240	99.93 149	99.22 376

XXY-MVS		99.71 56	99.67 65	99.81 54	99.89 40	99.72 95	99.59 80	99.82 122	99.39 227	99.82 112	99.84 76	99.38 76	99.91 185	99.38 115	99.93 149	99.80 67

PatchmatchNet1														98.28 292	99.92 158	99.44 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
SIFT-ConvMatch		98.16 405	98.37 371	97.52 483	99.54 316	99.20 277	96.97 517	98.47 485	98.09 422	99.14 393	99.40 376	95.93 406	99.05 533	97.87 334	99.92 158	94.31 537

usedtu_dtu_shiyan1		98.87 323	98.71 325	99.35 305	99.59 276	98.88 340	97.17 505	99.64 258	98.94 305	99.27 364	99.22 432	95.57 413	99.83 338	99.08 184	99.92 158	99.35 344

FE-MVSNET3		98.87 323	98.71 325	99.35 305	99.59 276	98.88 340	97.17 505	99.64 258	98.94 305	99.27 364	99.22 432	95.57 413	99.83 338	99.08 184	99.92 158	99.35 344

viewmanbaseed2359cas		99.50 127	99.47 132	99.61 191	99.73 197	99.52 181	99.03 277	99.83 115	99.49 194	99.65 227	99.64 249	99.18 109	99.71 444	98.73 245	99.92 158	99.58 221

pm-mvs1		99.79 34	99.79 34	99.78 76	99.91 31	99.83 33	99.76 23	99.87 80	99.73 112	99.89 72	99.87 56	99.63 37	99.87 258	99.54 87	99.92 158	99.63 176

EI-MVSNet		99.38 179	99.44 146	99.21 347	99.58 286	98.09 422	99.26 186	99.46 357	99.62 165	99.75 165	99.67 234	98.54 225	99.85 297	99.15 164	99.92 158	99.68 126

TranMVSNet+NR-MVSNet		99.54 116	99.47 132	99.76 87	99.58 286	99.64 136	99.30 167	99.63 262	99.61 170	99.71 193	99.56 320	98.76 188	99.96 69	99.14 171	99.92 158	99.68 126

lessismore_v0				99.64 167	99.86 60	99.38 226		90.66 554		99.89 72	99.83 83	94.56 434	99.97 44	99.56 83	99.92 158	99.57 228

SixPastTwentyTwo		99.42 163	99.30 188	99.76 87	99.92 29	99.67 120	99.70 38	99.14 442	99.65 156	99.89 72	99.90 36	96.20 398	99.94 98	99.42 111	99.92 158	99.67 135

MVSTER		98.47 372	98.22 388	99.24 344	99.06 459	98.35 405	99.08 262	99.46 357	99.27 246	99.75 165	99.66 240	88.61 507	99.85 297	99.14 171	99.92 158	99.52 268

N_pmnet		98.73 340	98.53 348	99.35 305	99.72 202	98.67 364	98.34 412	94.65 544	98.35 397	99.79 133	99.68 228	98.03 295	99.93 120	98.28 292	99.92 158	99.44 312

CSCG		99.37 184	99.29 194	99.60 195	99.71 207	99.46 197	99.43 121	99.85 95	98.79 335	99.41 327	99.60 295	98.92 164	99.92 154	98.02 317	99.92 158	99.43 319

CMPMVS	77.52 23	98.50 368	98.19 393	99.41 280	98.33 520	99.56 169	99.01 286	99.59 291	95.44 517	99.57 266	99.80 108	95.64 409	99.46 516	96.47 460	99.92 158	99.21 379
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LoFTR		99.29 206	99.26 202	99.36 301	99.70 223	99.05 308	98.66 366	99.95 38	98.85 322	99.86 96	99.75 163	98.14 284	99.93 120	98.54 271	99.91 172	99.10 408

SIFT-UMatch		98.07 410	98.27 384	97.46 489	99.57 296	98.99 315	96.93 520	99.02 451	98.53 371	99.26 368	99.23 431	95.43 419	99.31 522	96.51 455	99.91 172	94.09 541

viewcassd2359sk11		99.48 135	99.45 141	99.58 202	99.73 197	99.42 212	98.96 309	99.80 143	99.44 210	99.63 238	99.74 171	99.09 127	99.76 416	98.72 247	99.91 172	99.57 228

EG-PatchMatch MVS		99.57 102	99.56 110	99.62 184	99.77 159	99.33 241	99.26 186	99.76 178	99.32 238	99.80 126	99.78 133	99.29 91	99.87 258	99.15 164	99.91 172	99.66 149

dtuonly		98.93 314	99.11 233	98.38 445	99.72 202	95.75 506	97.07 512	99.91 57	99.04 290	99.65 227	99.41 370	98.32 263	99.83 338	98.97 201	99.90 176	99.55 236

MatchFormer		99.03 288	99.02 268	99.08 371	99.56 310	98.47 392	98.57 382	99.90 64	98.13 418	99.80 126	99.75 163	98.34 259	99.84 315	97.18 414	99.90 176	98.92 453

viewdifsd2359ckpt13		99.42 163	99.37 164	99.57 210	99.72 202	99.46 197	99.01 286	99.80 143	99.20 260	99.51 297	99.60 295	98.92 164	99.70 448	98.65 260	99.90 176	99.55 236

mamba_0408		99.54 116	99.55 112	99.54 227	99.71 207	99.24 264	99.27 181	99.79 152	99.72 116	99.78 139	99.64 249	99.36 81	99.93 120	98.74 240	99.90 176	99.45 297

SSM_04072		99.55 111	99.55 112	99.55 221	99.71 207	99.24 264	99.27 181	99.79 152	99.72 116	99.78 139	99.64 249	99.36 81	99.97 44	98.74 240	99.90 176	99.45 297

SSM_0407		99.56 106	99.56 110	99.54 227	99.71 207	99.24 264	99.15 229	99.84 105	99.80 95	99.78 139	99.70 206	99.44 65	99.93 120	98.74 240	99.90 176	99.45 297

MVSMamba_PlusPlus		99.55 111	99.58 100	99.47 252	99.68 240	99.40 220	99.52 94	99.70 217	99.92 45	99.77 151	99.86 63	98.28 267	99.96 69	99.54 87	99.90 176	99.05 429

miper_lstm_enhance		98.65 349	98.60 336	98.82 414	99.20 432	97.33 464	97.78 470	99.66 240	99.01 295	99.59 261	99.50 345	94.62 433	99.85 297	98.12 310	99.90 176	99.26 368

SPE-MVS-test		99.68 64	99.70 57	99.64 167	99.57 296	99.83 33	99.78 17	99.97 21	99.92 45	99.50 300	99.38 384	99.57 52	99.95 81	99.69 64	99.90 176	99.15 396

EI-MVSNet-UG-set		99.48 135	99.50 125	99.42 270	99.57 296	98.65 371	99.24 193	99.46 357	99.68 136	99.80 126	99.66 240	98.99 151	99.89 226	99.19 152	99.90 176	99.72 99

diffmvs		99.34 196	99.32 181	99.39 286	99.67 247	98.77 356	98.57 382	99.81 135	99.61 170	99.48 305	99.41 370	98.47 239	99.86 278	98.97 201	99.90 176	99.53 257
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
YYNet1		98.95 310	98.99 285	98.84 409	99.64 256	97.14 471	98.22 424	99.32 402	98.92 313	99.59 261	99.66 240	97.40 339	99.83 338	98.27 294	99.90 176	99.55 236

GBi-Net		99.42 163	99.31 183	99.73 113	99.49 345	99.77 63	99.68 48	99.70 217	99.44 210	99.62 248	99.83 83	97.21 350	99.90 204	98.96 204	99.90 176	99.53 257

FMVSNet5		97.80 426	97.25 446	99.42 270	98.83 490	98.97 320	99.38 132	99.80 143	98.87 319	99.25 370	99.69 215	80.60 531	99.91 185	98.96 204	99.90 176	99.38 334

test1		99.42 163	99.31 183	99.73 113	99.49 345	99.77 63	99.68 48	99.70 217	99.44 210	99.62 248	99.83 83	97.21 350	99.90 204	98.96 204	99.90 176	99.53 257

FMVSNet3		98.80 332	98.63 334	99.32 317	99.13 444	98.72 360	99.10 254	99.48 351	99.23 255	99.62 248	99.64 249	92.57 461	99.86 278	98.96 204	99.90 176	99.39 332

SIFT-PointCN		98.28 389	98.47 355	97.71 480	99.70 223	98.91 334	96.98 516	99.70 217	97.90 440	99.36 339	99.35 398	95.51 416	99.83 338	97.84 343	99.89 192	94.39 536

E3new		99.42 163	99.37 164	99.56 214	99.68 240	99.38 226	98.93 317	99.79 152	99.30 241	99.55 279	99.69 215	98.88 171	99.76 416	98.63 262	99.89 192	99.53 257

cl____		98.54 363	98.41 366	98.92 391	99.03 466	97.80 442	97.46 492	99.59 291	98.90 315	99.60 258	99.46 361	93.85 443	99.78 396	97.97 324	99.89 192	99.17 392

DIV-MVS_self_test		98.54 363	98.42 365	98.92 391	99.03 466	97.80 442	97.46 492	99.59 291	98.90 315	99.60 258	99.46 361	93.87 442	99.78 396	97.97 324	99.89 192	99.18 389

CS-MVS		99.67 76	99.70 57	99.58 202	99.53 325	99.84 26	99.79 15	99.96 30	99.90 49	99.61 255	99.41 370	99.51 61	99.95 81	99.66 69	99.89 192	98.96 445

EI-MVSNet-Vis-set		99.47 145	99.49 129	99.42 270	99.57 296	98.66 367	99.24 193	99.46 357	99.67 144	99.79 133	99.65 247	98.97 157	99.89 226	99.15 164	99.89 192	99.71 104

DSMNet-mixed		99.48 135	99.65 74	98.95 385	99.71 207	97.27 466	99.50 102	99.82 122	99.59 179	99.41 327	99.85 68	99.62 40	100.00 1	99.53 90	99.89 192	99.59 215

Vis-MVSNet (Re-imp)		98.77 335	98.58 341	99.34 309	99.78 146	98.88 340	99.61 73	99.56 308	99.11 283	99.24 373	99.56 320	93.00 457	99.78 396	97.43 386	99.89 192	99.35 344

EPP-MVSNet		99.17 251	99.00 278	99.66 153	99.80 123	99.43 209	99.70 38	99.24 423	99.48 197	99.56 274	99.77 145	94.89 427	99.93 120	98.72 247	99.89 192	99.63 176

CLD-MVS		98.76 336	98.57 342	99.33 312	99.57 296	98.97 320	97.53 488	99.55 315	96.41 502	99.27 364	99.13 445	99.07 134	99.78 396	96.73 441	99.89 192	99.23 374
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMH	98.42 6	99.59 101	99.54 116	99.72 122	99.86 60	99.62 144	99.56 87	99.79 152	98.77 340	99.80 126	99.85 68	99.64 35	99.85 297	98.70 251	99.89 192	99.70 107
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DenseAffine		99.17 251	99.06 252	99.49 244	99.76 164	99.33 241	98.43 407	99.97 21	99.11 283	99.17 387	99.61 285	97.05 359	99.76 416	98.56 268	99.88 203	99.38 334

viewdifsd2359ckpt09		99.24 220	99.16 218	99.49 244	99.70 223	99.22 270	98.88 323	99.81 135	98.70 348	99.38 336	99.37 388	98.22 276	99.76 416	98.48 274	99.88 203	99.51 271

testf1		99.63 86	99.60 93	99.72 122	99.94 18	99.95 2	99.47 112	99.89 68	99.43 217	99.88 82	99.80 108	99.26 97	99.90 204	98.81 228	99.88 203	99.32 355

APD_test2		99.63 86	99.60 93	99.72 122	99.94 18	99.95 2	99.47 112	99.89 68	99.43 217	99.88 82	99.80 108	99.26 97	99.90 204	98.81 228	99.88 203	99.32 355

BridgeMVS		99.50 127	99.50 125	99.50 240	99.42 373	99.49 184	99.52 94	99.75 184	99.86 65	99.78 139	99.71 196	98.20 279	99.90 204	99.39 114	99.88 203	99.10 408

GeoE		99.69 59	99.66 72	99.78 76	99.76 164	99.76 70	99.60 79	99.82 122	99.46 205	99.75 165	99.56 320	99.63 37	99.95 81	99.43 106	99.88 203	99.62 188

c3_l		98.72 341	98.71 325	98.72 422	99.12 446	97.22 468	97.68 478	99.56 308	98.90 315	99.54 283	99.48 354	96.37 388	99.73 437	97.88 331	99.88 203	99.21 379

VPA-MVSNet		99.66 77	99.62 85	99.79 72	99.68 240	99.75 79	99.62 67	99.69 226	99.85 71	99.80 126	99.81 98	98.81 177	99.91 185	99.47 100	99.88 203	99.70 107

MDA-MVSNet_test_wron		98.95 310	98.99 285	98.85 407	99.64 256	97.16 469	98.23 423	99.33 400	98.93 310	99.56 274	99.66 240	97.39 341	99.83 338	98.29 291	99.88 203	99.55 236

XVG-OURS		99.21 237	99.06 252	99.65 160	99.82 99	99.62 144	97.87 466	99.74 190	98.36 391	99.66 223	99.68 228	99.71 28	99.90 204	96.84 435	99.88 203	99.43 319

CDS-MVSNet		99.22 232	99.13 226	99.50 240	99.35 390	99.11 295	98.96 309	99.54 321	99.46 205	99.61 255	99.70 206	96.31 391	99.83 338	99.34 123	99.88 203	99.55 236
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS-MVSNet		99.03 288	98.85 309	99.55 221	99.80 123	99.25 259	99.73 30	99.15 440	99.37 229	99.61 255	99.71 196	94.73 431	99.81 378	97.70 357	99.88 203	99.58 221

USDC		98.96 307	98.93 296	99.05 375	99.54 316	97.99 428	97.07 512	99.80 143	98.21 412	99.75 165	99.77 145	98.43 246	99.64 487	97.90 329	99.88 203	99.51 271

ACMH+	98.40 8	99.50 127	99.43 149	99.71 128	99.86 60	99.76 70	99.32 158	99.77 170	99.53 187	99.77 151	99.76 155	99.26 97	99.78 396	97.77 345	99.88 203	99.60 208

mvsany_test1		99.44 155	99.45 141	99.40 283	99.37 383	98.64 374	97.90 465	99.59 291	99.27 246	99.92 59	99.82 91	99.74 26	99.93 120	99.55 85	99.87 217	99.63 176

SD-MVS		99.01 297	99.30 188	98.15 459	99.50 340	99.40 220	98.94 314	99.61 273	99.22 259	99.75 165	99.82 91	99.54 55	95.51 552	97.48 382	99.87 217	99.54 248
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
UniMVSNet (Re)		99.37 184	99.26 202	99.68 141	99.51 334	99.58 165	98.98 303	99.60 285	99.43 217	99.70 197	99.36 393	97.70 320	99.88 241	99.20 150	99.87 217	99.59 215

WR-MVS		99.11 270	98.93 296	99.66 153	99.30 411	99.42 212	98.42 408	99.37 386	99.04 290	99.57 266	99.20 438	96.89 366	99.86 278	98.66 257	99.87 217	99.70 107

NR-MVSNet		99.40 172	99.31 183	99.68 141	99.43 368	99.55 173	99.73 30	99.50 346	99.46 205	99.88 82	99.36 393	97.54 333	99.87 258	98.97 201	99.87 217	99.63 176

LPG-MVS_test		99.22 232	99.05 259	99.74 103	99.82 99	99.63 142	99.16 226	99.73 195	97.56 459	99.64 233	99.69 215	99.37 78	99.89 226	96.66 445	99.87 217	99.69 119

LGP-MVS_train				99.74 103	99.82 99	99.63 142		99.73 195	97.56 459	99.64 233	99.69 215	99.37 78	99.89 226	96.66 445	99.87 217	99.69 119

COLMAP_ROB	98.06 12	99.45 151	99.37 164	99.70 133	99.83 90	99.70 109	99.38 132	99.78 165	99.53 187	99.67 216	99.78 133	99.19 108	99.86 278	97.32 393	99.87 217	99.55 236
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SIFT-NCM-Cal		98.18 401	98.41 366	97.48 485	99.57 296	99.28 250	97.26 501	98.08 503	98.30 407	99.23 374	99.39 381	97.13 355	99.04 534	96.86 431	99.86 225	94.12 540

RoMa-SfM		99.32 201	99.23 211	99.59 198	99.77 159	99.53 176	98.89 321	99.88 74	98.78 337	99.65 227	99.52 338	97.78 315	99.90 204	98.96 204	99.86 225	99.35 344

BP-MVS1		98.72 341	98.46 357	99.50 240	99.53 325	99.00 313	99.34 149	98.53 480	99.65 156	99.73 182	99.38 384	90.62 493	99.96 69	99.50 95	99.86 225	99.55 236

test20.03		99.55 111	99.54 116	99.58 202	99.79 137	99.37 231	99.02 281	99.89 68	99.60 177	99.82 112	99.62 275	98.81 177	99.89 226	99.43 106	99.86 225	99.47 290

Baseline_NR-MVSNet		99.49 132	99.37 164	99.82 46	99.91 31	99.84 26	98.83 335	99.86 89	99.68 136	99.65 227	99.88 50	97.67 324	99.87 258	99.03 191	99.86 225	99.76 86

EC-MVSNet		99.69 59	99.69 60	99.68 141	99.71 207	99.91 4	99.76 23	99.96 30	99.86 65	99.51 297	99.39 381	99.57 52	99.93 120	99.64 73	99.86 225	99.20 384

MSDG		99.08 275	98.98 288	99.37 295	99.60 270	99.13 292	97.54 486	99.74 190	98.84 326	99.53 288	99.55 329	99.10 125	99.79 392	97.07 420	99.86 225	99.18 389

dtuonlycased		99.24 220	99.47 132	98.56 436	99.90 37	96.17 496	97.62 483	99.85 95	99.66 151	99.86 96	99.50 345	99.39 71	99.93 120	99.55 85	99.85 232	99.59 215

EGC-MVSNET		89.05 515	85.52 518	99.64 167	99.89 40	99.78 57	99.56 87	99.52 337	24.19 555	49.96 558	99.83 83	99.15 115	99.92 154	97.71 354	99.85 232	99.21 379

Patchmatch-RL test		98.60 354	98.36 373	99.33 312	99.77 159	99.07 305	98.27 419	99.87 80	98.91 314	99.74 176	99.72 186	90.57 495	99.79 392	98.55 269	99.85 232	99.11 405

APDe-MVS		99.48 135	99.36 169	99.85 32	99.55 314	99.81 47	99.50 102	99.69 226	98.99 297	99.75 165	99.71 196	98.79 182	99.93 120	98.46 276	99.85 232	99.80 67
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMP	97.51 14	99.05 284	98.84 311	99.67 145	99.78 146	99.55 173	98.88 323	99.66 240	97.11 487	99.47 307	99.60 295	99.07 134	99.89 226	96.18 475	99.85 232	99.58 221
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PMVS	92.94 21	98.82 329	98.81 316	98.85 407	99.84 81	97.99 428	99.20 205	99.47 354	99.71 122	99.42 321	99.82 91	98.09 290	99.47 514	93.88 526	99.85 232	99.07 426
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Anonymous20231206		99.35 191	99.31 183	99.47 252	99.74 193	99.06 307	99.28 177	99.74 190	99.23 255	99.72 188	99.53 334	97.63 332	99.88 241	99.11 180	99.84 238	99.48 286

HPM-MVS_fast		99.43 159	99.30 188	99.80 64	99.83 90	99.81 47	99.52 94	99.70 217	98.35 397	99.51 297	99.50 345	99.31 89	99.88 241	98.18 305	99.84 238	99.69 119

XVG-ACMP-BASELINE		99.23 223	99.10 242	99.63 175	99.82 99	99.58 165	98.83 335	99.72 204	98.36 391	99.60 258	99.71 196	98.92 164	99.91 185	97.08 419	99.84 238	99.40 328

new_pmnet		98.88 322	98.89 305	98.84 409	99.70 223	97.62 448	98.15 432	99.50 346	97.98 431	99.62 248	99.54 331	98.15 283	99.94 98	97.55 377	99.84 238	98.95 447

Test_1112_low_res		98.95 310	98.73 322	99.63 175	99.68 240	99.15 289	98.09 441	99.80 143	97.14 485	99.46 311	99.40 376	96.11 400	99.89 226	99.01 196	99.84 238	99.84 55

1112_ss		99.05 284	98.84 311	99.67 145	99.66 250	99.29 248	98.52 393	99.82 122	97.65 457	99.43 318	99.16 442	96.42 384	99.91 185	99.07 187	99.84 238	99.80 67

3Dnovator	99.15 2	99.43 159	99.36 169	99.65 160	99.39 377	99.42 212	99.70 38	99.56 308	99.23 255	99.35 343	99.80 108	99.17 111	99.95 81	98.21 300	99.84 238	99.59 215

LF4IMVS		99.01 297	98.92 300	99.27 335	99.71 207	99.28 250	98.59 376	99.77 170	98.32 405	99.39 335	99.41 370	98.62 208	99.84 315	96.62 451	99.84 238	98.69 476

SD_0403		97.42 446	96.90 462	98.98 381	99.54 316	97.90 436	99.52 94	99.54 321	99.34 234	97.87 500	98.85 484	98.72 195	99.64 487	78.93 553	99.83 246	99.40 328

ACMMP_NAP		99.28 208	99.11 233	99.79 72	99.75 182	99.81 47	98.95 312	99.53 332	98.27 409	99.53 288	99.73 176	98.75 190	99.87 258	97.70 357	99.83 246	99.68 126

AllTest		99.21 237	99.07 250	99.63 175	99.78 146	99.64 136	99.12 245	99.83 115	98.63 357	99.63 238	99.72 186	98.68 199	99.75 427	96.38 465	99.83 246	99.51 271

TestCases				99.63 175	99.78 146	99.64 136		99.83 115	98.63 357	99.63 238	99.72 186	98.68 199	99.75 427	96.38 465	99.83 246	99.51 271

PM-MVS		99.36 189	99.29 194	99.58 202	99.83 90	99.66 123	98.95 312	99.86 89	98.85 322	99.81 119	99.73 176	98.40 253	99.92 154	98.36 286	99.83 246	99.17 392

EPNet		98.13 406	97.77 426	99.18 353	94.57 556	97.99 428	99.24 193	97.96 508	99.74 111	97.29 520	99.62 275	93.13 454	99.97 44	98.59 264	99.83 246	99.58 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended		98.70 344	98.59 338	99.02 377	99.54 316	97.99 428	97.58 485	99.82 122	95.70 514	99.34 347	98.98 470	98.52 234	99.77 409	97.98 322	99.83 246	99.30 362

MVS-HIRNet		97.86 421	98.22 388	96.76 514	99.28 416	91.53 545	98.38 410	92.60 551	99.13 279	99.31 357	99.96 15	97.18 354	99.68 467	98.34 288	99.83 246	99.07 426

RPSCF		99.18 246	99.02 268	99.64 167	99.83 90	99.85 21	99.44 119	99.82 122	98.33 404	99.50 300	99.78 133	97.90 305	99.65 485	96.78 438	99.83 246	99.44 312

TinyColmap		98.97 304	98.93 296	99.07 372	99.46 360	98.19 412	97.75 471	99.75 184	98.79 335	99.54 283	99.70 206	98.97 157	99.62 490	96.63 449	99.83 246	99.41 325

reproduce_model		99.50 127	99.40 157	99.83 41	99.60 270	99.83 33	99.12 245	99.68 230	99.49 194	99.80 126	99.79 120	99.01 148	99.93 120	98.24 297	99.82 256	99.73 95

test_vis1_rt		99.45 151	99.46 138	99.41 280	99.71 207	98.63 376	98.99 300	99.96 30	99.03 292	99.95 45	99.12 449	98.75 190	99.84 315	99.82 50	99.82 256	99.77 81

MP-MVS-pluss		99.14 259	98.92 300	99.80 64	99.83 90	99.83 33	98.61 371	99.63 262	96.84 496	99.44 314	99.58 308	98.81 177	99.91 185	97.70 357	99.82 256	99.67 135
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MDA-MVSNet-bldmvs		99.06 280	99.05 259	99.07 372	99.80 123	97.83 439	98.89 321	99.72 204	99.29 242	99.63 238	99.70 206	96.47 382	99.89 226	98.17 307	99.82 256	99.50 277

jason		99.16 254	99.11 233	99.32 317	99.75 182	98.44 396	98.26 421	99.39 380	98.70 348	99.74 176	99.30 409	98.54 225	99.97 44	98.48 274	99.82 256	99.55 236
jason: jason.
HPM-MVS		99.25 216	99.07 250	99.78 76	99.81 112	99.75 79	99.61 73	99.67 235	97.72 454	99.35 343	99.25 423	99.23 103	99.92 154	97.21 409	99.82 256	99.67 135
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
114514_t		98.49 370	98.11 399	99.64 167	99.73 197	99.58 165	99.24 193	99.76 178	89.94 544	99.42 321	99.56 320	97.76 318	99.86 278	97.74 350	99.82 256	99.47 290

CP-MVS		99.23 223	99.05 259	99.75 98	99.66 250	99.66 123	99.38 132	99.62 265	98.38 389	99.06 405	99.27 417	98.79 182	99.94 98	97.51 381	99.82 256	99.66 149

PHI-MVS		99.11 270	98.95 294	99.59 198	99.13 444	99.59 160	99.17 220	99.65 250	97.88 444	99.25 370	99.46 361	98.97 157	99.80 388	97.26 402	99.82 256	99.37 338

wuyk23d		97.58 436	99.13 226	92.93 531	99.69 231	99.49 184	99.52 94	99.77 170	97.97 432	99.96 34	99.79 120	99.84 16	99.94 98	95.85 490	99.82 256	79.36 552

reproduce-ours		99.46 147	99.35 173	99.82 46	99.56 310	99.83 33	99.05 269	99.65 250	99.45 208	99.78 139	99.78 133	98.93 161	99.93 120	98.11 311	99.81 266	99.70 107

our_new_method		99.46 147	99.35 173	99.82 46	99.56 310	99.83 33	99.05 269	99.65 250	99.45 208	99.78 139	99.78 133	98.93 161	99.93 120	98.11 311	99.81 266	99.70 107

CVMVSNet		98.61 351	98.88 306	97.80 474	99.58 286	93.60 533	99.26 186	99.64 258	99.66 151	99.72 188	99.67 234	93.26 452	99.93 120	99.30 131	99.81 266	99.87 45

UniMVSNet_NR-MVSNet		99.37 184	99.25 206	99.72 122	99.47 356	99.56 169	98.97 305	99.61 273	99.43 217	99.67 216	99.28 415	97.85 310	99.95 81	99.17 159	99.81 266	99.65 158

DU-MVS		99.33 199	99.21 212	99.71 128	99.43 368	99.56 169	98.83 335	99.53 332	99.38 228	99.67 216	99.36 393	97.67 324	99.95 81	99.17 159	99.81 266	99.63 176

DeepPCF-MVS	98.42 6	99.18 246	99.02 268	99.67 145	99.22 427	99.75 79	97.25 502	99.47 354	98.72 345	99.66 223	99.70 206	99.29 91	99.63 489	98.07 316	99.81 266	99.62 188

ACMM	98.09 11	99.46 147	99.38 161	99.72 122	99.80 123	99.69 114	99.13 240	99.65 250	98.99 297	99.64 233	99.72 186	99.39 71	99.86 278	98.23 298	99.81 266	99.60 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SIFT-CM-Cal		97.96 419	98.15 396	97.39 492	99.61 267	99.15 289	96.75 525	98.41 491	98.04 427	99.03 408	99.54 331	95.24 423	99.41 517	96.97 424	99.80 273	93.61 547

ZNCC-MVS		99.22 232	99.04 265	99.77 80	99.76 164	99.73 90	99.28 177	99.56 308	98.19 414	99.14 393	99.29 413	98.84 176	99.92 154	97.53 380	99.80 273	99.64 170

test_0728_THIRD									99.18 263	99.62 248	99.61 285	98.58 215	99.91 185	97.72 352	99.80 273	99.77 81

SteuartSystems-ACMMP		99.30 204	99.14 223	99.76 87	99.87 55	99.66 123	99.18 215	99.60 285	98.55 367	99.57 266	99.67 234	99.03 146	99.94 98	97.01 421	99.80 273	99.69 119
Skip Steuart: Steuart Systems R&D Blog.
DP-MVS		99.48 135	99.39 158	99.74 103	99.57 296	99.62 144	99.29 175	99.61 273	99.87 62	99.74 176	99.76 155	98.69 198	99.87 258	98.20 301	99.80 273	99.75 89

PCF-MVS	96.03 18	96.73 467	95.86 483	99.33 312	99.44 365	99.16 287	96.87 522	99.44 363	86.58 546	98.95 415	99.40 376	94.38 437	99.88 241	87.93 542	99.80 273	98.95 447
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MASt3R-SfM		98.45 375	98.51 350	98.26 456	99.32 405	97.43 460	97.43 494	99.69 226	94.97 524	99.75 165	99.41 370	98.49 238	99.75 427	97.73 351	99.79 279	97.61 522

aaatest				99.74 103	99.76 164	99.65 129	99.38 132	99.78 165	99.58 181	99.81 119	99.66 240		99.90 204	97.69 363	99.79 279	99.67 135

aaEdge-Enhanced		99.26 214	99.10 242	99.73 113	99.60 270	99.65 129	98.75 353	99.45 362	99.31 240	99.65 227	99.66 240	98.00 301	99.86 278	97.69 363	99.79 279	99.67 135

SMA-MVS		99.19 242	99.00 278	99.73 113	99.46 360	99.73 90	99.13 240	99.52 337	97.40 471	99.57 266	99.64 249	98.93 161	99.83 338	97.61 373	99.79 279	99.63 176
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
MTAPA		99.35 191	99.20 213	99.80 64	99.81 112	99.81 47	99.33 155	99.53 332	99.27 246	99.42 321	99.63 265	98.21 277	99.95 81	97.83 344	99.79 279	99.65 158

ACMMP++															99.79 279

ACMMP		99.25 216	99.08 246	99.74 103	99.79 137	99.68 117	99.50 102	99.65 250	98.07 425	99.52 290	99.69 215	98.57 216	99.92 154	97.18 414	99.79 279	99.63 176
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
OMC-MVS		98.90 318	98.72 324	99.44 263	99.39 377	99.42 212	98.58 378	99.64 258	97.31 476	99.44 314	99.62 275	98.59 213	99.69 455	96.17 476	99.79 279	99.22 376

tfpnnormal		99.43 159	99.38 161	99.60 195	99.87 55	99.75 79	99.59 80	99.78 165	99.71 122	99.90 67	99.69 215	98.85 175	99.90 204	97.25 406	99.78 287	99.15 396

HQP_MVS		98.90 318	98.68 330	99.55 221	99.58 286	99.24 264	98.80 343	99.54 321	98.94 305	99.14 393	99.25 423	97.24 347	99.82 361	95.84 491	99.78 287	99.60 208

plane_prior5								99.54 321					99.82 361	95.84 491	99.78 287	99.60 208

mPP-MVS		99.19 242	99.00 278	99.76 87	99.76 164	99.68 117	99.38 132	99.54 321	98.34 401	99.01 410	99.50 345	98.53 230	99.93 120	97.18 414	99.78 287	99.66 149

RoMa-HiRes		99.38 179	99.30 188	99.64 167	99.81 112	99.47 189	99.11 250	99.94 41	99.03 292	99.55 279	99.56 320	97.71 319	99.92 154	99.19 152	99.77 291	99.54 248

ALIKED-MNN		98.03 412	97.78 425	98.78 417	98.84 489	98.97 320	98.16 431	99.74 190	97.31 476	96.60 531	98.85 484	96.61 375	99.48 513	94.16 520	99.77 291	97.91 519

TestfortrainingZip a		99.55 111	99.45 141	99.85 32	99.76 164	99.82 41	99.38 132	99.62 265	99.77 107	99.87 92	99.78 133	98.12 287	99.88 241	98.96 204	99.77 291	99.85 50

OPM-MVS		99.26 214	99.13 226	99.63 175	99.70 223	99.61 154	98.58 378	99.48 351	98.50 376	99.52 290	99.63 265	99.14 118	99.76 416	97.89 330	99.77 291	99.51 271
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVS_111021_LR		99.13 262	99.03 267	99.42 270	99.58 286	99.32 244	97.91 464	99.73 195	98.68 350	99.31 357	99.48 354	99.09 127	99.66 478	97.70 357	99.77 291	99.29 365

test-260524					99.64 256	99.70 109		99.58 300		99.69 201		97.64 331	99.87 258	98.68 254	99.76 296

DKM-HiRes		98.95 310	98.73 322	99.62 184	99.82 99	99.47 189	98.50 395	99.81 135	99.41 222	97.76 508	99.58 308	95.04 425	99.83 338	98.89 217	99.76 296	99.58 221

SIFT-NN-PointCN		97.97 417	98.24 386	97.14 506	99.59 276	98.71 361	96.75 525	99.56 308	97.02 490	97.91 497	99.27 417	96.85 368	98.39 543	97.47 383	99.76 296	94.31 537

MED-MVS		99.51 124	99.42 152	99.80 64	99.76 164	99.65 129	99.38 132	99.78 165	99.77 107	99.81 119	99.78 133	99.02 147	99.90 204	97.69 363	99.76 296	99.85 50

NormalMVS		99.09 274	98.91 304	99.62 184	99.78 146	99.11 295	99.36 144	99.77 170	99.82 85	99.68 208	99.53 334	93.30 450	99.99 7	99.24 139	99.76 296	99.74 91

lecture		99.56 106	99.48 130	99.81 54	99.78 146	99.86 18	99.50 102	99.70 217	99.59 179	99.75 165	99.71 196	98.94 160	99.92 154	98.59 264	99.76 296	99.66 149

MIMVSNet		98.43 377	98.20 390	99.11 363	99.53 325	98.38 403	99.58 82	98.61 475	98.96 301	99.33 349	99.76 155	90.92 485	99.81 378	97.38 389	99.76 296	99.15 396

MVS_111021_HR		99.12 265	99.02 268	99.40 283	99.50 340	99.11 295	97.92 462	99.71 208	98.76 343	99.08 401	99.47 358	99.17 111	99.54 504	97.85 338	99.76 296	99.54 248

DPE-MVS		99.14 259	98.92 300	99.82 46	99.57 296	99.77 63	98.74 354	99.60 285	98.55 367	99.76 160	99.69 215	98.23 275	99.92 154	96.39 464	99.75 304	99.76 86
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + MP.		99.34 196	99.24 208	99.63 175	99.82 99	99.37 231	99.26 186	99.35 391	98.77 340	99.57 266	99.70 206	99.27 96	99.88 241	97.71 354	99.75 304	99.65 158
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HFP-MVS		99.25 216	99.08 246	99.76 87	99.73 197	99.70 109	99.31 164	99.59 291	98.36 391	99.36 339	99.37 388	98.80 181	99.91 185	97.43 386	99.75 304	99.68 126

ACMMPR		99.23 223	99.06 252	99.76 87	99.74 193	99.69 114	99.31 164	99.59 291	98.36 391	99.35 343	99.38 384	98.61 210	99.93 120	97.43 386	99.75 304	99.67 135

MP-MVS		99.06 280	98.83 313	99.76 87	99.76 164	99.71 101	99.32 158	99.50 346	98.35 397	98.97 413	99.48 354	98.37 255	99.92 154	95.95 486	99.75 304	99.63 176
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
QAPM		98.40 381	97.99 406	99.65 160	99.39 377	99.47 189	99.67 53	99.52 337	91.70 541	98.78 439	99.80 108	98.55 220	99.95 81	94.71 514	99.75 304	99.53 257

DeepC-MVS_fast	98.47 5	99.23 223	99.12 230	99.56 214	99.28 416	99.22 270	98.99 300	99.40 377	99.08 285	99.58 263	99.64 249	98.90 170	99.83 338	97.44 385	99.75 304	99.63 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ALIKED-LG		98.78 333	98.66 331	99.14 359	99.02 472	99.40 220	98.74 354	99.79 152	98.62 361	99.18 386	99.38 384	97.54 333	99.77 409	95.94 488	99.74 311	98.25 501

GDP-MVS		98.81 331	98.57 342	99.50 240	99.53 325	99.12 294	99.28 177	99.86 89	99.53 187	99.57 266	99.32 403	90.88 488	99.98 26	99.46 101	99.74 311	99.42 324

GST-MVS		99.16 254	98.96 292	99.75 98	99.73 197	99.73 90	99.20 205	99.55 315	98.22 411	99.32 352	99.35 398	98.65 206	99.91 185	96.86 431	99.74 311	99.62 188

region2R		99.23 223	99.05 259	99.77 80	99.76 164	99.70 109	99.31 164	99.59 291	98.41 384	99.32 352	99.36 393	98.73 194	99.93 120	97.29 397	99.74 311	99.67 135

PGM-MVS		99.20 239	99.01 274	99.77 80	99.75 182	99.71 101	99.16 226	99.72 204	97.99 430	99.42 321	99.60 295	98.81 177	99.93 120	96.91 428	99.74 311	99.66 149

TransMVSNet (Re)		99.78 37	99.77 45	99.81 54	99.91 31	99.85 21	99.75 25	99.86 89	99.70 129	99.91 62	99.89 41	99.60 44	99.87 258	99.59 78	99.74 311	99.71 104

TSAR-MVS + GP.		99.12 265	99.04 265	99.38 290	99.34 399	99.16 287	98.15 432	99.29 410	98.18 415	99.63 238	99.62 275	99.18 109	99.68 467	98.20 301	99.74 311	99.30 362

ArgMatch-SfM		99.14 259	99.06 252	99.36 301	99.59 276	99.14 291	98.45 405	99.81 135	98.67 352	99.50 300	99.42 368	98.55 220	99.84 315	97.85 338	99.73 318	99.11 405

KD-MVS_self_test		99.63 86	99.59 96	99.76 87	99.84 81	99.90 7	99.37 140	99.79 152	99.83 81	99.88 82	99.85 68	98.42 248	99.90 204	99.60 77	99.73 318	99.49 282

balanced_ft_v1		99.37 184	99.36 169	99.38 290	99.10 453	99.38 226	99.68 48	99.72 204	99.72 116	99.36 339	99.77 145	97.66 328	99.94 98	99.52 91	99.73 318	98.83 464

XVS		99.27 212	99.11 233	99.75 98	99.71 207	99.71 101	99.37 140	99.61 273	99.29 242	98.76 440	99.47 358	98.47 239	99.88 241	97.62 371	99.73 318	99.67 135

X-MVStestdata		96.09 488	94.87 504	99.75 98	99.71 207	99.71 101	99.37 140	99.61 273	99.29 242	98.76 440	61.30 565	98.47 239	99.88 241	97.62 371	99.73 318	99.67 135

VDD-MVS		99.20 239	99.11 233	99.44 263	99.43 368	98.98 317	99.50 102	98.32 496	99.80 95	99.56 274	99.69 215	96.99 363	99.85 297	98.99 197	99.73 318	99.50 277

ab-mvs		99.33 199	99.28 197	99.47 252	99.57 296	99.39 224	99.78 17	99.43 367	98.87 319	99.57 266	99.82 91	98.06 293	99.87 258	98.69 253	99.73 318	99.15 396

TAPA-MVS	97.92 13	98.03 412	97.55 434	99.46 256	99.47 356	99.44 205	98.50 395	99.62 265	86.79 545	99.07 404	99.26 421	98.26 270	99.62 490	97.28 399	99.73 318	99.31 360
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
usedtu_dtu_shiyan2		99.44 155	99.33 180	99.78 76	99.86 60	99.76 70	99.54 90	99.79 152	99.66 151	99.66 223	99.79 120	96.76 371	99.96 69	99.15 164	99.72 326	99.62 188

DVP-MVS		99.32 201	99.17 217	99.77 80	99.69 231	99.80 51	99.14 233	99.31 406	99.16 272	99.62 248	99.61 285	98.35 257	99.91 185	97.88 331	99.72 326	99.61 203
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				99.83 41	99.70 223	99.79 54	99.14 233	99.61 273					99.92 154	97.88 331	99.72 326	99.77 81

3Dnovator+	98.92 3	99.35 191	99.24 208	99.67 145	99.35 390	99.47 189	99.62 67	99.50 346	99.44 210	99.12 397	99.78 133	98.77 187	99.94 98	97.87 334	99.72 326	99.62 188

ArgMatch-Sym		99.06 280	98.96 292	99.35 305	99.62 265	99.22 270	98.34 412	99.79 152	98.80 333	99.50 300	99.29 413	98.30 265	99.75 427	97.30 396	99.71 330	99.08 420

plane_prior						99.24 264	98.42 408		97.87 445						99.71 330

APD-MVS		98.87 323	98.59 338	99.71 128	99.50 340	99.62 144	99.01 286	99.57 303	96.80 498	99.54 283	99.63 265	98.29 266	99.91 185	95.24 505	99.71 330	99.61 203
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SIFT-UM-Cal		98.18 401	98.45 360	97.37 494	99.59 276	98.95 324	96.76 524	99.39 380	98.39 387	99.46 311	99.31 406	96.23 397	99.24 525	97.21 409	99.70 333	93.90 543

icg_test_0407_2		99.30 204	99.29 194	99.31 321	99.71 207	98.55 385	98.17 429	99.71 208	99.41 222	99.73 182	99.60 295	99.17 111	99.92 154	98.45 277	99.70 333	99.45 297

IMVS_0407		99.38 179	99.42 152	99.28 329	99.71 207	98.55 385	99.27 181	99.71 208	99.41 222	99.73 182	99.60 295	99.17 111	99.83 338	98.45 277	99.70 333	99.45 297

IMVS_0404		99.23 223	99.20 213	99.32 317	99.71 207	98.55 385	98.57 382	99.71 208	99.41 222	99.52 290	99.60 295	98.12 287	99.95 81	98.45 277	99.70 333	99.45 297

IMVS_0403		99.37 184	99.39 158	99.28 329	99.71 207	98.55 385	99.19 211	99.71 208	99.41 222	99.67 216	99.60 295	99.12 123	99.84 315	98.45 277	99.70 333	99.45 297

APD_test1		99.36 189	99.28 197	99.61 191	99.89 40	99.89 10	99.32 158	99.74 190	99.18 263	99.69 201	99.75 163	98.41 249	99.84 315	97.85 338	99.70 333	99.10 408

SED-MVS		99.40 172	99.28 197	99.77 80	99.69 231	99.82 41	99.20 205	99.54 321	99.13 279	99.82 112	99.63 265	98.91 167	99.92 154	97.85 338	99.70 333	99.58 221

IU-MVS					99.69 231	99.77 63		99.22 427	97.50 465	99.69 201				97.75 349	99.70 333	99.77 81

ambc				99.20 350	99.35 390	98.53 389	99.17 220	99.46 357		99.67 216	99.80 108	98.46 243	99.70 448	97.92 327	99.70 333	99.38 334

MSC_two_6792asdad				99.74 103	99.03 466	99.53 176		99.23 424					99.92 154	97.77 345	99.69 342	99.78 77

No_MVS				99.74 103	99.03 466	99.53 176		99.23 424					99.92 154	97.77 345	99.69 342	99.78 77

test_241102_TWO								99.54 321	99.13 279	99.76 160	99.63 265	98.32 263	99.92 154	97.85 338	99.69 342	99.75 89

MVSFormer		99.41 170	99.44 146	99.31 321	99.57 296	98.40 399	99.77 19	99.80 143	99.73 112	99.63 238	99.30 409	98.02 296	99.98 26	99.43 106	99.69 342	99.55 236

lupinMVS		98.96 307	98.87 307	99.24 344	99.57 296	98.40 399	98.12 437	99.18 436	98.28 408	99.63 238	99.13 445	98.02 296	99.97 44	98.22 299	99.69 342	99.35 344

SF-MVS		99.10 273	98.93 296	99.62 184	99.58 286	99.51 182	99.13 240	99.65 250	97.97 432	99.42 321	99.61 285	98.86 174	99.87 258	96.45 462	99.68 347	99.49 282

Anonymous20240529		99.42 163	99.34 175	99.65 160	99.53 325	99.60 158	99.63 64	99.39 380	99.47 202	99.76 160	99.78 133	98.13 285	99.86 278	98.70 251	99.68 347	99.49 282

MSLP-MVS++		99.05 284	99.09 244	98.91 396	99.21 429	98.36 404	98.82 339	99.47 354	98.85 322	98.90 423	99.56 320	98.78 185	99.09 531	98.57 267	99.68 347	99.26 368

DELS-MVS		99.34 196	99.30 188	99.48 250	99.51 334	99.36 235	98.12 437	99.53 332	99.36 233	99.41 327	99.61 285	99.22 104	99.87 258	99.21 146	99.68 347	99.20 384
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
PVSNet	97.47 15	98.42 378	98.44 362	98.35 446	99.46 360	96.26 493	96.70 528	99.34 395	97.68 456	99.00 411	99.13 445	97.40 339	99.72 439	97.59 375	99.68 347	99.08 420

LS3D		99.24 220	99.11 233	99.61 191	98.38 518	99.79 54	99.57 85	99.68 230	99.61 170	99.15 391	99.71 196	98.70 197	99.91 185	97.54 378	99.68 347	99.13 404

HQP3-MVS								99.37 386							99.67 353

CPTT-MVS		98.74 338	98.44 362	99.64 167	99.61 267	99.38 226	99.18 215	99.55 315	96.49 501	99.27 364	99.37 388	97.11 357	99.92 154	95.74 496	99.67 353	99.62 188

HQP-MVS		98.36 383	98.02 405	99.39 286	99.31 407	98.94 326	97.98 455	99.37 386	97.45 467	98.15 483	98.83 486	96.67 373	99.70 448	94.73 512	99.67 353	99.53 257

MVS_Test		99.28 208	99.31 183	99.19 351	99.35 390	98.79 354	99.36 144	99.49 350	99.17 270	99.21 380	99.67 234	98.78 185	99.66 478	99.09 182	99.66 356	99.10 408

CDPH-MVS		98.56 360	98.20 390	99.61 191	99.50 340	99.46 197	98.32 416	99.41 370	95.22 520	99.21 380	99.10 453	98.34 259	99.82 361	95.09 509	99.66 356	99.56 232

tttt0517		97.62 434	97.20 448	98.90 402	99.76 164	97.40 461	99.48 109	94.36 545	99.06 289	99.70 197	99.49 350	84.55 524	99.94 98	98.73 245	99.65 358	99.36 341

ITE_SJBPF				99.38 290	99.63 261	99.44 205		99.73 195	98.56 365	99.33 349	99.53 334	98.88 171	99.68 467	96.01 480	99.65 358	99.02 440

ttmdpeth		99.48 135	99.55 112	99.29 326	99.76 164	98.16 416	99.33 155	99.95 38	99.79 99	99.36 339	99.89 41	99.13 120	99.77 409	99.09 182	99.64 360	99.93 21

9.14			98.64 332		99.45 364		98.81 340	99.60 285	97.52 464	99.28 363	99.56 320	98.53 230	99.83 338	95.36 504	99.64 360

Patchmatch-test		98.10 408	97.98 408	98.48 439	99.27 418	96.48 487	99.40 127	99.07 446	98.81 331	99.23 374	99.57 316	90.11 500	99.87 258	96.69 442	99.64 360	99.09 414

SymmetryMVS		99.01 297	98.82 314	99.58 202	99.65 254	99.11 295	99.36 144	99.20 433	99.82 85	99.68 208	99.53 334	93.30 450	99.99 7	99.24 139	99.63 363	99.64 170

sss		98.90 318	98.77 321	99.27 335	99.48 350	98.44 396	98.72 357	99.32 402	97.94 438	99.37 338	99.35 398	96.31 391	99.91 185	98.85 220	99.63 363	99.47 290

GLUNet-SfM		95.26 505	95.06 502	95.87 527	94.84 554	90.39 553	90.24 548	99.92 47	92.30 538	99.16 388	99.25 423	94.69 432	98.01 545	85.55 549	99.62 365	99.21 379

cl22		97.56 437	97.28 443	98.40 443	98.37 519	96.75 482	97.24 503	99.37 386	97.31 476	99.41 327	99.22 432	87.30 510	99.37 520	97.70 357	99.62 365	99.08 420

miper_ehance_all_eth		98.59 357	98.59 338	98.59 432	98.98 473	97.07 472	97.49 491	99.52 337	98.50 376	99.52 290	99.37 388	96.41 386	99.71 444	97.86 336	99.62 365	99.00 442

miper_enhance_ethall		98.03 412	97.94 414	98.32 449	98.27 522	96.43 489	96.95 518	99.41 370	96.37 504	99.43 318	98.96 474	94.74 430	99.69 455	97.71 354	99.62 365	98.83 464

SCA		98.11 407	98.36 373	97.36 495	99.20 432	92.99 535	98.17 429	98.49 484	98.24 410	99.10 400	99.57 316	96.01 403	99.94 98	96.86 431	99.62 365	99.14 401

MS-PatchMatch		99.00 300	98.97 290	99.09 366	99.11 451	98.19 412	98.76 349	99.33 400	98.49 378	99.44 314	99.58 308	98.21 277	99.69 455	98.20 301	99.62 365	99.39 332

APD-MVS_3200maxsize		99.31 203	99.16 218	99.74 103	99.53 325	99.75 79	99.27 181	99.61 273	99.19 262	99.57 266	99.64 249	98.76 188	99.90 204	97.29 397	99.62 365	99.56 232

EPNet_dtu		97.62 434	97.79 424	97.11 507	96.67 548	92.31 539	98.51 394	98.04 505	99.24 253	95.77 538	99.47 358	93.78 445	99.66 478	98.98 199	99.62 365	99.37 338
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SR-MVS-dyc-post		99.27 212	99.11 233	99.73 113	99.54 316	99.74 87	99.26 186	99.62 265	99.16 272	99.52 290	99.64 249	98.41 249	99.91 185	97.27 400	99.61 373	99.54 248

RE-MVS-def			99.13 226		99.54 316	99.74 87	99.26 186	99.62 265	99.16 272	99.52 290	99.64 249	98.57 216		97.27 400	99.61 373	99.54 248

MG-MVS		98.52 365	98.39 369	98.94 386	99.15 441	97.39 462	98.18 426	99.21 430	98.89 318	99.23 374	99.63 265	97.37 342	99.74 434	94.22 519	99.61 373	99.69 119

DVP-MVS++		99.38 179	99.25 206	99.77 80	99.03 466	99.77 63	99.74 27	99.61 273	99.18 263	99.76 160	99.61 285	99.00 149	99.92 154	97.72 352	99.60 376	99.62 188

PC_three_1452									97.56 459	99.68 208	99.41 370	99.09 127	97.09 548	96.66 445	99.60 376	99.62 188

OPU-MVS				99.29 326	99.12 446	99.44 205	99.20 205				99.40 376	99.00 149	98.84 538	96.54 453	99.60 376	99.58 221

HPM-MVS++		98.96 307	98.70 329	99.74 103	99.52 332	99.71 101	98.86 327	99.19 434	98.47 380	98.59 455	99.06 457	98.08 292	99.91 185	96.94 426	99.60 376	99.60 208

mvsmamba		99.08 275	98.95 294	99.45 259	99.36 386	99.18 286	99.39 129	98.81 464	99.37 229	99.35 343	99.70 206	96.36 389	99.94 98	98.66 257	99.59 380	99.22 376

CNVR-MVS		98.99 303	98.80 319	99.56 214	99.25 422	99.43 209	98.54 389	99.27 414	98.58 364	98.80 435	99.43 366	98.53 230	99.70 448	97.22 408	99.59 380	99.54 248

Anonymous202405211		98.75 337	98.46 357	99.63 175	99.34 399	99.66 123	99.47 112	97.65 517	99.28 245	99.56 274	99.50 345	93.15 453	99.84 315	98.62 263	99.58 382	99.40 328

MVP-Stereo		99.16 254	99.08 246	99.43 267	99.48 350	99.07 305	99.08 262	99.55 315	98.63 357	99.31 357	99.68 228	98.19 280	99.78 396	98.18 305	99.58 382	99.45 297
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVStest1		98.22 398	98.09 400	98.62 429	99.04 464	96.23 494	99.20 205	99.92 47	99.44 210	99.98 14	99.87 56	85.87 521	99.67 473	99.91 33	99.57 384	99.95 15

ADS-MVSNet2		97.78 427	97.66 432	98.12 461	99.14 442	95.36 514	99.22 202	98.75 467	96.97 491	98.25 474	99.64 249	90.90 486	99.94 98	96.51 455	99.56 385	99.08 420

ADS-MVSNet		97.72 432	97.67 431	97.86 472	99.14 442	94.65 524	99.22 202	98.86 459	96.97 491	98.25 474	99.64 249	90.90 486	99.84 315	96.51 455	99.56 385	99.08 420

LCM-MVSNet-Re		99.28 208	99.15 222	99.67 145	99.33 404	99.76 70	99.34 149	99.97 21	98.93 310	99.91 62	99.79 120	98.68 199	99.93 120	96.80 437	99.56 385	99.30 362

API-MVS		98.38 382	98.39 369	98.35 446	98.83 490	99.26 256	99.14 233	99.18 436	98.59 363	98.66 448	98.78 490	98.61 210	99.57 499	94.14 521	99.56 385	96.21 531

xiu_mvs_v1_base_debu		99.23 223	99.34 175	98.91 396	99.59 276	98.23 408	98.47 400	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 389	98.51 488

xiu_mvs_v1_base		99.23 223	99.34 175	98.91 396	99.59 276	98.23 408	98.47 400	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 389	98.51 488

xiu_mvs_v1_base_debi		99.23 223	99.34 175	98.91 396	99.59 276	98.23 408	98.47 400	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 389	98.51 488

OpenMVS	98.12 10	98.23 396	97.89 419	99.26 339	99.19 434	99.26 256	99.65 62	99.69 226	91.33 542	98.14 487	99.77 145	98.28 267	99.96 69	95.41 502	99.55 389	98.58 483

MVE	92.54 22	96.66 470	96.11 477	98.31 451	99.68 240	97.55 450	97.94 460	95.60 542	99.37 229	90.68 550	98.70 496	96.56 377	98.61 541	86.94 547	99.55 389	98.77 472
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SR-MVS		99.19 242	99.00 278	99.74 103	99.51 334	99.72 95	99.18 215	99.60 285	98.85 322	99.47 307	99.58 308	98.38 254	99.92 154	96.92 427	99.54 394	99.57 228

thisisatest0530		97.45 444	96.95 458	98.94 386	99.68 240	97.73 444	99.09 259	94.19 547	98.61 362	99.56 274	99.30 409	84.30 526	99.93 120	98.27 294	99.54 394	99.16 394

tt0805		99.63 86	99.57 105	99.81 54	99.87 55	99.88 12	99.58 82	98.70 469	99.72 116	99.91 62	99.60 295	99.43 67	99.81 378	99.81 51	99.53 396	99.73 95

MSP-MVS		99.04 287	98.79 320	99.81 54	99.78 146	99.73 90	99.35 148	99.57 303	98.54 370	99.54 283	98.99 467	96.81 369	99.93 120	96.97 424	99.53 396	99.77 81
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
AdaColmap		98.60 354	98.35 375	99.38 290	99.12 446	99.22 270	98.67 364	99.42 369	97.84 449	98.81 433	99.27 417	97.32 345	99.81 378	95.14 507	99.53 396	99.10 408

ETV-MVS		99.18 246	99.18 216	99.16 354	99.34 399	99.28 250	99.12 245	99.79 152	99.48 197	98.93 417	98.55 505	99.40 70	99.93 120	98.51 273	99.52 399	98.28 498

SSC-MVS		99.52 122	99.42 152	99.83 41	99.86 60	99.65 129	99.52 94	99.81 135	99.87 62	99.81 119	99.79 120	96.78 370	99.99 7	99.83 46	99.51 400	99.86 47

EIA-MVS		99.12 265	99.01 274	99.45 259	99.36 386	99.62 144	99.34 149	99.79 152	98.41 384	98.84 430	98.89 481	98.75 190	99.84 315	98.15 309	99.51 400	98.89 458

MCST-MVS		99.02 291	98.81 316	99.65 160	99.58 286	99.49 184	98.58 378	99.07 446	98.40 386	99.04 407	99.25 423	98.51 236	99.80 388	97.31 394	99.51 400	99.65 158

mvs_anonymous		99.28 208	99.39 158	98.94 386	99.19 434	97.81 440	99.02 281	99.55 315	99.78 102	99.85 101	99.80 108	98.24 271	99.86 278	99.57 82	99.50 403	99.15 396

CNLPA		98.57 359	98.34 376	99.28 329	99.18 437	99.10 302	98.34 412	99.41 370	98.48 379	98.52 461	98.98 470	97.05 359	99.78 396	95.59 498	99.50 403	98.96 445

ZD-MVS					99.43 368	99.61 154		99.43 367	96.38 503	99.11 398	99.07 456	97.86 308	99.92 154	94.04 523	99.49 405

test_prior2							97.95 459		97.87 445	98.05 489	99.05 458	97.90 305		95.99 483	99.49 405

SIFT-MNN		97.55 439	97.74 427	96.98 510	99.38 380	98.85 346	96.92 521	98.61 475	98.36 391	98.63 451	99.10 453	92.51 464	97.85 546	96.63 449	99.48 407	94.25 539

PRO-TEST		99.17 251	99.14 223	99.28 329	99.04 464	98.92 333	99.24 193	99.76 178	99.69 132	99.41 327	99.17 441	98.06 293	99.85 297	98.39 284	99.47 408	99.06 428

SP-LightGlue		98.62 350	98.51 350	98.94 386	98.69 507	99.01 312	98.34 412	99.54 321	99.27 246	97.72 511	99.15 444	95.88 407	99.54 504	98.53 272	99.47 408	98.27 499

pmmvs3		98.08 409	97.80 422	98.91 396	99.41 375	97.69 446	97.87 466	99.66 240	95.87 509	99.50 300	99.51 342	90.35 497	99.97 44	98.55 269	99.47 408	99.08 420

test12				99.54 227	99.29 413	99.33 241		99.16 439		98.43 466		97.54 333	99.82 361		99.47 408	99.48 286

agg_prior2														94.58 515	99.46 412	99.50 277

test9_res														95.10 508	99.44 413	99.50 277

train_agg		98.35 386	97.95 410	99.57 210	99.35 390	99.35 238	98.11 439	99.41 370	94.90 525	97.92 495	98.99 467	98.02 296	99.85 297	95.38 503	99.44 413	99.50 277

VPNet		99.46 147	99.37 164	99.71 128	99.82 99	99.59 160	99.48 109	99.70 217	99.81 91	99.69 201	99.58 308	97.66 328	99.86 278	99.17 159	99.44 413	99.67 135

DP-MVS Recon		98.50 368	98.23 387	99.31 321	99.49 345	99.46 197	98.56 385	99.63 262	94.86 527	98.85 429	99.37 388	97.81 312	99.59 497	96.08 477	99.44 413	98.88 459

PMatch-Up-SfM		99.08 275	99.02 268	99.27 335	99.81 112	99.04 310	98.13 435	99.83 115	99.16 272	99.26 368	99.69 215	97.22 349	99.83 338	98.67 256	99.43 417	98.94 450

LFMVS		98.46 374	98.19 393	99.26 339	99.24 424	98.52 391	99.62 67	96.94 528	99.87 62	99.31 357	99.58 308	91.04 483	99.81 378	98.68 254	99.42 418	99.45 297

Fast-Effi-MVS+		99.02 291	98.87 307	99.46 256	99.38 380	99.50 183	99.04 274	99.79 152	97.17 483	98.62 452	98.74 492	99.34 85	99.95 81	98.32 290	99.41 419	98.92 453

PatchmatchNet		97.65 433	97.80 422	97.18 502	98.82 493	92.49 538	99.17 220	98.39 492	98.12 419	98.79 437	99.58 308	90.71 492	99.89 226	97.23 407	99.41 419	99.16 394
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thisisatest0515		96.98 460	96.42 469	98.66 428	99.42 373	97.47 454	97.27 500	94.30 546	97.24 479	99.15 391	98.86 483	85.01 522	99.87 258	97.10 417	99.39 421	98.63 477

原ACMM1				99.37 295	99.47 356	98.87 345		99.27 414	96.74 500	98.26 473	99.32 403	97.93 304	99.82 361	95.96 485	99.38 422	99.43 319

test222					99.51 334	99.08 304	97.83 468	99.29 410	95.21 521	98.68 447	99.31 406	97.28 346			99.38 422	99.43 319

F-COLMAP		98.74 338	98.45 360	99.62 184	99.57 296	99.47 189	98.84 332	99.65 250	96.31 505	98.93 417	99.19 440	97.68 323	99.87 258	96.52 454	99.37 424	99.53 257

DPM-MVS		98.28 389	97.94 414	99.32 317	99.36 386	99.11 295	97.31 499	98.78 466	96.88 494	98.84 430	99.11 452	97.77 316	99.61 495	94.03 524	99.36 425	99.23 374

旧先验1					99.49 345	99.29 248		99.26 416			99.39 381	97.67 324			99.36 425	99.46 295

dmvs_re		98.69 345	98.48 354	99.31 321	99.55 314	99.42 212	99.54 90	98.38 493	99.32 238	98.72 443	98.71 494	96.76 371	99.21 526	96.01 480	99.35 427	99.31 360

PS-MVSNAJ		99.00 300	99.08 246	98.76 419	99.37 383	98.10 421	98.00 453	99.51 342	99.47 202	99.41 327	98.50 508	99.28 93	99.97 44	98.83 222	99.34 428	98.20 506

testing3		96.48 476	95.63 489	99.01 378	99.23 426	97.81 440	98.90 320	99.10 445	98.72 345	97.84 503	97.92 522	72.44 553	99.85 297	97.21 409	99.33 429	99.35 344

xiu_mvs_v2_base		99.02 291	99.11 233	98.77 418	99.37 383	98.09 422	98.13 435	99.51 342	99.47 202	99.42 321	98.54 506	99.38 76	99.97 44	98.83 222	99.33 429	98.24 502

新几何1				99.52 234	99.50 340	99.22 270		99.26 416	95.66 515	98.60 454	99.28 415	97.67 324	99.89 226	95.95 486	99.32 431	99.45 297

VDDNet		98.97 304	98.82 314	99.42 270	99.71 207	98.81 350	99.62 67	98.68 470	99.81 91	99.38 336	99.80 108	94.25 438	99.85 297	98.79 231	99.32 431	99.59 215

FA-MVS(test-final)		98.52 365	98.32 378	99.10 365	99.48 350	98.67 364	99.77 19	98.60 478	97.35 474	99.63 238	99.80 108	93.07 455	99.84 315	97.92 327	99.30 433	98.78 469

VNet		99.18 246	99.06 252	99.56 214	99.24 424	99.36 235	99.33 155	99.31 406	99.67 144	99.47 307	99.57 316	96.48 381	99.84 315	99.15 164	99.30 433	99.47 290

PatchMatch-RL		98.68 346	98.47 355	99.30 325	99.44 365	99.28 250	98.14 434	99.54 321	97.12 486	99.11 398	99.25 423	97.80 313	99.70 448	96.51 455	99.30 433	98.93 451

Effi-MVS+-dtu		99.07 279	98.92 300	99.52 234	98.89 482	99.78 57	99.15 229	99.66 240	99.34 234	98.92 420	99.24 429	97.69 322	99.98 26	98.11 311	99.28 436	98.81 466

testdata				99.42 270	99.51 334	98.93 329		99.30 409	96.20 506	98.87 427	99.40 376	98.33 262	99.89 226	96.29 468	99.28 436	99.44 312

OpenMVS_ROB	97.31 17	97.36 450	96.84 463	98.89 403	99.29 413	99.45 203	98.87 326	99.48 351	86.54 547	99.44 314	99.74 171	97.34 343	99.86 278	91.61 532	99.28 436	97.37 526

NCCC		98.82 329	98.57 342	99.58 202	99.21 429	99.31 245	98.61 371	99.25 419	98.65 354	98.43 466	99.26 421	97.86 308	99.81 378	96.55 452	99.27 439	99.61 203

SIFT-NN-CMatch		97.30 451	97.34 441	97.18 502	99.54 316	98.85 346	96.02 536	95.77 541	97.05 489	97.55 514	98.70 496	96.35 390	98.75 539	95.82 493	99.26 440	93.95 542

testing3-2		96.51 475	96.43 468	96.74 516	99.36 386	91.38 547	99.10 254	97.87 513	99.48 197	98.57 458	98.71 494	76.65 545	99.66 478	98.87 219	99.26 440	99.18 389

testgi		99.29 206	99.26 202	99.37 295	99.75 182	98.81 350	98.84 332	99.89 68	98.38 389	99.75 165	99.04 460	99.36 81	99.86 278	99.08 184	99.25 442	99.45 297

PLC	97.35 16	98.36 383	97.99 406	99.48 250	99.32 405	99.24 264	98.50 395	99.51 342	95.19 522	98.58 456	98.96 474	96.95 364	99.83 338	95.63 497	99.25 442	99.37 338
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		99.20 239	99.12 230	99.43 267	99.25 422	99.69 114	99.05 269	99.82 122	99.50 192	98.97 413	99.05 458	98.98 155	99.98 26	98.20 301	99.24 444	98.62 478

PMMVS		98.49 370	98.29 383	99.11 363	98.96 475	98.42 398	97.54 486	99.32 402	97.53 463	98.47 464	98.15 517	97.88 307	99.82 361	97.46 384	99.24 444	99.09 414

WB-MVS		99.44 155	99.32 181	99.80 64	99.81 112	99.61 154	99.47 112	99.81 135	99.82 85	99.71 193	99.72 186	96.60 376	99.98 26	99.75 56	99.23 446	99.82 66

EPMVS		96.53 473	96.32 470	97.17 504	98.18 526	92.97 536	99.39 129	89.95 556	98.21 412	98.61 453	99.59 305	86.69 519	99.72 439	96.99 422	99.23 446	98.81 466

SIFT-NN-NCMNet		97.22 453	97.27 445	97.07 508	99.64 256	99.20 277	96.53 530	95.91 534	96.91 493	97.38 516	98.95 476	96.01 403	98.29 544	94.87 510	99.21 448	93.73 546

SP-SuperGlue		98.66 348	98.63 334	98.73 421	98.44 516	99.02 311	98.22 424	99.44 363	99.37 229	98.17 482	99.30 409	96.95 364	99.12 528	98.59 264	99.20 449	98.06 510

SP-DiffGlue		98.47 372	98.43 364	98.59 432	97.44 544	98.59 380	98.01 450	99.36 390	99.00 296	99.06 405	99.20 438	97.01 361	99.25 524	97.64 369	99.15 450	97.92 518

alignmvs		98.28 389	97.96 409	99.25 342	99.12 446	98.93 329	99.03 277	98.42 488	99.64 160	98.72 443	97.85 524	90.86 489	99.62 490	98.88 218	99.13 451	99.19 387

FE-MVS		97.85 422	97.42 439	99.15 356	99.44 365	98.75 358	99.77 19	98.20 500	95.85 510	99.33 349	99.80 108	88.86 506	99.88 241	96.40 463	99.12 452	98.81 466

cascas		96.99 459	96.82 465	97.48 485	97.57 542	95.64 509	96.43 532	99.56 308	91.75 540	97.13 526	97.61 531	95.58 412	98.63 540	96.68 443	99.11 453	98.18 507

XFeat-MNN		96.67 469	96.56 467	96.98 510	96.73 547	95.62 511	94.54 543	98.93 458	97.42 470	98.18 478	98.67 499	91.60 477	99.12 528	93.88 526	99.10 454	96.21 531

BH-RMVSNet		98.41 379	98.14 397	99.21 347	99.21 429	98.47 392	98.60 373	98.26 498	98.35 397	98.93 417	99.31 406	97.20 353	99.66 478	94.32 517	99.10 454	99.51 271

RRT-MVS		99.08 275	99.00 278	99.33 312	99.27 418	98.65 371	99.62 67	99.93 43	99.66 151	99.67 216	99.82 91	95.27 422	99.93 120	98.64 261	99.09 456	99.41 325

UWE-MVS		96.21 486	95.78 485	97.49 484	98.53 512	93.83 531	98.04 447	93.94 549	98.96 301	98.46 465	98.17 516	79.86 533	99.87 258	96.99 422	99.06 457	98.78 469

MAR-MVS		98.24 394	97.92 416	99.19 351	98.78 498	99.65 129	99.17 220	99.14 442	95.36 518	98.04 490	98.81 489	97.47 336	99.72 439	95.47 501	99.06 457	98.21 504
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
GA-MVS		97.99 416	97.68 430	98.93 390	99.52 332	98.04 426	97.19 504	99.05 449	98.32 405	98.81 433	98.97 472	89.89 503	99.41 517	98.33 289	99.05 459	99.34 350

EMVS		96.96 461	97.28 443	95.99 526	98.76 501	91.03 548	95.26 541	98.61 475	99.34 234	98.92 420	98.88 482	93.79 444	99.66 478	92.87 528	99.05 459	97.30 527

PMatch-SfM		98.91 315	98.81 316	99.22 346	99.79 137	98.89 338	98.18 426	99.61 273	99.18 263	99.03 408	99.61 285	96.13 399	99.80 388	98.71 249	99.04 461	98.99 443

E-PMN		97.14 458	97.43 438	96.27 522	98.79 496	91.62 544	95.54 538	99.01 454	99.44 210	98.88 424	99.12 449	92.78 458	99.68 467	94.30 518	99.03 462	97.50 523

tpmrst		97.73 429	98.07 402	96.73 517	98.71 505	92.00 540	99.10 254	98.86 459	98.52 373	98.92 420	99.54 331	91.90 472	99.82 361	98.02 317	99.03 462	98.37 495

PatchT		98.45 375	98.32 378	98.83 411	98.94 476	98.29 406	99.24 193	98.82 462	99.84 75	99.08 401	99.76 155	91.37 478	99.94 98	98.82 224	99.00 464	98.26 500

WB-MVSnew		98.34 388	98.14 397	98.96 383	98.14 529	97.90 436	98.27 419	97.26 525	98.63 357	98.80 435	98.00 520	97.77 316	99.90 204	97.37 390	98.98 465	99.09 414

CL-MVSNet_self_test		98.71 343	98.56 346	99.15 356	99.22 427	98.66 367	97.14 508	99.51 342	98.09 422	99.54 283	99.27 417	96.87 367	99.74 434	98.43 281	98.96 466	99.03 435

ALIKED-NN		96.66 470	96.26 472	97.88 470	97.49 543	98.59 380	96.71 527	99.15 440	95.50 516	93.58 547	98.39 510	94.52 435	97.74 547	92.05 531	98.94 467	97.29 528

test_yl		98.25 392	97.95 410	99.13 361	99.17 438	98.47 392	99.00 292	98.67 472	98.97 299	99.22 378	99.02 465	91.31 479	99.69 455	97.26 402	98.93 468	99.24 371

DCV-MVSNet		98.25 392	97.95 410	99.13 361	99.17 438	98.47 392	99.00 292	98.67 472	98.97 299	99.22 378	99.02 465	91.31 479	99.69 455	97.26 402	98.93 468	99.24 371

MGCFI-Net		99.02 291	99.01 274	99.06 374	99.11 451	98.60 378	99.63 64	99.67 235	99.63 162	98.58 456	97.65 528	99.07 134	99.57 499	98.85 220	98.92 470	99.03 435

sasdasda		99.02 291	99.00 278	99.09 366	99.10 453	98.70 362	99.61 73	99.66 240	99.63 162	98.64 449	97.65 528	99.04 144	99.54 504	98.79 231	98.92 470	99.04 432

canonicalmvs		99.02 291	99.00 278	99.09 366	99.10 453	98.70 362	99.61 73	99.66 240	99.63 162	98.64 449	97.65 528	99.04 144	99.54 504	98.79 231	98.92 470	99.04 432

MDTV_nov1_ep13			97.73 428		98.70 506	90.83 549	99.15 229	98.02 506	98.51 374	98.82 432	99.61 285	90.98 484	99.66 478	96.89 430	98.92 470

PAPM_NR		98.36 383	98.04 403	99.33 312	99.48 350	98.93 329	98.79 346	99.28 413	97.54 462	98.56 460	98.57 503	97.12 356	99.69 455	94.09 522	98.90 474	99.38 334

FPMVS		96.32 481	95.50 490	98.79 415	99.60 270	98.17 415	98.46 404	98.80 465	97.16 484	96.28 534	99.63 265	82.19 527	99.09 531	88.45 540	98.89 475	99.10 408

nomal-1		96.75 466	96.26 472	98.21 457	99.06 459	95.71 507	98.65 369	97.76 516	98.51 374	97.96 493	97.91 523	79.57 536	99.88 241	98.11 311	98.84 476	99.05 429

tpm cat1		96.78 464	96.98 457	96.16 524	98.85 487	90.59 552	99.08 262	99.32 402	92.37 537	97.73 510	99.46 361	91.15 482	99.69 455	96.07 478	98.80 477	98.21 504

test-LLR		97.15 456	96.95 458	97.74 477	98.18 526	95.02 521	97.38 495	96.10 530	98.00 428	97.81 505	98.58 501	90.04 501	99.91 185	97.69 363	98.78 478	98.31 496

test-mter		96.23 484	95.73 487	97.74 477	98.18 526	95.02 521	97.38 495	96.10 530	97.90 440	97.81 505	98.58 501	79.12 539	99.91 185	97.69 363	98.78 478	98.31 496

TESTMET0.1,1		96.24 483	95.84 484	97.41 491	98.24 523	93.84 530	97.38 495	95.84 538	98.43 381	97.81 505	98.56 504	79.77 535	99.89 226	97.77 345	98.77 480	98.52 487

CR-MVSNet		98.35 386	98.20 390	98.83 411	99.05 461	98.12 418	99.30 167	99.67 235	97.39 472	99.16 388	99.79 120	91.87 474	99.91 185	98.78 237	98.77 480	98.44 493

RPMNet		98.60 354	98.53 348	98.83 411	99.05 461	98.12 418	99.30 167	99.62 265	99.86 65	99.16 388	99.74 171	92.53 463	99.92 154	98.75 239	98.77 480	98.44 493

WTY-MVS		98.59 357	98.37 371	99.26 339	99.43 368	98.40 399	98.74 354	99.13 444	98.10 420	99.21 380	99.24 429	94.82 429	99.90 204	97.86 336	98.77 480	99.49 282

SP-MNN		97.94 420	97.82 421	98.31 451	98.30 521	97.67 447	97.81 469	97.93 510	98.14 417	97.16 525	98.64 500	96.31 391	99.21 526	97.34 391	98.75 484	98.05 512

UBG		96.53 473	95.95 480	98.29 454	98.87 485	96.31 492	98.48 399	98.07 504	98.83 327	97.32 518	96.54 553	79.81 534	99.62 490	96.84 435	98.74 485	98.95 447

Effi-MVS+		99.06 280	98.97 290	99.34 309	99.31 407	98.98 317	98.31 417	99.91 57	98.81 331	98.79 437	98.94 477	99.14 118	99.84 315	98.79 231	98.74 485	99.20 384

PAPR		97.56 437	97.07 453	99.04 376	98.80 494	98.11 420	97.63 481	99.25 419	94.56 531	98.02 492	98.25 514	97.43 338	99.68 467	90.90 535	98.74 485	99.33 351

Syy-MVS		98.17 404	97.85 420	99.15 356	98.50 514	98.79 354	98.60 373	99.21 430	97.89 442	96.76 528	96.37 556	95.47 418	99.57 499	99.10 181	98.73 488	99.09 414

myMVS_eth3d		95.63 501	94.73 505	98.34 448	98.50 514	96.36 490	98.60 373	99.21 430	97.89 442	96.76 528	96.37 556	72.10 554	99.57 499	94.38 516	98.73 488	99.09 414

tpmvs		97.39 448	97.69 429	96.52 519	98.41 517	91.76 542	99.30 167	98.94 457	97.74 451	97.85 502	99.55 329	92.40 468	99.73 437	96.25 470	98.73 488	98.06 510

dp		96.86 462	97.07 453	96.24 523	98.68 508	90.30 555	99.19 211	98.38 493	97.35 474	98.23 476	99.59 305	87.23 511	99.82 361	96.27 469	98.73 488	98.59 481

XVG-OURS-SEG-HR		99.16 254	98.99 285	99.66 153	99.84 81	99.64 136	98.25 422	99.73 195	98.39 387	99.63 238	99.43 366	99.70 31	99.90 204	97.34 391	98.64 492	99.44 312

thres600view7		96.60 472	96.16 476	97.93 468	99.63 261	96.09 500	99.18 215	97.57 518	98.77 340	98.72 443	97.32 536	87.04 513	99.72 439	88.57 539	98.62 493	97.98 515

thres200		96.09 488	95.68 488	97.33 498	99.48 350	96.22 495	98.53 391	97.57 518	98.06 426	98.37 469	96.73 550	86.84 517	99.61 495	86.99 546	98.57 494	96.16 533

1314		98.00 415	97.90 418	98.27 455	98.90 478	97.45 457	99.30 167	99.06 448	94.98 523	97.21 522	99.12 449	98.43 246	99.67 473	95.58 499	98.56 495	97.71 520

dmvs_testset		97.27 452	96.83 464	98.59 432	99.46 360	97.55 450	99.25 192	96.84 529	98.78 337	97.24 521	97.67 527	97.11 357	98.97 535	86.59 548	98.54 496	99.27 366

thres100view900		96.39 478	96.03 479	97.47 487	99.63 261	95.93 501	99.18 215	97.57 518	98.75 344	98.70 446	97.31 537	87.04 513	99.67 473	87.62 543	98.51 497	96.81 529

tfpn200view9		96.30 482	95.89 481	97.53 482	99.58 286	96.11 498	99.00 292	97.54 521	98.43 381	98.52 461	96.98 542	86.85 515	99.67 473	87.62 543	98.51 497	96.81 529

thres400		96.40 477	95.89 481	97.92 469	99.58 286	96.11 498	99.00 292	97.54 521	98.43 381	98.52 461	96.98 542	86.85 515	99.67 473	87.62 543	98.51 497	97.98 515

SIFT-NN-UMatch		97.18 455	97.24 447	97.01 509	99.57 296	98.65 371	96.33 534	97.31 524	97.07 488	97.48 515	98.73 493	94.39 436	98.87 537	95.75 495	98.50 500	93.50 549

UWE-MVS-28		95.64 500	95.47 491	96.14 525	97.98 532	90.39 553	98.49 398	95.81 540	99.02 294	98.03 491	98.19 515	84.49 525	99.28 523	88.75 538	98.47 501	98.75 474

TestfortrainingZip				99.38 290	99.17 438	99.25 259	99.38 132	98.82 462	98.93 310	99.68 208	99.49 350	98.11 289	99.56 503		98.44 502	99.32 355

myMVS_eth3d28		96.23 484	95.74 486	97.70 481	98.86 486	95.59 512	98.66 366	98.14 502	98.96 301	97.67 512	97.06 541	76.78 544	98.92 536	97.10 417	98.41 503	98.58 483

FBQ-MVS		96.06 490	95.42 492	97.98 464	98.90 478	95.77 505	98.71 360	98.20 500	98.34 401	97.83 504	97.34 534	74.90 550	99.39 519	96.20 474	98.40 504	98.78 469

MVS		95.72 499	94.63 507	98.99 379	98.56 511	97.98 433	99.30 167	98.86 459	72.71 551	97.30 519	99.08 455	98.34 259	99.74 434	89.21 536	98.33 505	99.26 368

BH-untuned		98.22 398	98.09 400	98.58 435	99.38 380	97.24 467	98.55 386	98.98 456	97.81 450	99.20 385	98.76 491	97.01 361	99.65 485	94.83 511	98.33 505	98.86 461

testing11		96.05 491	95.41 494	97.97 466	98.78 498	95.27 517	98.59 376	98.23 499	98.86 321	96.56 532	96.91 545	75.20 548	99.69 455	97.26 402	98.29 507	98.93 451

test_method		91.72 513	92.32 513	89.91 533	93.49 557	70.18 561	90.28 547	99.56 308	61.71 553	95.39 540	99.52 338	93.90 441	99.94 98	98.76 238	98.27 508	99.62 188

testing91		96.00 492	95.32 497	98.02 462	98.76 501	95.39 513	98.38 410	98.65 474	98.82 329	96.84 527	96.71 551	75.06 549	99.71 444	96.46 461	98.23 509	98.98 444

gg-mvs-nofinetune		95.87 495	95.17 501	97.97 466	98.19 525	96.95 474	99.69 45	89.23 557	99.89 55	96.24 536	99.94 19	81.19 528	99.51 511	93.99 525	98.20 510	97.44 524

HY-MVS	98.23 9	98.21 400	97.95 410	98.99 379	99.03 466	98.24 407	99.61 73	98.72 468	96.81 497	98.73 442	99.51 342	94.06 440	99.86 278	96.91 428	98.20 510	98.86 461

UnsupCasMVSNet_bld		98.55 361	98.27 384	99.40 283	99.56 310	99.37 231	97.97 458	99.68 230	97.49 466	99.08 401	99.35 398	95.41 420	99.82 361	97.70 357	98.19 512	99.01 441

tpm2		96.35 480	96.22 475	96.73 517	98.88 484	91.75 543	99.21 204	98.51 482	93.27 534	97.89 498	99.21 436	84.83 523	99.70 448	96.04 479	98.18 513	98.75 474

testing222		95.60 503	94.59 508	98.61 430	98.66 509	97.45 457	98.54 389	97.90 512	98.53 371	96.54 533	96.47 555	70.62 556	99.81 378	95.91 489	98.15 514	98.56 486

SIFT-NN		94.78 507	94.89 503	94.45 529	98.23 524	97.29 465	94.93 542	95.84 538	95.82 512	94.78 543	97.12 539	90.26 498	92.28 554	88.91 537	98.14 515	93.77 545

tmp_tt		95.75 498	95.42 492	96.76 514	89.90 558	94.42 525	98.86 327	97.87 513	78.01 549	99.30 362	99.69 215	97.70 320	95.89 549	99.29 134	98.14 515	99.95 15

baseline2		96.83 463	96.28 471	98.46 441	99.09 457	96.91 477	98.83 335	93.87 550	97.23 480	96.23 537	98.36 511	88.12 509	99.90 204	96.68 443	98.14 515	98.57 485

ETVMVS		96.14 487	95.22 499	98.89 403	98.80 494	98.01 427	98.66 366	98.35 495	98.71 347	97.18 523	96.31 558	74.23 552	99.75 427	96.64 448	98.13 518	98.90 456

CostFormer		96.71 468	96.79 466	96.46 521	98.90 478	90.71 551	99.41 122	98.68 470	94.69 529	98.14 487	99.34 402	86.32 520	99.80 388	97.60 374	98.07 519	98.88 459

testing99		95.86 496	95.19 500	97.87 471	98.76 501	95.03 520	98.62 370	98.44 487	98.68 350	96.67 530	96.66 552	74.31 551	99.69 455	96.51 455	98.03 520	98.90 456

AUN-MVS		97.82 423	97.38 440	99.14 359	99.27 418	98.53 389	98.72 357	99.02 451	98.10 420	97.18 523	99.03 464	89.26 505	99.85 297	97.94 326	97.91 521	99.03 435

DeepMVS_CX				97.98 464	99.69 231	96.95 474		99.26 416	75.51 550	95.74 539	98.28 513	96.47 382	99.62 490	91.23 534	97.89 522	97.38 525

hse-mvs2		98.52 365	98.30 381	99.16 354	99.29 413	98.60 378	98.77 348	99.02 451	99.68 136	99.32 352	99.04 460	92.50 465	99.85 297	99.24 139	97.87 523	99.03 435

PAPM		95.61 502	94.71 506	98.31 451	99.12 446	96.63 483	96.66 529	98.46 486	90.77 543	96.25 535	98.68 498	93.01 456	99.69 455	81.60 550	97.86 524	98.62 478

JIA-IIPM		98.06 411	97.92 416	98.50 438	98.59 510	97.02 473	98.80 343	98.51 482	99.88 60	97.89 498	99.87 56	91.89 473	99.90 204	98.16 308	97.68 525	98.59 481

ET-MVSNet_ETH3D		96.78 464	96.07 478	98.91 396	99.26 421	97.92 435	97.70 477	96.05 533	97.96 435	92.37 549	98.43 509	87.06 512	99.90 204	98.27 294	97.56 526	98.91 455

gbinet_0.2-2-1-0.02		97.52 442	97.07 453	98.88 405	97.35 545	97.35 463	97.17 505	99.25 419	97.86 447	98.41 468	96.54 553	90.74 491	99.85 297	98.80 230	97.51 527	99.43 319

SP-NN		96.37 479	96.23 474	96.77 513	96.83 546	96.95 474	96.47 531	97.07 527	96.75 499	93.41 548	97.75 525	94.13 439	95.69 550	96.25 470	97.43 528	97.68 521

XFeat-NN		93.89 509	93.91 511	93.83 530	95.49 550	92.69 537	90.85 546	97.98 507	94.69 529	95.08 542	96.98 542	88.36 508	94.23 553	88.42 541	97.34 529	94.57 535

dongtai		89.37 514	88.91 517	90.76 532	99.19 434	77.46 559	95.47 539	87.82 559	92.28 539	94.17 545	98.82 488	71.22 555	95.54 551	63.85 554	97.34 529	99.27 366

TR-MVS		97.44 445	97.15 450	98.32 449	98.53 512	97.46 455	98.47 400	97.91 511	96.85 495	98.21 477	98.51 507	96.42 384	99.51 511	92.16 530	97.29 531	97.98 515

BH-w/o		97.20 454	97.01 456	97.76 475	99.08 458	95.69 508	98.03 449	98.52 481	95.76 513	97.96 493	98.02 518	95.62 410	99.47 514	92.82 529	97.25 532	98.12 509

KD-MVS_2432*1600		95.89 493	95.41 494	97.31 499	94.96 551	93.89 528	97.09 509	99.22 427	97.23 480	98.88 424	99.04 460	79.23 537	99.54 504	96.24 472	96.81 533	98.50 491

miper_refine_blended		95.89 493	95.41 494	97.31 499	94.96 551	93.89 528	97.09 509	99.22 427	97.23 480	98.88 424	99.04 460	79.23 537	99.54 504	96.24 472	96.81 533	98.50 491

UnsupCasMVSNet_eth		98.83 328	98.57 342	99.59 198	99.68 240	99.45 203	98.99 300	99.67 235	99.48 197	99.55 279	99.36 393	94.92 426	99.86 278	98.95 211	96.57 535	99.45 297

wanda-best-256-512		97.53 440	97.14 451	98.72 422	97.71 537	96.86 479	97.00 514	99.34 395	97.73 452	98.18 478	96.82 547	91.92 469	99.84 315	99.02 194	96.53 536	99.45 297

FE-blended-shiyan7		97.53 440	97.14 451	98.72 422	97.71 537	96.86 479	97.00 514	99.34 395	97.73 452	98.18 478	96.82 547	91.92 469	99.84 315	99.02 194	96.53 536	99.45 297

blended_shiyan6		97.82 423	97.46 435	98.92 391	98.08 530	97.46 455	97.73 472	99.34 395	97.96 435	98.33 471	97.35 533	92.78 458	99.84 315	99.04 189	96.53 536	99.46 295

usedtu_blend_shiyan5		97.97 417	97.65 433	98.92 391	97.71 537	97.49 452	99.53 92	99.81 135	99.52 191	98.18 478	96.82 547	91.92 469	99.83 338	98.79 231	96.53 536	99.45 297

h-mvs33		98.61 351	98.34 376	99.44 263	99.60 270	98.67 364	99.27 181	99.44 363	99.68 136	99.32 352	99.49 350	92.50 465	100.00 1	99.24 139	96.51 540	99.65 158

GG-mvs-BLEND				97.36 495	97.59 540	96.87 478	99.70 38	88.49 558		94.64 544	97.26 538	80.66 530	99.12 528	91.50 533	96.50 541	96.08 534

blended_shiyan8		97.82 423	97.45 437	98.92 391	98.06 531	97.45 457	97.73 472	99.35 391	97.96 435	98.35 470	97.34 534	92.76 460	99.84 315	99.04 189	96.49 542	99.47 290

tpm		97.15 456	96.95 458	97.75 476	98.91 477	94.24 527	99.32 158	97.96 508	97.71 455	98.29 472	99.32 403	86.72 518	99.92 154	98.10 315	96.24 543	99.09 414

MonoMVSNet		98.23 396	98.32 378	97.99 463	98.97 474	96.62 484	99.49 107	98.42 488	99.62 165	99.40 333	99.79 120	95.51 416	98.58 542	97.68 368	95.98 544	98.76 473

test0.0.03 1		97.37 449	96.91 461	98.74 420	97.72 536	97.57 449	97.60 484	97.36 523	98.00 428	99.21 380	98.02 518	90.04 501	99.79 392	98.37 285	95.89 545	98.86 461

kuosan		85.65 516	84.57 519	88.90 534	97.91 534	77.11 560	96.37 533	87.62 560	85.24 548	85.45 554	96.83 546	69.94 557	90.98 555	45.90 556	95.83 546	98.62 478

IB-MVS	95.41 20	95.30 504	94.46 510	97.84 473	98.76 501	95.33 515	97.33 498	96.07 532	96.02 508	95.37 541	97.41 532	76.17 546	99.96 69	97.54 378	95.44 547	98.22 503
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
baseline1		97.73 429	97.33 442	98.96 383	99.30 411	97.73 444	99.40 127	98.42 488	99.33 237	99.46 311	99.21 436	91.18 481	99.82 361	98.35 287	91.26 548	99.32 355

MVS_clip		74.80 518	77.14 520	67.78 536	84.58 559	66.83 562	78.80 549	52.59 563	49.02 554	94.13 546	97.99 521	68.69 558	48.60 558	80.92 551	87.52 549	87.92 551

0.4-1-1-0.1		93.18 510	91.66 514	97.73 479	95.83 549	95.29 516	95.30 540	95.90 536	93.59 532	90.58 551	94.40 559	77.87 541	99.77 409	97.31 394	84.20 550	98.15 508

0.4-1-1-0.2		92.59 511	91.07 515	97.15 505	94.73 555	93.68 532	93.50 545	95.91 534	92.68 536	90.48 552	93.52 561	77.77 542	99.75 427	97.19 412	83.88 551	98.01 514

0.3-1-1-0.015		92.36 512	90.68 516	97.39 492	94.94 553	94.41 526	94.21 544	95.89 537	92.87 535	88.87 553	93.49 562	75.30 547	99.76 416	97.19 412	83.41 552	98.02 513

VLMVS_CLIP		76.68 517	76.70 521	76.61 535	60.81 560	61.63 563	78.48 550	91.77 552	64.66 552	83.93 555	93.59 560	55.35 559	75.94 556	79.82 552	81.86 553	92.28 550

PVSNet_0	95.53 19	95.85 497	95.31 498	97.47 487	98.78 498	93.48 534	95.72 537	99.40 377	96.18 507	97.37 517	97.73 526	95.73 408	99.58 498	95.49 500	81.40 554	99.36 341

blend_shiyan4		95.04 506	93.76 512	98.88 405	97.92 533	97.49 452	97.72 474	99.34 395	97.93 439	97.65 513	97.11 540	77.69 543	99.83 338	98.79 231	79.72 555	99.33 351

VLMVS		62.60 519	63.55 522	59.72 537	60.35 561	58.44 564	68.37 551	54.75 562	23.35 556	80.04 556	90.18 564	54.59 560	52.33 557	63.04 555	77.30 556	68.41 553

MVS_baseline		39.37 520	46.36 523	18.41 538	48.75 562	10.55 566	42.43 552	13.32 565	4.65 559	75.25 557	91.61 563	29.41 561	0.06 561	38.83 557	72.99 557	44.63 554

testmvs		28.94 522	33.33 524	15.79 540	26.03 563	9.81 567	96.77 523	15.67 564	11.55 558	23.87 560	50.74 568	19.03 563	8.53 560	23.21 559	33.07 558	29.03 556

test123		29.31 521	33.05 526	18.08 539	25.93 564	12.24 565	97.53 488	10.93 566	11.78 557	24.21 559	50.08 569	21.04 562	8.60 559	23.51 558	32.43 559	33.39 555

mmdepth		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

monomultidepth		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

test_blank		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uanet_test		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

DCPMVS		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		24.88 523	33.17 525	0.00 541	0.00 565	0.00 568	0.00 553	99.62 265	0.00 560	0.00 561	99.13 445	99.82 18	0.00 562	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		16.61 524	22.14 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	99.28 93	0.00 562	0.00 560	0.00 560	0.00 557

sosnet-low-res		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

sosnet		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uncertanet		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

Regformer		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

ab-mvs-re		8.26 535	11.02 538	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	99.16 442	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uanet		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

PatchmatchNet2					0.00 565	95.19 519	97.64 480	99.19 434	98.09 422
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.93 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						96.36 490								95.20 506

FOURS1					99.83 90	99.89 10	99.74 27	99.71 208	99.69 132	99.63 238

test_one_0601					99.63 261	99.76 70		99.55 315	99.23 255	99.31 357	99.61 285	98.59 213

eth-test2					0.00 565

eth-test					0.00 565

test_241102_ONE					99.69 231	99.82 41		99.54 321	99.12 282	99.82 112	99.49 350	98.91 167	99.52 510

save fliter					99.53 325	99.25 259	98.29 418	99.38 385	99.07 287

test0726					99.69 231	99.80 51	99.24 193	99.57 303	99.16 272	99.73 182	99.65 247	98.35 257

GSMVS																99.14 401

test_part2					99.62 265	99.67 120				99.55 279

sam_mvs1												90.81 490				99.14 401

sam_mvs												90.52 496

MTGPA								99.53 332

test_post1							99.14 233				51.63 567	89.54 504	99.82 361	96.86 431

test_post											52.41 566	90.25 499	99.86 278

patchmatchnet-post											99.62 275	90.58 494	99.94 98

MTMP							99.09 259	98.59 479

gm-plane-assit					97.59 540	89.02 557			93.47 533		98.30 512		99.84 315	96.38 465

TEST9					99.35 390	99.35 238	98.11 439	99.41 370	94.83 528	97.92 495	98.99 467	98.02 296	99.85 297

test_8					99.34 399	99.31 245	98.08 443	99.40 377	94.90 525	97.87 500	98.97 472	98.02 296	99.84 315

agg_prior					99.35 390	99.36 235		99.39 380		97.76 508			99.85 297

test_prior4						99.19 280	98.00 453

test_prior				99.46 256	99.35 390	99.22 270		99.39 380					99.69 455			99.48 286

旧先验2							97.94 460		95.33 519	98.94 416			99.88 241	96.75 439

新几何2							98.04 447

无先验							98.01 450	99.23 424	95.83 511				99.85 297	95.79 494		99.44 312

原ACMM2							97.92 462

testdata2													99.89 226	95.99 483

segment_acmp												98.37 255

testdata1							97.72 474		97.86 447

plane_prior7					99.58 286	99.38 226

plane_prior6					99.47 356	99.26 256						97.24 347

plane_prior4											99.25 423

plane_prior3						99.31 245			98.36 391	99.14 393

plane_prior2							98.80 343		98.94 305

plane_prior1					99.51 334

n2								0.00 567

nn								0.00 567

door-mid								99.83 115

test11								99.29 410

door								99.77 170

HQP5-MVS						98.94 326

HQP-NCC					99.31 407		97.98 455		97.45 467	98.15 483

ACMP_Plane					99.31 407		97.98 455		97.45 467	98.15 483

BP-MVS														94.73 512

HQP4-MVS										98.15 483			99.70 448			99.53 257

HQP2-MVS												96.67 373

NP-MVS					99.40 376	99.13 292					98.83 486

MDTV_nov1_ep13_2view						91.44 546	99.14 233		97.37 473	99.21 380		91.78 476		96.75 439		99.03 435

Test By Simon												98.41 249

High-resolution multi-view benchmark