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.

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 10	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 10	100.00 1	99.85 18

test_fmvs3		99.12 51	99.41 19	98.25 235	99.76 29	95.07 286	99.05 63	99.94 2	97.78 178	99.82 21	99.84 2	98.56 54	99.71 248	99.96 1	99.96 23	99.97 3

pmmvs6		99.67 3	99.70 3	99.60 11	99.90 4	99.27 23	99.53 8	99.76 29	99.64 18	99.84 20	99.83 3	99.50 8	99.87 101	99.36 37	99.92 52	99.64 60

test_f		98.67 115	98.87 72	98.05 252	99.72 42	95.59 264	98.51 121	99.81 23	96.30 283	99.78 28	99.82 4	96.14 209	98.63 396	99.82 7	99.93 41	99.95 6

mvsany_test3		98.87 78	98.92 69	98.74 175	99.38 138	96.94 223	98.58 109	99.10 228	96.49 273	99.96 4	99.81 5	98.18 80	99.45 347	98.97 64	99.79 112	99.83 20

UA-Net		99.47 13	99.40 20	99.70 2	99.49 113	99.29 20	99.80 3	99.72 33	99.82 3	99.04 142	99.81 5	98.05 91	99.96 12	98.85 70	99.99 5	99.86 17

ANet_high		99.57 7	99.67 5	99.28 84	99.89 6	98.09 134	99.14 53	99.93 4	99.82 3	99.93 6	99.81 5	99.17 18	99.94 35	99.31 39	100.00 1	99.82 23

test_fmvs2		98.70 104	98.97 67	97.89 259	99.54 96	94.05 312	98.55 112	99.92 6	96.78 261	99.72 33	99.78 8	96.60 190	99.67 268	99.91 2	99.90 65	99.94 7

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 16	99.69 4	99.58 56	99.90 2	99.86 18	99.78 8	99.58 6	99.95 23	99.00 62	99.95 29	99.78 31

OurMVSNet-221017-0		99.37 25	99.31 31	99.53 34	99.91 3	98.98 66	99.63 6	99.58 56	99.44 38	99.78 28	99.76 10	96.39 198	99.92 51	99.44 35	99.92 52	99.68 51

test_fmvsmconf0.01_n		99.57 7	99.63 7	99.36 63	99.87 12	98.13 130	98.08 164	99.95 1	99.45 36	99.98 2	99.75 11	99.80 1	99.97 5	99.82 7	99.99 5	99.99 1

MVS-HIRNet		94.32 335	95.62 304	90.42 389	98.46 318	75.36 413	96.29 313	89.13 405	95.25 315	95.38 373	99.75 11	92.88 296	99.19 379	94.07 319	99.39 243	96.72 390

gg-mvs-nofinetune		92.37 365	91.20 369	95.85 353	95.80 406	92.38 353	99.31 26	81.84 412	99.75 6	91.83 401	99.74 13	68.29 399	99.02 385	87.15 390	97.12 377	96.16 395

mvs_tets		99.63 5	99.67 5	99.49 48	99.88 9	98.61 91	99.34 19	99.71 34	99.27 57	99.90 12	99.74 13	99.68 4	99.97 5	99.55 28	99.99 5	99.88 14

test_djsdf		99.52 10	99.51 11	99.53 34	99.86 14	98.74 81	99.39 16	99.56 70	99.11 73	99.70 37	99.73 15	99.00 22	99.97 5	99.26 44	99.98 12	99.89 11

anonymousdsp		99.51 11	99.47 16	99.62 6	99.88 9	99.08 64	99.34 19	99.69 38	98.93 99	99.65 47	99.72 16	98.93 26	99.95 23	99.11 53	100.00 1	99.82 23

fmvsm_s_conf0.1_n_a		99.17 42	99.30 33	98.80 159	99.75 33	96.59 236	97.97 184	99.86 13	98.22 144	99.88 17	99.71 17	98.59 50	99.84 140	99.73 18	99.98 12	99.98 2

PS-MVSNAJss		99.46 14	99.49 12	99.35 69	99.90 4	98.15 127	99.20 44	99.65 47	99.48 32	99.92 8	99.71 17	98.07 88	99.96 12	99.53 29	100.00 1	99.93 8

JIA-IIPM		95.52 319	95.03 324	97.00 317	96.85 391	94.03 315	96.93 279	95.82 371	99.20 64	94.63 383	99.71 17	83.09 371	99.60 300	94.42 307	94.64 398	97.36 382

fmvsm_s_conf0.1_n		99.16 45	99.33 27	98.64 180	99.71 45	96.10 249	97.87 195	99.85 15	98.56 124	99.90 12	99.68 20	98.69 41	99.85 122	99.72 20	99.98 12	99.97 3

SDMVSNet		99.23 38	99.32 29	98.96 138	99.68 56	97.35 197	98.84 88	99.48 96	99.69 12	99.63 50	99.68 20	99.03 21	99.96 12	97.97 126	99.92 52	99.57 88

sd_testset		99.28 30	99.31 31	99.19 100	99.68 56	98.06 143	99.41 13	99.30 172	99.69 12	99.63 50	99.68 20	99.25 14	99.96 12	97.25 164	99.92 52	99.57 88

Anonymous20231211		99.27 31	99.27 36	99.26 89	99.29 159	98.18 125	99.49 9	99.51 85	99.70 11	99.80 26	99.68 20	96.84 173	99.83 157	99.21 49	99.91 59	99.77 33

jajsoiax		99.58 6	99.61 8	99.48 50	99.87 12	98.61 91	99.28 36	99.66 46	99.09 83	99.89 15	99.68 20	99.53 7	99.97 5	99.50 32	99.99 5	99.87 15

test_vis3_rt		99.14 46	99.17 45	99.07 119	99.78 23	98.38 108	98.92 77	99.94 2	97.80 176	99.91 11	99.67 25	97.15 157	98.91 391	99.76 15	99.56 210	99.92 9

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 22	99.85 16	99.11 60	99.90 1	99.78 27	99.63 20	99.78 28	99.67 25	99.48 9	99.81 180	99.30 40	99.97 19	99.77 33
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
test_fmvsmconf0.1_n		99.49 12	99.54 10	99.34 72	99.78 23	98.11 131	97.77 206	99.90 9	99.33 50	99.97 3	99.66 27	99.71 3	99.96 12	99.79 12	99.99 5	99.96 5

v7n		99.53 9	99.57 9	99.41 59	99.88 9	98.54 99	99.45 10	99.61 52	99.66 16	99.68 41	99.66 27	98.44 61	99.95 23	99.73 18	99.96 23	99.75 41

K. test v3		98.00 194	97.66 216	99.03 129	99.79 22	97.56 186	99.19 48	92.47 394	99.62 23	99.52 63	99.66 27	89.61 326	99.96 12	99.25 46	99.81 97	99.56 94

SixPastTwentyTwo		98.75 96	98.62 106	99.16 104	99.83 18	97.96 154	99.28 36	98.20 317	99.37 45	99.70 37	99.65 30	92.65 302	99.93 42	99.04 59	99.84 83	99.60 71

test_fmvs1_n		98.09 188	98.28 157	97.52 292	99.68 56	93.47 334	98.63 103	99.93 4	95.41 313	99.68 41	99.64 31	91.88 311	99.48 340	99.82 7	99.87 74	99.62 64

DSMNet-mixed		97.42 240	97.60 221	96.87 325	99.15 195	91.46 363	98.54 114	99.12 225	92.87 360	97.58 291	99.63 32	96.21 206	99.90 65	95.74 272	99.54 215	99.27 212

test_cas_vis1_n_1920		98.33 163	98.68 97	97.27 306	99.69 54	92.29 355	98.03 172	99.85 15	97.62 187	99.96 4	99.62 33	93.98 279	99.74 235	99.52 31	99.86 77	99.79 28

TransMVSNet (Re)		99.44 15	99.47 16	99.36 63	99.80 20	98.58 94	99.27 38	99.57 63	99.39 43	99.75 32	99.62 33	99.17 18	99.83 157	99.06 57	99.62 187	99.66 55

Gipuma		99.03 58	99.16 47	98.64 180	99.94 2	98.51 101	99.32 22	99.75 32	99.58 28	98.60 212	99.62 33	98.22 76	99.51 333	97.70 144	99.73 142	97.89 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Baseline_NR-MVSNet		98.98 65	98.86 75	99.36 63	99.82 19	98.55 96	97.47 244	99.57 63	99.37 45	99.21 119	99.61 36	96.76 182	99.83 157	98.06 119	99.83 90	99.71 44

TDRefinement		99.42 20	99.38 22	99.55 24	99.76 29	99.33 17	99.68 5	99.71 34	99.38 44	99.53 61	99.61 36	98.64 44	99.80 187	98.24 106	99.84 83	99.52 115

pm-mvs1		99.44 15	99.48 14	99.33 77	99.80 20	98.63 88	99.29 32	99.63 48	99.30 54	99.65 47	99.60 38	99.16 20	99.82 167	99.07 56	99.83 90	99.56 94

v10		98.97 66	99.11 53	98.55 200	99.44 127	96.21 248	98.90 78	99.55 74	98.73 109	99.48 69	99.60 38	96.63 189	99.83 157	99.70 21	99.99 5	99.61 70

test1111		96.49 292	96.82 266	95.52 361	99.42 133	87.08 394	99.22 41	87.14 407	99.11 73	99.46 72	99.58 40	88.69 332	99.86 109	98.80 72	99.95 29	99.62 64

test_fmvsmconf_n		99.44 15	99.48 14	99.31 82	99.64 68	98.10 133	97.68 217	99.84 18	99.29 55	99.92 8	99.57 41	99.60 5	99.96 12	99.74 17	99.98 12	99.89 11

test_vis1_n		98.31 166	98.50 122	97.73 275	99.76 29	94.17 310	98.68 100	99.91 7	96.31 281	99.79 27	99.57 41	92.85 298	99.42 352	99.79 12	99.84 83	99.60 71

test2506		92.39 363	91.89 365	93.89 379	99.38 138	82.28 409	99.32 22	66.03 415	99.08 85	98.77 190	99.57 41	66.26 405	99.84 140	98.71 81	99.95 29	99.54 105

ECVR-MVS		96.42 294	96.61 280	95.85 353	99.38 138	88.18 390	99.22 41	86.00 409	99.08 85	99.36 91	99.57 41	88.47 337	99.82 167	98.52 94	99.95 29	99.54 105

mamv4		99.44 15	99.39 21	99.58 16	99.30 157	99.74 2	99.04 64	99.81 23	99.77 5	99.82 21	99.57 41	97.82 107	99.98 4	99.53 29	99.89 69	99.01 256

v8		99.01 60	99.16 47	98.57 195	99.47 122	96.31 246	98.90 78	99.47 104	99.03 89	99.52 63	99.57 41	96.93 169	99.81 180	99.60 24	99.98 12	99.60 71

MIMVSNet1		99.38 24	99.32 29	99.55 24	99.86 14	99.19 38	99.41 13	99.59 54	99.59 26	99.71 35	99.57 41	97.12 158	99.90 65	99.21 49	99.87 74	99.54 105

fmvsm_s_conf0.5_n		99.09 54	99.26 38	98.61 188	99.55 91	96.09 252	97.74 211	99.81 23	98.55 125	99.85 19	99.55 48	98.60 49	99.84 140	99.69 23	99.98 12	99.89 11

test_vis1_n_1920		98.40 152	98.92 69	96.81 329	99.74 35	90.76 378	98.15 156	99.91 7	98.33 133	99.89 15	99.55 48	95.07 249	99.88 84	99.76 15	99.93 41	99.79 28

Anonymous20240521		98.69 107	98.87 72	98.16 243	99.77 26	95.11 285	99.08 57	99.44 114	99.34 49	99.33 96	99.55 48	94.10 278	99.94 35	99.25 46	99.96 23	99.42 159

GBi-Net		98.65 117	98.47 129	99.17 101	98.90 241	98.24 119	99.20 44	99.44 114	98.59 119	98.95 157	99.55 48	94.14 274	99.86 109	97.77 138	99.69 162	99.41 162

test1		98.65 117	98.47 129	99.17 101	98.90 241	98.24 119	99.20 44	99.44 114	98.59 119	98.95 157	99.55 48	94.14 274	99.86 109	97.77 138	99.69 162	99.41 162

FMVSNet1		99.17 42	99.17 45	99.17 101	99.55 91	98.24 119	99.20 44	99.44 114	99.21 62	99.43 77	99.55 48	97.82 107	99.86 109	98.42 99	99.89 69	99.41 162

fmvsm_s_conf0.5_n_a		99.10 53	99.20 43	98.78 165	99.55 91	96.59 236	97.79 203	99.82 22	98.21 145	99.81 25	99.53 54	98.46 60	99.84 140	99.70 21	99.97 19	99.90 10

KD-MVS_self_test		99.25 34	99.18 44	99.44 56	99.63 72	99.06 65	98.69 99	99.54 78	99.31 52	99.62 53	99.53 54	97.36 145	99.86 109	99.24 48	99.71 154	99.39 172

new-patchmatchnet		98.35 159	98.74 84	97.18 309	99.24 167	92.23 357	96.42 305	99.48 96	98.30 136	99.69 39	99.53 54	97.44 141	99.82 167	98.84 71	99.77 123	99.49 125

lessismore_v0				98.97 137	99.73 36	97.53 188		86.71 408		99.37 89	99.52 57	89.93 324	99.92 51	98.99 63	99.72 149	99.44 152

MVSMamba_PlusPlus		98.83 83	98.98 66	98.36 225	99.32 152	96.58 238	98.90 78	99.41 125	99.75 6	98.72 196	99.50 58	96.17 207	99.94 35	99.27 42	99.78 117	98.57 322

test_fmvsmvis_n_1920		99.26 33	99.49 12	98.54 203	99.66 62	96.97 219	98.00 178	99.85 15	99.24 59	99.92 8	99.50 58	99.39 11	99.95 23	99.89 3	99.98 12	98.71 306

FC-MVSNet-test		99.27 31	99.25 39	99.34 72	99.77 26	98.37 110	99.30 31	99.57 63	99.61 25	99.40 84	99.50 58	97.12 158	99.85 122	99.02 61	99.94 36	99.80 27

iter_conf05		99.03 58	99.22 41	98.46 213	99.32 152	96.55 240	99.55 7	99.70 37	99.75 6	99.82 21	99.50 58	96.17 207	99.94 35	99.27 42	99.86 77	98.88 282

VDDNet		98.21 179	97.95 193	99.01 132	99.58 75	97.74 175	99.01 66	97.29 342	99.67 15	98.97 153	99.50 58	90.45 321	99.80 187	97.88 132	99.20 273	99.48 135

DeepC-MVS	97.60 4	98.97 66	98.93 68	99.10 113	99.35 149	97.98 150	98.01 177	99.46 106	97.56 196	99.54 57	99.50 58	98.97 23	99.84 140	98.06 119	99.92 52	99.49 125
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XXY-MVS		99.14 46	99.15 52	99.10 113	99.76 29	97.74 175	98.85 86	99.62 49	98.48 127	99.37 89	99.49 64	98.75 36	99.86 109	98.20 109	99.80 107	99.71 44

Vis-MVSNet		99.34 26	99.36 23	99.27 87	99.73 36	98.26 117	99.17 49	99.78 27	99.11 73	99.27 107	99.48 65	98.82 31	99.95 23	98.94 65	99.93 41	99.59 77
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		98.53 138	98.45 132	98.79 162	97.94 350	96.96 221	99.08 57	98.54 302	99.10 80	96.82 335	99.47 66	96.55 192	99.84 140	98.56 93	99.94 36	99.55 101
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
EU-MVSNet		97.66 222	98.50 122	95.13 367	99.63 72	85.84 397	98.35 140	98.21 316	98.23 143	99.54 57	99.46 67	95.02 250	99.68 265	98.24 106	99.87 74	99.87 15

LCM-MVSNet-Re		98.64 119	98.48 127	99.11 111	98.85 252	98.51 101	98.49 124	99.83 20	98.37 130	99.69 39	99.46 67	98.21 78	99.92 51	94.13 317	99.30 258	98.91 277

mvs_anonymous		97.83 213	98.16 174	96.87 325	98.18 339	91.89 359	97.31 254	98.90 260	97.37 217	98.83 181	99.46 67	96.28 204	99.79 200	98.90 67	98.16 346	98.95 268

DTE-MVSNet		99.43 19	99.35 24	99.66 4	99.71 45	99.30 18	99.31 26	99.51 85	99.64 18	99.56 54	99.46 67	98.23 73	99.97 5	98.78 73	99.93 41	99.72 43

ACMH	96.65 7	99.25 34	99.24 40	99.26 89	99.72 42	98.38 108	99.07 60	99.55 74	98.30 136	99.65 47	99.45 71	99.22 15	99.76 223	98.44 97	99.77 123	99.64 60
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs1		97.72 217	97.94 195	97.07 316	98.66 293	92.39 352	97.68 217	99.81 23	95.20 317	99.54 57	99.44 72	91.56 313	99.41 353	99.78 14	99.77 123	99.40 171

VPA-MVSNet		99.30 29	99.30 33	99.28 84	99.49 113	98.36 113	99.00 68	99.45 110	99.63 20	99.52 63	99.44 72	98.25 71	99.88 84	99.09 55	99.84 83	99.62 64

EGC-MVSNET		85.24 373	80.54 376	99.34 72	99.77 26	99.20 35	99.08 57	99.29 180	12.08 411	20.84 412	99.42 74	97.55 129	99.85 122	97.08 176	99.72 149	98.96 267

PEN-MVS		99.41 21	99.34 26	99.62 6	99.73 36	99.14 53	99.29 32	99.54 78	99.62 23	99.56 54	99.42 74	98.16 84	99.96 12	98.78 73	99.93 41	99.77 33

PatchT		96.65 285	96.35 288	97.54 290	97.40 378	95.32 276	97.98 181	96.64 359	99.33 50	96.89 331	99.42 74	84.32 364	99.81 180	97.69 146	97.49 363	97.48 379

FIs		99.14 46	99.09 56	99.29 83	99.70 52	98.28 116	99.13 54	99.52 84	99.48 32	99.24 116	99.41 77	96.79 179	99.82 167	98.69 83	99.88 71	99.76 37

PS-CasMVS		99.40 22	99.33 27	99.62 6	99.71 45	99.10 61	99.29 32	99.53 81	99.53 30	99.46 72	99.41 77	98.23 73	99.95 23	98.89 69	99.95 29	99.81 26

ab-mvs		98.41 150	98.36 147	98.59 191	99.19 181	97.23 204	99.32 22	98.81 279	97.66 184	98.62 208	99.40 79	96.82 176	99.80 187	95.88 263	99.51 224	98.75 303

Anonymous20240529		98.93 71	98.87 72	99.12 109	99.19 181	98.22 124	99.01 66	98.99 250	99.25 58	99.54 57	99.37 80	97.04 162	99.80 187	97.89 129	99.52 222	99.35 191

CR-MVSNet		96.28 298	95.95 296	97.28 305	97.71 360	94.22 306	98.11 160	98.92 257	92.31 366	96.91 327	99.37 80	85.44 356	99.81 180	97.39 157	97.36 372	97.81 366

Patchmtry		97.35 244	96.97 255	98.50 210	97.31 381	96.47 241	98.18 152	98.92 257	98.95 98	98.78 187	99.37 80	85.44 356	99.85 122	95.96 261	99.83 90	99.17 237

EG-PatchMatch MVS		98.99 62	99.01 62	98.94 141	99.50 106	97.47 190	98.04 171	99.59 54	98.15 156	99.40 84	99.36 83	98.58 53	99.76 223	98.78 73	99.68 167	99.59 77

testf1		99.25 34	99.16 47	99.51 43	99.89 6	99.63 4	98.71 97	99.69 38	98.90 101	99.43 77	99.35 84	98.86 28	99.67 268	97.81 135	99.81 97	99.24 219

APD_test2		99.25 34	99.16 47	99.51 43	99.89 6	99.63 4	98.71 97	99.69 38	98.90 101	99.43 77	99.35 84	98.86 28	99.67 268	97.81 135	99.81 97	99.24 219

IterMVS-SCA-FT		97.85 210	98.18 170	96.87 325	99.27 162	91.16 373	95.53 348	99.25 192	99.10 80	99.41 81	99.35 84	93.10 291	99.96 12	98.65 85	99.94 36	99.49 125

PMVS	91.26 20	97.86 205	97.94 195	97.65 279	99.71 45	97.94 156	98.52 116	98.68 294	98.99 92	97.52 297	99.35 84	97.41 142	98.18 400	91.59 365	99.67 173	96.82 388
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WR-MVS_H		99.33 27	99.22 41	99.65 5	99.71 45	99.24 26	99.32 22	99.55 74	99.46 35	99.50 68	99.34 88	97.30 147	99.93 42	98.90 67	99.93 41	99.77 33

RPMNet		97.02 269	96.93 256	97.30 304	97.71 360	94.22 306	98.11 160	99.30 172	99.37 45	96.91 327	99.34 88	86.72 343	99.87 101	97.53 151	97.36 372	97.81 366

mvsany_test1		97.60 225	97.54 223	97.77 267	97.72 358	95.35 275	95.36 356	97.13 346	94.13 341	99.71 35	99.33 90	97.93 100	99.30 369	97.60 147	98.94 307	98.67 314

FA-MVS(test-final)		96.99 273	96.82 266	97.50 294	98.70 278	94.78 291	99.34 19	96.99 349	95.07 318	98.48 228	99.33 90	88.41 338	99.65 284	96.13 256	98.92 309	98.07 354

IterMVS		97.73 216	98.11 179	96.57 334	99.24 167	90.28 381	95.52 350	99.21 201	98.86 104	99.33 96	99.33 90	93.11 290	99.94 35	98.49 95	99.94 36	99.48 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator	98.27 2	98.81 87	98.73 86	99.05 126	98.76 265	97.81 170	99.25 39	99.30 172	98.57 122	98.55 221	99.33 90	97.95 99	99.90 65	97.16 168	99.67 173	99.44 152

IterMVS-LS		98.55 134	98.70 94	98.09 245	99.48 120	94.73 294	97.22 263	99.39 131	98.97 95	99.38 87	99.31 94	96.00 216	99.93 42	98.58 88	99.97 19	99.60 71
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_fmvsm_n_1920		99.33 27	99.45 18	98.99 134	99.57 79	97.73 177	97.93 185	99.83 20	99.22 60	99.93 6	99.30 95	99.42 10	99.96 12	99.85 5	99.99 5	99.29 209

patch_mono-2		98.51 142	98.63 104	98.17 241	99.38 138	94.78 291	97.36 250	99.69 38	98.16 155	98.49 227	99.29 96	97.06 161	99.97 5	98.29 105	99.91 59	99.76 37

FMVSNet2		98.49 143	98.40 139	98.75 171	98.90 241	97.14 214	98.61 106	99.13 224	98.59 119	99.19 121	99.28 97	94.14 274	99.82 167	97.97 126	99.80 107	99.29 209

3Dnovator+	97.89 3	98.69 107	98.51 120	99.24 94	98.81 260	98.40 106	99.02 65	99.19 207	98.99 92	98.07 258	99.28 97	97.11 160	99.84 140	96.84 200	99.32 253	99.47 142

VDD-MVS		98.56 130	98.39 142	99.07 119	99.13 198	98.07 140	98.59 108	97.01 348	99.59 26	99.11 128	99.27 99	94.82 256	99.79 200	98.34 102	99.63 184	99.34 193

PVSNet_Blended_VisFu		98.17 184	98.15 175	98.22 238	99.73 36	95.15 282	97.36 250	99.68 43	94.45 334	98.99 148	99.27 99	96.87 172	99.94 35	97.13 173	99.91 59	99.57 88

FE-MVS		95.66 315	94.95 327	97.77 267	98.53 312	95.28 277	99.40 15	96.09 367	93.11 356	97.96 265	99.26 101	79.10 387	99.77 217	92.40 356	98.71 320	98.27 345

dcpmvs_2		98.78 91	99.11 53	97.78 266	99.56 87	93.67 330	99.06 61	99.86 13	99.50 31	99.66 44	99.26 101	97.21 155	99.99 2	98.00 124	99.91 59	99.68 51

nrg030		99.40 22	99.35 24	99.54 27	99.58 75	99.13 56	98.98 71	99.48 96	99.68 14	99.46 72	99.26 101	98.62 47	99.73 240	99.17 52	99.92 52	99.76 37

CP-MVSNet		99.21 39	99.09 56	99.56 22	99.65 63	98.96 71	99.13 54	99.34 152	99.42 41	99.33 96	99.26 101	97.01 166	99.94 35	98.74 78	99.93 41	99.79 28

RPSCF		98.62 124	98.36 147	99.42 57	99.65 63	99.42 8	98.55 112	99.57 63	97.72 181	98.90 168	99.26 101	96.12 211	99.52 329	95.72 273	99.71 154	99.32 200

SSC-MVS		98.71 100	98.74 84	98.62 185	99.72 42	96.08 254	98.74 90	98.64 298	99.74 9	99.67 43	99.24 106	94.57 264	99.95 23	99.11 53	99.24 267	99.82 23

tfpnnormal		98.90 75	98.90 71	98.91 146	99.67 60	97.82 167	99.00 68	99.44 114	99.45 36	99.51 67	99.24 106	98.20 79	99.86 109	95.92 262	99.69 162	99.04 252

v1240		98.55 134	98.62 106	98.32 229	99.22 172	95.58 266	97.51 240	99.45 110	97.16 241	99.45 75	99.24 106	96.12 211	99.85 122	99.60 24	99.88 71	99.55 101

APDe-MVS		98.99 62	98.79 81	99.60 11	99.21 174	99.15 48	98.87 83	99.48 96	97.57 193	99.35 93	99.24 106	97.83 104	99.89 75	97.88 132	99.70 159	99.75 41
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mvsmamba		97.57 229	97.26 240	98.51 206	98.69 283	96.73 232	98.74 90	97.25 343	97.03 248	97.88 270	99.23 110	90.95 317	99.87 101	96.61 218	99.00 299	98.91 277

casdiffmvs_mvg		99.12 51	99.16 47	98.99 134	99.43 132	97.73 177	98.00 178	99.62 49	99.22 60	99.55 56	99.22 111	98.93 26	99.75 230	98.66 84	99.81 97	99.50 121
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ambc				98.24 237	98.82 258	95.97 256	98.62 105	99.00 249		99.27 107	99.21 112	96.99 167	99.50 334	96.55 229	99.50 231	99.26 215

TAMVS		98.24 176	98.05 185	98.80 159	99.07 209	97.18 210	97.88 192	98.81 279	96.66 267	99.17 126	99.21 112	94.81 258	99.77 217	96.96 187	99.88 71	99.44 152

bld_raw_conf03		98.38 158	98.39 142	98.33 228	98.69 283	96.58 238	98.90 78	99.41 125	97.57 193	98.72 196	99.20 114	95.48 239	99.86 109	97.76 142	99.78 117	98.57 322

v1192		98.60 126	98.66 100	98.41 219	99.27 162	95.88 258	97.52 238	99.36 141	97.41 213	99.33 96	99.20 114	96.37 201	99.82 167	99.57 26	99.92 52	99.55 101

APD_test1		98.83 83	98.66 100	99.34 72	99.78 23	99.47 7	98.42 134	99.45 110	98.28 141	98.98 149	99.19 116	97.76 111	99.58 310	96.57 222	99.55 213	98.97 265

balanced_conf03		98.63 121	98.72 88	98.38 222	98.66 293	96.68 235	98.90 78	99.42 123	98.99 92	98.97 153	99.19 116	95.81 228	99.85 122	98.77 76	99.77 123	98.60 318

pmmvs-eth3d		98.47 145	98.34 150	98.86 151	99.30 157	97.76 173	97.16 268	99.28 183	95.54 306	99.42 80	99.19 116	97.27 150	99.63 290	97.89 129	99.97 19	99.20 226

COLMAP_ROB	96.50 10	98.99 62	98.85 76	99.41 59	99.58 75	99.10 61	98.74 90	99.56 70	99.09 83	99.33 96	99.19 116	98.40 63	99.72 247	95.98 260	99.76 135	99.42 159
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v144192		98.54 136	98.57 114	98.45 215	99.21 174	95.98 255	97.63 225	99.36 141	97.15 243	99.32 102	99.18 120	95.84 227	99.84 140	99.50 32	99.91 59	99.54 105

PM-MVS		98.82 85	98.72 88	99.12 109	99.64 68	98.54 99	97.98 181	99.68 43	97.62 187	99.34 95	99.18 120	97.54 130	99.77 217	97.79 137	99.74 139	99.04 252

PVSNet_BlendedMVS		97.55 230	97.53 224	97.60 283	98.92 237	93.77 328	96.64 294	99.43 120	94.49 330	97.62 287	99.18 120	96.82 176	99.67 268	94.73 296	99.93 41	99.36 187

ACMH+	96.62 9	99.08 56	99.00 63	99.33 77	99.71 45	98.83 76	98.60 107	99.58 56	99.11 73	99.53 61	99.18 120	98.81 32	99.67 268	96.71 213	99.77 123	99.50 121

v1921920		98.54 136	98.60 111	98.38 222	99.20 178	95.76 263	97.56 234	99.36 141	97.23 235	99.38 87	99.17 124	96.02 214	99.84 140	99.57 26	99.90 65	99.54 105

casdiffmvs		98.95 69	99.00 63	98.81 157	99.38 138	97.33 198	97.82 199	99.57 63	99.17 71	99.35 93	99.17 124	98.35 68	99.69 256	98.46 96	99.73 142	99.41 162
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Patchmatch-RL test		97.26 251	97.02 253	97.99 256	99.52 101	95.53 268	96.13 323	99.71 34	97.47 204	99.27 107	99.16 126	84.30 365	99.62 293	97.89 129	99.77 123	98.81 292

V42		98.78 91	98.78 82	98.76 169	99.44 127	97.04 216	98.27 144	99.19 207	97.87 171	99.25 115	99.16 126	96.84 173	99.78 211	99.21 49	99.84 83	99.46 144

QAPM		97.31 247	96.81 268	98.82 155	98.80 263	97.49 189	99.06 61	99.19 207	90.22 384	97.69 284	99.16 126	96.91 170	99.90 65	90.89 378	99.41 241	99.07 246

wuyk23d		96.06 302	97.62 220	91.38 388	98.65 297	98.57 95	98.85 86	96.95 352	96.86 257	99.90 12	99.16 126	99.18 17	98.40 398	89.23 385	99.77 123	77.18 408

v1144		98.60 126	98.66 100	98.41 219	99.36 145	95.90 257	97.58 232	99.34 152	97.51 200	99.27 107	99.15 130	96.34 203	99.80 187	99.47 34	99.93 41	99.51 118

DP-MVS		98.93 71	98.81 80	99.28 84	99.21 174	98.45 105	98.46 129	99.33 157	99.63 20	99.48 69	99.15 130	97.23 153	99.75 230	97.17 167	99.66 178	99.63 63

OpenMVS	96.65 7	97.09 264	96.68 275	98.32 229	98.32 330	97.16 212	98.86 85	99.37 137	89.48 388	96.29 353	99.15 130	96.56 191	99.90 65	92.90 344	99.20 273	97.89 361

MM		98.22 177	97.99 190	98.91 146	98.66 293	96.97 219	97.89 191	94.44 382	99.54 29	98.95 157	99.14 133	93.50 286	99.92 51	99.80 11	99.96 23	99.85 18

EPP-MVSNet		98.30 167	98.04 186	99.07 119	99.56 87	97.83 164	99.29 32	98.07 323	99.03 89	98.59 214	99.13 134	92.16 307	99.90 65	96.87 197	99.68 167	99.49 125

ACMMP_NAP		98.75 96	98.48 127	99.57 17	99.58 75	99.29 20	97.82 199	99.25 192	96.94 252	98.78 187	99.12 135	98.02 92	99.84 140	97.13 173	99.67 173	99.59 77

fmvsm_l_conf0.5_n_a		99.19 41	99.27 36	98.94 141	99.65 63	97.05 215	97.80 202	99.76 29	98.70 112	99.78 28	99.11 136	98.79 34	99.95 23	99.85 5	99.96 23	99.83 20

MVS_Test		98.18 182	98.36 147	97.67 277	98.48 315	94.73 294	98.18 152	99.02 244	97.69 182	98.04 262	99.11 136	97.22 154	99.56 315	98.57 90	98.90 310	98.71 306

MDA-MVSNet-bldmvs		97.94 198	97.91 198	98.06 250	99.44 127	94.96 288	96.63 295	99.15 223	98.35 131	98.83 181	99.11 136	94.31 271	99.85 122	96.60 219	98.72 318	99.37 181

SMA-MVS		98.40 152	98.03 187	99.51 43	99.16 191	99.21 29	98.05 169	99.22 200	94.16 340	98.98 149	99.10 139	97.52 134	99.79 200	96.45 236	99.64 181	99.53 112
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
MIMVSNet		96.62 287	96.25 294	97.71 276	99.04 217	94.66 297	99.16 50	96.92 354	97.23 235	97.87 271	99.10 139	86.11 350	99.65 284	91.65 363	99.21 272	98.82 288

USDC		97.41 241	97.40 231	97.44 299	98.94 231	93.67 330	95.17 360	99.53 81	94.03 344	98.97 153	99.10 139	95.29 243	99.34 363	95.84 269	99.73 142	99.30 207

fmvsm_l_conf0.5_n		99.21 39	99.28 35	99.02 131	99.64 68	97.28 201	97.82 199	99.76 29	98.73 109	99.82 21	99.09 142	98.81 32	99.95 23	99.86 4	99.96 23	99.83 20

test0726					99.50 106	99.21 29	98.17 155	99.35 146	97.97 162	99.26 111	99.06 143	97.61 124

AllTest		98.44 148	98.20 167	99.16 104	99.50 106	98.55 96	98.25 146	99.58 56	96.80 259	98.88 173	99.06 143	97.65 118	99.57 312	94.45 305	99.61 192	99.37 181

TestCases				99.16 104	99.50 106	98.55 96		99.58 56	96.80 259	98.88 173	99.06 143	97.65 118	99.57 312	94.45 305	99.61 192	99.37 181

TranMVSNet+NR-MVSNet		99.17 42	99.07 59	99.46 55	99.37 144	98.87 74	98.39 136	99.42 123	99.42 41	99.36 91	99.06 143	98.38 64	99.95 23	98.34 102	99.90 65	99.57 88

LPG-MVS_test		98.71 100	98.46 131	99.47 53	99.57 79	98.97 67	98.23 147	99.48 96	96.60 268	99.10 131	99.06 143	98.71 39	99.83 157	95.58 280	99.78 117	99.62 64

LGP-MVS_train				99.47 53	99.57 79	98.97 67		99.48 96	96.60 268	99.10 131	99.06 143	98.71 39	99.83 157	95.58 280	99.78 117	99.62 64

baseline		98.96 68	99.02 61	98.76 169	99.38 138	97.26 203	98.49 124	99.50 87	98.86 104	99.19 121	99.06 143	98.23 73	99.69 256	98.71 81	99.76 135	99.33 198

VPNet		98.87 78	98.83 77	99.01 132	99.70 52	97.62 184	98.43 132	99.35 146	99.47 34	99.28 105	99.05 150	96.72 185	99.82 167	98.09 116	99.36 247	99.59 77

MVSTER		96.86 277	96.55 284	97.79 265	97.91 352	94.21 308	97.56 234	98.87 265	97.49 203	99.06 135	99.05 150	80.72 378	99.80 187	98.44 97	99.82 93	99.37 181

SD-MVS		98.40 152	98.68 97	97.54 290	98.96 229	97.99 147	97.88 192	99.36 141	98.20 149	99.63 50	99.04 152	98.76 35	95.33 408	96.56 226	99.74 139	99.31 204
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
FMVSNet5		96.01 304	95.20 321	98.41 219	97.53 371	96.10 249	98.74 90	99.50 87	97.22 238	98.03 263	99.04 152	69.80 398	99.88 84	97.27 162	99.71 154	99.25 216

IS-MVSNet		98.19 181	97.90 199	99.08 117	99.57 79	97.97 151	99.31 26	98.32 312	99.01 91	98.98 149	99.03 154	91.59 312	99.79 200	95.49 282	99.80 107	99.48 135

DVP-MVS++		98.90 75	98.70 94	99.51 43	98.43 322	99.15 48	99.43 11	99.32 159	98.17 152	99.26 111	99.02 155	98.18 80	99.88 84	97.07 177	99.45 236	99.49 125

test_one_0601					99.39 137	99.20 35		99.31 164	98.49 126	98.66 203	99.02 155	97.64 121

h-mvs33		97.77 214	97.33 238	99.10 113	99.21 174	97.84 163	98.35 140	98.57 301	99.11 73	98.58 216	99.02 155	88.65 335	99.96 12	98.11 114	96.34 386	99.49 125

SED-MVS		98.91 73	98.72 88	99.49 48	99.49 113	99.17 40	98.10 162	99.31 164	98.03 159	99.66 44	99.02 155	98.36 65	99.88 84	96.91 189	99.62 187	99.41 162

test_241102_TWO								99.30 172	98.03 159	99.26 111	99.02 155	97.51 135	99.88 84	96.91 189	99.60 194	99.66 55

DVP-MVS		98.77 94	98.52 119	99.52 39	99.50 106	99.21 29	98.02 174	98.84 274	97.97 162	99.08 133	99.02 155	97.61 124	99.88 84	96.99 183	99.63 184	99.48 135
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_THIRD									98.17 152	99.08 133	99.02 155	97.89 101	99.88 84	97.07 177	99.71 154	99.70 49

EI-MVSNet		98.40 152	98.51 120	98.04 253	99.10 202	94.73 294	97.20 264	98.87 265	98.97 95	99.06 135	99.02 155	96.00 216	99.80 187	98.58 88	99.82 93	99.60 71

CVMVSNet		96.25 299	97.21 244	93.38 385	99.10 202	80.56 412	97.20 264	98.19 319	96.94 252	99.00 147	99.02 155	89.50 328	99.80 187	96.36 241	99.59 198	99.78 31

LFMVS		97.20 257	96.72 272	98.64 180	98.72 271	96.95 222	98.93 76	94.14 388	99.74 9	98.78 187	99.01 164	84.45 362	99.73 240	97.44 154	99.27 262	99.25 216

v2v482		98.56 130	98.62 106	98.37 224	99.42 133	95.81 261	97.58 232	99.16 218	97.90 169	99.28 105	99.01 164	95.98 221	99.79 200	99.33 38	99.90 65	99.51 118

ACMMP		98.75 96	98.50 122	99.52 39	99.56 87	99.16 44	98.87 83	99.37 137	97.16 241	98.82 184	99.01 164	97.71 114	99.87 101	96.29 245	99.69 162	99.54 105
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
WB-MVS		98.52 141	98.55 115	98.43 217	99.65 63	95.59 264	98.52 116	98.77 285	99.65 17	99.52 63	99.00 167	94.34 270	99.93 42	98.65 85	98.83 312	99.76 37

DPE-MVS		98.59 128	98.26 161	99.57 17	99.27 162	99.15 48	97.01 273	99.39 131	97.67 183	99.44 76	98.99 168	97.53 132	99.89 75	95.40 284	99.68 167	99.66 55
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		98.57 129	98.23 165	99.60 11	99.69 54	99.35 13	97.16 268	99.38 133	94.87 324	98.97 153	98.99 168	98.01 93	99.88 84	97.29 161	99.70 159	99.58 83
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EI-MVSNet-UG-set		98.69 107	98.71 91	98.62 185	99.10 202	96.37 243	97.23 260	98.87 265	99.20 64	99.19 121	98.99 168	97.30 147	99.85 122	98.77 76	99.79 112	99.65 59

XVG-ACMP-BASELINE		98.56 130	98.34 150	99.22 97	99.54 96	98.59 93	97.71 214	99.46 106	97.25 229	98.98 149	98.99 168	97.54 130	99.84 140	95.88 263	99.74 139	99.23 221

APD-MVS_3200maxsize		98.84 82	98.61 110	99.53 34	99.19 181	99.27 23	98.49 124	99.33 157	98.64 113	99.03 145	98.98 172	97.89 101	99.85 122	96.54 230	99.42 240	99.46 144

XVG-OURS		98.53 138	98.34 150	99.11 111	99.50 106	98.82 78	95.97 329	99.50 87	97.30 224	99.05 140	98.98 172	99.35 12	99.32 366	95.72 273	99.68 167	99.18 233

v148		98.45 147	98.60 111	98.00 255	99.44 127	94.98 287	97.44 246	99.06 233	98.30 136	99.32 102	98.97 174	96.65 188	99.62 293	98.37 100	99.85 79	99.39 172

EI-MVSNet-Vis-set		98.68 112	98.70 94	98.63 184	99.09 205	96.40 242	97.23 260	98.86 270	99.20 64	99.18 125	98.97 174	97.29 149	99.85 122	98.72 80	99.78 117	99.64 60

CHOSEN 1792x2688		97.49 233	97.14 249	98.54 203	99.68 56	96.09 252	96.50 300	99.62 49	91.58 372	98.84 180	98.97 174	92.36 304	99.88 84	96.76 206	99.95 29	99.67 54

SR-MVS-dyc-post		98.81 87	98.55 115	99.57 17	99.20 178	99.38 9	98.48 127	99.30 172	98.64 113	98.95 157	98.96 177	97.49 139	99.86 109	96.56 226	99.39 243	99.45 148

RE-MVS-def			98.58 113		99.20 178	99.38 9	98.48 127	99.30 172	98.64 113	98.95 157	98.96 177	97.75 112		96.56 226	99.39 243	99.45 148

D2MVS		97.84 211	97.84 203	97.83 262	99.14 196	94.74 293	96.94 277	98.88 263	95.84 298	98.89 170	98.96 177	94.40 268	99.69 256	97.55 148	99.95 29	99.05 248

ACMM	96.08 12	98.91 73	98.73 86	99.48 50	99.55 91	99.14 53	98.07 166	99.37 137	97.62 187	99.04 142	98.96 177	98.84 30	99.79 200	97.43 155	99.65 179	99.49 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		98.08 189	97.92 197	98.57 195	98.96 229	96.79 227	97.90 190	99.18 211	96.41 277	98.46 229	98.95 181	95.93 224	99.60 300	96.51 232	98.98 303	99.31 204
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
YYNet1		97.60 225	97.67 213	97.39 302	99.04 217	93.04 341	95.27 357	98.38 311	97.25 229	98.92 166	98.95 181	95.48 239	99.73 240	96.99 183	98.74 316	99.41 162

MDA-MVSNet_test_wron		97.60 225	97.66 216	97.41 301	99.04 217	93.09 337	95.27 357	98.42 308	97.26 228	98.88 173	98.95 181	95.43 241	99.73 240	97.02 180	98.72 318	99.41 162

FMVSNet3		97.50 231	97.24 242	98.29 233	98.08 345	95.83 260	97.86 196	98.91 259	97.89 170	98.95 157	98.95 181	87.06 341	99.81 180	97.77 138	99.69 162	99.23 221

OPM-MVS		98.56 130	98.32 154	99.25 92	99.41 135	98.73 84	97.13 270	99.18 211	97.10 244	98.75 193	98.92 185	98.18 80	99.65 284	96.68 215	99.56 210	99.37 181
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ADS-MVSNet2		95.43 321	94.98 325	96.76 332	98.14 341	91.74 360	97.92 187	97.76 329	90.23 382	96.51 347	98.91 186	85.61 353	99.85 122	92.88 345	96.90 379	98.69 310

ADS-MVSNet		95.24 324	94.93 328	96.18 347	98.14 341	90.10 382	97.92 187	97.32 341	90.23 382	96.51 347	98.91 186	85.61 353	99.74 235	92.88 345	96.90 379	98.69 310

test_0402		98.76 95	98.71 91	98.93 143	99.56 87	98.14 129	98.45 131	99.34 152	99.28 56	98.95 157	98.91 186	98.34 69	99.79 200	95.63 277	99.91 59	98.86 285

test_241102_ONE					99.49 113	99.17 40		99.31 164	97.98 161	99.66 44	98.90 189	98.36 65	99.48 340

SF-MVS		98.53 138	98.27 160	99.32 79	99.31 154	98.75 80	98.19 151	99.41 125	96.77 262	98.83 181	98.90 189	97.80 109	99.82 167	95.68 276	99.52 222	99.38 179

MTAPA		98.88 77	98.64 103	99.61 9	99.67 60	99.36 12	98.43 132	99.20 203	98.83 108	98.89 170	98.90 189	96.98 168	99.92 51	97.16 168	99.70 159	99.56 94

test20.03		98.78 91	98.77 83	98.78 165	99.46 123	97.20 208	97.78 204	99.24 197	99.04 88	99.41 81	98.90 189	97.65 118	99.76 223	97.70 144	99.79 112	99.39 172

SteuartSystems-ACMMP		98.79 89	98.54 117	99.54 27	99.73 36	99.16 44	98.23 147	99.31 164	97.92 167	98.90 168	98.90 189	98.00 94	99.88 84	96.15 253	99.72 149	99.58 83
Skip Steuart: Steuart Systems R&D Blog.
N_pmnet		97.63 224	97.17 245	98.99 134	99.27 162	97.86 161	95.98 328	93.41 391	95.25 315	99.47 71	98.90 189	95.63 232	99.85 122	96.91 189	99.73 142	99.27 212

TSAR-MVS + MP.		98.63 121	98.49 126	99.06 125	99.64 68	97.90 158	98.51 121	98.94 252	96.96 250	99.24 116	98.89 195	97.83 104	99.81 180	96.88 196	99.49 232	99.48 135
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		98.66 116	98.37 146	99.55 24	99.53 99	99.18 39	98.23 147	99.49 94	97.01 249	98.69 199	98.88 196	98.00 94	99.89 75	95.87 266	99.59 198	99.58 83

TinyColmap		97.89 201	97.98 191	97.60 283	98.86 249	94.35 305	96.21 317	99.44 114	97.45 211	99.06 135	98.88 196	97.99 97	99.28 373	94.38 311	99.58 203	99.18 233

LS3D		98.63 121	98.38 145	99.36 63	97.25 382	99.38 9	99.12 56	99.32 159	99.21 62	98.44 231	98.88 196	97.31 146	99.80 187	96.58 220	99.34 251	98.92 274

Anonymous202405211		97.90 199	97.50 226	99.08 117	98.90 241	98.25 118	98.53 115	96.16 365	98.87 103	99.11 128	98.86 199	90.40 322	99.78 211	97.36 158	99.31 255	99.19 231

HPM-MVS_fast		99.01 60	98.82 78	99.57 17	99.71 45	99.35 13	99.00 68	99.50 87	97.33 220	98.94 164	98.86 199	98.75 36	99.82 167	97.53 151	99.71 154	99.56 94

CMPMVS	75.91 23	96.29 297	95.44 312	98.84 153	96.25 402	98.69 87	97.02 272	99.12 225	88.90 391	97.83 275	98.86 199	89.51 327	98.90 392	91.92 358	99.51 224	98.92 274
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SR-MVS		98.71 100	98.43 135	99.57 17	99.18 188	99.35 13	98.36 139	99.29 180	98.29 139	98.88 173	98.85 202	97.53 132	99.87 101	96.14 254	99.31 255	99.48 135

our_test_3		97.39 242	97.73 210	96.34 339	98.70 278	89.78 383	94.61 377	98.97 251	96.50 272	99.04 142	98.85 202	95.98 221	99.84 140	97.26 163	99.67 173	99.41 162

EPNet		96.14 301	95.44 312	98.25 235	90.76 413	95.50 270	97.92 187	94.65 380	98.97 95	92.98 396	98.85 202	89.12 330	99.87 101	95.99 259	99.68 167	99.39 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		97.64 223	97.49 227	98.08 248	99.14 196	95.12 284	96.70 292	99.05 236	93.77 347	98.62 208	98.83 205	93.23 287	99.75 230	98.33 104	99.76 135	99.36 187

PMMVS2		98.07 190	98.08 183	98.04 253	99.41 135	94.59 300	94.59 378	99.40 129	97.50 201	98.82 184	98.83 205	96.83 175	99.84 140	97.50 153	99.81 97	99.71 44

MDTV_nov1_ep13			95.22 320		97.06 388	83.20 407	97.74 211	96.16 365	94.37 336	96.99 323	98.83 205	83.95 367	99.53 325	93.90 322	97.95 357

Anonymous20231206		98.21 179	98.21 166	98.20 239	99.51 103	95.43 273	98.13 157	99.32 159	96.16 286	98.93 165	98.82 208	96.00 216	99.83 157	97.32 160	99.73 142	99.36 187

ACMP	95.32 15	98.41 150	98.09 180	99.36 63	99.51 103	98.79 79	97.68 217	99.38 133	95.76 300	98.81 186	98.82 208	98.36 65	99.82 167	94.75 295	99.77 123	99.48 135
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GeoE		99.05 57	98.99 65	99.25 92	99.44 127	98.35 114	98.73 94	99.56 70	98.42 129	98.91 167	98.81 210	98.94 25	99.91 60	98.35 101	99.73 142	99.49 125

VNet		98.42 149	98.30 155	98.79 162	98.79 264	97.29 200	98.23 147	98.66 295	99.31 52	98.85 178	98.80 211	94.80 259	99.78 211	98.13 113	99.13 284	99.31 204

tpmrst		95.07 326	95.46 310	93.91 378	97.11 385	84.36 405	97.62 226	96.96 351	94.98 320	96.35 352	98.80 211	85.46 355	99.59 304	95.60 278	96.23 388	97.79 369

ppachtmachnet_test		97.50 231	97.74 208	96.78 331	98.70 278	91.23 372	94.55 379	99.05 236	96.36 278	99.21 119	98.79 213	96.39 198	99.78 211	96.74 208	99.82 93	99.34 193

MVS_0304		97.44 238	97.01 254	98.72 176	96.42 399	96.74 231	97.20 264	91.97 398	98.46 128	98.30 240	98.79 213	92.74 300	99.91 60	99.30 40	99.94 36	99.52 115

miper_lstm_enhance		97.18 259	97.16 246	97.25 308	98.16 340	92.85 343	95.15 362	99.31 164	97.25 229	98.74 195	98.78 215	90.07 323	99.78 211	97.19 166	99.80 107	99.11 243

DeepPCF-MVS	96.93 5	98.32 164	98.01 188	99.23 96	98.39 327	98.97 67	95.03 364	99.18 211	96.88 255	99.33 96	98.78 215	98.16 84	99.28 373	96.74 208	99.62 187	99.44 152

patchmatchnet-post											98.77 217	84.37 363	99.85 122

APD-MVS		98.10 186	97.67 213	99.42 57	99.11 200	98.93 72	97.76 209	99.28 183	94.97 321	98.72 196	98.77 217	97.04 162	99.85 122	93.79 327	99.54 215	99.49 125
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DU-MVS		98.82 85	98.63 104	99.39 62	99.16 191	98.74 81	97.54 236	99.25 192	98.84 107	99.06 135	98.76 219	96.76 182	99.93 42	98.57 90	99.77 123	99.50 121

NR-MVSNet		98.95 69	98.82 78	99.36 63	99.16 191	98.72 86	99.22 41	99.20 203	99.10 80	99.72 33	98.76 219	96.38 200	99.86 109	98.00 124	99.82 93	99.50 121

eth_miper_zixun_eth		97.23 255	97.25 241	97.17 311	98.00 348	92.77 345	94.71 371	99.18 211	97.27 227	98.56 219	98.74 221	91.89 310	99.69 256	97.06 179	99.81 97	99.05 248

UniMVSNet (Re)		98.87 78	98.71 91	99.35 69	99.24 167	98.73 84	97.73 213	99.38 133	98.93 99	99.12 127	98.73 222	96.77 180	99.86 109	98.63 87	99.80 107	99.46 144

MG-MVS		96.77 281	96.61 280	97.26 307	98.31 331	93.06 338	95.93 334	98.12 322	96.45 276	97.92 266	98.73 222	93.77 284	99.39 356	91.19 373	99.04 293	99.33 198

c3_l		97.36 243	97.37 234	97.31 303	98.09 344	93.25 336	95.01 365	99.16 218	97.05 245	98.77 190	98.72 224	92.88 296	99.64 287	96.93 188	99.76 135	99.05 248

cl____		97.02 269	96.83 265	97.58 285	97.82 355	94.04 314	94.66 374	99.16 218	97.04 246	98.63 206	98.71 225	88.68 334	99.69 256	97.00 181	99.81 97	99.00 260

DIV-MVS_self_test		97.02 269	96.84 264	97.58 285	97.82 355	94.03 315	94.66 374	99.16 218	97.04 246	98.63 206	98.71 225	88.69 332	99.69 256	97.00 181	99.81 97	99.01 256

DELS-MVS		98.27 171	98.20 167	98.48 211	98.86 249	96.70 233	95.60 346	99.20 203	97.73 180	98.45 230	98.71 225	97.50 136	99.82 167	98.21 108	99.59 198	98.93 273
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
9.14			97.78 205		99.07 209		97.53 237	99.32 159	95.53 307	98.54 223	98.70 228	97.58 126	99.76 223	94.32 312	99.46 234

tpmvs		95.02 328	95.25 319	94.33 373	96.39 401	85.87 396	98.08 164	96.83 356	95.46 309	95.51 372	98.69 229	85.91 351	99.53 325	94.16 313	96.23 388	97.58 377

PatchmatchNet		95.58 317	95.67 303	95.30 366	97.34 380	87.32 393	97.65 223	96.65 358	95.30 314	97.07 318	98.69 229	84.77 359	99.75 230	94.97 291	98.64 327	98.83 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mPP-MVS		98.64 119	98.34 150	99.54 27	99.54 96	99.17 40	98.63 103	99.24 197	97.47 204	98.09 257	98.68 231	97.62 123	99.89 75	96.22 248	99.62 187	99.57 88

UnsupCasMVSNet_eth		97.89 201	97.60 221	98.75 171	99.31 154	97.17 211	97.62 226	99.35 146	98.72 111	98.76 192	98.68 231	92.57 303	99.74 235	97.76 142	95.60 394	99.34 193

SCA		96.41 295	96.66 278	95.67 357	98.24 335	88.35 388	95.85 339	96.88 355	96.11 287	97.67 285	98.67 233	93.10 291	99.85 122	94.16 313	99.22 270	98.81 292

Patchmatch-test		96.55 288	96.34 289	97.17 311	98.35 328	93.06 338	98.40 135	97.79 328	97.33 220	98.41 234	98.67 233	83.68 369	99.69 256	95.16 288	99.31 255	98.77 300

CDS-MVSNet		97.69 219	97.35 236	98.69 177	98.73 269	97.02 218	96.92 281	98.75 289	95.89 297	98.59 214	98.67 233	92.08 309	99.74 235	96.72 211	99.81 97	99.32 200
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MP-MVS		98.46 146	98.09 180	99.54 27	99.57 79	99.22 28	98.50 123	99.19 207	97.61 190	97.58 291	98.66 236	97.40 143	99.88 84	94.72 298	99.60 194	99.54 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	96.85 6	98.30 167	98.15 175	98.75 171	98.61 298	97.23 204	97.76 209	99.09 230	97.31 223	98.75 193	98.66 236	97.56 128	99.64 287	96.10 257	99.55 213	99.39 172
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MS-PatchMatch		97.68 220	97.75 207	97.45 298	98.23 337	93.78 327	97.29 256	98.84 274	96.10 288	98.64 205	98.65 238	96.04 213	99.36 359	96.84 200	99.14 282	99.20 226

pmmvs4		97.58 228	97.28 239	98.51 206	98.84 253	96.93 224	95.40 355	98.52 304	93.60 349	98.61 210	98.65 238	95.10 248	99.60 300	96.97 186	99.79 112	98.99 261

FPMVS		93.44 351	92.23 356	97.08 314	99.25 166	97.86 161	95.61 345	97.16 345	92.90 359	93.76 393	98.65 238	75.94 394	95.66 406	79.30 406	97.49 363	97.73 371

dp		93.47 350	93.59 343	93.13 387	96.64 395	81.62 411	97.66 221	96.42 363	92.80 361	96.11 356	98.64 241	78.55 391	99.59 304	93.31 338	92.18 405	98.16 349

EPMVS		93.72 347	93.27 346	95.09 369	96.04 404	87.76 391	98.13 157	85.01 410	94.69 327	96.92 325	98.64 241	78.47 392	99.31 367	95.04 289	96.46 385	98.20 347

XVS		98.72 99	98.45 132	99.53 34	99.46 123	99.21 29	98.65 101	99.34 152	98.62 117	97.54 295	98.63 243	97.50 136	99.83 157	96.79 202	99.53 219	99.56 94

CostFormer		93.97 343	93.78 340	94.51 372	97.53 371	85.83 398	97.98 181	95.96 369	89.29 390	94.99 378	98.63 243	78.63 389	99.62 293	94.54 301	96.50 384	98.09 353

MSLP-MVS++		98.02 192	98.14 177	97.64 281	98.58 305	95.19 281	97.48 242	99.23 199	97.47 204	97.90 268	98.62 245	97.04 162	98.81 394	97.55 148	99.41 241	98.94 272

Vis-MVSNet (Re-imp)		97.46 235	97.16 246	98.34 227	99.55 91	96.10 249	98.94 75	98.44 307	98.32 135	98.16 249	98.62 245	88.76 331	99.73 240	93.88 324	99.79 112	99.18 233

XVG-OURS-SEG-HR		98.49 143	98.28 157	99.14 107	99.49 113	98.83 76	96.54 297	99.48 96	97.32 222	99.11 128	98.61 247	99.33 13	99.30 369	96.23 247	98.38 335	99.28 211

ITE_SJBPF				98.87 150	99.22 172	98.48 103		99.35 146	97.50 201	98.28 243	98.60 248	97.64 121	99.35 362	93.86 325	99.27 262	98.79 298

UniMVSNet_NR-MVSNet		98.86 81	98.68 97	99.40 61	99.17 189	98.74 81	97.68 217	99.40 129	99.14 72	99.06 135	98.59 249	96.71 186	99.93 42	98.57 90	99.77 123	99.53 112

114514_t		96.50 291	95.77 298	98.69 177	99.48 120	97.43 194	97.84 198	99.55 74	81.42 404	96.51 347	98.58 250	95.53 235	99.67 268	93.41 337	99.58 203	98.98 262

HY-MVS	95.94 13	95.90 308	95.35 317	97.55 289	97.95 349	94.79 290	98.81 89	96.94 353	92.28 367	95.17 375	98.57 251	89.90 325	99.75 230	91.20 372	97.33 374	98.10 352

tpm		94.67 331	94.34 335	95.66 358	97.68 365	88.42 387	97.88 192	94.90 378	94.46 332	96.03 360	98.56 252	78.66 388	99.79 200	95.88 263	95.01 397	98.78 299

PC_three_1452									93.27 353	99.40 84	98.54 253	98.22 76	97.00 404	95.17 287	99.45 236	99.49 125

ACMMPR		98.70 104	98.42 137	99.54 27	99.52 101	99.14 53	98.52 116	99.31 164	97.47 204	98.56 219	98.54 253	97.75 112	99.88 84	96.57 222	99.59 198	99.58 83

new_pmnet		96.99 273	96.76 270	97.67 277	98.72 271	94.89 289	95.95 333	98.20 317	92.62 363	98.55 221	98.54 253	94.88 255	99.52 329	93.96 321	99.44 239	98.59 321

OPU-MVS				98.82 155	98.59 303	98.30 115	98.10 162				98.52 256	98.18 80	98.75 395	94.62 299	99.48 233	99.41 162

CS-MVS-test		99.13 49	99.09 56	99.26 89	99.13 198	98.97 67	99.31 26	99.88 11	99.44 38	98.16 249	98.51 257	98.64 44	99.93 42	98.91 66	99.85 79	98.88 282

region2R		98.69 107	98.40 139	99.54 27	99.53 99	99.17 40	98.52 116	99.31 164	97.46 209	98.44 231	98.51 257	97.83 104	99.88 84	96.46 235	99.58 203	99.58 83

TSAR-MVS + GP.		98.18 182	97.98 191	98.77 168	98.71 274	97.88 159	96.32 311	98.66 295	96.33 279	99.23 118	98.51 257	97.48 140	99.40 354	97.16 168	99.46 234	99.02 255

OMC-MVS		97.88 203	97.49 227	99.04 128	98.89 246	98.63 88	96.94 277	99.25 192	95.02 319	98.53 224	98.51 257	97.27 150	99.47 343	93.50 335	99.51 224	99.01 256

HFP-MVS		98.71 100	98.44 134	99.51 43	99.49 113	99.16 44	98.52 116	99.31 164	97.47 204	98.58 216	98.50 261	97.97 98	99.85 122	96.57 222	99.59 198	99.53 112

diffmvs		98.22 177	98.24 164	98.17 241	99.00 222	95.44 272	96.38 307	99.58 56	97.79 177	98.53 224	98.50 261	96.76 182	99.74 235	97.95 128	99.64 181	99.34 193
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WR-MVS		98.40 152	98.19 169	99.03 129	99.00 222	97.65 181	96.85 283	98.94 252	98.57 122	98.89 170	98.50 261	95.60 233	99.85 122	97.54 150	99.85 79	99.59 77

Test_1112_low_res		96.99 273	96.55 284	98.31 231	99.35 149	95.47 271	95.84 340	99.53 81	91.51 374	96.80 336	98.48 264	91.36 314	99.83 157	96.58 220	99.53 219	99.62 64

CS-MVS		99.13 49	99.10 55	99.24 94	99.06 213	99.15 48	99.36 18	99.88 11	99.36 48	98.21 246	98.46 265	98.68 42	99.93 42	99.03 60	99.85 79	98.64 315

miper_ehance_all_eth		97.06 266	97.03 252	97.16 313	97.83 354	93.06 338	94.66 374	99.09 230	95.99 293	98.69 199	98.45 266	92.73 301	99.61 299	96.79 202	99.03 294	98.82 288

PHI-MVS		98.29 170	97.95 193	99.34 72	98.44 321	99.16 44	98.12 159	99.38 133	96.01 292	98.06 259	98.43 267	97.80 109	99.67 268	95.69 275	99.58 203	99.20 226

tpm cat1		93.29 353	93.13 350	93.75 380	97.39 379	84.74 401	97.39 247	97.65 333	83.39 402	94.16 386	98.41 268	82.86 373	99.39 356	91.56 366	95.35 396	97.14 384

CP-MVS		98.70 104	98.42 137	99.52 39	99.36 145	99.12 58	98.72 95	99.36 141	97.54 199	98.30 240	98.40 269	97.86 103	99.89 75	96.53 231	99.72 149	99.56 94

ZNCC-MVS		98.68 112	98.40 139	99.54 27	99.57 79	99.21 29	98.46 129	99.29 180	97.28 226	98.11 255	98.39 270	98.00 94	99.87 101	96.86 199	99.64 181	99.55 101

GST-MVS		98.61 125	98.30 155	99.52 39	99.51 103	99.20 35	98.26 145	99.25 192	97.44 212	98.67 201	98.39 270	97.68 115	99.85 122	96.00 258	99.51 224	99.52 115

HPM-MVS		98.79 89	98.53 118	99.59 15	99.65 63	99.29 20	99.16 50	99.43 120	96.74 263	98.61 210	98.38 272	98.62 47	99.87 101	96.47 234	99.67 173	99.59 77
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				98.09 245	98.93 233	95.40 274		98.80 281	90.08 386	97.45 304	98.37 273	95.26 244	99.70 252	93.58 332	98.95 306	99.17 237

CPTT-MVS		97.84 211	97.36 235	99.27 87	99.31 154	98.46 104	98.29 142	99.27 186	94.90 323	97.83 275	98.37 273	94.90 252	99.84 140	93.85 326	99.54 215	99.51 118

EC-MVSNet		99.09 54	99.05 60	99.20 98	99.28 160	98.93 72	99.24 40	99.84 18	99.08 85	98.12 254	98.37 273	98.72 38	99.90 65	99.05 58	99.77 123	98.77 300

OpenMVS_ROB	95.38 14	95.84 310	95.18 322	97.81 264	98.41 326	97.15 213	97.37 249	98.62 299	83.86 400	98.65 204	98.37 273	94.29 272	99.68 265	88.41 386	98.62 330	96.60 391

tttt0517		95.64 316	94.98 325	97.64 281	99.36 145	93.81 326	98.72 95	90.47 402	98.08 158	98.67 201	98.34 277	73.88 396	99.92 51	97.77 138	99.51 224	99.20 226

旧先验1					98.82 258	97.45 192		98.76 286			98.34 277	95.50 238			99.01 298	99.23 221

CNVR-MVS		98.17 184	97.87 201	99.07 119	98.67 288	98.24 119	97.01 273	98.93 254	97.25 229	97.62 287	98.34 277	97.27 150	99.57 312	96.42 237	99.33 252	99.39 172

HyFIR lowres test		97.19 258	96.60 282	98.96 138	99.62 74	97.28 201	95.17 360	99.50 87	94.21 339	99.01 146	98.32 280	86.61 344	99.99 2	97.10 175	99.84 83	99.60 71

UnsupCasMVSNet_bld		97.30 248	96.92 258	98.45 215	99.28 160	96.78 230	96.20 318	99.27 186	95.42 310	98.28 243	98.30 281	93.16 289	99.71 248	94.99 290	97.37 370	98.87 284

MSDG		97.71 218	97.52 225	98.28 234	98.91 240	96.82 226	94.42 381	99.37 137	97.65 185	98.37 239	98.29 282	97.40 143	99.33 365	94.09 318	99.22 270	98.68 313

MVS_111021_HR		98.25 175	98.08 183	98.75 171	99.09 205	97.46 191	95.97 329	99.27 186	97.60 191	97.99 264	98.25 283	98.15 86	99.38 358	96.87 197	99.57 207	99.42 159

CANet_DTU		97.26 251	97.06 251	97.84 261	97.57 366	94.65 298	96.19 319	98.79 282	97.23 235	95.14 376	98.24 284	93.22 288	99.84 140	97.34 159	99.84 83	99.04 252

MVS_111021_LR		98.30 167	98.12 178	98.83 154	99.16 191	98.03 145	96.09 325	99.30 172	97.58 192	98.10 256	98.24 284	98.25 71	99.34 363	96.69 214	99.65 179	99.12 242

tpm2		93.09 355	92.58 354	94.62 371	97.56 367	86.53 395	97.66 221	95.79 372	86.15 397	94.07 389	98.23 286	75.95 393	99.53 325	90.91 377	96.86 382	97.81 366

CANet		97.87 204	97.76 206	98.19 240	97.75 357	95.51 269	96.76 288	99.05 236	97.74 179	96.93 324	98.21 287	95.59 234	99.89 75	97.86 134	99.93 41	99.19 231

LF4IMVS		97.90 199	97.69 212	98.52 205	99.17 189	97.66 180	97.19 267	99.47 104	96.31 281	97.85 274	98.20 288	96.71 186	99.52 329	94.62 299	99.72 149	98.38 339

CL-MVSNet_self_test		97.44 238	97.22 243	98.08 248	98.57 307	95.78 262	94.30 384	98.79 282	96.58 270	98.60 212	98.19 289	94.74 262	99.64 287	96.41 238	98.84 311	98.82 288

cl22		95.79 311	95.39 315	96.98 319	96.77 393	92.79 344	94.40 382	98.53 303	94.59 329	97.89 269	98.17 290	82.82 374	99.24 375	96.37 239	99.03 294	98.92 274

MVSFormer		98.26 173	98.43 135	97.77 267	98.88 247	93.89 324	99.39 16	99.56 70	99.11 73	98.16 249	98.13 291	93.81 282	99.97 5	99.26 44	99.57 207	99.43 156

jason		97.45 237	97.35 236	97.76 270	99.24 167	93.93 320	95.86 337	98.42 308	94.24 338	98.50 226	98.13 291	94.82 256	99.91 60	97.22 165	99.73 142	99.43 156
jason: jason.
ZD-MVS					99.01 221	98.84 75		99.07 232	94.10 342	98.05 261	98.12 293	96.36 202	99.86 109	92.70 352	99.19 276

test222					98.92 237	96.93 224	95.54 347	98.78 284	85.72 398	96.86 333	98.11 294	94.43 266			99.10 289	99.23 221

新几何1				98.91 146	98.94 231	97.76 173		98.76 286	87.58 395	96.75 338	98.10 295	94.80 259	99.78 211	92.73 351	99.00 299	99.20 226

原ACMM1				98.35 226	98.90 241	96.25 247		98.83 278	92.48 364	96.07 358	98.10 295	95.39 242	99.71 248	92.61 354	98.99 301	99.08 244

EPNet_dtu		94.93 329	94.78 330	95.38 365	93.58 409	87.68 392	96.78 286	95.69 375	97.35 219	89.14 406	98.09 297	88.15 339	99.49 337	94.95 292	99.30 258	98.98 262
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		95.03 327	94.40 333	96.93 321	97.70 362	92.53 349	95.08 363	97.71 331	88.57 392	97.71 282	98.08 298	79.39 385	99.82 167	96.19 250	99.11 288	98.43 334

DP-MVS Recon		97.33 246	96.92 258	98.57 195	99.09 205	97.99 147	96.79 285	99.35 146	93.18 354	97.71 282	98.07 299	95.00 251	99.31 367	93.97 320	99.13 284	98.42 336

test_vis1_rt		97.75 215	97.72 211	97.83 262	98.81 260	96.35 244	97.30 255	99.69 38	94.61 328	97.87 271	98.05 300	96.26 205	98.32 399	98.74 78	98.18 343	98.82 288

CSCG		98.68 112	98.50 122	99.20 98	99.45 126	98.63 88	98.56 111	99.57 63	97.87 171	98.85 178	98.04 301	97.66 117	99.84 140	96.72 211	99.81 97	99.13 241

F-COLMAP		97.30 248	96.68 275	99.14 107	99.19 181	98.39 107	97.27 259	99.30 172	92.93 358	96.62 342	98.00 302	95.73 230	99.68 265	92.62 353	98.46 334	99.35 191

Effi-MVS+-dtu		98.26 173	97.90 199	99.35 69	98.02 347	99.49 6	98.02 174	99.16 218	98.29 139	97.64 286	97.99 303	96.44 197	99.95 23	96.66 216	98.93 308	98.60 318

hse-mvs2		97.46 235	97.07 250	98.64 180	98.73 269	97.33 198	97.45 245	97.64 335	99.11 73	98.58 216	97.98 304	88.65 335	99.79 200	98.11 114	97.39 369	98.81 292

HQP_MVS		97.99 197	97.67 213	98.93 143	99.19 181	97.65 181	97.77 206	99.27 186	98.20 149	97.79 278	97.98 304	94.90 252	99.70 252	94.42 307	99.51 224	99.45 148

plane_prior4											97.98 304

BH-RMVSNet		96.83 278	96.58 283	97.58 285	98.47 316	94.05 312	96.67 293	97.36 338	96.70 266	97.87 271	97.98 304	95.14 247	99.44 349	90.47 380	98.58 332	99.25 216

AUN-MVS		96.24 300	95.45 311	98.60 190	98.70 278	97.22 206	97.38 248	97.65 333	95.95 295	95.53 371	97.96 308	82.11 377	99.79 200	96.31 243	97.44 366	98.80 297

NCCC		97.86 205	97.47 230	99.05 126	98.61 298	98.07 140	96.98 275	98.90 260	97.63 186	97.04 320	97.93 309	95.99 220	99.66 279	95.31 285	98.82 314	99.43 156

sss		97.21 256	96.93 256	98.06 250	98.83 255	95.22 280	96.75 289	98.48 306	94.49 330	97.27 312	97.90 310	92.77 299	99.80 187	96.57 222	99.32 253	99.16 240

test_yl		96.69 282	96.29 291	97.90 257	98.28 332	95.24 278	97.29 256	97.36 338	98.21 145	98.17 247	97.86 311	86.27 346	99.55 318	94.87 293	98.32 336	98.89 279

DCV-MVSNet		96.69 282	96.29 291	97.90 257	98.28 332	95.24 278	97.29 256	97.36 338	98.21 145	98.17 247	97.86 311	86.27 346	99.55 318	94.87 293	98.32 336	98.89 279

CDPH-MVS		97.26 251	96.66 278	99.07 119	99.00 222	98.15 127	96.03 327	99.01 247	91.21 378	97.79 278	97.85 313	96.89 171	99.69 256	92.75 350	99.38 246	99.39 172

HPM-MVS++		98.10 186	97.64 218	99.48 50	99.09 205	99.13 56	97.52 238	98.75 289	97.46 209	96.90 330	97.83 314	96.01 215	99.84 140	95.82 270	99.35 249	99.46 144

PatchMatch-RL		97.24 254	96.78 269	98.61 188	99.03 220	97.83 164	96.36 308	99.06 233	93.49 352	97.36 311	97.78 315	95.75 229	99.49 337	93.44 336	98.77 315	98.52 325

TAPA-MVS	96.21 11	96.63 286	95.95 296	98.65 179	98.93 233	98.09 134	96.93 279	99.28 183	83.58 401	98.13 253	97.78 315	96.13 210	99.40 354	93.52 333	99.29 260	98.45 330
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		95.96 307	95.44 312	97.52 292	98.51 314	93.99 318	98.39 136	96.09 367	98.21 145	98.40 238	97.76 317	86.88 342	99.63 290	95.42 283	89.27 406	98.95 268

WTY-MVS		96.67 284	96.27 293	97.87 260	98.81 260	94.61 299	96.77 287	97.92 327	94.94 322	97.12 315	97.74 318	91.11 316	99.82 167	93.89 323	98.15 347	99.18 233

test_method		79.78 374	79.50 377	80.62 390	80.21 415	45.76 418	70.82 406	98.41 310	31.08 410	80.89 410	97.71 319	84.85 358	97.37 403	91.51 367	80.03 407	98.75 303

MSP-MVS		98.40 152	98.00 189	99.61 9	99.57 79	99.25 25	98.57 110	99.35 146	97.55 198	99.31 104	97.71 319	94.61 263	99.88 84	96.14 254	99.19 276	99.70 49
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
MCST-MVS		98.00 194	97.63 219	99.10 113	99.24 167	98.17 126	96.89 282	98.73 292	95.66 301	97.92 266	97.70 321	97.17 156	99.66 279	96.18 252	99.23 269	99.47 142

AdaColmap		97.14 262	96.71 273	98.46 213	98.34 329	97.80 171	96.95 276	98.93 254	95.58 305	96.92 325	97.66 322	95.87 226	99.53 325	90.97 375	99.14 282	98.04 355

thisisatest0530		95.27 323	94.45 332	97.74 273	99.19 181	94.37 304	97.86 196	90.20 403	97.17 240	98.22 245	97.65 323	73.53 397	99.90 65	96.90 194	99.35 249	98.95 268

testgi		98.32 164	98.39 142	98.13 244	99.57 79	95.54 267	97.78 204	99.49 94	97.37 217	99.19 121	97.65 323	98.96 24	99.49 337	96.50 233	98.99 301	99.34 193

test_prior2							95.74 342		96.48 274	96.11 356	97.63 325	95.92 225		94.16 313	99.20 273

tt0805		98.69 107	98.62 106	98.90 149	99.75 33	99.30 18	99.15 52	96.97 350	98.86 104	98.87 177	97.62 326	98.63 46	98.96 388	99.41 36	98.29 339	98.45 330

cdsmvs_eth3d_5k		24.66 378	32.88 381	0.00 396	0.00 419	0.00 421	0.00 407	99.10 228	0.00 414	0.00 415	97.58 327	99.21 16	0.00 415	0.00 414	0.00 413	0.00 411

lupinMVS		97.06 266	96.86 262	97.65 279	98.88 247	93.89 324	95.48 351	97.97 325	93.53 350	98.16 249	97.58 327	93.81 282	99.91 60	96.77 205	99.57 207	99.17 237

TEST9					98.71 274	98.08 138	95.96 331	99.03 241	91.40 375	95.85 361	97.53 329	96.52 193	99.76 223

train_agg		97.10 263	96.45 287	99.07 119	98.71 274	98.08 138	95.96 331	99.03 241	91.64 370	95.85 361	97.53 329	96.47 195	99.76 223	93.67 329	99.16 279	99.36 187

Fast-Effi-MVS+-dtu		98.27 171	98.09 180	98.81 157	98.43 322	98.11 131	97.61 228	99.50 87	98.64 113	97.39 309	97.52 331	98.12 87	99.95 23	96.90 194	98.71 320	98.38 339

test_8					98.67 288	98.01 146	95.91 336	99.02 244	91.64 370	95.79 363	97.50 332	96.47 195	99.76 223

1112_ss		97.29 250	96.86 262	98.58 192	99.34 151	96.32 245	96.75 289	99.58 56	93.14 355	96.89 331	97.48 333	92.11 308	99.86 109	96.91 189	99.54 215	99.57 88

ab-mvs-re		8.12 382	10.83 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	97.48 333	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Effi-MVS+		98.02 192	97.82 204	98.62 185	98.53 312	97.19 209	97.33 252	99.68 43	97.30 224	96.68 339	97.46 335	98.56 54	99.80 187	96.63 217	98.20 342	98.86 285

PCF-MVS	92.86 18	94.36 334	93.00 351	98.42 218	98.70 278	97.56 186	93.16 396	99.11 227	79.59 405	97.55 294	97.43 336	92.19 306	99.73 240	79.85 405	99.45 236	97.97 360
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GA-MVS		95.86 309	95.32 318	97.49 295	98.60 300	94.15 311	93.83 391	97.93 326	95.49 308	96.68 339	97.42 337	83.21 370	99.30 369	96.22 248	98.55 333	99.01 256

CNLPA		97.17 260	96.71 273	98.55 200	98.56 308	98.05 144	96.33 310	98.93 254	96.91 254	97.06 319	97.39 338	94.38 269	99.45 347	91.66 362	99.18 278	98.14 350

PLC	94.65 16	96.51 289	95.73 300	98.85 152	98.75 267	97.91 157	96.42 305	99.06 233	90.94 381	95.59 364	97.38 339	94.41 267	99.59 304	90.93 376	98.04 356	99.05 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 278	96.75 271	97.08 314	98.74 268	93.33 335	96.71 291	98.26 314	96.72 264	98.44 231	97.37 340	95.20 245	99.47 343	91.89 359	97.43 367	98.44 332

PVSNet_Blended		96.88 276	96.68 275	97.47 297	98.92 237	93.77 328	94.71 371	99.43 120	90.98 380	97.62 287	97.36 341	96.82 176	99.67 268	94.73 296	99.56 210	98.98 262

miper_enhance_ethall		96.01 304	95.74 299	96.81 329	96.41 400	92.27 356	93.69 393	98.89 262	91.14 379	98.30 240	97.35 342	90.58 320	99.58 310	96.31 243	99.03 294	98.60 318

DPM-MVS		96.32 296	95.59 306	98.51 206	98.76 265	97.21 207	94.54 380	98.26 314	91.94 369	96.37 351	97.25 343	93.06 293	99.43 350	91.42 368	98.74 316	98.89 279

E-PMN		94.17 339	94.37 334	93.58 382	96.86 390	85.71 399	90.11 404	97.07 347	98.17 152	97.82 277	97.19 344	84.62 361	98.94 389	89.77 382	97.68 360	96.09 398

CLD-MVS		97.49 233	97.16 246	98.48 211	99.07 209	97.03 217	94.71 371	99.21 201	94.46 332	98.06 259	97.16 345	97.57 127	99.48 340	94.46 304	99.78 117	98.95 268
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 280x420		95.51 320	95.47 309	95.65 359	98.25 334	88.27 389	93.25 395	98.88 263	93.53 350	94.65 382	97.15 346	86.17 348	99.93 42	97.41 156	99.93 41	98.73 305

xiu_mvs_v1_base_debu		97.86 205	98.17 171	96.92 322	98.98 226	93.91 321	96.45 302	99.17 215	97.85 173	98.41 234	97.14 347	98.47 57	99.92 51	98.02 121	99.05 290	96.92 385

xiu_mvs_v1_base		97.86 205	98.17 171	96.92 322	98.98 226	93.91 321	96.45 302	99.17 215	97.85 173	98.41 234	97.14 347	98.47 57	99.92 51	98.02 121	99.05 290	96.92 385

xiu_mvs_v1_base_debi		97.86 205	98.17 171	96.92 322	98.98 226	93.91 321	96.45 302	99.17 215	97.85 173	98.41 234	97.14 347	98.47 57	99.92 51	98.02 121	99.05 290	96.92 385

NP-MVS					98.84 253	97.39 196					96.84 350

HQP-MVS		97.00 272	96.49 286	98.55 200	98.67 288	96.79 227	96.29 313	99.04 239	96.05 289	95.55 367	96.84 350	93.84 280	99.54 323	92.82 347	99.26 265	99.32 200

API-MVS		97.04 268	96.91 260	97.42 300	97.88 353	98.23 123	98.18 152	98.50 305	97.57 193	97.39 309	96.75 352	96.77 180	99.15 382	90.16 381	99.02 297	94.88 402

1314		95.74 312	95.60 305	96.17 348	97.53 371	92.75 346	98.07 166	98.31 313	91.22 377	94.25 385	96.68 353	95.53 235	99.03 384	91.64 364	97.18 376	96.74 389

TR-MVS		95.55 318	95.12 323	96.86 328	97.54 369	93.94 319	96.49 301	96.53 362	94.36 337	97.03 322	96.61 354	94.26 273	99.16 381	86.91 393	96.31 387	97.47 380

Fast-Effi-MVS+		97.67 221	97.38 233	98.57 195	98.71 274	97.43 194	97.23 260	99.45 110	94.82 325	96.13 355	96.51 355	98.52 56	99.91 60	96.19 250	98.83 312	98.37 341

xiu_mvs_v2_base		97.16 261	97.49 227	96.17 348	98.54 310	92.46 350	95.45 352	98.84 274	97.25 229	97.48 301	96.49 356	98.31 70	99.90 65	96.34 242	98.68 325	96.15 396

MVS		93.19 354	92.09 358	96.50 336	96.91 389	94.03 315	98.07 166	98.06 324	68.01 407	94.56 384	96.48 357	95.96 223	99.30 369	83.84 398	96.89 381	96.17 394

PAPM_NR		96.82 280	96.32 290	98.30 232	99.07 209	96.69 234	97.48 242	98.76 286	95.81 299	96.61 343	96.47 358	94.12 277	99.17 380	90.82 379	97.78 358	99.06 247

KD-MVS_2432*1600		92.87 359	91.99 361	95.51 362	91.37 411	89.27 384	94.07 386	98.14 320	95.42 310	97.25 313	96.44 359	67.86 400	99.24 375	91.28 370	96.08 391	98.02 356

miper_refine_blended		92.87 359	91.99 361	95.51 362	91.37 411	89.27 384	94.07 386	98.14 320	95.42 310	97.25 313	96.44 359	67.86 400	99.24 375	91.28 370	96.08 391	98.02 356

PVSNet	93.40 17	95.67 314	95.70 301	95.57 360	98.83 255	88.57 386	92.50 398	97.72 330	92.69 362	96.49 350	96.44 359	93.72 285	99.43 350	93.61 330	99.28 261	98.71 306

EMVS		93.83 345	94.02 337	93.23 386	96.83 392	84.96 400	89.77 405	96.32 364	97.92 167	97.43 306	96.36 362	86.17 348	98.93 390	87.68 389	97.73 359	95.81 399

MAR-MVS		96.47 293	95.70 301	98.79 162	97.92 351	99.12 58	98.28 143	98.60 300	92.16 368	95.54 370	96.17 363	94.77 261	99.52 329	89.62 383	98.23 340	97.72 372
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
PAPM		91.88 371	90.34 374	96.51 335	98.06 346	92.56 348	92.44 399	97.17 344	86.35 396	90.38 403	96.01 364	86.61 344	99.21 378	70.65 409	95.43 395	97.75 370

PS-MVSNAJ		97.08 265	97.39 232	96.16 350	98.56 308	92.46 350	95.24 359	98.85 273	97.25 229	97.49 300	95.99 365	98.07 88	99.90 65	96.37 239	98.67 326	96.12 397

dmvs_re		95.98 306	95.39 315	97.74 273	98.86 249	97.45 192	98.37 138	95.69 375	97.95 164	96.56 344	95.95 366	90.70 319	97.68 402	88.32 387	96.13 390	98.11 351

baseline2		93.73 346	92.83 352	96.42 338	97.70 362	91.28 369	96.84 284	89.77 404	93.96 346	92.44 399	95.93 367	79.14 386	99.77 217	92.94 343	96.76 383	98.21 346

alignmvs		97.35 244	96.88 261	98.78 165	98.54 310	98.09 134	97.71 214	97.69 332	99.20 64	97.59 290	95.90 368	88.12 340	99.55 318	98.18 110	98.96 305	98.70 309

ET-MVSNet_ETH3D		94.30 337	93.21 347	97.58 285	98.14 341	94.47 302	94.78 370	93.24 393	94.72 326	89.56 404	95.87 369	78.57 390	99.81 180	96.91 189	97.11 378	98.46 327

thisisatest0515		94.12 341	93.16 348	96.97 320	98.60 300	92.90 342	93.77 392	90.61 401	94.10 342	96.91 327	95.87 369	74.99 395	99.80 187	94.52 302	99.12 287	98.20 347

UWE-MVS		92.38 364	91.76 367	94.21 375	97.16 384	84.65 402	95.42 354	88.45 406	95.96 294	96.17 354	95.84 371	66.36 404	99.71 248	91.87 360	98.64 327	98.28 344

BH-w/o		95.13 325	94.89 329	95.86 352	98.20 338	91.31 367	95.65 344	97.37 337	93.64 348	96.52 346	95.70 372	93.04 294	99.02 385	88.10 388	95.82 393	97.24 383

PMMVS		96.51 289	95.98 295	98.09 245	97.53 371	95.84 259	94.92 367	98.84 274	91.58 372	96.05 359	95.58 373	95.68 231	99.66 279	95.59 279	98.09 350	98.76 302

EIA-MVS		98.00 194	97.74 208	98.80 159	98.72 271	98.09 134	98.05 169	99.60 53	97.39 215	96.63 341	95.55 374	97.68 115	99.80 187	96.73 210	99.27 262	98.52 325

ETV-MVS		98.03 191	97.86 202	98.56 199	98.69 283	98.07 140	97.51 240	99.50 87	98.10 157	97.50 299	95.51 375	98.41 62	99.88 84	96.27 246	99.24 267	97.71 373

MGCFI-Net		98.34 160	98.28 157	98.51 206	98.47 316	97.59 185	98.96 72	99.48 96	99.18 70	97.40 307	95.50 376	98.66 43	99.50 334	98.18 110	98.71 320	98.44 332

testing3		93.51 349	92.09 358	97.75 271	98.60 300	94.40 303	97.32 253	95.26 377	97.56 196	96.79 337	95.50 376	53.57 414	99.77 217	95.26 286	98.97 304	99.08 244

PAPR		95.29 322	94.47 331	97.75 271	97.50 376	95.14 283	94.89 368	98.71 293	91.39 376	95.35 374	95.48 378	94.57 264	99.14 383	84.95 396	97.37 370	98.97 265

sasdasda		98.34 160	98.26 161	98.58 192	98.46 318	97.82 167	98.96 72	99.46 106	99.19 68	97.46 302	95.46 379	98.59 50	99.46 345	98.08 117	98.71 320	98.46 327

canonicalmvs		98.34 160	98.26 161	98.58 192	98.46 318	97.82 167	98.96 72	99.46 106	99.19 68	97.46 302	95.46 379	98.59 50	99.46 345	98.08 117	98.71 320	98.46 327

MVE	83.40 22	92.50 362	91.92 364	94.25 374	98.83 255	91.64 361	92.71 397	83.52 411	95.92 296	86.46 409	95.46 379	95.20 245	95.40 407	80.51 404	98.64 327	95.73 400
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WB-MVSnew		95.73 313	95.57 307	96.23 345	96.70 394	90.70 379	96.07 326	93.86 389	95.60 304	97.04 320	95.45 382	96.00 216	99.55 318	91.04 374	98.31 338	98.43 334

test-LLR		93.90 344	93.85 338	94.04 376	96.53 396	84.62 403	94.05 388	92.39 395	96.17 284	94.12 387	95.07 383	82.30 375	99.67 268	95.87 266	98.18 343	97.82 364

test-mter		92.33 366	91.76 367	94.04 376	96.53 396	84.62 403	94.05 388	92.39 395	94.00 345	94.12 387	95.07 383	65.63 407	99.67 268	95.87 266	98.18 343	97.82 364

thres600view7		94.45 333	93.83 339	96.29 341	99.06 213	91.53 362	97.99 180	94.24 386	98.34 132	97.44 305	95.01 385	79.84 381	99.67 268	84.33 397	98.23 340	97.66 374

gm-plane-assit					94.83 407	81.97 410			88.07 394		94.99 386		99.60 300	91.76 361

thres100view900		94.19 338	93.67 342	95.75 356	99.06 213	91.35 366	98.03 172	94.24 386	98.33 133	97.40 307	94.98 387	79.84 381	99.62 293	83.05 399	98.08 351	96.29 392

cascas		94.79 330	94.33 336	96.15 351	96.02 405	92.36 354	92.34 400	99.26 191	85.34 399	95.08 377	94.96 388	92.96 295	98.53 397	94.41 310	98.59 331	97.56 378

TESTMET0.1,1		92.19 368	91.77 366	93.46 383	96.48 398	82.80 408	94.05 388	91.52 400	94.45 334	94.00 390	94.88 389	66.65 403	99.56 315	95.78 271	98.11 349	98.02 356

test0.0.03 1		94.51 332	93.69 341	96.99 318	96.05 403	93.61 333	94.97 366	93.49 390	96.17 284	97.57 293	94.88 389	82.30 375	99.01 387	93.60 331	94.17 401	98.37 341

DeepMVS_CX				93.44 384	98.24 335	94.21 308		94.34 383	64.28 408	91.34 402	94.87 391	89.45 329	92.77 409	77.54 407	93.14 402	93.35 404

dongtai		76.24 376	75.95 379	77.12 392	92.39 410	67.91 416	90.16 403	59.44 417	82.04 403	89.42 405	94.67 392	49.68 415	81.74 410	48.06 410	77.66 408	81.72 406

IB-MVS	91.63 19	92.24 367	90.90 371	96.27 342	97.22 383	91.24 371	94.36 383	93.33 392	92.37 365	92.24 400	94.58 393	66.20 406	99.89 75	93.16 341	94.63 399	97.66 374
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
tfpn200view9		94.03 342	93.44 344	95.78 355	98.93 233	91.44 364	97.60 229	94.29 384	97.94 165	97.10 316	94.31 394	79.67 383	99.62 293	83.05 399	98.08 351	96.29 392

thres400		94.14 340	93.44 344	96.24 344	98.93 233	91.44 364	97.60 229	94.29 384	97.94 165	97.10 316	94.31 394	79.67 383	99.62 293	83.05 399	98.08 351	97.66 374

testing11		93.08 356	92.02 360	96.26 343	97.56 367	90.83 377	96.32 311	95.70 373	96.47 275	92.66 398	93.73 396	64.36 409	99.59 304	93.77 328	97.57 361	98.37 341

thres200		93.72 347	93.14 349	95.46 364	98.66 293	91.29 368	96.61 296	94.63 381	97.39 215	96.83 334	93.71 397	79.88 380	99.56 315	82.40 402	98.13 348	95.54 401

dmvs_testset		92.94 358	92.21 357	95.13 367	98.59 303	90.99 374	97.65 223	92.09 397	96.95 251	94.00 390	93.55 398	92.34 305	96.97 405	72.20 408	92.52 403	97.43 381

testing91		93.32 352	92.27 355	96.47 337	97.54 369	91.25 370	96.17 322	96.76 357	97.18 239	93.65 394	93.50 399	65.11 408	99.63 290	93.04 342	97.45 365	98.53 324

testing99		93.04 357	91.98 363	96.23 345	97.53 371	90.70 379	96.35 309	95.94 370	96.87 256	93.41 395	93.43 400	63.84 410	99.59 304	93.24 340	97.19 375	98.40 337

PVSNet_0	89.98 21	91.15 372	90.30 375	93.70 381	97.72 358	84.34 406	90.24 402	97.42 336	90.20 385	93.79 392	93.09 401	90.90 318	98.89 393	86.57 394	72.76 409	97.87 363

testing222		91.96 369	90.37 373	96.72 333	97.47 377	92.59 347	96.11 324	94.76 379	96.83 258	92.90 397	92.87 402	57.92 412	99.55 318	86.93 392	97.52 362	98.00 359

tmp_tt		78.77 375	78.73 378	78.90 391	58.45 416	74.76 415	94.20 385	78.26 414	39.16 409	86.71 408	92.82 403	80.50 379	75.19 411	86.16 395	92.29 404	86.74 405

ETVMVS		92.60 361	91.08 370	97.18 309	97.70 362	93.65 332	96.54 297	95.70 373	96.51 271	94.68 381	92.39 404	61.80 411	99.50 334	86.97 391	97.41 368	98.40 337

Syy-MVS		96.04 303	95.56 308	97.49 295	97.10 386	94.48 301	96.18 320	96.58 360	95.65 302	94.77 379	92.29 405	91.27 315	99.36 359	98.17 112	98.05 354	98.63 316

myMVS_eth3d		91.92 370	90.45 372	96.30 340	97.10 386	90.90 375	96.18 320	96.58 360	95.65 302	94.77 379	92.29 405	53.88 413	99.36 359	89.59 384	98.05 354	98.63 316

GG-mvs-BLEND				94.76 370	94.54 408	92.13 358	99.31 26	80.47 413		88.73 407	91.01 407	67.59 402	98.16 401	82.30 403	94.53 400	93.98 403

kuosan		69.30 377	68.95 380	70.34 393	87.68 414	65.00 417	91.11 401	59.90 416	69.02 406	74.46 411	88.89 408	48.58 416	68.03 412	28.61 411	72.33 410	77.99 407

X-MVStestdata		94.32 335	92.59 353	99.53 34	99.46 123	99.21 29	98.65 101	99.34 152	98.62 117	97.54 295	45.85 409	97.50 136	99.83 157	96.79 202	99.53 219	99.56 94

testmvs		17.12 379	20.53 382	6.87 395	12.05 417	4.20 420	93.62 394	6.73 418	4.62 413	10.41 413	24.33 410	8.28 418	3.56 414	9.69 413	15.07 411	12.86 410

test123		17.04 380	20.11 383	7.82 394	10.25 418	4.91 419	94.80 369	4.47 419	4.93 412	10.00 414	24.28 411	9.69 417	3.64 413	10.14 412	12.43 412	14.92 409

test_post											21.25 412	83.86 368	99.70 252

test_post1							97.59 231				20.48 413	83.07 372	99.66 279	94.16 313

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		8.17 381	10.90 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	98.07 88	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						90.90 375								91.37 369

FOURS1					99.73 36	99.67 3	99.43 11	99.54 78	99.43 40	99.26 111

MSC_two_6792asdad				99.32 79	98.43 322	98.37 110		98.86 270					99.89 75	97.14 171	99.60 194	99.71 44

No_MVS				99.32 79	98.43 322	98.37 110		98.86 270					99.89 75	97.14 171	99.60 194	99.71 44

eth-test2					0.00 419

eth-test					0.00 419

IU-MVS					99.49 113	99.15 48		98.87 265	92.97 357	99.41 81				96.76 206	99.62 187	99.66 55

save fliter					99.11 200	97.97 151	96.53 299	99.02 244	98.24 142

test_0728_SECOND				99.60 11	99.50 106	99.23 27	98.02 174	99.32 159					99.88 84	96.99 183	99.63 184	99.68 51

GSMVS																98.81 292

test_part2					99.36 145	99.10 61				99.05 140

sam_mvs1												84.74 360				98.81 292

sam_mvs												84.29 366

MTGPA								99.20 203

MTMP							97.93 185	91.91 399

test9_res														93.28 339	99.15 281	99.38 179

agg_prior2														92.50 355	99.16 279	99.37 181

agg_prior					98.68 287	97.99 147		99.01 247		95.59 364			99.77 217

test_prior4						97.97 151	95.86 337

test_prior				98.95 140	98.69 283	97.95 155		99.03 241					99.59 304			99.30 207

旧先验2							95.76 341		88.56 393	97.52 297			99.66 279	94.48 303

新几何2							95.93 334

无先验							95.74 342	98.74 291	89.38 389				99.73 240	92.38 357		99.22 225

原ACMM2							95.53 348

testdata2													99.79 200	92.80 349

segment_acmp												97.02 165

testdata1							95.44 353		96.32 280

test12				98.93 143	98.58 305	97.83 164		98.66 295		96.53 345		95.51 237	99.69 256		99.13 284	99.27 212

plane_prior7					99.19 181	97.87 160

plane_prior6					98.99 225	97.70 179						94.90 252

plane_prior5								99.27 186					99.70 252	94.42 307	99.51 224	99.45 148

plane_prior3						97.78 172			97.41 213	97.79 278

plane_prior2							97.77 206		98.20 149

plane_prior1					99.05 216

plane_prior						97.65 181	97.07 271		96.72 264						99.36 247

n2								0.00 420

nn								0.00 420

door-mid								99.57 63

test11								98.87 265

door								99.41 125

HQP5-MVS						96.79 227

HQP-NCC					98.67 288		96.29 313		96.05 289	95.55 367

ACMP_Plane					98.67 288		96.29 313		96.05 289	95.55 367

BP-MVS														92.82 347

HQP4-MVS										95.56 366			99.54 323			99.32 200

HQP3-MVS								99.04 239							99.26 265

HQP2-MVS												93.84 280

MDTV_nov1_ep13_2view						74.92 414	97.69 216		90.06 387	97.75 281		85.78 352		93.52 333		98.69 310

ACMMP++_ref															99.77 123

ACMMP++															99.68 167

Test By Simon												96.52 193

High-resolution multi-view benchmark