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.

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 142	98.08 193	99.95 1	99.45 50	99.98 2	99.75 16	99.80 1	99.97 6	99.82 12	99.99 5	99.99 2

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 16	100.00 1	99.85 30

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 143	97.77 250	99.90 11	99.33 65	99.97 3	99.66 32	99.71 3	99.96 13	99.79 19	99.99 5	99.96 8

mvs_tets		99.63 6	99.67 6	99.49 54	99.88 9	98.61 102	99.34 23	99.71 46	99.27 73	99.90 14	99.74 18	99.68 4	99.97 6	99.55 43	99.99 5	99.88 20

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 76	98.10 145	97.68 264	99.84 22	99.29 71	99.92 8	99.57 49	99.60 5	99.96 13	99.74 27	99.98 12	99.89 16

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 93	99.90 3	99.86 24	99.78 13	99.58 6	99.95 25	99.00 87	99.95 38	99.78 47

jajsoiax		99.58 9	99.61 11	99.48 56	99.87 12	98.61 102	99.28 40	99.66 64	99.09 108	99.89 18	99.68 25	99.53 7	99.97 6	99.50 50	99.99 5	99.87 22

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 91	98.21 136	97.82 241	99.84 22	99.41 57	99.92 8	99.41 94	99.51 8	99.95 25	99.84 9	99.97 21	99.87 22

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 27	99.53 9	99.76 38	99.64 26	99.84 30	99.83 4	99.50 9	99.87 135	99.36 57	99.92 69	99.64 84

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 35	99.63 28	99.78 39	99.67 30	99.48 10	99.81 223	99.30 62	99.97 21	99.77 50
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_fmvsm_n_1920		99.33 30	99.45 23	98.99 151	99.57 102	97.73 193	97.93 225	99.83 25	99.22 78	99.93 6	99.30 123	99.42 11	99.96 13	99.85 6	99.99 5	99.29 270

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 252	99.66 69	96.97 253	98.00 211	99.85 18	99.24 75	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 387

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 155	99.59 91	97.18 239	97.44 305	99.83 25	99.56 39	99.91 12	99.34 113	99.36 13	99.93 53	99.83 10	99.98 12	99.85 30

XVG-OURS		98.53 184	98.34 200	99.11 126	99.50 136	98.82 88	95.97 404	99.50 131	97.30 292	99.05 189	98.98 223	99.35 14	99.32 449	95.72 353	99.68 216	99.18 307

XVG-OURS-SEG-HR		98.49 191	98.28 211	99.14 122	99.49 144	98.83 86	96.54 369	99.48 141	97.32 290	99.11 174	98.61 314	99.33 15	99.30 452	96.23 327	98.38 409	99.28 273

sd_testset		99.28 36	99.31 41	99.19 112	99.68 63	98.06 155	99.41 17	99.30 231	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 234	99.92 69	99.57 123

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 44	98.38 119	99.07 65	99.55 113	98.30 188	99.65 63	99.45 84	99.22 17	99.76 269	98.44 129	99.77 161	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cdsmvs_eth3d_5k		24.66 465	32.88 468	0.00 484	0.00 507	0.00 509	0.00 496	99.10 289	0.00 502	0.00 503	97.58 401	99.21 18	0.00 503	0.00 502	0.00 501	0.00 499

wuyk23d		96.06 373	97.62 284	91.38 476	98.65 366	98.57 106	98.85 93	96.95 431	96.86 330	99.90 14	99.16 164	99.18 19	98.40 483	89.23 471	99.77 161	77.18 496

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 105	99.27 42	99.57 100	99.39 58	99.75 44	99.62 40	99.17 20	99.83 193	99.06 82	99.62 243	99.66 78

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 146	99.14 58	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 61	100.00 1	99.82 36

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 99	99.29 36	99.63 72	99.30 70	99.65 63	99.60 45	99.16 22	99.82 206	99.07 80	99.83 122	99.56 129

fmvsm_s_conf0.5_n_6		99.08 76	99.21 57	98.69 216	99.36 187	96.51 281	97.62 275	99.68 59	98.43 177	99.85 27	99.10 181	99.12 23	99.88 115	99.77 22	99.92 69	99.67 76

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 72	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 52	99.93 56	99.60 100

SDMVSNet		99.23 45	99.32 39	98.96 158	99.68 63	97.35 216	98.84 95	99.48 141	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 170	99.92 69	99.57 123

tt0320-xc		99.64 5	99.68 5	99.50 53	99.72 44	98.98 71	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 38	99.61 98

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 91	99.39 20	99.56 109	99.11 98	99.70 51	99.73 20	99.00 27	99.97 6	99.26 65	99.98 12	99.89 16

DeepC-MVS	97.60 4	98.97 94	98.93 98	99.10 128	99.35 192	97.98 162	98.01 210	99.46 155	97.56 261	99.54 78	99.50 68	98.97 28	99.84 175	98.06 158	99.92 69	99.49 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testgi		98.32 216	98.39 191	98.13 302	99.57 102	95.54 318	97.78 247	99.49 139	97.37 285	99.19 166	97.65 397	98.96 29	99.49 418	96.50 312	98.99 373	99.34 251

fmvsm_s_conf0.5_n_3		99.22 46	99.37 31	98.78 195	99.46 158	96.58 277	97.65 270	99.72 44	99.47 47	99.86 24	99.50 68	98.94 30	99.89 97	99.75 26	99.97 21	99.86 28

GeoE		99.05 79	98.99 93	99.25 104	99.44 165	98.35 125	98.73 103	99.56 109	98.42 178	98.91 222	98.81 268	98.94 30	99.91 74	98.35 138	99.73 184	99.49 174

anonymousdsp		99.51 14	99.47 21	99.62 10	99.88 9	99.08 69	99.34 23	99.69 53	98.93 129	99.65 63	99.72 21	98.93 32	99.95 25	99.11 77	100.00 1	99.82 36

casdiffmvs_mvg		99.12 69	99.16 62	98.99 151	99.43 170	97.73 193	98.00 211	99.62 78	99.22 78	99.55 76	99.22 148	98.93 32	99.75 281	98.66 113	99.81 133	99.50 167
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testf1		99.25 40	99.16 62	99.51 48	99.89 6	99.63 3	98.71 106	99.69 53	98.90 133	99.43 106	99.35 109	98.86 34	99.67 335	97.81 182	99.81 133	99.24 285

APD_test2		99.25 40	99.16 62	99.51 48	99.89 6	99.63 3	98.71 106	99.69 53	98.90 133	99.43 106	99.35 109	98.86 34	99.67 335	97.81 182	99.81 133	99.24 285

ACMM	96.08 12	98.91 102	98.73 125	99.48 56	99.55 116	99.14 57	98.07 197	99.37 194	97.62 252	99.04 191	98.96 228	98.84 36	99.79 245	97.43 222	99.65 231	99.49 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 37	98.26 128	99.17 54	99.78 35	99.11 98	99.27 144	99.48 75	98.82 37	99.95 25	98.94 91	99.93 56	99.59 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_l_conf0.5_n		99.21 47	99.28 46	99.02 148	99.64 76	97.28 227	97.82 241	99.76 38	98.73 146	99.82 34	99.09 186	98.81 38	99.95 25	99.86 4	99.96 28	99.83 33

ACMH+	96.62 9	99.08 76	99.00 91	99.33 89	99.71 48	98.83 86	98.60 121	99.58 93	99.11 98	99.53 82	99.18 158	98.81 38	99.67 335	96.71 285	99.77 161	99.50 167

fmvsm_s_conf0.5_n_5		99.07 78	99.10 77	98.99 151	99.47 155	97.22 233	97.40 307	99.83 25	97.61 255	99.85 27	99.30 123	98.80 40	99.95 25	99.71 32	99.90 86	99.78 47

fmvsm_s_conf0.5_n_4		99.01 86	99.22 54	98.38 273	99.31 198	95.48 324	97.56 286	99.73 43	98.87 137	99.75 44	99.27 129	98.80 40	99.86 144	99.80 17	99.90 86	99.81 40

SSM_0407		98.86 113	98.96 97	98.55 247	99.27 210	96.50 282	98.04 202	99.66 64	99.09 108	99.22 161	99.02 201	98.79 42	99.87 135	97.87 179	99.72 192	99.27 275

SSM_0404		98.90 104	99.01 89	98.57 240	99.42 172	96.59 274	98.13 183	99.66 64	99.09 108	99.30 139	99.02 201	98.79 42	99.89 97	97.87 179	99.80 144	99.23 287

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 161	99.65 70	97.05 248	97.80 245	99.76 38	98.70 153	99.78 39	99.11 178	98.79 42	99.95 25	99.85 6	99.96 28	99.83 33

SD-MVS		98.40 201	98.68 138	97.54 366	98.96 295	97.99 159	97.88 233	99.36 198	98.20 202	99.63 66	99.04 198	98.76 45	95.33 496	96.56 305	99.74 181	99.31 264
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
tt0320		99.61 8	99.65 9	99.48 56	99.71 48	98.94 78	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 81	99.54 44	99.95 38	99.59 107

HPM-MVS_fast		99.01 86	98.82 116	99.57 21	99.71 48	99.35 16	99.00 73	99.50 131	97.33 288	98.94 218	98.86 252	98.75 46	99.82 206	97.53 211	99.71 201	99.56 129

XXY-MVS		99.14 62	99.15 67	99.10 128	99.76 30	97.74 191	98.85 93	99.62 78	98.48 175	99.37 120	99.49 74	98.75 46	99.86 144	98.20 148	99.80 144	99.71 63

EC-MVSNet		99.09 72	99.05 83	99.20 110	99.28 207	98.93 79	99.24 44	99.84 22	99.08 112	98.12 320	98.37 344	98.72 49	99.90 81	99.05 83	99.77 161	98.77 381

E5new		99.05 79	99.11 71	98.85 175	99.60 87	97.30 221	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

E6new		99.05 79	99.11 71	98.85 175	99.60 87	97.30 221	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

E6		99.05 79	99.11 71	98.85 175	99.60 87	97.30 221	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

E5		99.05 79	99.11 71	98.85 175	99.60 87	97.30 221	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

LPG-MVS_test		98.71 139	98.46 180	99.47 60	99.57 102	98.97 73	98.23 171	99.48 141	96.60 341	99.10 177	99.06 189	98.71 50	99.83 193	95.58 360	99.78 155	99.62 90

LGP-MVS_train				99.47 60	99.57 102	98.97 73		99.48 141	96.60 341	99.10 177	99.06 189	98.71 50	99.83 193	95.58 360	99.78 155	99.62 90

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 223	99.71 48	96.10 295	97.87 236	99.85 18	98.56 171	99.90 14	99.68 25	98.69 56	99.85 157	99.72 30	99.98 12	99.97 4

CS-MVS		99.13 66	99.10 77	99.24 106	99.06 271	99.15 52	99.36 22	99.88 14	99.36 63	98.21 311	98.46 335	98.68 57	99.93 53	99.03 85	99.85 106	98.64 396

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 254	99.51 130	95.82 310	97.62 275	99.78 35	99.72 14	99.90 14	99.48 75	98.66 58	99.89 97	99.85 6	99.93 56	99.89 16

MGCFI-Net		98.34 211	98.28 211	98.51 256	98.47 385	97.59 201	98.96 78	99.48 141	99.18 90	97.40 379	95.50 452	98.66 58	99.50 414	98.18 149	98.71 393	98.44 413

SPE-MVS-test		99.13 66	99.09 79	99.26 101	99.13 255	98.97 73	99.31 30	99.88 14	99.44 52	98.16 315	98.51 326	98.64 60	99.93 53	98.91 93	99.85 106	98.88 363

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 46	99.38 59	99.53 82	99.61 43	98.64 60	99.80 232	98.24 143	99.84 111	99.52 159

tt0805		98.69 148	98.62 150	98.90 171	99.75 34	99.30 22	99.15 57	96.97 429	98.86 139	98.87 234	97.62 400	98.63 62	98.96 471	99.41 56	98.29 413	98.45 410

nrg030		99.40 25	99.35 33	99.54 31	99.58 93	99.13 60	98.98 76	99.48 141	99.68 19	99.46 101	99.26 135	98.62 63	99.73 295	99.17 74	99.92 69	99.76 56

HPM-MVS		98.79 128	98.53 165	99.59 20	99.65 70	99.29 24	99.16 55	99.43 173	96.74 336	98.61 271	98.38 343	98.62 63	99.87 135	96.47 313	99.67 222	99.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n		99.09 72	99.26 50	98.61 233	99.55 116	96.09 298	97.74 257	99.81 31	98.55 172	99.85 27	99.55 56	98.60 65	99.84 175	99.69 35	99.98 12	99.89 16

mamba_0408		98.80 126	98.88 104	98.55 247	99.27 210	96.50 282	98.00 211	99.60 83	98.93 129	99.22 161	98.84 260	98.59 66	99.89 97	97.74 191	99.72 192	99.27 275

SSM_04072		98.80 126	98.88 104	98.56 245	99.27 210	96.50 282	98.00 211	99.60 83	98.93 129	99.22 161	98.84 260	98.59 66	99.90 81	97.74 191	99.72 192	99.27 275

sasdasda		98.34 211	98.26 215	98.58 237	98.46 387	97.82 183	98.96 78	99.46 155	99.19 87	97.46 373	95.46 455	98.59 66	99.46 428	98.08 156	98.71 393	98.46 407

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 188	99.75 34	96.59 274	97.97 223	99.86 16	98.22 196	99.88 21	99.71 22	98.59 66	99.84 175	99.73 28	99.98 12	99.98 3

canonicalmvs		98.34 211	98.26 215	98.58 237	98.46 387	97.82 183	98.96 78	99.46 155	99.19 87	97.46 373	95.46 455	98.59 66	99.46 428	98.08 156	98.71 393	98.46 407

EG-PatchMatch MVS		98.99 89	99.01 89	98.94 161	99.50 136	97.47 209	98.04 202	99.59 90	98.15 213	99.40 115	99.36 108	98.58 71	99.76 269	98.78 102	99.68 216	99.59 107

test_fmvs3		99.12 69	99.41 26	98.25 288	99.76 30	95.07 346	99.05 68	99.94 2	97.78 242	99.82 34	99.84 3	98.56 72	99.71 306	99.96 1	99.96 28	99.97 4

Effi-MVS+		98.02 252	97.82 267	98.62 229	98.53 381	97.19 237	97.33 317	99.68 59	97.30 292	96.68 417	97.46 409	98.56 72	99.80 232	96.63 295	98.20 416	98.86 365

Fast-Effi-MVS+		97.67 285	97.38 297	98.57 240	98.71 343	97.43 213	97.23 327	99.45 159	94.82 410	96.13 435	96.51 430	98.52 74	99.91 74	96.19 330	98.83 385	98.37 422

fmvsm_s_conf0.1_n_2		99.20 50	99.38 28	98.65 221	99.69 60	96.08 300	97.49 296	99.90 11	99.53 41	99.88 21	99.64 37	98.51 75	99.90 81	99.83 10	99.98 12	99.97 4

FE-MVSNET2		99.15 57	99.22 54	98.94 161	99.70 56	97.49 205	98.62 118	99.67 63	98.85 142	99.34 127	99.54 62	98.47 76	99.81 223	98.93 92	99.91 78	99.51 163

xiu_mvs_v1_base_debu		97.86 269	98.17 228	96.92 399	98.98 292	93.91 395	96.45 375	99.17 276	97.85 236	98.41 297	97.14 421	98.47 76	99.92 65	98.02 163	99.05 362	96.92 470

xiu_mvs_v1_base		97.86 269	98.17 228	96.92 399	98.98 292	93.91 395	96.45 375	99.17 276	97.85 236	98.41 297	97.14 421	98.47 76	99.92 65	98.02 163	99.05 362	96.92 470

xiu_mvs_v1_base_debi		97.86 269	98.17 228	96.92 399	98.98 292	93.91 395	96.45 375	99.17 276	97.85 236	98.41 297	97.14 421	98.47 76	99.92 65	98.02 163	99.05 362	96.92 470

fmvsm_s_conf0.5_n_2		99.14 62	99.31 41	98.63 227	99.49 144	96.08 300	97.38 310	99.81 31	99.48 44	99.84 30	99.57 49	98.46 80	99.89 97	99.82 12	99.97 21	99.91 13

fmvsm_s_conf0.5_n_a		99.10 71	99.20 58	98.78 195	99.55 116	96.59 274	97.79 246	99.82 30	98.21 198	99.81 36	99.53 64	98.46 80	99.84 175	99.70 33	99.97 21	99.90 15

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 110	99.45 14	99.61 81	99.66 23	99.68 57	99.66 32	98.44 82	99.95 25	99.73 28	99.96 28	99.75 60

ETV-MVS		98.03 251	97.86 265	98.56 245	98.69 352	98.07 152	97.51 293	99.50 131	98.10 216	97.50 370	95.51 451	98.41 83	99.88 115	96.27 326	99.24 337	97.71 457

viewmacassd2359aftdt		98.86 113	98.87 107	98.83 181	99.53 124	97.32 220	97.70 262	99.64 70	98.22 196	99.25 156	99.27 129	98.40 84	99.61 372	97.98 169	99.87 97	99.55 136

COLMAP_ROB	96.50 10	98.99 89	98.85 114	99.41 69	99.58 93	99.10 65	98.74 99	99.56 109	99.09 108	99.33 130	99.19 154	98.40 84	99.72 305	95.98 340	99.76 176	99.42 213
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TranMVSNet+NR-MVSNet		99.17 52	99.07 82	99.46 62	99.37 186	98.87 84	98.39 157	99.42 179	99.42 55	99.36 123	99.06 189	98.38 86	99.95 25	98.34 139	99.90 86	99.57 123

lecture		99.25 40	99.12 70	99.62 10	99.64 76	99.40 11	98.89 88	99.51 128	99.19 87	99.37 120	99.25 140	98.36 87	99.88 115	98.23 145	99.67 222	99.59 107

SED-MVS		98.91 102	98.72 127	99.49 54	99.49 144	99.17 44	98.10 190	99.31 223	98.03 220	99.66 60	99.02 201	98.36 87	99.88 115	96.91 261	99.62 243	99.41 216

test_241102_ONE					99.49 144	99.17 44		99.31 223	97.98 223	99.66 60	98.90 242	98.36 87	99.48 422

ACMP	95.32 15	98.41 198	98.09 237	99.36 74	99.51 130	98.79 89	97.68 264	99.38 190	95.76 384	98.81 243	98.82 265	98.36 87	99.82 206	94.75 376	99.77 161	99.48 185
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		98.95 97	99.00 91	98.81 185	99.38 180	97.33 218	97.82 241	99.57 100	99.17 91	99.35 125	99.17 162	98.35 91	99.69 321	98.46 128	99.73 184	99.41 216
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_0402		98.76 134	98.71 132	98.93 164	99.56 110	98.14 141	98.45 147	99.34 210	99.28 72	98.95 211	98.91 239	98.34 92	99.79 245	95.63 357	99.91 78	98.86 365

mmtdpeth		99.30 33	99.42 25	98.92 167	99.58 93	96.89 261	99.48 13	99.92 7	99.92 2	98.26 309	99.80 11	98.33 93	99.91 74	99.56 41	99.95 38	99.97 4

reproduce_model		99.15 57	98.97 95	99.67 4	99.33 196	99.44 9	98.15 181	99.47 150	99.12 97	99.52 87	99.32 121	98.31 94	99.90 81	97.78 185	99.73 184	99.66 78

xiu_mvs_v2_base		97.16 329	97.49 291	96.17 427	98.54 379	92.46 426	95.45 430	98.84 340	97.25 297	97.48 372	96.49 431	98.31 94	99.90 81	96.34 322	98.68 398	96.15 484

E4		98.87 109	98.88 104	98.81 185	99.52 127	97.23 230	97.62 275	99.61 81	98.58 166	99.18 170	99.33 116	98.29 96	99.69 321	97.99 168	99.83 122	99.52 159

viewdifsd2359ckpt07		98.71 139	98.86 111	98.26 286	99.43 170	95.65 314	97.20 332	99.66 64	99.20 82	99.29 140	99.01 212	98.29 96	99.73 295	97.92 173	99.75 180	99.39 226

reproduce-ours		99.09 72	98.90 101	99.67 4	99.27 210	99.49 5	98.00 211	99.42 179	99.05 115	99.48 96	99.27 129	98.29 96	99.89 97	97.61 202	99.71 201	99.62 90

our_new_method		99.09 72	98.90 101	99.67 4	99.27 210	99.49 5	98.00 211	99.42 179	99.05 115	99.48 96	99.27 129	98.29 96	99.89 97	97.61 202	99.71 201	99.62 90

icg_test_0407_2		98.20 235	98.38 193	97.65 351	99.03 278	94.03 385	95.78 418	99.45 159	98.16 208	99.06 181	98.71 287	98.27 100	99.68 331	97.50 214	99.45 297	99.22 292

IMVS_0407		98.39 207	98.64 146	97.66 349	99.03 278	94.03 385	98.10 190	99.45 159	98.16 208	99.06 181	98.71 287	98.27 100	99.71 306	97.50 214	99.45 297	99.22 292

viewdifsd2359ckpt11		98.84 116	99.04 84	98.24 290	99.56 110	95.51 320	97.38 310	99.70 51	99.16 92	99.57 71	99.40 97	98.26 102	99.71 306	98.55 124	99.82 127	99.50 167

viewmsd2359difaftdt		98.84 116	99.04 84	98.24 290	99.56 110	95.51 320	97.38 310	99.70 51	99.16 92	99.57 71	99.40 97	98.26 102	99.71 306	98.55 124	99.82 127	99.50 167

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 144	98.36 124	99.00 73	99.45 159	99.63 28	99.52 87	99.44 85	98.25 104	99.88 115	99.09 79	99.84 111	99.62 90

MVS_111021_LR		98.30 219	98.12 235	98.83 181	99.16 248	98.03 157	96.09 400	99.30 231	97.58 258	98.10 322	98.24 355	98.25 104	99.34 446	96.69 288	99.65 231	99.12 323

PS-CasMVS		99.40 25	99.33 37	99.62 10	99.71 48	99.10 65	99.29 36	99.53 122	99.53 41	99.46 101	99.41 94	98.23 106	99.95 25	98.89 96	99.95 38	99.81 40

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 48	99.30 22	99.31 30	99.51 128	99.64 26	99.56 73	99.46 80	98.23 106	99.97 6	98.78 102	99.93 56	99.72 62

baseline		98.96 96	99.02 87	98.76 202	99.38 180	97.26 229	98.49 140	99.50 131	98.86 139	99.19 166	99.06 189	98.23 106	99.69 321	98.71 110	99.76 176	99.33 257

PC_three_1452									93.27 438	99.40 115	98.54 321	98.22 109	97.00 492	95.17 367	99.45 297	99.49 174

Gipuma		99.03 84	99.16 62	98.64 223	99.94 2	98.51 112	99.32 26	99.75 41	99.58 38	98.60 273	99.62 40	98.22 109	99.51 413	97.70 195	99.73 184	97.89 445
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 188	99.48 152	96.56 279	97.97 223	99.69 53	99.63 28	99.84 30	99.54 62	98.21 111	99.94 41	99.76 23	99.95 38	99.88 20

LCM-MVSNet-Re		98.64 161	98.48 176	99.11 126	98.85 319	98.51 112	98.49 140	99.83 25	98.37 179	99.69 55	99.46 80	98.21 111	99.92 65	94.13 398	99.30 328	98.91 358

tfpnnormal		98.90 104	98.90 101	98.91 168	99.67 67	97.82 183	99.00 73	99.44 167	99.45 50	99.51 92	99.24 142	98.20 113	99.86 144	95.92 342	99.69 211	99.04 333

mvsany_test3		98.87 109	98.92 99	98.74 208	99.38 180	96.94 257	98.58 123	99.10 289	96.49 346	99.96 4	99.81 8	98.18 114	99.45 430	98.97 89	99.79 150	99.83 33

DVP-MVS++		98.90 104	98.70 135	99.51 48	98.43 391	99.15 52	99.43 15	99.32 218	98.17 205	99.26 148	99.02 201	98.18 114	99.88 115	97.07 248	99.45 297	99.49 174

OPU-MVS				98.82 183	98.59 372	98.30 126	98.10 190				98.52 325	98.18 114	98.75 479	94.62 380	99.48 293	99.41 216

OPM-MVS		98.56 175	98.32 206	99.25 104	99.41 175	98.73 94	97.13 339	99.18 272	97.10 312	98.75 253	98.92 236	98.18 114	99.65 355	96.68 289	99.56 266	99.37 237
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PEN-MVS		99.41 24	99.34 35	99.62 10	99.73 37	99.14 57	99.29 36	99.54 118	99.62 32	99.56 73	99.42 89	98.16 118	99.96 13	98.78 102	99.93 56	99.77 50

DeepPCF-MVS	96.93 5	98.32 216	98.01 247	99.23 108	98.39 396	98.97 73	95.03 445	99.18 272	96.88 326	99.33 130	98.78 273	98.16 118	99.28 456	96.74 280	99.62 243	99.44 204

MVS_111021_HR		98.25 229	98.08 240	98.75 204	99.09 262	97.46 210	95.97 404	99.27 246	97.60 257	97.99 333	98.25 354	98.15 120	99.38 441	96.87 269	99.57 263	99.42 213

mvs5depth		99.30 33	99.59 12	98.44 266	99.65 70	95.35 333	99.82 3	99.94 2	99.83 7	99.42 110	99.94 2	98.13 121	99.96 13	99.63 36	99.96 28	100.00 1

Fast-Effi-MVS+-dtu		98.27 224	98.09 237	98.81 185	98.43 391	98.11 143	97.61 280	99.50 131	98.64 155	97.39 381	97.52 405	98.12 122	99.95 25	96.90 266	98.71 393	98.38 420

fmvsm_s_conf0.5_n_7		98.83 119	99.04 84	98.20 295	99.30 202	94.83 355	97.23 327	99.36 198	98.64 155	99.84 30	99.43 88	98.10 123	99.91 74	99.56 41	99.96 28	99.87 22

pcd_1.5k_mvsjas		8.17 468	10.90 471	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	98.07 124	0.00 503	0.00 502	0.00 501	0.00 499

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 139	99.20 49	99.65 68	99.48 44	99.92 8	99.71 22	98.07 124	99.96 13	99.53 48	100.00 1	99.93 11

PS-MVSNAJ		97.08 333	97.39 296	96.16 429	98.56 377	92.46 426	95.24 439	98.85 339	97.25 297	97.49 371	95.99 441	98.07 124	99.90 81	96.37 319	98.67 399	96.12 485

UA-Net		99.47 16	99.40 27	99.70 2	99.49 144	99.29 24	99.80 4	99.72 44	99.82 8	99.04 191	99.81 8	98.05 127	99.96 13	98.85 98	99.99 5	99.86 28

ACMMP_NAP		98.75 135	98.48 176	99.57 21	99.58 93	99.29 24	97.82 241	99.25 253	96.94 321	98.78 247	99.12 176	98.02 128	99.84 175	97.13 244	99.67 222	99.59 107

MP-MVS-pluss		98.57 174	98.23 220	99.60 16	99.69 60	99.35 16	97.16 337	99.38 190	94.87 409	98.97 205	98.99 218	98.01 129	99.88 115	97.29 231	99.70 208	99.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZNCC-MVS		98.68 154	98.40 188	99.54 31	99.57 102	99.21 33	98.46 145	99.29 239	97.28 294	98.11 321	98.39 341	98.00 130	99.87 135	96.86 271	99.64 233	99.55 136

PGM-MVS		98.66 158	98.37 195	99.55 28	99.53 124	99.18 43	98.23 171	99.49 139	97.01 318	98.69 258	98.88 249	98.00 130	99.89 97	95.87 346	99.59 254	99.58 115

SteuartSystems-ACMMP		98.79 128	98.54 163	99.54 31	99.73 37	99.16 48	98.23 171	99.31 223	97.92 230	98.90 223	98.90 242	98.00 130	99.88 115	96.15 333	99.72 192	99.58 115
Skip Steuart: Steuart Systems R&D Blog.
TinyColmap		97.89 264	97.98 250	97.60 358	98.86 316	94.35 371	96.21 391	99.44 167	97.45 278	99.06 181	98.88 249	97.99 133	99.28 456	94.38 392	99.58 259	99.18 307

HFP-MVS		98.71 139	98.44 183	99.51 48	99.49 144	99.16 48	98.52 130	99.31 223	97.47 271	98.58 277	98.50 330	97.97 134	99.85 157	96.57 301	99.59 254	99.53 156

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 195	99.47 155	96.56 279	97.75 256	99.71 46	99.60 35	99.74 46	99.44 85	97.96 135	99.95 25	99.86 4	99.94 50	99.82 36

3Dnovator	98.27 2	98.81 124	98.73 125	99.05 142	98.76 333	97.81 186	99.25 43	99.30 231	98.57 168	98.55 283	99.33 116	97.95 136	99.90 81	97.16 239	99.67 222	99.44 204

mvsany_test1		97.60 289	97.54 287	97.77 333	97.72 430	95.35 333	95.36 434	97.13 425	94.13 426	99.71 49	99.33 116	97.93 137	99.30 452	97.60 204	98.94 380	98.67 395

fmvsm_s_conf0.5_n_8		99.13 66	99.26 50	98.74 208	99.51 130	96.44 286	97.65 270	99.65 68	99.66 23	99.78 39	99.48 75	97.92 138	99.93 53	99.72 30	99.95 38	99.87 22

E2		98.70 144	98.68 138	98.73 210	99.40 177	97.10 246	97.48 297	99.57 100	98.09 217	99.00 196	99.20 151	97.90 139	99.67 335	97.73 193	99.77 161	99.43 208

E3		98.69 148	98.68 138	98.73 210	99.40 177	97.10 246	97.48 297	99.57 100	98.09 217	99.00 196	99.20 151	97.90 139	99.67 335	97.73 193	99.77 161	99.43 208

IMVS_0403		98.34 211	98.56 160	97.66 349	99.03 278	94.03 385	97.98 219	99.45 159	98.16 208	98.89 226	98.71 287	97.90 139	99.74 288	97.50 214	99.45 297	99.22 292

SSC-MVS3.2		98.53 184	98.79 119	97.74 339	99.46 158	93.62 408	96.45 375	99.34 210	99.33 65	98.93 219	98.70 294	97.90 139	99.90 81	99.12 76	99.92 69	99.69 70

test_0728_THIRD									98.17 205	99.08 179	99.02 201	97.89 143	99.88 115	97.07 248	99.71 201	99.70 68

APD-MVS_3200maxsize		98.84 116	98.61 154	99.53 38	99.19 236	99.27 27	98.49 140	99.33 216	98.64 155	99.03 194	98.98 223	97.89 143	99.85 157	96.54 309	99.42 307	99.46 195

MED-MVS		98.90 104	98.72 127	99.45 63	99.58 93	98.93 79	98.68 109	99.60 83	98.14 214	99.53 82	98.77 275	97.87 145	99.83 193	96.67 290	99.64 233	99.58 115

TestfortrainingZip a		98.95 97	98.72 127	99.64 9	99.58 93	99.32 21	98.68 109	99.60 83	96.46 349	99.53 82	98.77 275	97.87 145	99.83 193	98.39 136	99.64 233	99.77 50

CP-MVS		98.70 144	98.42 186	99.52 44	99.36 187	99.12 62	98.72 104	99.36 198	97.54 265	98.30 303	98.40 340	97.86 147	99.89 97	96.53 310	99.72 192	99.56 129

TSAR-MVS + MP.		98.63 163	98.49 175	99.06 141	99.64 76	97.90 172	98.51 135	98.94 316	96.96 319	99.24 158	98.89 248	97.83 148	99.81 223	96.88 268	99.49 292	99.48 185
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
region2R		98.69 148	98.40 188	99.54 31	99.53 124	99.17 44	98.52 130	99.31 223	97.46 276	98.44 294	98.51 326	97.83 148	99.88 115	96.46 314	99.58 259	99.58 115

APDe-MVS		98.99 89	98.79 119	99.60 16	99.21 229	99.15 52	98.87 89	99.48 141	97.57 259	99.35 125	99.24 142	97.83 148	99.89 97	97.88 177	99.70 208	99.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 116	98.24 130	99.20 49	99.44 167	99.21 80	99.43 106	99.55 56	97.82 151	99.86 144	98.42 135	99.89 92	99.41 216

SF-MVS		98.53 184	98.27 214	99.32 91	99.31 198	98.75 90	98.19 175	99.41 183	96.77 335	98.83 238	98.90 242	97.80 152	99.82 206	95.68 356	99.52 279	99.38 235

PHI-MVS		98.29 222	97.95 254	99.34 83	98.44 390	99.16 48	98.12 187	99.38 190	96.01 372	98.06 326	98.43 338	97.80 152	99.67 335	95.69 355	99.58 259	99.20 297

viewmanbaseed2359cas		98.58 173	98.54 163	98.70 214	99.28 207	97.13 245	97.47 301	99.55 113	97.55 263	98.96 210	98.92 236	97.77 154	99.59 379	97.59 205	99.77 161	99.39 226

APD_test1		98.83 119	98.66 143	99.34 83	99.78 24	99.47 8	98.42 151	99.45 159	98.28 193	98.98 201	99.19 154	97.76 155	99.58 386	96.57 301	99.55 270	98.97 346

RE-MVS-def			98.58 158		99.20 233	99.38 12	98.48 143	99.30 231	98.64 155	98.95 211	98.96 228	97.75 156		96.56 305	99.39 310	99.45 200

ACMMPR		98.70 144	98.42 186	99.54 31	99.52 127	99.14 57	98.52 130	99.31 223	97.47 271	98.56 281	98.54 321	97.75 156	99.88 115	96.57 301	99.59 254	99.58 115

ACMMP		98.75 135	98.50 170	99.52 44	99.56 110	99.16 48	98.87 89	99.37 194	97.16 309	98.82 241	99.01 212	97.71 158	99.87 135	96.29 325	99.69 211	99.54 142
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
EIA-MVS		98.00 255	97.74 271	98.80 188	98.72 339	98.09 146	98.05 200	99.60 83	97.39 283	96.63 419	95.55 450	97.68 159	99.80 232	96.73 282	99.27 332	98.52 405

GST-MVS		98.61 167	98.30 208	99.52 44	99.51 130	99.20 39	98.26 169	99.25 253	97.44 279	98.67 261	98.39 341	97.68 159	99.85 157	96.00 338	99.51 282	99.52 159

CSCG		98.68 154	98.50 170	99.20 110	99.45 163	98.63 99	98.56 125	99.57 100	97.87 234	98.85 235	98.04 372	97.66 161	99.84 175	96.72 283	99.81 133	99.13 322

AllTest		98.44 196	98.20 222	99.16 118	99.50 136	98.55 107	98.25 170	99.58 93	96.80 332	98.88 230	99.06 189	97.65 162	99.57 388	94.45 386	99.61 248	99.37 237

TestCases				99.16 118	99.50 136	98.55 107		99.58 93	96.80 332	98.88 230	99.06 189	97.65 162	99.57 388	94.45 386	99.61 248	99.37 237

test20.03		98.78 130	98.77 122	98.78 195	99.46 158	97.20 236	97.78 247	99.24 258	99.04 117	99.41 112	98.90 242	97.65 162	99.76 269	97.70 195	99.79 150	99.39 226

test_one_0601					99.39 179	99.20 39		99.31 223	98.49 174	98.66 263	99.02 201	97.64 165

ITE_SJBPF				98.87 172	99.22 227	98.48 114		99.35 204	97.50 268	98.28 307	98.60 316	97.64 165	99.35 445	93.86 406	99.27 332	98.79 379

viewmambaseed2359dif		98.19 236	98.26 215	97.99 317	99.02 285	95.03 347	96.59 368	99.53 122	96.21 360	99.00 196	98.99 218	97.62 167	99.61 372	97.62 201	99.72 192	99.33 257

mPP-MVS		98.64 161	98.34 200	99.54 31	99.54 121	99.17 44	98.63 116	99.24 258	97.47 271	98.09 323	98.68 298	97.62 167	99.89 97	96.22 328	99.62 243	99.57 123

DVP-MVS		98.77 133	98.52 166	99.52 44	99.50 136	99.21 33	98.02 207	98.84 340	97.97 224	99.08 179	99.02 201	97.61 169	99.88 115	96.99 255	99.63 240	99.48 185
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
test0726					99.50 136	99.21 33	98.17 179	99.35 204	97.97 224	99.26 148	99.06 189	97.61 169

9.14			97.78 268		99.07 266		97.53 290	99.32 218	95.53 391	98.54 285	98.70 294	97.58 171	99.76 269	94.32 393	99.46 295

CLD-MVS		97.49 298	97.16 310	98.48 261	99.07 266	97.03 250	94.71 452	99.21 262	94.46 417	98.06 326	97.16 419	97.57 172	99.48 422	94.46 385	99.78 155	98.95 349
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DeepC-MVS_fast	96.85 6	98.30 219	98.15 232	98.75 204	98.61 367	97.23 230	97.76 253	99.09 291	97.31 291	98.75 253	98.66 303	97.56 173	99.64 359	96.10 337	99.55 270	99.39 226
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
viewcassd2359sk11		98.55 179	98.51 167	98.67 219	99.29 204	96.99 252	97.39 308	99.54 118	97.73 244	98.81 243	99.08 187	97.55 174	99.66 348	97.52 213	99.67 222	99.36 244

EGC-MVSNET		85.24 460	80.54 463	99.34 83	99.77 27	99.20 39	99.08 62	99.29 239	12.08 499	20.84 500	99.42 89	97.55 174	99.85 157	97.08 247	99.72 192	98.96 348

PM-MVS		98.82 122	98.72 127	99.12 124	99.64 76	98.54 110	97.98 219	99.68 59	97.62 252	99.34 127	99.18 158	97.54 176	99.77 263	97.79 184	99.74 181	99.04 333

XVG-ACMP-BASELINE		98.56 175	98.34 200	99.22 109	99.54 121	98.59 104	97.71 260	99.46 155	97.25 297	98.98 201	98.99 218	97.54 176	99.84 175	95.88 343	99.74 181	99.23 287

SR-MVS		98.71 139	98.43 184	99.57 21	99.18 244	99.35 16	98.36 160	99.29 239	98.29 191	98.88 230	98.85 255	97.53 178	99.87 135	96.14 334	99.31 325	99.48 185

DPE-MVS		98.59 171	98.26 215	99.57 21	99.27 210	99.15 52	97.01 342	99.39 188	97.67 248	99.44 105	98.99 218	97.53 178	99.89 97	95.40 364	99.68 216	99.66 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		98.40 201	98.03 245	99.51 48	99.16 248	99.21 33	98.05 200	99.22 261	94.16 425	98.98 201	99.10 181	97.52 180	99.79 245	96.45 315	99.64 233	99.53 156
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
test_241102_TWO								99.30 231	98.03 220	99.26 148	99.02 201	97.51 181	99.88 115	96.91 261	99.60 250	99.66 78

XVS		98.72 138	98.45 181	99.53 38	99.46 158	99.21 33	98.65 114	99.34 210	98.62 160	97.54 366	98.63 310	97.50 182	99.83 193	96.79 274	99.53 276	99.56 129

X-MVStestdata		94.32 414	92.59 433	99.53 38	99.46 158	99.21 33	98.65 114	99.34 210	98.62 160	97.54 366	45.85 497	97.50 182	99.83 193	96.79 274	99.53 276	99.56 129

DELS-MVS		98.27 224	98.20 222	98.48 261	98.86 316	96.70 271	95.60 424	99.20 264	97.73 244	98.45 293	98.71 287	97.50 182	99.82 206	98.21 147	99.59 254	98.93 354
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
SR-MVS-dyc-post		98.81 124	98.55 161	99.57 21	99.20 233	99.38 12	98.48 143	99.30 231	98.64 155	98.95 211	98.96 228	97.49 185	99.86 144	96.56 305	99.39 310	99.45 200

TSAR-MVS + GP.		98.18 238	97.98 250	98.77 200	98.71 343	97.88 173	96.32 385	98.66 363	96.33 354	99.23 160	98.51 326	97.48 186	99.40 437	97.16 239	99.46 295	99.02 336

viewdifsd2359ckpt13		98.39 207	98.29 210	98.70 214	99.26 219	97.19 237	97.51 293	99.48 141	96.94 321	98.58 277	98.82 265	97.47 187	99.55 395	97.21 236	99.33 321	99.34 251

new-patchmatchnet		98.35 210	98.74 123	97.18 385	99.24 221	92.23 433	96.42 379	99.48 141	98.30 188	99.69 55	99.53 64	97.44 188	99.82 206	98.84 99	99.77 161	99.49 174

PMVS	91.26 20	97.86 269	97.94 256	97.65 351	99.71 48	97.94 168	98.52 130	98.68 362	98.99 121	97.52 368	99.35 109	97.41 189	98.18 487	91.59 450	99.67 222	96.82 473
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS		98.46 194	98.09 237	99.54 31	99.57 102	99.22 32	98.50 137	99.19 268	97.61 255	97.58 362	98.66 303	97.40 190	99.88 115	94.72 379	99.60 250	99.54 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MSDG		97.71 282	97.52 289	98.28 285	98.91 306	96.82 263	94.42 465	99.37 194	97.65 250	98.37 302	98.29 353	97.40 190	99.33 448	94.09 399	99.22 341	98.68 394

diffmvs_AUTHOR		98.50 190	98.59 157	98.23 293	99.35 192	95.48 324	96.61 366	99.60 83	98.37 179	98.90 223	99.00 216	97.37 192	99.76 269	98.22 146	99.85 106	99.46 195

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 82	99.06 70	98.69 108	99.54 118	99.31 68	99.62 69	99.53 64	97.36 193	99.86 144	99.24 69	99.71 201	99.39 226

SD_0403		96.28 366	95.83 367	97.64 354	98.72 339	94.30 372	98.87 89	98.77 351	97.80 239	96.53 424	98.02 373	97.34 194	99.47 425	76.93 494	99.48 293	99.16 317

LS3D		98.63 163	98.38 193	99.36 74	97.25 457	99.38 12	99.12 61	99.32 218	99.21 80	98.44 294	98.88 249	97.31 195	99.80 232	96.58 299	99.34 319	98.92 355

EI-MVSNet-UG-set		98.69 148	98.71 132	98.62 229	99.10 259	96.37 288	97.23 327	98.87 331	99.20 82	99.19 166	98.99 218	97.30 196	99.85 157	98.77 105	99.79 150	99.65 83

WR-MVS_H		99.33 30	99.22 54	99.65 8	99.71 48	99.24 30	99.32 26	99.55 113	99.46 49	99.50 93	99.34 113	97.30 196	99.93 53	98.90 94	99.93 56	99.77 50

EI-MVSNet-Vis-set		98.68 154	98.70 135	98.63 227	99.09 262	96.40 287	97.23 327	98.86 336	99.20 82	99.18 170	98.97 225	97.29 198	99.85 157	98.72 109	99.78 155	99.64 84

pmmvs-eth3d		98.47 193	98.34 200	98.86 174	99.30 202	97.76 189	97.16 337	99.28 243	95.54 390	99.42 110	99.19 154	97.27 199	99.63 362	97.89 174	99.97 21	99.20 297

CNVR-MVS		98.17 240	97.87 264	99.07 135	98.67 357	98.24 130	97.01 342	98.93 319	97.25 297	97.62 358	98.34 348	97.27 199	99.57 388	96.42 316	99.33 321	99.39 226

OMC-MVS		97.88 266	97.49 291	99.04 144	98.89 312	98.63 99	96.94 346	99.25 253	95.02 404	98.53 286	98.51 326	97.27 199	99.47 425	93.50 416	99.51 282	99.01 337

DP-MVS		98.93 100	98.81 118	99.28 96	99.21 229	98.45 116	98.46 145	99.33 216	99.63 28	99.48 96	99.15 168	97.23 202	99.75 281	97.17 238	99.66 230	99.63 89

MVS_Test		98.18 238	98.36 196	97.67 347	98.48 384	94.73 360	98.18 176	99.02 306	97.69 247	98.04 329	99.11 178	97.22 203	99.56 391	98.57 120	98.90 383	98.71 387

E3new		98.41 198	98.34 200	98.62 229	99.19 236	96.90 260	97.32 318	99.50 131	97.40 282	98.63 266	98.92 236	97.21 204	99.65 355	97.34 226	99.52 279	99.31 264

dcpmvs_2		98.78 130	99.11 71	97.78 332	99.56 110	93.67 405	99.06 66	99.86 16	99.50 43	99.66 60	99.26 135	97.21 204	99.99 2	98.00 166	99.91 78	99.68 71

MCST-MVS		98.00 255	97.63 283	99.10 128	99.24 221	98.17 138	96.89 351	98.73 359	95.66 385	97.92 337	97.70 395	97.17 206	99.66 348	96.18 332	99.23 340	99.47 193

test_vis3_rt		99.14 62	99.17 60	99.07 135	99.78 24	98.38 119	98.92 83	99.94 2	97.80 239	99.91 12	99.67 30	97.15 207	98.91 474	99.76 23	99.56 266	99.92 12

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 121	99.30 35	99.57 100	99.61 34	99.40 115	99.50 68	97.12 208	99.85 157	99.02 86	99.94 50	99.80 42

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 42	99.41 17	99.59 90	99.59 36	99.71 49	99.57 49	97.12 208	99.90 81	99.21 70	99.87 97	99.54 142

3Dnovator+	97.89 3	98.69 148	98.51 167	99.24 106	98.81 328	98.40 117	99.02 70	99.19 268	98.99 121	98.07 325	99.28 127	97.11 210	99.84 175	96.84 272	99.32 323	99.47 193

patch_mono-2		98.51 189	98.63 148	98.17 298	99.38 180	94.78 357	97.36 315	99.69 53	98.16 208	98.49 290	99.29 126	97.06 211	99.97 6	98.29 142	99.91 78	99.76 56

Anonymous20240529		98.93 100	98.87 107	99.12 124	99.19 236	98.22 135	99.01 71	98.99 312	99.25 74	99.54 78	99.37 104	97.04 212	99.80 232	97.89 174	99.52 279	99.35 249

MSLP-MVS++		98.02 252	98.14 234	97.64 354	98.58 374	95.19 341	97.48 297	99.23 260	97.47 271	97.90 339	98.62 312	97.04 212	98.81 477	97.55 208	99.41 308	98.94 353

APD-MVS		98.10 244	97.67 277	99.42 67	99.11 257	98.93 79	97.76 253	99.28 243	94.97 406	98.72 256	98.77 275	97.04 212	99.85 157	93.79 408	99.54 272	99.49 174
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
segment_acmp												97.02 215

CP-MVSNet		99.21 47	99.09 79	99.56 26	99.65 70	98.96 77	99.13 59	99.34 210	99.42 55	99.33 130	99.26 135	97.01 216	99.94 41	98.74 107	99.93 56	99.79 44

ambc				98.24 290	98.82 325	95.97 304	98.62 118	99.00 311		99.27 144	99.21 149	96.99 217	99.50 414	96.55 308	99.50 290	99.26 281

MTAPA		98.88 108	98.64 146	99.61 14	99.67 67	99.36 15	98.43 148	99.20 264	98.83 144	98.89 226	98.90 242	96.98 218	99.92 65	97.16 239	99.70 208	99.56 129

ME-MVS		98.61 167	98.33 205	99.44 65	99.24 221	98.93 79	97.45 303	99.06 294	98.14 214	99.06 181	98.77 275	96.97 219	99.82 206	96.67 290	99.64 233	99.58 115

FE-MVSNET		98.59 171	98.50 170	98.87 172	99.58 93	97.30 221	98.08 193	99.74 42	96.94 321	98.97 205	99.10 181	96.94 220	99.74 288	97.33 228	99.86 104	99.55 136

v8		99.01 86	99.16 62	98.57 240	99.47 155	96.31 291	98.90 84	99.47 150	99.03 118	99.52 87	99.57 49	96.93 221	99.81 223	99.60 37	99.98 12	99.60 100

QAPM		97.31 315	96.81 336	98.82 183	98.80 331	97.49 205	99.06 66	99.19 268	90.22 469	97.69 355	99.16 164	96.91 222	99.90 81	90.89 463	99.41 308	99.07 327

CDPH-MVS		97.26 319	96.66 346	99.07 135	99.00 288	98.15 139	96.03 402	99.01 309	91.21 463	97.79 349	97.85 385	96.89 223	99.69 321	92.75 434	99.38 313	99.39 226

PVSNet_Blended_VisFu		98.17 240	98.15 232	98.22 294	99.73 37	95.15 342	97.36 315	99.68 59	94.45 419	98.99 200	99.27 129	96.87 224	99.94 41	97.13 244	99.91 78	99.57 123

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 204	98.18 137	99.49 12	99.51 128	99.70 15	99.80 37	99.68 25	96.84 225	99.83 193	99.21 70	99.91 78	99.77 50

V42		98.78 130	98.78 121	98.76 202	99.44 165	97.04 249	98.27 168	99.19 268	97.87 234	99.25 156	99.16 164	96.84 225	99.78 257	99.21 70	99.84 111	99.46 195

PMMVS2		98.07 248	98.08 240	98.04 314	99.41 175	94.59 366	94.59 460	99.40 186	97.50 268	98.82 241	98.83 262	96.83 227	99.84 175	97.50 214	99.81 133	99.71 63

PVSNet_BlendedMVS		97.55 294	97.53 288	97.60 358	98.92 303	93.77 402	96.64 364	99.43 173	94.49 415	97.62 358	99.18 158	96.82 228	99.67 335	94.73 377	99.93 56	99.36 244

PVSNet_Blended		96.88 344	96.68 343	97.47 373	98.92 303	93.77 402	94.71 452	99.43 173	90.98 465	97.62 358	97.36 415	96.82 228	99.67 335	94.73 377	99.56 266	98.98 342

ab-mvs		98.41 198	98.36 196	98.59 236	99.19 236	97.23 230	99.32 26	98.81 345	97.66 249	98.62 269	99.40 97	96.82 228	99.80 232	95.88 343	99.51 282	98.75 384

IMVS_0404		98.07 248	98.20 222	97.69 344	99.03 278	94.03 385	96.67 362	99.45 159	98.16 208	98.03 330	98.71 287	96.80 231	99.82 206	97.50 214	99.45 297	99.22 292

FIs		99.14 62	99.09 79	99.29 95	99.70 56	98.28 127	99.13 59	99.52 127	99.48 44	99.24 158	99.41 94	96.79 232	99.82 206	98.69 112	99.88 93	99.76 56

UniMVSNet (Re)		98.87 109	98.71 132	99.35 80	99.24 221	98.73 94	97.73 259	99.38 190	98.93 129	99.12 173	98.73 284	96.77 233	99.86 144	98.63 116	99.80 144	99.46 195

API-MVS		97.04 336	96.91 328	97.42 376	97.88 424	98.23 134	98.18 176	98.50 378	97.57 259	97.39 381	96.75 426	96.77 233	99.15 465	90.16 467	99.02 369	94.88 490

diffmvs		98.22 231	98.24 219	98.17 298	99.00 288	95.44 328	96.38 381	99.58 93	97.79 241	98.53 286	98.50 330	96.76 235	99.74 288	97.95 172	99.64 233	99.34 251
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DU-MVS		98.82 122	98.63 148	99.39 72	99.16 248	98.74 91	97.54 289	99.25 253	98.84 143	99.06 181	98.76 281	96.76 235	99.93 53	98.57 120	99.77 161	99.50 167

Baseline_NR-MVSNet		98.98 93	98.86 111	99.36 74	99.82 19	98.55 107	97.47 301	99.57 100	99.37 60	99.21 164	99.61 43	96.76 235	99.83 193	98.06 158	99.83 122	99.71 63

VPNet		98.87 109	98.83 115	99.01 149	99.70 56	97.62 200	98.43 148	99.35 204	99.47 47	99.28 142	99.05 196	96.72 238	99.82 206	98.09 155	99.36 314	99.59 107

UniMVSNet_NR-MVSNet		98.86 113	98.68 138	99.40 71	99.17 246	98.74 91	97.68 264	99.40 186	99.14 96	99.06 181	98.59 317	96.71 239	99.93 53	98.57 120	99.77 161	99.53 156

LF4IMVS		97.90 262	97.69 276	98.52 255	99.17 246	97.66 196	97.19 336	99.47 150	96.31 356	97.85 345	98.20 359	96.71 239	99.52 407	94.62 380	99.72 192	98.38 420

Elysia		99.15 57	99.14 68	99.18 113	99.63 82	97.92 169	98.50 137	99.43 173	99.67 20	99.70 51	99.13 173	96.66 241	99.98 4	99.54 44	99.96 28	99.64 84

StellarMVS		99.15 57	99.14 68	99.18 113	99.63 82	97.92 169	98.50 137	99.43 173	99.67 20	99.70 51	99.13 173	96.66 241	99.98 4	99.54 44	99.96 28	99.64 84

v148		98.45 195	98.60 155	98.00 316	99.44 165	94.98 348	97.44 305	99.06 294	98.30 188	99.32 136	98.97 225	96.65 243	99.62 365	98.37 137	99.85 106	99.39 226

v10		98.97 94	99.11 71	98.55 247	99.44 165	96.21 294	98.90 84	99.55 113	98.73 146	99.48 96	99.60 45	96.63 244	99.83 193	99.70 33	99.99 5	99.61 98

test_fmvs2		98.70 144	98.97 95	97.89 324	99.54 121	94.05 382	98.55 126	99.92 7	96.78 334	99.72 47	99.78 13	96.60 245	99.67 335	99.91 2	99.90 86	99.94 10

OpenMVS	96.65 7	97.09 332	96.68 343	98.32 280	98.32 399	97.16 242	98.86 92	99.37 194	89.48 474	96.29 433	99.15 168	96.56 246	99.90 81	92.90 428	99.20 345	97.89 445

UGNet		98.53 184	98.45 181	98.79 192	97.94 421	96.96 255	99.08 62	98.54 375	99.10 105	96.82 410	99.47 78	96.55 247	99.84 175	98.56 123	99.94 50	99.55 136
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
TEST9					98.71 343	98.08 150	95.96 406	99.03 303	91.40 460	95.85 443	97.53 403	96.52 248	99.76 269

Test By Simon												96.52 248

train_agg		97.10 331	96.45 356	99.07 135	98.71 343	98.08 150	95.96 406	99.03 303	91.64 455	95.85 443	97.53 403	96.47 250	99.76 269	93.67 410	99.16 351	99.36 244

test_8					98.67 357	98.01 158	95.91 412	99.02 306	91.64 455	95.79 445	97.50 406	96.47 250	99.76 269

Effi-MVS+-dtu		98.26 226	97.90 262	99.35 80	98.02 418	99.49 5	98.02 207	99.16 279	98.29 191	97.64 357	97.99 375	96.44 252	99.95 25	96.66 293	98.93 381	98.60 399

ppachtmachnet_test		97.50 295	97.74 271	96.78 408	98.70 347	91.23 451	94.55 461	99.05 298	96.36 353	99.21 164	98.79 271	96.39 253	99.78 257	96.74 280	99.82 127	99.34 251

OurMVSNet-221017-0		99.37 28	99.31 41	99.53 38	99.91 3	98.98 71	99.63 7	99.58 93	99.44 52	99.78 39	99.76 15	96.39 253	99.92 65	99.44 54	99.92 69	99.68 71

NR-MVSNet		98.95 97	98.82 116	99.36 74	99.16 248	98.72 96	99.22 46	99.20 264	99.10 105	99.72 47	98.76 281	96.38 255	99.86 144	98.00 166	99.82 127	99.50 167

v1192		98.60 169	98.66 143	98.41 269	99.27 210	95.88 306	97.52 291	99.36 198	97.41 280	99.33 130	99.20 151	96.37 256	99.82 206	99.57 39	99.92 69	99.55 136

ZD-MVS					99.01 287	98.84 85		99.07 293	94.10 427	98.05 328	98.12 364	96.36 257	99.86 144	92.70 436	99.19 348

usedtu_dtu_shiyan2		98.99 89	98.86 111	99.39 72	99.73 37	98.71 97	99.05 68	99.47 150	99.16 92	99.49 94	99.12 176	96.34 258	99.93 53	98.05 160	99.36 314	99.54 142

viewdifsd2359ckpt09		98.13 243	97.92 259	98.77 200	99.18 244	97.35 216	97.29 322	99.53 122	95.81 382	98.09 323	98.47 334	96.34 258	99.66 348	97.02 251	99.51 282	99.29 270

v1144		98.60 169	98.66 143	98.41 269	99.36 187	95.90 305	97.58 284	99.34 210	97.51 267	99.27 144	99.15 168	96.34 258	99.80 232	99.47 53	99.93 56	99.51 163

mvs_anonymous		97.83 277	98.16 231	96.87 402	98.18 408	91.89 435	97.31 320	98.90 325	97.37 285	98.83 238	99.46 80	96.28 261	99.79 245	98.90 94	98.16 420	98.95 349

test_vis1_rt		97.75 279	97.72 274	97.83 328	98.81 328	96.35 289	97.30 321	99.69 53	94.61 413	97.87 342	98.05 371	96.26 262	98.32 484	98.74 107	98.18 417	98.82 368

DSMNet-mixed		97.42 305	97.60 285	96.87 402	99.15 252	91.46 441	98.54 128	99.12 286	92.87 445	97.58 362	99.63 39	96.21 263	99.90 81	95.74 352	99.54 272	99.27 275

MVSMamba_PlusPlus		98.83 119	98.98 94	98.36 277	99.32 197	96.58 277	98.90 84	99.41 183	99.75 10	98.72 256	99.50 68	96.17 264	99.94 41	99.27 64	99.78 155	98.57 403

test_f		98.67 157	98.87 107	98.05 313	99.72 44	95.59 315	98.51 135	99.81 31	96.30 358	99.78 39	99.82 5	96.14 265	98.63 481	99.82 12	99.93 56	99.95 9

KinetiMVS		99.03 84	99.02 87	99.03 145	99.70 56	97.48 208	98.43 148	99.29 239	99.70 15	99.60 70	99.07 188	96.13 266	99.94 41	99.42 55	99.87 97	99.68 71

TAPA-MVS	96.21 11	96.63 354	95.95 365	98.65 221	98.93 299	98.09 146	96.93 348	99.28 243	83.58 489	98.13 319	97.78 389	96.13 266	99.40 437	93.52 414	99.29 330	98.45 410
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1240		98.55 179	98.62 150	98.32 280	99.22 227	95.58 317	97.51 293	99.45 159	97.16 309	99.45 104	99.24 142	96.12 268	99.85 157	99.60 37	99.88 93	99.55 136

RPSCF		98.62 166	98.36 196	99.42 67	99.65 70	99.42 10	98.55 126	99.57 100	97.72 246	98.90 223	99.26 135	96.12 268	99.52 407	95.72 353	99.71 201	99.32 260

MS-PatchMatch		97.68 284	97.75 270	97.45 374	98.23 406	93.78 401	97.29 322	98.84 340	96.10 367	98.64 265	98.65 305	96.04 270	99.36 442	96.84 272	99.14 354	99.20 297

v1921920		98.54 182	98.60 155	98.38 273	99.20 233	95.76 313	97.56 286	99.36 198	97.23 303	99.38 118	99.17 162	96.02 271	99.84 175	99.57 39	99.90 86	99.54 142

HPM-MVS++		98.10 244	97.64 282	99.48 56	99.09 262	99.13 60	97.52 291	98.75 356	97.46 276	96.90 405	97.83 386	96.01 272	99.84 175	95.82 350	99.35 317	99.46 195

WB-MVSnew		95.73 387	95.57 379	96.23 424	96.70 472	90.70 460	96.07 401	93.86 473	95.60 388	97.04 395	95.45 458	96.00 273	99.55 395	91.04 459	98.31 412	98.43 415

Anonymous20231206		98.21 233	98.21 221	98.20 295	99.51 130	95.43 329	98.13 183	99.32 218	96.16 365	98.93 219	98.82 265	96.00 273	99.83 193	97.32 230	99.73 184	99.36 244

EI-MVSNet		98.40 201	98.51 167	98.04 314	99.10 259	94.73 360	97.20 332	98.87 331	98.97 124	99.06 181	99.02 201	96.00 273	99.80 232	98.58 118	99.82 127	99.60 100

IterMVS-LS		98.55 179	98.70 135	98.09 306	99.48 152	94.73 360	97.22 331	99.39 188	98.97 124	99.38 118	99.31 122	96.00 273	99.93 53	98.58 118	99.97 21	99.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NCCC		97.86 269	97.47 294	99.05 142	98.61 367	98.07 152	96.98 344	98.90 325	97.63 251	97.04 395	97.93 381	95.99 277	99.66 348	95.31 365	98.82 387	99.43 208

our_test_3		97.39 308	97.73 273	96.34 418	98.70 347	89.78 466	94.61 459	98.97 315	96.50 345	99.04 191	98.85 255	95.98 278	99.84 175	97.26 233	99.67 222	99.41 216

v2v482		98.56 175	98.62 150	98.37 276	99.42 172	95.81 311	97.58 284	99.16 279	97.90 232	99.28 142	99.01 212	95.98 278	99.79 245	99.33 59	99.90 86	99.51 163

MVS		93.19 435	92.09 440	96.50 414	96.91 466	94.03 385	98.07 197	98.06 399	68.01 495	94.56 466	96.48 432	95.96 280	99.30 452	83.84 484	96.89 459	96.17 482

ttmdpeth		97.91 261	98.02 246	97.58 360	98.69 352	94.10 381	98.13 183	98.90 325	97.95 226	97.32 384	99.58 47	95.95 281	98.75 479	96.41 317	99.22 341	99.87 22

MVP-Stereo		98.08 247	97.92 259	98.57 240	98.96 295	96.79 265	97.90 231	99.18 272	96.41 352	98.46 292	98.95 232	95.93 282	99.60 375	96.51 311	98.98 376	99.31 264
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							95.74 420		96.48 347	96.11 436	97.63 399	95.92 283		94.16 394	99.20 345

AdaColmap		97.14 330	96.71 341	98.46 263	98.34 398	97.80 187	96.95 345	98.93 319	95.58 389	96.92 400	97.66 396	95.87 284	99.53 403	90.97 460	99.14 354	98.04 437

v144192		98.54 182	98.57 159	98.45 264	99.21 229	95.98 303	97.63 274	99.36 198	97.15 311	99.32 136	99.18 158	95.84 285	99.84 175	99.50 50	99.91 78	99.54 142

balanced_conf03		98.63 163	98.72 127	98.38 273	98.66 362	96.68 273	98.90 84	99.42 179	98.99 121	98.97 205	99.19 154	95.81 286	99.85 157	98.77 105	99.77 161	98.60 399

PatchMatch-RL		97.24 322	96.78 337	98.61 233	99.03 278	97.83 178	96.36 382	99.06 294	93.49 437	97.36 383	97.78 389	95.75 287	99.49 418	93.44 417	98.77 388	98.52 405

F-COLMAP		97.30 316	96.68 343	99.14 122	99.19 236	98.39 118	97.27 326	99.30 231	92.93 443	96.62 420	98.00 374	95.73 288	99.68 331	92.62 437	98.46 408	99.35 249

PMMVS		96.51 357	95.98 364	98.09 306	97.53 445	95.84 308	94.92 448	98.84 340	91.58 457	96.05 440	95.58 449	95.68 289	99.66 348	95.59 359	98.09 424	98.76 383

N_pmnet		97.63 288	97.17 309	98.99 151	99.27 210	97.86 175	95.98 403	93.41 475	95.25 399	99.47 100	98.90 242	95.63 290	99.85 157	96.91 261	99.73 184	99.27 275

WR-MVS		98.40 201	98.19 226	99.03 145	99.00 288	97.65 197	96.85 352	98.94 316	98.57 168	98.89 226	98.50 330	95.60 291	99.85 157	97.54 210	99.85 106	99.59 107

CANet		97.87 268	97.76 269	98.19 297	97.75 429	95.51 320	96.76 357	99.05 298	97.74 243	96.93 399	98.21 358	95.59 292	99.89 97	97.86 181	99.93 56	99.19 303

1314		95.74 386	95.60 376	96.17 427	97.53 445	92.75 422	98.07 197	98.31 387	91.22 462	94.25 468	96.68 427	95.53 293	99.03 467	91.64 449	97.18 453	96.74 475

114514_t		96.50 359	95.77 368	98.69 216	99.48 152	97.43 213	97.84 240	99.55 113	81.42 492	96.51 427	98.58 318	95.53 293	99.67 335	93.41 418	99.58 259	98.98 342

test12				98.93 164	98.58 374	97.83 178		98.66 363		96.53 424		95.51 295	99.69 321		99.13 356	99.27 275

旧先验1					98.82 325	97.45 211		98.76 353			98.34 348	95.50 296			99.01 370	99.23 287

YYNet1		97.60 289	97.67 277	97.39 378	99.04 275	93.04 417	95.27 437	98.38 385	97.25 297	98.92 221	98.95 232	95.48 297	99.73 295	96.99 255	98.74 389	99.41 216

MDA-MVSNet_test_wron		97.60 289	97.66 280	97.41 377	99.04 275	93.09 413	95.27 437	98.42 382	97.26 296	98.88 230	98.95 232	95.43 298	99.73 295	97.02 251	98.72 391	99.41 216

原ACMM1				98.35 278	98.90 307	96.25 292		98.83 344	92.48 449	96.07 438	98.10 366	95.39 299	99.71 306	92.61 438	98.99 373	99.08 325

balanced_ft_v1		98.28 223	98.35 199	98.10 305	98.08 415	96.23 293	99.23 45	99.26 251	98.34 182	97.46 373	99.42 89	95.38 300	99.88 115	98.60 117	99.34 319	98.17 430

USDC		97.41 306	97.40 295	97.44 375	98.94 297	93.67 405	95.17 441	99.53 122	94.03 429	98.97 205	99.10 181	95.29 301	99.34 446	95.84 349	99.73 184	99.30 268

testdata				98.09 306	98.93 299	95.40 330		98.80 347	90.08 471	97.45 376	98.37 344	95.26 302	99.70 313	93.58 413	98.95 379	99.17 311

BH-untuned		96.83 346	96.75 339	97.08 390	98.74 336	93.33 411	96.71 360	98.26 389	96.72 337	98.44 294	97.37 414	95.20 303	99.47 425	91.89 443	97.43 444	98.44 413

MVE	83.40 22	92.50 444	91.92 446	94.25 459	98.83 322	91.64 438	92.71 483	83.52 499	95.92 377	86.46 494	95.46 455	95.20 303	95.40 495	80.51 490	98.64 400	95.73 488
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
BH-RMVSNet		96.83 346	96.58 351	97.58 360	98.47 385	94.05 382	96.67 362	97.36 416	96.70 339	97.87 342	97.98 376	95.14 305	99.44 432	90.47 466	98.58 405	99.25 282

pmmvs4		97.58 292	97.28 303	98.51 256	98.84 320	96.93 258	95.40 433	98.52 377	93.60 434	98.61 271	98.65 305	95.10 306	99.60 375	96.97 258	99.79 150	98.99 341

test_vis1_n_1920		98.40 201	98.92 99	96.81 406	99.74 36	90.76 459	98.15 181	99.91 9	98.33 184	99.89 18	99.55 56	95.07 307	99.88 115	99.76 23	99.93 56	99.79 44

EU-MVSNet		97.66 286	98.50 170	95.13 451	99.63 82	85.84 482	98.35 161	98.21 391	98.23 195	99.54 78	99.46 80	95.02 308	99.68 331	98.24 143	99.87 97	99.87 22

DP-MVS Recon		97.33 314	96.92 326	98.57 240	99.09 262	97.99 159	96.79 354	99.35 204	93.18 439	97.71 353	98.07 370	95.00 309	99.31 450	93.97 401	99.13 356	98.42 417

HQP_MVS		97.99 258	97.67 277	98.93 164	99.19 236	97.65 197	97.77 250	99.27 246	98.20 202	97.79 349	97.98 376	94.90 310	99.70 313	94.42 388	99.51 282	99.45 200

plane_prior6					98.99 291	97.70 195						94.90 310

CPTT-MVS		97.84 275	97.36 299	99.27 99	99.31 198	98.46 115	98.29 164	99.27 246	94.90 408	97.83 346	98.37 344	94.90 310	99.84 175	93.85 407	99.54 272	99.51 163

new_pmnet		96.99 341	96.76 338	97.67 347	98.72 339	94.89 352	95.95 408	98.20 392	92.62 448	98.55 283	98.54 321	94.88 313	99.52 407	93.96 402	99.44 304	98.59 402

VDD-MVS		98.56 175	98.39 191	99.07 135	99.13 255	98.07 152	98.59 122	97.01 427	99.59 36	99.11 174	99.27 129	94.82 314	99.79 245	98.34 139	99.63 240	99.34 251

jason		97.45 302	97.35 300	97.76 336	99.24 221	93.93 394	95.86 413	98.42 382	94.24 423	98.50 289	98.13 362	94.82 314	99.91 74	97.22 235	99.73 184	99.43 208
jason: jason.
TAMVS		98.24 230	98.05 243	98.80 188	99.07 266	97.18 239	97.88 233	98.81 345	96.66 340	99.17 172	99.21 149	94.81 316	99.77 263	96.96 259	99.88 93	99.44 204

新几何1				98.91 168	98.94 297	97.76 189		98.76 353	87.58 483	96.75 413	98.10 366	94.80 317	99.78 257	92.73 435	99.00 371	99.20 297

VNet		98.42 197	98.30 208	98.79 192	98.79 332	97.29 226	98.23 171	98.66 363	99.31 68	98.85 235	98.80 269	94.80 317	99.78 257	98.13 152	99.13 356	99.31 264

MAR-MVS		96.47 361	95.70 371	98.79 192	97.92 422	99.12 62	98.28 165	98.60 368	92.16 453	95.54 452	96.17 438	94.77 319	99.52 407	89.62 469	98.23 414	97.72 456
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
CL-MVSNet_self_test		97.44 303	97.22 307	98.08 309	98.57 376	95.78 312	94.30 468	98.79 348	96.58 343	98.60 273	98.19 360	94.74 320	99.64 359	96.41 317	98.84 384	98.82 368

MSP-MVS		98.40 201	98.00 248	99.61 14	99.57 102	99.25 29	98.57 124	99.35 204	97.55 263	99.31 138	97.71 393	94.61 321	99.88 115	96.14 334	99.19 348	99.70 68
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
SSC-MVS		98.71 139	98.74 123	98.62 229	99.72 44	96.08 300	98.74 99	98.64 366	99.74 12	99.67 59	99.24 142	94.57 322	99.95 25	99.11 77	99.24 337	99.82 36

PAPR		95.29 399	94.47 410	97.75 337	97.50 451	95.14 343	94.89 449	98.71 361	91.39 461	95.35 456	95.48 454	94.57 322	99.14 466	84.95 482	97.37 447	98.97 346

test222					98.92 303	96.93 258	95.54 425	98.78 350	85.72 486	96.86 408	98.11 365	94.43 324			99.10 361	99.23 287

PLC	94.65 16	96.51 357	95.73 370	98.85 175	98.75 335	97.91 171	96.42 379	99.06 294	90.94 466	95.59 446	97.38 413	94.41 325	99.59 379	90.93 461	98.04 430	99.05 329
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		97.84 275	97.84 266	97.83 328	99.14 253	94.74 359	96.94 346	98.88 329	95.84 379	98.89 226	98.96 228	94.40 326	99.69 321	97.55 208	99.95 38	99.05 329

CNLPA		97.17 328	96.71 341	98.55 247	98.56 377	98.05 156	96.33 384	98.93 319	96.91 325	97.06 393	97.39 412	94.38 327	99.45 430	91.66 447	99.18 350	98.14 432

WB-MVS		98.52 188	98.55 161	98.43 267	99.65 70	95.59 315	98.52 130	98.77 351	99.65 25	99.52 87	99.00 216	94.34 328	99.93 53	98.65 114	98.83 385	99.76 56

MDA-MVSNet-bldmvs		97.94 260	97.91 261	98.06 311	99.44 165	94.96 349	96.63 365	99.15 284	98.35 181	98.83 238	99.11 178	94.31 329	99.85 157	96.60 298	98.72 391	99.37 237

OpenMVS_ROB	95.38 14	95.84 384	95.18 397	97.81 330	98.41 395	97.15 243	97.37 314	98.62 367	83.86 488	98.65 264	98.37 344	94.29 330	99.68 331	88.41 472	98.62 403	96.60 477

TR-MVS		95.55 392	95.12 398	96.86 405	97.54 443	93.94 393	96.49 374	96.53 441	94.36 422	97.03 397	96.61 429	94.26 331	99.16 464	86.91 479	96.31 465	97.47 464

GBi-Net		98.65 159	98.47 178	99.17 115	98.90 307	98.24 130	99.20 49	99.44 167	98.59 163	98.95 211	99.55 56	94.14 332	99.86 144	97.77 186	99.69 211	99.41 216

test1		98.65 159	98.47 178	99.17 115	98.90 307	98.24 130	99.20 49	99.44 167	98.59 163	98.95 211	99.55 56	94.14 332	99.86 144	97.77 186	99.69 211	99.41 216

FMVSNet2		98.49 191	98.40 188	98.75 204	98.90 307	97.14 244	98.61 120	99.13 285	98.59 163	99.19 166	99.28 127	94.14 332	99.82 206	97.97 170	99.80 144	99.29 270

PAPM_NR		96.82 348	96.32 359	98.30 283	99.07 266	96.69 272	97.48 297	98.76 353	95.81 382	96.61 421	96.47 433	94.12 335	99.17 463	90.82 464	97.78 434	99.06 328

Anonymous20240521		98.69 148	98.87 107	98.16 300	99.77 27	95.11 345	99.08 62	99.44 167	99.34 64	99.33 130	99.55 56	94.10 336	99.94 41	99.25 67	99.96 28	99.42 213

test_cas_vis1_n_1920		98.33 215	98.68 138	97.27 382	99.69 60	92.29 431	98.03 204	99.85 18	97.62 252	99.96 4	99.62 40	93.98 337	99.74 288	99.52 49	99.86 104	99.79 44

HQP2-MVS												93.84 338

HQP-MVS		97.00 340	96.49 355	98.55 247	98.67 357	96.79 265	96.29 387	99.04 301	96.05 368	95.55 449	96.84 424	93.84 338	99.54 401	92.82 431	99.26 335	99.32 260

MVSFormer		98.26 226	98.43 184	97.77 333	98.88 313	93.89 398	99.39 20	99.56 109	99.11 98	98.16 315	98.13 362	93.81 340	99.97 6	99.26 65	99.57 263	99.43 208

lupinMVS		97.06 334	96.86 330	97.65 351	98.88 313	93.89 398	95.48 429	97.97 400	93.53 435	98.16 315	97.58 401	93.81 340	99.91 74	96.77 277	99.57 263	99.17 311

MG-MVS		96.77 349	96.61 348	97.26 383	98.31 400	93.06 414	95.93 409	98.12 397	96.45 351	97.92 337	98.73 284	93.77 342	99.39 439	91.19 458	99.04 365	99.33 257

PVSNet	93.40 17	95.67 388	95.70 371	95.57 441	98.83 322	88.57 470	92.50 484	97.72 405	92.69 447	96.49 430	96.44 434	93.72 343	99.43 433	93.61 411	99.28 331	98.71 387

MM		98.22 231	97.99 249	98.91 168	98.66 362	96.97 253	97.89 232	94.44 466	99.54 40	98.95 211	99.14 171	93.50 344	99.92 65	99.80 17	99.96 28	99.85 30

AstraMVS		98.16 242	98.07 242	98.41 269	99.51 130	95.86 307	98.00 211	95.14 461	98.97 124	99.43 106	99.24 142	93.25 345	99.84 175	99.21 70	99.87 97	99.54 142

pmmvs5		97.64 287	97.49 291	98.08 309	99.14 253	95.12 344	96.70 361	99.05 298	93.77 432	98.62 269	98.83 262	93.23 346	99.75 281	98.33 141	99.76 176	99.36 244

CANet_DTU		97.26 319	97.06 318	97.84 327	97.57 440	94.65 364	96.19 393	98.79 348	97.23 303	95.14 458	98.24 355	93.22 347	99.84 175	97.34 226	99.84 111	99.04 333

UnsupCasMVSNet_bld		97.30 316	96.92 326	98.45 264	99.28 207	96.78 268	96.20 392	99.27 246	95.42 394	98.28 307	98.30 352	93.16 348	99.71 306	94.99 370	97.37 447	98.87 364

IterMVS		97.73 280	98.11 236	96.57 412	99.24 221	90.28 462	95.52 428	99.21 262	98.86 139	99.33 130	99.33 116	93.11 349	99.94 41	98.49 127	99.94 50	99.48 185
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		97.85 274	98.18 227	96.87 402	99.27 210	91.16 452	95.53 426	99.25 253	99.10 105	99.41 112	99.35 109	93.10 350	99.96 13	98.65 114	99.94 50	99.49 174

SCA		96.41 363	96.66 346	95.67 438	98.24 404	88.35 472	95.85 415	96.88 434	96.11 366	97.67 356	98.67 300	93.10 350	99.85 157	94.16 394	99.22 341	98.81 373

DPM-MVS		96.32 364	95.59 378	98.51 256	98.76 333	97.21 235	94.54 462	98.26 389	91.94 454	96.37 431	97.25 417	93.06 352	99.43 433	91.42 453	98.74 389	98.89 360

BH-w/o		95.13 403	94.89 404	95.86 433	98.20 407	91.31 446	95.65 422	97.37 415	93.64 433	96.52 426	95.70 448	93.04 353	99.02 468	88.10 474	95.82 476	97.24 468

cascas		94.79 409	94.33 415	96.15 430	96.02 487	92.36 430	92.34 486	99.26 251	85.34 487	95.08 459	94.96 464	92.96 354	98.53 482	94.41 391	98.59 404	97.56 462

c3_l		97.36 311	97.37 298	97.31 379	98.09 414	93.25 412	95.01 446	99.16 279	97.05 314	98.77 250	98.72 286	92.88 355	99.64 359	96.93 260	99.76 176	99.05 329

MVS-HIRNet		94.32 414	95.62 374	90.42 477	98.46 387	75.36 501	96.29 387	89.13 492	95.25 399	95.38 455	99.75 16	92.88 355	99.19 462	94.07 400	99.39 310	96.72 476

test_vis1_n		98.31 218	98.50 170	97.73 342	99.76 30	94.17 377	98.68 109	99.91 9	96.31 356	99.79 38	99.57 49	92.85 357	99.42 435	99.79 19	99.84 111	99.60 100

sss		97.21 324	96.93 324	98.06 311	98.83 322	95.22 340	96.75 358	98.48 379	94.49 415	97.27 385	97.90 382	92.77 358	99.80 232	96.57 301	99.32 323	99.16 317

MGCNet		97.44 303	97.01 321	98.72 212	96.42 481	96.74 269	97.20 332	91.97 485	98.46 176	98.30 303	98.79 271	92.74 359	99.91 74	99.30 62	99.94 50	99.52 159

miper_ehance_all_eth		97.06 334	97.03 319	97.16 389	97.83 426	93.06 414	94.66 456	99.09 291	95.99 374	98.69 258	98.45 336	92.73 360	99.61 372	96.79 274	99.03 366	98.82 368

SixPastTwentyTwo		98.75 135	98.62 150	99.16 118	99.83 18	97.96 166	99.28 40	98.20 392	99.37 60	99.70 51	99.65 36	92.65 361	99.93 53	99.04 84	99.84 111	99.60 100

UnsupCasMVSNet_eth		97.89 264	97.60 285	98.75 204	99.31 198	97.17 241	97.62 275	99.35 204	98.72 152	98.76 252	98.68 298	92.57 362	99.74 288	97.76 190	95.60 477	99.34 251

CHOSEN 1792x2688		97.49 298	97.14 313	98.54 252	99.68 63	96.09 298	96.50 373	99.62 78	91.58 457	98.84 237	98.97 225	92.36 363	99.88 115	96.76 278	99.95 38	99.67 76

dmvs_testset		92.94 439	92.21 439	95.13 451	98.59 372	90.99 454	97.65 270	92.09 481	96.95 320	94.00 473	93.55 474	92.34 364	96.97 493	72.20 495	92.52 487	97.43 465

LuminaMVS		98.39 207	98.20 222	98.98 155	99.50 136	97.49 205	97.78 247	97.69 407	98.75 145	99.49 94	99.25 140	92.30 365	99.94 41	99.14 75	99.88 93	99.50 167

PCF-MVS	92.86 18	94.36 413	93.00 431	98.42 268	98.70 347	97.56 202	93.16 482	99.11 288	79.59 493	97.55 365	97.43 410	92.19 366	99.73 295	79.85 491	99.45 297	97.97 442
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPP-MVSNet		98.30 219	98.04 244	99.07 135	99.56 110	97.83 178	99.29 36	98.07 398	99.03 118	98.59 275	99.13 173	92.16 367	99.90 81	96.87 269	99.68 216	99.49 174

usedtu_dtu_shiyan1		97.37 309	97.13 314	98.11 303	99.03 278	95.40 330	94.47 463	98.99 312	96.87 327	97.97 334	97.81 387	92.12 368	99.75 281	97.49 219	99.43 305	99.16 317

FE-MVSNET3		97.37 309	97.13 314	98.11 303	99.03 278	95.40 330	94.47 463	98.99 312	96.87 327	97.97 334	97.81 387	92.12 368	99.75 281	97.49 219	99.43 305	99.16 317

1112_ss		97.29 318	96.86 330	98.58 237	99.34 195	96.32 290	96.75 358	99.58 93	93.14 440	96.89 406	97.48 407	92.11 370	99.86 144	96.91 261	99.54 272	99.57 123

CDS-MVSNet		97.69 283	97.35 300	98.69 216	98.73 337	97.02 251	96.92 350	98.75 356	95.89 378	98.59 275	98.67 300	92.08 371	99.74 288	96.72 283	99.81 133	99.32 260
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
eth_miper_zixun_eth		97.23 323	97.25 305	97.17 387	98.00 419	92.77 421	94.71 452	99.18 272	97.27 295	98.56 281	98.74 283	91.89 372	99.69 321	97.06 250	99.81 133	99.05 329

test_fmvs1_n		98.09 246	98.28 211	97.52 368	99.68 63	93.47 410	98.63 116	99.93 5	95.41 397	99.68 57	99.64 37	91.88 373	99.48 422	99.82 12	99.87 97	99.62 90

RRT-MVS		97.88 266	97.98 250	97.61 357	98.15 410	93.77 402	98.97 77	99.64 70	99.16 92	98.69 258	99.42 89	91.60 374	99.89 97	97.63 200	98.52 407	99.16 317

IS-MVSNet		98.19 236	97.90 262	99.08 133	99.57 102	97.97 163	99.31 30	98.32 386	99.01 120	98.98 201	99.03 200	91.59 375	99.79 245	95.49 362	99.80 144	99.48 185

test_fmvs1		97.72 281	97.94 256	97.07 392	98.66 362	92.39 428	97.68 264	99.81 31	95.20 402	99.54 78	99.44 85	91.56 376	99.41 436	99.78 21	99.77 161	99.40 225

VortexMVS		97.98 259	98.31 207	97.02 393	98.88 313	91.45 442	98.03 204	99.47 150	98.65 154	99.55 76	99.47 78	91.49 377	99.81 223	99.32 60	99.91 78	99.80 42

Test_1112_low_res		96.99 341	96.55 352	98.31 282	99.35 192	95.47 327	95.84 416	99.53 122	91.51 459	96.80 411	98.48 333	91.36 378	99.83 193	96.58 299	99.53 276	99.62 90

guyue		98.01 254	97.93 258	98.26 286	99.45 163	95.48 324	98.08 193	96.24 444	98.89 135	99.34 127	99.14 171	91.32 379	99.82 206	99.07 80	99.83 122	99.48 185

Syy-MVS		96.04 374	95.56 380	97.49 371	97.10 461	94.48 367	96.18 395	96.58 439	95.65 386	94.77 461	92.29 485	91.27 380	99.36 442	98.17 151	98.05 428	98.63 397

WTY-MVS		96.67 352	96.27 362	97.87 326	98.81 328	94.61 365	96.77 356	97.92 402	94.94 407	97.12 388	97.74 392	91.11 381	99.82 206	93.89 404	98.15 421	99.18 307

mvsmamba		97.57 293	97.26 304	98.51 256	98.69 352	96.73 270	98.74 99	97.25 421	97.03 317	97.88 341	99.23 147	90.95 382	99.87 135	96.61 297	99.00 371	98.91 358

MonoMVSNet		96.25 368	96.53 354	95.39 447	96.57 474	91.01 453	98.82 97	97.68 409	98.57 168	98.03 330	99.37 104	90.92 383	97.78 489	94.99 370	93.88 485	97.38 466

PVSNet_0	89.98 21	91.15 455	90.30 457	93.70 467	97.72 430	84.34 491	90.24 489	97.42 414	90.20 470	93.79 476	93.09 478	90.90 384	98.89 476	86.57 480	72.76 497	97.87 447

dmvs_re		95.98 378	95.39 387	97.74 339	98.86 316	97.45 211	98.37 159	95.69 457	97.95 226	96.56 422	95.95 442	90.70 385	97.68 490	88.32 473	96.13 468	98.11 433

miper_enhance_ethall		96.01 375	95.74 369	96.81 406	96.41 482	92.27 432	93.69 479	98.89 328	91.14 464	98.30 303	97.35 416	90.58 386	99.58 386	96.31 323	99.03 366	98.60 399

VDDNet		98.21 233	97.95 254	99.01 149	99.58 93	97.74 191	99.01 71	97.29 420	99.67 20	98.97 205	99.50 68	90.45 387	99.80 232	97.88 177	99.20 345	99.48 185

Anonymous202405211		97.90 262	97.50 290	99.08 133	98.90 307	98.25 129	98.53 129	96.16 445	98.87 137	99.11 174	98.86 252	90.40 388	99.78 257	97.36 225	99.31 325	99.19 303

miper_lstm_enhance		97.18 327	97.16 310	97.25 384	98.16 409	92.85 419	95.15 443	99.31 223	97.25 297	98.74 255	98.78 273	90.07 389	99.78 257	97.19 237	99.80 144	99.11 324

lessismore_v0				98.97 157	99.73 37	97.53 204		86.71 496		99.37 120	99.52 67	89.93 390	99.92 65	98.99 88	99.72 192	99.44 204

HY-MVS	95.94 13	95.90 381	95.35 389	97.55 365	97.95 420	94.79 356	98.81 98	96.94 432	92.28 452	95.17 457	98.57 319	89.90 391	99.75 281	91.20 457	97.33 451	98.10 434

NormalMVS		98.26 226	97.97 253	99.15 121	99.64 76	97.83 178	98.28 165	99.43 173	99.24 75	98.80 245	98.85 255	89.76 392	99.94 41	98.04 161	99.67 222	99.68 71

SymmetryMVS		98.05 250	97.71 275	99.09 132	99.29 204	97.83 178	98.28 165	97.64 412	99.24 75	98.80 245	98.85 255	89.76 392	99.94 41	98.04 161	99.50 290	99.49 174

K. test v3		98.00 255	97.66 280	99.03 145	99.79 23	97.56 202	99.19 53	92.47 478	99.62 32	99.52 87	99.66 32	89.61 394	99.96 13	99.25 67	99.81 133	99.56 129

CMPMVS	75.91 23	96.29 365	95.44 384	98.84 180	96.25 484	98.69 98	97.02 341	99.12 286	88.90 478	97.83 346	98.86 252	89.51 395	98.90 475	91.92 442	99.51 282	98.92 355
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		96.25 368	97.21 308	93.38 472	99.10 259	80.56 500	97.20 332	98.19 394	96.94 321	99.00 196	99.02 201	89.50 396	99.80 232	96.36 321	99.59 254	99.78 47

DeepMVS_CX				93.44 470	98.24 404	94.21 375		94.34 467	64.28 496	91.34 487	94.87 467	89.45 397	92.77 497	77.54 493	93.14 486	93.35 492

EPNet		96.14 372	95.44 384	98.25 288	90.76 501	95.50 323	97.92 228	94.65 464	98.97 124	92.98 480	98.85 255	89.12 398	99.87 135	95.99 339	99.68 216	99.39 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		97.46 300	97.16 310	98.34 279	99.55 116	96.10 295	98.94 81	98.44 380	98.32 186	98.16 315	98.62 312	88.76 399	99.73 295	93.88 405	99.79 150	99.18 307

test1111		96.49 360	96.82 334	95.52 443	99.42 172	87.08 479	99.22 46	87.14 495	99.11 98	99.46 101	99.58 47	88.69 400	99.86 144	98.80 100	99.95 38	99.62 90

DIV-MVS_self_test		97.02 337	96.84 332	97.58 360	97.82 427	94.03 385	94.66 456	99.16 279	97.04 315	98.63 266	98.71 287	88.69 400	99.69 321	97.00 253	99.81 133	99.01 337

cl____		97.02 337	96.83 333	97.58 360	97.82 427	94.04 384	94.66 456	99.16 279	97.04 315	98.63 266	98.71 287	88.68 402	99.69 321	97.00 253	99.81 133	99.00 340

h-mvs33		97.77 278	97.33 302	99.10 128	99.21 229	97.84 177	98.35 161	98.57 372	99.11 98	98.58 277	99.02 201	88.65 403	99.96 13	98.11 153	96.34 464	99.49 174

hse-mvs2		97.46 300	97.07 317	98.64 223	98.73 337	97.33 218	97.45 303	97.64 412	99.11 98	98.58 277	97.98 376	88.65 403	99.79 245	98.11 153	97.39 446	98.81 373

ECVR-MVS		96.42 362	96.61 348	95.85 434	99.38 180	88.18 474	99.22 46	86.00 497	99.08 112	99.36 123	99.57 49	88.47 405	99.82 206	98.52 126	99.95 38	99.54 142

FA-MVS(test-final)		96.99 341	96.82 334	97.50 370	98.70 347	94.78 357	99.34 23	96.99 428	95.07 403	98.48 291	99.33 116	88.41 406	99.65 355	96.13 336	98.92 382	98.07 436

EPNet_dtu		94.93 408	94.78 405	95.38 448	93.58 494	87.68 476	96.78 355	95.69 457	97.35 287	89.14 491	98.09 368	88.15 407	99.49 418	94.95 373	99.30 328	98.98 342
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
alignmvs		97.35 312	96.88 329	98.78 195	98.54 379	98.09 146	97.71 260	97.69 407	99.20 82	97.59 361	95.90 444	88.12 408	99.55 395	98.18 149	98.96 378	98.70 390

blended_shiyan6		95.99 377	95.33 390	97.95 319	97.06 463	94.89 352	95.34 435	98.58 370	96.17 361	97.06 393	92.41 482	87.64 409	99.76 269	97.64 199	96.09 469	99.19 303

blended_shiyan8		95.98 378	95.33 390	97.94 320	97.05 465	94.87 354	95.34 435	98.59 369	96.17 361	97.09 391	92.39 483	87.62 410	99.76 269	97.65 198	96.05 475	99.20 297

FMVSNet3		97.50 295	97.24 306	98.29 284	98.08 415	95.83 309	97.86 237	98.91 324	97.89 233	98.95 211	98.95 232	87.06 411	99.81 223	97.77 186	99.69 211	99.23 287

baseline1		95.96 380	95.44 384	97.52 368	98.51 383	93.99 392	98.39 157	96.09 448	98.21 198	98.40 301	97.76 391	86.88 412	99.63 362	95.42 363	89.27 490	98.95 349

RPMNet		97.02 337	96.93 324	97.30 380	97.71 433	94.22 373	98.11 188	99.30 231	99.37 60	96.91 402	99.34 113	86.72 413	99.87 135	97.53 211	97.36 449	97.81 450

HyFIR lowres test		97.19 326	96.60 350	98.96 158	99.62 86	97.28 227	95.17 441	99.50 131	94.21 424	99.01 195	98.32 351	86.61 414	99.99 2	97.10 246	99.84 111	99.60 100

PAPM		91.88 454	90.34 456	96.51 413	98.06 417	92.56 424	92.44 485	97.17 423	86.35 484	90.38 488	96.01 440	86.61 414	99.21 461	70.65 497	95.43 478	97.75 454

test_yl		96.69 350	96.29 360	97.90 322	98.28 401	95.24 338	97.29 322	97.36 416	98.21 198	98.17 312	97.86 383	86.27 416	99.55 395	94.87 374	98.32 410	98.89 360

DCV-MVSNet		96.69 350	96.29 360	97.90 322	98.28 401	95.24 338	97.29 322	97.36 416	98.21 198	98.17 312	97.86 383	86.27 416	99.55 395	94.87 374	98.32 410	98.89 360

CHOSEN 280x420		95.51 394	95.47 381	95.65 440	98.25 403	88.27 473	93.25 481	98.88 329	93.53 435	94.65 464	97.15 420	86.17 418	99.93 53	97.41 223	99.93 56	98.73 386

EMVS		93.83 424	94.02 416	93.23 473	96.83 469	84.96 485	89.77 492	96.32 443	97.92 230	97.43 378	96.36 437	86.17 418	98.93 473	87.68 475	97.73 436	95.81 487

MIMVSNet		96.62 355	96.25 363	97.71 343	99.04 275	94.66 363	99.16 55	96.92 433	97.23 303	97.87 342	99.10 181	86.11 420	99.65 355	91.65 448	99.21 344	98.82 368

wanda-best-256-512		95.48 395	94.74 407	97.68 345	96.53 475	94.12 379	94.17 470	98.57 372	95.84 379	96.71 414	91.16 488	86.05 421	99.76 269	97.57 206	96.09 469	99.17 311

FE-blended-shiyan7		95.48 395	94.74 407	97.68 345	96.53 475	94.12 379	94.17 470	98.57 372	95.84 379	96.71 414	91.16 488	86.05 421	99.76 269	97.57 206	96.09 469	99.17 311

usedtu_blend_shiyan5		96.20 371	95.62 374	97.94 320	96.53 475	94.93 350	98.83 96	99.59 90	98.89 135	96.71 414	91.16 488	86.05 421	99.73 295	96.70 286	96.09 469	99.17 311

tpmvs		95.02 406	95.25 393	94.33 458	96.39 483	85.87 481	98.08 193	96.83 435	95.46 393	95.51 454	98.69 296	85.91 424	99.53 403	94.16 394	96.23 466	97.58 461

MDTV_nov1_ep13_2view						74.92 502	97.69 263		90.06 472	97.75 352		85.78 425		93.52 414		98.69 391

ADS-MVSNet2		95.43 398	94.98 400	96.76 409	98.14 411	91.74 436	97.92 228	97.76 404	90.23 467	96.51 427	98.91 239	85.61 426	99.85 157	92.88 429	96.90 457	98.69 391

ADS-MVSNet		95.24 401	94.93 403	96.18 426	98.14 411	90.10 464	97.92 228	97.32 419	90.23 467	96.51 427	98.91 239	85.61 426	99.74 288	92.88 429	96.90 457	98.69 391

tpmrst		95.07 404	95.46 382	93.91 464	97.11 460	84.36 490	97.62 275	96.96 430	94.98 405	96.35 432	98.80 269	85.46 428	99.59 379	95.60 358	96.23 466	97.79 453

CR-MVSNet		96.28 366	95.95 365	97.28 381	97.71 433	94.22 373	98.11 188	98.92 322	92.31 451	96.91 402	99.37 104	85.44 429	99.81 223	97.39 224	97.36 449	97.81 450

Patchmtry		97.35 312	96.97 322	98.50 260	97.31 456	96.47 285	98.18 176	98.92 322	98.95 128	98.78 247	99.37 104	85.44 429	99.85 157	95.96 341	99.83 122	99.17 311

gbinet_0.2-2-1-0.02		95.44 397	94.55 409	98.14 301	95.99 488	95.34 335	94.71 452	98.29 388	96.00 373	96.05 440	90.50 492	84.99 431	99.79 245	97.33 228	97.07 456	99.28 273

test_method		79.78 461	79.50 464	80.62 478	80.21 503	45.76 506	70.82 495	98.41 384	31.08 498	80.89 498	97.71 393	84.85 432	97.37 491	91.51 452	80.03 494	98.75 384

PatchmatchNet		95.58 391	95.67 373	95.30 450	97.34 455	87.32 478	97.65 270	96.65 437	95.30 398	97.07 392	98.69 296	84.77 433	99.75 281	94.97 372	98.64 400	98.83 367
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												84.74 434				98.81 373

E-PMN		94.17 418	94.37 413	93.58 468	96.86 467	85.71 484	90.11 491	97.07 426	98.17 205	97.82 348	97.19 418	84.62 435	98.94 472	89.77 468	97.68 437	96.09 486

LFMVS		97.20 325	96.72 340	98.64 223	98.72 339	96.95 256	98.93 82	94.14 472	99.74 12	98.78 247	99.01 212	84.45 436	99.73 295	97.44 221	99.27 332	99.25 282

patchmatchnet-post											98.77 275	84.37 437	99.85 157

PatchT		96.65 353	96.35 357	97.54 366	97.40 453	95.32 336	97.98 219	96.64 438	99.33 65	96.89 406	99.42 89	84.32 438	99.81 223	97.69 197	97.49 440	97.48 463

Patchmatch-RL test		97.26 319	97.02 320	97.99 317	99.52 127	95.53 319	96.13 398	99.71 46	97.47 271	99.27 144	99.16 164	84.30 439	99.62 365	97.89 174	99.77 161	98.81 373

sam_mvs												84.29 440

BP-MVS1		97.40 307	96.97 322	98.71 213	99.07 266	96.81 264	98.34 163	97.18 422	98.58 166	98.17 312	98.61 314	84.01 441	99.94 41	98.97 89	99.78 155	99.37 237

MDTV_nov1_ep13			95.22 395		97.06 463	83.20 493	97.74 257	96.16 445	94.37 421	96.99 398	98.83 262	83.95 442	99.53 403	93.90 403	97.95 432

test_post											21.25 500	83.86 443	99.70 313

Patchmatch-test		96.55 356	96.34 358	97.17 387	98.35 397	93.06 414	98.40 156	97.79 403	97.33 288	98.41 297	98.67 300	83.68 444	99.69 321	95.16 368	99.31 325	98.77 381

GDP-MVS		97.50 295	97.11 316	98.67 219	99.02 285	96.85 262	98.16 180	99.71 46	98.32 186	98.52 288	98.54 321	83.39 445	99.95 25	98.79 101	99.56 266	99.19 303

GA-MVS		95.86 382	95.32 392	97.49 371	98.60 369	94.15 378	93.83 477	97.93 401	95.49 392	96.68 417	97.42 411	83.21 446	99.30 452	96.22 328	98.55 406	99.01 337

JIA-IIPM		95.52 393	95.03 399	97.00 394	96.85 468	94.03 385	96.93 348	95.82 453	99.20 82	94.63 465	99.71 22	83.09 447	99.60 375	94.42 388	94.64 481	97.36 467

test_post1							97.59 283				20.48 501	83.07 448	99.66 348	94.16 394

tpm cat1		93.29 433	93.13 430	93.75 466	97.39 454	84.74 486	97.39 308	97.65 410	83.39 490	94.16 469	98.41 339	82.86 449	99.39 439	91.56 451	95.35 479	97.14 469

cl22		95.79 385	95.39 387	96.98 396	96.77 471	92.79 420	94.40 466	98.53 376	94.59 414	97.89 340	98.17 361	82.82 450	99.24 458	96.37 319	99.03 366	98.92 355

test-LLR		93.90 423	93.85 418	94.04 462	96.53 475	84.62 488	94.05 474	92.39 479	96.17 361	94.12 470	95.07 459	82.30 451	99.67 335	95.87 346	98.18 417	97.82 448

test0.0.03 1		94.51 411	93.69 421	96.99 395	96.05 485	93.61 409	94.97 447	93.49 474	96.17 361	97.57 364	94.88 465	82.30 451	99.01 470	93.60 412	94.17 484	98.37 422

AUN-MVS		96.24 370	95.45 383	98.60 235	98.70 347	97.22 233	97.38 310	97.65 410	95.95 376	95.53 453	97.96 380	82.11 453	99.79 245	96.31 323	97.44 443	98.80 378

MVSTER		96.86 345	96.55 352	97.79 331	97.91 423	94.21 375	97.56 286	98.87 331	97.49 270	99.06 181	99.05 196	80.72 454	99.80 232	98.44 129	99.82 127	99.37 237

tmp_tt		78.77 462	78.73 465	78.90 479	58.45 504	74.76 503	94.20 469	78.26 502	39.16 497	86.71 493	92.82 481	80.50 455	75.19 499	86.16 481	92.29 488	86.74 493

thres200		93.72 427	93.14 429	95.46 446	98.66 362	91.29 447	96.61 366	94.63 465	97.39 283	96.83 409	93.71 473	79.88 456	99.56 391	82.40 488	98.13 422	95.54 489

thres100view900		94.19 417	93.67 422	95.75 437	99.06 271	91.35 445	98.03 204	94.24 470	98.33 184	97.40 379	94.98 463	79.84 457	99.62 365	83.05 485	98.08 425	96.29 480

thres600view7		94.45 412	93.83 419	96.29 420	99.06 271	91.53 440	97.99 218	94.24 470	98.34 182	97.44 377	95.01 461	79.84 457	99.67 335	84.33 483	98.23 414	97.66 458

tfpn200view9		94.03 421	93.44 424	95.78 436	98.93 299	91.44 443	97.60 281	94.29 468	97.94 228	97.10 389	94.31 470	79.67 459	99.62 365	83.05 485	98.08 425	96.29 480

thres400		94.14 419	93.44 424	96.24 423	98.93 299	91.44 443	97.60 281	94.29 468	97.94 228	97.10 389	94.31 470	79.67 459	99.62 365	83.05 485	98.08 425	97.66 458

pmmvs3		95.03 405	94.40 412	96.93 398	97.70 435	92.53 425	95.08 444	97.71 406	88.57 480	97.71 353	98.08 369	79.39 461	99.82 206	96.19 330	99.11 360	98.43 415

baseline2		93.73 426	92.83 432	96.42 416	97.70 435	91.28 448	96.84 353	89.77 491	93.96 431	92.44 483	95.93 443	79.14 462	99.77 263	92.94 426	96.76 461	98.21 427

FE-MVS		95.66 389	94.95 402	97.77 333	98.53 381	95.28 337	99.40 19	96.09 448	93.11 441	97.96 336	99.26 135	79.10 463	99.77 263	92.40 440	98.71 393	98.27 426

tpm		94.67 410	94.34 414	95.66 439	97.68 438	88.42 471	97.88 233	94.90 462	94.46 417	96.03 442	98.56 320	78.66 464	99.79 245	95.88 343	95.01 480	98.78 380

CostFormer		93.97 422	93.78 420	94.51 457	97.53 445	85.83 483	97.98 219	95.96 450	89.29 476	94.99 460	98.63 310	78.63 465	99.62 365	94.54 382	96.50 462	98.09 435

ET-MVSNet_ETH3D		94.30 416	93.21 427	97.58 360	98.14 411	94.47 368	94.78 451	93.24 477	94.72 411	89.56 489	95.87 445	78.57 466	99.81 223	96.91 261	97.11 455	98.46 407

dp		93.47 430	93.59 423	93.13 474	96.64 473	81.62 499	97.66 268	96.42 442	92.80 446	96.11 436	98.64 308	78.55 467	99.59 379	93.31 419	92.18 489	98.16 431

EPMVS		93.72 427	93.27 426	95.09 453	96.04 486	87.76 475	98.13 183	85.01 498	94.69 412	96.92 400	98.64 308	78.47 468	99.31 450	95.04 369	96.46 463	98.20 428

tpm2		93.09 436	92.58 434	94.62 456	97.56 441	86.53 480	97.66 268	95.79 454	86.15 485	94.07 472	98.23 357	75.95 469	99.53 403	90.91 462	96.86 460	97.81 450

FPMVS		93.44 431	92.23 438	97.08 390	99.25 220	97.86 175	95.61 423	97.16 424	92.90 444	93.76 477	98.65 305	75.94 470	95.66 494	79.30 492	97.49 440	97.73 455

thisisatest0515		94.12 420	93.16 428	96.97 397	98.60 369	92.90 418	93.77 478	90.61 488	94.10 427	96.91 402	95.87 445	74.99 471	99.80 232	94.52 383	99.12 359	98.20 428

tttt0517		95.64 390	94.98 400	97.64 354	99.36 187	93.81 400	98.72 104	90.47 489	98.08 219	98.67 261	98.34 348	73.88 472	99.92 65	97.77 186	99.51 282	99.20 297

thisisatest0530		95.27 400	94.45 411	97.74 339	99.19 236	94.37 370	97.86 237	90.20 490	97.17 308	98.22 310	97.65 397	73.53 473	99.90 81	96.90 266	99.35 317	98.95 349

MVStest1		95.86 382	95.60 376	96.63 411	95.87 489	91.70 437	97.93 225	98.94 316	98.03 220	99.56 73	99.66 32	71.83 474	98.26 485	99.35 58	99.24 337	99.91 13

UWE-MVS-28		90.22 456	89.28 459	93.02 475	94.50 493	82.87 494	96.52 372	87.51 494	95.21 401	92.36 484	96.04 439	71.57 475	98.25 486	72.04 496	97.77 435	97.94 443

WBMVS		95.18 402	94.78 405	96.37 417	97.68 438	89.74 467	95.80 417	98.73 359	97.54 265	98.30 303	98.44 337	70.06 476	99.82 206	96.62 296	99.87 97	99.54 142

FMVSNet5		96.01 375	95.20 396	98.41 269	97.53 445	96.10 295	98.74 99	99.50 131	97.22 306	98.03 330	99.04 198	69.80 477	99.88 115	97.27 232	99.71 201	99.25 282

UBG		93.25 434	92.32 435	96.04 431	97.72 430	90.16 463	95.92 411	95.91 452	96.03 371	93.95 475	93.04 479	69.60 478	99.52 407	90.72 465	97.98 431	98.45 410

gg-mvs-nofinetune		92.37 447	91.20 451	95.85 434	95.80 490	92.38 429	99.31 30	81.84 500	99.75 10	91.83 486	99.74 18	68.29 479	99.02 468	87.15 476	97.12 454	96.16 483

KD-MVS_2432*1600		92.87 441	91.99 443	95.51 444	91.37 498	89.27 468	94.07 472	98.14 395	95.42 394	97.25 386	96.44 434	67.86 480	99.24 458	91.28 455	96.08 473	98.02 438

miper_refine_blended		92.87 441	91.99 443	95.51 444	91.37 498	89.27 468	94.07 472	98.14 395	95.42 394	97.25 386	96.44 434	67.86 480	99.24 458	91.28 455	96.08 473	98.02 438

reproduce_monomvs		95.00 407	95.25 393	94.22 460	97.51 450	83.34 492	97.86 237	98.44 380	98.51 173	99.29 140	99.30 123	67.68 482	99.56 391	98.89 96	99.81 133	99.77 50

GG-mvs-BLEND				94.76 455	94.54 492	92.13 434	99.31 30	80.47 501		88.73 492	91.01 491	67.59 483	98.16 488	82.30 489	94.53 483	93.98 491

TESTMET0.1,1		92.19 450	91.77 448	93.46 469	96.48 480	82.80 495	94.05 474	91.52 487	94.45 419	94.00 473	94.88 465	66.65 484	99.56 391	95.78 351	98.11 423	98.02 438

UWE-MVS		92.38 446	91.76 449	94.21 461	97.16 459	84.65 487	95.42 432	88.45 493	95.96 375	96.17 434	95.84 447	66.36 485	99.71 306	91.87 444	98.64 400	98.28 425

test2506		92.39 445	91.89 447	93.89 465	99.38 180	82.28 496	99.32 26	66.03 503	99.08 112	98.77 250	99.57 49	66.26 486	99.84 175	98.71 110	99.95 38	99.54 142

IB-MVS	91.63 19	92.24 449	90.90 453	96.27 421	97.22 458	91.24 450	94.36 467	93.33 476	92.37 450	92.24 485	94.58 469	66.20 487	99.89 97	93.16 423	94.63 482	97.66 458
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
myMVS_eth3d28		92.92 440	92.31 436	94.77 454	97.84 425	87.59 477	96.19 393	96.11 447	97.08 313	94.27 467	93.49 476	66.07 488	98.78 478	91.78 445	97.93 433	97.92 444

testing3-2		93.78 425	93.91 417	93.39 471	98.82 325	81.72 498	97.76 253	95.28 459	98.60 162	96.54 423	96.66 428	65.85 489	99.62 365	96.65 294	98.99 373	98.82 368

test-mter		92.33 448	91.76 449	94.04 462	96.53 475	84.62 488	94.05 474	92.39 479	94.00 430	94.12 470	95.07 459	65.63 490	99.67 335	95.87 346	98.18 417	97.82 448

testing91		93.32 432	92.27 437	96.47 415	97.54 443	91.25 449	96.17 397	96.76 436	97.18 307	93.65 478	93.50 475	65.11 491	99.63 362	93.04 424	97.45 442	98.53 404

testing11		93.08 437	92.02 442	96.26 422	97.56 441	90.83 457	96.32 385	95.70 455	96.47 348	92.66 482	93.73 472	64.36 492	99.59 379	93.77 409	97.57 438	98.37 422

testing99		93.04 438	91.98 445	96.23 424	97.53 445	90.70 460	96.35 383	95.94 451	96.87 327	93.41 479	93.43 477	63.84 493	99.59 379	93.24 422	97.19 452	98.40 418

0.4-1-1-0.1		88.42 457	85.91 460	95.94 432	93.08 495	91.54 439	90.99 488	92.04 483	89.96 473	84.83 495	83.25 494	63.75 494	99.52 407	93.25 421	82.07 491	96.75 474

0.4-1-1-0.2		87.49 458	84.89 461	95.31 449	91.33 500	90.08 465	88.47 494	92.07 482	88.70 479	84.06 496	81.08 496	63.62 495	99.49 418	92.93 427	81.71 492	96.37 479

blend_shiyan4		92.09 451	90.16 458	97.88 325	96.78 470	94.93 350	95.24 439	98.58 370	96.22 359	96.07 438	91.42 487	63.46 496	99.73 295	96.70 286	76.98 496	98.98 342

ETVMVS		92.60 443	91.08 452	97.18 385	97.70 435	93.65 407	96.54 369	95.70 455	96.51 344	94.68 463	92.39 483	61.80 497	99.50 414	86.97 477	97.41 445	98.40 418

0.3-1-1-0.015		87.27 459	84.50 462	95.57 441	91.70 497	90.77 458	89.41 493	92.04 483	88.98 477	82.46 497	81.35 495	60.36 498	99.50 414	92.96 425	81.23 493	96.45 478

testing222		91.96 452	90.37 455	96.72 410	97.47 452	92.59 423	96.11 399	94.76 463	96.83 331	92.90 481	92.87 480	57.92 499	99.55 395	86.93 478	97.52 439	98.00 441

myMVS_eth3d		91.92 453	90.45 454	96.30 419	97.10 461	90.90 455	96.18 395	96.58 439	95.65 386	94.77 461	92.29 485	53.88 500	99.36 442	89.59 470	98.05 428	98.63 397

testing3		93.51 429	92.09 440	97.75 337	98.60 369	94.40 369	97.32 318	95.26 460	97.56 261	96.79 412	95.50 452	53.57 501	99.77 263	95.26 366	98.97 377	99.08 325

dongtai		76.24 463	75.95 466	77.12 480	92.39 496	67.91 504	90.16 490	59.44 505	82.04 491	89.42 490	94.67 468	49.68 502	81.74 498	48.06 498	77.66 495	81.72 494

kuosan		69.30 464	68.95 467	70.34 481	87.68 502	65.00 505	91.11 487	59.90 504	69.02 494	74.46 499	88.89 493	48.58 503	68.03 500	28.61 499	72.33 498	77.99 495

test123		17.04 467	20.11 470	7.82 482	10.25 506	4.91 507	94.80 450	4.47 507	4.93 500	10.00 502	24.28 499	9.69 504	3.64 501	10.14 500	12.43 500	14.92 497

testmvs		17.12 466	20.53 469	6.87 483	12.05 505	4.20 508	93.62 480	6.73 506	4.62 501	10.41 501	24.33 498	8.28 505	3.56 502	9.69 501	15.07 499	12.86 498

mmdepth		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

monomultidepth		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

test_blank		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uanet_test		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

DCPMVS		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

sosnet-low-res		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

sosnet		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uncertanet		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

Regformer		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

ab-mvs-re		8.12 469	10.83 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	97.48 407	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uanet		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

MED-MVS test				99.45 63	99.58 93	98.93 79	98.68 109	99.60 83	96.46 349	99.53 82	98.77 275		99.83 193	96.67 290	99.64 233	99.58 115

TestfortrainingZip							98.68 109

WAC-MVS						90.90 455								91.37 454

FOURS1					99.73 37	99.67 2	99.43 15	99.54 118	99.43 54	99.26 148

MSC_two_6792asdad				99.32 91	98.43 391	98.37 121		98.86 336					99.89 97	97.14 242	99.60 250	99.71 63

No_MVS				99.32 91	98.43 391	98.37 121		98.86 336					99.89 97	97.14 242	99.60 250	99.71 63

eth-test2					0.00 507

eth-test					0.00 507

IU-MVS					99.49 144	99.15 52		98.87 331	92.97 442	99.41 112				96.76 278	99.62 243	99.66 78

save fliter					99.11 257	97.97 163	96.53 371	99.02 306	98.24 194

test_0728_SECOND				99.60 16	99.50 136	99.23 31	98.02 207	99.32 218					99.88 115	96.99 255	99.63 240	99.68 71

GSMVS																98.81 373

test_part2					99.36 187	99.10 65				99.05 189

MTGPA								99.20 264

MTMP							97.93 225	91.91 486

gm-plane-assit					94.83 491	81.97 497			88.07 482		94.99 462		99.60 375	91.76 446

test9_res														93.28 420	99.15 353	99.38 235

agg_prior2														92.50 439	99.16 351	99.37 237

agg_prior					98.68 356	97.99 159		99.01 309		95.59 446			99.77 263

test_prior4						97.97 163	95.86 413

test_prior				98.95 160	98.69 352	97.95 167		99.03 303					99.59 379			99.30 268

旧先验2							95.76 419		88.56 481	97.52 368			99.66 348	94.48 384

新几何2							95.93 409

无先验							95.74 420	98.74 358	89.38 475				99.73 295	92.38 441		99.22 292

原ACMM2							95.53 426

testdata2													99.79 245	92.80 433

testdata1							95.44 431		96.32 355

plane_prior7					99.19 236	97.87 174

plane_prior5								99.27 246					99.70 313	94.42 388	99.51 282	99.45 200

plane_prior4											97.98 376

plane_prior3						97.78 188			97.41 280	97.79 349

plane_prior2							97.77 250		98.20 202

plane_prior1					99.05 274

plane_prior						97.65 197	97.07 340		96.72 337						99.36 314

n2								0.00 508

nn								0.00 508

door-mid								99.57 100

test11								98.87 331

door								99.41 183

HQP5-MVS						96.79 265

HQP-NCC					98.67 357		96.29 387		96.05 368	95.55 449

ACMP_Plane					98.67 357		96.29 387		96.05 368	95.55 449

BP-MVS														92.82 431

HQP4-MVS										95.56 448			99.54 401			99.32 260

HQP3-MVS								99.04 301							99.26 335

NP-MVS					98.84 320	97.39 215					96.84 424

ACMMP++_ref															99.77 161

ACMMP++															99.68 216

High-resolution multi-view benchmark