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 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 16	100.00 1	99.85 29

mvs5depth		99.30 34	99.59 12	98.44 254	99.65 68	95.35 318	99.82 3	99.94 2	99.83 7	99.42 107	99.94 2	98.13 114	99.96 14	99.63 35	99.96 28	100.00 1

test_fmvs3		99.12 68	99.41 26	98.25 276	99.76 30	95.07 330	99.05 67	99.94 2	97.78 230	99.82 33	99.84 3	98.56 68	99.71 292	99.96 1	99.96 28	99.97 4

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 28	99.53 9	99.76 39	99.64 27	99.84 30	99.83 4	99.50 9	99.87 133	99.36 57	99.92 68	99.64 83

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 43	98.98 72	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 80	99.54 43	99.95 38	99.61 97

tt0320		99.61 8	99.65 9	99.48 57	99.71 47	98.94 79	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 80	99.54 43	99.95 38	99.59 106

test_f		98.67 147	98.87 101	98.05 297	99.72 43	95.59 302	98.51 130	99.81 31	96.30 342	99.78 39	99.82 5	96.14 254	98.63 456	99.82 12	99.93 55	99.95 9

mvsany_test3		98.87 102	98.92 94	98.74 199	99.38 168	96.94 247	98.58 118	99.10 275	96.49 331	99.96 4	99.81 8	98.18 107	99.45 405	98.97 89	99.79 144	99.83 32

UA-Net		99.47 16	99.40 27	99.70 2	99.49 135	99.29 25	99.80 4	99.72 45	99.82 8	99.04 182	99.81 8	98.05 120	99.96 14	98.85 97	99.99 5	99.86 27

ANet_high		99.57 10	99.67 6	99.28 94	99.89 6	98.09 144	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 42	99.31 61	100.00 1	99.82 35

sc_t1		99.62 7	99.66 8	99.53 39	99.82 19	99.09 69	99.50 11	99.63 71	99.88 4	99.86 24	99.80 11	99.03 24	99.89 96	99.48 52	99.93 55	99.60 99

mmtdpeth		99.30 34	99.42 25	98.92 164	99.58 87	96.89 250	99.48 13	99.92 7	99.92 2	98.26 297	99.80 11	98.33 88	99.91 73	99.56 40	99.95 38	99.97 4

test_fmvs2		98.70 136	98.97 90	97.89 305	99.54 113	94.05 360	98.55 121	99.92 7	96.78 319	99.72 47	99.78 13	96.60 235	99.67 316	99.91 2	99.90 84	99.94 10

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 20	99.69 5	99.58 84	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 87	99.95 38	99.78 46

OurMVSNet-221017-0		99.37 29	99.31 41	99.53 39	99.91 3	98.98 72	99.63 7	99.58 84	99.44 52	99.78 39	99.76 15	96.39 243	99.92 64	99.44 54	99.92 68	99.68 70

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 72	99.87 12	98.13 140	98.08 184	99.95 1	99.45 50	99.98 2	99.75 16	99.80 1	99.97 7	99.82 12	99.99 5	99.99 2

MVS-HIRNet		94.32 394	95.62 360	90.42 452	98.46 373	75.36 476	96.29 373	89.13 467	95.25 377	95.38 433	99.75 16	92.88 343	99.19 437	94.07 378	99.39 297	96.72 453

gg-mvs-nofinetune		92.37 427	91.20 431	95.85 411	95.80 468	92.38 407	99.31 30	81.84 475	99.75 11	91.83 464	99.74 18	68.29 459	99.02 443	87.15 451	97.12 439	96.16 458

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 100	99.34 23	99.71 47	99.27 73	99.90 14	99.74 18	99.68 4	99.97 7	99.55 42	99.99 5	99.88 20

test_djsdf		99.52 13	99.51 15	99.53 39	99.86 14	98.74 90	99.39 20	99.56 98	99.11 97	99.70 51	99.73 20	99.00 27	99.97 7	99.26 65	99.98 12	99.89 16

anonymousdsp		99.51 14	99.47 21	99.62 10	99.88 9	99.08 70	99.34 23	99.69 54	98.93 128	99.65 63	99.72 21	98.93 32	99.95 26	99.11 77	100.00 1	99.82 35

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 180	99.75 34	96.59 263	97.97 214	99.86 16	98.22 187	99.88 21	99.71 22	98.59 62	99.84 173	99.73 27	99.98 12	99.98 3

PS-MVSNAJss		99.46 17	99.49 16	99.35 78	99.90 4	98.15 137	99.20 48	99.65 67	99.48 44	99.92 8	99.71 22	98.07 117	99.96 14	99.53 47	100.00 1	99.93 11

JIA-IIPM		95.52 376	95.03 382	97.00 372	96.85 451	94.03 363	96.93 334	95.82 431	99.20 82	94.63 443	99.71 22	83.09 427	99.60 353	94.42 366	94.64 460	97.36 445

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 212	99.71 47	96.10 282	97.87 226	99.85 18	98.56 163	99.90 14	99.68 25	98.69 52	99.85 155	99.72 29	99.98 12	99.97 4

SDMVSNet		99.23 46	99.32 39	98.96 156	99.68 61	97.35 213	98.84 94	99.48 129	99.69 18	99.63 66	99.68 25	99.03 24	99.96 14	97.97 162	99.92 68	99.57 120

sd_testset		99.28 37	99.31 41	99.19 110	99.68 61	98.06 153	99.41 17	99.30 218	99.69 18	99.63 66	99.68 25	99.25 16	99.96 14	97.25 216	99.92 68	99.57 120

Anonymous20231211		99.27 38	99.27 47	99.26 99	99.29 193	98.18 135	99.49 12	99.51 117	99.70 16	99.80 37	99.68 25	96.84 215	99.83 191	99.21 70	99.91 77	99.77 49

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 100	99.28 40	99.66 63	99.09 107	99.89 18	99.68 25	99.53 7	99.97 7	99.50 50	99.99 5	99.87 21

test_vis3_rt		99.14 61	99.17 59	99.07 133	99.78 24	98.38 117	98.92 82	99.94 2	97.80 227	99.91 12	99.67 30	97.15 197	98.91 449	99.76 23	99.56 256	99.92 12

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 27	99.85 16	99.11 65	99.90 1	99.78 36	99.63 29	99.78 39	99.67 30	99.48 10	99.81 219	99.30 62	99.97 21	99.77 49
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
MVStest1		95.86 365	95.60 361	96.63 389	95.87 467	91.70 415	97.93 215	98.94 300	98.03 208	99.56 73	99.66 32	71.83 454	98.26 460	99.35 58	99.24 322	99.91 13

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 81	99.78 24	98.11 141	97.77 240	99.90 11	99.33 65	99.97 3	99.66 32	99.71 3	99.96 14	99.79 19	99.99 5	99.96 8

v7n		99.53 12	99.57 13	99.41 68	99.88 9	98.54 108	99.45 14	99.61 76	99.66 24	99.68 57	99.66 32	98.44 77	99.95 26	99.73 27	99.96 28	99.75 59

K. test v3		98.00 243	97.66 268	99.03 143	99.79 23	97.56 200	99.19 52	92.47 456	99.62 32	99.52 85	99.66 32	89.61 380	99.96 14	99.25 67	99.81 127	99.56 126

SixPastTwentyTwo		98.75 127	98.62 140	99.16 116	99.83 18	97.96 164	99.28 40	98.20 370	99.37 60	99.70 51	99.65 36	92.65 349	99.93 53	99.04 84	99.84 110	99.60 99

fmvsm_s_conf0.1_n_2		99.20 50	99.38 29	98.65 210	99.69 58	96.08 287	97.49 285	99.90 11	99.53 41	99.88 21	99.64 37	98.51 71	99.90 80	99.83 10	99.98 12	99.97 4

test_fmvs1_n		98.09 234	98.28 199	97.52 346	99.68 61	93.47 388	98.63 112	99.93 5	95.41 375	99.68 57	99.64 37	91.88 359	99.48 397	99.82 12	99.87 96	99.62 89

DSMNet-mixed		97.42 293	97.60 273	96.87 380	99.15 240	91.46 418	98.54 123	99.12 272	92.87 423	97.58 348	99.63 39	96.21 252	99.90 80	95.74 330	99.54 262	99.27 261

test_cas_vis1_n_1920		98.33 204	98.68 130	97.27 360	99.69 58	92.29 409	98.03 195	99.85 18	97.62 240	99.96 4	99.62 40	93.98 325	99.74 276	99.52 49	99.86 103	99.79 43

TransMVSNet (Re)		99.44 19	99.47 21	99.36 72	99.80 21	98.58 103	99.27 42	99.57 91	99.39 58	99.75 44	99.62 40	99.17 20	99.83 191	99.06 82	99.62 233	99.66 77

Gipuma		99.03 79	99.16 61	98.64 212	99.94 2	98.51 110	99.32 26	99.75 42	99.58 38	98.60 261	99.62 40	98.22 103	99.51 390	97.70 185	99.73 176	97.89 423
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Baseline_NR-MVSNet		98.98 87	98.86 105	99.36 72	99.82 19	98.55 105	97.47 288	99.57 91	99.37 60	99.21 156	99.61 43	96.76 225	99.83 191	98.06 152	99.83 117	99.71 62

TDRefinement		99.42 24	99.38 29	99.55 29	99.76 30	99.33 21	99.68 6	99.71 47	99.38 59	99.53 82	99.61 43	98.64 56	99.80 227	98.24 137	99.84 110	99.52 151

pm-mvs1		99.44 19	99.48 18	99.33 87	99.80 21	98.63 97	99.29 36	99.63 71	99.30 70	99.65 63	99.60 45	99.16 22	99.82 202	99.07 80	99.83 117	99.56 126

v10		98.97 88	99.11 70	98.55 235	99.44 155	96.21 281	98.90 83	99.55 102	98.73 143	99.48 93	99.60 45	96.63 234	99.83 191	99.70 32	99.99 5	99.61 97

ttmdpeth		97.91 249	98.02 234	97.58 338	98.69 338	94.10 359	98.13 174	98.90 309	97.95 214	97.32 369	99.58 47	95.95 270	98.75 454	96.41 295	99.22 326	99.87 21

test1111		96.49 346	96.82 320	95.52 419	99.42 162	87.08 454	99.22 45	87.14 470	99.11 97	99.46 98	99.58 47	88.69 386	99.86 142	98.80 99	99.95 38	99.62 89

fmvsm_s_conf0.5_n_2		99.14 61	99.31 41	98.63 216	99.49 135	96.08 287	97.38 297	99.81 31	99.48 44	99.84 30	99.57 49	98.46 75	99.89 96	99.82 12	99.97 21	99.91 13

test_fmvsmconf_n		99.44 19	99.48 18	99.31 92	99.64 74	98.10 143	97.68 254	99.84 22	99.29 71	99.92 8	99.57 49	99.60 5	99.96 14	99.74 26	99.98 12	99.89 16

test_vis1_n		98.31 207	98.50 160	97.73 322	99.76 30	94.17 357	98.68 107	99.91 9	96.31 340	99.79 38	99.57 49	92.85 345	99.42 410	99.79 19	99.84 110	99.60 99

test2506		92.39 425	91.89 427	93.89 440	99.38 168	82.28 471	99.32 26	66.03 478	99.08 111	98.77 239	99.57 49	66.26 466	99.84 173	98.71 109	99.95 38	99.54 139

ECVR-MVS		96.42 348	96.61 334	95.85 411	99.38 168	88.18 449	99.22 45	86.00 472	99.08 111	99.36 120	99.57 49	88.47 391	99.82 202	98.52 124	99.95 38	99.54 139

mamv4		99.44 19	99.39 28	99.58 21	99.30 190	99.74 2	99.04 68	99.81 31	99.77 10	99.82 33	99.57 49	97.82 141	99.98 4	99.53 47	99.89 90	99.01 316

v8		99.01 81	99.16 61	98.57 228	99.47 145	96.31 279	98.90 83	99.47 138	99.03 117	99.52 85	99.57 49	96.93 211	99.81 219	99.60 36	99.98 12	99.60 99

MIMVSNet1		99.38 28	99.32 39	99.55 29	99.86 14	99.19 43	99.41 17	99.59 82	99.59 36	99.71 49	99.57 49	97.12 198	99.90 80	99.21 70	99.87 96	99.54 139

fmvsm_s_conf0.5_n		99.09 71	99.26 50	98.61 221	99.55 108	96.09 285	97.74 247	99.81 31	98.55 164	99.85 27	99.55 57	98.60 61	99.84 173	99.69 34	99.98 12	99.89 16

test_vis1_n_1920		98.40 190	98.92 94	96.81 384	99.74 36	90.76 435	98.15 172	99.91 9	98.33 175	99.89 18	99.55 57	95.07 295	99.88 114	99.76 23	99.93 55	99.79 43

Anonymous20240521		98.69 139	98.87 101	98.16 288	99.77 27	95.11 329	99.08 61	99.44 154	99.34 64	99.33 126	99.55 57	94.10 324	99.94 42	99.25 67	99.96 28	99.42 201

GBi-Net		98.65 149	98.47 168	99.17 113	98.90 293	98.24 128	99.20 48	99.44 154	98.59 156	98.95 200	99.55 57	94.14 320	99.86 142	97.77 178	99.69 203	99.41 204

test1		98.65 149	98.47 168	99.17 113	98.90 293	98.24 128	99.20 48	99.44 154	98.59 156	98.95 200	99.55 57	94.14 320	99.86 142	97.77 178	99.69 203	99.41 204

FMVSNet1		99.17 52	99.17 59	99.17 113	99.55 108	98.24 128	99.20 48	99.44 154	99.21 80	99.43 103	99.55 57	97.82 141	99.86 142	98.42 129	99.89 90	99.41 204

fmvsm_s_conf0.5_n_a		99.10 70	99.20 57	98.78 186	99.55 108	96.59 263	97.79 236	99.82 30	98.21 189	99.81 36	99.53 63	98.46 75	99.84 173	99.70 32	99.97 21	99.90 15

KD-MVS_self_test		99.25 41	99.18 58	99.44 64	99.63 80	99.06 71	98.69 106	99.54 107	99.31 68	99.62 69	99.53 63	97.36 184	99.86 142	99.24 69	99.71 193	99.39 214

new-patchmatchnet		98.35 199	98.74 116	97.18 363	99.24 210	92.23 411	96.42 365	99.48 129	98.30 179	99.69 55	99.53 63	97.44 179	99.82 202	98.84 98	99.77 155	99.49 164

lessismore_v0				98.97 155	99.73 37	97.53 202		86.71 471		99.37 117	99.52 66	89.93 376	99.92 64	98.99 88	99.72 184	99.44 194

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 186	99.46 148	96.58 266	97.65 260	99.72 45	99.47 47	99.86 24	99.50 67	98.94 30	99.89 96	99.75 25	99.97 21	99.86 27

MVSMamba_PlusPlus		98.83 111	98.98 89	98.36 265	99.32 185	96.58 266	98.90 83	99.41 170	99.75 11	98.72 245	99.50 67	96.17 253	99.94 42	99.27 64	99.78 149	98.57 382

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 240	99.66 67	96.97 243	98.00 202	99.85 18	99.24 75	99.92 8	99.50 67	99.39 12	99.95 26	99.89 3	99.98 12	98.71 366

FC-MVSNet-test		99.27 38	99.25 52	99.34 81	99.77 27	98.37 119	99.30 35	99.57 91	99.61 34	99.40 112	99.50 67	97.12 198	99.85 155	99.02 86	99.94 49	99.80 41

VDDNet		98.21 221	97.95 242	99.01 147	99.58 87	97.74 189	99.01 70	97.29 398	99.67 21	98.97 194	99.50 67	90.45 373	99.80 227	97.88 169	99.20 330	99.48 175

DeepC-MVS	97.60 4	98.97 88	98.93 93	99.10 126	99.35 180	97.98 160	98.01 201	99.46 142	97.56 249	99.54 78	99.50 67	98.97 28	99.84 173	98.06 152	99.92 68	99.49 164
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 61	99.15 66	99.10 126	99.76 30	97.74 189	98.85 92	99.62 73	98.48 167	99.37 117	99.49 73	98.75 46	99.86 142	98.20 142	99.80 138	99.71 62

fmvsm_s_conf0.5_n_9		99.17 52	99.38 29	98.53 242	99.51 121	95.82 297	97.62 265	99.78 36	99.72 15	99.90 14	99.48 74	98.66 54	99.89 96	99.85 6	99.93 55	99.89 16

fmvsm_s_conf0.5_n_8		99.13 65	99.26 50	98.74 199	99.51 121	96.44 274	97.65 260	99.65 67	99.66 24	99.78 39	99.48 74	97.92 131	99.93 53	99.72 29	99.95 38	99.87 21

Vis-MVSNet		99.34 30	99.36 33	99.27 97	99.73 37	98.26 126	99.17 53	99.78 36	99.11 97	99.27 140	99.48 74	98.82 37	99.95 26	98.94 91	99.93 55	99.59 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VortexMVS		97.98 247	98.31 195	97.02 371	98.88 299	91.45 419	98.03 195	99.47 138	98.65 147	99.55 76	99.47 77	91.49 363	99.81 219	99.32 60	99.91 77	99.80 41

UGNet		98.53 174	98.45 171	98.79 183	97.94 406	96.96 245	99.08 61	98.54 354	99.10 104	96.82 393	99.47 77	96.55 237	99.84 173	98.56 121	99.94 49	99.55 133
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		97.66 274	98.50 160	95.13 426	99.63 80	85.84 457	98.35 152	98.21 369	98.23 186	99.54 78	99.46 79	95.02 296	99.68 312	98.24 137	99.87 96	99.87 21

LCM-MVSNet-Re		98.64 151	98.48 166	99.11 124	98.85 305	98.51 110	98.49 135	99.83 25	98.37 171	99.69 55	99.46 79	98.21 105	99.92 64	94.13 376	99.30 313	98.91 337

mvs_anonymous		97.83 265	98.16 219	96.87 380	98.18 394	91.89 413	97.31 306	98.90 309	97.37 272	98.83 227	99.46 79	96.28 250	99.79 240	98.90 93	98.16 405	98.95 328

DTE-MVSNet		99.43 23	99.35 34	99.66 7	99.71 47	99.30 23	99.31 30	99.51 117	99.64 27	99.56 73	99.46 79	98.23 100	99.97 7	98.78 101	99.93 55	99.72 61

ACMH	96.65 7	99.25 41	99.24 53	99.26 99	99.72 43	98.38 117	99.07 64	99.55 102	98.30 179	99.65 63	99.45 83	99.22 17	99.76 263	98.44 127	99.77 155	99.64 83
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 186	99.47 145	96.56 268	97.75 246	99.71 47	99.60 35	99.74 46	99.44 84	97.96 128	99.95 26	99.86 4	99.94 49	99.82 35

test_fmvs1		97.72 269	97.94 244	97.07 370	98.66 348	92.39 406	97.68 254	99.81 31	95.20 380	99.54 78	99.44 84	91.56 362	99.41 411	99.78 21	99.77 155	99.40 213

VPA-MVSNet		99.30 34	99.30 44	99.28 94	99.49 135	98.36 122	99.00 72	99.45 146	99.63 29	99.52 85	99.44 84	98.25 98	99.88 114	99.09 79	99.84 110	99.62 89

fmvsm_s_conf0.5_n_7		98.83 111	99.04 79	98.20 283	99.30 190	94.83 335	97.23 313	99.36 185	98.64 148	99.84 30	99.43 87	98.10 116	99.91 73	99.56 40	99.96 28	99.87 21

EGC-MVSNET		85.24 436	80.54 439	99.34 81	99.77 27	99.20 40	99.08 61	99.29 226	12.08 474	20.84 475	99.42 88	97.55 165	99.85 155	97.08 229	99.72 184	98.96 327

RRT-MVS		97.88 254	97.98 238	97.61 335	98.15 396	93.77 380	98.97 76	99.64 69	99.16 92	98.69 247	99.42 88	91.60 360	99.89 96	97.63 188	98.52 392	99.16 298

PEN-MVS		99.41 25	99.34 36	99.62 10	99.73 37	99.14 58	99.29 36	99.54 107	99.62 32	99.56 73	99.42 88	98.16 111	99.96 14	98.78 101	99.93 55	99.77 49

PatchT		96.65 339	96.35 343	97.54 344	97.40 438	95.32 320	97.98 210	96.64 416	99.33 65	96.89 389	99.42 88	84.32 418	99.81 219	97.69 187	97.49 425	97.48 441

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 81	99.59 85	98.21 134	97.82 231	99.84 22	99.41 57	99.92 8	99.41 92	99.51 8	99.95 26	99.84 9	99.97 21	99.87 21

FIs		99.14 61	99.09 74	99.29 93	99.70 55	98.28 125	99.13 58	99.52 116	99.48 44	99.24 150	99.41 92	96.79 222	99.82 202	98.69 111	99.88 92	99.76 55

PS-CasMVS		99.40 26	99.33 37	99.62 10	99.71 47	99.10 66	99.29 36	99.53 111	99.53 41	99.46 98	99.41 92	98.23 100	99.95 26	98.89 95	99.95 38	99.81 39

viewdifsd2359ckpt11		98.84 108	99.04 79	98.24 278	99.56 102	95.51 307	97.38 297	99.70 52	99.16 92	99.57 71	99.40 95	98.26 96	99.71 292	98.55 122	99.82 121	99.50 157

viewmsd2359difaftdt		98.84 108	99.04 79	98.24 278	99.56 102	95.51 307	97.38 297	99.70 52	99.16 92	99.57 71	99.40 95	98.26 96	99.71 292	98.55 122	99.82 121	99.50 157

ab-mvs		98.41 188	98.36 186	98.59 224	99.19 225	97.23 223	99.32 26	98.81 329	97.66 237	98.62 257	99.40 95	96.82 218	99.80 227	95.88 321	99.51 271	98.75 363

MonoMVSNet		96.25 354	96.53 340	95.39 423	96.57 456	91.01 430	98.82 95	97.68 387	98.57 160	98.03 318	99.37 98	90.92 369	97.78 464	94.99 348	93.88 464	97.38 444

Anonymous20240529		98.93 94	98.87 101	99.12 122	99.19 225	98.22 133	99.01 70	98.99 298	99.25 74	99.54 78	99.37 98	97.04 202	99.80 227	97.89 166	99.52 269	99.35 237

CR-MVSNet		96.28 352	95.95 351	97.28 359	97.71 418	94.22 353	98.11 179	98.92 306	92.31 429	96.91 385	99.37 98	85.44 410	99.81 219	97.39 208	97.36 434	97.81 428

Patchmtry		97.35 298	96.97 308	98.50 248	97.31 441	96.47 273	98.18 167	98.92 306	98.95 127	98.78 236	99.37 98	85.44 410	99.85 155	95.96 319	99.83 117	99.17 295

EG-PatchMatch MVS		98.99 84	99.01 84	98.94 159	99.50 127	97.47 206	98.04 193	99.59 82	98.15 204	99.40 112	99.36 102	98.58 67	99.76 263	98.78 101	99.68 208	99.59 106

testf1		99.25 41	99.16 61	99.51 49	99.89 6	99.63 4	98.71 104	99.69 54	98.90 132	99.43 103	99.35 103	98.86 34	99.67 316	97.81 174	99.81 127	99.24 271

APD_test2		99.25 41	99.16 61	99.51 49	99.89 6	99.63 4	98.71 104	99.69 54	98.90 132	99.43 103	99.35 103	98.86 34	99.67 316	97.81 174	99.81 127	99.24 271

IterMVS-SCA-FT		97.85 262	98.18 215	96.87 380	99.27 199	91.16 429	95.53 412	99.25 239	99.10 104	99.41 109	99.35 103	93.10 338	99.96 14	98.65 113	99.94 49	99.49 164

PMVS	91.26 20	97.86 257	97.94 244	97.65 329	99.71 47	97.94 166	98.52 125	98.68 346	98.99 120	97.52 354	99.35 103	97.41 180	98.18 462	91.59 425	99.67 214	96.82 451
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 153	99.59 85	97.18 231	97.44 292	99.83 25	99.56 39	99.91 12	99.34 107	99.36 13	99.93 53	99.83 10	99.98 12	99.85 29

WR-MVS_H		99.33 31	99.22 54	99.65 8	99.71 47	99.24 31	99.32 26	99.55 102	99.46 49	99.50 91	99.34 107	97.30 187	99.93 53	98.90 93	99.93 55	99.77 49

RPMNet		97.02 323	96.93 310	97.30 358	97.71 418	94.22 353	98.11 179	99.30 218	99.37 60	96.91 385	99.34 107	86.72 397	99.87 133	97.53 197	97.36 434	97.81 428

mvsany_test1		97.60 277	97.54 275	97.77 313	97.72 415	95.35 318	95.36 420	97.13 403	94.13 404	99.71 49	99.33 110	97.93 130	99.30 427	97.60 192	98.94 365	98.67 374

FA-MVS(test-final)		96.99 327	96.82 320	97.50 348	98.70 333	94.78 337	99.34 23	96.99 406	95.07 381	98.48 279	99.33 110	88.41 392	99.65 334	96.13 314	98.92 367	98.07 414

IterMVS		97.73 268	98.11 224	96.57 390	99.24 210	90.28 438	95.52 414	99.21 248	98.86 137	99.33 126	99.33 110	93.11 337	99.94 42	98.49 125	99.94 49	99.48 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator	98.27 2	98.81 116	98.73 118	99.05 140	98.76 319	97.81 184	99.25 43	99.30 218	98.57 160	98.55 271	99.33 110	97.95 129	99.90 80	97.16 221	99.67 214	99.44 194

reproduce_model		99.15 57	98.97 90	99.67 4	99.33 184	99.44 10	98.15 172	99.47 138	99.12 96	99.52 85	99.32 114	98.31 89	99.90 80	97.78 177	99.73 176	99.66 77

IterMVS-LS		98.55 169	98.70 127	98.09 290	99.48 143	94.73 340	97.22 317	99.39 175	98.97 123	99.38 115	99.31 115	96.00 262	99.93 53	98.58 116	99.97 21	99.60 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_s_conf0.5_n_5		99.07 77	99.10 72	98.99 149	99.47 145	97.22 225	97.40 294	99.83 25	97.61 243	99.85 27	99.30 116	98.80 40	99.95 26	99.71 31	99.90 84	99.78 46

reproduce_monomvs		95.00 387	95.25 376	94.22 435	97.51 435	83.34 467	97.86 227	98.44 359	98.51 165	99.29 136	99.30 116	67.68 462	99.56 369	98.89 95	99.81 127	99.77 49

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 149	99.57 94	97.73 191	97.93 215	99.83 25	99.22 78	99.93 6	99.30 116	99.42 11	99.96 14	99.85 6	99.99 5	99.29 257

patch_mono-2		98.51 179	98.63 138	98.17 286	99.38 168	94.78 337	97.36 302	99.69 54	98.16 199	98.49 278	99.29 119	97.06 201	99.97 7	98.29 136	99.91 77	99.76 55

FMVSNet2		98.49 181	98.40 178	98.75 195	98.90 293	97.14 236	98.61 115	99.13 271	98.59 156	99.19 158	99.28 120	94.14 320	99.82 202	97.97 162	99.80 138	99.29 257

3Dnovator+	97.89 3	98.69 139	98.51 157	99.24 104	98.81 314	98.40 115	99.02 69	99.19 254	98.99 120	98.07 313	99.28 120	97.11 200	99.84 173	96.84 254	99.32 308	99.47 183

viewmacassd2359aftdt		98.86 105	98.87 101	98.83 174	99.53 116	97.32 217	97.70 252	99.64 69	98.22 187	99.25 148	99.27 122	98.40 79	99.61 350	97.98 161	99.87 96	99.55 133

fmvsm_s_conf0.5_n_4		99.01 81	99.22 54	98.38 261	99.31 186	95.48 311	97.56 275	99.73 44	98.87 135	99.75 44	99.27 122	98.80 40	99.86 142	99.80 17	99.90 84	99.81 39

reproduce-ours		99.09 71	98.90 96	99.67 4	99.27 199	99.49 6	98.00 202	99.42 166	99.05 114	99.48 93	99.27 122	98.29 91	99.89 96	97.61 190	99.71 193	99.62 89

our_new_method		99.09 71	98.90 96	99.67 4	99.27 199	99.49 6	98.00 202	99.42 166	99.05 114	99.48 93	99.27 122	98.29 91	99.89 96	97.61 190	99.71 193	99.62 89

VDD-MVS		98.56 165	98.39 181	99.07 133	99.13 243	98.07 150	98.59 117	97.01 405	99.59 36	99.11 165	99.27 122	94.82 302	99.79 240	98.34 133	99.63 230	99.34 239

PVSNet_Blended_VisFu		98.17 228	98.15 220	98.22 282	99.73 37	95.15 326	97.36 302	99.68 59	94.45 397	98.99 189	99.27 122	96.87 214	99.94 42	97.13 226	99.91 77	99.57 120

FE-MVS		95.66 372	94.95 385	97.77 313	98.53 367	95.28 321	99.40 19	96.09 426	93.11 419	97.96 322	99.26 128	79.10 443	99.77 257	92.40 415	98.71 378	98.27 405

dcpmvs_2		98.78 122	99.11 70	97.78 312	99.56 102	93.67 383	99.06 65	99.86 16	99.50 43	99.66 60	99.26 128	97.21 195	99.99 2	98.00 159	99.91 77	99.68 70

nrg030		99.40 26	99.35 34	99.54 32	99.58 87	99.13 61	98.98 75	99.48 129	99.68 20	99.46 98	99.26 128	98.62 59	99.73 283	99.17 74	99.92 68	99.76 55

CP-MVSNet		99.21 48	99.09 74	99.56 27	99.65 68	98.96 78	99.13 58	99.34 197	99.42 55	99.33 126	99.26 128	97.01 206	99.94 42	98.74 106	99.93 55	99.79 43

RPSCF		98.62 156	98.36 186	99.42 66	99.65 68	99.42 11	98.55 121	99.57 91	97.72 234	98.90 212	99.26 128	96.12 257	99.52 385	95.72 331	99.71 193	99.32 248

lecture		99.25 41	99.12 69	99.62 10	99.64 74	99.40 12	98.89 87	99.51 117	99.19 87	99.37 117	99.25 133	98.36 82	99.88 114	98.23 139	99.67 214	99.59 106

LuminaMVS		98.39 196	98.20 210	98.98 153	99.50 127	97.49 203	97.78 237	97.69 385	98.75 142	99.49 92	99.25 133	92.30 353	99.94 42	99.14 75	99.88 92	99.50 157

AstraMVS		98.16 230	98.07 230	98.41 257	99.51 121	95.86 294	98.00 202	95.14 439	98.97 123	99.43 103	99.24 135	93.25 333	99.84 173	99.21 70	99.87 96	99.54 139

SSC-MVS		98.71 131	98.74 116	98.62 218	99.72 43	96.08 287	98.74 97	98.64 350	99.74 13	99.67 59	99.24 135	94.57 310	99.95 26	99.11 77	99.24 322	99.82 35

tfpnnormal		98.90 98	98.90 96	98.91 165	99.67 65	97.82 181	99.00 72	99.44 154	99.45 50	99.51 90	99.24 135	98.20 106	99.86 142	95.92 320	99.69 203	99.04 312

v1240		98.55 169	98.62 140	98.32 268	99.22 216	95.58 304	97.51 282	99.45 146	97.16 296	99.45 101	99.24 135	96.12 257	99.85 155	99.60 36	99.88 92	99.55 133

APDe-MVS		98.99 84	98.79 112	99.60 16	99.21 218	99.15 53	98.87 88	99.48 129	97.57 247	99.35 122	99.24 135	97.83 138	99.89 96	97.88 169	99.70 200	99.75 59
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mvsmamba		97.57 281	97.26 292	98.51 244	98.69 338	96.73 259	98.74 97	97.25 399	97.03 304	97.88 327	99.23 140	90.95 368	99.87 133	96.61 275	99.00 356	98.91 337

casdiffmvs_mvg		99.12 68	99.16 61	98.99 149	99.43 160	97.73 191	98.00 202	99.62 73	99.22 78	99.55 76	99.22 141	98.93 32	99.75 271	98.66 112	99.81 127	99.50 157
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				98.24 278	98.82 311	95.97 291	98.62 114	99.00 297		99.27 140	99.21 142	96.99 207	99.50 391	96.55 286	99.50 279	99.26 267

TAMVS		98.24 218	98.05 231	98.80 180	99.07 254	97.18 231	97.88 223	98.81 329	96.66 325	99.17 163	99.21 142	94.81 304	99.77 257	96.96 241	99.88 92	99.44 194

v1192		98.60 159	98.66 133	98.41 257	99.27 199	95.88 293	97.52 280	99.36 185	97.41 268	99.33 126	99.20 144	96.37 246	99.82 202	99.57 38	99.92 68	99.55 133

APD_test1		98.83 111	98.66 133	99.34 81	99.78 24	99.47 9	98.42 146	99.45 146	98.28 184	98.98 190	99.19 145	97.76 146	99.58 364	96.57 279	99.55 260	98.97 325

balanced_conf03		98.63 153	98.72 120	98.38 261	98.66 348	96.68 262	98.90 83	99.42 166	98.99 120	98.97 194	99.19 145	95.81 275	99.85 155	98.77 104	99.77 155	98.60 378

pmmvs-eth3d		98.47 183	98.34 189	98.86 171	99.30 190	97.76 187	97.16 323	99.28 230	95.54 368	99.42 107	99.19 145	97.27 190	99.63 340	97.89 166	99.97 21	99.20 283

COLMAP_ROB	96.50 10	98.99 84	98.85 107	99.41 68	99.58 87	99.10 66	98.74 97	99.56 98	99.09 107	99.33 126	99.19 145	98.40 79	99.72 291	95.98 318	99.76 168	99.42 201
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v144192		98.54 172	98.57 149	98.45 252	99.21 218	95.98 290	97.63 264	99.36 185	97.15 298	99.32 132	99.18 149	95.84 274	99.84 173	99.50 50	99.91 77	99.54 139

PM-MVS		98.82 114	98.72 120	99.12 122	99.64 74	98.54 108	97.98 210	99.68 59	97.62 240	99.34 124	99.18 149	97.54 167	99.77 257	97.79 176	99.74 173	99.04 312

PVSNet_BlendedMVS		97.55 282	97.53 276	97.60 336	98.92 289	93.77 380	96.64 350	99.43 160	94.49 393	97.62 344	99.18 149	96.82 218	99.67 316	94.73 355	99.93 55	99.36 232

ACMH+	96.62 9	99.08 75	99.00 86	99.33 87	99.71 47	98.83 85	98.60 116	99.58 84	99.11 97	99.53 82	99.18 149	98.81 38	99.67 316	96.71 267	99.77 155	99.50 157

v1921920		98.54 172	98.60 145	98.38 261	99.20 222	95.76 300	97.56 275	99.36 185	97.23 290	99.38 115	99.17 153	96.02 260	99.84 173	99.57 38	99.90 84	99.54 139

casdiffmvs		98.95 91	99.00 86	98.81 178	99.38 168	97.33 215	97.82 231	99.57 91	99.17 91	99.35 122	99.17 153	98.35 86	99.69 303	98.46 126	99.73 176	99.41 204
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		97.26 305	97.02 306	97.99 301	99.52 119	95.53 306	96.13 384	99.71 47	97.47 259	99.27 140	99.16 155	84.30 419	99.62 343	97.89 166	99.77 155	98.81 352

V42		98.78 122	98.78 114	98.76 193	99.44 155	97.04 239	98.27 159	99.19 254	97.87 222	99.25 148	99.16 155	96.84 215	99.78 251	99.21 70	99.84 110	99.46 185

QAPM		97.31 301	96.81 322	98.82 176	98.80 317	97.49 203	99.06 65	99.19 254	90.22 447	97.69 341	99.16 155	96.91 212	99.90 80	90.89 438	99.41 295	99.07 306

wuyk23d		96.06 358	97.62 272	91.38 451	98.65 352	98.57 104	98.85 92	96.95 409	96.86 315	99.90 14	99.16 155	99.18 19	98.40 458	89.23 446	99.77 155	77.18 471

v1144		98.60 159	98.66 133	98.41 257	99.36 175	95.90 292	97.58 273	99.34 197	97.51 255	99.27 140	99.15 159	96.34 248	99.80 227	99.47 53	99.93 55	99.51 154

DP-MVS		98.93 94	98.81 111	99.28 94	99.21 218	98.45 114	98.46 140	99.33 203	99.63 29	99.48 93	99.15 159	97.23 193	99.75 271	97.17 220	99.66 222	99.63 88

OpenMVS	96.65 7	97.09 318	96.68 329	98.32 268	98.32 385	97.16 234	98.86 91	99.37 181	89.48 451	96.29 413	99.15 159	96.56 236	99.90 80	92.90 403	99.20 330	97.89 423

guyue		98.01 242	97.93 246	98.26 274	99.45 153	95.48 311	98.08 184	96.24 422	98.89 134	99.34 124	99.14 162	91.32 365	99.82 202	99.07 80	99.83 117	99.48 175

MM		98.22 219	97.99 237	98.91 165	98.66 348	96.97 243	97.89 222	94.44 444	99.54 40	98.95 200	99.14 162	93.50 332	99.92 64	99.80 17	99.96 28	99.85 29

Elysia		99.15 57	99.14 67	99.18 111	99.63 80	97.92 167	98.50 132	99.43 160	99.67 21	99.70 51	99.13 164	96.66 231	99.98 4	99.54 43	99.96 28	99.64 83

StellarMVS		99.15 57	99.14 67	99.18 111	99.63 80	97.92 167	98.50 132	99.43 160	99.67 21	99.70 51	99.13 164	96.66 231	99.98 4	99.54 43	99.96 28	99.64 83

EPP-MVSNet		98.30 208	98.04 232	99.07 133	99.56 102	97.83 176	99.29 36	98.07 376	99.03 117	98.59 263	99.13 164	92.16 355	99.90 80	96.87 251	99.68 208	99.49 164

ACMMP_NAP		98.75 127	98.48 166	99.57 22	99.58 87	99.29 25	97.82 231	99.25 239	96.94 308	98.78 236	99.12 167	98.02 121	99.84 173	97.13 226	99.67 214	99.59 106

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 159	99.65 68	97.05 238	97.80 235	99.76 39	98.70 146	99.78 39	99.11 168	98.79 42	99.95 26	99.85 6	99.96 28	99.83 32

MVS_Test		98.18 226	98.36 186	97.67 325	98.48 370	94.73 340	98.18 167	99.02 292	97.69 235	98.04 317	99.11 168	97.22 194	99.56 369	98.57 118	98.90 368	98.71 366

MDA-MVSNet-bldmvs		97.94 248	97.91 249	98.06 295	99.44 155	94.96 333	96.63 351	99.15 270	98.35 173	98.83 227	99.11 168	94.31 317	99.85 155	96.60 276	98.72 376	99.37 225

FE-MVSNET		98.59 161	98.50 160	98.87 169	99.58 87	97.30 218	98.08 184	99.74 43	96.94 308	98.97 194	99.10 171	96.94 210	99.74 276	97.33 211	99.86 103	99.55 133

fmvsm_s_conf0.5_n_6		99.08 75	99.21 56	98.69 205	99.36 175	96.51 269	97.62 265	99.68 59	98.43 169	99.85 27	99.10 171	99.12 23	99.88 114	99.77 22	99.92 68	99.67 75

SMA-MVS		98.40 190	98.03 233	99.51 49	99.16 236	99.21 34	98.05 191	99.22 247	94.16 403	98.98 190	99.10 171	97.52 171	99.79 240	96.45 293	99.64 225	99.53 148
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		96.62 341	96.25 349	97.71 323	99.04 263	94.66 343	99.16 54	96.92 411	97.23 290	97.87 328	99.10 171	86.11 404	99.65 334	91.65 423	99.21 329	98.82 347

USDC		97.41 294	97.40 283	97.44 353	98.94 283	93.67 383	95.17 424	99.53 111	94.03 407	98.97 194	99.10 171	95.29 289	99.34 421	95.84 327	99.73 176	99.30 255

fmvsm_l_conf0.5_n		99.21 48	99.28 46	99.02 146	99.64 74	97.28 220	97.82 231	99.76 39	98.73 143	99.82 33	99.09 176	98.81 38	99.95 26	99.86 4	99.96 28	99.83 32

viewcassd2359sk11		98.55 169	98.51 157	98.67 208	99.29 193	96.99 242	97.39 295	99.54 107	97.73 232	98.81 232	99.08 177	97.55 165	99.66 327	97.52 199	99.67 214	99.36 232

KinetiMVS		99.03 79	99.02 82	99.03 143	99.70 55	97.48 205	98.43 143	99.29 226	99.70 16	99.60 70	99.07 178	96.13 255	99.94 42	99.42 55	99.87 96	99.68 70

test0726					99.50 127	99.21 34	98.17 170	99.35 191	97.97 212	99.26 144	99.06 179	97.61 160

AllTest		98.44 186	98.20 210	99.16 116	99.50 127	98.55 105	98.25 161	99.58 84	96.80 317	98.88 219	99.06 179	97.65 153	99.57 366	94.45 364	99.61 238	99.37 225

TestCases				99.16 116	99.50 127	98.55 105		99.58 84	96.80 317	98.88 219	99.06 179	97.65 153	99.57 366	94.45 364	99.61 238	99.37 225

TranMVSNet+NR-MVSNet		99.17 52	99.07 77	99.46 63	99.37 174	98.87 83	98.39 148	99.42 166	99.42 55	99.36 120	99.06 179	98.38 81	99.95 26	98.34 133	99.90 84	99.57 120

LPG-MVS_test		98.71 131	98.46 170	99.47 61	99.57 94	98.97 74	98.23 162	99.48 129	96.60 326	99.10 168	99.06 179	98.71 50	99.83 191	95.58 338	99.78 149	99.62 89

LGP-MVS_train				99.47 61	99.57 94	98.97 74		99.48 129	96.60 326	99.10 168	99.06 179	98.71 50	99.83 191	95.58 338	99.78 149	99.62 89

baseline		98.96 90	99.02 82	98.76 193	99.38 168	97.26 222	98.49 135	99.50 120	98.86 137	99.19 158	99.06 179	98.23 100	99.69 303	98.71 109	99.76 168	99.33 245

VPNet		98.87 102	98.83 108	99.01 147	99.70 55	97.62 198	98.43 143	99.35 191	99.47 47	99.28 138	99.05 186	96.72 228	99.82 202	98.09 149	99.36 301	99.59 106

MVSTER		96.86 331	96.55 338	97.79 311	97.91 408	94.21 355	97.56 275	98.87 315	97.49 258	99.06 172	99.05 186	80.72 434	99.80 227	98.44 127	99.82 121	99.37 225

SD-MVS		98.40 190	98.68 130	97.54 344	98.96 281	97.99 157	97.88 223	99.36 185	98.20 193	99.63 66	99.04 188	98.76 45	95.33 471	96.56 283	99.74 173	99.31 252
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		96.01 360	95.20 379	98.41 257	97.53 430	96.10 282	98.74 97	99.50 120	97.22 293	98.03 318	99.04 188	69.80 457	99.88 114	97.27 214	99.71 193	99.25 268

IS-MVSNet		98.19 224	97.90 250	99.08 131	99.57 94	97.97 161	99.31 30	98.32 365	99.01 119	98.98 190	99.03 190	91.59 361	99.79 240	95.49 340	99.80 138	99.48 175

SSM_0407		98.86 105	98.96 92	98.55 235	99.27 199	96.50 270	98.04 193	99.66 63	99.09 107	99.22 153	99.02 191	98.79 42	99.87 133	97.87 171	99.72 184	99.27 261

SSM_0404		98.90 98	99.01 84	98.57 228	99.42 162	96.59 263	98.13 174	99.66 63	99.09 107	99.30 135	99.02 191	98.79 42	99.89 96	97.87 171	99.80 138	99.23 273

DVP-MVS++		98.90 98	98.70 127	99.51 49	98.43 377	99.15 53	99.43 15	99.32 205	98.17 196	99.26 144	99.02 191	98.18 107	99.88 114	97.07 230	99.45 286	99.49 164

test_one_0601					99.39 167	99.20 40		99.31 210	98.49 166	98.66 252	99.02 191	97.64 156

h-mvs33		97.77 266	97.33 290	99.10 126	99.21 218	97.84 175	98.35 152	98.57 353	99.11 97	98.58 265	99.02 191	88.65 389	99.96 14	98.11 147	96.34 448	99.49 164

SED-MVS		98.91 96	98.72 120	99.49 55	99.49 135	99.17 45	98.10 181	99.31 210	98.03 208	99.66 60	99.02 191	98.36 82	99.88 114	96.91 243	99.62 233	99.41 204

test_241102_TWO								99.30 218	98.03 208	99.26 144	99.02 191	97.51 172	99.88 114	96.91 243	99.60 240	99.66 77

DVP-MVS		98.77 125	98.52 156	99.52 45	99.50 127	99.21 34	98.02 198	98.84 324	97.97 212	99.08 170	99.02 191	97.61 160	99.88 114	96.99 237	99.63 230	99.48 175
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									98.17 196	99.08 170	99.02 191	97.89 134	99.88 114	97.07 230	99.71 193	99.70 67

EI-MVSNet		98.40 190	98.51 157	98.04 298	99.10 247	94.73 340	97.20 318	98.87 315	98.97 123	99.06 172	99.02 191	96.00 262	99.80 227	98.58 116	99.82 121	99.60 99

CVMVSNet		96.25 354	97.21 296	93.38 447	99.10 247	80.56 475	97.20 318	98.19 372	96.94 308	99.00 187	99.02 191	89.50 382	99.80 227	96.36 299	99.59 244	99.78 46

viewdifsd2359ckpt07		98.71 131	98.86 105	98.26 274	99.43 160	95.65 301	97.20 318	99.66 63	99.20 82	99.29 136	99.01 202	98.29 91	99.73 283	97.92 165	99.75 172	99.39 214

LFMVS		97.20 311	96.72 326	98.64 212	98.72 325	96.95 246	98.93 81	94.14 450	99.74 13	98.78 236	99.01 202	84.45 416	99.73 283	97.44 205	99.27 317	99.25 268

v2v482		98.56 165	98.62 140	98.37 264	99.42 162	95.81 298	97.58 273	99.16 265	97.90 220	99.28 138	99.01 202	95.98 267	99.79 240	99.33 59	99.90 84	99.51 154

ACMMP		98.75 127	98.50 160	99.52 45	99.56 102	99.16 49	98.87 88	99.37 181	97.16 296	98.82 230	99.01 202	97.71 149	99.87 133	96.29 303	99.69 203	99.54 139
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
diffmvs_AUTHOR		98.50 180	98.59 147	98.23 281	99.35 180	95.48 311	96.61 352	99.60 77	98.37 171	98.90 212	99.00 206	97.37 183	99.76 263	98.22 140	99.85 105	99.46 185

WB-MVS		98.52 178	98.55 151	98.43 255	99.65 68	95.59 302	98.52 125	98.77 335	99.65 26	99.52 85	99.00 206	94.34 316	99.93 53	98.65 113	98.83 370	99.76 55

viewmambaseed2359dif		98.19 224	98.26 203	97.99 301	99.02 271	95.03 331	96.59 354	99.53 111	96.21 343	99.00 187	98.99 208	97.62 158	99.61 350	97.62 189	99.72 184	99.33 245

DPE-MVS		98.59 161	98.26 203	99.57 22	99.27 199	99.15 53	97.01 328	99.39 175	97.67 236	99.44 102	98.99 208	97.53 169	99.89 96	95.40 342	99.68 208	99.66 77
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		98.57 164	98.23 208	99.60 16	99.69 58	99.35 17	97.16 323	99.38 177	94.87 387	98.97 194	98.99 208	98.01 122	99.88 114	97.29 213	99.70 200	99.58 114
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EI-MVSNet-UG-set		98.69 139	98.71 124	98.62 218	99.10 247	96.37 276	97.23 313	98.87 315	99.20 82	99.19 158	98.99 208	97.30 187	99.85 155	98.77 104	99.79 144	99.65 82

XVG-ACMP-BASELINE		98.56 165	98.34 189	99.22 107	99.54 113	98.59 102	97.71 250	99.46 142	97.25 284	98.98 190	98.99 208	97.54 167	99.84 173	95.88 321	99.74 173	99.23 273

APD-MVS_3200maxsize		98.84 108	98.61 144	99.53 39	99.19 225	99.27 28	98.49 135	99.33 203	98.64 148	99.03 185	98.98 213	97.89 134	99.85 155	96.54 287	99.42 294	99.46 185

XVG-OURS		98.53 174	98.34 189	99.11 124	99.50 127	98.82 87	95.97 390	99.50 120	97.30 279	99.05 180	98.98 213	99.35 14	99.32 424	95.72 331	99.68 208	99.18 291

v148		98.45 185	98.60 145	98.00 300	99.44 155	94.98 332	97.44 292	99.06 280	98.30 179	99.32 132	98.97 215	96.65 233	99.62 343	98.37 131	99.85 105	99.39 214

EI-MVSNet-Vis-set		98.68 144	98.70 127	98.63 216	99.09 250	96.40 275	97.23 313	98.86 320	99.20 82	99.18 162	98.97 215	97.29 189	99.85 155	98.72 108	99.78 149	99.64 83

CHOSEN 1792x2688		97.49 286	97.14 301	98.54 240	99.68 61	96.09 285	96.50 359	99.62 73	91.58 435	98.84 226	98.97 215	92.36 351	99.88 114	96.76 260	99.95 38	99.67 75

SR-MVS-dyc-post		98.81 116	98.55 151	99.57 22	99.20 222	99.38 13	98.48 138	99.30 218	98.64 148	98.95 200	98.96 218	97.49 176	99.86 142	96.56 283	99.39 297	99.45 190

RE-MVS-def			98.58 148		99.20 222	99.38 13	98.48 138	99.30 218	98.64 148	98.95 200	98.96 218	97.75 147		96.56 283	99.39 297	99.45 190

D2MVS		97.84 263	97.84 254	97.83 308	99.14 241	94.74 339	96.94 332	98.88 313	95.84 359	98.89 215	98.96 218	94.40 314	99.69 303	97.55 194	99.95 38	99.05 308

ACMM	96.08 12	98.91 96	98.73 118	99.48 57	99.55 108	99.14 58	98.07 188	99.37 181	97.62 240	99.04 182	98.96 218	98.84 36	99.79 240	97.43 206	99.65 223	99.49 164
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		98.08 235	97.92 247	98.57 228	98.96 281	96.79 254	97.90 221	99.18 258	96.41 336	98.46 280	98.95 222	95.93 271	99.60 353	96.51 289	98.98 361	99.31 252
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
YYNet1		97.60 277	97.67 265	97.39 356	99.04 263	93.04 395	95.27 421	98.38 364	97.25 284	98.92 210	98.95 222	95.48 286	99.73 283	96.99 237	98.74 374	99.41 204

MDA-MVSNet_test_wron		97.60 277	97.66 268	97.41 355	99.04 263	93.09 391	95.27 421	98.42 361	97.26 283	98.88 219	98.95 222	95.43 287	99.73 283	97.02 233	98.72 376	99.41 204

FMVSNet3		97.50 283	97.24 294	98.29 272	98.08 401	95.83 296	97.86 227	98.91 308	97.89 221	98.95 200	98.95 222	87.06 395	99.81 219	97.77 178	99.69 203	99.23 273

viewmanbaseed2359cas		98.58 163	98.54 153	98.70 203	99.28 196	97.13 237	97.47 288	99.55 102	97.55 251	98.96 199	98.92 226	97.77 145	99.59 357	97.59 193	99.77 155	99.39 214

OPM-MVS		98.56 165	98.32 194	99.25 102	99.41 165	98.73 93	97.13 325	99.18 258	97.10 299	98.75 242	98.92 226	98.18 107	99.65 334	96.68 269	99.56 256	99.37 225
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ADS-MVSNet2		95.43 378	94.98 383	96.76 387	98.14 397	91.74 414	97.92 218	97.76 382	90.23 445	96.51 407	98.91 228	85.61 407	99.85 155	92.88 404	96.90 441	98.69 370

ADS-MVSNet		95.24 381	94.93 386	96.18 404	98.14 397	90.10 440	97.92 218	97.32 397	90.23 445	96.51 407	98.91 228	85.61 407	99.74 276	92.88 404	96.90 441	98.69 370

test_0402		98.76 126	98.71 124	98.93 161	99.56 102	98.14 139	98.45 142	99.34 197	99.28 72	98.95 200	98.91 228	98.34 87	99.79 240	95.63 335	99.91 77	98.86 344

test_241102_ONE					99.49 135	99.17 45		99.31 210	97.98 211	99.66 60	98.90 231	98.36 82	99.48 397

SF-MVS		98.53 174	98.27 202	99.32 89	99.31 186	98.75 89	98.19 166	99.41 170	96.77 320	98.83 227	98.90 231	97.80 143	99.82 202	95.68 334	99.52 269	99.38 223

MTAPA		98.88 101	98.64 136	99.61 14	99.67 65	99.36 16	98.43 143	99.20 250	98.83 141	98.89 215	98.90 231	96.98 208	99.92 64	97.16 221	99.70 200	99.56 126

test20.03		98.78 122	98.77 115	98.78 186	99.46 148	97.20 228	97.78 237	99.24 244	99.04 116	99.41 109	98.90 231	97.65 153	99.76 263	97.70 185	99.79 144	99.39 214

SteuartSystems-ACMMP		98.79 120	98.54 153	99.54 32	99.73 37	99.16 49	98.23 162	99.31 210	97.92 218	98.90 212	98.90 231	98.00 123	99.88 114	96.15 311	99.72 184	99.58 114
Skip Steuart: Steuart Systems R&D Blog.
N_pmnet		97.63 276	97.17 297	98.99 149	99.27 199	97.86 173	95.98 389	93.41 453	95.25 377	99.47 97	98.90 231	95.63 279	99.85 155	96.91 243	99.73 176	99.27 261

TSAR-MVS + MP.		98.63 153	98.49 165	99.06 139	99.64 74	97.90 170	98.51 130	98.94 300	96.96 306	99.24 150	98.89 237	97.83 138	99.81 219	96.88 250	99.49 281	99.48 175
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		98.66 148	98.37 185	99.55 29	99.53 116	99.18 44	98.23 162	99.49 127	97.01 305	98.69 247	98.88 238	98.00 123	99.89 96	95.87 324	99.59 244	99.58 114

TinyColmap		97.89 252	97.98 238	97.60 336	98.86 302	94.35 351	96.21 377	99.44 154	97.45 266	99.06 172	98.88 238	97.99 126	99.28 431	94.38 370	99.58 249	99.18 291

LS3D		98.63 153	98.38 183	99.36 72	97.25 442	99.38 13	99.12 60	99.32 205	99.21 80	98.44 282	98.88 238	97.31 186	99.80 227	96.58 277	99.34 305	98.92 334

Anonymous202405211		97.90 250	97.50 278	99.08 131	98.90 293	98.25 127	98.53 124	96.16 423	98.87 135	99.11 165	98.86 241	90.40 374	99.78 251	97.36 209	99.31 310	99.19 288

HPM-MVS_fast		99.01 81	98.82 109	99.57 22	99.71 47	99.35 17	99.00 72	99.50 120	97.33 275	98.94 207	98.86 241	98.75 46	99.82 202	97.53 197	99.71 193	99.56 126

CMPMVS	75.91 23	96.29 351	95.44 369	98.84 173	96.25 463	98.69 96	97.02 327	99.12 272	88.90 454	97.83 332	98.86 241	89.51 381	98.90 450	91.92 417	99.51 271	98.92 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NormalMVS		98.26 214	97.97 241	99.15 119	99.64 74	97.83 176	98.28 156	99.43 160	99.24 75	98.80 234	98.85 244	89.76 378	99.94 42	98.04 154	99.67 214	99.68 70

SymmetryMVS		98.05 238	97.71 263	99.09 130	99.29 193	97.83 176	98.28 156	97.64 390	99.24 75	98.80 234	98.85 244	89.76 378	99.94 42	98.04 154	99.50 279	99.49 164

SR-MVS		98.71 131	98.43 174	99.57 22	99.18 232	99.35 17	98.36 151	99.29 226	98.29 182	98.88 219	98.85 244	97.53 169	99.87 133	96.14 312	99.31 310	99.48 175

our_test_3		97.39 296	97.73 261	96.34 396	98.70 333	89.78 441	94.61 441	98.97 299	96.50 330	99.04 182	98.85 244	95.98 267	99.84 173	97.26 215	99.67 214	99.41 204

EPNet		96.14 357	95.44 369	98.25 276	90.76 476	95.50 310	97.92 218	94.65 442	98.97 123	92.98 458	98.85 244	89.12 384	99.87 133	95.99 317	99.68 208	99.39 214
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mamba_0408		98.80 118	98.88 99	98.55 235	99.27 199	96.50 270	98.00 202	99.60 77	98.93 128	99.22 153	98.84 249	98.59 62	99.89 96	97.74 183	99.72 184	99.27 261

SSM_04072		98.80 118	98.88 99	98.56 233	99.27 199	96.50 270	98.00 202	99.60 77	98.93 128	99.22 153	98.84 249	98.59 62	99.90 80	97.74 183	99.72 184	99.27 261

pmmvs5		97.64 275	97.49 279	98.08 293	99.14 241	95.12 328	96.70 347	99.05 284	93.77 410	98.62 257	98.83 251	93.23 334	99.75 271	98.33 135	99.76 168	99.36 232

PMMVS2		98.07 236	98.08 228	98.04 298	99.41 165	94.59 346	94.59 442	99.40 173	97.50 256	98.82 230	98.83 251	96.83 217	99.84 173	97.50 200	99.81 127	99.71 62

MDTV_nov1_ep13			95.22 378		97.06 448	83.20 468	97.74 247	96.16 423	94.37 399	96.99 381	98.83 251	83.95 422	99.53 381	93.90 381	97.95 417

viewdifsd2359ckpt13		98.39 196	98.29 198	98.70 203	99.26 208	97.19 229	97.51 282	99.48 129	96.94 308	98.58 265	98.82 254	97.47 178	99.55 373	97.21 218	99.33 306	99.34 239

Anonymous20231206		98.21 221	98.21 209	98.20 283	99.51 121	95.43 316	98.13 174	99.32 205	96.16 346	98.93 208	98.82 254	96.00 262	99.83 191	97.32 212	99.73 176	99.36 232

ACMP	95.32 15	98.41 188	98.09 225	99.36 72	99.51 121	98.79 88	97.68 254	99.38 177	95.76 362	98.81 232	98.82 254	98.36 82	99.82 202	94.75 354	99.77 155	99.48 175
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GeoE		99.05 78	98.99 88	99.25 102	99.44 155	98.35 123	98.73 101	99.56 98	98.42 170	98.91 211	98.81 257	98.94 30	99.91 73	98.35 132	99.73 176	99.49 164

VNet		98.42 187	98.30 196	98.79 183	98.79 318	97.29 219	98.23 162	98.66 347	99.31 68	98.85 224	98.80 258	94.80 305	99.78 251	98.13 146	99.13 341	99.31 252

tpmrst		95.07 384	95.46 367	93.91 439	97.11 445	84.36 465	97.62 265	96.96 408	94.98 383	96.35 412	98.80 258	85.46 409	99.59 357	95.60 336	96.23 450	97.79 431

ppachtmachnet_test		97.50 283	97.74 259	96.78 386	98.70 333	91.23 428	94.55 443	99.05 284	96.36 337	99.21 156	98.79 260	96.39 243	99.78 251	96.74 262	99.82 121	99.34 239

MGCNet		97.44 291	97.01 307	98.72 201	96.42 460	96.74 258	97.20 318	91.97 460	98.46 168	98.30 291	98.79 260	92.74 347	99.91 73	99.30 62	99.94 49	99.52 151

miper_lstm_enhance		97.18 313	97.16 298	97.25 362	98.16 395	92.85 397	95.15 426	99.31 210	97.25 284	98.74 244	98.78 262	90.07 375	99.78 251	97.19 219	99.80 138	99.11 303

DeepPCF-MVS	96.93 5	98.32 205	98.01 235	99.23 106	98.39 382	98.97 74	95.03 428	99.18 258	96.88 313	99.33 126	98.78 262	98.16 111	99.28 431	96.74 262	99.62 233	99.44 194

TestfortrainingZip a		98.95 91	98.72 120	99.64 9	99.58 87	99.32 22	98.68 107	99.60 77	96.46 334	99.53 82	98.77 264	97.87 136	99.83 191	98.39 130	99.64 225	99.77 49

MED-MVS		98.61 157	98.33 193	99.44 64	99.24 210	98.93 80	97.45 290	99.06 280	98.14 205	99.06 172	98.77 264	96.97 209	99.82 202	96.67 270	99.64 225	99.58 114

patchmatchnet-post											98.77 264	84.37 417	99.85 155

APD-MVS		98.10 232	97.67 265	99.42 66	99.11 245	98.93 80	97.76 243	99.28 230	94.97 384	98.72 245	98.77 264	97.04 202	99.85 155	93.79 386	99.54 262	99.49 164
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DU-MVS		98.82 114	98.63 138	99.39 71	99.16 236	98.74 90	97.54 278	99.25 239	98.84 140	99.06 172	98.76 268	96.76 225	99.93 53	98.57 118	99.77 155	99.50 157

NR-MVSNet		98.95 91	98.82 109	99.36 72	99.16 236	98.72 95	99.22 45	99.20 250	99.10 104	99.72 47	98.76 268	96.38 245	99.86 142	98.00 159	99.82 121	99.50 157

eth_miper_zixun_eth		97.23 309	97.25 293	97.17 365	98.00 404	92.77 399	94.71 435	99.18 258	97.27 282	98.56 269	98.74 270	91.89 358	99.69 303	97.06 232	99.81 127	99.05 308

UniMVSNet (Re)		98.87 102	98.71 124	99.35 78	99.24 210	98.73 93	97.73 249	99.38 177	98.93 128	99.12 164	98.73 271	96.77 223	99.86 142	98.63 115	99.80 138	99.46 185

MG-MVS		96.77 335	96.61 334	97.26 361	98.31 386	93.06 392	95.93 395	98.12 375	96.45 335	97.92 323	98.73 271	93.77 330	99.39 414	91.19 433	99.04 350	99.33 245

c3_l		97.36 297	97.37 286	97.31 357	98.09 400	93.25 390	95.01 429	99.16 265	97.05 301	98.77 239	98.72 273	92.88 343	99.64 337	96.93 242	99.76 168	99.05 308

icg_test_0407_2		98.20 223	98.38 183	97.65 329	99.03 266	94.03 363	95.78 404	99.45 146	98.16 199	99.06 172	98.71 274	98.27 94	99.68 312	97.50 200	99.45 286	99.22 278

IMVS_0407		98.39 196	98.64 136	97.66 327	99.03 266	94.03 363	98.10 181	99.45 146	98.16 199	99.06 172	98.71 274	98.27 94	99.71 292	97.50 200	99.45 286	99.22 278

IMVS_0404		98.07 236	98.20 210	97.69 324	99.03 266	94.03 363	96.67 348	99.45 146	98.16 199	98.03 318	98.71 274	96.80 221	99.82 202	97.50 200	99.45 286	99.22 278

IMVS_0403		98.34 200	98.56 150	97.66 327	99.03 266	94.03 363	97.98 210	99.45 146	98.16 199	98.89 215	98.71 274	97.90 132	99.74 276	97.50 200	99.45 286	99.22 278

cl____		97.02 323	96.83 319	97.58 338	97.82 412	94.04 362	94.66 438	99.16 265	97.04 302	98.63 255	98.71 274	88.68 388	99.69 303	97.00 235	99.81 127	99.00 320

DIV-MVS_self_test		97.02 323	96.84 318	97.58 338	97.82 412	94.03 363	94.66 438	99.16 265	97.04 302	98.63 255	98.71 274	88.69 386	99.69 303	97.00 235	99.81 127	99.01 316

DELS-MVS		98.27 212	98.20 210	98.48 249	98.86 302	96.70 260	95.60 410	99.20 250	97.73 232	98.45 281	98.71 274	97.50 173	99.82 202	98.21 141	99.59 244	98.93 333
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
SSC-MVS3.2		98.53 174	98.79 112	97.74 319	99.46 148	93.62 386	96.45 361	99.34 197	99.33 65	98.93 208	98.70 281	97.90 132	99.90 80	99.12 76	99.92 68	99.69 69

9.14			97.78 256		99.07 254		97.53 279	99.32 205	95.53 369	98.54 273	98.70 281	97.58 162	99.76 263	94.32 371	99.46 284

tpmvs		95.02 386	95.25 376	94.33 433	96.39 462	85.87 456	98.08 184	96.83 413	95.46 371	95.51 432	98.69 283	85.91 405	99.53 381	94.16 372	96.23 450	97.58 439

PatchmatchNet		95.58 374	95.67 359	95.30 425	97.34 440	87.32 453	97.65 260	96.65 415	95.30 376	97.07 376	98.69 283	84.77 413	99.75 271	94.97 350	98.64 385	98.83 346
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mPP-MVS		98.64 151	98.34 189	99.54 32	99.54 113	99.17 45	98.63 112	99.24 244	97.47 259	98.09 311	98.68 285	97.62 158	99.89 96	96.22 306	99.62 233	99.57 120

UnsupCasMVSNet_eth		97.89 252	97.60 273	98.75 195	99.31 186	97.17 233	97.62 265	99.35 191	98.72 145	98.76 241	98.68 285	92.57 350	99.74 276	97.76 182	95.60 456	99.34 239

SCA		96.41 349	96.66 332	95.67 415	98.24 390	88.35 447	95.85 401	96.88 412	96.11 347	97.67 342	98.67 287	93.10 338	99.85 155	94.16 372	99.22 326	98.81 352

Patchmatch-test		96.55 342	96.34 344	97.17 365	98.35 383	93.06 392	98.40 147	97.79 381	97.33 275	98.41 285	98.67 287	83.68 424	99.69 303	95.16 346	99.31 310	98.77 360

CDS-MVSNet		97.69 271	97.35 288	98.69 205	98.73 323	97.02 241	96.92 336	98.75 340	95.89 358	98.59 263	98.67 287	92.08 357	99.74 276	96.72 265	99.81 127	99.32 248
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MP-MVS		98.46 184	98.09 225	99.54 32	99.57 94	99.22 33	98.50 132	99.19 254	97.61 243	97.58 348	98.66 290	97.40 181	99.88 114	94.72 357	99.60 240	99.54 139
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	96.85 6	98.30 208	98.15 220	98.75 195	98.61 353	97.23 223	97.76 243	99.09 277	97.31 278	98.75 242	98.66 290	97.56 164	99.64 337	96.10 315	99.55 260	99.39 214
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MS-PatchMatch		97.68 272	97.75 258	97.45 352	98.23 392	93.78 379	97.29 308	98.84 324	96.10 348	98.64 254	98.65 292	96.04 259	99.36 417	96.84 254	99.14 339	99.20 283

pmmvs4		97.58 280	97.28 291	98.51 244	98.84 306	96.93 248	95.40 419	98.52 356	93.60 412	98.61 259	98.65 292	95.10 294	99.60 353	96.97 240	99.79 144	98.99 321

FPMVS		93.44 411	92.23 418	97.08 368	99.25 209	97.86 173	95.61 409	97.16 402	92.90 422	93.76 455	98.65 292	75.94 450	95.66 469	79.30 467	97.49 425	97.73 433

dp		93.47 410	93.59 403	93.13 449	96.64 455	81.62 474	97.66 258	96.42 420	92.80 424	96.11 416	98.64 295	78.55 447	99.59 357	93.31 397	92.18 468	98.16 409

EPMVS		93.72 407	93.27 406	95.09 428	96.04 465	87.76 450	98.13 174	85.01 473	94.69 390	96.92 383	98.64 295	78.47 448	99.31 425	95.04 347	96.46 447	98.20 407

XVS		98.72 130	98.45 171	99.53 39	99.46 148	99.21 34	98.65 110	99.34 197	98.62 153	97.54 352	98.63 297	97.50 173	99.83 191	96.79 256	99.53 266	99.56 126

CostFormer		93.97 402	93.78 400	94.51 432	97.53 430	85.83 458	97.98 210	95.96 428	89.29 453	94.99 438	98.63 297	78.63 445	99.62 343	94.54 360	96.50 446	98.09 413

MSLP-MVS++		98.02 240	98.14 222	97.64 332	98.58 360	95.19 325	97.48 286	99.23 246	97.47 259	97.90 325	98.62 299	97.04 202	98.81 452	97.55 194	99.41 295	98.94 332

Vis-MVSNet (Re-imp)		97.46 288	97.16 298	98.34 267	99.55 108	96.10 282	98.94 80	98.44 359	98.32 177	98.16 303	98.62 299	88.76 385	99.73 283	93.88 383	99.79 144	99.18 291

BP-MVS1		97.40 295	96.97 308	98.71 202	99.07 254	96.81 253	98.34 154	97.18 400	98.58 159	98.17 300	98.61 301	84.01 421	99.94 42	98.97 89	99.78 149	99.37 225

XVG-OURS-SEG-HR		98.49 181	98.28 199	99.14 120	99.49 135	98.83 85	96.54 355	99.48 129	97.32 277	99.11 165	98.61 301	99.33 15	99.30 427	96.23 305	98.38 394	99.28 260

ITE_SJBPF				98.87 169	99.22 216	98.48 112		99.35 191	97.50 256	98.28 295	98.60 303	97.64 156	99.35 420	93.86 384	99.27 317	98.79 358

UniMVSNet_NR-MVSNet		98.86 105	98.68 130	99.40 70	99.17 234	98.74 90	97.68 254	99.40 173	99.14 95	99.06 172	98.59 304	96.71 229	99.93 53	98.57 118	99.77 155	99.53 148

114514_t		96.50 345	95.77 354	98.69 205	99.48 143	97.43 210	97.84 230	99.55 102	81.42 467	96.51 407	98.58 305	95.53 282	99.67 316	93.41 396	99.58 249	98.98 322

HY-MVS	95.94 13	95.90 364	95.35 374	97.55 343	97.95 405	94.79 336	98.81 96	96.94 410	92.28 430	95.17 435	98.57 306	89.90 377	99.75 271	91.20 432	97.33 436	98.10 412

tpm		94.67 390	94.34 394	95.66 416	97.68 423	88.42 446	97.88 223	94.90 440	94.46 395	96.03 420	98.56 307	78.66 444	99.79 240	95.88 321	95.01 459	98.78 359

GDP-MVS		97.50 283	97.11 302	98.67 208	99.02 271	96.85 251	98.16 171	99.71 47	98.32 177	98.52 276	98.54 308	83.39 425	99.95 26	98.79 100	99.56 256	99.19 288

PC_three_1452									93.27 416	99.40 112	98.54 308	98.22 103	97.00 467	95.17 345	99.45 286	99.49 164

ACMMPR		98.70 136	98.42 176	99.54 32	99.52 119	99.14 58	98.52 125	99.31 210	97.47 259	98.56 269	98.54 308	97.75 147	99.88 114	96.57 279	99.59 244	99.58 114

new_pmnet		96.99 327	96.76 324	97.67 325	98.72 325	94.89 334	95.95 394	98.20 370	92.62 426	98.55 271	98.54 308	94.88 301	99.52 385	93.96 380	99.44 293	98.59 381

OPU-MVS				98.82 176	98.59 358	98.30 124	98.10 181				98.52 312	98.18 107	98.75 454	94.62 358	99.48 282	99.41 204

SPE-MVS-test		99.13 65	99.09 74	99.26 99	99.13 243	98.97 74	99.31 30	99.88 14	99.44 52	98.16 303	98.51 313	98.64 56	99.93 53	98.91 92	99.85 105	98.88 342

region2R		98.69 139	98.40 178	99.54 32	99.53 116	99.17 45	98.52 125	99.31 210	97.46 264	98.44 282	98.51 313	97.83 138	99.88 114	96.46 292	99.58 249	99.58 114

TSAR-MVS + GP.		98.18 226	97.98 238	98.77 191	98.71 329	97.88 171	96.32 371	98.66 347	96.33 338	99.23 152	98.51 313	97.48 177	99.40 412	97.16 221	99.46 284	99.02 315

OMC-MVS		97.88 254	97.49 279	99.04 142	98.89 298	98.63 97	96.94 332	99.25 239	95.02 382	98.53 274	98.51 313	97.27 190	99.47 400	93.50 394	99.51 271	99.01 316

HFP-MVS		98.71 131	98.44 173	99.51 49	99.49 135	99.16 49	98.52 125	99.31 210	97.47 259	98.58 265	98.50 317	97.97 127	99.85 155	96.57 279	99.59 244	99.53 148

diffmvs		98.22 219	98.24 207	98.17 286	99.00 274	95.44 315	96.38 367	99.58 84	97.79 229	98.53 274	98.50 317	96.76 225	99.74 276	97.95 164	99.64 225	99.34 239
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		98.40 190	98.19 214	99.03 143	99.00 274	97.65 195	96.85 338	98.94 300	98.57 160	98.89 215	98.50 317	95.60 280	99.85 155	97.54 196	99.85 105	99.59 106

Test_1112_low_res		96.99 327	96.55 338	98.31 270	99.35 180	95.47 314	95.84 402	99.53 111	91.51 437	96.80 394	98.48 320	91.36 364	99.83 191	96.58 277	99.53 266	99.62 89

viewdifsd2359ckpt09		98.13 231	97.92 247	98.77 191	99.18 232	97.35 213	97.29 308	99.53 111	95.81 360	98.09 311	98.47 321	96.34 248	99.66 327	97.02 233	99.51 271	99.29 257

CS-MVS		99.13 65	99.10 72	99.24 104	99.06 259	99.15 53	99.36 22	99.88 14	99.36 63	98.21 299	98.46 322	98.68 53	99.93 53	99.03 85	99.85 105	98.64 375

miper_ehance_all_eth		97.06 320	97.03 305	97.16 367	97.83 411	93.06 392	94.66 438	99.09 277	95.99 354	98.69 247	98.45 323	92.73 348	99.61 350	96.79 256	99.03 351	98.82 347

WBMVS		95.18 382	94.78 388	96.37 395	97.68 423	89.74 442	95.80 403	98.73 343	97.54 253	98.30 291	98.44 324	70.06 456	99.82 202	96.62 274	99.87 96	99.54 139

PHI-MVS		98.29 211	97.95 242	99.34 81	98.44 376	99.16 49	98.12 178	99.38 177	96.01 353	98.06 314	98.43 325	97.80 143	99.67 316	95.69 333	99.58 249	99.20 283

tpm cat1		93.29 413	93.13 410	93.75 441	97.39 439	84.74 461	97.39 295	97.65 388	83.39 465	94.16 447	98.41 326	82.86 429	99.39 414	91.56 426	95.35 458	97.14 447

CP-MVS		98.70 136	98.42 176	99.52 45	99.36 175	99.12 63	98.72 102	99.36 185	97.54 253	98.30 291	98.40 327	97.86 137	99.89 96	96.53 288	99.72 184	99.56 126

ZNCC-MVS		98.68 144	98.40 178	99.54 32	99.57 94	99.21 34	98.46 140	99.29 226	97.28 281	98.11 309	98.39 328	98.00 123	99.87 133	96.86 253	99.64 225	99.55 133

GST-MVS		98.61 157	98.30 196	99.52 45	99.51 121	99.20 40	98.26 160	99.25 239	97.44 267	98.67 250	98.39 328	97.68 150	99.85 155	96.00 316	99.51 271	99.52 151

HPM-MVS		98.79 120	98.53 155	99.59 20	99.65 68	99.29 25	99.16 54	99.43 160	96.74 321	98.61 259	98.38 330	98.62 59	99.87 133	96.47 291	99.67 214	99.59 106
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				98.09 290	98.93 285	95.40 317		98.80 331	90.08 449	97.45 361	98.37 331	95.26 290	99.70 299	93.58 391	98.95 364	99.17 295

CPTT-MVS		97.84 263	97.36 287	99.27 97	99.31 186	98.46 113	98.29 155	99.27 233	94.90 386	97.83 332	98.37 331	94.90 298	99.84 173	93.85 385	99.54 262	99.51 154

EC-MVSNet		99.09 71	99.05 78	99.20 108	99.28 196	98.93 80	99.24 44	99.84 22	99.08 111	98.12 308	98.37 331	98.72 49	99.90 80	99.05 83	99.77 155	98.77 360

OpenMVS_ROB	95.38 14	95.84 367	95.18 380	97.81 310	98.41 381	97.15 235	97.37 301	98.62 351	83.86 463	98.65 253	98.37 331	94.29 318	99.68 312	88.41 447	98.62 388	96.60 454

tttt0517		95.64 373	94.98 383	97.64 332	99.36 175	93.81 378	98.72 102	90.47 464	98.08 207	98.67 250	98.34 335	73.88 452	99.92 64	97.77 178	99.51 271	99.20 283

旧先验1					98.82 311	97.45 208		98.76 337			98.34 335	95.50 285			99.01 355	99.23 273

CNVR-MVS		98.17 228	97.87 252	99.07 133	98.67 343	98.24 128	97.01 328	98.93 303	97.25 284	97.62 344	98.34 335	97.27 190	99.57 366	96.42 294	99.33 306	99.39 214

HyFIR lowres test		97.19 312	96.60 336	98.96 156	99.62 84	97.28 220	95.17 424	99.50 120	94.21 402	99.01 186	98.32 338	86.61 398	99.99 2	97.10 228	99.84 110	99.60 99

UnsupCasMVSNet_bld		97.30 302	96.92 312	98.45 252	99.28 196	96.78 257	96.20 378	99.27 233	95.42 372	98.28 295	98.30 339	93.16 336	99.71 292	94.99 348	97.37 432	98.87 343

MSDG		97.71 270	97.52 277	98.28 273	98.91 292	96.82 252	94.42 445	99.37 181	97.65 238	98.37 290	98.29 340	97.40 181	99.33 423	94.09 377	99.22 326	98.68 373

MVS_111021_HR		98.25 217	98.08 228	98.75 195	99.09 250	97.46 207	95.97 390	99.27 233	97.60 245	97.99 321	98.25 341	98.15 113	99.38 416	96.87 251	99.57 253	99.42 201

CANet_DTU		97.26 305	97.06 304	97.84 307	97.57 425	94.65 344	96.19 379	98.79 332	97.23 290	95.14 436	98.24 342	93.22 335	99.84 173	97.34 210	99.84 110	99.04 312

MVS_111021_LR		98.30 208	98.12 223	98.83 174	99.16 236	98.03 155	96.09 386	99.30 218	97.58 246	98.10 310	98.24 342	98.25 98	99.34 421	96.69 268	99.65 223	99.12 302

tpm2		93.09 416	92.58 414	94.62 431	97.56 426	86.53 455	97.66 258	95.79 432	86.15 460	94.07 450	98.23 344	75.95 449	99.53 381	90.91 437	96.86 444	97.81 428

CANet		97.87 256	97.76 257	98.19 285	97.75 414	95.51 307	96.76 343	99.05 284	97.74 231	96.93 382	98.21 345	95.59 281	99.89 96	97.86 173	99.93 55	99.19 288

LF4IMVS		97.90 250	97.69 264	98.52 243	99.17 234	97.66 194	97.19 322	99.47 138	96.31 340	97.85 331	98.20 346	96.71 229	99.52 385	94.62 358	99.72 184	98.38 399

CL-MVSNet_self_test		97.44 291	97.22 295	98.08 293	98.57 362	95.78 299	94.30 448	98.79 332	96.58 328	98.60 261	98.19 347	94.74 308	99.64 337	96.41 295	98.84 369	98.82 347

cl22		95.79 368	95.39 372	96.98 374	96.77 453	92.79 398	94.40 446	98.53 355	94.59 392	97.89 326	98.17 348	82.82 430	99.24 433	96.37 297	99.03 351	98.92 334

MVSFormer		98.26 214	98.43 174	97.77 313	98.88 299	93.89 376	99.39 20	99.56 98	99.11 97	98.16 303	98.13 349	93.81 328	99.97 7	99.26 65	99.57 253	99.43 198

jason		97.45 290	97.35 288	97.76 316	99.24 210	93.93 372	95.86 399	98.42 361	94.24 401	98.50 277	98.13 349	94.82 302	99.91 73	97.22 217	99.73 176	99.43 198
jason: jason.
ZD-MVS					99.01 273	98.84 84		99.07 279	94.10 405	98.05 316	98.12 351	96.36 247	99.86 142	92.70 411	99.19 333

test222					98.92 289	96.93 248	95.54 411	98.78 334	85.72 461	96.86 391	98.11 352	94.43 312			99.10 346	99.23 273

新几何1				98.91 165	98.94 283	97.76 187		98.76 337	87.58 458	96.75 396	98.10 353	94.80 305	99.78 251	92.73 410	99.00 356	99.20 283

原ACMM1				98.35 266	98.90 293	96.25 280		98.83 328	92.48 427	96.07 418	98.10 353	95.39 288	99.71 292	92.61 413	98.99 358	99.08 304

EPNet_dtu		94.93 388	94.78 388	95.38 424	93.58 472	87.68 451	96.78 341	95.69 435	97.35 274	89.14 469	98.09 355	88.15 393	99.49 394	94.95 351	99.30 313	98.98 322
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		95.03 385	94.40 392	96.93 376	97.70 420	92.53 403	95.08 427	97.71 384	88.57 455	97.71 339	98.08 356	79.39 441	99.82 202	96.19 308	99.11 345	98.43 394

DP-MVS Recon		97.33 300	96.92 312	98.57 228	99.09 250	97.99 157	96.79 340	99.35 191	93.18 417	97.71 339	98.07 357	95.00 297	99.31 425	93.97 379	99.13 341	98.42 396

test_vis1_rt		97.75 267	97.72 262	97.83 308	98.81 314	96.35 277	97.30 307	99.69 54	94.61 391	97.87 328	98.05 358	96.26 251	98.32 459	98.74 106	98.18 402	98.82 347

CSCG		98.68 144	98.50 160	99.20 108	99.45 153	98.63 97	98.56 120	99.57 91	97.87 222	98.85 224	98.04 359	97.66 152	99.84 173	96.72 265	99.81 127	99.13 301

SD_0403		96.28 352	95.83 353	97.64 332	98.72 325	94.30 352	98.87 88	98.77 335	97.80 227	96.53 404	98.02 360	97.34 185	99.47 400	76.93 469	99.48 282	99.16 298

F-COLMAP		97.30 302	96.68 329	99.14 120	99.19 225	98.39 116	97.27 312	99.30 218	92.93 421	96.62 400	98.00 361	95.73 277	99.68 312	92.62 412	98.46 393	99.35 237

Effi-MVS+-dtu		98.26 214	97.90 250	99.35 78	98.02 403	99.49 6	98.02 198	99.16 265	98.29 182	97.64 343	97.99 362	96.44 242	99.95 26	96.66 271	98.93 366	98.60 378

hse-mvs2		97.46 288	97.07 303	98.64 212	98.73 323	97.33 215	97.45 290	97.64 390	99.11 97	98.58 265	97.98 363	88.65 389	99.79 240	98.11 147	97.39 431	98.81 352

HQP_MVS		97.99 246	97.67 265	98.93 161	99.19 225	97.65 195	97.77 240	99.27 233	98.20 193	97.79 335	97.98 363	94.90 298	99.70 299	94.42 366	99.51 271	99.45 190

plane_prior4											97.98 363

BH-RMVSNet		96.83 332	96.58 337	97.58 338	98.47 371	94.05 360	96.67 348	97.36 394	96.70 324	97.87 328	97.98 363	95.14 293	99.44 407	90.47 441	98.58 390	99.25 268

AUN-MVS		96.24 356	95.45 368	98.60 223	98.70 333	97.22 225	97.38 297	97.65 388	95.95 356	95.53 431	97.96 367	82.11 433	99.79 240	96.31 301	97.44 428	98.80 357

NCCC		97.86 257	97.47 282	99.05 140	98.61 353	98.07 150	96.98 330	98.90 309	97.63 239	97.04 378	97.93 368	95.99 266	99.66 327	95.31 343	98.82 372	99.43 198

sss		97.21 310	96.93 310	98.06 295	98.83 308	95.22 324	96.75 344	98.48 358	94.49 393	97.27 370	97.90 369	92.77 346	99.80 227	96.57 279	99.32 308	99.16 298

test_yl		96.69 336	96.29 346	97.90 303	98.28 387	95.24 322	97.29 308	97.36 394	98.21 189	98.17 300	97.86 370	86.27 400	99.55 373	94.87 352	98.32 395	98.89 339

DCV-MVSNet		96.69 336	96.29 346	97.90 303	98.28 387	95.24 322	97.29 308	97.36 394	98.21 189	98.17 300	97.86 370	86.27 400	99.55 373	94.87 352	98.32 395	98.89 339

CDPH-MVS		97.26 305	96.66 332	99.07 133	99.00 274	98.15 137	96.03 388	99.01 295	91.21 441	97.79 335	97.85 372	96.89 213	99.69 303	92.75 409	99.38 300	99.39 214

HPM-MVS++		98.10 232	97.64 270	99.48 57	99.09 250	99.13 61	97.52 280	98.75 340	97.46 264	96.90 388	97.83 373	96.01 261	99.84 173	95.82 328	99.35 303	99.46 185

PatchMatch-RL		97.24 308	96.78 323	98.61 221	99.03 266	97.83 176	96.36 368	99.06 280	93.49 415	97.36 368	97.78 374	95.75 276	99.49 394	93.44 395	98.77 373	98.52 384

TAPA-MVS	96.21 11	96.63 340	95.95 351	98.65 210	98.93 285	98.09 144	96.93 334	99.28 230	83.58 464	98.13 307	97.78 374	96.13 255	99.40 412	93.52 392	99.29 315	98.45 389
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		95.96 363	95.44 369	97.52 346	98.51 369	93.99 370	98.39 148	96.09 426	98.21 189	98.40 289	97.76 376	86.88 396	99.63 340	95.42 341	89.27 469	98.95 328

WTY-MVS		96.67 338	96.27 348	97.87 306	98.81 314	94.61 345	96.77 342	97.92 380	94.94 385	97.12 373	97.74 377	91.11 367	99.82 202	93.89 382	98.15 406	99.18 291

test_method		79.78 437	79.50 440	80.62 453	80.21 478	45.76 481	70.82 470	98.41 363	31.08 473	80.89 473	97.71 378	84.85 412	97.37 466	91.51 427	80.03 470	98.75 363

MSP-MVS		98.40 190	98.00 236	99.61 14	99.57 94	99.25 30	98.57 119	99.35 191	97.55 251	99.31 134	97.71 378	94.61 309	99.88 114	96.14 312	99.19 333	99.70 67
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		98.00 243	97.63 271	99.10 126	99.24 210	98.17 136	96.89 337	98.73 343	95.66 363	97.92 323	97.70 380	97.17 196	99.66 327	96.18 310	99.23 325	99.47 183

AdaColmap		97.14 316	96.71 327	98.46 251	98.34 384	97.80 185	96.95 331	98.93 303	95.58 367	96.92 383	97.66 381	95.87 273	99.53 381	90.97 435	99.14 339	98.04 415

thisisatest0530		95.27 380	94.45 391	97.74 319	99.19 225	94.37 350	97.86 227	90.20 465	97.17 295	98.22 298	97.65 382	73.53 453	99.90 80	96.90 248	99.35 303	98.95 328

testgi		98.32 205	98.39 181	98.13 289	99.57 94	95.54 305	97.78 237	99.49 127	97.37 272	99.19 158	97.65 382	98.96 29	99.49 394	96.50 290	98.99 358	99.34 239

test_prior2							95.74 406		96.48 332	96.11 416	97.63 384	95.92 272		94.16 372	99.20 330

tt0805		98.69 139	98.62 140	98.90 168	99.75 34	99.30 23	99.15 56	96.97 407	98.86 137	98.87 223	97.62 385	98.63 58	98.96 446	99.41 56	98.29 398	98.45 389

cdsmvs_eth3d_5k		24.66 441	32.88 444	0.00 459	0.00 482	0.00 484	0.00 471	99.10 275	0.00 477	0.00 478	97.58 386	99.21 18	0.00 478	0.00 477	0.00 476	0.00 474

lupinMVS		97.06 320	96.86 316	97.65 329	98.88 299	93.89 376	95.48 415	97.97 378	93.53 413	98.16 303	97.58 386	93.81 328	99.91 73	96.77 259	99.57 253	99.17 295

TEST9					98.71 329	98.08 148	95.96 392	99.03 289	91.40 438	95.85 421	97.53 388	96.52 238	99.76 263

train_agg		97.10 317	96.45 342	99.07 133	98.71 329	98.08 148	95.96 392	99.03 289	91.64 433	95.85 421	97.53 388	96.47 240	99.76 263	93.67 388	99.16 336	99.36 232

Fast-Effi-MVS+-dtu		98.27 212	98.09 225	98.81 178	98.43 377	98.11 141	97.61 269	99.50 120	98.64 148	97.39 366	97.52 390	98.12 115	99.95 26	96.90 248	98.71 378	98.38 399

test_8					98.67 343	98.01 156	95.91 398	99.02 292	91.64 433	95.79 423	97.50 391	96.47 240	99.76 263

1112_ss		97.29 304	96.86 316	98.58 225	99.34 183	96.32 278	96.75 344	99.58 84	93.14 418	96.89 389	97.48 392	92.11 356	99.86 142	96.91 243	99.54 262	99.57 120

ab-mvs-re		8.12 445	10.83 448	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	97.48 392	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

Effi-MVS+		98.02 240	97.82 255	98.62 218	98.53 367	97.19 229	97.33 304	99.68 59	97.30 279	96.68 397	97.46 394	98.56 68	99.80 227	96.63 273	98.20 401	98.86 344

PCF-MVS	92.86 18	94.36 393	93.00 411	98.42 256	98.70 333	97.56 200	93.16 460	99.11 274	79.59 468	97.55 351	97.43 395	92.19 354	99.73 283	79.85 466	99.45 286	97.97 420
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GA-MVS		95.86 365	95.32 375	97.49 349	98.60 355	94.15 358	93.83 455	97.93 379	95.49 370	96.68 397	97.42 396	83.21 426	99.30 427	96.22 306	98.55 391	99.01 316

CNLPA		97.17 314	96.71 327	98.55 235	98.56 363	98.05 154	96.33 370	98.93 303	96.91 312	97.06 377	97.39 397	94.38 315	99.45 405	91.66 422	99.18 335	98.14 410

PLC	94.65 16	96.51 343	95.73 356	98.85 172	98.75 321	97.91 169	96.42 365	99.06 280	90.94 444	95.59 424	97.38 398	94.41 313	99.59 357	90.93 436	98.04 415	99.05 308
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 332	96.75 325	97.08 368	98.74 322	93.33 389	96.71 346	98.26 367	96.72 322	98.44 282	97.37 399	95.20 291	99.47 400	91.89 418	97.43 429	98.44 392

PVSNet_Blended		96.88 330	96.68 329	97.47 351	98.92 289	93.77 380	94.71 435	99.43 160	90.98 443	97.62 344	97.36 400	96.82 218	99.67 316	94.73 355	99.56 256	98.98 322

miper_enhance_ethall		96.01 360	95.74 355	96.81 384	96.41 461	92.27 410	93.69 457	98.89 312	91.14 442	98.30 291	97.35 401	90.58 372	99.58 364	96.31 301	99.03 351	98.60 378

DPM-MVS		96.32 350	95.59 363	98.51 244	98.76 319	97.21 227	94.54 444	98.26 367	91.94 432	96.37 411	97.25 402	93.06 340	99.43 408	91.42 428	98.74 374	98.89 339

E-PMN		94.17 398	94.37 393	93.58 443	96.86 450	85.71 459	90.11 468	97.07 404	98.17 196	97.82 334	97.19 403	84.62 415	98.94 447	89.77 443	97.68 422	96.09 461

CLD-MVS		97.49 286	97.16 298	98.48 249	99.07 254	97.03 240	94.71 435	99.21 248	94.46 395	98.06 314	97.16 404	97.57 163	99.48 397	94.46 363	99.78 149	98.95 328
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 280x420		95.51 377	95.47 366	95.65 417	98.25 389	88.27 448	93.25 459	98.88 313	93.53 413	94.65 442	97.15 405	86.17 402	99.93 53	97.41 207	99.93 55	98.73 365

xiu_mvs_v1_base_debu		97.86 257	98.17 216	96.92 377	98.98 278	93.91 373	96.45 361	99.17 262	97.85 224	98.41 285	97.14 406	98.47 72	99.92 64	98.02 156	99.05 347	96.92 448

xiu_mvs_v1_base		97.86 257	98.17 216	96.92 377	98.98 278	93.91 373	96.45 361	99.17 262	97.85 224	98.41 285	97.14 406	98.47 72	99.92 64	98.02 156	99.05 347	96.92 448

xiu_mvs_v1_base_debi		97.86 257	98.17 216	96.92 377	98.98 278	93.91 373	96.45 361	99.17 262	97.85 224	98.41 285	97.14 406	98.47 72	99.92 64	98.02 156	99.05 347	96.92 448

NP-MVS					98.84 306	97.39 212					96.84 409

HQP-MVS		97.00 326	96.49 341	98.55 235	98.67 343	96.79 254	96.29 373	99.04 287	96.05 349	95.55 427	96.84 409	93.84 326	99.54 379	92.82 406	99.26 320	99.32 248

API-MVS		97.04 322	96.91 314	97.42 354	97.88 409	98.23 132	98.18 167	98.50 357	97.57 247	97.39 366	96.75 411	96.77 223	99.15 440	90.16 442	99.02 354	94.88 465

1314		95.74 369	95.60 361	96.17 405	97.53 430	92.75 400	98.07 188	98.31 366	91.22 440	94.25 446	96.68 412	95.53 282	99.03 442	91.64 424	97.18 438	96.74 452

testing3-2		93.78 405	93.91 397	93.39 446	98.82 311	81.72 473	97.76 243	95.28 437	98.60 155	96.54 403	96.66 413	65.85 469	99.62 343	96.65 272	98.99 358	98.82 347

TR-MVS		95.55 375	95.12 381	96.86 383	97.54 428	93.94 371	96.49 360	96.53 419	94.36 400	97.03 380	96.61 414	94.26 319	99.16 439	86.91 454	96.31 449	97.47 442

Fast-Effi-MVS+		97.67 273	97.38 285	98.57 228	98.71 329	97.43 210	97.23 313	99.45 146	94.82 388	96.13 415	96.51 415	98.52 70	99.91 73	96.19 308	98.83 370	98.37 401

xiu_mvs_v2_base		97.16 315	97.49 279	96.17 405	98.54 365	92.46 404	95.45 416	98.84 324	97.25 284	97.48 358	96.49 416	98.31 89	99.90 80	96.34 300	98.68 383	96.15 459

MVS		93.19 415	92.09 420	96.50 392	96.91 449	94.03 363	98.07 188	98.06 377	68.01 470	94.56 444	96.48 417	95.96 269	99.30 427	83.84 459	96.89 443	96.17 457

PAPM_NR		96.82 334	96.32 345	98.30 271	99.07 254	96.69 261	97.48 286	98.76 337	95.81 360	96.61 401	96.47 418	94.12 323	99.17 438	90.82 439	97.78 419	99.06 307

KD-MVS_2432*1600		92.87 421	91.99 423	95.51 420	91.37 474	89.27 443	94.07 450	98.14 373	95.42 372	97.25 371	96.44 419	67.86 460	99.24 433	91.28 430	96.08 453	98.02 416

miper_refine_blended		92.87 421	91.99 423	95.51 420	91.37 474	89.27 443	94.07 450	98.14 373	95.42 372	97.25 371	96.44 419	67.86 460	99.24 433	91.28 430	96.08 453	98.02 416

PVSNet	93.40 17	95.67 371	95.70 357	95.57 418	98.83 308	88.57 445	92.50 462	97.72 383	92.69 425	96.49 410	96.44 419	93.72 331	99.43 408	93.61 389	99.28 316	98.71 366

EMVS		93.83 404	94.02 396	93.23 448	96.83 452	84.96 460	89.77 469	96.32 421	97.92 218	97.43 363	96.36 422	86.17 402	98.93 448	87.68 450	97.73 421	95.81 462

MAR-MVS		96.47 347	95.70 357	98.79 183	97.92 407	99.12 63	98.28 156	98.60 352	92.16 431	95.54 430	96.17 423	94.77 307	99.52 385	89.62 444	98.23 399	97.72 434
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
UWE-MVS-28		90.22 435	89.28 438	93.02 450	94.50 471	82.87 469	96.52 358	87.51 469	95.21 379	92.36 462	96.04 424	71.57 455	98.25 461	72.04 471	97.77 420	97.94 421

PAPM		91.88 433	90.34 436	96.51 391	98.06 402	92.56 402	92.44 463	97.17 401	86.35 459	90.38 466	96.01 425	86.61 398	99.21 436	70.65 472	95.43 457	97.75 432

PS-MVSNAJ		97.08 319	97.39 284	96.16 407	98.56 363	92.46 404	95.24 423	98.85 323	97.25 284	97.49 357	95.99 426	98.07 117	99.90 80	96.37 297	98.67 384	96.12 460

dmvs_re		95.98 362	95.39 372	97.74 319	98.86 302	97.45 208	98.37 150	95.69 435	97.95 214	96.56 402	95.95 427	90.70 371	97.68 465	88.32 448	96.13 452	98.11 411

baseline2		93.73 406	92.83 412	96.42 394	97.70 420	91.28 425	96.84 339	89.77 466	93.96 409	92.44 461	95.93 428	79.14 442	99.77 257	92.94 402	96.76 445	98.21 406

alignmvs		97.35 298	96.88 315	98.78 186	98.54 365	98.09 144	97.71 250	97.69 385	99.20 82	97.59 347	95.90 429	88.12 394	99.55 373	98.18 143	98.96 363	98.70 369

ET-MVSNet_ETH3D		94.30 396	93.21 407	97.58 338	98.14 397	94.47 348	94.78 434	93.24 455	94.72 389	89.56 467	95.87 430	78.57 446	99.81 219	96.91 243	97.11 440	98.46 386

thisisatest0515		94.12 400	93.16 408	96.97 375	98.60 355	92.90 396	93.77 456	90.61 463	94.10 405	96.91 385	95.87 430	74.99 451	99.80 227	94.52 361	99.12 344	98.20 407

UWE-MVS		92.38 426	91.76 429	94.21 436	97.16 444	84.65 462	95.42 418	88.45 468	95.96 355	96.17 414	95.84 432	66.36 465	99.71 292	91.87 419	98.64 385	98.28 404

BH-w/o		95.13 383	94.89 387	95.86 410	98.20 393	91.31 423	95.65 408	97.37 393	93.64 411	96.52 406	95.70 433	93.04 341	99.02 443	88.10 449	95.82 455	97.24 446

PMMVS		96.51 343	95.98 350	98.09 290	97.53 430	95.84 295	94.92 431	98.84 324	91.58 435	96.05 419	95.58 434	95.68 278	99.66 327	95.59 337	98.09 409	98.76 362

EIA-MVS		98.00 243	97.74 259	98.80 180	98.72 325	98.09 144	98.05 191	99.60 77	97.39 270	96.63 399	95.55 435	97.68 150	99.80 227	96.73 264	99.27 317	98.52 384

ETV-MVS		98.03 239	97.86 253	98.56 233	98.69 338	98.07 150	97.51 282	99.50 120	98.10 206	97.50 356	95.51 436	98.41 78	99.88 114	96.27 304	99.24 322	97.71 435

MGCFI-Net		98.34 200	98.28 199	98.51 244	98.47 371	97.59 199	98.96 77	99.48 129	99.18 90	97.40 364	95.50 437	98.66 54	99.50 391	98.18 143	98.71 378	98.44 392

testing3		93.51 409	92.09 420	97.75 317	98.60 355	94.40 349	97.32 305	95.26 438	97.56 249	96.79 395	95.50 437	53.57 477	99.77 257	95.26 344	98.97 362	99.08 304

PAPR		95.29 379	94.47 390	97.75 317	97.50 436	95.14 327	94.89 432	98.71 345	91.39 439	95.35 434	95.48 439	94.57 310	99.14 441	84.95 457	97.37 432	98.97 325

sasdasda		98.34 200	98.26 203	98.58 225	98.46 373	97.82 181	98.96 77	99.46 142	99.19 87	97.46 359	95.46 440	98.59 62	99.46 403	98.08 150	98.71 378	98.46 386

canonicalmvs		98.34 200	98.26 203	98.58 225	98.46 373	97.82 181	98.96 77	99.46 142	99.19 87	97.46 359	95.46 440	98.59 62	99.46 403	98.08 150	98.71 378	98.46 386

MVE	83.40 22	92.50 424	91.92 426	94.25 434	98.83 308	91.64 416	92.71 461	83.52 474	95.92 357	86.46 472	95.46 440	95.20 291	95.40 470	80.51 465	98.64 385	95.73 463
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WB-MVSnew		95.73 370	95.57 364	96.23 402	96.70 454	90.70 436	96.07 387	93.86 451	95.60 366	97.04 378	95.45 443	96.00 262	99.55 373	91.04 434	98.31 397	98.43 394

test-LLR		93.90 403	93.85 398	94.04 437	96.53 457	84.62 463	94.05 452	92.39 457	96.17 344	94.12 448	95.07 444	82.30 431	99.67 316	95.87 324	98.18 402	97.82 426

test-mter		92.33 428	91.76 429	94.04 437	96.53 457	84.62 463	94.05 452	92.39 457	94.00 408	94.12 448	95.07 444	65.63 470	99.67 316	95.87 324	98.18 402	97.82 426

thres600view7		94.45 392	93.83 399	96.29 398	99.06 259	91.53 417	97.99 209	94.24 448	98.34 174	97.44 362	95.01 446	79.84 437	99.67 316	84.33 458	98.23 399	97.66 436

gm-plane-assit					94.83 469	81.97 472			88.07 457		94.99 447		99.60 353	91.76 421

thres100view900		94.19 397	93.67 402	95.75 414	99.06 259	91.35 422	98.03 195	94.24 448	98.33 175	97.40 364	94.98 448	79.84 437	99.62 343	83.05 460	98.08 410	96.29 455

cascas		94.79 389	94.33 395	96.15 408	96.02 466	92.36 408	92.34 464	99.26 238	85.34 462	95.08 437	94.96 449	92.96 342	98.53 457	94.41 369	98.59 389	97.56 440

TESTMET0.1,1		92.19 430	91.77 428	93.46 444	96.48 459	82.80 470	94.05 452	91.52 462	94.45 397	94.00 451	94.88 450	66.65 464	99.56 369	95.78 329	98.11 408	98.02 416

test0.0.03 1		94.51 391	93.69 401	96.99 373	96.05 464	93.61 387	94.97 430	93.49 452	96.17 344	97.57 350	94.88 450	82.30 431	99.01 445	93.60 390	94.17 463	98.37 401

DeepMVS_CX				93.44 445	98.24 390	94.21 355		94.34 445	64.28 471	91.34 465	94.87 452	89.45 383	92.77 472	77.54 468	93.14 465	93.35 467

dongtai		76.24 439	75.95 442	77.12 455	92.39 473	67.91 479	90.16 467	59.44 480	82.04 466	89.42 468	94.67 453	49.68 478	81.74 473	48.06 473	77.66 471	81.72 469

IB-MVS	91.63 19	92.24 429	90.90 433	96.27 399	97.22 443	91.24 427	94.36 447	93.33 454	92.37 428	92.24 463	94.58 454	66.20 467	99.89 96	93.16 400	94.63 461	97.66 436
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		94.03 401	93.44 404	95.78 413	98.93 285	91.44 420	97.60 270	94.29 446	97.94 216	97.10 374	94.31 455	79.67 439	99.62 343	83.05 460	98.08 410	96.29 455

thres400		94.14 399	93.44 404	96.24 401	98.93 285	91.44 420	97.60 270	94.29 446	97.94 216	97.10 374	94.31 455	79.67 439	99.62 343	83.05 460	98.08 410	97.66 436

testing11		93.08 417	92.02 422	96.26 400	97.56 426	90.83 434	96.32 371	95.70 433	96.47 333	92.66 460	93.73 457	64.36 472	99.59 357	93.77 387	97.57 423	98.37 401

thres200		93.72 407	93.14 409	95.46 422	98.66 348	91.29 424	96.61 352	94.63 443	97.39 270	96.83 392	93.71 458	79.88 436	99.56 369	82.40 463	98.13 407	95.54 464

dmvs_testset		92.94 419	92.21 419	95.13 426	98.59 358	90.99 431	97.65 260	92.09 459	96.95 307	94.00 451	93.55 459	92.34 352	96.97 468	72.20 470	92.52 466	97.43 443

testing91		93.32 412	92.27 417	96.47 393	97.54 428	91.25 426	96.17 383	96.76 414	97.18 294	93.65 456	93.50 460	65.11 471	99.63 340	93.04 401	97.45 427	98.53 383

myMVS_eth3d28		92.92 420	92.31 416	94.77 429	97.84 410	87.59 452	96.19 379	96.11 425	97.08 300	94.27 445	93.49 461	66.07 468	98.78 453	91.78 420	97.93 418	97.92 422

testing99		93.04 418	91.98 425	96.23 402	97.53 430	90.70 436	96.35 369	95.94 429	96.87 314	93.41 457	93.43 462	63.84 473	99.59 357	93.24 399	97.19 437	98.40 397

PVSNet_0	89.98 21	91.15 434	90.30 437	93.70 442	97.72 415	84.34 466	90.24 466	97.42 392	90.20 448	93.79 454	93.09 463	90.90 370	98.89 451	86.57 455	72.76 472	97.87 425

UBG		93.25 414	92.32 415	96.04 409	97.72 415	90.16 439	95.92 397	95.91 430	96.03 352	93.95 453	93.04 464	69.60 458	99.52 385	90.72 440	97.98 416	98.45 389

testing222		91.96 431	90.37 435	96.72 388	97.47 437	92.59 401	96.11 385	94.76 441	96.83 316	92.90 459	92.87 465	57.92 475	99.55 373	86.93 453	97.52 424	98.00 419

tmp_tt		78.77 438	78.73 441	78.90 454	58.45 479	74.76 478	94.20 449	78.26 477	39.16 472	86.71 471	92.82 466	80.50 435	75.19 474	86.16 456	92.29 467	86.74 468

ETVMVS		92.60 423	91.08 432	97.18 363	97.70 420	93.65 385	96.54 355	95.70 433	96.51 329	94.68 441	92.39 467	61.80 474	99.50 391	86.97 452	97.41 430	98.40 397

Syy-MVS		96.04 359	95.56 365	97.49 349	97.10 446	94.48 347	96.18 381	96.58 417	95.65 364	94.77 439	92.29 468	91.27 366	99.36 417	98.17 145	98.05 413	98.63 376

myMVS_eth3d		91.92 432	90.45 434	96.30 397	97.10 446	90.90 432	96.18 381	96.58 417	95.65 364	94.77 439	92.29 468	53.88 476	99.36 417	89.59 445	98.05 413	98.63 376

GG-mvs-BLEND				94.76 430	94.54 470	92.13 412	99.31 30	80.47 476		88.73 470	91.01 470	67.59 463	98.16 463	82.30 464	94.53 462	93.98 466

kuosan		69.30 440	68.95 443	70.34 456	87.68 477	65.00 480	91.11 465	59.90 479	69.02 469	74.46 474	88.89 471	48.58 479	68.03 475	28.61 474	72.33 473	77.99 470

X-MVStestdata		94.32 394	92.59 413	99.53 39	99.46 148	99.21 34	98.65 110	99.34 197	98.62 153	97.54 352	45.85 472	97.50 173	99.83 191	96.79 256	99.53 266	99.56 126

testmvs		17.12 442	20.53 445	6.87 458	12.05 480	4.20 483	93.62 458	6.73 481	4.62 476	10.41 476	24.33 473	8.28 481	3.56 477	9.69 476	15.07 474	12.86 473

test123		17.04 443	20.11 446	7.82 457	10.25 481	4.91 482	94.80 433	4.47 482	4.93 475	10.00 477	24.28 474	9.69 480	3.64 476	10.14 475	12.43 475	14.92 472

test_post											21.25 475	83.86 423	99.70 299

test_post1							97.59 272				20.48 476	83.07 428	99.66 327	94.16 372

mmdepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

monomultidepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

test_blank		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet_test		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

DCPMVS		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

pcd_1.5k_mvsjas		8.17 444	10.90 447	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	98.07 117	0.00 478	0.00 477	0.00 476	0.00 474

sosnet-low-res		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

sosnet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uncertanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

Regformer		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

TestfortrainingZip							98.68 107

WAC-MVS						90.90 432								91.37 429

FOURS1					99.73 37	99.67 3	99.43 15	99.54 107	99.43 54	99.26 144

MSC_two_6792asdad				99.32 89	98.43 377	98.37 119		98.86 320					99.89 96	97.14 224	99.60 240	99.71 62

No_MVS				99.32 89	98.43 377	98.37 119		98.86 320					99.89 96	97.14 224	99.60 240	99.71 62

eth-test2					0.00 482

eth-test					0.00 482

IU-MVS					99.49 135	99.15 53		98.87 315	92.97 420	99.41 109				96.76 260	99.62 233	99.66 77

save fliter					99.11 245	97.97 161	96.53 357	99.02 292	98.24 185

test_0728_SECOND				99.60 16	99.50 127	99.23 32	98.02 198	99.32 205					99.88 114	96.99 237	99.63 230	99.68 70

GSMVS																98.81 352

test_part2					99.36 175	99.10 66				99.05 180

sam_mvs1												84.74 414				98.81 352

sam_mvs												84.29 420

MTGPA								99.20 250

MTMP							97.93 215	91.91 461

test9_res														93.28 398	99.15 338	99.38 223

agg_prior2														92.50 414	99.16 336	99.37 225

agg_prior					98.68 342	97.99 157		99.01 295		95.59 424			99.77 257

test_prior4						97.97 161	95.86 399

test_prior				98.95 158	98.69 338	97.95 165		99.03 289					99.59 357			99.30 255

旧先验2							95.76 405		88.56 456	97.52 354			99.66 327	94.48 362

新几何2							95.93 395

无先验							95.74 406	98.74 342	89.38 452				99.73 283	92.38 416		99.22 278

原ACMM2							95.53 412

testdata2													99.79 240	92.80 408

segment_acmp												97.02 205

testdata1							95.44 417		96.32 339

test12				98.93 161	98.58 360	97.83 176		98.66 347		96.53 404		95.51 284	99.69 303		99.13 341	99.27 261

plane_prior7					99.19 225	97.87 172

plane_prior6					98.99 277	97.70 193						94.90 298

plane_prior5								99.27 233					99.70 299	94.42 366	99.51 271	99.45 190

plane_prior3						97.78 186			97.41 268	97.79 335

plane_prior2							97.77 240		98.20 193

plane_prior1					99.05 262

plane_prior						97.65 195	97.07 326		96.72 322						99.36 301

n2								0.00 483

nn								0.00 483

door-mid								99.57 91

test11								98.87 315

door								99.41 170

HQP5-MVS						96.79 254

HQP-NCC					98.67 343		96.29 373		96.05 349	95.55 427

ACMP_Plane					98.67 343		96.29 373		96.05 349	95.55 427

BP-MVS														92.82 406

HQP4-MVS										95.56 426			99.54 379			99.32 248

HQP3-MVS								99.04 287							99.26 320

HQP2-MVS												93.84 326

MDTV_nov1_ep13_2view						74.92 477	97.69 253		90.06 450	97.75 338		85.78 406		93.52 392		98.69 370

ACMMP++_ref															99.77 155

ACMMP++															99.68 208

Test By Simon												96.52 238

High-resolution multi-view benchmark