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 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 1	100.00 1	99.85 9

mvs_tets		99.63 5	99.67 5	99.49 52	99.88 7	98.61 98	99.34 20	99.71 14	99.27 52	99.90 4	99.74 8	99.68 2	99.97 4	99.55 8	99.99 5	99.88 3

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 18	99.34 15	99.69 4	99.58 31	99.90 2	99.86 7	99.78 5	99.58 3	99.95 17	99.00 40	99.95 18	99.78 15

jajsoiax		99.58 6	99.61 7	99.48 55	99.87 10	98.61 98	99.28 36	99.66 22	99.09 75	99.89 6	99.68 14	99.53 4	99.97 4	99.50 10	99.99 5	99.87 5

pmmvs6		99.67 3	99.70 3	99.60 13	99.90 4	99.27 21	99.53 7	99.76 10	99.64 13	99.84 8	99.83 2	99.50 5	99.87 94	99.36 17	99.92 42	99.64 47

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 24	99.85 15	99.11 60	99.90 1	99.78 8	99.63 15	99.78 10	99.67 17	99.48 6	99.81 174	99.30 22	99.97 12	99.77 17
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
XVG-OURS		98.53 122	98.34 129	99.11 116	99.50 85	98.82 82	95.97 299	99.50 64	97.30 207	99.05 118	98.98 140	99.35 7	99.32 343	95.72 246	99.68 147	99.18 227

XVG-OURS-SEG-HR		98.49 127	98.28 136	99.14 112	99.49 92	98.83 80	96.54 273	99.48 74	97.32 205	99.11 105	98.61 225	99.33 8	99.30 346	96.23 221	98.38 316	99.28 205

ACMH	96.65 7	99.25 29	99.24 27	99.26 94	99.72 36	98.38 115	99.07 60	99.55 49	98.30 123	99.65 23	99.45 54	99.22 9	99.76 221	98.44 74	99.77 102	99.64 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cdsmvs_eth3d_5k		24.66 346	32.88 349	0.00 364	0.00 387	0.00 388	0.00 375	99.10 210	0.00 382	0.00 383	97.58 309	99.21 10	0.00 383	0.00 381	0.00 381	0.00 379

wuyk23d		96.06 285	97.62 195	91.38 358	98.65 278	98.57 102	98.85 79	96.95 337	96.86 236	99.90 4	99.16 97	99.18 11	98.40 371	89.23 357	99.77 102	77.18 376

TransMVSNet (Re)		99.44 13	99.47 12	99.36 69	99.80 22	98.58 101	99.27 38	99.57 38	99.39 39	99.75 12	99.62 23	99.17 12	99.83 150	99.06 35	99.62 167	99.66 42

ANet_high		99.57 7	99.67 5	99.28 88	99.89 6	98.09 140	99.14 52	99.93 1	99.82 3	99.93 2	99.81 3	99.17 12	99.94 26	99.31 20	100.00 1	99.82 10

pm-mvs1		99.44 13	99.48 11	99.33 80	99.80 22	98.63 95	99.29 32	99.63 24	99.30 50	99.65 23	99.60 28	99.16 14	99.82 160	99.07 34	99.83 74	99.56 79

test_djsdf		99.52 9	99.51 9	99.53 38	99.86 12	98.74 87	99.39 16	99.56 45	99.11 65	99.70 15	99.73 10	99.00 15	99.97 4	99.26 23	99.98 9	99.89 2

DeepC-MVS	97.60 4	98.97 52	98.93 51	99.10 118	99.35 128	97.98 156	98.01 160	99.46 82	97.56 179	99.54 32	99.50 43	98.97 16	99.84 135	98.06 95	99.92 42	99.49 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testgi		98.32 144	98.39 122	98.13 236	99.57 62	95.54 257	97.78 179	99.49 72	97.37 200	99.19 97	97.65 305	98.96 17	99.49 321	96.50 205	98.99 290	99.34 185

GeoE		99.05 43	98.99 49	99.25 97	99.44 108	98.35 120	98.73 84	99.56 45	98.42 117	98.91 144	98.81 186	98.94 18	99.91 50	98.35 79	99.73 119	99.49 114

anonymousdsp		99.51 10	99.47 12	99.62 6	99.88 7	99.08 64	99.34 20	99.69 17	98.93 92	99.65 23	99.72 11	98.93 19	99.95 17	99.11 32	100.00 1	99.82 10

ACMM	96.08 12	98.91 59	98.73 66	99.48 55	99.55 73	99.14 53	98.07 148	99.37 109	97.62 172	99.04 120	98.96 145	98.84 20	99.79 196	97.43 128	99.65 159	99.49 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet		99.34 23	99.36 17	99.27 91	99.73 30	98.26 123	99.17 49	99.78 8	99.11 65	99.27 82	99.48 48	98.82 21	99.95 17	98.94 42	99.93 33	99.59 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH+	96.62 9	99.08 41	99.00 46	99.33 80	99.71 37	98.83 80	98.60 93	99.58 31	99.11 65	99.53 35	99.18 91	98.81 22	99.67 263	96.71 186	99.77 102	99.50 110

SD-MVS		98.40 137	98.68 76	97.54 275	98.96 211	97.99 152	97.88 170	99.36 113	98.20 135	99.63 26	99.04 120	98.76 23	95.33 378	96.56 198	99.74 116	99.31 197
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
HPM-MVS_fast		99.01 45	98.82 58	99.57 18	99.71 37	99.35 12	99.00 67	99.50 64	97.33 203	98.94 141	98.86 172	98.75 24	99.82 160	97.53 124	99.71 131	99.56 79

XXY-MVS		99.14 35	99.15 34	99.10 118	99.76 28	97.74 183	98.85 79	99.62 25	98.48 115	99.37 64	99.49 46	98.75 24	99.86 103	98.20 87	99.80 89	99.71 30

DROMVSNet		99.09 39	99.05 43	99.20 103	99.28 136	98.93 72	99.24 40	99.84 6	99.08 77	98.12 233	98.37 254	98.72 26	99.90 56	99.05 36	99.77 102	98.77 290

LPG-MVS_test		98.71 86	98.46 109	99.47 58	99.57 62	98.97 67	98.23 131	99.48 74	96.60 245	99.10 108	99.06 111	98.71 27	99.83 150	95.58 255	99.78 98	99.62 51

LGP-MVS_train				99.47 58	99.57 62	98.97 67		99.48 74	96.60 245	99.10 108	99.06 111	98.71 27	99.83 150	95.58 255	99.78 98	99.62 51

CS-MVS		99.13 37	99.10 38	99.24 99	99.06 192	99.15 48	99.36 19	99.88 3	99.36 44	98.21 225	98.46 245	98.68 29	99.93 31	99.03 38	99.85 64	98.64 303

CS-MVS-test		99.13 37	99.09 39	99.26 94	99.13 175	98.97 67	99.31 26	99.88 3	99.44 33	98.16 228	98.51 235	98.64 30	99.93 31	98.91 43	99.85 64	98.88 274

TDRefinement		99.42 16	99.38 15	99.55 26	99.76 28	99.33 16	99.68 5	99.71 14	99.38 40	99.53 35	99.61 25	98.64 30	99.80 183	98.24 84	99.84 68	99.52 103

nrg030		99.40 19	99.35 18	99.54 31	99.58 58	99.13 56	98.98 70	99.48 74	99.68 9	99.46 46	99.26 79	98.62 32	99.73 236	99.17 31	99.92 42	99.76 21

HPM-MVS		98.79 72	98.53 95	99.59 17	99.65 50	99.29 18	99.16 50	99.43 94	96.74 240	98.61 189	98.38 253	98.62 32	99.87 94	96.47 206	99.67 153	99.59 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
canonicalmvs		98.34 143	98.26 138	98.58 192	98.46 297	97.82 175	98.96 71	99.46 82	99.19 61	97.46 280	95.46 359	98.59 34	99.46 328	98.08 94	98.71 305	98.46 309

EG-PatchMatch MVS		98.99 47	99.01 45	98.94 146	99.50 85	97.47 196	98.04 154	99.59 29	98.15 142	99.40 57	99.36 66	98.58 35	99.76 221	98.78 51	99.68 147	99.59 63

Effi-MVS+		98.02 170	97.82 180	98.62 187	98.53 291	97.19 214	97.33 224	99.68 19	97.30 207	96.68 314	97.46 318	98.56 36	99.80 183	96.63 191	98.20 321	98.86 276

abl_6		98.99 47	98.78 62	99.61 9	99.45 106	99.46 4	98.60 93	99.50 64	98.59 108	99.24 91	99.04 120	98.54 37	99.89 66	96.45 208	99.62 167	99.50 110

Fast-Effi-MVS+		97.67 200	97.38 210	98.57 194	98.71 259	97.43 199	97.23 231	99.45 85	94.82 294	96.13 330	96.51 339	98.52 38	99.91 50	96.19 224	98.83 298	98.37 317

xiu_mvs_v1_base_debu		97.86 184	98.17 148	96.92 301	98.98 208	93.91 304	96.45 279	99.17 193	97.85 159	98.41 214	97.14 331	98.47 39	99.92 40	98.02 97	99.05 279	96.92 355

xiu_mvs_v1_base		97.86 184	98.17 148	96.92 301	98.98 208	93.91 304	96.45 279	99.17 193	97.85 159	98.41 214	97.14 331	98.47 39	99.92 40	98.02 97	99.05 279	96.92 355

xiu_mvs_v1_base_debi		97.86 184	98.17 148	96.92 301	98.98 208	93.91 304	96.45 279	99.17 193	97.85 159	98.41 214	97.14 331	98.47 39	99.92 40	98.02 97	99.05 279	96.92 355

v7n		99.53 8	99.57 8	99.41 65	99.88 7	98.54 106	99.45 10	99.61 27	99.66 12	99.68 19	99.66 18	98.44 42	99.95 17	99.73 2	99.96 15	99.75 24

ETV-MVS		98.03 168	97.86 178	98.56 198	98.69 267	98.07 146	97.51 211	99.50 64	98.10 143	97.50 277	95.51 357	98.41 43	99.88 77	96.27 220	99.24 253	97.71 343

COLMAP_ROB	96.50 10	98.99 47	98.85 56	99.41 65	99.58 58	99.10 61	98.74 82	99.56 45	99.09 75	99.33 71	99.19 89	98.40 44	99.72 244	95.98 233	99.76 112	99.42 149
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 33	99.07 42	99.46 60	99.37 123	98.87 75	98.39 119	99.42 97	99.42 36	99.36 66	99.06 111	98.38 45	99.95 17	98.34 80	99.90 55	99.57 74

SED-MVS		98.91 59	98.72 68	99.49 52	99.49 92	99.17 39	98.10 145	99.31 138	98.03 146	99.66 20	99.02 124	98.36 46	99.88 77	96.91 162	99.62 167	99.41 152

test_241102_ONE					99.49 92	99.17 39		99.31 138	97.98 148	99.66 20	98.90 159	98.36 46	99.48 324

ACMP	95.32 15	98.41 135	98.09 158	99.36 69	99.51 82	98.79 85	97.68 191	99.38 105	95.76 274	98.81 167	98.82 184	98.36 46	99.82 160	94.75 269	99.77 102	99.48 124
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		98.95 55	99.00 46	98.81 163	99.38 118	97.33 202	97.82 177	99.57 38	99.17 62	99.35 68	99.17 95	98.35 49	99.69 251	98.46 73	99.73 119	99.41 152

test_0402		98.76 79	98.71 70	98.93 147	99.56 69	98.14 138	98.45 115	99.34 125	99.28 51	98.95 135	98.91 156	98.34 50	99.79 196	95.63 252	99.91 48	98.86 276

xiu_mvs_v2_base		97.16 240	97.49 202	96.17 320	98.54 289	92.46 330	95.45 324	98.84 261	97.25 212	97.48 279	96.49 340	98.31 51	99.90 56	96.34 216	98.68 307	96.15 366

VPA-MVSNet		99.30 25	99.30 24	99.28 88	99.49 92	98.36 119	99.00 67	99.45 85	99.63 15	99.52 37	99.44 55	98.25 52	99.88 77	99.09 33	99.84 68	99.62 51

MVS_111021_LR		98.30 146	98.12 156	98.83 160	99.16 168	98.03 150	96.09 296	99.30 148	97.58 176	98.10 236	98.24 265	98.25 52	99.34 340	96.69 187	99.65 159	99.12 236

PS-CasMVS		99.40 19	99.33 21	99.62 6	99.71 37	99.10 61	99.29 32	99.53 58	99.53 25	99.46 46	99.41 60	98.23 54	99.95 17	98.89 46	99.95 18	99.81 12

DTE-MVSNet		99.43 15	99.35 18	99.66 4	99.71 37	99.30 17	99.31 26	99.51 62	99.64 13	99.56 30	99.46 50	98.23 54	99.97 4	98.78 51	99.93 33	99.72 28

baseline		98.96 54	99.02 44	98.76 173	99.38 118	97.26 207	98.49 108	99.50 64	98.86 95	99.19 97	99.06 111	98.23 54	99.69 251	98.71 57	99.76 112	99.33 191

PC_three_1452									93.27 321	99.40 57	98.54 231	98.22 57	97.00 375	95.17 261	99.45 220	99.49 114

Gipuma		99.03 44	99.16 31	98.64 182	99.94 2	98.51 108	99.32 22	99.75 11	99.58 23	98.60 191	99.62 23	98.22 57	99.51 319	97.70 118	99.73 119	97.89 331
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet-Re		98.64 101	98.48 105	99.11 116	98.85 235	98.51 108	98.49 108	99.83 7	98.37 118	99.69 17	99.46 50	98.21 59	99.92 40	94.13 292	99.30 244	98.91 270

tfpnnormal		98.90 61	98.90 52	98.91 150	99.67 47	97.82 175	99.00 67	99.44 88	99.45 32	99.51 40	99.24 83	98.20 60	99.86 103	95.92 235	99.69 142	99.04 246

DVP-MVS++		98.90 61	98.70 73	99.51 47	98.43 300	99.15 48	99.43 12	99.32 132	98.17 138	99.26 86	99.02 124	98.18 61	99.88 77	97.07 150	99.45 220	99.49 114

OPU-MVS				98.82 161	98.59 283	98.30 121	98.10 145				98.52 234	98.18 61	98.75 369	94.62 273	99.48 217	99.41 152

OPM-MVS		98.56 113	98.32 133	99.25 97	99.41 115	98.73 90	97.13 243	99.18 187	97.10 226	98.75 174	98.92 155	98.18 61	99.65 276	96.68 188	99.56 194	99.37 173
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PEN-MVS		99.41 17	99.34 20	99.62 6	99.73 30	99.14 53	99.29 32	99.54 54	99.62 18	99.56 30	99.42 57	98.16 64	99.96 11	98.78 51	99.93 33	99.77 17

DeepPCF-MVS	96.93 5	98.32 144	98.01 166	99.23 101	98.39 305	98.97 67	95.03 334	99.18 187	96.88 235	99.33 71	98.78 190	98.16 64	99.28 349	96.74 181	99.62 167	99.44 142

MVS_111021_HR		98.25 154	98.08 161	98.75 175	99.09 184	97.46 197	95.97 299	99.27 161	97.60 175	97.99 244	98.25 264	98.15 66	99.38 337	96.87 170	99.57 189	99.42 149

Fast-Effi-MVS+-dtu		98.27 150	98.09 158	98.81 163	98.43 300	98.11 139	97.61 199	99.50 64	98.64 102	97.39 285	97.52 313	98.12 67	99.95 17	96.90 167	98.71 305	98.38 315

pcd_1.5k_mvsjas		8.17 349	10.90 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	98.07 68	0.00 383	0.00 381	0.00 381	0.00 379

PS-MVSNAJss		99.46 12	99.49 10	99.35 74	99.90 4	98.15 136	99.20 44	99.65 23	99.48 28	99.92 3	99.71 12	98.07 68	99.96 11	99.53 9	100.00 1	99.93 1

PS-MVSNAJ		97.08 245	97.39 209	96.16 322	98.56 287	92.46 330	95.24 329	98.85 260	97.25 212	97.49 278	95.99 349	98.07 68	99.90 56	96.37 213	98.67 308	96.12 367

UA-Net		99.47 11	99.40 14	99.70 2	99.49 92	99.29 18	99.80 3	99.72 13	99.82 3	99.04 120	99.81 3	98.05 71	99.96 11	98.85 48	99.99 5	99.86 8

ACMMP_NAP		98.75 81	98.48 105	99.57 18	99.58 58	99.29 18	97.82 177	99.25 167	96.94 232	98.78 168	99.12 105	98.02 72	99.84 135	97.13 146	99.67 153	99.59 63

MP-MVS-pluss		98.57 112	98.23 142	99.60 13	99.69 45	99.35 12	97.16 241	99.38 105	94.87 293	98.97 132	98.99 136	98.01 73	99.88 77	97.29 134	99.70 136	99.58 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZNCC-MVS		98.68 95	98.40 119	99.54 31	99.57 62	99.21 27	98.46 113	99.29 155	97.28 209	98.11 235	98.39 251	98.00 74	99.87 94	96.86 172	99.64 161	99.55 87

PGM-MVS		98.66 98	98.37 125	99.55 26	99.53 78	99.18 38	98.23 131	99.49 72	97.01 230	98.69 178	98.88 168	98.00 74	99.89 66	95.87 239	99.59 179	99.58 69

SteuartSystems-ACMMP		98.79 72	98.54 94	99.54 31	99.73 30	99.16 43	98.23 131	99.31 138	97.92 153	98.90 145	98.90 159	98.00 74	99.88 77	96.15 227	99.72 126	99.58 69
Skip Steuart: Steuart Systems R&D Blog.
TinyColmap		97.89 180	97.98 168	97.60 268	98.86 233	94.35 290	96.21 292	99.44 88	97.45 193	99.06 113	98.88 168	97.99 77	99.28 349	94.38 285	99.58 185	99.18 227

HFP-MVS		98.71 86	98.44 113	99.51 47	99.49 92	99.16 43	98.52 102	99.31 138	97.47 186	98.58 195	98.50 239	97.97 78	99.85 118	96.57 195	99.59 179	99.53 99

#test#		98.50 126	98.16 151	99.51 47	99.49 92	99.16 43	98.03 155	99.31 138	96.30 257	98.58 195	98.50 239	97.97 78	99.85 118	95.68 249	99.59 179	99.53 99

3Dnovator	98.27 2	98.81 70	98.73 66	99.05 132	98.76 250	97.81 177	99.25 39	99.30 148	98.57 112	98.55 201	99.33 71	97.95 80	99.90 56	97.16 140	99.67 153	99.44 142

test_0728_THIRD									98.17 138	99.08 111	99.02 124	97.89 81	99.88 77	97.07 150	99.71 131	99.70 35

APD-MVS_3200maxsize		98.84 67	98.61 87	99.53 38	99.19 157	99.27 21	98.49 108	99.33 130	98.64 102	99.03 123	98.98 140	97.89 81	99.85 118	96.54 202	99.42 224	99.46 134

CP-MVS		98.70 89	98.42 117	99.52 43	99.36 124	99.12 58	98.72 85	99.36 113	97.54 181	98.30 220	98.40 249	97.86 83	99.89 66	96.53 203	99.72 126	99.56 79

TSAR-MVS + MP.		98.63 103	98.49 103	99.06 130	99.64 53	97.90 166	98.51 106	98.94 238	96.96 231	99.24 91	98.89 167	97.83 84	99.81 174	96.88 169	99.49 215	99.48 124
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 91	98.40 119	99.54 31	99.53 78	99.17 39	98.52 102	99.31 138	97.46 191	98.44 210	98.51 235	97.83 84	99.88 77	96.46 207	99.58 185	99.58 69

APDe-MVS		98.99 47	98.79 61	99.60 13	99.21 150	99.15 48	98.87 76	99.48 74	97.57 177	99.35 68	99.24 83	97.83 84	99.89 66	97.88 107	99.70 136	99.75 24

FMVSNet1		99.17 33	99.17 30	99.17 106	99.55 73	98.24 125	99.20 44	99.44 88	99.21 54	99.43 52	99.55 35	97.82 87	99.86 103	98.42 76	99.89 58	99.41 152

xxxxxxxxxxxxxcwj		98.44 132	98.24 140	99.06 130	99.11 177	97.97 157	96.53 274	99.54 54	98.24 129	98.83 161	98.90 159	97.80 88	99.82 160	95.68 249	99.52 204	99.38 170

SF-MVS		98.53 122	98.27 137	99.32 82	99.31 131	98.75 86	98.19 135	99.41 98	96.77 239	98.83 161	98.90 159	97.80 88	99.82 160	95.68 249	99.52 204	99.38 170

PHI-MVS		98.29 149	97.95 170	99.34 77	98.44 299	99.16 43	98.12 142	99.38 105	96.01 266	98.06 239	98.43 247	97.80 88	99.67 263	95.69 248	99.58 185	99.20 220

RE-MVS-def			98.58 91		99.20 154	99.38 6	98.48 111	99.30 148	98.64 102	98.95 135	98.96 145	97.75 91		96.56 198	99.39 228	99.45 138

ACMMPR		98.70 89	98.42 117	99.54 31	99.52 80	99.14 53	98.52 102	99.31 138	97.47 186	98.56 199	98.54 231	97.75 91	99.88 77	96.57 195	99.59 179	99.58 69

ACMMP		98.75 81	98.50 100	99.52 43	99.56 69	99.16 43	98.87 76	99.37 109	97.16 223	98.82 165	99.01 133	97.71 93	99.87 94	96.29 219	99.69 142	99.54 91
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		98.00 172	97.74 184	98.80 165	98.72 256	98.09 140	98.05 152	99.60 28	97.39 198	96.63 316	95.55 356	97.68 94	99.80 183	96.73 183	99.27 248	98.52 307

GST-MVS		98.61 106	98.30 134	99.52 43	99.51 82	99.20 33	98.26 129	99.25 167	97.44 194	98.67 180	98.39 251	97.68 94	99.85 118	96.00 231	99.51 207	99.52 103

CSCG		98.68 95	98.50 100	99.20 103	99.45 106	98.63 95	98.56 98	99.57 38	97.87 157	98.85 157	98.04 282	97.66 96	99.84 135	96.72 184	99.81 81	99.13 235

AllTest		98.44 132	98.20 144	99.16 109	99.50 85	98.55 103	98.25 130	99.58 31	96.80 237	98.88 153	99.06 111	97.65 97	99.57 300	94.45 279	99.61 173	99.37 173

TestCases				99.16 109	99.50 85	98.55 103		99.58 31	96.80 237	98.88 153	99.06 111	97.65 97	99.57 300	94.45 279	99.61 173	99.37 173

test20.03		98.78 75	98.77 64	98.78 170	99.46 103	97.20 213	97.78 179	99.24 172	99.04 80	99.41 54	98.90 159	97.65 97	99.76 221	97.70 118	99.79 94	99.39 163

test_one_0601					99.39 117	99.20 33		99.31 138	98.49 114	98.66 182	99.02 124	97.64 100

ITE_SJBPF				98.87 155	99.22 148	98.48 110		99.35 119	97.50 183	98.28 222	98.60 226	97.64 100	99.35 339	93.86 301	99.27 248	98.79 288

mPP-MVS		98.64 101	98.34 129	99.54 31	99.54 76	99.17 39	98.63 90	99.24 172	97.47 186	98.09 237	98.68 206	97.62 102	99.89 66	96.22 222	99.62 167	99.57 74

DVP-MVS		98.77 78	98.52 96	99.52 43	99.50 85	99.21 27	98.02 157	98.84 261	97.97 149	99.08 111	99.02 124	97.61 103	99.88 77	96.99 156	99.63 164	99.48 124
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 85	99.21 27	98.17 139	99.35 119	97.97 149	99.26 86	99.06 111	97.61 103

9.14			97.78 181		99.07 188		97.53 208	99.32 132	95.53 279	98.54 203	98.70 203	97.58 105	99.76 221	94.32 286	99.46 218

CLD-MVS		97.49 212	97.16 224	98.48 208	99.07 188	97.03 221	94.71 341	99.21 176	94.46 300	98.06 239	97.16 329	97.57 106	99.48 324	94.46 278	99.78 98	98.95 261
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	98.30 146	98.15 153	98.75 175	98.61 279	97.23 208	97.76 184	99.09 211	97.31 206	98.75 174	98.66 211	97.56 107	99.64 278	96.10 230	99.55 196	99.39 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EGC-MVSNET		85.24 343	80.54 346	99.34 77	99.77 25	99.20 33	99.08 57	99.29 155	12.08 379	20.84 380	99.42 57	97.55 108	99.85 118	97.08 149	99.72 126	98.96 260

PM-MVS		98.82 68	98.72 68	99.12 114	99.64 53	98.54 106	97.98 162	99.68 19	97.62 172	99.34 70	99.18 91	97.54 109	99.77 214	97.79 110	99.74 116	99.04 246

XVG-ACMP-BASELINE		98.56 113	98.34 129	99.22 102	99.54 76	98.59 100	97.71 188	99.46 82	97.25 212	98.98 129	98.99 136	97.54 109	99.84 135	95.88 236	99.74 116	99.23 215

SR-MVS		98.71 86	98.43 115	99.57 18	99.18 164	99.35 12	98.36 122	99.29 155	98.29 126	98.88 153	98.85 175	97.53 111	99.87 94	96.14 228	99.31 241	99.48 124

DPE-MVS		98.59 111	98.26 138	99.57 18	99.27 138	99.15 48	97.01 246	99.39 103	97.67 168	99.44 51	98.99 136	97.53 111	99.89 66	95.40 259	99.68 147	99.66 42
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		98.40 137	98.03 165	99.51 47	99.16 168	99.21 27	98.05 152	99.22 175	94.16 309	98.98 129	99.10 108	97.52 113	99.79 196	96.45 208	99.64 161	99.53 99
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								99.30 148	98.03 146	99.26 86	99.02 124	97.51 114	99.88 77	96.91 162	99.60 175	99.66 42

XVS		98.72 85	98.45 111	99.53 38	99.46 103	99.21 27	98.65 88	99.34 125	98.62 106	97.54 273	98.63 220	97.50 115	99.83 150	96.79 175	99.53 201	99.56 79

X-MVStestdata		94.32 314	92.59 332	99.53 38	99.46 103	99.21 27	98.65 88	99.34 125	98.62 106	97.54 273	45.85 377	97.50 115	99.83 150	96.79 175	99.53 201	99.56 79

DELS-MVS		98.27 150	98.20 144	98.48 208	98.86 233	96.70 232	95.60 318	99.20 178	97.73 164	98.45 209	98.71 200	97.50 115	99.82 160	98.21 86	99.59 179	98.93 266
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
test1172		98.76 79	98.49 103	99.57 18	99.18 164	99.37 9	98.39 119	99.31 138	98.43 116	98.90 145	98.88 168	97.49 118	99.86 103	96.43 210	99.37 232	99.48 124

SR-MVS-dyc-post		98.81 70	98.55 93	99.57 18	99.20 154	99.38 6	98.48 111	99.30 148	98.64 102	98.95 135	98.96 145	97.49 118	99.86 103	96.56 198	99.39 228	99.45 138

TSAR-MVS + GP.		98.18 160	97.98 168	98.77 172	98.71 259	97.88 167	96.32 287	98.66 283	96.33 254	99.23 94	98.51 235	97.48 120	99.40 333	97.16 140	99.46 218	99.02 249

Regformer-4		98.73 84	98.68 76	98.89 153	99.02 201	97.22 210	97.17 239	99.06 215	99.21 54	99.17 102	98.85 175	97.45 121	99.86 103	98.48 72	99.70 136	99.60 57

new-patchmatchnet		98.35 142	98.74 65	97.18 290	99.24 143	92.23 335	96.42 282	99.48 74	98.30 123	99.69 17	99.53 39	97.44 122	99.82 160	98.84 49	99.77 102	99.49 114

PMVS	91.26 20	97.86 184	97.94 172	97.65 264	99.71 37	97.94 164	98.52 102	98.68 282	98.99 84	97.52 275	99.35 67	97.41 123	98.18 372	91.59 340	99.67 153	96.82 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS		98.46 130	98.09 158	99.54 31	99.57 62	99.22 26	98.50 107	99.19 183	97.61 174	97.58 269	98.66 211	97.40 124	99.88 77	94.72 272	99.60 175	99.54 91
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MSDG		97.71 197	97.52 200	98.28 227	98.91 223	96.82 227	94.42 351	99.37 109	97.65 170	98.37 219	98.29 263	97.40 124	99.33 342	94.09 293	99.22 255	98.68 302

Regformer-2		98.60 108	98.46 109	99.02 138	98.85 235	97.71 185	96.91 255	99.09 211	98.98 86	99.01 124	98.64 216	97.37 126	99.84 135	97.75 117	99.57 189	99.52 103

KD-MVS_self_test		99.25 29	99.18 29	99.44 61	99.63 55	99.06 65	98.69 87	99.54 54	99.31 48	99.62 28	99.53 39	97.36 127	99.86 103	99.24 27	99.71 131	99.39 163

LS3D		98.63 103	98.38 124	99.36 69	97.25 357	99.38 6	99.12 55	99.32 132	99.21 54	98.44 210	98.88 168	97.31 128	99.80 183	96.58 193	99.34 237	98.92 267

EI-MVSNet-UG-set		98.69 91	98.71 70	98.62 187	99.10 181	96.37 238	97.23 231	98.87 251	99.20 57	99.19 97	98.99 136	97.30 129	99.85 118	98.77 54	99.79 94	99.65 46

WR-MVS_H		99.33 24	99.22 28	99.65 5	99.71 37	99.24 24	99.32 22	99.55 49	99.46 31	99.50 42	99.34 69	97.30 129	99.93 31	98.90 44	99.93 33	99.77 17

EI-MVSNet-Vis-set		98.68 95	98.70 73	98.63 185	99.09 184	96.40 237	97.23 231	98.86 257	99.20 57	99.18 101	98.97 142	97.29 131	99.85 118	98.72 56	99.78 98	99.64 47

pmmvs-eth3d		98.47 129	98.34 129	98.86 157	99.30 134	97.76 180	97.16 241	99.28 158	95.54 277	99.42 53	99.19 89	97.27 132	99.63 281	97.89 104	99.97 12	99.20 220

CNVR-MVS		98.17 162	97.87 177	99.07 125	98.67 272	98.24 125	97.01 246	98.93 240	97.25 212	97.62 265	98.34 258	97.27 132	99.57 300	96.42 211	99.33 238	99.39 163

OMC-MVS		97.88 182	97.49 202	99.04 134	98.89 229	98.63 95	96.94 250	99.25 167	95.02 288	98.53 204	98.51 235	97.27 132	99.47 326	93.50 311	99.51 207	99.01 250

Regformer-1		98.55 117	98.44 113	98.87 155	98.85 235	97.29 204	96.91 255	98.99 235	98.97 87	98.99 127	98.64 216	97.26 135	99.81 174	97.79 110	99.57 189	99.51 106

bld_raw_conf005		99.41 17	99.38 15	99.51 47	99.85 15	98.88 74	99.44 11	99.74 12	99.68 9	99.51 40	99.61 25	97.25 136	99.91 50	99.37 16	99.95 18	99.72 28

Regformer-3		98.61 106	98.61 87	98.63 185	99.02 201	96.53 235	97.17 239	98.84 261	99.13 64	99.10 108	98.85 175	97.24 137	99.79 196	98.41 77	99.70 136	99.57 74

DP-MVS		98.93 57	98.81 60	99.28 88	99.21 150	98.45 112	98.46 113	99.33 130	99.63 15	99.48 43	99.15 101	97.23 138	99.75 228	97.17 139	99.66 158	99.63 50

MVS_Test		98.18 160	98.36 126	97.67 262	98.48 295	94.73 280	98.18 136	99.02 228	97.69 167	98.04 242	99.11 106	97.22 139	99.56 303	98.57 65	98.90 296	98.71 296

dcpmvs_2		98.78 75	99.11 36	97.78 256	99.56 69	93.67 313	99.06 62	99.86 5	99.50 26	99.66 20	99.26 79	97.21 140	99.99 2	98.00 100	99.91 48	99.68 38

MCST-MVS		98.00 172	97.63 194	99.10 118	99.24 143	98.17 135	96.89 257	98.73 279	95.66 275	97.92 245	97.70 303	97.17 141	99.66 271	96.18 226	99.23 254	99.47 132

FC-MVSNet-test		99.27 26	99.25 26	99.34 77	99.77 25	98.37 116	99.30 31	99.57 38	99.61 20	99.40 57	99.50 43	97.12 142	99.85 118	99.02 39	99.94 28	99.80 13

MIMVSNet1		99.38 21	99.32 22	99.55 26	99.86 12	99.19 37	99.41 14	99.59 29	99.59 21	99.71 14	99.57 31	97.12 142	99.90 56	99.21 28	99.87 62	99.54 91

3Dnovator+	97.89 3	98.69 91	98.51 98	99.24 99	98.81 245	98.40 113	99.02 64	99.19 183	98.99 84	98.07 238	99.28 75	97.11 144	99.84 135	96.84 173	99.32 239	99.47 132

patch_mono-2		98.51 125	98.63 82	98.17 233	99.38 118	94.78 278	97.36 222	99.69 17	98.16 141	98.49 207	99.29 74	97.06 145	99.97 4	98.29 83	99.91 48	99.76 21

Anonymous20240529		98.93 57	98.87 53	99.12 114	99.19 157	98.22 130	99.01 65	98.99 235	99.25 53	99.54 32	99.37 63	97.04 146	99.80 183	97.89 104	99.52 204	99.35 183

MSLP-MVS++		98.02 170	98.14 155	97.64 266	98.58 284	95.19 269	97.48 213	99.23 174	97.47 186	97.90 247	98.62 222	97.04 146	98.81 368	97.55 121	99.41 225	98.94 265

APD-MVS		98.10 164	97.67 188	99.42 62	99.11 177	98.93 72	97.76 184	99.28 158	94.97 290	98.72 177	98.77 192	97.04 146	99.85 118	93.79 303	99.54 197	99.49 114
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
segment_acmp												97.02 149

CP-MVSNet		99.21 32	99.09 39	99.56 24	99.65 50	98.96 71	99.13 53	99.34 125	99.42 36	99.33 71	99.26 79	97.01 150	99.94 26	98.74 55	99.93 33	99.79 14

ambc				98.24 229	98.82 243	95.97 248	98.62 91	99.00 234		99.27 82	99.21 86	96.99 151	99.50 320	96.55 201	99.50 214	99.26 210

zzz-MVS		98.79 72	98.52 96	99.61 9	99.67 47	99.36 10	97.33 224	99.20 178	98.83 98	98.89 148	98.90 159	96.98 152	99.92 40	97.16 140	99.70 136	99.56 79

MTAPA		98.88 63	98.64 81	99.61 9	99.67 47	99.36 10	98.43 116	99.20 178	98.83 98	98.89 148	98.90 159	96.98 152	99.92 40	97.16 140	99.70 136	99.56 79

v8		99.01 45	99.16 31	98.57 194	99.47 102	96.31 240	98.90 74	99.47 80	99.03 81	99.52 37	99.57 31	96.93 154	99.81 174	99.60 4	99.98 9	99.60 57

QAPM		97.31 226	96.81 246	98.82 161	98.80 247	97.49 195	99.06 62	99.19 183	90.22 352	97.69 261	99.16 97	96.91 155	99.90 56	90.89 351	99.41 225	99.07 240

CDPH-MVS		97.26 230	96.66 256	99.07 125	99.00 204	98.15 136	96.03 297	99.01 231	91.21 346	97.79 255	97.85 294	96.89 156	99.69 251	92.75 325	99.38 231	99.39 163

PVSNet_Blended_VisFu		98.17 162	98.15 153	98.22 230	99.73 30	95.15 270	97.36 222	99.68 19	94.45 302	98.99 127	99.27 77	96.87 157	99.94 26	97.13 146	99.91 48	99.57 74

Anonymous20231211		99.27 26	99.27 25	99.26 94	99.29 135	98.18 132	99.49 8	99.51 62	99.70 8	99.80 9	99.68 14	96.84 158	99.83 150	99.21 28	99.91 48	99.77 17

V42		98.78 75	98.78 62	98.76 173	99.44 108	97.04 220	98.27 128	99.19 183	97.87 157	99.25 90	99.16 97	96.84 158	99.78 208	99.21 28	99.84 68	99.46 134

PMMVS2		98.07 167	98.08 161	98.04 244	99.41 115	94.59 286	94.59 348	99.40 100	97.50 183	98.82 165	98.83 181	96.83 160	99.84 135	97.50 126	99.81 81	99.71 30

PVSNet_BlendedMVS		97.55 208	97.53 199	97.60 268	98.92 220	93.77 311	96.64 270	99.43 94	94.49 298	97.62 265	99.18 91	96.82 161	99.67 263	94.73 270	99.93 33	99.36 179

PVSNet_Blended		96.88 256	96.68 253	97.47 279	98.92 220	93.77 311	94.71 341	99.43 94	90.98 348	97.62 265	97.36 324	96.82 161	99.67 263	94.73 270	99.56 194	98.98 255

ab-mvs		98.41 135	98.36 126	98.59 191	99.19 157	97.23 208	99.32 22	98.81 267	97.66 169	98.62 187	99.40 62	96.82 161	99.80 183	95.88 236	99.51 207	98.75 293

FIs		99.14 35	99.09 39	99.29 85	99.70 43	98.28 122	99.13 53	99.52 61	99.48 28	99.24 91	99.41 60	96.79 164	99.82 160	98.69 59	99.88 59	99.76 21

UniMVSNet (Re)		98.87 64	98.71 70	99.35 74	99.24 143	98.73 90	97.73 187	99.38 105	98.93 92	99.12 104	98.73 197	96.77 165	99.86 103	98.63 62	99.80 89	99.46 134

API-MVS		97.04 249	96.91 239	97.42 282	97.88 333	98.23 129	98.18 136	98.50 292	97.57 177	97.39 285	96.75 336	96.77 165	99.15 358	90.16 354	99.02 286	94.88 372

diffmvs		98.22 156	98.24 140	98.17 233	99.00 204	95.44 262	96.38 284	99.58 31	97.79 162	98.53 204	98.50 239	96.76 167	99.74 232	97.95 103	99.64 161	99.34 185

DU-MVS		98.82 68	98.63 82	99.39 68	99.16 168	98.74 87	97.54 207	99.25 167	98.84 97	99.06 113	98.76 194	96.76 167	99.93 31	98.57 65	99.77 102	99.50 110

Baseline_NR-MVSNet		98.98 51	98.86 55	99.36 69	99.82 21	98.55 103	97.47 215	99.57 38	99.37 41	99.21 95	99.61 25	96.76 167	99.83 150	98.06 95	99.83 74	99.71 30

VPNet		98.87 64	98.83 57	99.01 139	99.70 43	97.62 191	98.43 116	99.35 119	99.47 30	99.28 80	99.05 118	96.72 170	99.82 160	98.09 93	99.36 233	99.59 63

UniMVSNet_NR-MVSNet		98.86 66	98.68 76	99.40 67	99.17 166	98.74 87	97.68 191	99.40 100	99.14 63	99.06 113	98.59 227	96.71 171	99.93 31	98.57 65	99.77 102	99.53 99

LF4IMVS		97.90 178	97.69 187	98.52 203	99.17 166	97.66 187	97.19 238	99.47 80	96.31 256	97.85 251	98.20 269	96.71 171	99.52 315	94.62 273	99.72 126	98.38 315

v148		98.45 131	98.60 89	98.00 246	99.44 108	94.98 274	97.44 218	99.06 215	98.30 123	99.32 77	98.97 142	96.65 173	99.62 283	98.37 78	99.85 64	99.39 163

v10		98.97 52	99.11 36	98.55 199	99.44 108	96.21 242	98.90 74	99.55 49	98.73 100	99.48 43	99.60 28	96.63 174	99.83 150	99.70 3	99.99 5	99.61 56

ETH3D-3000-0.1		98.03 168	97.62 195	99.29 85	99.11 177	98.80 84	97.47 215	99.32 132	95.54 277	98.43 213	98.62 222	96.61 175	99.77 214	93.95 297	99.49 215	99.30 200

OpenMVS	96.65 7	97.09 244	96.68 253	98.32 222	98.32 308	97.16 217	98.86 78	99.37 109	89.48 356	96.29 329	99.15 101	96.56 176	99.90 56	92.90 319	99.20 258	97.89 331

UGNet		98.53 122	98.45 111	98.79 167	97.94 329	96.96 223	99.08 57	98.54 289	99.10 72	96.82 311	99.47 49	96.55 177	99.84 135	98.56 68	99.94 28	99.55 87
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					98.71 259	98.08 144	95.96 301	99.03 224	91.40 343	95.85 337	97.53 311	96.52 178	99.76 221

Test By Simon												96.52 178

agg_prior1		97.06 246	96.40 267	99.03 135	98.68 270	97.99 152	95.76 311	99.01 231	91.73 337	95.59 340	97.50 314	96.49 180	99.77 214	93.71 304	99.14 269	99.34 185

train_agg		97.10 242	96.45 266	99.07 125	98.71 259	98.08 144	95.96 301	99.03 224	91.64 338	95.85 337	97.53 311	96.47 181	99.76 221	93.67 305	99.16 265	99.36 179

test_8					98.67 272	98.01 151	95.91 306	99.02 228	91.64 338	95.79 339	97.50 314	96.47 181	99.76 221

test_low_dy_conf_001		99.26 28	99.16 31	99.55 26	99.86 12	98.86 76	99.37 18	98.87 251	99.42 36	99.46 46	99.68 14	96.44 183	99.93 31	99.39 15	99.94 28	99.87 5

Effi-MVS+-dtu		98.26 152	97.90 175	99.35 74	98.02 325	99.49 3	98.02 157	99.16 196	98.29 126	97.64 264	97.99 284	96.44 183	99.95 17	96.66 189	98.93 295	98.60 304

mvs-test1		97.83 192	97.48 205	98.89 153	98.02 325	99.20 33	97.20 235	99.16 196	98.29 126	96.46 326	97.17 328	96.44 183	99.92 40	96.66 189	97.90 334	97.54 349

ppachtmachnet_test		97.50 210	97.74 184	96.78 309	98.70 263	91.23 349	94.55 349	99.05 219	96.36 253	99.21 95	98.79 189	96.39 186	99.78 208	96.74 181	99.82 77	99.34 185

OurMVSNet-221017-0		99.37 22	99.31 23	99.53 38	99.91 3	98.98 66	99.63 6	99.58 31	99.44 33	99.78 10	99.76 6	96.39 186	99.92 40	99.44 14	99.92 42	99.68 38

NR-MVSNet		98.95 55	98.82 58	99.36 69	99.16 168	98.72 92	99.22 41	99.20 178	99.10 72	99.72 13	98.76 194	96.38 188	99.86 103	98.00 100	99.82 77	99.50 110

v1192		98.60 108	98.66 79	98.41 215	99.27 138	95.88 250	97.52 209	99.36 113	97.41 196	99.33 71	99.20 88	96.37 189	99.82 160	99.57 6	99.92 42	99.55 87

ZD-MVS					99.01 203	98.84 79		99.07 214	94.10 310	98.05 241	98.12 275	96.36 190	99.86 103	92.70 327	99.19 262

v1144		98.60 108	98.66 79	98.41 215	99.36 124	95.90 249	97.58 203	99.34 125	97.51 182	99.27 82	99.15 101	96.34 191	99.80 183	99.47 12	99.93 33	99.51 106

mvs_anonymous		97.83 192	98.16 151	96.87 304	98.18 317	91.89 337	97.31 226	98.90 246	97.37 200	98.83 161	99.46 50	96.28 192	99.79 196	98.90 44	98.16 324	98.95 261

DSMNet-mixed		97.42 219	97.60 197	96.87 304	99.15 172	91.46 341	98.54 100	99.12 206	92.87 327	97.58 269	99.63 22	96.21 193	99.90 56	95.74 245	99.54 197	99.27 207

TAPA-MVS	96.21 11	96.63 267	95.95 278	98.65 181	98.93 216	98.09 140	96.93 252	99.28 158	83.58 371	98.13 232	97.78 297	96.13 194	99.40 333	93.52 309	99.29 246	98.45 311
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1240		98.55 117	98.62 84	98.32 222	99.22 148	95.58 256	97.51 211	99.45 85	97.16 223	99.45 50	99.24 83	96.12 195	99.85 118	99.60 4	99.88 59	99.55 87

RPSCF		98.62 105	98.36 126	99.42 62	99.65 50	99.42 5	98.55 99	99.57 38	97.72 166	98.90 145	99.26 79	96.12 195	99.52 315	95.72 246	99.71 131	99.32 193

MS-PatchMatch		97.68 199	97.75 183	97.45 280	98.23 315	93.78 310	97.29 227	98.84 261	96.10 262	98.64 184	98.65 213	96.04 197	99.36 338	96.84 173	99.14 269	99.20 220

v1921920		98.54 120	98.60 89	98.38 218	99.20 154	95.76 255	97.56 205	99.36 113	97.23 218	99.38 61	99.17 95	96.02 198	99.84 135	99.57 6	99.90 55	99.54 91

HPM-MVS++		98.10 164	97.64 193	99.48 55	99.09 184	99.13 56	97.52 209	98.75 276	97.46 191	96.90 306	97.83 295	96.01 199	99.84 135	95.82 243	99.35 235	99.46 134

Anonymous20231206		98.21 157	98.21 143	98.20 231	99.51 82	95.43 263	98.13 140	99.32 132	96.16 260	98.93 142	98.82 184	96.00 200	99.83 150	97.32 133	99.73 119	99.36 179

EI-MVSNet		98.40 137	98.51 98	98.04 244	99.10 181	94.73 280	97.20 235	98.87 251	98.97 87	99.06 113	99.02 124	96.00 200	99.80 183	98.58 63	99.82 77	99.60 57

IterMVS-LS		98.55 117	98.70 73	98.09 237	99.48 100	94.73 280	97.22 234	99.39 103	98.97 87	99.38 61	99.31 73	96.00 200	99.93 31	98.58 63	99.97 12	99.60 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NCCC		97.86 184	97.47 206	99.05 132	98.61 279	98.07 146	96.98 248	98.90 246	97.63 171	97.04 297	97.93 290	95.99 203	99.66 271	95.31 260	98.82 299	99.43 146

our_test_3		97.39 221	97.73 186	96.34 315	98.70 263	89.78 353	94.61 347	98.97 237	96.50 248	99.04 120	98.85 175	95.98 204	99.84 135	97.26 136	99.67 153	99.41 152

v2v482		98.56 113	98.62 84	98.37 219	99.42 113	95.81 253	97.58 203	99.16 196	97.90 155	99.28 80	99.01 133	95.98 204	99.79 196	99.33 18	99.90 55	99.51 106

MVS		93.19 331	92.09 335	96.50 313	96.91 361	94.03 298	98.07 148	98.06 312	68.01 375	94.56 358	96.48 341	95.96 206	99.30 346	83.84 368	96.89 353	96.17 364

MVP-Stereo		98.08 166	97.92 173	98.57 194	98.96 211	96.79 228	97.90 169	99.18 187	96.41 252	98.46 208	98.95 149	95.93 207	99.60 290	96.51 204	98.98 292	99.31 197
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior3		97.48 214	97.00 232	98.95 144	98.69 267	97.95 162	95.74 313	99.03 224	96.48 249	96.11 331	97.63 307	95.92 208	99.59 294	94.16 287	99.20 258	99.30 200

test_prior2							95.74 313		96.48 249	96.11 331	97.63 307	95.92 208		94.16 287	99.20 258

AdaColmap		97.14 241	96.71 251	98.46 210	98.34 307	97.80 178	96.95 249	98.93 240	95.58 276	96.92 301	97.66 304	95.87 210	99.53 311	90.97 348	99.14 269	98.04 326

mvsmamba		99.24 31	99.15 34	99.49 52	99.83 19	98.85 77	99.41 14	99.55 49	99.54 24	99.40 57	99.52 41	95.86 211	99.91 50	99.32 19	99.95 18	99.70 35

v144192		98.54 120	98.57 92	98.45 211	99.21 150	95.98 247	97.63 196	99.36 113	97.15 225	99.32 77	99.18 91	95.84 212	99.84 135	99.50 10	99.91 48	99.54 91

ETH3D cwj APD-0.16		97.55 208	97.00 232	99.19 105	98.51 293	98.64 94	96.85 258	99.13 204	94.19 308	97.65 263	98.40 249	95.78 213	99.81 174	93.37 314	99.16 265	99.12 236

PatchMatch-RL		97.24 233	96.78 247	98.61 189	99.03 199	97.83 172	96.36 285	99.06 215	93.49 320	97.36 287	97.78 297	95.75 214	99.49 321	93.44 312	98.77 300	98.52 307

F-COLMAP		97.30 227	96.68 253	99.14 112	99.19 157	98.39 114	97.27 230	99.30 148	92.93 325	96.62 317	98.00 283	95.73 215	99.68 260	92.62 328	98.46 315	99.35 183

PMMVS		96.51 271	95.98 277	98.09 237	97.53 348	95.84 251	94.92 337	98.84 261	91.58 340	96.05 335	95.58 355	95.68 216	99.66 271	95.59 254	98.09 328	98.76 292

N_pmnet		97.63 204	97.17 223	98.99 141	99.27 138	97.86 169	95.98 298	93.41 364	95.25 286	99.47 45	98.90 159	95.63 217	99.85 118	96.91 162	99.73 119	99.27 207

WR-MVS		98.40 137	98.19 146	99.03 135	99.00 204	97.65 188	96.85 258	98.94 238	98.57 112	98.89 148	98.50 239	95.60 218	99.85 118	97.54 123	99.85 64	99.59 63

CANet		97.87 183	97.76 182	98.19 232	97.75 338	95.51 259	96.76 264	99.05 219	97.74 163	96.93 300	98.21 268	95.59 219	99.89 66	97.86 109	99.93 33	99.19 225

1314		95.74 293	95.60 287	96.17 320	97.53 348	92.75 327	98.07 148	98.31 300	91.22 345	94.25 359	96.68 337	95.53 220	99.03 360	91.64 339	97.18 348	96.74 359

114514_t		96.50 273	95.77 280	98.69 179	99.48 100	97.43 199	97.84 176	99.55 49	81.42 373	96.51 322	98.58 228	95.53 220	99.67 263	93.41 313	99.58 185	98.98 255

test12				98.93 147	98.58 284	97.83 172		98.66 283		96.53 320		95.51 222	99.69 251		99.13 272	99.27 207

testtj		97.79 194	97.25 218	99.42 62	99.03 199	98.85 77	97.78 179	99.18 187	95.83 272	98.12 233	98.50 239	95.50 223	99.86 103	92.23 333	99.07 278	99.54 91

旧先验1					98.82 243	97.45 198		98.76 273			98.34 258	95.50 223			99.01 288	99.23 215

YYNet1		97.60 205	97.67 188	97.39 284	99.04 196	93.04 322	95.27 327	98.38 298	97.25 212	98.92 143	98.95 149	95.48 225	99.73 236	96.99 156	98.74 301	99.41 152

MDA-MVSNet_test_wron		97.60 205	97.66 191	97.41 283	99.04 196	93.09 318	95.27 327	98.42 295	97.26 211	98.88 153	98.95 149	95.43 226	99.73 236	97.02 153	98.72 303	99.41 152

原ACMM1				98.35 220	98.90 224	96.25 241		98.83 266	92.48 331	96.07 334	98.10 277	95.39 227	99.71 245	92.61 329	98.99 290	99.08 239

USDC		97.41 220	97.40 208	97.44 281	98.94 214	93.67 313	95.17 330	99.53 58	94.03 312	98.97 132	99.10 108	95.29 228	99.34 340	95.84 242	99.73 119	99.30 200

testdata				98.09 237	98.93 216	95.40 264		98.80 269	90.08 354	97.45 281	98.37 254	95.26 229	99.70 247	93.58 308	98.95 294	99.17 231

bld_raw_dy_0_64		99.07 42	99.00 46	99.29 85	99.85 15	98.18 132	99.11 56	99.40 100	99.33 46	99.38 61	99.44 55	95.21 230	99.97 4	99.31 20	99.98 9	99.73 27

BH-untuned		96.83 258	96.75 249	97.08 294	98.74 253	93.33 316	96.71 267	98.26 301	96.72 241	98.44 210	97.37 323	95.20 231	99.47 326	91.89 335	97.43 341	98.44 312

MVE	83.40 22	92.50 335	91.92 338	94.25 345	98.83 240	91.64 339	92.71 367	83.52 381	95.92 269	86.46 378	95.46 359	95.20 231	95.40 377	80.51 374	98.64 309	95.73 370
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
BH-RMVSNet		96.83 258	96.58 262	97.58 270	98.47 296	94.05 296	96.67 269	97.36 327	96.70 243	97.87 249	97.98 285	95.14 233	99.44 330	90.47 353	98.58 313	99.25 211

pmmvs4		97.58 207	97.28 217	98.51 205	98.84 238	96.93 225	95.40 326	98.52 291	93.60 317	98.61 189	98.65 213	95.10 234	99.60 290	96.97 159	99.79 94	98.99 254

EU-MVSNet		97.66 201	98.50 100	95.13 339	99.63 55	85.84 367	98.35 123	98.21 303	98.23 131	99.54 32	99.46 50	95.02 235	99.68 260	98.24 84	99.87 62	99.87 5

DP-MVS Recon		97.33 225	96.92 237	98.57 194	99.09 184	97.99 152	96.79 261	99.35 119	93.18 322	97.71 259	98.07 281	95.00 236	99.31 344	93.97 295	99.13 272	98.42 314

HQP_MVS		97.99 175	97.67 188	98.93 147	99.19 157	97.65 188	97.77 182	99.27 161	98.20 135	97.79 255	97.98 285	94.90 237	99.70 247	94.42 281	99.51 207	99.45 138

plane_prior6					98.99 207	97.70 186						94.90 237

CPTT-MVS		97.84 190	97.36 212	99.27 91	99.31 131	98.46 111	98.29 126	99.27 161	94.90 292	97.83 252	98.37 254	94.90 237	99.84 135	93.85 302	99.54 197	99.51 106

new_pmnet		96.99 254	96.76 248	97.67 262	98.72 256	94.89 276	95.95 303	98.20 304	92.62 330	98.55 201	98.54 231	94.88 240	99.52 315	93.96 296	99.44 223	98.59 306

VDD-MVS		98.56 113	98.39 122	99.07 125	99.13 175	98.07 146	98.59 95	97.01 335	99.59 21	99.11 105	99.27 77	94.82 241	99.79 196	98.34 80	99.63 164	99.34 185

jason		97.45 217	97.35 213	97.76 258	99.24 143	93.93 303	95.86 307	98.42 295	94.24 306	98.50 206	98.13 272	94.82 241	99.91 50	97.22 137	99.73 119	99.43 146
jason: jason.
TAMVS		98.24 155	98.05 163	98.80 165	99.07 188	97.18 215	97.88 170	98.81 267	96.66 244	99.17 102	99.21 86	94.81 243	99.77 214	96.96 160	99.88 59	99.44 142

新几何1				98.91 150	98.94 214	97.76 180		98.76 273	87.58 365	96.75 313	98.10 277	94.80 244	99.78 208	92.73 326	99.00 289	99.20 220

VNet		98.42 134	98.30 134	98.79 167	98.79 249	97.29 204	98.23 131	98.66 283	99.31 48	98.85 157	98.80 187	94.80 244	99.78 208	98.13 89	99.13 272	99.31 197

RRT_MVS		99.09 39	98.94 50	99.55 26	99.87 10	98.82 82	99.48 9	98.16 307	99.49 27	99.59 29	99.65 20	94.79 246	99.95 17	99.45 13	99.96 15	99.88 3

MAR-MVS		96.47 275	95.70 283	98.79 167	97.92 330	99.12 58	98.28 127	98.60 287	92.16 335	95.54 347	96.17 347	94.77 247	99.52 315	89.62 356	98.23 319	97.72 342
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		97.44 218	97.22 221	98.08 240	98.57 286	95.78 254	94.30 354	98.79 270	96.58 247	98.60 191	98.19 270	94.74 248	99.64 278	96.41 212	98.84 297	98.82 279

MSP-MVS		98.40 137	98.00 167	99.61 9	99.57 62	99.25 23	98.57 97	99.35 119	97.55 180	99.31 79	97.71 301	94.61 249	99.88 77	96.14 228	99.19 262	99.70 35
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
PAPR		95.29 301	94.47 310	97.75 259	97.50 352	95.14 271	94.89 338	98.71 281	91.39 344	95.35 351	95.48 358	94.57 250	99.14 359	84.95 366	97.37 343	98.97 259

1121		96.73 262	96.00 276	98.91 150	98.95 213	97.76 180	98.07 148	98.73 279	87.65 364	96.54 319	98.13 272	94.52 251	99.73 236	92.38 331	99.02 286	99.24 214

test222					98.92 220	96.93 225	95.54 319	98.78 272	85.72 368	96.86 309	98.11 276	94.43 252			99.10 277	99.23 215

PLC	94.65 16	96.51 271	95.73 282	98.85 158	98.75 252	97.91 165	96.42 282	99.06 215	90.94 349	95.59 340	97.38 322	94.41 253	99.59 294	90.93 349	98.04 332	99.05 242
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		97.84 190	97.84 179	97.83 253	99.14 173	94.74 279	96.94 250	98.88 249	95.84 271	98.89 148	98.96 145	94.40 254	99.69 251	97.55 121	99.95 18	99.05 242

CNLPA		97.17 239	96.71 251	98.55 199	98.56 287	98.05 149	96.33 286	98.93 240	96.91 234	97.06 296	97.39 321	94.38 255	99.45 329	91.66 337	99.18 264	98.14 323

MDA-MVSNet-bldmvs		97.94 176	97.91 174	98.06 242	99.44 108	94.96 275	96.63 271	99.15 202	98.35 119	98.83 161	99.11 106	94.31 256	99.85 118	96.60 192	98.72 303	99.37 173

OpenMVS_ROB	95.38 14	95.84 291	95.18 302	97.81 254	98.41 304	97.15 218	97.37 221	98.62 286	83.86 370	98.65 183	98.37 254	94.29 257	99.68 260	88.41 359	98.62 311	96.60 361

TR-MVS		95.55 297	95.12 303	96.86 307	97.54 347	93.94 302	96.49 278	96.53 344	94.36 305	97.03 298	96.61 338	94.26 258	99.16 357	86.91 363	96.31 359	97.47 351

GBi-Net		98.65 99	98.47 107	99.17 106	98.90 224	98.24 125	99.20 44	99.44 88	98.59 108	98.95 135	99.55 35	94.14 259	99.86 103	97.77 112	99.69 142	99.41 152

test1		98.65 99	98.47 107	99.17 106	98.90 224	98.24 125	99.20 44	99.44 88	98.59 108	98.95 135	99.55 35	94.14 259	99.86 103	97.77 112	99.69 142	99.41 152

FMVSNet2		98.49 127	98.40 119	98.75 175	98.90 224	97.14 219	98.61 92	99.13 204	98.59 108	99.19 97	99.28 75	94.14 259	99.82 160	97.97 102	99.80 89	99.29 204

PAPM_NR		96.82 260	96.32 270	98.30 225	99.07 188	96.69 233	97.48 213	98.76 273	95.81 273	96.61 318	96.47 342	94.12 262	99.17 356	90.82 352	97.78 335	99.06 241

Anonymous20240521		98.69 91	98.87 53	98.16 235	99.77 25	95.11 273	99.08 57	99.44 88	99.34 45	99.33 71	99.55 35	94.10 263	99.94 26	99.25 25	99.96 15	99.42 149

HQP2-MVS												93.84 264

HQP-MVS		97.00 253	96.49 265	98.55 199	98.67 272	96.79 228	96.29 288	99.04 222	96.05 263	95.55 344	96.84 334	93.84 264	99.54 309	92.82 322	99.26 251	99.32 193

MVSFormer		98.26 152	98.43 115	97.77 257	98.88 230	93.89 307	99.39 16	99.56 45	99.11 65	98.16 228	98.13 272	93.81 266	99.97 4	99.26 23	99.57 189	99.43 146

lupinMVS		97.06 246	96.86 241	97.65 264	98.88 230	93.89 307	95.48 323	97.97 314	93.53 318	98.16 228	97.58 309	93.81 266	99.91 50	96.77 178	99.57 189	99.17 231

MG-MVS		96.77 261	96.61 259	97.26 288	98.31 309	93.06 319	95.93 304	98.12 310	96.45 251	97.92 245	98.73 197	93.77 268	99.39 335	91.19 347	99.04 282	99.33 191

PVSNet	93.40 17	95.67 294	95.70 283	95.57 332	98.83 240	88.57 356	92.50 368	97.72 319	92.69 329	96.49 325	96.44 343	93.72 269	99.43 331	93.61 306	99.28 247	98.71 296

MVS_0304		97.64 202	97.35 213	98.52 203	97.87 334	96.69 233	98.59 95	98.05 313	97.44 194	93.74 367	98.85 175	93.69 270	99.88 77	98.11 90	99.81 81	98.98 255

ETH3 D test6400		96.46 276	95.59 288	99.08 122	98.88 230	98.21 131	96.53 274	99.18 187	88.87 360	97.08 294	97.79 296	93.64 271	99.77 214	88.92 358	99.40 227	99.28 205

pmmvs5		97.64 202	97.49 202	98.08 240	99.14 173	95.12 272	96.70 268	99.05 219	93.77 315	98.62 187	98.83 181	93.23 272	99.75 228	98.33 82	99.76 112	99.36 179

CANet_DTU		97.26 230	97.06 229	97.84 252	97.57 345	94.65 284	96.19 294	98.79 270	97.23 218	95.14 353	98.24 265	93.22 273	99.84 135	97.34 132	99.84 68	99.04 246

UnsupCasMVSNet_bld		97.30 227	96.92 237	98.45 211	99.28 136	96.78 231	96.20 293	99.27 161	95.42 282	98.28 222	98.30 262	93.16 274	99.71 245	94.99 264	97.37 343	98.87 275

IterMVS		97.73 196	98.11 157	96.57 311	99.24 143	90.28 351	95.52 322	99.21 176	98.86 95	99.33 71	99.33 71	93.11 275	99.94 26	98.49 71	99.94 28	99.48 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		97.85 189	98.18 147	96.87 304	99.27 138	91.16 350	95.53 320	99.25 167	99.10 72	99.41 54	99.35 67	93.10 276	99.96 11	98.65 61	99.94 28	99.49 114

SCA		96.41 278	96.66 256	95.67 329	98.24 313	88.35 358	95.85 309	96.88 340	96.11 261	97.67 262	98.67 208	93.10 276	99.85 118	94.16 287	99.22 255	98.81 282

DPM-MVS		96.32 279	95.59 288	98.51 205	98.76 250	97.21 212	94.54 350	98.26 301	91.94 336	96.37 327	97.25 326	93.06 278	99.43 331	91.42 343	98.74 301	98.89 271

BH-w/o		95.13 304	94.89 308	95.86 324	98.20 316	91.31 345	95.65 316	97.37 326	93.64 316	96.52 321	95.70 354	93.04 279	99.02 361	88.10 360	95.82 364	97.24 353

cascas		94.79 309	94.33 315	96.15 323	96.02 375	92.36 333	92.34 370	99.26 166	85.34 369	95.08 354	94.96 366	92.96 280	98.53 370	94.41 284	98.59 312	97.56 348

c3_l		97.36 222	97.37 211	97.31 285	98.09 322	93.25 317	95.01 335	99.16 196	97.05 227	98.77 171	98.72 199	92.88 281	99.64 278	96.93 161	99.76 112	99.05 242

MVS-HIRNet		94.32 314	95.62 286	90.42 359	98.46 297	75.36 382	96.29 288	89.13 376	95.25 286	95.38 350	99.75 7	92.88 281	99.19 355	94.07 294	99.39 228	96.72 360

sss		97.21 235	96.93 235	98.06 242	98.83 240	95.22 268	96.75 265	98.48 293	94.49 298	97.27 288	97.90 291	92.77 283	99.80 183	96.57 195	99.32 239	99.16 234

miper_ehance_all_eth		97.06 246	97.03 230	97.16 293	97.83 335	93.06 319	94.66 344	99.09 211	95.99 267	98.69 178	98.45 246	92.73 284	99.61 289	96.79 175	99.03 283	98.82 279

SixPastTwentyTwo		98.75 81	98.62 84	99.16 109	99.83 19	97.96 161	99.28 36	98.20 304	99.37 41	99.70 15	99.65 20	92.65 285	99.93 31	99.04 37	99.84 68	99.60 57

UnsupCasMVSNet_eth		97.89 180	97.60 197	98.75 175	99.31 131	97.17 216	97.62 197	99.35 119	98.72 101	98.76 173	98.68 206	92.57 286	99.74 232	97.76 116	95.60 365	99.34 185

CHOSEN 1792x2688		97.49 212	97.14 227	98.54 202	99.68 46	96.09 245	96.50 277	99.62 25	91.58 340	98.84 160	98.97 142	92.36 287	99.88 77	96.76 179	99.95 18	99.67 41

PCF-MVS	92.86 18	94.36 313	93.00 330	98.42 214	98.70 263	97.56 192	93.16 366	99.11 208	79.59 374	97.55 272	97.43 319	92.19 288	99.73 236	79.85 375	99.45 220	97.97 330
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPP-MVSNet		98.30 146	98.04 164	99.07 125	99.56 69	97.83 172	99.29 32	98.07 311	99.03 81	98.59 193	99.13 104	92.16 289	99.90 56	96.87 170	99.68 147	99.49 114

1112_ss		97.29 229	96.86 241	98.58 192	99.34 130	96.32 239	96.75 265	99.58 31	93.14 323	96.89 307	97.48 316	92.11 290	99.86 103	96.91 162	99.54 197	99.57 74

CDS-MVSNet		97.69 198	97.35 213	98.69 179	98.73 254	97.02 222	96.92 254	98.75 276	95.89 270	98.59 193	98.67 208	92.08 291	99.74 232	96.72 184	99.81 81	99.32 193
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
eth_miper_zixun_eth		97.23 234	97.25 218	97.17 291	98.00 327	92.77 326	94.71 341	99.18 187	97.27 210	98.56 199	98.74 196	91.89 292	99.69 251	97.06 152	99.81 81	99.05 242

IS-MVSNet		98.19 159	97.90 175	99.08 122	99.57 62	97.97 157	99.31 26	98.32 299	99.01 83	98.98 129	99.03 123	91.59 293	99.79 196	95.49 257	99.80 89	99.48 124

Test_1112_low_res		96.99 254	96.55 263	98.31 224	99.35 128	95.47 261	95.84 310	99.53 58	91.51 342	96.80 312	98.48 244	91.36 294	99.83 150	96.58 193	99.53 201	99.62 51

WTY-MVS		96.67 265	96.27 273	97.87 251	98.81 245	94.61 285	96.77 263	97.92 316	94.94 291	97.12 291	97.74 300	91.11 295	99.82 160	93.89 299	98.15 325	99.18 227

PVSNet_0	89.98 21	91.15 342	90.30 345	93.70 351	97.72 339	84.34 375	90.24 371	97.42 325	90.20 353	93.79 365	93.09 374	90.90 296	98.89 367	86.57 364	72.76 378	97.87 333

miper_enhance_ethall		96.01 286	95.74 281	96.81 308	96.41 370	92.27 334	93.69 363	98.89 248	91.14 347	98.30 220	97.35 325	90.58 297	99.58 299	96.31 217	99.03 283	98.60 304

VDDNet		98.21 157	97.95 170	99.01 139	99.58 58	97.74 183	99.01 65	97.29 331	99.67 11	98.97 132	99.50 43	90.45 298	99.80 183	97.88 107	99.20 258	99.48 124

Anonymous202405211		97.90 178	97.50 201	99.08 122	98.90 224	98.25 124	98.53 101	96.16 347	98.87 94	99.11 105	98.86 172	90.40 299	99.78 208	97.36 131	99.31 241	99.19 225

miper_lstm_enhance		97.18 238	97.16 224	97.25 289	98.16 318	92.85 324	95.15 332	99.31 138	97.25 212	98.74 176	98.78 190	90.07 300	99.78 208	97.19 138	99.80 89	99.11 238

lessismore_v0				98.97 142	99.73 30	97.53 194		86.71 378		99.37 64	99.52 41	89.93 301	99.92 40	98.99 41	99.72 126	99.44 142

HY-MVS	95.94 13	95.90 289	95.35 297	97.55 274	97.95 328	94.79 277	98.81 81	96.94 338	92.28 334	95.17 352	98.57 229	89.90 302	99.75 228	91.20 346	97.33 347	98.10 324

K. test v3		98.00 172	97.66 191	99.03 135	99.79 24	97.56 192	99.19 48	92.47 367	99.62 18	99.52 37	99.66 18	89.61 303	99.96 11	99.25 25	99.81 81	99.56 79

CMPMVS	75.91 23	96.29 280	95.44 293	98.84 159	96.25 372	98.69 93	97.02 245	99.12 206	88.90 359	97.83 252	98.86 172	89.51 304	98.90 366	91.92 334	99.51 207	98.92 267
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		96.25 282	97.21 222	93.38 355	99.10 181	80.56 381	97.20 235	98.19 306	96.94 232	99.00 126	99.02 124	89.50 305	99.80 183	96.36 215	99.59 179	99.78 15

DeepMVS_CX				93.44 354	98.24 313	94.21 293		94.34 357	64.28 376	91.34 372	94.87 369	89.45 306	92.77 379	77.54 377	93.14 373	93.35 374

EPNet		96.14 284	95.44 293	98.25 228	90.76 382	95.50 260	97.92 166	94.65 355	98.97 87	92.98 368	98.85 175	89.12 307	99.87 94	95.99 232	99.68 147	99.39 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		97.46 215	97.16 224	98.34 221	99.55 73	96.10 243	98.94 72	98.44 294	98.32 122	98.16 228	98.62 222	88.76 308	99.73 236	93.88 300	99.79 94	99.18 227

test1111		96.49 274	96.82 245	95.52 333	99.42 113	87.08 364	99.22 41	87.14 377	99.11 65	99.46 46	99.58 30	88.69 309	99.86 103	98.80 50	99.95 18	99.62 51

DIV-MVS_self_test		97.02 250	96.84 243	97.58 270	97.82 336	94.03 298	94.66 344	99.16 196	97.04 228	98.63 185	98.71 200	88.69 309	99.69 251	97.00 154	99.81 81	99.01 250

cl____		97.02 250	96.83 244	97.58 270	97.82 336	94.04 297	94.66 344	99.16 196	97.04 228	98.63 185	98.71 200	88.68 311	99.69 251	97.00 154	99.81 81	99.00 253

h-mvs33		97.77 195	97.33 216	99.10 118	99.21 150	97.84 171	98.35 123	98.57 288	99.11 65	98.58 195	99.02 124	88.65 312	99.96 11	98.11 90	96.34 358	99.49 114

hse-mvs2		97.46 215	97.07 228	98.64 182	98.73 254	97.33 202	97.45 217	97.64 324	99.11 65	98.58 195	97.98 285	88.65 312	99.79 196	98.11 90	97.39 342	98.81 282

ECVR-MVS		96.42 277	96.61 259	95.85 325	99.38 118	88.18 360	99.22 41	86.00 379	99.08 77	99.36 66	99.57 31	88.47 314	99.82 160	98.52 69	99.95 18	99.54 91

EPNet_dtu		94.93 308	94.78 309	95.38 337	93.58 379	87.68 362	96.78 262	95.69 353	97.35 202	89.14 375	98.09 279	88.15 315	99.49 321	94.95 266	99.30 244	98.98 255
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
alignmvs		97.35 223	96.88 240	98.78 170	98.54 289	98.09 140	97.71 188	97.69 321	99.20 57	97.59 268	95.90 351	88.12 316	99.55 306	98.18 88	98.96 293	98.70 298

FMVSNet3		97.50 210	97.24 220	98.29 226	98.08 323	95.83 252	97.86 174	98.91 245	97.89 156	98.95 135	98.95 149	87.06 317	99.81 174	97.77 112	99.69 142	99.23 215

baseline1		95.96 288	95.44 293	97.52 277	98.51 293	93.99 301	98.39 119	96.09 349	98.21 132	98.40 218	97.76 299	86.88 318	99.63 281	95.42 258	89.27 376	98.95 261

RPMNet		97.02 250	96.93 235	97.30 286	97.71 340	94.22 291	98.11 143	99.30 148	99.37 41	96.91 303	99.34 69	86.72 319	99.87 94	97.53 124	97.36 345	97.81 336

HyFIR lowres test		97.19 237	96.60 261	98.96 143	99.62 57	97.28 206	95.17 330	99.50 64	94.21 307	99.01 124	98.32 261	86.61 320	99.99 2	97.10 148	99.84 68	99.60 57

PAPM		91.88 341	90.34 344	96.51 312	98.06 324	92.56 328	92.44 369	97.17 332	86.35 366	90.38 373	96.01 348	86.61 320	99.21 354	70.65 378	95.43 366	97.75 340

test_yl		96.69 263	96.29 271	97.90 249	98.28 310	95.24 266	97.29 227	97.36 327	98.21 132	98.17 226	97.86 292	86.27 322	99.55 306	94.87 267	98.32 317	98.89 271

DCV-MVSNet		96.69 263	96.29 271	97.90 249	98.28 310	95.24 266	97.29 227	97.36 327	98.21 132	98.17 226	97.86 292	86.27 322	99.55 306	94.87 267	98.32 317	98.89 271

CHOSEN 280x420		95.51 299	95.47 290	95.65 331	98.25 312	88.27 359	93.25 365	98.88 249	93.53 318	94.65 356	97.15 330	86.17 324	99.93 31	97.41 129	99.93 33	98.73 295

EMVS		93.83 324	94.02 316	93.23 356	96.83 364	84.96 370	89.77 373	96.32 346	97.92 153	97.43 283	96.36 346	86.17 324	98.93 365	87.68 361	97.73 336	95.81 369

MIMVSNet		96.62 268	96.25 274	97.71 261	99.04 196	94.66 283	99.16 50	96.92 339	97.23 218	97.87 249	99.10 108	86.11 326	99.65 276	91.65 338	99.21 257	98.82 279

tpmvs		95.02 307	95.25 299	94.33 344	96.39 371	85.87 366	98.08 147	96.83 341	95.46 281	95.51 349	98.69 204	85.91 327	99.53 311	94.16 287	96.23 360	97.58 347

MDTV_nov1_ep13_2view						74.92 383	97.69 190		90.06 355	97.75 258		85.78 328		93.52 309		98.69 299

ADS-MVSNet2		95.43 300	94.98 305	96.76 310	98.14 319	91.74 338	97.92 166	97.76 318	90.23 350	96.51 322	98.91 156	85.61 329	99.85 118	92.88 320	96.90 351	98.69 299

ADS-MVSNet		95.24 303	94.93 307	96.18 319	98.14 319	90.10 352	97.92 166	97.32 330	90.23 350	96.51 322	98.91 156	85.61 329	99.74 232	92.88 320	96.90 351	98.69 299

tpmrst		95.07 305	95.46 291	93.91 348	97.11 359	84.36 374	97.62 197	96.96 336	94.98 289	96.35 328	98.80 187	85.46 331	99.59 294	95.60 253	96.23 360	97.79 339

CR-MVSNet		96.28 281	95.95 278	97.28 287	97.71 340	94.22 291	98.11 143	98.92 243	92.31 333	96.91 303	99.37 63	85.44 332	99.81 174	97.39 130	97.36 345	97.81 336

Patchmtry		97.35 223	96.97 234	98.50 207	97.31 356	96.47 236	98.18 136	98.92 243	98.95 91	98.78 168	99.37 63	85.44 332	99.85 118	95.96 234	99.83 74	99.17 231

test_method		79.78 344	79.50 347	80.62 360	80.21 383	45.76 385	70.82 374	98.41 297	31.08 378	80.89 379	97.71 301	84.85 334	97.37 374	91.51 342	80.03 377	98.75 293

PatchmatchNet		95.58 296	95.67 285	95.30 338	97.34 355	87.32 363	97.65 195	96.65 342	95.30 285	97.07 295	98.69 204	84.77 335	99.75 228	94.97 265	98.64 309	98.83 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												84.74 336				98.81 282

E-PMN		94.17 318	94.37 313	93.58 352	96.86 362	85.71 369	90.11 372	97.07 334	98.17 138	97.82 254	97.19 327	84.62 337	98.94 364	89.77 355	97.68 337	96.09 368

LFMVS		97.20 236	96.72 250	98.64 182	98.72 256	96.95 224	98.93 73	94.14 362	99.74 7	98.78 168	99.01 133	84.45 338	99.73 236	97.44 127	99.27 248	99.25 211

patchmatchnet-post											98.77 192	84.37 339	99.85 118

PatchT		96.65 266	96.35 268	97.54 275	97.40 353	95.32 265	97.98 162	96.64 343	99.33 46	96.89 307	99.42 57	84.32 340	99.81 174	97.69 120	97.49 338	97.48 350

Patchmatch-RL test		97.26 230	97.02 231	97.99 247	99.52 80	95.53 258	96.13 295	99.71 14	97.47 186	99.27 82	99.16 97	84.30 341	99.62 283	97.89 104	99.77 102	98.81 282

sam_mvs												84.29 342

MDTV_nov1_ep13			95.22 300		97.06 360	83.20 376	97.74 186	96.16 347	94.37 304	96.99 299	98.83 181	83.95 343	99.53 311	93.90 298	97.95 333

test_post											21.25 380	83.86 344	99.70 247

Patchmatch-test		96.55 269	96.34 269	97.17 291	98.35 306	93.06 319	98.40 118	97.79 317	97.33 203	98.41 214	98.67 208	83.68 345	99.69 251	95.16 262	99.31 241	98.77 290

GA-MVS		95.86 290	95.32 298	97.49 278	98.60 281	94.15 295	93.83 361	97.93 315	95.49 280	96.68 314	97.42 320	83.21 346	99.30 346	96.22 222	98.55 314	99.01 250

JIA-IIPM		95.52 298	95.03 304	97.00 296	96.85 363	94.03 298	96.93 252	95.82 351	99.20 57	94.63 357	99.71 12	83.09 347	99.60 290	94.42 281	94.64 369	97.36 352

test_post1							97.59 202				20.48 381	83.07 348	99.66 271	94.16 287

tpm cat1		93.29 330	93.13 329	93.75 350	97.39 354	84.74 371	97.39 219	97.65 322	83.39 372	94.16 360	98.41 248	82.86 349	99.39 335	91.56 341	95.35 367	97.14 354

cl22		95.79 292	95.39 296	96.98 298	96.77 365	92.79 325	94.40 352	98.53 290	94.59 297	97.89 248	98.17 271	82.82 350	99.24 351	96.37 213	99.03 283	98.92 267

test-LLR		93.90 323	93.85 317	94.04 346	96.53 367	84.62 372	94.05 358	92.39 368	96.17 258	94.12 361	95.07 361	82.30 351	99.67 263	95.87 239	98.18 322	97.82 334

test0.0.03 1		94.51 311	93.69 320	96.99 297	96.05 373	93.61 315	94.97 336	93.49 363	96.17 258	97.57 271	94.88 367	82.30 351	99.01 363	93.60 307	94.17 372	98.37 317

test_part1		97.91 177	97.46 207	99.27 91	98.80 247	98.18 132	99.07 60	99.36 113	99.75 5	99.63 26	99.49 46	82.20 353	99.89 66	98.87 47	99.95 18	99.74 26

AUN-MVS		96.24 283	95.45 292	98.60 190	98.70 263	97.22 210	97.38 220	97.65 322	95.95 268	95.53 348	97.96 289	82.11 354	99.79 196	96.31 217	97.44 340	98.80 287

MVSTER		96.86 257	96.55 263	97.79 255	97.91 331	94.21 293	97.56 205	98.87 251	97.49 185	99.06 113	99.05 118	80.72 355	99.80 183	98.44 74	99.82 77	99.37 173

tmp_tt		78.77 345	78.73 348	78.90 361	58.45 384	74.76 384	94.20 355	78.26 384	39.16 377	86.71 377	92.82 375	80.50 356	75.19 380	86.16 365	92.29 374	86.74 375

thres200		93.72 326	93.14 328	95.46 336	98.66 277	91.29 346	96.61 272	94.63 356	97.39 198	96.83 310	93.71 373	79.88 357	99.56 303	82.40 372	98.13 326	95.54 371

thres100view900		94.19 317	93.67 321	95.75 328	99.06 192	91.35 344	98.03 155	94.24 360	98.33 121	97.40 284	94.98 365	79.84 358	99.62 283	83.05 369	98.08 329	96.29 362

thres600view7		94.45 312	93.83 318	96.29 316	99.06 192	91.53 340	97.99 161	94.24 360	98.34 120	97.44 282	95.01 363	79.84 358	99.67 263	84.33 367	98.23 319	97.66 344

tfpn200view9		94.03 321	93.44 323	95.78 327	98.93 216	91.44 342	97.60 200	94.29 358	97.94 151	97.10 292	94.31 371	79.67 360	99.62 283	83.05 369	98.08 329	96.29 362

thres400		94.14 319	93.44 323	96.24 318	98.93 216	91.44 342	97.60 200	94.29 358	97.94 151	97.10 292	94.31 371	79.67 360	99.62 283	83.05 369	98.08 329	97.66 344

pmmvs3		95.03 306	94.40 312	96.93 300	97.70 342	92.53 329	95.08 333	97.71 320	88.57 361	97.71 259	98.08 280	79.39 362	99.82 160	96.19 224	99.11 276	98.43 313

iter_conf_final		97.10 242	96.65 258	98.45 211	98.53 291	96.08 246	98.30 125	99.11 208	98.10 143	98.85 157	98.95 149	79.38 363	99.87 94	98.68 60	99.91 48	99.40 161

baseline2		93.73 325	92.83 331	96.42 314	97.70 342	91.28 347	96.84 260	89.77 375	93.96 314	92.44 369	95.93 350	79.14 364	99.77 214	92.94 318	96.76 355	98.21 319

tpm		94.67 310	94.34 314	95.66 330	97.68 344	88.42 357	97.88 170	94.90 354	94.46 300	96.03 336	98.56 230	78.66 365	99.79 196	95.88 236	95.01 368	98.78 289

CostFormer		93.97 322	93.78 319	94.51 343	97.53 348	85.83 368	97.98 162	95.96 350	89.29 358	94.99 355	98.63 220	78.63 366	99.62 283	94.54 275	96.50 356	98.09 325

ET-MVSNet_ETH3D		94.30 316	93.21 326	97.58 270	98.14 319	94.47 288	94.78 340	93.24 366	94.72 295	89.56 374	95.87 352	78.57 367	99.81 174	96.91 162	97.11 350	98.46 309

dp		93.47 328	93.59 322	93.13 357	96.64 366	81.62 380	97.66 193	96.42 345	92.80 328	96.11 331	98.64 216	78.55 368	99.59 294	93.31 315	92.18 375	98.16 322

EPMVS		93.72 326	93.27 325	95.09 340	96.04 374	87.76 361	98.13 140	85.01 380	94.69 296	96.92 301	98.64 216	78.47 369	99.31 344	95.04 263	96.46 357	98.20 320

tpm2		93.09 332	92.58 333	94.62 342	97.56 346	86.53 365	97.66 193	95.79 352	86.15 367	94.07 363	98.23 267	75.95 370	99.53 311	90.91 350	96.86 354	97.81 336

FPMVS		93.44 329	92.23 334	97.08 294	99.25 142	97.86 169	95.61 317	97.16 333	92.90 326	93.76 366	98.65 213	75.94 371	95.66 376	79.30 376	97.49 338	97.73 341

iter_conf05		96.54 270	96.07 275	97.92 248	97.90 332	94.50 287	97.87 173	99.14 203	97.73 164	98.89 148	98.95 149	75.75 372	99.87 94	98.50 70	99.92 42	99.40 161

thisisatest0515		94.12 320	93.16 327	96.97 299	98.60 281	92.90 323	93.77 362	90.61 372	94.10 310	96.91 303	95.87 352	74.99 373	99.80 183	94.52 276	99.12 275	98.20 320

tttt0517		95.64 295	94.98 305	97.64 266	99.36 124	93.81 309	98.72 85	90.47 373	98.08 145	98.67 180	98.34 258	73.88 374	99.92 40	97.77 112	99.51 207	99.20 220

thisisatest0530		95.27 302	94.45 311	97.74 260	99.19 157	94.37 289	97.86 174	90.20 374	97.17 222	98.22 224	97.65 305	73.53 375	99.90 56	96.90 167	99.35 235	98.95 261

FMVSNet5		96.01 286	95.20 301	98.41 215	97.53 348	96.10 243	98.74 82	99.50 64	97.22 221	98.03 243	99.04 120	69.80 376	99.88 77	97.27 135	99.71 131	99.25 211

gg-mvs-nofinetune		92.37 337	91.20 342	95.85 325	95.80 376	92.38 332	99.31 26	81.84 382	99.75 5	91.83 371	99.74 8	68.29 377	99.02 361	87.15 362	97.12 349	96.16 365

KD-MVS_2432*1600		92.87 333	91.99 336	95.51 334	91.37 380	89.27 354	94.07 356	98.14 308	95.42 282	97.25 289	96.44 343	67.86 378	99.24 351	91.28 344	96.08 362	98.02 327

miper_refine_blended		92.87 333	91.99 336	95.51 334	91.37 380	89.27 354	94.07 356	98.14 308	95.42 282	97.25 289	96.44 343	67.86 378	99.24 351	91.28 344	96.08 362	98.02 327

GG-mvs-BLEND				94.76 341	94.54 378	92.13 336	99.31 26	80.47 383		88.73 376	91.01 376	67.59 380	98.16 373	82.30 373	94.53 371	93.98 373

TESTMET0.1,1		92.19 340	91.77 340	93.46 353	96.48 369	82.80 377	94.05 358	91.52 371	94.45 302	94.00 364	94.88 367	66.65 381	99.56 303	95.78 244	98.11 327	98.02 327

test2506		92.39 336	91.89 339	93.89 349	99.38 118	82.28 378	99.32 22	66.03 385	99.08 77	98.77 171	99.57 31	66.26 382	99.84 135	98.71 57	99.95 18	99.54 91

IB-MVS	91.63 19	92.24 339	90.90 343	96.27 317	97.22 358	91.24 348	94.36 353	93.33 365	92.37 332	92.24 370	94.58 370	66.20 383	99.89 66	93.16 317	94.63 370	97.66 344
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
test-mter		92.33 338	91.76 341	94.04 346	96.53 367	84.62 372	94.05 358	92.39 368	94.00 313	94.12 361	95.07 361	65.63 384	99.67 263	95.87 239	98.18 322	97.82 334

test123		17.04 348	20.11 351	7.82 362	10.25 386	4.91 386	94.80 339	4.47 387	4.93 380	10.00 382	24.28 379	9.69 385	3.64 381	10.14 379	12.43 380	14.92 377

testmvs		17.12 347	20.53 350	6.87 363	12.05 385	4.20 387	93.62 364	6.73 386	4.62 381	10.41 381	24.33 378	8.28 386	3.56 382	9.69 380	15.07 379	12.86 378

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.12 350	10.83 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	97.48 316	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					99.73 30	99.67 2	99.43 12	99.54 54	99.43 35	99.26 86

MSC_two_6792asdad				99.32 82	98.43 300	98.37 116		98.86 257					99.89 66	97.14 144	99.60 175	99.71 30

No_MVS				99.32 82	98.43 300	98.37 116		98.86 257					99.89 66	97.14 144	99.60 175	99.71 30

eth-test2					0.00 387

eth-test					0.00 387

IU-MVS					99.49 92	99.15 48		98.87 251	92.97 324	99.41 54				96.76 179	99.62 167	99.66 42

save fliter					99.11 177	97.97 157	96.53 274	99.02 228	98.24 129

test_0728_SECOND				99.60 13	99.50 85	99.23 25	98.02 157	99.32 132					99.88 77	96.99 156	99.63 164	99.68 38

GSMVS																98.81 282

test_part2					99.36 124	99.10 61				99.05 118

MTGPA								99.20 178

MTMP							97.93 165	91.91 370

gm-plane-assit					94.83 377	81.97 379			88.07 363		94.99 364		99.60 290	91.76 336

test9_res														93.28 316	99.15 268	99.38 170

agg_prior2														92.50 330	99.16 265	99.37 173

agg_prior					98.68 270	97.99 152		99.01 231		95.59 340			99.77 214

test_prior4						97.97 157	95.86 307

test_prior				98.95 144	98.69 267	97.95 162		99.03 224					99.59 294			99.30 200

旧先验2							95.76 311		88.56 362	97.52 275			99.66 271	94.48 277

新几何2							95.93 304

无先验							95.74 313	98.74 278	89.38 357				99.73 236	92.38 331		99.22 219

原ACMM2							95.53 320

testdata2													99.79 196	92.80 324

testdata1							95.44 325		96.32 255

plane_prior7					99.19 157	97.87 168

plane_prior5								99.27 161					99.70 247	94.42 281	99.51 207	99.45 138

plane_prior4											97.98 285

plane_prior3						97.78 179			97.41 196	97.79 255

plane_prior2							97.77 182		98.20 135

plane_prior1					99.05 195

plane_prior						97.65 188	97.07 244		96.72 241						99.36 233

n2								0.00 388

nn								0.00 388

door-mid								99.57 38

test11								98.87 251

door								99.41 98

HQP5-MVS						96.79 228

HQP-NCC					98.67 272		96.29 288		96.05 263	95.55 344

ACMP_Plane					98.67 272		96.29 288		96.05 263	95.55 344

BP-MVS														92.82 322

HQP4-MVS										95.56 343			99.54 309			99.32 193

HQP3-MVS								99.04 222							99.26 251

NP-MVS					98.84 238	97.39 201					96.84 334

ACMMP++_ref															99.77 102

ACMMP++															99.68 147

High-resolution multi-view benchmark