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.

jajsoiax		99.89 3	99.89 3	99.89 8	99.96 4	99.78 39	99.70 23	99.86 20	99.89 12	99.98 4	99.90 22	99.94 1	99.98 6	99.75 13	100.00 1	99.90 5

mvs_tets		99.90 2	99.90 2	99.90 5	99.96 4	99.79 36	99.72 20	99.88 16	99.92 7	99.98 4	99.93 14	99.94 1	99.98 6	99.77 12	100.00 1	99.92 3

wuyk23d		97.58 285	99.13 117	92.93 339	99.69 121	99.49 120	99.52 63	99.77 61	97.97 259	99.96 9	99.79 59	99.84 3	99.94 54	95.85 302	99.82 140	79.36 353

cdsmvs_eth3d_5k		24.88 328	33.17 330	0.00 342	0.00 363	0.00 364	0.00 354	99.62 135	0.00 359	0.00 360	99.13 289	99.82 4	0.00 360	0.00 358	0.00 358	0.00 356

LTVRE_ROB	99.19 1	99.88 5	99.87 5	99.88 12	99.91 15	99.90 5	99.96 1	99.92 5	99.90 8	99.97 7	99.87 31	99.81 5	99.95 43	99.54 26	99.99 13	99.80 24
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
LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 8	99.78 6	100.00 1	99.92 1	100.00 1	99.87 10

test_djsdf		99.84 9	99.81 10	99.91 3	99.94 10	99.84 18	99.77 12	99.80 47	99.73 38	99.97 7	99.92 17	99.77 7	99.98 6	99.43 37	100.00 1	99.90 5

pmmvs6		99.86 7	99.86 7	99.83 22	99.94 10	99.90 5	99.83 7	99.91 8	99.85 20	99.94 12	99.95 12	99.73 8	99.90 123	99.65 17	99.97 30	99.69 51

UniMVSNet_ETH3D		99.85 8	99.83 8	99.90 5	99.89 22	99.91 3	99.89 5	99.71 92	99.93 5	99.95 11	99.89 26	99.71 9	99.96 34	99.51 30	99.97 30	99.84 15

XVG-OURS		99.21 130	99.06 141	99.65 99	99.82 44	99.62 95	97.87 313	99.74 77	98.36 229	99.66 113	99.68 124	99.71 9	99.90 123	96.84 259	99.88 99	99.43 204

XVG-OURS-SEG-HR		99.16 144	98.99 166	99.66 94	99.84 34	99.64 89	98.25 275	99.73 80	98.39 226	99.63 123	99.43 233	99.70 11	99.90 123	97.34 225	98.64 324	99.44 198

DeepC-MVS	98.90 4	99.62 34	99.61 30	99.67 87	99.72 108	99.44 134	99.24 125	99.71 92	99.27 119	99.93 15	99.90 22	99.70 11	99.93 67	98.99 94	99.99 13	99.64 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMH	98.42 6	99.59 37	99.54 45	99.72 75	99.86 30	99.62 95	99.56 61	99.79 53	98.77 190	99.80 60	99.85 36	99.64 13	99.85 203	98.70 123	99.89 91	99.70 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		99.79 13	99.79 12	99.78 38	99.91 15	99.83 22	99.76 14	99.87 18	99.73 38	99.89 27	99.87 31	99.63 14	99.87 165	99.54 26	99.92 73	99.63 93

DSMNet-mixed		99.48 54	99.65 23	98.95 253	99.71 111	97.27 303	99.50 65	99.82 37	99.59 76	99.41 195	99.85 36	99.62 15	100.00 1	99.53 28	99.89 91	99.59 125

Vis-MVSNet		99.75 16	99.74 16	99.79 35	99.88 24	99.66 82	99.69 29	99.92 5	99.67 52	99.77 73	99.75 80	99.61 16	99.98 6	99.35 47	99.98 22	99.72 42
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high		99.88 5	99.87 5	99.91 3	99.99 1	99.91 3	99.65 44	100.00 1	99.90 8	100.00 1	99.97 9	99.61 16	99.97 17	99.75 13	100.00 1	99.84 15

TransMVSNet (Re)		99.78 14	99.77 13	99.81 27	99.91 15	99.85 13	99.75 15	99.86 20	99.70 45	99.91 21	99.89 26	99.60 18	99.87 165	99.59 21	99.74 181	99.71 45

PMMVS2		99.48 54	99.45 57	99.57 136	99.76 85	98.99 216	98.09 289	99.90 11	98.95 165	99.78 68	99.58 184	99.57 19	99.93 67	99.48 33	99.95 48	99.79 30

SD-MVS		99.01 177	99.30 87	98.15 302	99.50 195	99.40 147	98.94 204	99.61 142	99.22 130	99.75 80	99.82 48	99.54 20	95.51 357	97.48 218	99.87 107	99.54 149
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
test_part1		99.89 3	99.88 4	99.94 2	99.91 15	99.92 2	99.92 3	99.90 11	99.98 2	99.99 3	99.97 9	99.50 21	99.98 6	99.73 16	100.00 1	99.92 3

anonymousdsp		99.80 12	99.77 13	99.90 5	99.96 4	99.88 8	99.73 17	99.85 24	99.70 45	99.92 19	99.93 14	99.45 22	99.97 17	99.36 46	100.00 1	99.85 14

ETV-MVS		99.18 139	99.18 108	99.16 235	99.34 254	99.28 173	99.12 167	99.79 53	99.48 85	98.93 263	98.55 337	99.40 23	99.93 67	98.51 133	99.52 254	98.28 328

xiu_mvs_v1_base_debu		99.23 116	99.34 76	98.91 260	99.59 149	98.23 265	98.47 257	99.66 113	99.61 68	99.68 105	98.94 317	99.39 24	99.97 17	99.18 71	99.55 244	98.51 319

xiu_mvs_v1_base		99.23 116	99.34 76	98.91 260	99.59 149	98.23 265	98.47 257	99.66 113	99.61 68	99.68 105	98.94 317	99.39 24	99.97 17	99.18 71	99.55 244	98.51 319

xiu_mvs_v1_base_debi		99.23 116	99.34 76	98.91 260	99.59 149	98.23 265	98.47 257	99.66 113	99.61 68	99.68 105	98.94 317	99.39 24	99.97 17	99.18 71	99.55 244	98.51 319

ACMM	98.09 11	99.46 61	99.38 68	99.72 75	99.80 57	99.69 75	99.13 163	99.65 124	98.99 159	99.64 119	99.72 93	99.39 24	99.86 185	98.23 152	99.81 148	99.60 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v2_base		99.02 173	99.11 124	98.77 277	99.37 240	98.09 276	98.13 284	99.51 206	99.47 90	99.42 187	98.54 338	99.38 28	99.97 17	98.83 111	99.33 284	98.24 330

XXY-MVS		99.71 19	99.67 21	99.81 27	99.89 22	99.72 62	99.59 56	99.82 37	99.39 104	99.82 50	99.84 41	99.38 28	99.91 103	99.38 43	99.93 69	99.80 24

LPG-MVS_test		99.22 125	99.05 145	99.74 61	99.82 44	99.63 93	99.16 152	99.73 80	97.56 279	99.64 119	99.69 113	99.37 30	99.89 137	96.66 269	99.87 107	99.69 51

LGP-MVS_train				99.74 61	99.82 44	99.63 93		99.73 80	97.56 279	99.64 119	99.69 113	99.37 30	99.89 137	96.66 269	99.87 107	99.69 51

TDRefinement		99.72 18	99.70 18	99.77 40	99.90 20	99.85 13	99.86 6	99.92 5	99.69 48	99.78 68	99.92 17	99.37 30	99.88 152	98.93 106	99.95 48	99.60 116

testgi		99.29 104	99.26 99	99.37 197	99.75 95	98.81 234	98.84 214	99.89 13	98.38 227	99.75 80	99.04 303	99.36 33	99.86 185	99.08 88	99.25 293	99.45 193

Fast-Effi-MVS+		99.02 173	98.87 186	99.46 167	99.38 237	99.50 119	99.04 182	99.79 53	97.17 298	98.62 293	98.74 330	99.34 34	99.95 43	98.32 144	99.41 271	98.92 296

casdiffmvs		99.63 31	99.61 30	99.67 87	99.79 67	99.59 106	99.13 163	99.85 24	99.79 33	99.76 75	99.72 93	99.33 35	99.82 240	99.21 64	99.94 61	99.59 125

new-patchmatchnet		99.35 88	99.57 39	98.71 282	99.82 44	96.62 317	98.55 248	99.75 72	99.50 83	99.88 33	99.87 31	99.31 36	99.88 152	99.43 37	100.00 1	99.62 105

HPM-MVS_fast		99.43 65	99.30 87	99.80 30	99.83 38	99.81 29	99.52 63	99.70 96	98.35 234	99.51 171	99.50 213	99.31 36	99.88 152	98.18 159	99.84 121	99.69 51

CS-MVS		99.09 161	99.03 153	99.25 223	99.45 219	99.49 120	99.41 78	99.82 37	99.10 149	98.03 325	98.48 341	99.30 38	99.89 137	98.30 146	99.41 271	98.35 325

EG-PatchMatch MVS		99.57 39	99.56 43	99.62 119	99.77 81	99.33 165	99.26 117	99.76 66	99.32 113	99.80 60	99.78 66	99.29 39	99.87 165	99.15 78	99.91 82	99.66 74

DeepPCF-MVS	98.42 6	99.18 139	99.02 155	99.67 87	99.22 278	99.75 49	97.25 340	99.47 220	98.72 195	99.66 113	99.70 107	99.29 39	99.63 330	98.07 168	99.81 148	99.62 105

pcd_1.5k_mvsjas		16.61 329	22.14 332	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	100.00 1	99.28 41	0.00 360	0.00 358	0.00 358	0.00 356

PS-MVSNAJss		99.84 9	99.82 9	99.89 8	99.96 4	99.77 41	99.68 32	99.85 24	99.95 4	99.98 4	99.92 17	99.28 41	99.98 6	99.75 13	100.00 1	99.94 2

PS-MVSNAJ		99.00 179	99.08 135	98.76 278	99.37 240	98.10 275	98.00 299	99.51 206	99.47 90	99.41 195	98.50 340	99.28 41	99.97 17	98.83 111	99.34 282	98.20 334

TSAR-MVS + MP.		99.34 93	99.24 102	99.63 110	99.82 44	99.37 155	99.26 117	99.35 257	98.77 190	99.57 146	99.70 107	99.27 44	99.88 152	97.71 198	99.75 173	99.65 82
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMH+	98.40 8	99.50 50	99.43 62	99.71 79	99.86 30	99.76 47	99.32 98	99.77 61	99.53 80	99.77 73	99.76 76	99.26 45	99.78 268	97.77 193	99.88 99	99.60 116

HPM-MVS		99.25 112	99.07 139	99.78 38	99.81 51	99.75 49	99.61 51	99.67 109	97.72 273	99.35 206	99.25 273	99.23 46	99.92 85	97.21 239	99.82 140	99.67 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DELS-MVS		99.34 93	99.30 87	99.48 162	99.51 189	99.36 158	98.12 285	99.53 195	99.36 108	99.41 195	99.61 169	99.22 47	99.87 165	99.21 64	99.68 206	99.20 252
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
pmmvs-eth3d		99.48 54	99.47 53	99.51 153	99.77 81	99.41 146	98.81 221	99.66 113	99.42 103	99.75 80	99.66 134	99.20 48	99.76 278	98.98 96	99.99 13	99.36 221

COLMAP_ROB	98.06 12	99.45 63	99.37 71	99.70 83	99.83 38	99.70 71	99.38 84	99.78 58	99.53 80	99.67 109	99.78 66	99.19 49	99.86 185	97.32 226	99.87 107	99.55 142
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TSAR-MVS + GP.		99.12 152	99.04 151	99.38 194	99.34 254	99.16 199	98.15 281	99.29 271	98.18 249	99.63 123	99.62 160	99.18 50	99.68 311	98.20 155	99.74 181	99.30 233

MVS_111021_HR		99.12 152	99.02 155	99.40 187	99.50 195	99.11 204	97.92 310	99.71 92	98.76 193	99.08 251	99.47 225	99.17 51	99.54 340	97.85 188	99.76 170	99.54 149

3Dnovator	99.15 2	99.43 65	99.36 74	99.65 99	99.39 234	99.42 142	99.70 23	99.56 176	99.23 127	99.35 206	99.80 53	99.17 51	99.95 43	98.21 154	99.84 121	99.59 125

UA-Net		99.78 14	99.76 15	99.86 17	99.72 108	99.71 64	99.91 4	99.95 4	99.96 3	99.71 98	99.91 20	99.15 53	99.97 17	99.50 32	100.00 1	99.90 5

baseline		99.63 31	99.62 26	99.66 94	99.80 57	99.62 95	99.44 75	99.80 47	99.71 42	99.72 93	99.69 113	99.15 53	99.83 230	99.32 52	99.94 61	99.53 154

OPM-MVS		99.26 111	99.13 117	99.63 110	99.70 118	99.61 101	98.58 242	99.48 216	98.50 215	99.52 166	99.63 151	99.14 55	99.76 278	97.89 181	99.77 168	99.51 166

Effi-MVS+		99.06 164	98.97 170	99.34 202	99.31 261	98.98 217	98.31 270	99.91 8	98.81 184	98.79 280	98.94 317	99.14 55	99.84 219	98.79 115	98.74 320	99.20 252

testing_2		99.58 38	99.56 43	99.62 119	99.81 51	99.44 134	99.14 156	99.43 232	99.69 48	99.82 50	99.79 59	99.14 55	99.79 264	99.31 55	99.95 48	99.63 93

v7n		99.82 11	99.80 11	99.88 12	99.96 4	99.84 18	99.82 9	99.82 37	99.84 22	99.94 12	99.91 20	99.13 58	99.96 34	99.83 9	99.99 13	99.83 19

nrg030		99.70 20	99.66 22	99.82 24	99.76 85	99.84 18	99.61 51	99.70 96	99.93 5	99.78 68	99.68 124	99.10 59	99.78 268	99.45 35	99.96 41	99.83 19

MSDG		99.08 162	98.98 169	99.37 197	99.60 147	99.13 202	97.54 326	99.74 77	98.84 182	99.53 164	99.55 201	99.10 59	99.79 264	97.07 247	99.86 114	99.18 256

v1240		99.56 42	99.58 36	99.51 153	99.80 57	99.00 215	99.00 189	99.65 124	99.15 142	99.90 23	99.75 80	99.09 61	99.88 152	99.90 2	99.96 41	99.67 64

abl_6		99.36 86	99.23 104	99.75 56	99.71 111	99.74 55	99.33 95	99.76 66	99.07 152	99.65 117	99.63 151	99.09 61	99.92 85	97.13 244	99.76 170	99.58 130

MVS_111021_LR		99.13 150	99.03 153	99.42 178	99.58 152	99.32 167	97.91 312	99.73 80	98.68 197	99.31 215	99.48 220	99.09 61	99.66 320	97.70 200	99.77 168	99.29 236

v1921920		99.56 42	99.57 39	99.55 143	99.75 95	99.11 204	99.05 180	99.61 142	99.15 142	99.88 33	99.71 100	99.08 64	99.87 165	99.90 2	99.97 30	99.66 74

v1192		99.57 39	99.57 39	99.57 136	99.77 81	99.22 190	99.04 182	99.60 153	99.18 133	99.87 38	99.72 93	99.08 64	99.85 203	99.89 5	99.98 22	99.66 74

test_0402		99.22 125	99.14 114	99.45 171	99.79 67	99.43 139	99.28 113	99.68 105	99.54 78	99.40 200	99.56 195	99.07 66	99.82 240	96.01 294	99.96 41	99.11 270

ACMP	97.51 14	99.05 167	98.84 190	99.67 87	99.78 73	99.55 115	98.88 207	99.66 113	97.11 302	99.47 176	99.60 176	99.07 66	99.89 137	96.18 289	99.85 117	99.58 130
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		98.76 211	98.57 216	99.33 204	99.57 162	98.97 219	97.53 328	99.55 181	96.41 314	99.27 221	99.13 289	99.07 66	99.78 268	96.73 265	99.89 91	99.23 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PVSNet_Blended_VisFu		99.40 75	99.38 68	99.44 173	99.90 20	98.66 243	98.94 204	99.91 8	97.97 259	99.79 65	99.73 87	99.05 69	99.97 17	99.15 78	99.99 13	99.68 57

canonicalmvs		99.02 173	99.00 161	99.09 241	99.10 300	98.70 240	99.61 51	99.66 113	99.63 63	98.64 292	97.65 352	99.04 70	99.54 340	98.79 115	98.92 309	99.04 286

SteuartSystems-ACMMP		99.30 102	99.14 114	99.76 46	99.87 28	99.66 82	99.18 141	99.60 153	98.55 209	99.57 146	99.67 130	99.03 71	99.94 54	97.01 248	99.80 153	99.69 51
Skip Steuart: Steuart Systems R&D Blog.
OPU-MVS				99.29 214	99.12 294	99.44 134	99.20 135				99.40 237	99.00 72	98.84 353	96.54 274	99.60 235	99.58 130

EI-MVSNet-UG-set		99.48 54	99.50 51	99.42 178	99.57 162	98.65 245	99.24 125	99.46 224	99.68 50	99.80 60	99.66 134	98.99 73	99.89 137	99.19 69	99.90 83	99.72 42

Fast-Effi-MVS+-dtu		99.20 132	99.12 121	99.43 176	99.25 274	99.69 75	99.05 180	99.82 37	99.50 83	98.97 259	99.05 300	98.98 74	99.98 6	98.20 155	99.24 295	98.62 311

FMVSNet1		99.66 25	99.63 25	99.73 69	99.78 73	99.77 41	99.68 32	99.70 96	99.67 52	99.82 50	99.83 42	98.98 74	99.90 123	99.24 63	99.97 30	99.53 154

EI-MVSNet-Vis-set		99.47 60	99.49 52	99.42 178	99.57 162	98.66 243	99.24 125	99.46 224	99.67 52	99.79 65	99.65 139	98.97 76	99.89 137	99.15 78	99.89 91	99.71 45

PHI-MVS		99.11 156	98.95 174	99.59 127	99.13 292	99.59 106	99.17 146	99.65 124	97.88 265	99.25 223	99.46 228	98.97 76	99.80 261	97.26 233	99.82 140	99.37 218

TinyColmap		98.97 183	98.93 175	99.07 245	99.46 216	98.19 268	97.75 317	99.75 72	98.79 187	99.54 160	99.70 107	98.97 76	99.62 331	96.63 271	99.83 131	99.41 208

Regformer-4		99.45 63	99.44 59	99.50 156	99.52 184	98.94 222	99.17 146	99.53 195	99.64 60	99.76 75	99.60 176	98.96 79	99.90 123	98.91 107	99.84 121	99.67 64

SMA-MVS		99.19 135	99.00 161	99.73 69	99.46 216	99.73 58	99.13 163	99.52 203	97.40 289	99.57 146	99.64 141	98.93 80	99.83 230	97.61 210	99.79 158	99.63 93
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
XVG-ACMP-BASELINE		99.23 116	99.10 132	99.63 110	99.82 44	99.58 109	98.83 216	99.72 89	98.36 229	99.60 138	99.71 100	98.92 81	99.91 103	97.08 246	99.84 121	99.40 210

CSCG		99.37 83	99.29 92	99.60 125	99.71 111	99.46 127	99.43 77	99.85 24	98.79 187	99.41 195	99.60 176	98.92 81	99.92 85	98.02 169	99.92 73	99.43 204

SED-MVS		99.40 75	99.28 94	99.77 40	99.69 121	99.82 26	99.20 135	99.54 186	99.13 144	99.82 50	99.63 151	98.91 83	99.92 85	97.85 188	99.70 200	99.58 130

test_241102_ONE					99.69 121	99.82 26		99.54 186	99.12 147	99.82 50	99.49 218	98.91 83	99.52 342

Gipuma		99.57 39	99.59 33	99.49 159	99.98 3	99.71 64	99.72 20	99.84 30	99.81 28	99.94 12	99.78 66	98.91 83	99.71 292	98.41 136	99.95 48	99.05 285
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-3		99.41 72	99.41 64	99.40 187	99.52 184	98.70 240	99.17 146	99.44 229	99.62 64	99.75 80	99.60 176	98.90 86	99.85 203	98.89 108	99.84 121	99.65 82

DeepC-MVS_fast	98.47 5	99.23 116	99.12 121	99.56 140	99.28 269	99.22 190	98.99 194	99.40 243	99.08 150	99.58 143	99.64 141	98.90 86	99.83 230	97.44 220	99.75 173	99.63 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ITE_SJBPF				99.38 194	99.63 141	99.44 134		99.73 80	98.56 207	99.33 211	99.53 205	98.88 88	99.68 311	96.01 294	99.65 220	99.02 289

xxxxxxxxxxxxxcwj		99.11 156	98.96 172	99.54 147	99.53 179	99.25 181	98.29 271	99.76 66	99.07 152	99.42 187	99.61 169	98.86 89	99.87 165	96.45 280	99.68 206	99.49 177

SF-MVS		99.10 160	98.93 175	99.62 119	99.58 152	99.51 118	99.13 163	99.65 124	97.97 259	99.42 187	99.61 169	98.86 89	99.87 165	96.45 280	99.68 206	99.49 177

tfpnnormal		99.43 65	99.38 68	99.60 125	99.87 28	99.75 49	99.59 56	99.78 58	99.71 42	99.90 23	99.69 113	98.85 91	99.90 123	97.25 236	99.78 164	99.15 262

ZNCC-MVS		99.22 125	99.04 151	99.77 40	99.76 85	99.73 58	99.28 113	99.56 176	98.19 248	99.14 244	99.29 265	98.84 92	99.92 85	97.53 216	99.80 153	99.64 88

MP-MVS-pluss		99.14 148	98.92 179	99.80 30	99.83 38	99.83 22	98.61 238	99.63 132	96.84 308	99.44 181	99.58 184	98.81 93	99.91 103	97.70 200	99.82 140	99.67 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VPA-MVSNet		99.66 25	99.62 26	99.79 35	99.68 129	99.75 49	99.62 47	99.69 102	99.85 20	99.80 60	99.81 51	98.81 93	99.91 103	99.47 34	99.88 99	99.70 48

test20.03		99.55 45	99.54 45	99.58 131	99.79 67	99.37 155	99.02 185	99.89 13	99.60 74	99.82 50	99.62 160	98.81 93	99.89 137	99.43 37	99.86 114	99.47 187

PGM-MVS		99.20 132	99.01 158	99.77 40	99.75 95	99.71 64	99.16 152	99.72 89	97.99 257	99.42 187	99.60 176	98.81 93	99.93 67	96.91 253	99.74 181	99.66 74

HFP-MVS		99.25 112	99.08 135	99.76 46	99.73 104	99.70 71	99.31 102	99.59 160	98.36 229	99.36 204	99.37 243	98.80 97	99.91 103	97.43 221	99.75 173	99.68 57

#test#		99.12 152	98.90 183	99.76 46	99.73 104	99.70 71	99.10 170	99.59 160	97.60 278	99.36 204	99.37 243	98.80 97	99.91 103	96.84 259	99.75 173	99.68 57

Regformer-2		99.34 93	99.27 97	99.53 149	99.41 230	99.10 208	98.99 194	99.53 195	99.47 90	99.66 113	99.52 207	98.80 97	99.89 137	98.31 145	99.74 181	99.60 116

APDe-MVS		99.48 54	99.36 74	99.85 19	99.55 173	99.81 29	99.50 65	99.69 102	98.99 159	99.75 80	99.71 100	98.79 100	99.93 67	98.46 135	99.85 117	99.80 24

CP-MVS		99.23 116	99.05 145	99.75 56	99.66 135	99.66 82	99.38 84	99.62 135	98.38 227	99.06 255	99.27 269	98.79 100	99.94 54	97.51 217	99.82 140	99.66 74

Regformer-1		99.32 99	99.27 97	99.47 164	99.41 230	98.95 221	98.99 194	99.48 216	99.48 85	99.66 113	99.52 207	98.78 102	99.87 165	98.36 139	99.74 181	99.60 116

MSLP-MVS++		99.05 167	99.09 133	98.91 260	99.21 279	98.36 261	98.82 220	99.47 220	98.85 179	98.90 269	99.56 195	98.78 102	99.09 351	98.57 130	99.68 206	99.26 239

MVS_Test		99.28 105	99.31 82	99.19 232	99.35 244	98.79 236	99.36 91	99.49 214	99.17 136	99.21 233	99.67 130	98.78 102	99.66 320	99.09 87	99.66 217	99.10 272

3Dnovator+	98.92 3	99.35 88	99.24 102	99.67 87	99.35 244	99.47 123	99.62 47	99.50 209	99.44 96	99.12 247	99.78 66	98.77 105	99.94 54	97.87 185	99.72 194	99.62 105

APD-MVS_3200maxsize		99.31 101	99.16 110	99.74 61	99.53 179	99.75 49	99.27 116	99.61 142	99.19 132	99.57 146	99.64 141	98.76 106	99.90 123	97.29 228	99.62 225	99.56 139

TranMVSNet+NR-MVSNet		99.54 47	99.47 53	99.76 46	99.58 152	99.64 89	99.30 105	99.63 132	99.61 68	99.71 98	99.56 195	98.76 106	99.96 34	99.14 84	99.92 73	99.68 57

EIA-MVS		99.12 152	99.01 158	99.45 171	99.36 242	99.62 95	99.34 93	99.79 53	98.41 223	98.84 274	98.89 322	98.75 108	99.84 219	98.15 163	99.51 255	98.89 298

ACMMP_NAP		99.28 105	99.11 124	99.79 35	99.75 95	99.81 29	98.95 202	99.53 195	98.27 243	99.53 164	99.73 87	98.75 108	99.87 165	97.70 200	99.83 131	99.68 57

v10		99.69 22	99.69 19	99.66 94	99.81 51	99.39 149	99.66 39	99.75 72	99.60 74	99.92 19	99.87 31	98.75 108	99.86 185	99.90 2	99.99 13	99.73 41

region2R		99.23 116	99.05 145	99.77 40	99.76 85	99.70 71	99.31 102	99.59 160	98.41 223	99.32 213	99.36 248	98.73 111	99.93 67	97.29 228	99.74 181	99.67 64

LS3D		99.24 115	99.11 124	99.61 123	98.38 344	99.79 36	99.57 59	99.68 105	99.61 68	99.15 242	99.71 100	98.70 112	99.91 103	97.54 214	99.68 206	99.13 269

DP-MVS		99.48 54	99.39 66	99.74 61	99.57 162	99.62 95	99.29 112	99.61 142	99.87 15	99.74 88	99.76 76	98.69 113	99.87 165	98.20 155	99.80 153	99.75 39

AllTest		99.21 130	99.07 139	99.63 110	99.78 73	99.64 89	99.12 167	99.83 32	98.63 201	99.63 123	99.72 93	98.68 114	99.75 282	96.38 283	99.83 131	99.51 166

TestCases				99.63 110	99.78 73	99.64 89		99.83 32	98.63 201	99.63 123	99.72 93	98.68 114	99.75 282	96.38 283	99.83 131	99.51 166

LCM-MVSNet-Re		99.28 105	99.15 113	99.67 87	99.33 259	99.76 47	99.34 93	99.97 2	98.93 169	99.91 21	99.79 59	98.68 114	99.93 67	96.80 261	99.56 240	99.30 233

v1144		99.54 47	99.53 49	99.59 127	99.79 67	99.28 173	99.10 170	99.61 142	99.20 131	99.84 43	99.73 87	98.67 117	99.84 219	99.86 8	99.98 22	99.64 88

DTE-MVSNet		99.68 23	99.61 30	99.88 12	99.80 57	99.87 9	99.67 36	99.71 92	99.72 41	99.84 43	99.78 66	98.67 117	99.97 17	99.30 56	99.95 48	99.80 24

v144192		99.55 45	99.54 45	99.58 131	99.78 73	99.20 196	99.11 169	99.62 135	99.18 133	99.89 27	99.72 93	98.66 119	99.87 165	99.88 6	99.97 30	99.66 74

v8		99.68 23	99.69 19	99.65 99	99.80 57	99.40 147	99.66 39	99.76 66	99.64 60	99.93 15	99.85 36	98.66 119	99.84 219	99.88 6	99.99 13	99.71 45

GST-MVS		99.16 144	98.96 172	99.75 56	99.73 104	99.73 58	99.20 135	99.55 181	98.22 245	99.32 213	99.35 253	98.65 121	99.91 103	96.86 256	99.74 181	99.62 105

ppachtmachnet_test		98.89 197	99.12 121	98.20 301	99.66 135	95.24 334	97.63 322	99.68 105	99.08 150	99.78 68	99.62 160	98.65 121	99.88 152	98.02 169	99.96 41	99.48 182

PS-CasMVS		99.66 25	99.58 36	99.89 8	99.80 57	99.85 13	99.66 39	99.73 80	99.62 64	99.84 43	99.71 100	98.62 123	99.96 34	99.30 56	99.96 41	99.86 12

LF4IMVS		99.01 177	98.92 179	99.27 218	99.71 111	99.28 173	98.59 241	99.77 61	98.32 240	99.39 201	99.41 235	98.62 123	99.84 219	96.62 272	99.84 121	98.69 309

ACMMPR		99.23 116	99.06 141	99.76 46	99.74 101	99.69 75	99.31 102	99.59 160	98.36 229	99.35 206	99.38 242	98.61 125	99.93 67	97.43 221	99.75 173	99.67 64

API-MVS		98.38 250	98.39 232	98.35 294	98.83 324	99.26 177	99.14 156	99.18 289	98.59 205	98.66 291	98.78 328	98.61 125	99.57 339	94.14 331	99.56 240	96.21 350

OMC-MVS		98.90 194	98.72 200	99.44 173	99.39 234	99.42 142	98.58 242	99.64 130	97.31 294	99.44 181	99.62 160	98.59 127	99.69 300	96.17 290	99.79 158	99.22 247

test_0728_THIRD									99.18 133	99.62 130	99.61 169	98.58 128	99.91 103	97.72 197	99.80 153	99.77 32

RE-MVS-def			99.13 117		99.54 174	99.74 55	99.26 117	99.62 135	99.16 138	99.52 166	99.64 141	98.57 129		97.27 231	99.61 232	99.54 149

ACMMP		99.25 112	99.08 135	99.74 61	99.79 67	99.68 78	99.50 65	99.65 124	98.07 253	99.52 166	99.69 113	98.57 129	99.92 85	97.18 241	99.79 158	99.63 93
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
PEN-MVS		99.66 25	99.59 33	99.89 8	99.83 38	99.87 9	99.66 39	99.73 80	99.70 45	99.84 43	99.73 87	98.56 131	99.96 34	99.29 59	99.94 61	99.83 19

V42		99.56 42	99.54 45	99.63 110	99.79 67	99.46 127	99.39 82	99.59 160	99.24 125	99.86 39	99.70 107	98.55 132	99.82 240	99.79 11	99.95 48	99.60 116

QAPM		98.40 249	97.99 260	99.65 99	99.39 234	99.47 123	99.67 36	99.52 203	91.70 347	98.78 282	99.80 53	98.55 132	99.95 43	94.71 325	99.75 173	99.53 154

EI-MVSNet		99.38 81	99.44 59	99.21 229	99.58 152	98.09 276	99.26 117	99.46 224	99.62 64	99.75 80	99.67 130	98.54 134	99.85 203	99.15 78	99.92 73	99.68 57

jason		99.16 144	99.11 124	99.32 208	99.75 95	98.44 254	98.26 274	99.39 246	98.70 196	99.74 88	99.30 262	98.54 134	99.97 17	98.48 134	99.82 140	99.55 142
jason: jason.
OurMVSNet-221017-0		99.75 16	99.71 17	99.84 20	99.96 4	99.83 22	99.83 7	99.85 24	99.80 31	99.93 15	99.93 14	98.54 134	99.93 67	99.59 21	99.98 22	99.76 36

IterMVS-LS		99.41 72	99.47 53	99.25 223	99.81 51	98.09 276	98.85 213	99.76 66	99.62 64	99.83 48	99.64 141	98.54 134	99.97 17	99.15 78	99.99 13	99.68 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
9.14			98.64 207		99.45 219		98.81 221	99.60 153	97.52 283	99.28 220	99.56 195	98.53 138	99.83 230	95.36 316	99.64 222

mPP-MVS		99.19 135	99.00 161	99.76 46	99.76 85	99.68 78	99.38 84	99.54 186	98.34 238	99.01 257	99.50 213	98.53 138	99.93 67	97.18 241	99.78 164	99.66 74

CNVR-MVS		98.99 182	98.80 196	99.56 140	99.25 274	99.43 139	98.54 251	99.27 275	98.58 206	98.80 279	99.43 233	98.53 138	99.70 294	97.22 238	99.59 237	99.54 149

PVSNet_BlendedMVS		99.03 171	99.01 158	99.09 241	99.54 174	97.99 280	98.58 242	99.82 37	97.62 277	99.34 209	99.71 100	98.52 141	99.77 276	97.98 174	99.97 30	99.52 164

PVSNet_Blended		98.70 218	98.59 212	99.02 249	99.54 174	97.99 280	97.58 325	99.82 37	95.70 326	99.34 209	98.98 310	98.52 141	99.77 276	97.98 174	99.83 131	99.30 233

MCST-MVS		99.02 173	98.81 194	99.65 99	99.58 152	99.49 120	98.58 242	99.07 296	98.40 225	99.04 256	99.25 273	98.51 143	99.80 261	97.31 227	99.51 255	99.65 82

UGNet		99.38 81	99.34 76	99.49 159	98.90 315	98.90 230	99.70 23	99.35 257	99.86 17	98.57 298	99.81 51	98.50 144	99.93 67	99.38 43	99.98 22	99.66 74
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
XVS		99.27 109	99.11 124	99.75 56	99.71 111	99.71 64	99.37 88	99.61 142	99.29 115	98.76 284	99.47 225	98.47 145	99.88 152	97.62 208	99.73 188	99.67 64

X-MVStestdata		96.09 317	94.87 325	99.75 56	99.71 111	99.71 64	99.37 88	99.61 142	99.29 115	98.76 284	61.30 362	98.47 145	99.88 152	97.62 208	99.73 188	99.67 64

diffmvs		99.34 93	99.32 81	99.39 190	99.67 134	98.77 237	98.57 246	99.81 46	99.61 68	99.48 174	99.41 235	98.47 145	99.86 185	98.97 98	99.90 83	99.53 154

ambc				99.20 231	99.35 244	98.53 248	99.17 146	99.46 224		99.67 109	99.80 53	98.46 148	99.70 294	97.92 179	99.70 200	99.38 215

FC-MVSNet-test		99.70 20	99.65 23	99.86 17	99.88 24	99.86 12	99.72 20	99.78 58	99.90 8	99.82 50	99.83 42	98.45 149	99.87 165	99.51 30	99.97 30	99.86 12

1314		98.00 272	97.90 273	98.27 300	98.90 315	97.45 299	99.30 105	99.06 298	94.98 334	97.21 344	99.12 293	98.43 150	99.67 316	95.58 310	98.56 327	97.71 342

USDC		98.96 186	98.93 175	99.05 247	99.54 174	97.99 280	97.07 343	99.80 47	98.21 246	99.75 80	99.77 73	98.43 150	99.64 329	97.90 180	99.88 99	99.51 166

CL-MVSNet_2432*1600		99.63 31	99.59 33	99.76 46	99.84 34	99.90 5	99.37 88	99.79 53	99.83 25	99.88 33	99.85 36	98.42 152	99.90 123	99.60 20	99.73 188	99.49 177

test1172		99.23 116	99.05 145	99.74 61	99.52 184	99.75 49	99.20 135	99.61 142	98.97 161	99.48 174	99.58 184	98.41 153	99.91 103	97.15 243	99.55 244	99.57 136

SR-MVS-dyc-post		99.27 109	99.11 124	99.73 69	99.54 174	99.74 55	99.26 117	99.62 135	99.16 138	99.52 166	99.64 141	98.41 153	99.91 103	97.27 231	99.61 232	99.54 149

v148		99.40 75	99.41 64	99.39 190	99.76 85	98.94 222	99.09 174	99.59 160	99.17 136	99.81 57	99.61 169	98.41 153	99.69 300	99.32 52	99.94 61	99.53 154

Test By Simon												98.41 153

PM-MVS		99.36 86	99.29 92	99.58 131	99.83 38	99.66 82	98.95 202	99.86 20	98.85 179	99.81 57	99.73 87	98.40 157	99.92 85	98.36 139	99.83 131	99.17 258

SR-MVS		99.19 135	99.00 161	99.74 61	99.51 189	99.72 62	99.18 141	99.60 153	98.85 179	99.47 176	99.58 184	98.38 158	99.92 85	96.92 252	99.54 250	99.57 136

segment_acmp												98.37 159

MP-MVS		99.06 164	98.83 192	99.76 46	99.76 85	99.71 64	99.32 98	99.50 209	98.35 234	98.97 259	99.48 220	98.37 159	99.92 85	95.95 300	99.75 173	99.63 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DVP-MVS		99.32 99	99.17 109	99.77 40	99.69 121	99.80 34	99.14 156	99.31 266	99.16 138	99.62 130	99.61 169	98.35 161	99.91 103	97.88 182	99.72 194	99.61 112

test0726					99.69 121	99.80 34	99.24 125	99.57 171	99.16 138	99.73 92	99.65 139	98.35 161

MVS		95.72 323	94.63 327	98.99 250	98.56 340	97.98 285	99.30 105	98.86 304	72.71 356	97.30 341	99.08 297	98.34 163	99.74 284	89.21 346	98.33 332	99.26 239

CDPH-MVS		98.56 230	98.20 247	99.61 123	99.50 195	99.46 127	98.32 269	99.41 236	95.22 331	99.21 233	99.10 296	98.34 163	99.82 240	95.09 320	99.66 217	99.56 139

testdata				99.42 178	99.51 189	98.93 226		99.30 269	96.20 318	98.87 271	99.40 237	98.33 165	99.89 137	96.29 286	99.28 289	99.44 198

test_241102_TWO								99.54 186	99.13 144	99.76 75	99.63 151	98.32 166	99.92 85	97.85 188	99.69 203	99.75 39

APD-MVS		98.87 200	98.59 212	99.71 79	99.50 195	99.62 95	99.01 187	99.57 171	96.80 310	99.54 160	99.63 151	98.29 167	99.91 103	95.24 317	99.71 198	99.61 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.12 10	98.23 262	97.89 274	99.26 220	99.19 284	99.26 177	99.65 44	99.69 102	91.33 348	98.14 320	99.77 73	98.28 168	99.96 34	95.41 314	99.55 244	98.58 315

FIs		99.65 30	99.58 36	99.84 20	99.84 34	99.85 13	99.66 39	99.75 72	99.86 17	99.74 88	99.79 59	98.27 169	99.85 203	99.37 45	99.93 69	99.83 19

TAPA-MVS	97.92 13	98.03 270	97.55 284	99.46 167	99.47 211	99.44 134	98.50 255	99.62 135	86.79 351	99.07 254	99.26 271	98.26 170	99.62 331	97.28 230	99.73 188	99.31 232
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v2v482		99.50 50	99.47 53	99.58 131	99.78 73	99.25 181	99.14 156	99.58 169	99.25 123	99.81 57	99.62 160	98.24 171	99.84 219	99.83 9	99.97 30	99.64 88

pmmvs4		99.13 150	99.06 141	99.36 200	99.57 162	99.10 208	98.01 297	99.25 280	98.78 189	99.58 143	99.44 232	98.24 171	99.76 278	98.74 120	99.93 69	99.22 247

mvs_anonymous		99.28 105	99.39 66	98.94 254	99.19 284	97.81 288	99.02 185	99.55 181	99.78 34	99.85 40	99.80 53	98.24 171	99.86 185	99.57 24	99.50 257	99.15 262

DPE-MVS		99.14 148	98.92 179	99.82 24	99.57 162	99.77 41	98.74 231	99.60 153	98.55 209	99.76 75	99.69 113	98.23 174	99.92 85	96.39 282	99.75 173	99.76 36

zzz-MVS		99.30 102	99.14 114	99.80 30	99.81 51	99.81 29	98.73 233	99.53 195	99.27 119	99.42 187	99.63 151	98.21 175	99.95 43	97.83 191	99.79 158	99.65 82

MTAPA		99.35 88	99.20 106	99.80 30	99.81 51	99.81 29	99.33 95	99.53 195	99.27 119	99.42 187	99.63 151	98.21 175	99.95 43	97.83 191	99.79 158	99.65 82

MS-PatchMatch		99.00 179	98.97 170	99.09 241	99.11 299	98.19 268	98.76 230	99.33 260	98.49 217	99.44 181	99.58 184	98.21 175	99.69 300	98.20 155	99.62 225	99.39 213

our_test_3		98.85 202	99.09 133	98.13 303	99.66 135	94.90 337	97.72 318	99.58 169	99.07 152	99.64 119	99.62 160	98.19 178	99.93 67	98.41 136	99.95 48	99.55 142

MVP-Stereo		99.16 144	99.08 135	99.43 176	99.48 206	99.07 212	99.08 177	99.55 181	98.63 201	99.31 215	99.68 124	98.19 178	99.78 268	98.18 159	99.58 238	99.45 193
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WR-MVS_H		99.61 36	99.53 49	99.87 15	99.80 57	99.83 22	99.67 36	99.75 72	99.58 77	99.85 40	99.69 113	98.18 180	99.94 54	99.28 61	99.95 48	99.83 19

new_pmnet		98.88 198	98.89 184	98.84 270	99.70 118	97.62 294	98.15 281	99.50 209	97.98 258	99.62 130	99.54 203	98.15 181	99.94 54	97.55 213	99.84 121	98.95 293

D2MVS		99.22 125	99.19 107	99.29 214	99.69 121	98.74 238	98.81 221	99.41 236	98.55 209	99.68 105	99.69 113	98.13 182	99.87 165	98.82 113	99.98 22	99.24 242

Anonymous20240529		99.42 68	99.34 76	99.65 99	99.53 179	99.60 103	99.63 46	99.39 246	99.47 90	99.76 75	99.78 66	98.13 182	99.86 185	98.70 123	99.68 206	99.49 177

EU-MVSNet		99.39 79	99.62 26	98.72 280	99.88 24	96.44 319	99.56 61	99.85 24	99.90 8	99.90 23	99.85 36	98.09 184	99.83 230	99.58 23	99.95 48	99.90 5

PMVS	92.94 21	98.82 205	98.81 194	98.85 268	99.84 34	97.99 280	99.20 135	99.47 220	99.71 42	99.42 187	99.82 48	98.09 184	99.47 345	93.88 336	99.85 117	99.07 283
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
HPM-MVS++		98.96 186	98.70 204	99.74 61	99.52 184	99.71 64	98.86 211	99.19 288	98.47 219	98.59 296	99.06 299	98.08 186	99.91 103	96.94 251	99.60 235	99.60 116

ab-mvs		99.33 97	99.28 94	99.47 164	99.57 162	99.39 149	99.78 11	99.43 232	98.87 177	99.57 146	99.82 48	98.06 187	99.87 165	98.69 125	99.73 188	99.15 262

agg_prior1		98.33 256	97.92 270	99.57 136	99.35 244	99.36 158	97.99 301	99.39 246	94.85 338	97.76 337	98.98 310	98.03 188	99.85 203	95.49 311	99.44 265	99.51 166

N_pmnet		98.73 216	98.53 221	99.35 201	99.72 108	98.67 242	98.34 266	94.65 352	98.35 234	99.79 65	99.68 124	98.03 188	99.93 67	98.28 148	99.92 73	99.44 198

TEST9					99.35 244	99.35 162	98.11 287	99.41 236	94.83 339	97.92 328	98.99 307	98.02 190	99.85 203

train_agg		98.35 254	97.95 264	99.57 136	99.35 244	99.35 162	98.11 287	99.41 236	94.90 335	97.92 328	98.99 307	98.02 190	99.85 203	95.38 315	99.44 265	99.50 172

test_8					99.34 254	99.31 168	98.08 291	99.40 243	94.90 335	97.87 332	98.97 313	98.02 190	99.84 219

MVSFormer		99.41 72	99.44 59	99.31 211	99.57 162	98.40 257	99.77 12	99.80 47	99.73 38	99.63 123	99.30 262	98.02 190	99.98 6	99.43 37	99.69 203	99.55 142

lupinMVS		98.96 186	98.87 186	99.24 226	99.57 162	98.40 257	98.12 285	99.18 289	98.28 242	99.63 123	99.13 289	98.02 190	99.97 17	98.22 153	99.69 203	99.35 224

Anonymous20231211		99.62 34	99.57 39	99.76 46	99.61 145	99.60 103	99.81 10	99.73 80	99.82 27	99.90 23	99.90 22	97.97 195	99.86 185	99.42 41	99.96 41	99.80 24

MIMVSNet1		99.66 25	99.62 26	99.80 30	99.94 10	99.87 9	99.69 29	99.77 61	99.78 34	99.93 15	99.89 26	97.94 196	99.92 85	99.65 17	99.98 22	99.62 105

原ACMM1				99.37 197	99.47 211	98.87 233		99.27 275	96.74 311	98.26 311	99.32 258	97.93 197	99.82 240	95.96 299	99.38 275	99.43 204

ETH3D-3000-0.1		98.77 209	98.50 223	99.59 127	99.47 211	99.53 117	98.77 229	99.60 153	97.33 293	99.23 227	99.50 213	97.91 198	99.83 230	95.02 321	99.67 213	99.41 208

test_prior3		98.62 222	98.34 238	99.46 167	99.35 244	99.22 190	97.95 306	99.39 246	97.87 266	98.05 322	99.05 300	97.90 199	99.69 300	95.99 296	99.49 259	99.48 182

test_prior2							97.95 306		97.87 266	98.05 322	99.05 300	97.90 199		95.99 296	99.49 259

RPSCF		99.18 139	99.02 155	99.64 106	99.83 38	99.85 13	99.44 75	99.82 37	98.33 239	99.50 172	99.78 66	97.90 199	99.65 327	96.78 262	99.83 131	99.44 198

PMMVS		98.49 240	98.29 241	99.11 239	98.96 312	98.42 256	97.54 326	99.32 262	97.53 282	98.47 305	98.15 347	97.88 202	99.82 240	97.46 219	99.24 295	99.09 275

ZD-MVS					99.43 224	99.61 101		99.43 232	96.38 315	99.11 248	99.07 298	97.86 203	99.92 85	94.04 333	99.49 259

NCCC		98.82 205	98.57 216	99.58 131	99.21 279	99.31 168	98.61 238	99.25 280	98.65 199	98.43 306	99.26 271	97.86 203	99.81 256	96.55 273	99.27 292	99.61 112

UniMVSNet_NR-MVSNet		99.37 83	99.25 101	99.72 75	99.47 211	99.56 112	98.97 200	99.61 142	99.43 101	99.67 109	99.28 267	97.85 205	99.95 43	99.17 74	99.81 148	99.65 82

TAMVS		99.49 52	99.45 57	99.63 110	99.48 206	99.42 142	99.45 72	99.57 171	99.66 56	99.78 68	99.83 42	97.85 205	99.86 185	99.44 36	99.96 41	99.61 112

DP-MVS Recon		98.50 237	98.23 244	99.31 211	99.49 200	99.46 127	98.56 247	99.63 132	94.86 337	98.85 273	99.37 243	97.81 207	99.59 337	96.08 291	99.44 265	98.88 299

PatchMatch-RL		98.68 219	98.47 224	99.30 213	99.44 222	99.28 173	98.14 283	99.54 186	97.12 301	99.11 248	99.25 273	97.80 208	99.70 294	96.51 276	99.30 287	98.93 295

CP-MVSNet		99.54 47	99.43 62	99.87 15	99.76 85	99.82 26	99.57 59	99.61 142	99.54 78	99.80 60	99.64 141	97.79 209	99.95 43	99.21 64	99.94 61	99.84 15

DPM-MVS		98.28 257	97.94 268	99.32 208	99.36 242	99.11 204	97.31 338	98.78 309	96.88 305	98.84 274	99.11 295	97.77 210	99.61 335	94.03 334	99.36 280	99.23 245

114514_t		98.49 240	98.11 255	99.64 106	99.73 104	99.58 109	99.24 125	99.76 66	89.94 350	99.42 187	99.56 195	97.76 211	99.86 185	97.74 196	99.82 140	99.47 187

tmp_tt		95.75 322	95.42 322	96.76 328	89.90 360	94.42 339	98.86 211	97.87 335	78.01 354	99.30 219	99.69 113	97.70 212	95.89 356	99.29 59	98.14 338	99.95 1

UniMVSNet (Re)		99.37 83	99.26 99	99.68 85	99.51 189	99.58 109	98.98 198	99.60 153	99.43 101	99.70 100	99.36 248	97.70 212	99.88 152	99.20 67	99.87 107	99.59 125

Effi-MVS+-dtu		99.07 163	98.92 179	99.52 150	98.89 318	99.78 39	99.15 154	99.66 113	99.34 109	98.92 266	99.24 278	97.69 214	99.98 6	98.11 165	99.28 289	98.81 305

mvs-test1		98.83 203	98.70 204	99.22 228	98.89 318	99.65 87	98.88 207	99.66 113	99.34 109	98.29 309	98.94 317	97.69 214	99.96 34	98.11 165	98.54 328	98.04 338

F-COLMAP		98.74 214	98.45 226	99.62 119	99.57 162	99.47 123	98.84 214	99.65 124	96.31 317	98.93 263	99.19 286	97.68 216	99.87 165	96.52 275	99.37 279	99.53 154

新几何1				99.52 150	99.50 195	99.22 190		99.26 277	95.66 327	98.60 295	99.28 267	97.67 217	99.89 137	95.95 300	99.32 285	99.45 193

旧先验1					99.49 200	99.29 171		99.26 277			99.39 241	97.67 217			99.36 280	99.46 191

DU-MVS		99.33 97	99.21 105	99.71 79	99.43 224	99.56 112	98.83 216	99.53 195	99.38 105	99.67 109	99.36 248	97.67 217	99.95 43	99.17 74	99.81 148	99.63 93

Baseline_NR-MVSNet		99.49 52	99.37 71	99.82 24	99.91 15	99.84 18	98.83 216	99.86 20	99.68 50	99.65 117	99.88 29	97.67 217	99.87 165	99.03 91	99.86 114	99.76 36

CANet		99.11 156	99.05 145	99.28 216	98.83 324	98.56 247	98.71 236	99.41 236	99.25 123	99.23 227	99.22 280	97.66 221	99.94 54	99.19 69	99.97 30	99.33 227

VPNet		99.46 61	99.37 71	99.71 79	99.82 44	99.59 106	99.48 69	99.70 96	99.81 28	99.69 103	99.58 184	97.66 221	99.86 185	99.17 74	99.44 265	99.67 64

Anonymous20231206		99.35 88	99.31 82	99.47 164	99.74 101	99.06 214	99.28 113	99.74 77	99.23 127	99.72 93	99.53 205	97.63 223	99.88 152	99.11 86	99.84 121	99.48 182

ETH3D cwj APD-0.16		98.50 237	98.16 253	99.51 153	99.04 307	99.39 149	98.47 257	99.47 220	96.70 312	98.78 282	99.33 257	97.62 224	99.86 185	94.69 326	99.38 275	99.28 238

test12				99.54 147	99.29 267	99.33 165		99.16 291		98.43 306		97.54 225	99.82 240		99.47 262	99.48 182

NR-MVSNet		99.40 75	99.31 82	99.68 85	99.43 224	99.55 115	99.73 17	99.50 209	99.46 94	99.88 33	99.36 248	97.54 225	99.87 165	98.97 98	99.87 107	99.63 93

MAR-MVS		98.24 261	97.92 270	99.19 232	98.78 332	99.65 87	99.17 146	99.14 293	95.36 329	98.04 324	98.81 327	97.47 227	99.72 288	95.47 313	99.06 301	98.21 332
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
CHOSEN 1792x2688		99.39 79	99.30 87	99.65 99	99.88 24	99.25 181	98.78 228	99.88 16	98.66 198	99.96 9	99.79 59	97.45 228	99.93 67	99.34 48	99.99 13	99.78 31

PAPR		97.56 286	97.07 295	99.04 248	98.80 329	98.11 274	97.63 322	99.25 280	94.56 341	98.02 326	98.25 346	97.43 229	99.68 311	90.90 345	98.74 320	99.33 227

YYNet1		98.95 189	98.99 166	98.84 270	99.64 139	97.14 307	98.22 277	99.32 262	98.92 171	99.59 141	99.66 134	97.40 230	99.83 230	98.27 149	99.90 83	99.55 142

PVSNet	97.47 15	98.42 246	98.44 227	98.35 294	99.46 216	96.26 321	96.70 348	99.34 259	97.68 275	99.00 258	99.13 289	97.40 230	99.72 288	97.59 212	99.68 206	99.08 278

1121		98.56 230	98.24 243	99.52 150	99.49 200	99.24 186	99.30 105	99.22 285	95.77 324	98.52 301	99.29 265	97.39 232	99.85 203	95.79 305	99.34 282	99.46 191

MDA-MVSNet_test_wron		98.95 189	98.99 166	98.85 268	99.64 139	97.16 306	98.23 276	99.33 260	98.93 169	99.56 153	99.66 134	97.39 232	99.83 230	98.29 147	99.88 99	99.55 142

MG-MVS		98.52 236	98.39 232	98.94 254	99.15 289	97.39 301	98.18 278	99.21 287	98.89 176	99.23 227	99.63 151	97.37 234	99.74 284	94.22 330	99.61 232	99.69 51

OpenMVS_ROB	97.31 17	97.36 292	96.84 303	98.89 267	99.29 267	99.45 132	98.87 210	99.48 216	86.54 353	99.44 181	99.74 83	97.34 235	99.86 185	91.61 341	99.28 289	97.37 346

AdaColmap		98.60 224	98.35 237	99.38 194	99.12 294	99.22 190	98.67 237	99.42 235	97.84 270	98.81 277	99.27 269	97.32 236	99.81 256	95.14 318	99.53 252	99.10 272

test222					99.51 189	99.08 211	97.83 315	99.29 271	95.21 332	98.68 290	99.31 260	97.28 237			99.38 275	99.43 204

HQP_MVS		98.90 194	98.68 206	99.55 143	99.58 152	99.24 186	98.80 224	99.54 186	98.94 166	99.14 244	99.25 273	97.24 238	99.82 240	95.84 303	99.78 164	99.60 116

plane_prior6					99.47 211	99.26 177						97.24 238

GBi-Net		99.42 68	99.31 82	99.73 69	99.49 200	99.77 41	99.68 32	99.70 96	99.44 96	99.62 130	99.83 42	97.21 240	99.90 123	98.96 100	99.90 83	99.53 154

test1		99.42 68	99.31 82	99.73 69	99.49 200	99.77 41	99.68 32	99.70 96	99.44 96	99.62 130	99.83 42	97.21 240	99.90 123	98.96 100	99.90 83	99.53 154

FMVSNet2		99.35 88	99.28 94	99.55 143	99.49 200	99.35 162	99.45 72	99.57 171	99.44 96	99.70 100	99.74 83	97.21 240	99.87 165	99.03 91	99.94 61	99.44 198

BH-RMVSNet		98.41 247	98.14 254	99.21 229	99.21 279	98.47 251	98.60 240	98.26 329	98.35 234	98.93 263	99.31 260	97.20 243	99.66 320	94.32 328	99.10 300	99.51 166

MVS-HIRNet		97.86 274	98.22 245	96.76 328	99.28 269	91.53 354	98.38 265	92.60 357	99.13 144	99.31 215	99.96 11	97.18 244	99.68 311	98.34 142	99.83 131	99.07 283

PAPM_NR		98.36 251	98.04 258	99.33 204	99.48 206	98.93 226	98.79 227	99.28 274	97.54 281	98.56 299	98.57 335	97.12 245	99.69 300	94.09 332	98.90 311	99.38 215

CPTT-MVS		98.74 214	98.44 227	99.64 106	99.61 145	99.38 152	99.18 141	99.55 181	96.49 313	99.27 221	99.37 243	97.11 246	99.92 85	95.74 307	99.67 213	99.62 105

testtj		98.56 230	98.17 252	99.72 75	99.45 219	99.60 103	98.88 207	99.50 209	96.88 305	99.18 239	99.48 220	97.08 247	99.92 85	93.69 337	99.38 275	99.63 93

CNLPA		98.57 229	98.34 238	99.28 216	99.18 286	99.10 208	98.34 266	99.41 236	98.48 218	98.52 301	98.98 310	97.05 248	99.78 268	95.59 309	99.50 257	98.96 292

BH-untuned		98.22 263	98.09 256	98.58 286	99.38 237	97.24 304	98.55 248	98.98 302	97.81 271	99.20 238	98.76 329	97.01 249	99.65 327	94.83 322	98.33 332	98.86 301

VDD-MVS		99.20 132	99.11 124	99.44 173	99.43 224	98.98 217	99.50 65	98.32 328	99.80 31	99.56 153	99.69 113	96.99 250	99.85 203	98.99 94	99.73 188	99.50 172

PLC	97.35 16	98.36 251	97.99 260	99.48 162	99.32 260	99.24 186	98.50 255	99.51 206	95.19 333	98.58 297	98.96 315	96.95 251	99.83 230	95.63 308	99.25 293	99.37 218
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		99.11 156	98.93 175	99.66 94	99.30 265	99.42 142	98.42 263	99.37 253	99.04 157	99.57 146	99.20 284	96.89 252	99.86 185	98.66 127	99.87 107	99.70 48

MSP-MVS		99.04 170	98.79 197	99.81 27	99.78 73	99.73 58	99.35 92	99.57 171	98.54 212	99.54 160	98.99 307	96.81 253	99.93 67	96.97 250	99.53 252	99.77 32

HQP2-MVS												96.67 254

HQP-MVS		98.36 251	98.02 259	99.39 190	99.31 261	98.94 222	97.98 302	99.37 253	97.45 286	98.15 316	98.83 325	96.67 254	99.70 294	94.73 323	99.67 213	99.53 154

CANet_DTU		98.91 192	98.85 188	99.09 241	98.79 330	98.13 271	98.18 278	99.31 266	99.48 85	98.86 272	99.51 210	96.56 256	99.95 43	99.05 90	99.95 48	99.19 254

pmmvs5		99.19 135	99.11 124	99.42 178	99.76 85	98.88 231	98.55 248	99.73 80	98.82 183	99.72 93	99.62 160	96.56 256	99.82 240	99.32 52	99.95 48	99.56 139

MVE	92.54 22	96.66 307	96.11 311	98.31 298	99.68 129	97.55 296	97.94 308	95.60 350	99.37 106	90.68 357	98.70 331	96.56 256	98.61 355	86.94 354	99.55 244	98.77 307
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
VNet		99.18 139	99.06 141	99.56 140	99.24 276	99.36 158	99.33 95	99.31 266	99.67 52	99.47 176	99.57 192	96.48 259	99.84 219	99.15 78	99.30 287	99.47 187

MDA-MVSNet-bldmvs		99.06 164	99.05 145	99.07 245	99.80 57	97.83 287	98.89 206	99.72 89	99.29 115	99.63 123	99.70 107	96.47 260	99.89 137	98.17 161	99.82 140	99.50 172

DeepMVS_CX				97.98 305	99.69 121	96.95 310		99.26 277	75.51 355	95.74 353	98.28 345	96.47 260	99.62 331	91.23 343	97.89 343	97.38 345

1112_ss		99.05 167	98.84 190	99.67 87	99.66 135	99.29 171	98.52 253	99.82 37	97.65 276	99.43 185	99.16 287	96.42 262	99.91 103	99.07 89	99.84 121	99.80 24

TR-MVS		97.44 289	97.15 294	98.32 296	98.53 341	97.46 298	98.47 257	97.91 334	96.85 307	98.21 315	98.51 339	96.42 262	99.51 343	92.16 340	97.29 347	97.98 339

miper_ehance_all_eth		98.59 227	98.59 212	98.59 285	98.98 311	97.07 308	97.49 331	99.52 203	98.50 215	99.52 166	99.37 243	96.41 264	99.71 292	97.86 186	99.62 225	99.00 291

cl_fuxian		98.72 217	98.71 201	98.72 280	99.12 294	97.22 305	97.68 321	99.56 176	98.90 173	99.54 160	99.48 220	96.37 265	99.73 286	97.88 182	99.88 99	99.21 249

sss		98.90 194	98.77 198	99.27 218	99.48 206	98.44 254	98.72 234	99.32 262	97.94 263	99.37 203	99.35 253	96.31 266	99.91 103	98.85 110	99.63 224	99.47 187

CDS-MVSNet		99.22 125	99.13 117	99.50 156	99.35 244	99.11 204	98.96 201	99.54 186	99.46 94	99.61 136	99.70 107	96.31 266	99.83 230	99.34 48	99.88 99	99.55 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
eth_miper_zixun_eth		98.68 219	98.71 201	98.60 284	99.10 300	96.84 314	97.52 330	99.54 186	98.94 166	99.58 143	99.48 220	96.25 268	99.76 278	98.01 172	99.93 69	99.21 249

SixPastTwentyTwo		99.42 68	99.30 87	99.76 46	99.92 14	99.67 80	99.70 23	99.14 293	99.65 58	99.89 27	99.90 22	96.20 269	99.94 54	99.42 41	99.92 73	99.67 64

MVS_0304		98.88 198	98.71 201	99.39 190	98.85 322	98.91 229	99.45 72	99.30 269	98.56 207	97.26 343	99.68 124	96.18 270	99.96 34	99.17 74	99.94 61	99.29 236

Test_1112_low_res		98.95 189	98.73 199	99.63 110	99.68 129	99.15 201	98.09 289	99.80 47	97.14 300	99.46 179	99.40 237	96.11 271	99.89 137	99.01 93	99.84 121	99.84 15

IterMVS		98.97 183	99.16 110	98.42 291	99.74 101	95.64 330	98.06 294	99.83 32	99.83 25	99.85 40	99.74 83	96.10 272	99.99 4	99.27 62	100.00 1	99.63 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		99.00 179	99.16 110	98.51 287	99.75 95	95.90 327	98.07 292	99.84 30	99.84 22	99.89 27	99.73 87	96.01 273	99.99 4	99.33 50	100.00 1	99.63 93

SCA		98.11 266	98.36 235	97.36 322	99.20 282	92.99 345	98.17 280	98.49 322	98.24 244	99.10 250	99.57 192	96.01 273	99.94 54	96.86 256	99.62 225	99.14 266

ETH3 D test6400		97.76 278	97.19 293	99.50 156	99.38 237	99.26 177	98.34 266	99.49 214	92.99 344	98.54 300	99.20 284	95.92 275	99.82 240	91.14 344	99.66 217	99.40 210

PVSNet_0	95.53 19	95.85 321	95.31 323	97.47 319	98.78 332	93.48 343	95.72 351	99.40 243	96.18 319	97.37 340	97.73 351	95.73 276	99.58 338	95.49 311	81.40 355	99.36 221

CMPMVS	77.52 23	98.50 237	98.19 250	99.41 185	98.33 346	99.56 112	99.01 187	99.59 160	95.44 328	99.57 146	99.80 53	95.64 277	99.46 347	96.47 279	99.92 73	99.21 249
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
BH-w/o		97.20 294	97.01 297	97.76 312	99.08 303	95.69 329	98.03 296	98.52 319	95.76 325	97.96 327	98.02 348	95.62 278	99.47 345	92.82 339	97.25 348	98.12 336

cascas		96.99 298	96.82 304	97.48 318	97.57 357	95.64 330	96.43 350	99.56 176	91.75 346	97.13 346	97.61 353	95.58 279	98.63 354	96.68 267	99.11 299	98.18 335

UnsupCasMVSNet_bld		98.55 233	98.27 242	99.40 187	99.56 172	99.37 155	97.97 305	99.68 105	97.49 285	99.08 251	99.35 253	95.41 280	99.82 240	97.70 200	98.19 336	99.01 290

UnsupCasMVSNet_eth		98.83 203	98.57 216	99.59 127	99.68 129	99.45 132	98.99 194	99.67 109	99.48 85	99.55 158	99.36 248	94.92 281	99.86 185	98.95 104	96.57 349	99.45 193

EPP-MVSNet		99.17 143	99.00 161	99.66 94	99.80 57	99.43 139	99.70 23	99.24 283	99.48 85	99.56 153	99.77 73	94.89 282	99.93 67	98.72 122	99.89 91	99.63 93

WTY-MVS		98.59 227	98.37 234	99.26 220	99.43 224	98.40 257	98.74 231	99.13 295	98.10 251	99.21 233	99.24 278	94.82 283	99.90 123	97.86 186	98.77 316	99.49 177

miper_enhance_ethall		98.03 270	97.94 268	98.32 296	98.27 347	96.43 320	96.95 344	99.41 236	96.37 316	99.43 185	98.96 315	94.74 284	99.69 300	97.71 198	99.62 225	98.83 304

IS-MVSNet		99.03 171	98.85 188	99.55 143	99.80 57	99.25 181	99.73 17	99.15 292	99.37 106	99.61 136	99.71 100	94.73 285	99.81 256	97.70 200	99.88 99	99.58 130

miper_lstm_enhance		98.65 221	98.60 210	98.82 275	99.20 282	97.33 302	97.78 316	99.66 113	99.01 158	99.59 141	99.50 213	94.62 286	99.85 203	98.12 164	99.90 83	99.26 239

lessismore_v0				99.64 106	99.86 30	99.38 152		90.66 358		99.89 27	99.83 42	94.56 287	99.97 17	99.56 25	99.92 73	99.57 136

PCF-MVS	96.03 18	96.73 305	95.86 316	99.33 204	99.44 222	99.16 199	96.87 346	99.44 229	86.58 352	98.95 261	99.40 237	94.38 288	99.88 152	87.93 349	99.80 153	98.95 293
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDDNet		98.97 183	98.82 193	99.42 178	99.71 111	98.81 234	99.62 47	98.68 312	99.81 28	99.38 202	99.80 53	94.25 289	99.85 203	98.79 115	99.32 285	99.59 125

HY-MVS	98.23 9	98.21 264	97.95 264	98.99 250	99.03 308	98.24 264	99.61 51	98.72 311	96.81 309	98.73 286	99.51 210	94.06 290	99.86 185	96.91 253	98.20 334	98.86 301

cl-mvsnet1		98.54 234	98.42 229	98.92 258	99.03 308	97.80 289	97.46 332	99.59 160	98.90 173	99.60 138	99.46 228	93.87 291	99.78 268	97.97 176	99.89 91	99.18 256

cl-mvsnet_		98.54 234	98.41 230	98.92 258	99.03 308	97.80 289	97.46 332	99.59 160	98.90 173	99.60 138	99.46 228	93.85 292	99.78 268	97.97 176	99.89 91	99.17 258

EMVS		96.96 300	97.28 288	95.99 338	98.76 334	91.03 356	95.26 353	98.61 316	99.34 109	98.92 266	98.88 323	93.79 293	99.66 320	92.87 338	99.05 302	97.30 347

EPNet_dtu		97.62 283	97.79 277	97.11 327	96.67 358	92.31 348	98.51 254	98.04 330	99.24 125	95.77 352	99.47 225	93.78 294	99.66 320	98.98 96	99.62 225	99.37 218
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
K. test v3		98.87 200	98.60 210	99.69 84	99.93 13	99.46 127	99.74 16	94.97 351	99.78 34	99.88 33	99.88 29	93.66 295	99.97 17	99.61 19	99.95 48	99.64 88

CHOSEN 280x420		98.41 247	98.41 230	98.40 292	99.34 254	95.89 328	96.94 345	99.44 229	98.80 186	99.25 223	99.52 207	93.51 296	99.98 6	98.94 105	99.98 22	99.32 230

CVMVSNet		98.61 223	98.88 185	97.80 311	99.58 152	93.60 342	99.26 117	99.64 130	99.66 56	99.72 93	99.67 130	93.26 297	99.93 67	99.30 56	99.81 148	99.87 10

Anonymous202405211		98.75 212	98.46 225	99.63 110	99.34 254	99.66 82	99.47 71	97.65 337	99.28 118	99.56 153	99.50 213	93.15 298	99.84 219	98.62 128	99.58 238	99.40 210

EPNet		98.13 265	97.77 278	99.18 234	94.57 359	97.99 280	99.24 125	97.96 332	99.74 37	97.29 342	99.62 160	93.13 299	99.97 17	98.59 129	99.83 131	99.58 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM		95.61 324	94.71 326	98.31 298	99.12 294	96.63 316	96.66 349	98.46 323	90.77 349	96.25 349	98.68 332	93.01 300	99.69 300	81.60 355	97.86 344	98.62 311

Vis-MVSNet (Re-imp)		98.77 209	98.58 215	99.34 202	99.78 73	98.88 231	99.61 51	99.56 176	99.11 148	99.24 226	99.56 195	93.00 301	99.78 268	97.43 221	99.89 91	99.35 224

E-PMN		97.14 297	97.43 285	96.27 335	98.79 330	91.62 353	95.54 352	99.01 301	99.44 96	98.88 270	99.12 293	92.78 302	99.68 311	94.30 329	99.03 304	97.50 343

FMVSNet3		98.80 207	98.63 209	99.32 208	99.13 292	98.72 239	99.10 170	99.48 216	99.23 127	99.62 130	99.64 141	92.57 303	99.86 185	98.96 100	99.90 83	99.39 213

HyFIR lowres test		98.91 192	98.64 207	99.73 69	99.85 33	99.47 123	98.07 292	99.83 32	98.64 200	99.89 27	99.60 176	92.57 303	100.00 1	99.33 50	99.97 30	99.72 42

RPMNet		98.60 224	98.53 221	98.83 272	99.05 305	98.12 272	99.30 105	99.62 135	99.86 17	99.16 240	99.74 83	92.53 305	99.92 85	98.75 119	98.77 316	98.44 322

tpmvs		97.39 290	97.69 280	96.52 333	98.41 343	91.76 351	99.30 105	98.94 303	97.74 272	97.85 333	99.55 201	92.40 306	99.73 286	96.25 288	98.73 322	98.06 337

RRT_MVS		98.75 212	98.54 219	99.41 185	98.14 353	98.61 246	98.98 198	99.66 113	99.31 114	99.84 43	99.75 80	91.98 307	99.98 6	99.20 67	99.95 48	99.62 105

tpmrst		97.73 279	98.07 257	96.73 330	98.71 336	92.00 349	99.10 170	98.86 304	98.52 213	98.92 266	99.54 203	91.90 308	99.82 240	98.02 169	99.03 304	98.37 324

JIA-IIPM		98.06 269	97.92 270	98.50 288	98.59 339	97.02 309	98.80 224	98.51 320	99.88 14	97.89 330	99.87 31	91.89 309	99.90 123	98.16 162	97.68 345	98.59 313

CR-MVSNet		98.35 254	98.20 247	98.83 272	99.05 305	98.12 272	99.30 105	99.67 109	97.39 290	99.16 240	99.79 59	91.87 310	99.91 103	98.78 118	98.77 316	98.44 322

Patchmtry		98.78 208	98.54 219	99.49 159	98.89 318	99.19 197	99.32 98	99.67 109	99.65 58	99.72 93	99.79 59	91.87 310	99.95 43	98.00 173	99.97 30	99.33 227

MDTV_nov1_ep13_2view						91.44 355	99.14 156		97.37 291	99.21 233		91.78 312		96.75 263		99.03 287

PatchT		98.45 244	98.32 240	98.83 272	98.94 313	98.29 263	99.24 125	98.82 307	99.84 22	99.08 251	99.76 76	91.37 313	99.94 54	98.82 113	99.00 306	98.26 329

test_yl		98.25 259	97.95 264	99.13 237	99.17 287	98.47 251	99.00 189	98.67 314	98.97 161	99.22 231	99.02 305	91.31 314	99.69 300	97.26 233	98.93 307	99.24 242

DCV-MVSNet		98.25 259	97.95 264	99.13 237	99.17 287	98.47 251	99.00 189	98.67 314	98.97 161	99.22 231	99.02 305	91.31 314	99.69 300	97.26 233	98.93 307	99.24 242

baseline1		97.73 279	97.33 287	98.96 252	99.30 265	97.73 291	99.40 80	98.42 324	99.33 112	99.46 179	99.21 282	91.18 316	99.82 240	98.35 141	91.26 354	99.32 230

tpm cat1		96.78 303	96.98 298	96.16 337	98.85 322	90.59 359	99.08 177	99.32 262	92.37 345	97.73 339	99.46 228	91.15 317	99.69 300	96.07 292	98.80 313	98.21 332

LFMVS		98.46 243	98.19 250	99.26 220	99.24 276	98.52 250	99.62 47	96.94 344	99.87 15	99.31 215	99.58 184	91.04 318	99.81 256	98.68 126	99.42 270	99.45 193

MDTV_nov1_ep13			97.73 279		98.70 337	90.83 357	99.15 154	98.02 331	98.51 214	98.82 276	99.61 169	90.98 319	99.66 320	96.89 255	98.92 309

MIMVSNet		98.43 245	98.20 247	99.11 239	99.53 179	98.38 260	99.58 58	98.61 316	98.96 164	99.33 211	99.76 76	90.92 320	99.81 256	97.38 224	99.76 170	99.15 262

ADS-MVSNet2		97.78 277	97.66 283	98.12 304	99.14 290	95.36 332	99.22 132	98.75 310	96.97 303	98.25 312	99.64 141	90.90 321	99.94 54	96.51 276	99.56 240	99.08 278

ADS-MVSNet		97.72 281	97.67 282	97.86 309	99.14 290	94.65 338	99.22 132	98.86 304	96.97 303	98.25 312	99.64 141	90.90 321	99.84 219	96.51 276	99.56 240	99.08 278

alignmvs		98.28 257	97.96 263	99.25 223	99.12 294	98.93 226	99.03 184	98.42 324	99.64 60	98.72 287	97.85 350	90.86 323	99.62 331	98.88 109	99.13 298	99.19 254

sam_mvs1												90.81 324				99.14 266

PatchmatchNet		97.65 282	97.80 275	97.18 325	98.82 327	92.49 347	99.17 146	98.39 326	98.12 250	98.79 280	99.58 184	90.71 325	99.89 137	97.23 237	99.41 271	99.16 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											99.62 160	90.58 326	99.94 54

Patchmatch-RL test		98.60 224	98.36 235	99.33 204	99.77 81	99.07 212	98.27 273	99.87 18	98.91 172	99.74 88	99.72 93	90.57 327	99.79 264	98.55 131	99.85 117	99.11 270

sam_mvs												90.52 328

pmmvs3		98.08 268	97.80 275	98.91 260	99.41 230	97.69 293	97.87 313	99.66 113	95.87 322	99.50 172	99.51 210	90.35 329	99.97 17	98.55 131	99.47 262	99.08 278

test_post											52.41 363	90.25 330	99.86 185

Patchmatch-test		98.10 267	97.98 262	98.48 289	99.27 271	96.48 318	99.40 80	99.07 296	98.81 184	99.23 227	99.57 192	90.11 331	99.87 165	96.69 266	99.64 222	99.09 275

test-LLR		97.15 295	96.95 299	97.74 314	98.18 350	95.02 335	97.38 334	96.10 345	98.00 255	97.81 334	98.58 333	90.04 332	99.91 103	97.69 206	98.78 314	98.31 326

test0.0.03 1		97.37 291	96.91 302	98.74 279	97.72 354	97.57 295	97.60 324	97.36 343	98.00 255	99.21 233	98.02 348	90.04 332	99.79 264	98.37 138	95.89 352	98.86 301

GA-MVS		97.99 273	97.68 281	98.93 257	99.52 184	98.04 279	97.19 342	99.05 299	98.32 240	98.81 277	98.97 313	89.89 334	99.41 348	98.33 143	99.05 302	99.34 226

test_post1							99.14 156				51.63 364	89.54 335	99.82 240	96.86 256

AUN-MVS		97.82 275	97.38 286	99.14 236	99.27 271	98.53 248	98.72 234	99.02 300	98.10 251	97.18 345	99.03 304	89.26 336	99.85 203	97.94 178	97.91 342	99.03 287

MVSTER		98.47 242	98.22 245	99.24 226	99.06 304	98.35 262	99.08 177	99.46 224	99.27 119	99.75 80	99.66 134	88.61 337	99.85 203	99.14 84	99.92 73	99.52 164

baseline2		96.83 302	96.28 308	98.46 290	99.09 302	96.91 312	98.83 216	93.87 356	97.23 297	96.23 351	98.36 343	88.12 338	99.90 123	96.68 267	98.14 338	98.57 316

cl-mvsnet2		97.56 286	97.28 288	98.40 292	98.37 345	96.75 315	97.24 341	99.37 253	97.31 294	99.41 195	99.22 280	87.30 339	99.37 349	97.70 200	99.62 225	99.08 278

dp		96.86 301	97.07 295	96.24 336	98.68 338	90.30 360	99.19 140	98.38 327	97.35 292	98.23 314	99.59 182	87.23 340	99.82 240	96.27 287	98.73 322	98.59 313

ET-MVSNet_ETH3D		96.78 303	96.07 312	98.91 260	99.26 273	97.92 286	97.70 320	96.05 348	97.96 262	92.37 356	98.43 342	87.06 341	99.90 123	98.27 149	97.56 346	98.91 297

thres100view900		96.39 311	96.03 313	97.47 319	99.63 141	95.93 326	99.18 141	97.57 338	98.75 194	98.70 289	97.31 357	87.04 342	99.67 316	87.62 350	98.51 329	96.81 348

thres600view7		96.60 308	96.16 310	97.93 307	99.63 141	96.09 325	99.18 141	97.57 338	98.77 190	98.72 287	97.32 356	87.04 342	99.72 288	88.57 347	98.62 325	97.98 339

tfpn200view9		96.30 314	95.89 314	97.53 317	99.58 152	96.11 323	99.00 189	97.54 341	98.43 220	98.52 301	96.98 359	86.85 344	99.67 316	87.62 350	98.51 329	96.81 348

thres400		96.40 310	95.89 314	97.92 308	99.58 152	96.11 323	99.00 189	97.54 341	98.43 220	98.52 301	96.98 359	86.85 344	99.67 316	87.62 350	98.51 329	97.98 339

thres200		96.09 317	95.68 320	97.33 324	99.48 206	96.22 322	98.53 252	97.57 338	98.06 254	98.37 308	96.73 361	86.84 346	99.61 335	86.99 353	98.57 326	96.16 351

tpm		97.15 295	96.95 299	97.75 313	98.91 314	94.24 340	99.32 98	97.96 332	97.71 274	98.29 309	99.32 258	86.72 347	99.92 85	98.10 167	96.24 351	99.09 275

EPMVS		96.53 309	96.32 307	97.17 326	98.18 350	92.97 346	99.39 82	89.95 359	98.21 246	98.61 294	99.59 182	86.69 348	99.72 288	96.99 249	99.23 297	98.81 305

CostFormer		96.71 306	96.79 305	96.46 334	98.90 315	90.71 358	99.41 78	98.68 312	94.69 340	98.14 320	99.34 256	86.32 349	99.80 261	97.60 211	98.07 340	98.88 299

thisisatest0515		96.98 299	96.42 306	98.66 283	99.42 229	97.47 297	97.27 339	94.30 354	97.24 296	99.15 242	98.86 324	85.01 350	99.87 165	97.10 245	99.39 274	98.63 310

tpm2		96.35 312	96.22 309	96.73 330	98.88 321	91.75 352	99.21 134	98.51 320	93.27 343	97.89 330	99.21 282	84.83 351	99.70 294	96.04 293	98.18 337	98.75 308

tttt0517		97.62 283	97.20 292	98.90 266	99.76 85	97.40 300	99.48 69	94.36 353	99.06 156	99.70 100	99.49 218	84.55 352	99.94 54	98.73 121	99.65 220	99.36 221

thisisatest0530		97.45 288	96.95 299	98.94 254	99.68 129	97.73 291	99.09 174	94.19 355	98.61 204	99.56 153	99.30 262	84.30 353	99.93 67	98.27 149	99.54 250	99.16 260

FPMVS		96.32 313	95.50 321	98.79 276	99.60 147	98.17 270	98.46 262	98.80 308	97.16 299	96.28 348	99.63 151	82.19 354	99.09 351	88.45 348	98.89 312	99.10 272

gg-mvs-nofinetune		95.87 320	95.17 324	97.97 306	98.19 349	96.95 310	99.69 29	89.23 360	99.89 12	96.24 350	99.94 13	81.19 355	99.51 343	93.99 335	98.20 334	97.44 344

DWT-MVSNet_test		96.03 319	95.80 318	96.71 332	98.50 342	91.93 350	99.25 124	97.87 335	95.99 321	96.81 347	97.61 353	81.02 356	99.66 320	97.20 240	97.98 341	98.54 317

GG-mvs-BLEND				97.36 322	97.59 355	96.87 313	99.70 23	88.49 361		94.64 355	97.26 358	80.66 357	99.12 350	91.50 342	96.50 350	96.08 352

FMVSNet5		97.80 276	97.25 290	99.42 178	98.83 324	98.97 219	99.38 84	99.80 47	98.87 177	99.25 223	99.69 113	80.60 358	99.91 103	98.96 100	99.90 83	99.38 215

TESTMET0.1,1		96.24 315	95.84 317	97.41 321	98.24 348	93.84 341	97.38 334	95.84 349	98.43 220	97.81 334	98.56 336	79.77 359	99.89 137	97.77 193	98.77 316	98.52 318

test-mter		96.23 316	95.73 319	97.74 314	98.18 350	95.02 335	97.38 334	96.10 345	97.90 264	97.81 334	98.58 333	79.12 360	99.91 103	97.69 206	98.78 314	98.31 326

RRT_test8_iter05		97.35 293	97.25 290	97.63 316	98.81 328	93.13 344	99.26 117	99.89 13	99.51 82	99.83 48	99.68 124	79.03 361	99.88 152	99.53 28	99.72 194	99.89 9

IB-MVS	95.41 20	95.30 325	94.46 328	97.84 310	98.76 334	95.33 333	97.33 337	96.07 347	96.02 320	95.37 354	97.41 355	76.17 362	99.96 34	97.54 214	95.44 353	98.22 331
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
test123		29.31 326	33.05 331	18.08 340	25.93 362	12.24 362	97.53 328	10.93 363	11.78 357	24.21 358	50.08 366	21.04 363	8.60 358	23.51 356	32.43 357	33.39 354

testmvs		28.94 327	33.33 329	15.79 341	26.03 361	9.81 363	96.77 347	15.67 362	11.55 358	23.87 359	50.74 365	19.03 364	8.53 359	23.21 357	33.07 356	29.03 355

uanet_test		8.33 330	11.11 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	100.00 1	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet-low-res		8.33 330	11.11 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	100.00 1	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet		8.33 330	11.11 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	100.00 1	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uncertanet		8.33 330	11.11 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	100.00 1	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

Regformer		8.33 330	11.11 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	100.00 1	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ab-mvs-re		8.26 336	11.02 339	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	99.16 287	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uanet		8.33 330	11.11 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	100.00 1	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

IU-MVS					99.69 121	99.77 41		99.22 285	97.50 284	99.69 103				97.75 195	99.70 200	99.77 32

save fliter					99.53 179	99.25 181	98.29 271	99.38 252	99.07 152

test_0728_SECOND				99.83 22	99.70 118	99.79 36	99.14 156	99.61 142					99.92 85	97.88 182	99.72 194	99.77 32

GSMVS																99.14 266

test_part2					99.62 144	99.67 80				99.55 158

MTGPA								99.53 195

MTMP							99.09 174	98.59 318

gm-plane-assit					97.59 355	89.02 361			93.47 342		98.30 344		99.84 219	96.38 283

test9_res														95.10 319	99.44 265	99.50 172

agg_prior2														94.58 327	99.46 264	99.50 172

agg_prior					99.35 244	99.36 158		99.39 246		97.76 337			99.85 203

test_prior4						99.19 197	98.00 299

test_prior				99.46 167	99.35 244	99.22 190		99.39 246					99.69 300			99.48 182

旧先验2							97.94 308		95.33 330	98.94 262			99.88 152	96.75 263

新几何2							98.04 295

无先验							98.01 297	99.23 284	95.83 323				99.85 203	95.79 305		99.44 198

原ACMM2							97.92 310

testdata2													99.89 137	95.99 296

testdata1							97.72 318		97.86 269

plane_prior7					99.58 152	99.38 152

plane_prior5								99.54 186					99.82 240	95.84 303	99.78 164	99.60 116

plane_prior4											99.25 273

plane_prior3						99.31 168			98.36 229	99.14 244

plane_prior2							98.80 224		98.94 166

plane_prior1					99.51 189

plane_prior						99.24 186	98.42 263		97.87 266						99.71 198

n2								0.00 364

nn								0.00 364

door-mid								99.83 32

test11								99.29 271

door								99.77 61

HQP5-MVS						98.94 222

HQP-NCC					99.31 261		97.98 302		97.45 286	98.15 316

ACMP_Plane					99.31 261		97.98 302		97.45 286	98.15 316

BP-MVS														94.73 323

HQP4-MVS										98.15 316			99.70 294			99.53 154

HQP3-MVS								99.37 253							99.67 213

NP-MVS					99.40 233	99.13 202					98.83 325

ACMMP++_ref															99.94 61

ACMMP++															99.79 158

High-resolution multi-view benchmark