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 7	99.96 4	99.78 41	99.70 28	99.86 22	99.89 11	99.98 3	99.90 22	99.94 1	99.98 7	99.75 13	100.00 1	99.90 4

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 38	99.72 23	99.88 18	99.92 6	99.98 3	99.93 14	99.94 1	99.98 7	99.77 12	100.00 1	99.92 3

wuyk23d		97.58 295	99.13 121	92.93 354	99.69 123	99.49 128	99.52 72	99.77 63	97.97 272	99.96 8	99.79 64	99.84 3	99.94 57	95.85 319	99.82 146	79.36 370

cdsmvs_eth3d_5k		24.88 342	33.17 344	0.00 358	0.00 381	0.00 382	0.00 369	99.62 140	0.00 376	0.00 377	99.13 302	99.82 4	0.00 377	0.00 375	0.00 375	0.00 373

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 11	99.91 15	99.90 5	99.96 1	99.92 7	99.90 7	99.97 6	99.87 32	99.81 5	99.95 45	99.54 28	99.99 12	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 9	99.78 6	100.00 1	99.92 1	100.00 1	99.87 9

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 19	99.77 11	99.80 49	99.73 43	99.97 6	99.92 17	99.77 7	99.98 7	99.43 41	100.00 1	99.90 4

pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 5	99.83 6	99.91 10	99.85 24	99.94 11	99.95 12	99.73 8	99.90 132	99.65 16	99.97 33	99.69 55

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 95	99.93 4	99.95 10	99.89 26	99.71 9	99.96 35	99.51 33	99.97 33	99.84 14

XVG-OURS		99.21 134	99.06 145	99.65 104	99.82 45	99.62 101	97.87 323	99.74 80	98.36 241	99.66 120	99.68 128	99.71 9	99.90 132	96.84 273	99.88 103	99.43 215

XVG-OURS-SEG-HR		99.16 148	98.99 169	99.66 99	99.84 34	99.64 95	98.25 285	99.73 83	98.39 238	99.63 130	99.43 243	99.70 11	99.90 132	97.34 239	98.64 336	99.44 209

DeepC-MVS	98.90 4	99.62 35	99.61 31	99.67 92	99.72 109	99.44 142	99.24 135	99.71 95	99.27 130	99.93 14	99.90 22	99.70 11	99.93 71	98.99 104	99.99 12	99.64 95
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 38	99.54 45	99.72 79	99.86 30	99.62 101	99.56 70	99.79 55	98.77 202	99.80 62	99.85 41	99.64 13	99.85 216	98.70 134	99.89 95	99.70 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GeoE		99.69 21	99.66 22	99.78 37	99.76 85	99.76 50	99.60 63	99.82 39	99.46 104	99.75 83	99.56 201	99.63 14	99.95 45	99.43 41	99.88 103	99.62 111

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 24	99.76 13	99.87 20	99.73 43	99.89 26	99.87 32	99.63 14	99.87 177	99.54 28	99.92 77	99.63 100

DSMNet-mixed		99.48 54	99.65 24	98.95 263	99.71 112	97.27 315	99.50 74	99.82 39	99.59 85	99.41 205	99.85 41	99.62 16	100.00 1	99.53 30	99.89 95	99.59 135

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 24	99.66 88	99.69 34	99.92 7	99.67 61	99.77 75	99.75 84	99.61 17	99.98 7	99.35 54	99.98 24	99.72 45
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 50	100.00 1	99.90 7	100.00 1	99.97 10	99.61 17	99.97 17	99.75 13	100.00 1	99.84 14

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 14	99.75 15	99.86 22	99.70 52	99.91 20	99.89 26	99.60 19	99.87 177	99.59 21	99.74 188	99.71 48

PMMVS2		99.48 54	99.45 57	99.57 140	99.76 85	98.99 223	98.09 299	99.90 14	98.95 177	99.78 70	99.58 190	99.57 20	99.93 71	99.48 36	99.95 52	99.79 30

DROMVSNet		99.69 21	99.69 18	99.68 89	99.71 112	99.91 2	99.76 13	99.96 4	99.86 19	99.51 180	99.39 252	99.57 20	99.93 71	99.64 18	99.86 119	99.20 263

SD-MVS		99.01 180	99.30 90	98.15 312	99.50 204	99.40 154	98.94 213	99.61 147	99.22 142	99.75 83	99.82 53	99.54 22	95.51 374	97.48 232	99.87 112	99.54 160
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
anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 9	99.73 20	99.85 26	99.70 52	99.92 18	99.93 14	99.45 23	99.97 17	99.36 53	100.00 1	99.85 13

ETV-MVS		99.18 143	99.18 112	99.16 243	99.34 264	99.28 180	99.12 176	99.79 55	99.48 95	98.93 278	98.55 355	99.40 24	99.93 71	98.51 144	99.52 266	98.28 345

xiu_mvs_v1_base_debu		99.23 120	99.34 79	98.91 270	99.59 155	98.23 277	98.47 267	99.66 118	99.61 77	99.68 111	98.94 333	99.39 25	99.97 17	99.18 80	99.55 256	98.51 334

xiu_mvs_v1_base		99.23 120	99.34 79	98.91 270	99.59 155	98.23 277	98.47 267	99.66 118	99.61 77	99.68 111	98.94 333	99.39 25	99.97 17	99.18 80	99.55 256	98.51 334

xiu_mvs_v1_base_debi		99.23 120	99.34 79	98.91 270	99.59 155	98.23 277	98.47 267	99.66 118	99.61 77	99.68 111	98.94 333	99.39 25	99.97 17	99.18 80	99.55 256	98.51 334

ACMM	98.09 11	99.46 61	99.38 71	99.72 79	99.80 57	99.69 81	99.13 172	99.65 129	98.99 171	99.64 126	99.72 97	99.39 25	99.86 197	98.23 163	99.81 154	99.60 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v2_base		99.02 176	99.11 128	98.77 287	99.37 250	98.09 288	98.13 294	99.51 214	99.47 100	99.42 197	98.54 356	99.38 29	99.97 17	98.83 121	99.33 295	98.24 347

XXY-MVS		99.71 18	99.67 21	99.81 26	99.89 21	99.72 67	99.59 65	99.82 39	99.39 115	99.82 52	99.84 46	99.38 29	99.91 112	99.38 50	99.93 73	99.80 24

LPG-MVS_test		99.22 129	99.05 149	99.74 63	99.82 45	99.63 99	99.16 162	99.73 83	97.56 292	99.64 126	99.69 117	99.37 31	99.89 149	96.66 283	99.87 112	99.69 55

LGP-MVS_train				99.74 63	99.82 45	99.63 99		99.73 83	97.56 292	99.64 126	99.69 117	99.37 31	99.89 149	96.66 283	99.87 112	99.69 55

TDRefinement		99.72 17	99.70 17	99.77 40	99.90 19	99.85 14	99.86 5	99.92 7	99.69 55	99.78 70	99.92 17	99.37 31	99.88 164	98.93 116	99.95 52	99.60 126

testgi		99.29 108	99.26 102	99.37 205	99.75 96	98.81 242	98.84 223	99.89 15	98.38 239	99.75 83	99.04 316	99.36 34	99.86 197	99.08 98	99.25 304	99.45 204

Fast-Effi-MVS+		99.02 176	98.87 189	99.46 173	99.38 247	99.50 127	99.04 191	99.79 55	97.17 314	98.62 310	98.74 348	99.34 35	99.95 45	98.32 156	99.41 283	98.92 310

casdiffmvs		99.63 32	99.61 31	99.67 92	99.79 67	99.59 112	99.13 172	99.85 26	99.79 38	99.76 77	99.72 97	99.33 36	99.82 254	99.21 73	99.94 65	99.59 135

new-patchmatchnet		99.35 92	99.57 40	98.71 292	99.82 45	96.62 329	98.55 258	99.75 75	99.50 93	99.88 32	99.87 32	99.31 37	99.88 164	99.43 41	100.00 1	99.62 111

HPM-MVS_fast		99.43 66	99.30 90	99.80 29	99.83 38	99.81 31	99.52 72	99.70 100	98.35 246	99.51 180	99.50 222	99.31 37	99.88 164	98.18 170	99.84 127	99.69 55

EG-PatchMatch MVS		99.57 39	99.56 44	99.62 124	99.77 81	99.33 172	99.26 127	99.76 68	99.32 124	99.80 62	99.78 70	99.29 39	99.87 177	99.15 87	99.91 86	99.66 78

DeepPCF-MVS	98.42 6	99.18 143	99.02 158	99.67 92	99.22 289	99.75 54	97.25 350	99.47 229	98.72 207	99.66 120	99.70 111	99.29 39	99.63 344	98.07 179	99.81 154	99.62 111

pcd_1.5k_mvsjas		16.61 343	22.14 346	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	99.28 41	0.00 377	0.00 375	0.00 375	0.00 373

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 43	99.68 37	99.85 26	99.95 3	99.98 3	99.92 17	99.28 41	99.98 7	99.75 13	100.00 1	99.94 2

PS-MVSNAJ		99.00 182	99.08 139	98.76 288	99.37 250	98.10 287	98.00 309	99.51 214	99.47 100	99.41 205	98.50 358	99.28 41	99.97 17	98.83 121	99.34 293	98.20 351

TSAR-MVS + MP.		99.34 97	99.24 106	99.63 115	99.82 45	99.37 162	99.26 127	99.35 266	98.77 202	99.57 154	99.70 111	99.27 44	99.88 164	97.71 212	99.75 180	99.65 86
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 83	99.86 30	99.76 50	99.32 107	99.77 63	99.53 89	99.77 75	99.76 80	99.26 45	99.78 281	97.77 204	99.88 103	99.60 126

CS-MVS-test		99.43 66	99.40 68	99.53 153	99.51 197	99.84 19	99.60 63	99.94 6	99.52 91	99.10 264	98.89 338	99.24 46	99.90 132	99.11 95	99.66 226	98.84 318

HPM-MVS		99.25 116	99.07 143	99.78 37	99.81 52	99.75 54	99.61 58	99.67 114	97.72 286	99.35 216	99.25 286	99.23 47	99.92 91	97.21 253	99.82 146	99.67 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		99.40 77	99.43 62	99.29 222	99.44 230	99.72 67	99.36 99	99.91 10	99.71 47	99.28 233	98.83 342	99.22 48	99.86 197	99.40 48	99.77 174	98.29 344

DELS-MVS		99.34 97	99.30 90	99.48 168	99.51 197	99.36 165	98.12 295	99.53 203	99.36 119	99.41 205	99.61 173	99.22 48	99.87 177	99.21 73	99.68 215	99.20 263
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	99.51 158	99.77 81	99.41 153	98.81 230	99.66 118	99.42 114	99.75 83	99.66 138	99.20 50	99.76 291	98.98 106	99.99 12	99.36 232

COLMAP_ROB	98.06 12	99.45 63	99.37 74	99.70 87	99.83 38	99.70 77	99.38 92	99.78 60	99.53 89	99.67 116	99.78 70	99.19 51	99.86 197	97.32 240	99.87 112	99.55 152
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TSAR-MVS + GP.		99.12 156	99.04 155	99.38 202	99.34 264	99.16 206	98.15 291	99.29 280	98.18 261	99.63 130	99.62 164	99.18 52	99.68 325	98.20 166	99.74 188	99.30 244

MVS_111021_HR		99.12 156	99.02 158	99.40 195	99.50 204	99.11 211	97.92 320	99.71 95	98.76 205	99.08 266	99.47 235	99.17 53	99.54 354	97.85 199	99.76 177	99.54 160

3Dnovator	99.15 2	99.43 66	99.36 77	99.65 104	99.39 244	99.42 149	99.70 28	99.56 182	99.23 138	99.35 216	99.80 58	99.17 53	99.95 45	98.21 165	99.84 127	99.59 135

UA-Net		99.78 13	99.76 14	99.86 16	99.72 109	99.71 70	99.91 3	99.95 5	99.96 2	99.71 103	99.91 20	99.15 55	99.97 17	99.50 35	100.00 1	99.90 4

baseline		99.63 32	99.62 27	99.66 99	99.80 57	99.62 101	99.44 84	99.80 49	99.71 47	99.72 98	99.69 117	99.15 55	99.83 244	99.32 60	99.94 65	99.53 165

OPM-MVS		99.26 115	99.13 121	99.63 115	99.70 120	99.61 107	98.58 252	99.48 225	98.50 227	99.52 175	99.63 155	99.14 57	99.76 291	97.89 192	99.77 174	99.51 177
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Effi-MVS+		99.06 167	98.97 173	99.34 210	99.31 271	98.98 224	98.31 280	99.91 10	98.81 196	98.79 297	98.94 333	99.14 57	99.84 233	98.79 125	98.74 332	99.20 263

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 19	99.82 8	99.82 39	99.84 27	99.94 11	99.91 20	99.13 59	99.96 35	99.83 9	99.99 12	99.83 18

nrg030		99.70 19	99.66 22	99.82 23	99.76 85	99.84 19	99.61 58	99.70 100	99.93 4	99.78 70	99.68 128	99.10 60	99.78 281	99.45 39	99.96 45	99.83 18

MSDG		99.08 165	98.98 172	99.37 205	99.60 151	99.13 209	97.54 336	99.74 80	98.84 194	99.53 173	99.55 208	99.10 60	99.79 278	97.07 261	99.86 119	99.18 268

PC_three_1452									97.56 292	99.68 111	99.41 245	99.09 62	97.09 372	96.66 283	99.60 245	99.62 111

v1240		99.56 42	99.58 37	99.51 158	99.80 57	99.00 222	99.00 198	99.65 129	99.15 155	99.90 22	99.75 84	99.09 62	99.88 164	99.90 2	99.96 45	99.67 68

abl_6		99.36 90	99.23 108	99.75 57	99.71 112	99.74 60	99.33 104	99.76 68	99.07 164	99.65 124	99.63 155	99.09 62	99.92 91	97.13 258	99.76 177	99.58 140

MVS_111021_LR		99.13 154	99.03 157	99.42 185	99.58 160	99.32 174	97.91 322	99.73 83	98.68 209	99.31 227	99.48 230	99.09 62	99.66 334	97.70 214	99.77 174	99.29 247

v1921920		99.56 42	99.57 40	99.55 147	99.75 96	99.11 211	99.05 189	99.61 147	99.15 155	99.88 32	99.71 104	99.08 66	99.87 177	99.90 2	99.97 33	99.66 78

v1192		99.57 39	99.57 40	99.57 140	99.77 81	99.22 197	99.04 191	99.60 159	99.18 145	99.87 39	99.72 97	99.08 66	99.85 216	99.89 5	99.98 24	99.66 78

test_0402		99.22 129	99.14 118	99.45 177	99.79 67	99.43 146	99.28 122	99.68 109	99.54 87	99.40 210	99.56 201	99.07 68	99.82 254	96.01 311	99.96 45	99.11 282

ACMP	97.51 14	99.05 170	98.84 193	99.67 92	99.78 73	99.55 121	98.88 216	99.66 118	97.11 318	99.47 186	99.60 182	99.07 68	99.89 149	96.18 306	99.85 123	99.58 140
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		98.76 214	98.57 219	99.33 212	99.57 170	98.97 226	97.53 338	99.55 188	96.41 330	99.27 235	99.13 302	99.07 68	99.78 281	96.73 279	99.89 95	99.23 256
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 77	99.38 71	99.44 179	99.90 19	98.66 252	98.94 213	99.91 10	97.97 272	99.79 67	99.73 91	99.05 71	99.97 17	99.15 87	99.99 12	99.68 61

canonicalmvs		99.02 176	99.00 164	99.09 251	99.10 312	98.70 248	99.61 58	99.66 118	99.63 72	98.64 309	97.65 369	99.04 72	99.54 354	98.79 125	98.92 321	99.04 298

SteuartSystems-ACMMP		99.30 106	99.14 118	99.76 47	99.87 28	99.66 88	99.18 151	99.60 159	98.55 221	99.57 154	99.67 134	99.03 73	99.94 57	97.01 262	99.80 159	99.69 55
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++		99.38 84	99.25 104	99.77 40	99.03 320	99.77 43	99.74 17	99.61 147	99.18 145	99.76 77	99.61 173	99.00 74	99.92 91	97.72 210	99.60 245	99.62 111

OPU-MVS				99.29 222	99.12 306	99.44 142	99.20 145				99.40 248	99.00 74	98.84 369	96.54 289	99.60 245	99.58 140

EI-MVSNet-UG-set		99.48 54	99.50 51	99.42 185	99.57 170	98.65 255	99.24 135	99.46 233	99.68 57	99.80 62	99.66 138	98.99 76	99.89 149	99.19 78	99.90 87	99.72 45

Fast-Effi-MVS+-dtu		99.20 136	99.12 125	99.43 183	99.25 285	99.69 81	99.05 189	99.82 39	99.50 93	98.97 274	99.05 313	98.98 77	99.98 7	98.20 166	99.24 306	98.62 326

FMVSNet1		99.66 26	99.63 26	99.73 73	99.78 73	99.77 43	99.68 37	99.70 100	99.67 61	99.82 52	99.83 47	98.98 77	99.90 132	99.24 70	99.97 33	99.53 165

EI-MVSNet-Vis-set		99.47 60	99.49 52	99.42 185	99.57 170	98.66 252	99.24 135	99.46 233	99.67 61	99.79 67	99.65 143	98.97 79	99.89 149	99.15 87	99.89 95	99.71 48

PHI-MVS		99.11 160	98.95 177	99.59 131	99.13 304	99.59 112	99.17 156	99.65 129	97.88 278	99.25 237	99.46 238	98.97 79	99.80 275	97.26 247	99.82 146	99.37 229

TinyColmap		98.97 186	98.93 178	99.07 255	99.46 225	98.19 280	97.75 327	99.75 75	98.79 199	99.54 168	99.70 111	98.97 79	99.62 345	96.63 286	99.83 137	99.41 219

Regformer-4		99.45 63	99.44 59	99.50 161	99.52 192	98.94 230	99.17 156	99.53 203	99.64 69	99.76 77	99.60 182	98.96 82	99.90 132	98.91 117	99.84 127	99.67 68

SMA-MVS		99.19 139	99.00 164	99.73 73	99.46 225	99.73 63	99.13 172	99.52 211	97.40 303	99.57 154	99.64 145	98.93 83	99.83 244	97.61 224	99.79 164	99.63 100
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		99.23 120	99.10 136	99.63 115	99.82 45	99.58 115	98.83 225	99.72 92	98.36 241	99.60 146	99.71 104	98.92 84	99.91 112	97.08 260	99.84 127	99.40 221

CSCG		99.37 87	99.29 95	99.60 129	99.71 112	99.46 135	99.43 86	99.85 26	98.79 199	99.41 205	99.60 182	98.92 84	99.92 91	98.02 180	99.92 77	99.43 215

SED-MVS		99.40 77	99.28 97	99.77 40	99.69 123	99.82 28	99.20 145	99.54 194	99.13 157	99.82 52	99.63 155	98.91 86	99.92 91	97.85 199	99.70 207	99.58 140

test_241102_ONE					99.69 123	99.82 28		99.54 194	99.12 160	99.82 52	99.49 227	98.91 86	99.52 358

Gipuma		99.57 39	99.59 34	99.49 164	99.98 3	99.71 70	99.72 23	99.84 32	99.81 33	99.94 11	99.78 70	98.91 86	99.71 306	98.41 148	99.95 52	99.05 297
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-3		99.41 74	99.41 66	99.40 195	99.52 192	98.70 248	99.17 156	99.44 238	99.62 73	99.75 83	99.60 182	98.90 89	99.85 216	98.89 118	99.84 127	99.65 86

DeepC-MVS_fast	98.47 5	99.23 120	99.12 125	99.56 144	99.28 280	99.22 197	98.99 203	99.40 252	99.08 162	99.58 151	99.64 145	98.90 89	99.83 244	97.44 234	99.75 180	99.63 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ITE_SJBPF				99.38 202	99.63 144	99.44 142		99.73 83	98.56 219	99.33 221	99.53 213	98.88 91	99.68 325	96.01 311	99.65 230	99.02 303

xxxxxxxxxxxxxcwj		99.11 160	98.96 175	99.54 151	99.53 187	99.25 188	98.29 281	99.76 68	99.07 164	99.42 197	99.61 173	98.86 92	99.87 177	96.45 295	99.68 215	99.49 188

SF-MVS		99.10 164	98.93 178	99.62 124	99.58 160	99.51 126	99.13 172	99.65 129	97.97 272	99.42 197	99.61 173	98.86 92	99.87 177	96.45 295	99.68 215	99.49 188

tfpnnormal		99.43 66	99.38 71	99.60 129	99.87 28	99.75 54	99.59 65	99.78 60	99.71 47	99.90 22	99.69 117	98.85 94	99.90 132	97.25 250	99.78 170	99.15 274

ZNCC-MVS		99.22 129	99.04 155	99.77 40	99.76 85	99.73 63	99.28 122	99.56 182	98.19 260	99.14 258	99.29 277	98.84 95	99.92 91	97.53 230	99.80 159	99.64 95

MP-MVS-pluss		99.14 152	98.92 182	99.80 29	99.83 38	99.83 24	98.61 248	99.63 137	96.84 324	99.44 191	99.58 190	98.81 96	99.91 112	97.70 214	99.82 146	99.67 68
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VPA-MVSNet		99.66 26	99.62 27	99.79 34	99.68 132	99.75 54	99.62 53	99.69 106	99.85 24	99.80 62	99.81 56	98.81 96	99.91 112	99.47 37	99.88 103	99.70 51

test20.03		99.55 45	99.54 45	99.58 135	99.79 67	99.37 162	99.02 194	99.89 15	99.60 83	99.82 52	99.62 164	98.81 96	99.89 149	99.43 41	99.86 119	99.47 198

PGM-MVS		99.20 136	99.01 161	99.77 40	99.75 96	99.71 70	99.16 162	99.72 92	97.99 270	99.42 197	99.60 182	98.81 96	99.93 71	96.91 267	99.74 188	99.66 78

HFP-MVS		99.25 116	99.08 139	99.76 47	99.73 105	99.70 77	99.31 111	99.59 166	98.36 241	99.36 214	99.37 255	98.80 100	99.91 112	97.43 235	99.75 180	99.68 61

#test#		99.12 156	98.90 186	99.76 47	99.73 105	99.70 77	99.10 179	99.59 166	97.60 291	99.36 214	99.37 255	98.80 100	99.91 112	96.84 273	99.75 180	99.68 61

Regformer-2		99.34 97	99.27 100	99.53 153	99.41 239	99.10 215	98.99 203	99.53 203	99.47 100	99.66 120	99.52 215	98.80 100	99.89 149	98.31 157	99.74 188	99.60 126

APDe-MVS		99.48 54	99.36 77	99.85 18	99.55 181	99.81 31	99.50 74	99.69 106	98.99 171	99.75 83	99.71 104	98.79 103	99.93 71	98.46 146	99.85 123	99.80 24

CP-MVS		99.23 120	99.05 149	99.75 57	99.66 138	99.66 88	99.38 92	99.62 140	98.38 239	99.06 270	99.27 281	98.79 103	99.94 57	97.51 231	99.82 146	99.66 78

Regformer-1		99.32 103	99.27 100	99.47 170	99.41 239	98.95 229	98.99 203	99.48 225	99.48 95	99.66 120	99.52 215	98.78 105	99.87 177	98.36 151	99.74 188	99.60 126

MSLP-MVS++		99.05 170	99.09 137	98.91 270	99.21 291	98.36 273	98.82 229	99.47 229	98.85 191	98.90 284	99.56 201	98.78 105	99.09 367	98.57 141	99.68 215	99.26 250

MVS_Test		99.28 109	99.31 85	99.19 240	99.35 254	98.79 244	99.36 99	99.49 223	99.17 149	99.21 247	99.67 134	98.78 105	99.66 334	99.09 97	99.66 226	99.10 284

3Dnovator+	98.92 3	99.35 92	99.24 106	99.67 92	99.35 254	99.47 131	99.62 53	99.50 218	99.44 107	99.12 261	99.78 70	98.77 108	99.94 57	97.87 196	99.72 201	99.62 111

APD-MVS_3200maxsize		99.31 105	99.16 114	99.74 63	99.53 187	99.75 54	99.27 125	99.61 147	99.19 144	99.57 154	99.64 145	98.76 109	99.90 132	97.29 242	99.62 235	99.56 149

TranMVSNet+NR-MVSNet		99.54 47	99.47 53	99.76 47	99.58 160	99.64 95	99.30 114	99.63 137	99.61 77	99.71 103	99.56 201	98.76 109	99.96 35	99.14 93	99.92 77	99.68 61

EIA-MVS		99.12 156	99.01 161	99.45 177	99.36 252	99.62 101	99.34 102	99.79 55	98.41 235	98.84 291	98.89 338	98.75 111	99.84 233	98.15 174	99.51 267	98.89 312

ACMMP_NAP		99.28 109	99.11 128	99.79 34	99.75 96	99.81 31	98.95 211	99.53 203	98.27 255	99.53 173	99.73 91	98.75 111	99.87 177	97.70 214	99.83 137	99.68 61

v10		99.69 21	99.69 18	99.66 99	99.81 52	99.39 156	99.66 45	99.75 75	99.60 83	99.92 18	99.87 32	98.75 111	99.86 197	99.90 2	99.99 12	99.73 44

region2R		99.23 120	99.05 149	99.77 40	99.76 85	99.70 77	99.31 111	99.59 166	98.41 235	99.32 223	99.36 260	98.73 114	99.93 71	97.29 242	99.74 188	99.67 68

LS3D		99.24 119	99.11 128	99.61 127	98.38 359	99.79 38	99.57 68	99.68 109	99.61 77	99.15 256	99.71 104	98.70 115	99.91 112	97.54 228	99.68 215	99.13 281

DP-MVS		99.48 54	99.39 69	99.74 63	99.57 170	99.62 101	99.29 121	99.61 147	99.87 17	99.74 92	99.76 80	98.69 116	99.87 177	98.20 166	99.80 159	99.75 42

AllTest		99.21 134	99.07 143	99.63 115	99.78 73	99.64 95	99.12 176	99.83 34	98.63 213	99.63 130	99.72 97	98.68 117	99.75 295	96.38 298	99.83 137	99.51 177

TestCases				99.63 115	99.78 73	99.64 95		99.83 34	98.63 213	99.63 130	99.72 97	98.68 117	99.75 295	96.38 298	99.83 137	99.51 177

LCM-MVSNet-Re		99.28 109	99.15 117	99.67 92	99.33 269	99.76 50	99.34 102	99.97 2	98.93 181	99.91 20	99.79 64	98.68 117	99.93 71	96.80 275	99.56 252	99.30 244

v1144		99.54 47	99.53 49	99.59 131	99.79 67	99.28 180	99.10 179	99.61 147	99.20 143	99.84 45	99.73 91	98.67 120	99.84 233	99.86 8	99.98 24	99.64 95

DTE-MVSNet		99.68 24	99.61 31	99.88 11	99.80 57	99.87 10	99.67 41	99.71 95	99.72 46	99.84 45	99.78 70	98.67 120	99.97 17	99.30 63	99.95 52	99.80 24

v144192		99.55 45	99.54 45	99.58 135	99.78 73	99.20 203	99.11 178	99.62 140	99.18 145	99.89 26	99.72 97	98.66 122	99.87 177	99.88 6	99.97 33	99.66 78

v8		99.68 24	99.69 18	99.65 104	99.80 57	99.40 154	99.66 45	99.76 68	99.64 69	99.93 14	99.85 41	98.66 122	99.84 233	99.88 6	99.99 12	99.71 48

GST-MVS		99.16 148	98.96 175	99.75 57	99.73 105	99.73 63	99.20 145	99.55 188	98.22 257	99.32 223	99.35 265	98.65 124	99.91 112	96.86 270	99.74 188	99.62 111

ppachtmachnet_test		98.89 200	99.12 125	98.20 311	99.66 138	95.24 346	97.63 332	99.68 109	99.08 162	99.78 70	99.62 164	98.65 124	99.88 164	98.02 180	99.96 45	99.48 193

PS-CasMVS		99.66 26	99.58 37	99.89 7	99.80 57	99.85 14	99.66 45	99.73 83	99.62 73	99.84 45	99.71 104	98.62 126	99.96 35	99.30 63	99.96 45	99.86 11

LF4IMVS		99.01 180	98.92 182	99.27 227	99.71 112	99.28 180	98.59 251	99.77 63	98.32 252	99.39 211	99.41 245	98.62 126	99.84 233	96.62 287	99.84 127	98.69 324

ACMMPR		99.23 120	99.06 145	99.76 47	99.74 102	99.69 81	99.31 111	99.59 166	98.36 241	99.35 216	99.38 254	98.61 128	99.93 71	97.43 235	99.75 180	99.67 68

API-MVS		98.38 258	98.39 237	98.35 304	98.83 339	99.26 184	99.14 166	99.18 302	98.59 217	98.66 308	98.78 346	98.61 128	99.57 353	94.14 348	99.56 252	96.21 367

test_one_0601					99.63 144	99.76 50		99.55 188	99.23 138	99.31 227	99.61 173	98.59 130

OMC-MVS		98.90 197	98.72 203	99.44 179	99.39 244	99.42 149	98.58 252	99.64 135	97.31 308	99.44 191	99.62 164	98.59 130	99.69 314	96.17 307	99.79 164	99.22 258

test_0728_THIRD									99.18 145	99.62 138	99.61 173	98.58 132	99.91 112	97.72 210	99.80 159	99.77 35

RE-MVS-def			99.13 121		99.54 182	99.74 60	99.26 127	99.62 140	99.16 151	99.52 175	99.64 145	98.57 133		97.27 245	99.61 242	99.54 160

ACMMP		99.25 116	99.08 139	99.74 63	99.79 67	99.68 84	99.50 74	99.65 129	98.07 266	99.52 175	99.69 117	98.57 133	99.92 91	97.18 255	99.79 164	99.63 100
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 26	99.59 34	99.89 7	99.83 38	99.87 10	99.66 45	99.73 83	99.70 52	99.84 45	99.73 91	98.56 135	99.96 35	99.29 66	99.94 65	99.83 18

V42		99.56 42	99.54 45	99.63 115	99.79 67	99.46 135	99.39 90	99.59 166	99.24 136	99.86 40	99.70 111	98.55 136	99.82 254	99.79 11	99.95 52	99.60 126

QAPM		98.40 257	97.99 270	99.65 104	99.39 244	99.47 131	99.67 41	99.52 211	91.70 363	98.78 299	99.80 58	98.55 136	99.95 45	94.71 342	99.75 180	99.53 165

EI-MVSNet		99.38 84	99.44 59	99.21 237	99.58 160	98.09 288	99.26 127	99.46 233	99.62 73	99.75 83	99.67 134	98.54 138	99.85 216	99.15 87	99.92 77	99.68 61

jason		99.16 148	99.11 128	99.32 216	99.75 96	98.44 266	98.26 284	99.39 255	98.70 208	99.74 92	99.30 274	98.54 138	99.97 17	98.48 145	99.82 146	99.55 152
jason: jason.
OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 24	99.83 6	99.85 26	99.80 36	99.93 14	99.93 14	98.54 138	99.93 71	99.59 21	99.98 24	99.76 39

IterMVS-LS		99.41 74	99.47 53	99.25 232	99.81 52	98.09 288	98.85 222	99.76 68	99.62 73	99.83 50	99.64 145	98.54 138	99.97 17	99.15 87	99.99 12	99.68 61
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
9.14			98.64 210		99.45 228		98.81 230	99.60 159	97.52 297	99.28 233	99.56 201	98.53 142	99.83 244	95.36 333	99.64 232

mPP-MVS		99.19 139	99.00 164	99.76 47	99.76 85	99.68 84	99.38 92	99.54 194	98.34 250	99.01 272	99.50 222	98.53 142	99.93 71	97.18 255	99.78 170	99.66 78

CNVR-MVS		98.99 185	98.80 199	99.56 144	99.25 285	99.43 146	98.54 261	99.27 284	98.58 218	98.80 296	99.43 243	98.53 142	99.70 308	97.22 252	99.59 249	99.54 160

PVSNet_BlendedMVS		99.03 174	99.01 161	99.09 251	99.54 182	97.99 292	98.58 252	99.82 39	97.62 290	99.34 219	99.71 104	98.52 145	99.77 289	97.98 185	99.97 33	99.52 175

PVSNet_Blended		98.70 222	98.59 215	99.02 259	99.54 182	97.99 292	97.58 335	99.82 39	95.70 342	99.34 219	98.98 326	98.52 145	99.77 289	97.98 185	99.83 137	99.30 244

MCST-MVS		99.02 176	98.81 197	99.65 104	99.58 160	99.49 128	98.58 252	99.07 309	98.40 237	99.04 271	99.25 286	98.51 147	99.80 275	97.31 241	99.51 267	99.65 86

UGNet		99.38 84	99.34 79	99.49 164	98.90 330	98.90 238	99.70 28	99.35 266	99.86 19	98.57 315	99.81 56	98.50 148	99.93 71	99.38 50	99.98 24	99.66 78
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		99.27 113	99.11 128	99.75 57	99.71 112	99.71 70	99.37 96	99.61 147	99.29 126	98.76 301	99.47 235	98.47 149	99.88 164	97.62 222	99.73 195	99.67 68

X-MVStestdata		96.09 327	94.87 337	99.75 57	99.71 112	99.71 70	99.37 96	99.61 147	99.29 126	98.76 301	61.30 379	98.47 149	99.88 164	97.62 222	99.73 195	99.67 68

diffmvs		99.34 97	99.32 84	99.39 198	99.67 137	98.77 245	98.57 256	99.81 48	99.61 77	99.48 184	99.41 245	98.47 149	99.86 197	98.97 108	99.90 87	99.53 165

ambc				99.20 239	99.35 254	98.53 259	99.17 156	99.46 233		99.67 116	99.80 58	98.46 152	99.70 308	97.92 190	99.70 207	99.38 226

FC-MVSNet-test		99.70 19	99.65 24	99.86 16	99.88 24	99.86 13	99.72 23	99.78 60	99.90 7	99.82 52	99.83 47	98.45 153	99.87 177	99.51 33	99.97 33	99.86 11

1314		98.00 280	97.90 283	98.27 310	98.90 330	97.45 311	99.30 114	99.06 311	94.98 350	97.21 360	99.12 306	98.43 154	99.67 330	95.58 327	98.56 339	97.71 359

USDC		98.96 189	98.93 178	99.05 257	99.54 182	97.99 292	97.07 356	99.80 49	98.21 258	99.75 83	99.77 77	98.43 154	99.64 343	97.90 191	99.88 103	99.51 177

KD-MVS_self_test		99.63 32	99.59 34	99.76 47	99.84 34	99.90 5	99.37 96	99.79 55	99.83 30	99.88 32	99.85 41	98.42 156	99.90 132	99.60 20	99.73 195	99.49 188

test1172		99.23 120	99.05 149	99.74 63	99.52 192	99.75 54	99.20 145	99.61 147	98.97 173	99.48 184	99.58 190	98.41 157	99.91 112	97.15 257	99.55 256	99.57 146

SR-MVS-dyc-post		99.27 113	99.11 128	99.73 73	99.54 182	99.74 60	99.26 127	99.62 140	99.16 151	99.52 175	99.64 145	98.41 157	99.91 112	97.27 245	99.61 242	99.54 160

v148		99.40 77	99.41 66	99.39 198	99.76 85	98.94 230	99.09 183	99.59 166	99.17 149	99.81 59	99.61 173	98.41 157	99.69 314	99.32 60	99.94 65	99.53 165

Test By Simon												98.41 157

PM-MVS		99.36 90	99.29 95	99.58 135	99.83 38	99.66 88	98.95 211	99.86 22	98.85 191	99.81 59	99.73 91	98.40 161	99.92 91	98.36 151	99.83 137	99.17 270

SR-MVS		99.19 139	99.00 164	99.74 63	99.51 197	99.72 67	99.18 151	99.60 159	98.85 191	99.47 186	99.58 190	98.38 162	99.92 91	96.92 266	99.54 262	99.57 146

segment_acmp												98.37 163

MP-MVS		99.06 167	98.83 195	99.76 47	99.76 85	99.71 70	99.32 107	99.50 218	98.35 246	98.97 274	99.48 230	98.37 163	99.92 91	95.95 317	99.75 180	99.63 100
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DVP-MVS		99.32 103	99.17 113	99.77 40	99.69 123	99.80 36	99.14 166	99.31 275	99.16 151	99.62 138	99.61 173	98.35 165	99.91 112	97.88 193	99.72 201	99.61 122
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					99.69 123	99.80 36	99.24 135	99.57 177	99.16 151	99.73 96	99.65 143	98.35 165

MVS		95.72 335	94.63 339	98.99 260	98.56 355	97.98 297	99.30 114	98.86 318	72.71 372	97.30 357	99.08 310	98.34 167	99.74 297	89.21 363	98.33 344	99.26 250

CDPH-MVS		98.56 237	98.20 255	99.61 127	99.50 204	99.46 135	98.32 279	99.41 245	95.22 347	99.21 247	99.10 309	98.34 167	99.82 254	95.09 337	99.66 226	99.56 149

testdata				99.42 185	99.51 197	98.93 234		99.30 278	96.20 334	98.87 288	99.40 248	98.33 169	99.89 149	96.29 301	99.28 300	99.44 209

test_241102_TWO								99.54 194	99.13 157	99.76 77	99.63 155	98.32 170	99.92 91	97.85 199	99.69 210	99.75 42

APD-MVS		98.87 203	98.59 215	99.71 83	99.50 204	99.62 101	99.01 196	99.57 177	96.80 326	99.54 168	99.63 155	98.29 171	99.91 112	95.24 334	99.71 205	99.61 122
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.12 10	98.23 270	97.89 284	99.26 229	99.19 296	99.26 184	99.65 50	99.69 106	91.33 364	98.14 337	99.77 77	98.28 172	99.96 35	95.41 331	99.55 256	98.58 330

FIs		99.65 31	99.58 37	99.84 19	99.84 34	99.85 14	99.66 45	99.75 75	99.86 19	99.74 92	99.79 64	98.27 173	99.85 216	99.37 52	99.93 73	99.83 18

TAPA-MVS	97.92 13	98.03 278	97.55 294	99.46 173	99.47 220	99.44 142	98.50 265	99.62 140	86.79 367	99.07 269	99.26 284	98.26 174	99.62 345	97.28 244	99.73 195	99.31 243
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v2v482		99.50 50	99.47 53	99.58 135	99.78 73	99.25 188	99.14 166	99.58 175	99.25 134	99.81 59	99.62 164	98.24 175	99.84 233	99.83 9	99.97 33	99.64 95

pmmvs4		99.13 154	99.06 145	99.36 208	99.57 170	99.10 215	98.01 307	99.25 289	98.78 201	99.58 151	99.44 242	98.24 175	99.76 291	98.74 131	99.93 73	99.22 258

mvs_anonymous		99.28 109	99.39 69	98.94 264	99.19 296	97.81 300	99.02 194	99.55 188	99.78 39	99.85 42	99.80 58	98.24 175	99.86 197	99.57 25	99.50 269	99.15 274

DPE-MVS		99.14 152	98.92 182	99.82 23	99.57 170	99.77 43	98.74 241	99.60 159	98.55 221	99.76 77	99.69 117	98.23 178	99.92 91	96.39 297	99.75 180	99.76 39
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
zzz-MVS		99.30 106	99.14 118	99.80 29	99.81 52	99.81 31	98.73 243	99.53 203	99.27 130	99.42 197	99.63 155	98.21 179	99.95 45	97.83 202	99.79 164	99.65 86

MTAPA		99.35 92	99.20 110	99.80 29	99.81 52	99.81 31	99.33 104	99.53 203	99.27 130	99.42 197	99.63 155	98.21 179	99.95 45	97.83 202	99.79 164	99.65 86

MS-PatchMatch		99.00 182	98.97 173	99.09 251	99.11 311	98.19 280	98.76 240	99.33 269	98.49 229	99.44 191	99.58 190	98.21 179	99.69 314	98.20 166	99.62 235	99.39 224

our_test_3		98.85 205	99.09 137	98.13 313	99.66 138	94.90 349	97.72 328	99.58 175	99.07 164	99.64 126	99.62 164	98.19 182	99.93 71	98.41 148	99.95 52	99.55 152

MVP-Stereo		99.16 148	99.08 139	99.43 183	99.48 215	99.07 219	99.08 186	99.55 188	98.63 213	99.31 227	99.68 128	98.19 182	99.78 281	98.18 170	99.58 250	99.45 204
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WR-MVS_H		99.61 37	99.53 49	99.87 14	99.80 57	99.83 24	99.67 41	99.75 75	99.58 86	99.85 42	99.69 117	98.18 184	99.94 57	99.28 68	99.95 52	99.83 18

new_pmnet		98.88 201	98.89 187	98.84 280	99.70 120	97.62 306	98.15 291	99.50 218	97.98 271	99.62 138	99.54 210	98.15 185	99.94 57	97.55 227	99.84 127	98.95 307

D2MVS		99.22 129	99.19 111	99.29 222	99.69 123	98.74 246	98.81 230	99.41 245	98.55 221	99.68 111	99.69 117	98.13 186	99.87 177	98.82 123	99.98 24	99.24 253

Anonymous20240529		99.42 70	99.34 79	99.65 104	99.53 187	99.60 109	99.63 52	99.39 255	99.47 100	99.76 77	99.78 70	98.13 186	99.86 197	98.70 134	99.68 215	99.49 188

EU-MVSNet		99.39 82	99.62 27	98.72 290	99.88 24	96.44 331	99.56 70	99.85 26	99.90 7	99.90 22	99.85 41	98.09 188	99.83 244	99.58 24	99.95 52	99.90 4

PMVS	92.94 21	98.82 208	98.81 197	98.85 278	99.84 34	97.99 292	99.20 145	99.47 229	99.71 47	99.42 197	99.82 53	98.09 188	99.47 361	93.88 353	99.85 123	99.07 295
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
HPM-MVS++		98.96 189	98.70 207	99.74 63	99.52 192	99.71 70	98.86 220	99.19 301	98.47 231	98.59 313	99.06 312	98.08 190	99.91 112	96.94 265	99.60 245	99.60 126

ab-mvs		99.33 101	99.28 97	99.47 170	99.57 170	99.39 156	99.78 10	99.43 242	98.87 189	99.57 154	99.82 53	98.06 191	99.87 177	98.69 136	99.73 195	99.15 274

agg_prior1		98.33 264	97.92 280	99.57 140	99.35 254	99.36 165	97.99 311	99.39 255	94.85 354	97.76 353	98.98 326	98.03 192	99.85 216	95.49 328	99.44 277	99.51 177

N_pmnet		98.73 219	98.53 225	99.35 209	99.72 109	98.67 250	98.34 276	94.65 367	98.35 246	99.79 67	99.68 128	98.03 192	99.93 71	98.28 159	99.92 77	99.44 209

TEST9					99.35 254	99.35 169	98.11 297	99.41 245	94.83 355	97.92 344	98.99 323	98.02 194	99.85 216

train_agg		98.35 262	97.95 274	99.57 140	99.35 254	99.35 169	98.11 297	99.41 245	94.90 351	97.92 344	98.99 323	98.02 194	99.85 216	95.38 332	99.44 277	99.50 183

test_8					99.34 264	99.31 175	98.08 301	99.40 252	94.90 351	97.87 348	98.97 329	98.02 194	99.84 233

MVSFormer		99.41 74	99.44 59	99.31 219	99.57 170	98.40 269	99.77 11	99.80 49	99.73 43	99.63 130	99.30 274	98.02 194	99.98 7	99.43 41	99.69 210	99.55 152

lupinMVS		98.96 189	98.87 189	99.24 234	99.57 170	98.40 269	98.12 295	99.18 302	98.28 254	99.63 130	99.13 302	98.02 194	99.97 17	98.22 164	99.69 210	99.35 235

Anonymous20231211		99.62 35	99.57 40	99.76 47	99.61 149	99.60 109	99.81 9	99.73 83	99.82 32	99.90 22	99.90 22	97.97 199	99.86 197	99.42 46	99.96 45	99.80 24

MIMVSNet1		99.66 26	99.62 27	99.80 29	99.94 10	99.87 10	99.69 34	99.77 63	99.78 39	99.93 14	99.89 26	97.94 200	99.92 91	99.65 16	99.98 24	99.62 111

原ACMM1				99.37 205	99.47 220	98.87 241		99.27 284	96.74 327	98.26 328	99.32 270	97.93 201	99.82 254	95.96 316	99.38 286	99.43 215

ETH3D-3000-0.1		98.77 212	98.50 227	99.59 131	99.47 220	99.53 123	98.77 238	99.60 159	97.33 307	99.23 241	99.50 222	97.91 202	99.83 244	95.02 338	99.67 222	99.41 219

test_prior3		98.62 228	98.34 243	99.46 173	99.35 254	99.22 197	97.95 316	99.39 255	97.87 279	98.05 339	99.05 313	97.90 203	99.69 314	95.99 313	99.49 271	99.48 193

test_prior2							97.95 316		97.87 279	98.05 339	99.05 313	97.90 203		95.99 313	99.49 271

RPSCF		99.18 143	99.02 158	99.64 111	99.83 38	99.85 14	99.44 84	99.82 39	98.33 251	99.50 182	99.78 70	97.90 203	99.65 341	96.78 276	99.83 137	99.44 209

PMMVS		98.49 248	98.29 248	99.11 249	98.96 327	98.42 268	97.54 336	99.32 271	97.53 296	98.47 322	98.15 364	97.88 206	99.82 254	97.46 233	99.24 306	99.09 287

ZD-MVS					99.43 233	99.61 107		99.43 242	96.38 331	99.11 262	99.07 311	97.86 207	99.92 91	94.04 350	99.49 271

NCCC		98.82 208	98.57 219	99.58 135	99.21 291	99.31 175	98.61 248	99.25 289	98.65 211	98.43 323	99.26 284	97.86 207	99.81 270	96.55 288	99.27 303	99.61 122

UniMVSNet_NR-MVSNet		99.37 87	99.25 104	99.72 79	99.47 220	99.56 118	98.97 209	99.61 147	99.43 112	99.67 116	99.28 279	97.85 209	99.95 45	99.17 83	99.81 154	99.65 86

TAMVS		99.49 52	99.45 57	99.63 115	99.48 215	99.42 149	99.45 81	99.57 177	99.66 65	99.78 70	99.83 47	97.85 209	99.86 197	99.44 40	99.96 45	99.61 122

DP-MVS Recon		98.50 245	98.23 252	99.31 219	99.49 209	99.46 135	98.56 257	99.63 137	94.86 353	98.85 290	99.37 255	97.81 211	99.59 351	96.08 308	99.44 277	98.88 313

PatchMatch-RL		98.68 224	98.47 228	99.30 221	99.44 230	99.28 180	98.14 293	99.54 194	97.12 317	99.11 262	99.25 286	97.80 212	99.70 308	96.51 291	99.30 298	98.93 309

CP-MVSNet		99.54 47	99.43 62	99.87 14	99.76 85	99.82 28	99.57 68	99.61 147	99.54 87	99.80 62	99.64 145	97.79 213	99.95 45	99.21 73	99.94 65	99.84 14

DPM-MVS		98.28 265	97.94 278	99.32 216	99.36 252	99.11 211	97.31 348	98.78 323	96.88 321	98.84 291	99.11 308	97.77 214	99.61 349	94.03 351	99.36 291	99.23 256

114514_t		98.49 248	98.11 264	99.64 111	99.73 105	99.58 115	99.24 135	99.76 68	89.94 366	99.42 197	99.56 201	97.76 215	99.86 197	97.74 209	99.82 146	99.47 198

tmp_tt		95.75 334	95.42 332	96.76 341	89.90 378	94.42 351	98.86 220	97.87 349	78.01 370	99.30 232	99.69 117	97.70 216	95.89 373	99.29 66	98.14 351	99.95 1

UniMVSNet (Re)		99.37 87	99.26 102	99.68 89	99.51 197	99.58 115	98.98 207	99.60 159	99.43 112	99.70 106	99.36 260	97.70 216	99.88 164	99.20 76	99.87 112	99.59 135

Effi-MVS+-dtu		99.07 166	98.92 182	99.52 155	98.89 333	99.78 41	99.15 164	99.66 118	99.34 120	98.92 281	99.24 291	97.69 218	99.98 7	98.11 176	99.28 300	98.81 320

mvs-test1		98.83 206	98.70 207	99.22 236	98.89 333	99.65 93	98.88 216	99.66 118	99.34 120	98.29 326	98.94 333	97.69 218	99.96 35	98.11 176	98.54 340	98.04 355

F-COLMAP		98.74 217	98.45 230	99.62 124	99.57 170	99.47 131	98.84 223	99.65 129	96.31 333	98.93 278	99.19 299	97.68 220	99.87 177	96.52 290	99.37 290	99.53 165

新几何1				99.52 155	99.50 204	99.22 197		99.26 286	95.66 343	98.60 312	99.28 279	97.67 221	99.89 149	95.95 317	99.32 296	99.45 204

旧先验1					99.49 209	99.29 178		99.26 286			99.39 252	97.67 221			99.36 291	99.46 202

DU-MVS		99.33 101	99.21 109	99.71 83	99.43 233	99.56 118	98.83 225	99.53 203	99.38 116	99.67 116	99.36 260	97.67 221	99.95 45	99.17 83	99.81 154	99.63 100

Baseline_NR-MVSNet		99.49 52	99.37 74	99.82 23	99.91 15	99.84 19	98.83 225	99.86 22	99.68 57	99.65 124	99.88 29	97.67 221	99.87 177	99.03 101	99.86 119	99.76 39

CANet		99.11 160	99.05 149	99.28 225	98.83 339	98.56 258	98.71 246	99.41 245	99.25 134	99.23 241	99.22 293	97.66 225	99.94 57	99.19 78	99.97 33	99.33 238

VPNet		99.46 61	99.37 74	99.71 83	99.82 45	99.59 112	99.48 78	99.70 100	99.81 33	99.69 109	99.58 190	97.66 225	99.86 197	99.17 83	99.44 277	99.67 68

Anonymous20231206		99.35 92	99.31 85	99.47 170	99.74 102	99.06 221	99.28 122	99.74 80	99.23 138	99.72 98	99.53 213	97.63 227	99.88 164	99.11 95	99.84 127	99.48 193

ETH3D cwj APD-0.16		98.50 245	98.16 261	99.51 158	99.04 319	99.39 156	98.47 267	99.47 229	96.70 328	98.78 299	99.33 269	97.62 228	99.86 197	94.69 343	99.38 286	99.28 249

test12				99.54 151	99.29 277	99.33 172		99.16 304		98.43 323		97.54 229	99.82 254		99.47 274	99.48 193

NR-MVSNet		99.40 77	99.31 85	99.68 89	99.43 233	99.55 121	99.73 20	99.50 218	99.46 104	99.88 32	99.36 260	97.54 229	99.87 177	98.97 108	99.87 112	99.63 100

MAR-MVS		98.24 269	97.92 280	99.19 240	98.78 347	99.65 93	99.17 156	99.14 306	95.36 345	98.04 341	98.81 345	97.47 231	99.72 302	95.47 330	99.06 312	98.21 349
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 82	99.30 90	99.65 104	99.88 24	99.25 188	98.78 237	99.88 18	98.66 210	99.96 8	99.79 64	97.45 232	99.93 71	99.34 55	99.99 12	99.78 32

PAPR		97.56 296	97.07 305	99.04 258	98.80 344	98.11 286	97.63 332	99.25 289	94.56 357	98.02 342	98.25 363	97.43 233	99.68 325	90.90 362	98.74 332	99.33 238

YYNet1		98.95 192	98.99 169	98.84 280	99.64 142	97.14 319	98.22 287	99.32 271	98.92 183	99.59 149	99.66 138	97.40 234	99.83 244	98.27 160	99.90 87	99.55 152

PVSNet	97.47 15	98.42 254	98.44 232	98.35 304	99.46 225	96.26 333	96.70 361	99.34 268	97.68 288	99.00 273	99.13 302	97.40 234	99.72 302	97.59 226	99.68 215	99.08 290

1121		98.56 237	98.24 251	99.52 155	99.49 209	99.24 193	99.30 114	99.22 296	95.77 340	98.52 318	99.29 277	97.39 236	99.85 216	95.79 322	99.34 293	99.46 202

MDA-MVSNet_test_wron		98.95 192	98.99 169	98.85 278	99.64 142	97.16 318	98.23 286	99.33 269	98.93 181	99.56 161	99.66 138	97.39 236	99.83 244	98.29 158	99.88 103	99.55 152

MG-MVS		98.52 243	98.39 237	98.94 264	99.15 301	97.39 313	98.18 288	99.21 300	98.89 188	99.23 241	99.63 155	97.37 238	99.74 297	94.22 347	99.61 242	99.69 55

OpenMVS_ROB	97.31 17	97.36 302	96.84 313	98.89 277	99.29 277	99.45 140	98.87 219	99.48 225	86.54 369	99.44 191	99.74 87	97.34 239	99.86 197	91.61 358	99.28 300	97.37 363

AdaColmap		98.60 231	98.35 242	99.38 202	99.12 306	99.22 197	98.67 247	99.42 244	97.84 283	98.81 294	99.27 281	97.32 240	99.81 270	95.14 335	99.53 264	99.10 284

test222					99.51 197	99.08 218	97.83 325	99.29 280	95.21 348	98.68 307	99.31 272	97.28 241			99.38 286	99.43 215

HQP_MVS		98.90 197	98.68 209	99.55 147	99.58 160	99.24 193	98.80 233	99.54 194	98.94 178	99.14 258	99.25 286	97.24 242	99.82 254	95.84 320	99.78 170	99.60 126

plane_prior6					99.47 220	99.26 184						97.24 242

GBi-Net		99.42 70	99.31 85	99.73 73	99.49 209	99.77 43	99.68 37	99.70 100	99.44 107	99.62 138	99.83 47	97.21 244	99.90 132	98.96 110	99.90 87	99.53 165

test1		99.42 70	99.31 85	99.73 73	99.49 209	99.77 43	99.68 37	99.70 100	99.44 107	99.62 138	99.83 47	97.21 244	99.90 132	98.96 110	99.90 87	99.53 165

FMVSNet2		99.35 92	99.28 97	99.55 147	99.49 209	99.35 169	99.45 81	99.57 177	99.44 107	99.70 106	99.74 87	97.21 244	99.87 177	99.03 101	99.94 65	99.44 209

BH-RMVSNet		98.41 255	98.14 263	99.21 237	99.21 291	98.47 263	98.60 250	98.26 343	98.35 246	98.93 278	99.31 272	97.20 247	99.66 334	94.32 345	99.10 311	99.51 177

MVS-HIRNet		97.86 282	98.22 253	96.76 341	99.28 280	91.53 368	98.38 275	92.60 372	99.13 157	99.31 227	99.96 11	97.18 248	99.68 325	98.34 154	99.83 137	99.07 295

PAPM_NR		98.36 259	98.04 267	99.33 212	99.48 215	98.93 234	98.79 236	99.28 283	97.54 295	98.56 316	98.57 353	97.12 249	99.69 314	94.09 349	98.90 323	99.38 226

CPTT-MVS		98.74 217	98.44 232	99.64 111	99.61 149	99.38 159	99.18 151	99.55 188	96.49 329	99.27 235	99.37 255	97.11 250	99.92 91	95.74 324	99.67 222	99.62 111

testtj		98.56 237	98.17 260	99.72 79	99.45 228	99.60 109	98.88 216	99.50 218	96.88 321	99.18 253	99.48 230	97.08 251	99.92 91	93.69 354	99.38 286	99.63 100

CNLPA		98.57 236	98.34 243	99.28 225	99.18 298	99.10 215	98.34 276	99.41 245	98.48 230	98.52 318	98.98 326	97.05 252	99.78 281	95.59 326	99.50 269	98.96 306

BH-untuned		98.22 271	98.09 265	98.58 296	99.38 247	97.24 316	98.55 258	98.98 316	97.81 284	99.20 252	98.76 347	97.01 253	99.65 341	94.83 339	98.33 344	98.86 315

VDD-MVS		99.20 136	99.11 128	99.44 179	99.43 233	98.98 224	99.50 74	98.32 342	99.80 36	99.56 161	99.69 117	96.99 254	99.85 216	98.99 104	99.73 195	99.50 183

PLC	97.35 16	98.36 259	97.99 270	99.48 168	99.32 270	99.24 193	98.50 265	99.51 214	95.19 349	98.58 314	98.96 331	96.95 255	99.83 244	95.63 325	99.25 304	99.37 229
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		99.11 160	98.93 178	99.66 99	99.30 275	99.42 149	98.42 273	99.37 262	99.04 169	99.57 154	99.20 297	96.89 256	99.86 197	98.66 138	99.87 112	99.70 51

CL-MVSNet_self_test		98.71 221	98.56 222	99.15 245	99.22 289	98.66 252	97.14 353	99.51 214	98.09 265	99.54 168	99.27 281	96.87 257	99.74 297	98.43 147	98.96 318	99.03 299

MSP-MVS		99.04 173	98.79 200	99.81 26	99.78 73	99.73 63	99.35 101	99.57 177	98.54 224	99.54 168	98.99 323	96.81 258	99.93 71	96.97 264	99.53 264	99.77 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
HQP2-MVS												96.67 259

HQP-MVS		98.36 259	98.02 269	99.39 198	99.31 271	98.94 230	97.98 312	99.37 262	97.45 300	98.15 333	98.83 342	96.67 259	99.70 308	94.73 340	99.67 222	99.53 165

CANet_DTU		98.91 195	98.85 191	99.09 251	98.79 345	98.13 283	98.18 288	99.31 275	99.48 95	98.86 289	99.51 219	96.56 261	99.95 45	99.05 100	99.95 52	99.19 266

pmmvs5		99.19 139	99.11 128	99.42 185	99.76 85	98.88 239	98.55 258	99.73 83	98.82 195	99.72 98	99.62 164	96.56 261	99.82 254	99.32 60	99.95 52	99.56 149

MVE	92.54 22	96.66 317	96.11 321	98.31 308	99.68 132	97.55 308	97.94 318	95.60 365	99.37 117	90.68 374	98.70 349	96.56 261	98.61 371	86.94 371	99.55 256	98.77 322
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
VNet		99.18 143	99.06 145	99.56 144	99.24 287	99.36 165	99.33 104	99.31 275	99.67 61	99.47 186	99.57 198	96.48 264	99.84 233	99.15 87	99.30 298	99.47 198

MDA-MVSNet-bldmvs		99.06 167	99.05 149	99.07 255	99.80 57	97.83 299	98.89 215	99.72 92	99.29 126	99.63 130	99.70 111	96.47 265	99.89 149	98.17 172	99.82 146	99.50 183

DeepMVS_CX				97.98 315	99.69 123	96.95 322		99.26 286	75.51 371	95.74 369	98.28 362	96.47 265	99.62 345	91.23 360	97.89 356	97.38 362

1112_ss		99.05 170	98.84 193	99.67 92	99.66 138	99.29 178	98.52 263	99.82 39	97.65 289	99.43 195	99.16 300	96.42 267	99.91 112	99.07 99	99.84 127	99.80 24

TR-MVS		97.44 299	97.15 304	98.32 306	98.53 356	97.46 310	98.47 267	97.91 348	96.85 323	98.21 332	98.51 357	96.42 267	99.51 359	92.16 357	97.29 361	97.98 356

miper_ehance_all_eth		98.59 234	98.59 215	98.59 295	98.98 326	97.07 320	97.49 341	99.52 211	98.50 227	99.52 175	99.37 255	96.41 269	99.71 306	97.86 197	99.62 235	99.00 305

Anonymous20240521		99.44 65	99.42 65	99.49 164	99.89 21	98.96 228	99.62 53	99.76 68	99.85 24	99.82 52	99.88 29	96.39 270	99.97 17	99.59 21	99.98 24	99.55 152

c3_l		98.72 220	98.71 204	98.72 290	99.12 306	97.22 317	97.68 331	99.56 182	98.90 185	99.54 168	99.48 230	96.37 271	99.73 300	97.88 193	99.88 103	99.21 260

sss		98.90 197	98.77 201	99.27 227	99.48 215	98.44 266	98.72 244	99.32 271	97.94 276	99.37 213	99.35 265	96.31 272	99.91 112	98.85 120	99.63 234	99.47 198

CDS-MVSNet		99.22 129	99.13 121	99.50 161	99.35 254	99.11 211	98.96 210	99.54 194	99.46 104	99.61 144	99.70 111	96.31 272	99.83 244	99.34 55	99.88 103	99.55 152
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
eth_miper_zixun_eth		98.68 224	98.71 204	98.60 294	99.10 312	96.84 326	97.52 340	99.54 194	98.94 178	99.58 151	99.48 230	96.25 274	99.76 291	98.01 183	99.93 73	99.21 260

SixPastTwentyTwo		99.42 70	99.30 90	99.76 47	99.92 14	99.67 86	99.70 28	99.14 306	99.65 67	99.89 26	99.90 22	96.20 275	99.94 57	99.42 46	99.92 77	99.67 68

MVS_0304		98.88 201	98.71 204	99.39 198	98.85 337	98.91 237	99.45 81	99.30 278	98.56 219	97.26 359	99.68 128	96.18 276	99.96 35	99.17 83	99.94 65	99.29 247

Test_1112_low_res		98.95 192	98.73 202	99.63 115	99.68 132	99.15 208	98.09 299	99.80 49	97.14 316	99.46 189	99.40 248	96.11 277	99.89 149	99.01 103	99.84 127	99.84 14

IterMVS		98.97 186	99.16 114	98.42 301	99.74 102	95.64 342	98.06 304	99.83 34	99.83 30	99.85 42	99.74 87	96.10 278	99.99 5	99.27 69	100.00 1	99.63 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		99.00 182	99.16 114	98.51 297	99.75 96	95.90 339	98.07 302	99.84 32	99.84 27	99.89 26	99.73 91	96.01 279	99.99 5	99.33 58	100.00 1	99.63 100

SCA		98.11 274	98.36 240	97.36 332	99.20 294	92.99 359	98.17 290	98.49 336	98.24 256	99.10 264	99.57 198	96.01 279	99.94 57	96.86 270	99.62 235	99.14 278

ETH3 D test6400		97.76 286	97.19 303	99.50 161	99.38 247	99.26 184	98.34 276	99.49 223	92.99 360	98.54 317	99.20 297	95.92 281	99.82 254	91.14 361	99.66 226	99.40 221

PVSNet_0	95.53 19	95.85 333	95.31 335	97.47 329	98.78 347	93.48 357	95.72 365	99.40 252	96.18 335	97.37 356	97.73 368	95.73 282	99.58 352	95.49 328	81.40 372	99.36 232

CMPMVS	77.52 23	98.50 245	98.19 258	99.41 193	98.33 361	99.56 118	99.01 196	99.59 166	95.44 344	99.57 154	99.80 58	95.64 283	99.46 363	96.47 294	99.92 77	99.21 260
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
BH-w/o		97.20 304	97.01 307	97.76 322	99.08 315	95.69 341	98.03 306	98.52 333	95.76 341	97.96 343	98.02 365	95.62 284	99.47 361	92.82 356	97.25 362	98.12 353

cascas		96.99 308	96.82 314	97.48 328	97.57 372	95.64 342	96.43 363	99.56 182	91.75 362	97.13 362	97.61 370	95.58 285	98.63 370	96.68 281	99.11 310	98.18 352

UnsupCasMVSNet_bld		98.55 240	98.27 249	99.40 195	99.56 180	99.37 162	97.97 315	99.68 109	97.49 299	99.08 266	99.35 265	95.41 286	99.82 254	97.70 214	98.19 349	99.01 304

bset_n11_16_dypcd		98.69 223	98.45 230	99.42 185	99.69 123	98.52 261	96.06 364	96.80 359	99.71 47	99.73 96	99.54 210	95.14 287	99.96 35	99.39 49	99.95 52	99.79 30

UnsupCasMVSNet_eth		98.83 206	98.57 219	99.59 131	99.68 132	99.45 140	98.99 203	99.67 114	99.48 95	99.55 166	99.36 260	94.92 288	99.86 197	98.95 114	96.57 365	99.45 204

EPP-MVSNet		99.17 147	99.00 164	99.66 99	99.80 57	99.43 146	99.70 28	99.24 292	99.48 95	99.56 161	99.77 77	94.89 289	99.93 71	98.72 133	99.89 95	99.63 100

WTY-MVS		98.59 234	98.37 239	99.26 229	99.43 233	98.40 269	98.74 241	99.13 308	98.10 263	99.21 247	99.24 291	94.82 290	99.90 132	97.86 197	98.77 328	99.49 188

miper_enhance_ethall		98.03 278	97.94 278	98.32 306	98.27 362	96.43 332	96.95 357	99.41 245	96.37 332	99.43 195	98.96 331	94.74 291	99.69 314	97.71 212	99.62 235	98.83 319

IS-MVSNet		99.03 174	98.85 191	99.55 147	99.80 57	99.25 188	99.73 20	99.15 305	99.37 117	99.61 144	99.71 104	94.73 292	99.81 270	97.70 214	99.88 103	99.58 140

miper_lstm_enhance		98.65 226	98.60 213	98.82 285	99.20 294	97.33 314	97.78 326	99.66 118	99.01 170	99.59 149	99.50 222	94.62 293	99.85 216	98.12 175	99.90 87	99.26 250

lessismore_v0				99.64 111	99.86 30	99.38 159		90.66 374		99.89 26	99.83 47	94.56 294	99.97 17	99.56 26	99.92 77	99.57 146

PCF-MVS	96.03 18	96.73 315	95.86 326	99.33 212	99.44 230	99.16 206	96.87 359	99.44 238	86.58 368	98.95 276	99.40 248	94.38 295	99.88 164	87.93 366	99.80 159	98.95 307
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDDNet		98.97 186	98.82 196	99.42 185	99.71 112	98.81 242	99.62 53	98.68 326	99.81 33	99.38 212	99.80 58	94.25 296	99.85 216	98.79 125	99.32 296	99.59 135

HY-MVS	98.23 9	98.21 272	97.95 274	98.99 260	99.03 320	98.24 276	99.61 58	98.72 325	96.81 325	98.73 303	99.51 219	94.06 297	99.86 197	96.91 267	98.20 347	98.86 315

test_method		91.72 339	92.32 342	89.91 355	93.49 377	70.18 379	90.28 368	99.56 182	61.71 373	95.39 370	99.52 215	93.90 298	99.94 57	98.76 129	98.27 346	99.62 111

DIV-MVS_self_test		98.54 241	98.42 234	98.92 268	99.03 320	97.80 301	97.46 342	99.59 166	98.90 185	99.60 146	99.46 238	93.87 299	99.78 281	97.97 187	99.89 95	99.18 268

cl____		98.54 241	98.41 235	98.92 268	99.03 320	97.80 301	97.46 342	99.59 166	98.90 185	99.60 146	99.46 238	93.85 300	99.78 281	97.97 187	99.89 95	99.17 270

EMVS		96.96 310	97.28 298	95.99 352	98.76 349	91.03 370	95.26 367	98.61 330	99.34 120	98.92 281	98.88 340	93.79 301	99.66 334	92.87 355	99.05 313	97.30 364

EPNet_dtu		97.62 293	97.79 287	97.11 339	96.67 373	92.31 362	98.51 264	98.04 344	99.24 136	95.77 368	99.47 235	93.78 302	99.66 334	98.98 106	99.62 235	99.37 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1111		97.74 287	98.16 261	96.49 347	99.60 151	89.86 376	99.71 27	91.21 373	99.89 11	99.88 32	99.87 32	93.73 303	99.90 132	99.56 26	99.99 12	99.70 51

K. test v3		98.87 203	98.60 213	99.69 88	99.93 13	99.46 135	99.74 17	94.97 366	99.78 39	99.88 32	99.88 29	93.66 304	99.97 17	99.61 19	99.95 52	99.64 95

ECVR-MVS		97.73 288	98.04 267	96.78 340	99.59 155	90.81 372	99.72 23	90.43 375	99.89 11	99.86 40	99.86 38	93.60 305	99.89 149	99.46 38	99.99 12	99.65 86

CHOSEN 280x420		98.41 255	98.41 235	98.40 302	99.34 264	95.89 340	96.94 358	99.44 238	98.80 198	99.25 237	99.52 215	93.51 306	99.98 7	98.94 115	99.98 24	99.32 241

CVMVSNet		98.61 229	98.88 188	97.80 321	99.58 160	93.60 356	99.26 127	99.64 135	99.66 65	99.72 98	99.67 134	93.26 307	99.93 71	99.30 63	99.81 154	99.87 9

Anonymous202405211		98.75 215	98.46 229	99.63 115	99.34 264	99.66 88	99.47 80	97.65 351	99.28 129	99.56 161	99.50 222	93.15 308	99.84 233	98.62 139	99.58 250	99.40 221

EPNet		98.13 273	97.77 288	99.18 242	94.57 376	97.99 292	99.24 135	97.96 346	99.74 42	97.29 358	99.62 164	93.13 309	99.97 17	98.59 140	99.83 137	99.58 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM		95.61 336	94.71 338	98.31 308	99.12 306	96.63 328	96.66 362	98.46 337	90.77 365	96.25 365	98.68 350	93.01 310	99.69 314	81.60 372	97.86 358	98.62 326

Vis-MVSNet (Re-imp)		98.77 212	98.58 218	99.34 210	99.78 73	98.88 239	99.61 58	99.56 182	99.11 161	99.24 240	99.56 201	93.00 311	99.78 281	97.43 235	99.89 95	99.35 235

E-PMN		97.14 307	97.43 295	96.27 349	98.79 345	91.62 367	95.54 366	99.01 315	99.44 107	98.88 285	99.12 306	92.78 312	99.68 325	94.30 346	99.03 315	97.50 360

FMVSNet3		98.80 210	98.63 212	99.32 216	99.13 304	98.72 247	99.10 179	99.48 225	99.23 138	99.62 138	99.64 145	92.57 313	99.86 197	98.96 110	99.90 87	99.39 224

HyFIR lowres test		98.91 195	98.64 210	99.73 73	99.85 33	99.47 131	98.07 302	99.83 34	98.64 212	99.89 26	99.60 182	92.57 313	100.00 1	99.33 58	99.97 33	99.72 45

RPMNet		98.60 231	98.53 225	98.83 282	99.05 317	98.12 284	99.30 114	99.62 140	99.86 19	99.16 254	99.74 87	92.53 315	99.92 91	98.75 130	98.77 328	98.44 339

h-mvs33		98.61 229	98.34 243	99.44 179	99.60 151	98.67 250	99.27 125	99.44 238	99.68 57	99.32 223	99.49 227	92.50 316	100.00 1	99.24 70	96.51 366	99.65 86

hse-mvs2		98.52 243	98.30 247	99.16 243	99.29 277	98.60 257	98.77 238	99.02 313	99.68 57	99.32 223	99.04 316	92.50 316	99.85 216	99.24 70	97.87 357	99.03 299

tpmvs		97.39 300	97.69 290	96.52 346	98.41 358	91.76 365	99.30 114	98.94 317	97.74 285	97.85 349	99.55 208	92.40 318	99.73 300	96.25 303	98.73 334	98.06 354

RRT_MVS		98.75 215	98.54 223	99.41 193	98.14 368	98.61 256	98.98 207	99.66 118	99.31 125	99.84 45	99.75 84	91.98 319	99.98 7	99.20 76	99.95 52	99.62 111

tpmrst		97.73 288	98.07 266	96.73 343	98.71 351	92.00 363	99.10 179	98.86 318	98.52 225	98.92 281	99.54 210	91.90 320	99.82 254	98.02 180	99.03 315	98.37 341

JIA-IIPM		98.06 277	97.92 280	98.50 298	98.59 354	97.02 321	98.80 233	98.51 334	99.88 16	97.89 346	99.87 32	91.89 321	99.90 132	98.16 173	97.68 359	98.59 328

CR-MVSNet		98.35 262	98.20 255	98.83 282	99.05 317	98.12 284	99.30 114	99.67 114	97.39 304	99.16 254	99.79 64	91.87 322	99.91 112	98.78 128	98.77 328	98.44 339

Patchmtry		98.78 211	98.54 223	99.49 164	98.89 333	99.19 204	99.32 107	99.67 114	99.65 67	99.72 98	99.79 64	91.87 322	99.95 45	98.00 184	99.97 33	99.33 238

MDTV_nov1_ep13_2view						91.44 369	99.14 166		97.37 305	99.21 247		91.78 324		96.75 277		99.03 299

PatchT		98.45 252	98.32 246	98.83 282	98.94 328	98.29 275	99.24 135	98.82 321	99.84 27	99.08 266	99.76 80	91.37 325	99.94 57	98.82 123	99.00 317	98.26 346

test_yl		98.25 267	97.95 274	99.13 247	99.17 299	98.47 263	99.00 198	98.67 328	98.97 173	99.22 245	99.02 321	91.31 326	99.69 314	97.26 247	98.93 319	99.24 253

DCV-MVSNet		98.25 267	97.95 274	99.13 247	99.17 299	98.47 263	99.00 198	98.67 328	98.97 173	99.22 245	99.02 321	91.31 326	99.69 314	97.26 247	98.93 319	99.24 253

baseline1		97.73 288	97.33 297	98.96 262	99.30 275	97.73 303	99.40 88	98.42 338	99.33 123	99.46 189	99.21 295	91.18 328	99.82 254	98.35 153	91.26 371	99.32 241

tpm cat1		96.78 313	96.98 308	96.16 351	98.85 337	90.59 374	99.08 186	99.32 271	92.37 361	97.73 355	99.46 238	91.15 329	99.69 314	96.07 309	98.80 325	98.21 349

LFMVS		98.46 251	98.19 258	99.26 229	99.24 287	98.52 261	99.62 53	96.94 358	99.87 17	99.31 227	99.58 190	91.04 330	99.81 270	98.68 137	99.42 282	99.45 204

MDTV_nov1_ep13			97.73 289		98.70 352	90.83 371	99.15 164	98.02 345	98.51 226	98.82 293	99.61 173	90.98 331	99.66 334	96.89 269	98.92 321

MIMVSNet		98.43 253	98.20 255	99.11 249	99.53 187	98.38 272	99.58 67	98.61 330	98.96 176	99.33 221	99.76 80	90.92 332	99.81 270	97.38 238	99.76 177	99.15 274

ADS-MVSNet2		97.78 285	97.66 293	98.12 314	99.14 302	95.36 344	99.22 142	98.75 324	96.97 319	98.25 329	99.64 145	90.90 333	99.94 57	96.51 291	99.56 252	99.08 290

ADS-MVSNet		97.72 291	97.67 292	97.86 319	99.14 302	94.65 350	99.22 142	98.86 318	96.97 319	98.25 329	99.64 145	90.90 333	99.84 233	96.51 291	99.56 252	99.08 290

alignmvs		98.28 265	97.96 273	99.25 232	99.12 306	98.93 234	99.03 193	98.42 338	99.64 69	98.72 304	97.85 367	90.86 335	99.62 345	98.88 119	99.13 309	99.19 266

sam_mvs1												90.81 336				99.14 278

PatchmatchNet		97.65 292	97.80 285	97.18 337	98.82 342	92.49 361	99.17 156	98.39 340	98.12 262	98.79 297	99.58 190	90.71 337	99.89 149	97.23 251	99.41 283	99.16 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											99.62 164	90.58 338	99.94 57

Patchmatch-RL test		98.60 231	98.36 240	99.33 212	99.77 81	99.07 219	98.27 283	99.87 20	98.91 184	99.74 92	99.72 97	90.57 339	99.79 278	98.55 142	99.85 123	99.11 282

sam_mvs												90.52 340

pmmvs3		98.08 276	97.80 285	98.91 270	99.41 239	97.69 305	97.87 323	99.66 118	95.87 338	99.50 182	99.51 219	90.35 341	99.97 17	98.55 142	99.47 274	99.08 290

test_post											52.41 380	90.25 342	99.86 197

Patchmatch-test		98.10 275	97.98 272	98.48 299	99.27 282	96.48 330	99.40 88	99.07 309	98.81 196	99.23 241	99.57 198	90.11 343	99.87 177	96.69 280	99.64 232	99.09 287

test-LLR		97.15 305	96.95 309	97.74 324	98.18 365	95.02 347	97.38 344	96.10 360	98.00 268	97.81 350	98.58 351	90.04 344	99.91 112	97.69 220	98.78 326	98.31 342

test0.0.03 1		97.37 301	96.91 312	98.74 289	97.72 369	97.57 307	97.60 334	97.36 357	98.00 268	99.21 247	98.02 365	90.04 344	99.79 278	98.37 150	95.89 369	98.86 315

GA-MVS		97.99 281	97.68 291	98.93 267	99.52 192	98.04 291	97.19 352	99.05 312	98.32 252	98.81 294	98.97 329	89.89 346	99.41 364	98.33 155	99.05 313	99.34 237

test_post1							99.14 166				51.63 381	89.54 347	99.82 254	96.86 270

AUN-MVS		97.82 283	97.38 296	99.14 246	99.27 282	98.53 259	98.72 244	99.02 313	98.10 263	97.18 361	99.03 320	89.26 348	99.85 216	97.94 189	97.91 355	99.03 299

MVSTER		98.47 250	98.22 253	99.24 234	99.06 316	98.35 274	99.08 186	99.46 233	99.27 130	99.75 83	99.66 138	88.61 349	99.85 216	99.14 93	99.92 77	99.52 175

baseline2		96.83 312	96.28 318	98.46 300	99.09 314	96.91 324	98.83 225	93.87 371	97.23 311	96.23 367	98.36 360	88.12 350	99.90 132	96.68 281	98.14 351	98.57 331

cl22		97.56 296	97.28 298	98.40 302	98.37 360	96.75 327	97.24 351	99.37 262	97.31 308	99.41 205	99.22 293	87.30 351	99.37 365	97.70 214	99.62 235	99.08 290

dp		96.86 311	97.07 305	96.24 350	98.68 353	90.30 375	99.19 150	98.38 341	97.35 306	98.23 331	99.59 188	87.23 352	99.82 254	96.27 302	98.73 334	98.59 328

ET-MVSNet_ETH3D		96.78 313	96.07 322	98.91 270	99.26 284	97.92 298	97.70 330	96.05 363	97.96 275	92.37 373	98.43 359	87.06 353	99.90 132	98.27 160	97.56 360	98.91 311

thres100view900		96.39 321	96.03 323	97.47 329	99.63 144	95.93 338	99.18 151	97.57 352	98.75 206	98.70 306	97.31 374	87.04 354	99.67 330	87.62 367	98.51 341	96.81 365

thres600view7		96.60 318	96.16 320	97.93 317	99.63 144	96.09 337	99.18 151	97.57 352	98.77 202	98.72 304	97.32 373	87.04 354	99.72 302	88.57 364	98.62 337	97.98 356

tfpn200view9		96.30 324	95.89 324	97.53 327	99.58 160	96.11 335	99.00 198	97.54 355	98.43 232	98.52 318	96.98 376	86.85 356	99.67 330	87.62 367	98.51 341	96.81 365

thres400		96.40 320	95.89 324	97.92 318	99.58 160	96.11 335	99.00 198	97.54 355	98.43 232	98.52 318	96.98 376	86.85 356	99.67 330	87.62 367	98.51 341	97.98 356

thres200		96.09 327	95.68 330	97.33 334	99.48 215	96.22 334	98.53 262	97.57 352	98.06 267	98.37 325	96.73 378	86.84 358	99.61 349	86.99 370	98.57 338	96.16 368

test_part1		98.63 227	98.26 250	99.75 57	99.40 242	99.49 128	99.67 41	99.68 109	99.86 19	99.88 32	99.86 38	86.73 359	99.93 71	99.34 55	99.97 33	99.81 23

tpm		97.15 305	96.95 309	97.75 323	98.91 329	94.24 352	99.32 107	97.96 346	97.71 287	98.29 326	99.32 270	86.72 360	99.92 91	98.10 178	96.24 368	99.09 287

EPMVS		96.53 319	96.32 317	97.17 338	98.18 365	92.97 360	99.39 90	89.95 376	98.21 258	98.61 311	99.59 188	86.69 361	99.72 302	96.99 263	99.23 308	98.81 320

CostFormer		96.71 316	96.79 315	96.46 348	98.90 330	90.71 373	99.41 87	98.68 326	94.69 356	98.14 337	99.34 268	86.32 362	99.80 275	97.60 225	98.07 353	98.88 313

thisisatest0515		96.98 309	96.42 316	98.66 293	99.42 238	97.47 309	97.27 349	94.30 369	97.24 310	99.15 256	98.86 341	85.01 363	99.87 177	97.10 259	99.39 285	98.63 325

tpm2		96.35 322	96.22 319	96.73 343	98.88 336	91.75 366	99.21 144	98.51 334	93.27 359	97.89 346	99.21 295	84.83 364	99.70 308	96.04 310	98.18 350	98.75 323

tttt0517		97.62 293	97.20 302	98.90 276	99.76 85	97.40 312	99.48 78	94.36 368	99.06 168	99.70 106	99.49 227	84.55 365	99.94 57	98.73 132	99.65 230	99.36 232

thisisatest0530		97.45 298	96.95 309	98.94 264	99.68 132	97.73 303	99.09 183	94.19 370	98.61 216	99.56 161	99.30 274	84.30 366	99.93 71	98.27 160	99.54 262	99.16 272

FPMVS		96.32 323	95.50 331	98.79 286	99.60 151	98.17 282	98.46 272	98.80 322	97.16 315	96.28 364	99.63 155	82.19 367	99.09 367	88.45 365	98.89 324	99.10 284

gg-mvs-nofinetune		95.87 332	95.17 336	97.97 316	98.19 364	96.95 322	99.69 34	89.23 377	99.89 11	96.24 366	99.94 13	81.19 368	99.51 359	93.99 352	98.20 347	97.44 361

DWT-MVSNet_test		96.03 329	95.80 328	96.71 345	98.50 357	91.93 364	99.25 134	97.87 349	95.99 337	96.81 363	97.61 370	81.02 369	99.66 334	97.20 254	97.98 354	98.54 332

GG-mvs-BLEND				97.36 332	97.59 370	96.87 325	99.70 28	88.49 378		94.64 372	97.26 375	80.66 370	99.12 366	91.50 359	96.50 367	96.08 369

FMVSNet5		97.80 284	97.25 300	99.42 185	98.83 339	98.97 226	99.38 92	99.80 49	98.87 189	99.25 237	99.69 117	80.60 371	99.91 112	98.96 110	99.90 87	99.38 226

TESTMET0.1,1		96.24 325	95.84 327	97.41 331	98.24 363	93.84 355	97.38 344	95.84 364	98.43 232	97.81 350	98.56 354	79.77 372	99.89 149	97.77 204	98.77 328	98.52 333

KD-MVS_2432*1600		95.89 330	95.41 333	97.31 335	94.96 374	93.89 353	97.09 354	99.22 296	97.23 311	98.88 285	99.04 316	79.23 373	99.54 354	96.24 304	96.81 363	98.50 337

miper_refine_blended		95.89 330	95.41 333	97.31 335	94.96 374	93.89 353	97.09 354	99.22 296	97.23 311	98.88 285	99.04 316	79.23 373	99.54 354	96.24 304	96.81 363	98.50 337

test-mter		96.23 326	95.73 329	97.74 324	98.18 365	95.02 347	97.38 344	96.10 360	97.90 277	97.81 350	98.58 351	79.12 375	99.91 112	97.69 220	98.78 326	98.31 342

RRT_test8_iter05		97.35 303	97.25 300	97.63 326	98.81 343	93.13 358	99.26 127	99.89 15	99.51 92	99.83 50	99.68 128	79.03 376	99.88 164	99.53 30	99.72 201	99.89 8

test2506		94.73 338	94.59 340	95.15 353	99.59 155	85.90 378	99.75 15	74.01 379	99.89 11	99.71 103	99.86 38	79.00 377	99.90 132	99.52 32	99.99 12	99.65 86

IB-MVS	95.41 20	95.30 337	94.46 341	97.84 320	98.76 349	95.33 345	97.33 347	96.07 362	96.02 336	95.37 371	97.41 372	76.17 378	99.96 35	97.54 228	95.44 370	98.22 348
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		29.31 340	33.05 345	18.08 356	25.93 380	12.24 380	97.53 338	10.93 381	11.78 374	24.21 375	50.08 383	21.04 379	8.60 375	23.51 373	32.43 374	33.39 371

testmvs		28.94 341	33.33 343	15.79 357	26.03 379	9.81 381	96.77 360	15.67 380	11.55 375	23.87 376	50.74 382	19.03 380	8.53 376	23.21 374	33.07 373	29.03 372

test_blank		8.33 344	11.11 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet_test		8.33 344	11.11 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

sosnet-low-res		8.33 344	11.11 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

sosnet		8.33 344	11.11 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uncertanet		8.33 344	11.11 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

Regformer		8.33 344	11.11 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

ab-mvs-re		8.26 351	11.02 354	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	99.16 300	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet		8.33 344	11.11 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	100.00 1	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

FOURS1					99.83 38	99.89 8	99.74 17	99.71 95	99.69 55	99.63 130

MSC_two_6792asdad				99.74 63	99.03 320	99.53 123		99.23 293					99.92 91	97.77 204	99.69 210	99.78 32

No_MVS				99.74 63	99.03 320	99.53 123		99.23 293					99.92 91	97.77 204	99.69 210	99.78 32

eth-test2					0.00 381

eth-test					0.00 381

IU-MVS					99.69 123	99.77 43		99.22 296	97.50 298	99.69 109				97.75 208	99.70 207	99.77 35

save fliter					99.53 187	99.25 188	98.29 281	99.38 261	99.07 164

test_0728_SECOND				99.83 21	99.70 120	99.79 38	99.14 166	99.61 147					99.92 91	97.88 193	99.72 201	99.77 35

GSMVS																99.14 278

test_part2					99.62 148	99.67 86				99.55 166

MTGPA								99.53 203

MTMP							99.09 183	98.59 332

gm-plane-assit					97.59 370	89.02 377			93.47 358		98.30 361		99.84 233	96.38 298

test9_res														95.10 336	99.44 277	99.50 183

agg_prior2														94.58 344	99.46 276	99.50 183

agg_prior					99.35 254	99.36 165		99.39 255		97.76 353			99.85 216

test_prior4						99.19 204	98.00 309

test_prior				99.46 173	99.35 254	99.22 197		99.39 255					99.69 314			99.48 193

旧先验2							97.94 318		95.33 346	98.94 277			99.88 164	96.75 277

新几何2							98.04 305

无先验							98.01 307	99.23 293	95.83 339				99.85 216	95.79 322		99.44 209

原ACMM2							97.92 320

testdata2													99.89 149	95.99 313

testdata1							97.72 328		97.86 282

plane_prior7					99.58 160	99.38 159

plane_prior5								99.54 194					99.82 254	95.84 320	99.78 170	99.60 126

plane_prior4											99.25 286

plane_prior3						99.31 175			98.36 241	99.14 258

plane_prior2							98.80 233		98.94 178

plane_prior1					99.51 197

plane_prior						99.24 193	98.42 273		97.87 279						99.71 205

n2								0.00 382

nn								0.00 382

door-mid								99.83 34

test11								99.29 280

door								99.77 63

HQP5-MVS						98.94 230

HQP-NCC					99.31 271		97.98 312		97.45 300	98.15 333

ACMP_Plane					99.31 271		97.98 312		97.45 300	98.15 333

BP-MVS														94.73 340

HQP4-MVS										98.15 333			99.70 308			99.53 165

HQP3-MVS								99.37 262							99.67 222

NP-MVS					99.40 242	99.13 209					98.83 342

ACMMP++_ref															99.94 65

ACMMP++															99.79 164

High-resolution multi-view benchmark