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.

UA-Net		99.42 40	99.29 47	99.80 43	99.62 130	99.55 80	99.50 132	99.70 15	98.79 54	99.77 36	99.96 1	97.45 121	99.96 19	98.92 72	99.90 23	99.89 2

DeepC-MVS	98.35 2	99.30 58	99.19 64	99.64 80	99.82 38	99.23 120	99.62 70	99.55 67	98.94 38	99.63 79	99.95 2	95.82 177	99.94 57	99.37 25	99.97 3	99.73 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OurMVSNet-221017-0		97.88 221	97.77 214	98.19 276	98.71 319	96.53 296	99.88 1	99.00 304	97.79 154	98.78 255	99.94 3	91.68 299	99.35 268	97.21 251	96.99 265	98.69 257

ECVR-MVS11		98.04 199	98.11 174	97.83 300	99.74 72	93.82 346	99.58 91	95.40 368	99.12 8	99.65 74	99.93 4	90.73 314	99.84 139	99.43 22	99.38 142	99.82 38

ECVR-MVS		98.04 199	98.05 183	98.00 289	99.74 72	94.37 341	99.59 83	94.98 369	99.13 7	99.66 68	99.93 4	90.67 315	99.84 139	99.40 23	99.38 142	99.80 54

SixPastTwentyTwo		97.50 274	97.33 270	98.03 284	98.65 324	96.23 306	99.77 27	98.68 338	97.14 220	97.90 317	99.93 4	90.45 316	99.18 296	97.00 265	96.43 275	98.67 269

SD-MVS		99.41 44	99.52 6	99.05 170	99.74 72	99.68 54	99.46 156	99.52 91	99.11 9	99.88 5	99.91 7	99.43 1	97.70 355	98.72 108	99.93 10	99.77 69
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
ACMH	97.28 8	98.10 189	97.99 189	98.44 255	99.41 189	96.96 282	99.60 77	99.56 57	98.09 120	98.15 308	99.91 7	90.87 313	99.70 207	98.88 76	97.45 249	98.67 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		97.55 268	97.02 285	99.16 161	99.49 168	98.12 227	99.38 193	99.30 265	95.35 316	99.68 57	99.90 9	82.62 360	99.93 72	99.31 33	98.13 221	99.42 178

QAPM		98.67 147	98.30 164	99.80 43	99.20 242	99.67 57	99.77 27	99.72 11	94.74 327	98.73 259	99.90 9	95.78 178	99.98 6	96.96 269	99.88 36	99.76 74

3Dnovator	97.25 9	99.24 69	99.05 77	99.81 41	99.12 260	99.66 59	99.84 9	99.74 10	99.09 13	98.92 234	99.90 9	95.94 171	99.98 6	98.95 67	99.92 11	99.79 59

Anonymous20240529		98.09 190	97.68 224	99.34 133	99.66 114	98.44 211	99.40 184	99.43 202	93.67 337	99.22 178	99.89 12	90.23 321	99.93 72	99.26 39	98.33 206	99.66 115

CHOSEN 1792x2688		99.19 72	99.10 72	99.45 120	99.89 9	98.52 203	99.39 188	99.94 1	98.73 58	99.11 199	99.89 12	95.50 187	99.94 57	99.50 10	99.97 3	99.89 2

RPSCF		98.22 174	98.62 141	96.99 323	99.82 38	91.58 360	99.72 35	99.44 194	96.61 263	99.66 68	99.89 12	95.92 172	99.82 157	97.46 238	99.10 166	99.57 145

3Dnovator+	97.12 13	99.18 74	98.97 94	99.82 38	99.17 253	99.68 54	99.81 15	99.51 104	99.20 4	98.72 260	99.89 12	95.68 182	99.97 11	98.86 85	99.86 51	99.81 44

COLMAP_ROB	97.56 6	98.86 122	98.75 124	99.17 160	99.88 12	98.53 199	99.34 209	99.59 43	97.55 179	98.70 267	99.89 12	95.83 176	99.90 109	98.10 177	99.90 23	99.08 204
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_djsdf		98.67 147	98.57 148	98.98 179	98.70 320	98.91 166	99.88 1	99.46 173	97.55 179	99.22 178	99.88 17	95.73 180	99.28 278	99.03 59	97.62 233	98.75 241

DP-MVS		99.16 78	98.95 98	99.78 48	99.77 51	99.53 85	99.41 176	99.50 124	97.03 234	99.04 215	99.88 17	97.39 122	99.92 83	98.66 118	99.90 23	99.87 12

TDRefinement		95.42 312	94.57 318	97.97 291	89.83 370	96.11 308	99.48 148	98.75 327	96.74 252	96.68 340	99.88 17	88.65 337	99.71 201	98.37 156	82.74 359	98.09 337

EPP-MVSNet		99.13 82	98.99 90	99.53 102	99.65 119	99.06 143	99.81 15	99.33 248	97.43 195	99.60 90	99.88 17	97.14 131	99.84 139	99.13 51	98.94 178	99.69 105

OpenMVS	96.50 16	98.47 155	98.12 173	99.52 108	99.04 276	99.53 85	99.82 13	99.72 11	94.56 330	98.08 310	99.88 17	94.73 219	99.98 6	97.47 237	99.76 100	99.06 210

lessismore_v0				97.79 304	98.69 321	95.44 324		94.75 370		95.71 349	99.87 22	88.69 336	99.32 273	95.89 299	94.93 312	98.62 291

Vis-MVSNet		99.12 88	98.97 94	99.56 94	99.78 46	99.10 138	99.68 45	99.66 27	98.49 72	99.86 12	99.87 22	94.77 216	99.84 139	99.19 44	99.41 141	99.74 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH+	97.24 10	97.92 218	97.78 212	98.32 266	99.46 177	96.68 292	99.56 104	99.54 74	98.41 80	97.79 322	99.87 22	90.18 322	99.66 216	98.05 186	97.18 261	98.62 291

ACMMP_NAP		99.47 23	99.34 28	99.88 6	99.87 16	99.86 13	99.47 153	99.48 145	98.05 130	99.76 40	99.86 25	98.82 47	99.93 72	98.82 97	99.91 16	99.84 20

RRT_MVS		98.60 152	98.44 153	99.05 170	98.88 294	99.14 133	99.49 142	99.38 222	97.76 157	99.29 160	99.86 25	95.38 190	99.36 264	98.81 98	97.16 262	98.64 281

casdiffmvs		99.13 82	98.98 93	99.56 94	99.65 119	99.16 128	99.56 104	99.50 124	98.33 92	99.41 131	99.86 25	95.92 172	99.83 150	99.45 19	99.16 158	99.70 102

PVSNet_Blended_VisFu		99.36 51	99.28 51	99.61 85	99.86 22	99.07 142	99.47 153	99.93 2	97.66 170	99.71 50	99.86 25	97.73 116	99.96 19	99.47 17	99.82 80	99.79 59

IS-MVSNet		99.05 103	98.87 107	99.57 92	99.73 79	99.32 108	99.75 31	99.20 283	98.02 134	99.56 98	99.86 25	96.54 152	99.67 213	98.09 178	99.13 162	99.73 87

USDC		97.34 281	97.20 279	97.75 305	99.07 270	95.20 328	98.51 340	99.04 302	97.99 135	98.31 301	99.86 25	89.02 332	99.55 236	95.67 306	97.36 256	98.49 311

TSAR-MVS + MP.		99.58 5	99.50 8	99.81 41	99.91 1	99.66 59	99.63 64	99.39 216	98.91 43	99.78 34	99.85 31	99.36 2	99.94 57	98.84 90	99.88 36	99.82 38
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tmp_tt		82.80 332	81.52 335	86.66 347	66.61 377	68.44 375	92.79 366	97.92 352	68.96 366	80.04 369	99.85 31	85.77 353	96.15 365	97.86 197	43.89 370	95.39 361

AllTest		98.87 119	98.72 125	99.31 139	99.86 22	98.48 209	99.56 104	99.61 35	97.85 145	99.36 146	99.85 31	95.95 169	99.85 134	96.66 286	99.83 74	99.59 140

TestCases				99.31 139	99.86 22	98.48 209		99.61 35	97.85 145	99.36 146	99.85 31	95.95 169	99.85 134	96.66 286	99.83 74	99.59 140

VDD-MVS		97.73 249	97.35 265	98.88 201	99.47 176	97.12 264	99.34 209	98.85 322	98.19 106	99.67 63	99.85 31	82.98 358	99.92 83	99.49 14	98.32 210	99.60 136

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 51	99.89 4	99.75 31	99.56 57	99.02 18	99.88 5	99.85 31	99.18 10	99.96 19	99.22 41	99.92 11	99.90 1

DeepPCF-MVS	98.18 3	98.81 134	99.37 21	97.12 322	99.60 138	91.75 359	98.61 333	99.44 194	99.35 1	99.83 19	99.85 31	98.70 65	99.81 161	99.02 61	99.91 16	99.81 44

ACMM	97.58 5	98.37 165	98.34 160	98.48 246	99.41 189	97.10 265	99.56 104	99.45 185	98.53 69	99.04 215	99.85 31	93.00 262	99.71 201	98.74 104	97.45 249	98.64 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 64	99.12 70	99.74 59	99.18 247	99.75 43	99.56 104	99.57 51	98.45 76	99.49 114	99.85 31	97.77 115	99.94 57	98.33 160	99.84 65	99.52 155

DPE-MVS		99.46 25	99.32 32	99.91 2	99.78 46	99.88 8	99.36 200	99.51 104	98.73 58	99.88 5	99.84 40	98.72 63	99.96 19	98.16 174	99.87 40	99.88 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVG-OURS		98.73 142	98.68 130	98.88 201	99.70 97	97.73 246	98.92 305	99.55 67	98.52 70	99.45 119	99.84 40	95.27 195	99.91 94	98.08 182	98.84 186	99.00 216

baseline		99.15 79	99.02 85	99.53 102	99.66 114	99.14 133	99.72 35	99.48 145	98.35 88	99.42 127	99.84 40	96.07 165	99.79 169	99.51 9	99.14 161	99.67 112

ACMMP		99.45 27	99.32 32	99.82 38	99.89 9	99.67 57	99.62 70	99.69 18	98.12 115	99.63 79	99.84 40	98.73 62	99.96 19	98.55 139	99.83 74	99.81 44
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
EI-MVSNet-UG-set		99.58 5	99.57 1	99.64 80	99.78 46	99.14 133	99.60 77	99.45 185	99.01 21	99.90 3	99.83 44	98.98 26	99.93 72	99.59 2	99.95 6	99.86 13

EI-MVSNet		98.67 147	98.67 131	98.68 228	99.35 203	97.97 232	99.50 132	99.38 222	96.93 243	99.20 184	99.83 44	97.87 111	99.36 264	98.38 154	97.56 238	98.71 249

CVMVSNet		98.57 153	98.67 131	98.30 268	99.35 203	95.59 317	99.50 132	99.55 67	98.60 66	99.39 138	99.83 44	94.48 230	99.45 243	98.75 103	98.56 199	99.85 16

LPG-MVS_test		98.22 174	98.13 172	98.49 244	99.33 208	97.05 271	99.58 91	99.55 67	97.46 188	99.24 173	99.83 44	92.58 278	99.72 195	98.09 178	97.51 242	98.68 262

LGP-MVS_train				98.49 244	99.33 208	97.05 271		99.55 67	97.46 188	99.24 173	99.83 44	92.58 278	99.72 195	98.09 178	97.51 242	98.68 262

SteuartSystems-ACMMP		99.54 10	99.42 14	99.87 12	99.82 38	99.81 27	99.59 83	99.51 104	98.62 64	99.79 29	99.83 44	99.28 4	99.97 11	98.48 144	99.90 23	99.84 20
Skip Steuart: Steuart Systems R&D Blog.
XXY-MVS		98.38 164	98.09 178	99.24 154	99.26 228	99.32 108	99.56 104	99.55 67	97.45 191	98.71 261	99.83 44	93.23 258	99.63 228	98.88 76	96.32 278	98.76 239

SR-MVS-dyc-post		99.45 27	99.31 39	99.85 28	99.76 54	99.82 23	99.63 64	99.52 91	98.38 83	99.76 40	99.82 51	98.53 75	99.95 46	98.61 125	99.81 83	99.77 69

RE-MVS-def			99.34 28		99.76 54	99.82 23	99.63 64	99.52 91	98.38 83	99.76 40	99.82 51	98.75 59		98.61 125	99.81 83	99.77 69

test0726					99.85 26	99.89 4	99.62 70	99.50 124	99.10 10	99.86 12	99.82 51	98.94 34

SMA-MVS		99.44 31	99.30 43	99.85 28	99.73 79	99.83 17	99.56 104	99.47 163	97.45 191	99.78 34	99.82 51	99.18 10	99.91 94	98.79 99	99.89 33	99.81 44
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
nrg030		98.64 150	98.42 155	99.28 149	99.05 275	99.69 52	99.81 15	99.46 173	98.04 131	99.01 218	99.82 51	96.69 148	99.38 257	99.34 30	94.59 316	98.78 233

FC-MVSNet-test		98.75 141	98.62 141	99.15 163	99.08 269	99.45 98	99.86 8	99.60 40	98.23 102	98.70 267	99.82 51	96.80 142	99.22 288	99.07 57	96.38 276	98.79 232

EI-MVSNet-Vis-set		99.58 5	99.56 3	99.64 80	99.78 46	99.15 132	99.61 76	99.45 185	99.01 21	99.89 4	99.82 51	99.01 19	99.92 83	99.56 5	99.95 6	99.85 16

APD-MVS_3200maxsize		99.48 20	99.35 26	99.85 28	99.76 54	99.83 17	99.63 64	99.54 74	98.36 87	99.79 29	99.82 51	98.86 43	99.95 46	98.62 122	99.81 83	99.78 67

EU-MVSNet		97.98 210	98.03 185	97.81 303	98.72 317	96.65 293	99.66 52	99.66 27	98.09 120	98.35 299	99.82 51	95.25 198	98.01 348	97.41 243	95.30 303	98.78 233

APD-MVS		99.27 64	99.08 75	99.84 35	99.75 64	99.79 33	99.50 132	99.50 124	97.16 219	99.77 36	99.82 51	98.78 51	99.94 57	97.56 228	99.86 51	99.80 54
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.12 88	99.08 75	99.24 154	99.46 177	98.55 197	99.51 126	99.46 173	98.09 120	99.45 119	99.82 51	98.34 93	99.51 238	98.70 110	98.93 179	99.67 112

DeepC-MVS_fast	98.69 1	99.49 16	99.39 18	99.77 50	99.63 124	99.59 73	99.36 200	99.46 173	99.07 16	99.79 29	99.82 51	98.85 44	99.92 83	98.68 115	99.87 40	99.82 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.13 82	99.02 85	99.45 120	99.57 144	98.63 191	99.07 269	99.34 241	98.99 28	99.61 86	99.82 51	97.98 110	99.87 125	97.00 265	99.80 87	99.85 16

DVP-MVS++.		99.59 3	99.50 8	99.88 6	99.51 156	99.88 8	99.87 5	99.51 104	98.99 28	99.88 5	99.81 64	99.27 5	99.96 19	98.85 87	99.80 87	99.81 44

test_one_0601					99.81 41	99.88 8		99.49 132	98.97 34	99.65 74	99.81 64	99.09 14

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 33	99.90 2	99.60 77	99.48 145	99.08 14	99.91 1	99.81 64	99.20 7	99.96 19	98.91 73	99.85 58	99.79 59

test_241102_TWO								99.48 145	99.08 14	99.88 5	99.81 64	98.94 34	99.96 19	98.91 73	99.84 65	99.88 7

OPM-MVS		98.19 178	98.10 175	98.45 252	98.88 294	97.07 269	99.28 221	99.38 222	98.57 67	99.22 178	99.81 64	92.12 289	99.66 216	98.08 182	97.54 240	98.61 300
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
zzz-MVS		99.49 16	99.36 23	99.89 4	99.90 4	99.86 13	99.36 200	99.47 163	98.79 54	99.68 57	99.81 64	98.43 84	99.97 11	98.88 76	99.90 23	99.83 31

MTAPA		99.52 14	99.39 18	99.89 4	99.90 4	99.86 13	99.66 52	99.47 163	98.79 54	99.68 57	99.81 64	98.43 84	99.97 11	98.88 76	99.90 23	99.83 31

FIs		98.78 138	98.63 136	99.23 156	99.18 247	99.54 82	99.83 12	99.59 43	98.28 95	98.79 254	99.81 64	96.75 146	99.37 260	99.08 56	96.38 276	98.78 233

mvs_tets		98.40 163	98.23 167	98.91 193	98.67 323	98.51 205	99.66 52	99.53 85	98.19 106	98.65 276	99.81 64	92.75 268	99.44 248	99.31 33	97.48 248	98.77 237

mvs_anonymous		99.03 106	98.99 90	99.16 161	99.38 198	98.52 203	99.51 126	99.38 222	97.79 154	99.38 141	99.81 64	97.30 127	99.45 243	99.35 26	98.99 176	99.51 161

TSAR-MVS + GP.		99.36 51	99.36 23	99.36 132	99.67 105	98.61 194	99.07 269	99.33 248	99.00 25	99.82 22	99.81 64	99.06 16	99.84 139	99.09 55	99.42 140	99.65 119

abl_6		99.44 31	99.31 39	99.83 36	99.85 26	99.75 43	99.66 52	99.59 43	98.13 113	99.82 22	99.81 64	98.60 72	99.96 19	98.46 148	99.88 36	99.79 59

RRT_test8_iter05		97.72 251	97.60 232	98.08 281	99.23 234	96.08 309	99.63 64	99.49 132	97.54 182	98.94 231	99.81 64	87.99 345	99.35 268	99.21 43	96.51 273	98.81 230

EPNet		98.86 122	98.71 127	99.30 143	97.20 355	98.18 222	99.62 70	98.91 316	99.28 2	98.63 278	99.81 64	95.96 168	99.99 1	99.24 40	99.72 109	99.73 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ab-mvs		98.86 122	98.63 136	99.54 96	99.64 121	99.19 123	99.44 161	99.54 74	97.77 156	99.30 157	99.81 64	94.20 238	99.93 72	99.17 47	98.82 187	99.49 165

OMC-MVS		99.08 99	99.04 80	99.20 157	99.67 105	98.22 221	99.28 221	99.52 91	98.07 125	99.66 68	99.81 64	97.79 114	99.78 174	97.79 203	99.81 83	99.60 136

xxxxxxxxxxxxxcwj		99.43 35	99.32 32	99.75 54	99.76 54	99.59 73	99.14 257	99.53 85	99.00 25	99.71 50	99.80 80	98.95 31	99.93 72	98.19 169	99.84 65	99.74 80

SF-MVS		99.38 49	99.24 59	99.79 46	99.79 44	99.68 54	99.57 97	99.54 74	97.82 153	99.71 50	99.80 80	98.95 31	99.93 72	98.19 169	99.84 65	99.74 80

DVP-MVS		99.57 8	99.47 10	99.88 6	99.85 26	99.89 4	99.57 97	99.37 231	99.10 10	99.81 24	99.80 80	98.94 34	99.96 19	98.93 70	99.86 51	99.81 44
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									98.99 28	99.81 24	99.80 80	99.09 14	99.96 19	98.85 87	99.90 23	99.88 7

jajsoiax		98.43 158	98.28 165	98.88 201	98.60 330	98.43 212	99.82 13	99.53 85	98.19 106	98.63 278	99.80 80	93.22 260	99.44 248	99.22 41	97.50 244	98.77 237

Regformer-3		99.57 8	99.53 5	99.68 68	99.76 54	99.29 113	99.58 91	99.44 194	99.01 21	99.87 11	99.80 80	98.97 27	99.91 94	99.44 21	99.92 11	99.83 31

Regformer-4		99.59 3	99.54 4	99.73 61	99.76 54	99.41 102	99.58 91	99.49 132	99.02 18	99.88 5	99.80 80	99.00 25	99.94 57	99.45 19	99.92 11	99.84 20

PGM-MVS		99.45 27	99.31 39	99.86 21	99.87 16	99.78 40	99.58 91	99.65 32	97.84 147	99.71 50	99.80 80	99.12 13	99.97 11	98.33 160	99.87 40	99.83 31

TransMVSNet (Re)		97.15 286	96.58 290	98.86 208	99.12 260	98.85 172	99.49 142	98.91 316	95.48 314	97.16 334	99.80 80	93.38 256	99.11 307	94.16 329	91.73 344	98.62 291

K. test v3		97.10 288	96.79 289	98.01 287	98.72 317	96.33 303	99.87 5	97.05 360	97.59 174	96.16 345	99.80 80	88.71 335	99.04 313	96.69 284	96.55 271	98.65 279

DELS-MVS		99.48 20	99.42 14	99.65 75	99.72 84	99.40 104	99.05 274	99.66 27	99.14 6	99.57 97	99.80 80	98.46 82	99.94 57	99.57 4	99.84 65	99.60 136
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
CSCG		99.32 56	99.32 32	99.32 138	99.85 26	98.29 217	99.71 37	99.66 27	98.11 117	99.41 131	99.80 80	98.37 91	99.96 19	98.99 63	99.96 5	99.72 93

test1172		99.43 35	99.29 47	99.85 28	99.75 64	99.82 23	99.60 77	99.56 57	98.28 95	99.74 44	99.79 92	98.53 75	99.95 46	98.55 139	99.78 94	99.79 59

SR-MVS		99.43 35	99.29 47	99.86 21	99.75 64	99.83 17	99.59 83	99.62 33	98.21 105	99.73 46	99.79 92	98.68 66	99.96 19	98.44 150	99.77 97	99.79 59

MP-MVS-pluss		99.37 50	99.20 63	99.88 6	99.90 4	99.87 12	99.30 215	99.52 91	97.18 217	99.60 90	99.79 92	98.79 50	99.95 46	98.83 93	99.91 16	99.83 31
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pm-mvs1		97.68 259	97.28 274	98.88 201	99.06 272	98.62 192	99.50 132	99.45 185	96.32 284	97.87 318	99.79 92	92.47 282	99.35 268	97.54 230	93.54 331	98.67 269

LFMVS		97.90 220	97.35 265	99.54 96	99.52 154	99.01 148	99.39 188	98.24 346	97.10 227	99.65 74	99.79 92	84.79 356	99.91 94	99.28 36	98.38 205	99.69 105

TinyColmap		97.12 287	96.89 287	97.83 300	99.07 270	95.52 321	98.57 336	98.74 330	97.58 176	97.81 321	99.79 92	88.16 343	99.56 234	95.10 316	97.21 259	98.39 325

ACMP	97.20 11	98.06 193	97.94 197	98.45 252	99.37 200	97.01 276	99.44 161	99.49 132	97.54 182	98.45 291	99.79 92	91.95 292	99.72 195	97.91 193	97.49 247	98.62 291
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GeoE		98.85 130	98.62 141	99.53 102	99.61 134	99.08 140	99.80 19	99.51 104	97.10 227	99.31 155	99.78 99	95.23 199	99.77 176	98.21 167	99.03 172	99.75 75

9.14			99.10 72		99.72 84		99.40 184	99.51 104	97.53 184	99.64 78	99.78 99	98.84 45	99.91 94	97.63 219	99.82 80

pmmvs6		96.53 296	96.09 299	97.82 302	98.69 321	95.47 322	99.37 196	99.47 163	93.46 341	97.41 327	99.78 99	87.06 350	99.33 272	96.92 274	92.70 341	98.65 279

MSLP-MVS++		99.46 25	99.47 10	99.44 125	99.60 138	99.16 128	99.41 176	99.71 13	98.98 31	99.45 119	99.78 99	99.19 9	99.54 237	99.28 36	99.84 65	99.63 130

VNet		99.11 93	98.90 103	99.73 61	99.52 154	99.56 78	99.41 176	99.39 216	99.01 21	99.74 44	99.78 99	95.56 185	99.92 83	99.52 7	98.18 216	99.72 93

114514_t		98.93 116	98.67 131	99.72 64	99.85 26	99.53 85	99.62 70	99.59 43	92.65 346	99.71 50	99.78 99	98.06 108	99.90 109	98.84 90	99.91 16	99.74 80

Vis-MVSNet (Re-imp)		98.87 119	98.72 125	99.31 139	99.71 90	98.88 168	99.80 19	99.44 194	97.91 141	99.36 146	99.78 99	95.49 188	99.43 252	97.91 193	99.11 163	99.62 132

UniMVSNet_ETH3D		97.32 282	96.81 288	98.87 205	99.40 194	97.46 253	99.51 126	99.53 85	95.86 311	98.54 286	99.77 106	82.44 361	99.66 216	98.68 115	97.52 241	99.50 164

anonymousdsp		98.44 157	98.28 165	98.94 185	98.50 335	98.96 157	99.77 27	99.50 124	97.07 229	98.87 242	99.77 106	94.76 217	99.28 278	98.66 118	97.60 234	98.57 306

CDS-MVSNet		99.09 97	99.03 82	99.25 152	99.42 186	98.73 183	99.45 157	99.46 173	98.11 117	99.46 118	99.77 106	98.01 109	99.37 260	98.70 110	98.92 181	99.66 115
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.98 112	98.80 118	99.53 102	99.76 54	99.19 123	98.75 322	99.55 67	97.25 211	99.47 116	99.77 106	97.82 113	99.87 125	96.93 272	99.90 23	99.54 149

CHOSEN 280x420		99.12 88	99.13 69	99.08 166	99.66 114	97.89 238	98.43 343	99.71 13	98.88 44	99.62 83	99.76 110	96.63 149	99.70 207	99.46 18	99.99 1	99.66 115

PS-MVSNAJss		98.92 117	98.92 100	98.90 195	98.78 309	98.53 199	99.78 25	99.54 74	98.07 125	99.00 223	99.76 110	99.01 19	99.37 260	99.13 51	97.23 258	98.81 230

Regformer-1		99.53 12	99.47 10	99.72 64	99.71 90	99.44 99	99.49 142	99.46 173	98.95 37	99.83 19	99.76 110	99.01 19	99.93 72	99.17 47	99.87 40	99.80 54

Regformer-2		99.54 10	99.47 10	99.75 54	99.71 90	99.52 88	99.49 142	99.49 132	98.94 38	99.83 19	99.76 110	99.01 19	99.94 57	99.15 50	99.87 40	99.80 54

MVS_Test		99.10 96	98.97 94	99.48 114	99.49 168	99.14 133	99.67 48	99.34 241	97.31 205	99.58 95	99.76 110	97.65 118	99.82 157	98.87 80	99.07 169	99.46 173

ETH3D-3000-0.1		99.21 70	99.02 85	99.77 50	99.73 79	99.69 52	99.38 193	99.51 104	97.45 191	99.61 86	99.75 115	98.51 78	99.91 94	97.45 240	99.83 74	99.71 100

CANet_DTU		98.97 114	98.87 107	99.25 152	99.33 208	98.42 214	99.08 268	99.30 265	99.16 5	99.43 124	99.75 115	95.27 195	99.97 11	98.56 136	99.95 6	99.36 183

mPP-MVS		99.44 31	99.30 43	99.86 21	99.88 12	99.79 33	99.69 40	99.48 145	98.12 115	99.50 111	99.75 115	98.78 51	99.97 11	98.57 133	99.89 33	99.83 31

HPM-MVS_fast		99.51 15	99.40 17	99.85 28	99.91 1	99.79 33	99.76 30	99.56 57	97.72 162	99.76 40	99.75 115	99.13 12	99.92 83	99.07 57	99.92 11	99.85 16

HyFIR lowres test		99.11 93	98.92 100	99.65 75	99.90 4	99.37 105	99.02 283	99.91 3	97.67 169	99.59 93	99.75 115	95.90 174	99.73 191	99.53 6	99.02 174	99.86 13

ITE_SJBPF				98.08 281	99.29 221	96.37 301		98.92 313	98.34 89	98.83 248	99.75 115	91.09 310	99.62 229	95.82 300	97.40 254	98.25 332

test_241102_ONE					99.84 33	99.90 2		99.48 145	99.07 16	99.91 1	99.74 121	99.20 7	99.76 180

testtj		99.12 88	98.87 107	99.86 21	99.72 84	99.79 33	99.44 161	99.51 104	97.29 207	99.59 93	99.74 121	98.15 105	99.96 19	96.74 280	99.69 115	99.81 44

Anonymous202405211		98.30 170	97.98 190	99.26 151	99.57 144	98.16 223	99.41 176	98.55 342	96.03 309	99.19 187	99.74 121	91.87 293	99.92 83	99.16 49	98.29 211	99.70 102

tttt0517		98.42 159	98.14 171	99.28 149	99.66 114	98.38 215	99.74 34	96.85 361	97.68 166	99.79 29	99.74 121	91.39 306	99.89 117	98.83 93	99.56 133	99.57 145

XVS		99.53 12	99.42 14	99.87 12	99.85 26	99.83 17	99.69 40	99.68 19	98.98 31	99.37 143	99.74 121	98.81 48	99.94 57	98.79 99	99.86 51	99.84 20

MP-MVS		99.33 55	99.15 67	99.87 12	99.88 12	99.82 23	99.66 52	99.46 173	98.09 120	99.48 115	99.74 121	98.29 96	99.96 19	97.93 192	99.87 40	99.82 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_111021_LR		99.41 44	99.33 30	99.65 75	99.77 51	99.51 90	98.94 304	99.85 6	98.82 49	99.65 74	99.74 121	98.51 78	99.80 166	98.83 93	99.89 33	99.64 126

VPNet		97.84 229	97.44 253	99.01 175	99.21 240	98.94 162	99.48 148	99.57 51	98.38 83	99.28 162	99.73 128	88.89 334	99.39 255	99.19 44	93.27 334	98.71 249

MVSTER		98.49 154	98.32 162	99.00 177	99.35 203	99.02 146	99.54 116	99.38 222	97.41 198	99.20 184	99.73 128	93.86 250	99.36 264	98.87 80	97.56 238	98.62 291

MVS_111021_HR		99.41 44	99.32 32	99.66 71	99.72 84	99.47 95	98.95 302	99.85 6	98.82 49	99.54 103	99.73 128	98.51 78	99.74 184	98.91 73	99.88 36	99.77 69

PHI-MVS		99.30 58	99.17 66	99.70 67	99.56 148	99.52 88	99.58 91	99.80 8	97.12 223	99.62 83	99.73 128	98.58 73	99.90 109	98.61 125	99.91 16	99.68 109

IterMVS-SCA-FT		97.82 234	97.75 218	98.06 283	99.57 144	96.36 302	99.02 283	99.49 132	97.18 217	98.71 261	99.72 132	92.72 271	99.14 299	97.44 241	95.86 290	98.67 269

diffmvs		99.14 80	99.02 85	99.51 110	99.61 134	98.96 157	99.28 221	99.49 132	98.46 75	99.72 49	99.71 133	96.50 153	99.88 122	99.31 33	99.11 163	99.67 112

XVG-OURS-SEG-HR		98.69 145	98.62 141	98.89 198	99.71 90	97.74 245	99.12 259	99.54 74	98.44 79	99.42 127	99.71 133	94.20 238	99.92 83	98.54 141	98.90 183	99.00 216

EPNet_dtu		98.03 201	97.96 193	98.23 274	98.27 339	95.54 320	99.23 240	98.75 327	99.02 18	97.82 320	99.71 133	96.11 164	99.48 239	93.04 340	99.65 125	99.69 105
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		99.42 40	99.30 43	99.78 48	99.62 130	99.71 49	99.26 235	99.52 91	98.82 49	99.39 138	99.71 133	98.96 28	99.85 134	98.59 130	99.80 87	99.77 69

PC_three_1452									98.18 109	99.84 14	99.70 137	99.31 3	98.52 340	98.30 164	99.80 87	99.81 44

OPU-MVS				99.64 80	99.56 148	99.72 47	99.60 77				99.70 137	99.27 5	99.42 253	98.24 166	99.80 87	99.79 59

tfpnnormal		97.84 229	97.47 245	98.98 179	99.20 242	99.22 122	99.64 62	99.61 35	96.32 284	98.27 304	99.70 137	93.35 257	99.44 248	95.69 304	95.40 301	98.27 330

v7n		97.87 223	97.52 239	98.92 189	98.76 313	98.58 195	99.84 9	99.46 173	96.20 294	98.91 235	99.70 137	94.89 208	99.44 248	96.03 297	93.89 327	98.75 241

testdata				99.54 96	99.75 64	98.95 159		99.51 104	97.07 229	99.43 124	99.70 137	98.87 42	99.94 57	97.76 206	99.64 126	99.72 93

IterMVS		97.83 231	97.77 214	98.02 286	99.58 142	96.27 305	99.02 283	99.48 145	97.22 215	98.71 261	99.70 137	92.75 268	99.13 302	97.46 238	96.00 284	98.67 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS	97.08 14	97.66 263	97.06 284	99.47 117	99.61 134	99.09 139	98.04 356	99.25 275	91.24 351	98.51 287	99.70 137	94.55 228	99.91 94	92.76 344	99.85 58	99.42 178
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LTVRE_ROB	97.16 12	98.02 203	97.90 200	98.40 259	99.23 234	96.80 288	99.70 38	99.60 40	97.12 223	98.18 307	99.70 137	91.73 298	99.72 195	98.39 152	97.45 249	98.68 262
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_part1		97.75 245	97.24 278	99.29 146	99.59 140	99.63 65	99.65 59	99.49 132	96.17 297	98.44 292	99.69 145	89.80 325	99.47 240	98.68 115	93.66 329	98.78 233

HFP-MVS		99.49 16	99.37 21	99.86 21	99.87 16	99.80 29	99.66 52	99.67 22	98.15 111	99.68 57	99.69 145	99.06 16	99.96 19	98.69 113	99.87 40	99.84 20

#test#		99.43 35	99.29 47	99.86 21	99.87 16	99.80 29	99.55 113	99.67 22	97.83 148	99.68 57	99.69 145	99.06 16	99.96 19	98.39 152	99.87 40	99.84 20

旧先验1					99.74 72	99.59 73		99.54 74			99.69 145	98.47 81			99.68 120	99.73 87

ACMMPR		99.49 16	99.36 23	99.86 21	99.87 16	99.79 33	99.66 52	99.67 22	98.15 111	99.67 63	99.69 145	98.95 31	99.96 19	98.69 113	99.87 40	99.84 20

CPTT-MVS		99.11 93	98.90 103	99.74 59	99.80 43	99.46 97	99.59 83	99.49 132	97.03 234	99.63 79	99.69 145	97.27 129	99.96 19	97.82 201	99.84 65	99.81 44

DROMVSNet		99.44 31	99.39 18	99.58 90	99.56 148	99.49 91	99.88 1	99.58 49	98.38 83	99.73 46	99.69 145	98.20 100	99.70 207	99.64 1	99.82 80	99.54 149

GST-MVS		99.40 47	99.24 59	99.85 28	99.86 22	99.79 33	99.60 77	99.67 22	97.97 136	99.63 79	99.68 152	98.52 77	99.95 46	98.38 154	99.86 51	99.81 44

Anonymous20231211		97.88 221	97.54 238	98.90 195	99.71 90	98.53 199	99.48 148	99.57 51	94.16 333	98.81 250	99.68 152	93.23 258	99.42 253	98.84 90	94.42 319	98.76 239

region2R		99.48 20	99.35 26	99.87 12	99.88 12	99.80 29	99.65 59	99.66 27	98.13 113	99.66 68	99.68 152	98.96 28	99.96 19	98.62 122	99.87 40	99.84 20

PS-CasMVS		97.93 215	97.59 234	98.95 184	98.99 282	99.06 143	99.68 45	99.52 91	97.13 221	98.31 301	99.68 152	92.44 286	99.05 312	98.51 142	94.08 325	98.75 241

HY-MVS	97.30 7	98.85 130	98.64 135	99.47 117	99.42 186	99.08 140	99.62 70	99.36 232	97.39 200	99.28 162	99.68 152	96.44 156	99.92 83	98.37 156	98.22 212	99.40 181

DP-MVS Recon		99.12 88	98.95 98	99.65 75	99.74 72	99.70 51	99.27 226	99.57 51	96.40 282	99.42 127	99.68 152	98.75 59	99.80 166	97.98 188	99.72 109	99.44 176

ETH3D cwj APD-0.16		99.06 101	98.84 113	99.72 64	99.51 156	99.60 70	99.23 240	99.44 194	97.04 232	99.39 138	99.67 158	98.30 95	99.92 83	97.27 247	99.69 115	99.64 126

ADS-MVSNet2		98.02 203	98.07 182	97.87 297	99.33 208	95.19 329	99.23 240	99.08 297	96.24 291	99.10 202	99.67 158	94.11 242	98.93 332	96.81 277	99.05 170	99.48 166

ADS-MVSNet		98.20 177	98.08 179	98.56 238	99.33 208	96.48 298	99.23 240	99.15 289	96.24 291	99.10 202	99.67 158	94.11 242	99.71 201	96.81 277	99.05 170	99.48 166

DTE-MVSNet		97.51 273	97.19 280	98.46 251	98.63 326	98.13 226	99.84 9	99.48 145	96.68 256	97.97 316	99.67 158	92.92 264	98.56 339	96.88 276	92.60 342	98.70 253

Baseline_NR-MVSNet		97.76 241	97.45 248	98.68 228	99.09 267	98.29 217	99.41 176	98.85 322	95.65 313	98.63 278	99.67 158	94.82 210	99.10 309	98.07 185	92.89 338	98.64 281

CMPMVS	69.68 23	94.13 323	94.90 315	91.84 343	97.24 354	80.01 368	98.52 339	99.48 145	89.01 355	91.99 359	99.67 158	85.67 354	99.13 302	95.44 309	97.03 264	96.39 359
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
原ACMM1				99.65 75	99.73 79	99.33 107		99.47 163	97.46 188	99.12 197	99.66 164	98.67 69	99.91 94	97.70 215	99.69 115	99.71 100

thisisatest0530		98.35 166	98.03 185	99.31 139	99.63 124	98.56 196	99.54 116	96.75 363	97.53 184	99.73 46	99.65 165	91.25 309	99.89 117	98.62 122	99.56 133	99.48 166

test222					99.75 64	99.49 91	98.91 307	99.49 132	96.42 280	99.34 152	99.65 165	98.28 97			99.69 115	99.72 93

1121		99.09 97	98.87 107	99.75 54	99.74 72	99.60 70	99.27 226	99.48 145	96.82 250	99.25 172	99.65 165	98.38 89	99.93 72	97.53 231	99.67 122	99.73 87

MVSFormer		99.17 76	99.12 70	99.29 146	99.51 156	98.94 162	99.88 1	99.46 173	97.55 179	99.80 27	99.65 165	97.39 122	99.28 278	99.03 59	99.85 58	99.65 119

jason		99.13 82	99.03 82	99.45 120	99.46 177	98.87 169	99.12 259	99.26 273	98.03 133	99.79 29	99.65 165	97.02 136	99.85 134	99.02 61	99.90 23	99.65 119
jason: jason.
BH-RMVSNet		98.41 161	98.08 179	99.40 128	99.41 189	98.83 176	99.30 215	98.77 326	97.70 164	98.94 231	99.65 165	92.91 266	99.74 184	96.52 288	99.55 135	99.64 126

sss		99.17 76	99.05 77	99.53 102	99.62 130	98.97 153	99.36 200	99.62 33	97.83 148	99.67 63	99.65 165	97.37 126	99.95 46	99.19 44	99.19 157	99.68 109

h-mvs33		97.70 256	97.28 274	98.97 181	99.70 97	97.27 258	99.36 200	99.45 185	98.94 38	99.66 68	99.64 172	94.93 204	99.99 1	99.48 15	84.36 356	99.65 119

ZNCC-MVS		99.47 23	99.33 30	99.87 12	99.87 16	99.81 27	99.64 62	99.67 22	98.08 124	99.55 102	99.64 172	98.91 39	99.96 19	98.72 108	99.90 23	99.82 38

新几何1				99.75 54	99.75 64	99.59 73		99.54 74	96.76 251	99.29 160	99.64 172	98.43 84	99.94 57	96.92 274	99.66 123	99.72 93

PEN-MVS		97.76 241	97.44 253	98.72 225	98.77 312	98.54 198	99.78 25	99.51 104	97.06 231	98.29 303	99.64 172	92.63 277	98.89 335	98.09 178	93.16 335	98.72 247

CP-MVSNet		98.09 190	97.78 212	99.01 175	98.97 287	99.24 119	99.67 48	99.46 173	97.25 211	98.48 290	99.64 172	93.79 251	99.06 311	98.63 121	94.10 324	98.74 245

LF4IMVS		97.52 271	97.46 247	97.70 308	98.98 285	95.55 318	99.29 219	98.82 325	98.07 125	98.66 270	99.64 172	89.97 323	99.61 230	97.01 264	96.68 266	97.94 348

bset_n11_16_dypcd		98.16 182	97.97 191	98.73 223	98.26 340	98.28 219	97.99 357	98.01 351	97.68 166	99.10 202	99.63 178	95.68 182	99.15 298	98.78 102	96.55 271	98.75 241

HPM-MVS		99.42 40	99.28 51	99.83 36	99.90 4	99.72 47	99.81 15	99.54 74	97.59 174	99.68 57	99.63 178	98.91 39	99.94 57	98.58 131	99.91 16	99.84 20
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NCCC		99.34 53	99.19 64	99.79 46	99.61 134	99.65 62	99.30 215	99.48 145	98.86 45	99.21 181	99.63 178	98.72 63	99.90 109	98.25 165	99.63 128	99.80 54

CP-MVS		99.45 27	99.32 32	99.85 28	99.83 37	99.75 43	99.69 40	99.52 91	98.07 125	99.53 105	99.63 178	98.93 38	99.97 11	98.74 104	99.91 16	99.83 31

AdaColmap		99.01 110	98.80 118	99.66 71	99.56 148	99.54 82	99.18 249	99.70 15	98.18 109	99.35 149	99.63 178	96.32 159	99.90 109	97.48 235	99.77 97	99.55 147

TAPA-MVS	97.07 15	97.74 248	97.34 268	98.94 185	99.70 97	97.53 251	99.25 237	99.51 104	91.90 348	99.30 157	99.63 178	98.78 51	99.64 223	88.09 359	99.87 40	99.65 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ppachtmachnet_test		97.49 277	97.45 248	97.61 309	98.62 327	95.24 327	98.80 317	99.46 173	96.11 304	98.22 305	99.62 184	96.45 155	98.97 329	93.77 331	95.97 288	98.61 300

MCST-MVS		99.43 35	99.30 43	99.82 38	99.79 44	99.74 46	99.29 219	99.40 212	98.79 54	99.52 108	99.62 184	98.91 39	99.90 109	98.64 120	99.75 102	99.82 38

WTY-MVS		99.06 101	98.88 106	99.61 85	99.62 130	99.16 128	99.37 196	99.56 57	98.04 131	99.53 105	99.62 184	96.84 141	99.94 57	98.85 87	98.49 203	99.72 93

MDTV_nov1_ep13			98.32 162		99.11 262	94.44 340	99.27 226	98.74 330	97.51 186	99.40 136	99.62 184	94.78 213	99.76 180	97.59 222	98.81 189

CANet		99.25 68	99.14 68	99.59 87	99.41 189	99.16 128	99.35 206	99.57 51	98.82 49	99.51 110	99.61 188	96.46 154	99.95 46	99.59 2	99.98 2	99.65 119

HQP_MVS		98.27 173	98.22 168	98.44 255	99.29 221	96.97 280	99.39 188	99.47 163	98.97 34	99.11 199	99.61 188	92.71 273	99.69 211	97.78 204	97.63 231	98.67 269

plane_prior4											99.61 188

ETH3 D test6400		98.70 143	98.35 159	99.73 61	99.69 100	99.60 70	99.16 251	99.45 185	95.42 315	99.27 165	99.60 191	97.39 122	99.91 94	95.36 313	99.83 74	99.70 102

baseline1		98.31 168	97.95 195	99.38 131	99.50 166	98.74 182	99.59 83	98.93 311	98.41 80	99.14 194	99.60 191	94.59 225	99.79 169	98.48 144	93.29 333	99.61 134

TranMVSNet+NR-MVSNet		97.93 215	97.66 226	98.76 222	98.78 309	98.62 192	99.65 59	99.49 132	97.76 157	98.49 289	99.60 191	94.23 237	98.97 329	98.00 187	92.90 337	98.70 253

tpmrst		98.33 167	98.48 152	97.90 296	99.16 255	94.78 337	99.31 213	99.11 293	97.27 209	99.45 119	99.59 194	95.33 193	99.84 139	98.48 144	98.61 193	99.09 203

IterMVS-LS		98.46 156	98.42 155	98.58 235	99.59 140	98.00 230	99.37 196	99.43 202	96.94 242	99.07 209	99.59 194	97.87 111	99.03 315	98.32 162	95.62 296	98.71 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		99.19 72	99.04 80	99.64 80	99.78 46	99.27 116	99.42 174	99.54 74	97.29 207	99.41 131	99.59 194	98.42 87	99.93 72	98.19 169	99.69 115	99.73 87

pmmvs4		98.13 186	97.90 200	98.81 216	98.61 329	98.87 169	98.99 290	99.21 282	96.44 278	99.06 213	99.58 197	95.90 174	99.11 307	97.18 257	96.11 282	98.46 318

1112_ss		98.98 112	98.77 121	99.59 87	99.68 104	99.02 146	99.25 237	99.48 145	97.23 214	99.13 195	99.58 197	96.93 140	99.90 109	98.87 80	98.78 190	99.84 20

ab-mvs-re		8.30 342	11.06 345	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	99.58 197	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

PatchmatchNet		98.31 168	98.36 157	98.19 276	99.16 255	95.32 326	99.27 226	98.92 313	97.37 201	99.37 143	99.58 197	94.90 207	99.70 207	97.43 242	99.21 155	99.54 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		98.19 178	98.16 169	98.27 273	99.30 217	95.55 318	99.07 269	98.97 307	97.57 177	99.43 124	99.57 201	92.72 271	99.74 184	97.58 223	99.20 156	99.52 155

Patchmatch-test		97.93 215	97.65 227	98.77 221	99.18 247	97.07 269	99.03 280	99.14 291	96.16 299	98.74 258	99.57 201	94.56 227	99.72 195	93.36 336	99.11 163	99.52 155

PVSNet	96.02 17	98.85 130	98.84 113	98.89 198	99.73 79	97.28 257	98.32 349	99.60 40	97.86 143	99.50 111	99.57 201	96.75 146	99.86 128	98.56 136	99.70 114	99.54 149

cdsmvs_eth3d_5k		24.64 341	32.85 344	0.00 357	0.00 380	0.00 381	0.00 368	99.51 104	0.00 375	0.00 376	99.56 204	96.58 150	0.00 376	0.00 374	0.00 374	0.00 372

1314		98.68 146	98.54 150	99.11 165	98.89 293	98.65 189	99.27 226	99.49 132	96.89 244	97.99 315	99.56 204	97.72 117	99.83 150	97.74 209	99.27 152	98.84 229

lupinMVS		99.13 82	99.01 89	99.46 119	99.51 156	98.94 162	99.05 274	99.16 288	97.86 143	99.80 27	99.56 204	97.39 122	99.86 128	98.94 68	99.85 58	99.58 144

miper_lstm_enhance		98.00 208	97.91 199	98.28 272	99.34 207	97.43 254	98.88 309	99.36 232	96.48 275	98.80 252	99.55 207	95.98 167	98.91 333	97.27 247	95.50 300	98.51 310

DPM-MVS		98.95 115	98.71 127	99.66 71	99.63 124	99.55 80	98.64 332	99.10 294	97.93 139	99.42 127	99.55 207	98.67 69	99.80 166	95.80 302	99.68 120	99.61 134

CDPH-MVS		99.13 82	98.91 102	99.80 43	99.75 64	99.71 49	99.15 255	99.41 206	96.60 265	99.60 90	99.55 207	98.83 46	99.90 109	97.48 235	99.83 74	99.78 67

dp		97.75 245	97.80 208	97.59 310	99.10 265	93.71 349	99.32 211	98.88 320	96.48 275	99.08 208	99.55 207	92.67 276	99.82 157	96.52 288	98.58 196	99.24 192

CLD-MVS		98.16 182	98.10 175	98.33 264	99.29 221	96.82 287	98.75 322	99.44 194	97.83 148	99.13 195	99.55 207	92.92 264	99.67 213	98.32 162	97.69 230	98.48 312
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ZD-MVS					99.71 90	99.79 33		99.61 35	96.84 247	99.56 98	99.54 212	98.58 73	99.96 19	96.93 272	99.75 102

cl____		98.01 206	97.84 207	98.55 240	99.25 232	97.97 232	98.71 326	99.34 241	96.47 277	98.59 284	99.54 212	95.65 184	99.21 293	97.21 251	95.77 291	98.46 318

DIV-MVS_self_test		98.01 206	97.85 206	98.48 246	99.24 233	97.95 236	98.71 326	99.35 237	96.50 270	98.60 283	99.54 212	95.72 181	99.03 315	97.21 251	95.77 291	98.46 318

MVS		97.28 283	96.55 291	99.48 114	98.78 309	98.95 159	99.27 226	99.39 216	83.53 361	98.08 310	99.54 212	96.97 138	99.87 125	94.23 327	99.16 158	99.63 130

pmmvs5		97.52 271	97.30 273	98.16 278	98.57 332	96.73 289	99.27 226	98.90 318	96.14 302	98.37 297	99.53 216	91.54 305	99.14 299	97.51 233	95.87 289	98.63 289

HPM-MVS++		99.39 48	99.23 61	99.87 12	99.75 64	99.84 16	99.43 167	99.51 104	98.68 62	99.27 165	99.53 216	98.64 71	99.96 19	98.44 150	99.80 87	99.79 59

PatchMatch-RL		98.84 133	98.62 141	99.52 108	99.71 90	99.28 114	99.06 272	99.77 9	97.74 161	99.50 111	99.53 216	95.41 189	99.84 139	97.17 258	99.64 126	99.44 176

eth_miper_zixun_eth		98.05 198	97.96 193	98.33 264	99.26 228	97.38 255	98.56 338	99.31 261	96.65 259	98.88 240	99.52 219	96.58 150	99.12 306	97.39 244	95.53 299	98.47 314

test_prior3		99.21 70	99.05 77	99.68 68	99.67 105	99.48 93	98.96 298	99.56 57	98.34 89	99.01 218	99.52 219	98.68 66	99.83 150	97.96 189	99.74 105	99.74 80

test_prior2							98.96 298		98.34 89	99.01 218	99.52 219	98.68 66		97.96 189	99.74 105

test_0402		96.64 294	96.24 296	97.85 298	98.85 302	96.43 300	99.44 161	99.26 273	93.52 339	96.98 338	99.52 219	88.52 339	99.20 295	92.58 346	97.50 244	97.93 349

test_yl		98.86 122	98.63 136	99.54 96	99.49 168	99.18 125	99.50 132	99.07 299	98.22 103	99.61 86	99.51 223	95.37 191	99.84 139	98.60 128	98.33 206	99.59 140

DCV-MVSNet		98.86 122	98.63 136	99.54 96	99.49 168	99.18 125	99.50 132	99.07 299	98.22 103	99.61 86	99.51 223	95.37 191	99.84 139	98.60 128	98.33 206	99.59 140

v148		97.79 239	97.55 235	98.50 243	98.74 314	97.72 247	99.54 116	99.33 248	96.26 289	98.90 237	99.51 223	94.68 221	99.14 299	97.83 200	93.15 336	98.63 289

DU-MVS		98.08 192	97.79 209	98.96 182	98.87 298	98.98 150	99.41 176	99.45 185	97.87 142	98.71 261	99.50 226	94.82 210	99.22 288	98.57 133	92.87 339	98.68 262

NR-MVSNet		97.97 213	97.61 231	99.02 174	98.87 298	99.26 117	99.47 153	99.42 204	97.63 172	97.08 336	99.50 226	95.07 202	99.13 302	97.86 197	93.59 330	98.68 262

XVG-ACMP-BASELINE		97.83 231	97.71 222	98.20 275	99.11 262	96.33 303	99.41 176	99.52 91	98.06 129	99.05 214	99.50 226	89.64 328	99.73 191	97.73 210	97.38 255	98.53 308

MSP-MVS		99.42 40	99.27 53	99.88 6	99.89 9	99.80 29	99.67 48	99.50 124	98.70 60	99.77 36	99.49 229	98.21 99	99.95 46	98.46 148	99.77 97	99.88 7
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
TEST9					99.67 105	99.65 62	99.05 274	99.41 206	96.22 293	98.95 229	99.49 229	98.77 54	99.91 94

train_agg		99.02 107	98.77 121	99.77 50	99.67 105	99.65 62	99.05 274	99.41 206	96.28 286	98.95 229	99.49 229	98.76 56	99.91 94	97.63 219	99.72 109	99.75 75

agg_prior1		99.01 110	98.76 123	99.76 53	99.67 105	99.62 66	98.99 290	99.40 212	96.26 289	98.87 242	99.49 229	98.77 54	99.91 94	97.69 216	99.72 109	99.75 75

PVSNet_Blended		99.08 99	98.97 94	99.42 127	99.76 54	98.79 180	98.78 319	99.91 3	96.74 252	99.67 63	99.49 229	97.53 119	99.88 122	98.98 64	99.85 58	99.60 136

CNLPA		99.14 80	98.99 90	99.59 87	99.58 142	99.41 102	99.16 251	99.44 194	98.45 76	99.19 187	99.49 229	98.08 107	99.89 117	97.73 210	99.75 102	99.48 166

test_8					99.67 105	99.61 68	99.03 280	99.41 206	96.28 286	98.93 233	99.48 235	98.76 56	99.91 94

EPMVS		97.82 234	97.65 227	98.35 263	98.88 294	95.98 310	99.49 142	94.71 371	97.57 177	99.26 170	99.48 235	92.46 285	99.71 201	97.87 196	99.08 168	99.35 184

PLC	97.94 4	99.02 107	98.85 112	99.53 102	99.66 114	99.01 148	99.24 239	99.52 91	96.85 246	99.27 165	99.48 235	98.25 98	99.91 94	97.76 206	99.62 129	99.65 119
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu		99.29 61	99.27 53	99.34 133	99.63 124	98.97 153	99.12 259	99.51 104	98.86 45	99.84 14	99.47 238	98.18 101	99.99 1	99.50 10	99.31 149	99.08 204

xiu_mvs_v1_base		99.29 61	99.27 53	99.34 133	99.63 124	98.97 153	99.12 259	99.51 104	98.86 45	99.84 14	99.47 238	98.18 101	99.99 1	99.50 10	99.31 149	99.08 204

xiu_mvs_v1_base_debi		99.29 61	99.27 53	99.34 133	99.63 124	98.97 153	99.12 259	99.51 104	98.86 45	99.84 14	99.47 238	98.18 101	99.99 1	99.50 10	99.31 149	99.08 204

v1921920		97.80 238	97.45 248	98.84 212	98.80 305	98.53 199	99.52 122	99.34 241	96.15 301	99.24 173	99.47 238	93.98 246	99.29 277	95.40 311	95.13 307	98.69 257

UniMVSNet_NR-MVSNet		98.22 174	97.97 191	98.96 182	98.92 291	98.98 150	99.48 148	99.53 85	97.76 157	98.71 261	99.46 242	96.43 157	99.22 288	98.57 133	92.87 339	98.69 257

testgi		97.65 264	97.50 242	98.13 280	99.36 202	96.45 299	99.42 174	99.48 145	97.76 157	97.87 318	99.45 243	91.09 310	98.81 336	94.53 323	98.52 201	99.13 198

EIA-MVS		99.18 74	99.09 74	99.45 120	99.49 168	99.18 125	99.67 48	99.53 85	97.66 170	99.40 136	99.44 244	98.10 106	99.81 161	98.94 68	99.62 129	99.35 184

CS-MVS-test		99.30 58	99.25 57	99.45 120	99.46 177	99.23 120	99.80 19	99.57 51	98.28 95	99.53 105	99.44 244	98.16 104	99.79 169	99.38 24	99.61 131	99.34 186

tpm2		97.44 279	97.34 268	97.74 306	99.15 258	94.36 342	99.45 157	98.94 310	93.45 342	98.90 237	99.44 244	91.35 307	99.59 232	97.31 245	98.07 223	99.29 190

thisisatest0515		98.14 185	97.79 209	99.19 158	99.50 166	98.50 206	98.61 333	96.82 362	96.95 240	99.54 103	99.43 247	91.66 302	99.86 128	98.08 182	99.51 137	99.22 193

mvs-test1		98.86 122	98.84 113	98.89 198	99.33 208	97.77 244	99.44 161	99.30 265	98.47 73	99.10 202	99.43 247	96.78 143	99.95 46	98.73 106	99.02 174	98.96 222

WR-MVS		98.06 193	97.73 220	99.06 168	98.86 301	99.25 118	99.19 248	99.35 237	97.30 206	98.66 270	99.43 247	93.94 247	99.21 293	98.58 131	94.28 321	98.71 249

hse-mvs2		97.50 274	97.14 281	98.59 232	99.49 168	97.05 271	99.28 221	99.22 279	98.94 38	99.66 68	99.42 250	94.93 204	99.65 220	99.48 15	83.80 358	99.08 204

v8		97.95 214	97.63 230	98.93 187	98.95 289	98.81 179	99.80 19	99.41 206	96.03 309	99.10 202	99.42 250	94.92 206	99.30 276	96.94 271	94.08 325	98.66 277

tpmvs		97.98 210	98.02 187	97.84 299	99.04 276	94.73 338	99.31 213	99.20 283	96.10 308	98.76 257	99.42 250	94.94 203	99.81 161	96.97 268	98.45 204	98.97 220

UGNet		98.87 119	98.69 129	99.40 128	99.22 238	98.72 184	99.44 161	99.68 19	99.24 3	99.18 190	99.42 250	92.74 270	99.96 19	99.34 30	99.94 9	99.53 154
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
AUN-MVS		96.88 290	96.31 295	98.59 232	99.48 175	97.04 274	99.27 226	99.22 279	97.44 194	98.51 287	99.41 254	91.97 291	99.66 216	97.71 213	83.83 357	99.07 209

CS-MVS		99.34 53	99.31 39	99.43 126	99.44 184	99.47 95	99.68 45	99.56 57	98.41 80	99.62 83	99.41 254	98.35 92	99.76 180	99.52 7	99.76 100	99.05 211

Effi-MVS+		98.81 134	98.59 147	99.48 114	99.46 177	99.12 137	98.08 355	99.50 124	97.50 187	99.38 141	99.41 254	96.37 158	99.81 161	99.11 53	98.54 200	99.51 161

v10		97.85 226	97.52 239	98.86 208	98.99 282	98.67 187	99.75 31	99.41 206	95.70 312	98.98 225	99.41 254	94.75 218	99.23 285	96.01 298	94.63 315	98.67 269

v144192		97.92 218	97.60 232	98.87 205	98.83 304	98.65 189	99.55 113	99.34 241	96.20 294	99.32 154	99.40 258	94.36 233	99.26 282	96.37 293	95.03 309	98.70 253

NP-MVS					99.23 234	96.92 283					99.40 258

HQP-MVS		98.02 203	97.90 200	98.37 262	99.19 244	96.83 285	98.98 294	99.39 216	98.24 99	98.66 270	99.40 258	92.47 282	99.64 223	97.19 255	97.58 236	98.64 281

MAR-MVS		98.86 122	98.63 136	99.54 96	99.37 200	99.66 59	99.45 157	99.54 74	96.61 263	99.01 218	99.40 258	97.09 133	99.86 128	97.68 218	99.53 136	99.10 199
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
API-MVS		99.04 104	99.03 82	99.06 168	99.40 194	99.31 111	99.55 113	99.56 57	98.54 68	99.33 153	99.39 262	98.76 56	99.78 174	96.98 267	99.78 94	98.07 338

CR-MVSNet		98.17 181	97.93 198	98.87 205	99.18 247	98.49 207	99.22 245	99.33 248	96.96 238	99.56 98	99.38 263	94.33 234	99.00 320	94.83 321	98.58 196	99.14 196

Patchmtry		97.75 245	97.40 259	98.81 216	99.10 265	98.87 169	99.11 265	99.33 248	94.83 325	98.81 250	99.38 263	94.33 234	99.02 317	96.10 295	95.57 297	98.53 308

BH-untuned		98.42 159	98.36 157	98.59 232	99.49 168	96.70 290	99.27 226	99.13 292	97.24 213	98.80 252	99.38 263	95.75 179	99.74 184	97.07 263	99.16 158	99.33 188

V42		98.06 193	97.79 209	98.86 208	98.98 285	98.84 173	99.69 40	99.34 241	96.53 269	99.30 157	99.37 266	94.67 222	99.32 273	97.57 227	94.66 314	98.42 321

VPA-MVSNet		98.29 171	97.95 195	99.30 143	99.16 255	99.54 82	99.50 132	99.58 49	98.27 98	99.35 149	99.37 266	92.53 280	99.65 220	99.35 26	94.46 317	98.72 247

PVSNet_BlendedMVS		98.86 122	98.80 118	99.03 173	99.76 54	98.79 180	99.28 221	99.91 3	97.42 197	99.67 63	99.37 266	97.53 119	99.88 122	98.98 64	97.29 257	98.42 321

D2MVS		98.41 161	98.50 151	98.15 279	99.26 228	96.62 294	99.40 184	99.61 35	97.71 163	98.98 225	99.36 269	96.04 166	99.67 213	98.70 110	97.41 253	98.15 336

MVP-Stereo		97.81 236	97.75 218	97.99 290	97.53 348	96.60 295	98.96 298	98.85 322	97.22 215	97.23 331	99.36 269	95.28 194	99.46 242	95.51 308	99.78 94	97.92 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1240		97.69 257	97.32 271	98.79 219	98.85 302	98.43 212	99.48 148	99.36 232	96.11 304	99.27 165	99.36 269	93.76 253	99.24 284	94.46 324	95.23 304	98.70 253

v1144		97.98 210	97.69 223	98.85 211	98.87 298	98.66 188	99.54 116	99.35 237	96.27 288	99.23 177	99.35 272	94.67 222	99.23 285	96.73 281	95.16 306	98.68 262

v2v482		98.06 193	97.77 214	98.92 189	98.90 292	98.82 177	99.57 97	99.36 232	96.65 259	99.19 187	99.35 272	94.20 238	99.25 283	97.72 212	94.97 310	98.69 257

CostFormer		97.72 251	97.73 220	97.71 307	99.15 258	94.02 345	99.54 116	99.02 303	94.67 328	99.04 215	99.35 272	92.35 288	99.77 176	98.50 143	97.94 225	99.34 186

our_test_3		97.65 264	97.68 224	97.55 312	98.62 327	94.97 333	98.84 313	99.30 265	96.83 249	98.19 306	99.34 275	97.01 137	99.02 317	95.00 319	96.01 283	98.64 281

c3_l		98.12 188	98.04 184	98.38 261	99.30 217	97.69 250	98.81 316	99.33 248	96.67 257	98.83 248	99.34 275	97.11 132	98.99 321	97.58 223	95.34 302	98.48 312

Fast-Effi-MVS+-dtu		98.77 140	98.83 117	98.60 231	99.41 189	96.99 278	99.52 122	99.49 132	98.11 117	99.24 173	99.34 275	96.96 139	99.79 169	97.95 191	99.45 138	99.02 215

Fast-Effi-MVS+		98.70 143	98.43 154	99.51 110	99.51 156	99.28 114	99.52 122	99.47 163	96.11 304	99.01 218	99.34 275	96.20 163	99.84 139	97.88 195	98.82 187	99.39 182

v1192		97.81 236	97.44 253	98.91 193	98.88 294	98.68 186	99.51 126	99.34 241	96.18 296	99.20 184	99.34 275	94.03 245	99.36 264	95.32 314	95.18 305	98.69 257

tpm		97.67 262	97.55 235	98.03 284	99.02 279	95.01 332	99.43 167	98.54 343	96.44 278	99.12 197	99.34 275	91.83 295	99.60 231	97.75 208	96.46 274	99.48 166

PAPM		97.59 267	97.09 283	99.07 167	99.06 272	98.26 220	98.30 350	99.10 294	94.88 324	98.08 310	99.34 275	96.27 161	99.64 223	89.87 353	98.92 181	99.31 189

GBi-Net		97.68 259	97.48 243	98.29 269	99.51 156	97.26 260	99.43 167	99.48 145	96.49 271	99.07 209	99.32 282	90.26 318	98.98 322	97.10 260	96.65 267	98.62 291

test1		97.68 259	97.48 243	98.29 269	99.51 156	97.26 260	99.43 167	99.48 145	96.49 271	99.07 209	99.32 282	90.26 318	98.98 322	97.10 260	96.65 267	98.62 291

FMVSNet1		96.84 291	96.36 294	98.29 269	99.32 215	97.26 260	99.43 167	99.48 145	95.11 319	98.55 285	99.32 282	83.95 357	98.98 322	95.81 301	96.26 279	98.62 291

MS-PatchMatch		97.24 285	97.32 271	96.99 323	98.45 337	93.51 353	98.82 315	99.32 258	97.41 198	98.13 309	99.30 285	88.99 333	99.56 234	95.68 305	99.80 87	97.90 351

GA-MVS		97.85 226	97.47 245	99.00 177	99.38 198	97.99 231	98.57 336	99.15 289	97.04 232	98.90 237	99.30 285	89.83 324	99.38 257	96.70 283	98.33 206	99.62 132

miper_ehance_all_eth		98.18 180	98.10 175	98.41 257	99.23 234	97.72 247	98.72 325	99.31 261	96.60 265	98.88 240	99.29 287	97.29 128	99.13 302	97.60 221	95.99 285	98.38 326

FMVSNet2		97.72 251	97.36 263	98.80 218	99.51 156	98.84 173	99.45 157	99.42 204	96.49 271	98.86 247	99.29 287	90.26 318	98.98 322	96.44 290	96.56 270	98.58 305

TESTMET0.1,1		97.55 268	97.27 277	98.40 259	98.93 290	96.53 296	98.67 328	97.61 357	96.96 238	98.64 277	99.28 289	88.63 338	99.45 243	97.30 246	99.38 142	99.21 194

FMVSNet3		98.03 201	97.76 217	98.84 212	99.39 197	98.98 150	99.40 184	99.38 222	96.67 257	99.07 209	99.28 289	92.93 263	98.98 322	97.10 260	96.65 267	98.56 307

PAPM_NR		99.04 104	98.84 113	99.66 71	99.74 72	99.44 99	99.39 188	99.38 222	97.70 164	99.28 162	99.28 289	98.34 93	99.85 134	96.96 269	99.45 138	99.69 105

ETV-MVS		99.26 66	99.21 62	99.40 128	99.46 177	99.30 112	99.56 104	99.52 91	98.52 70	99.44 123	99.27 292	98.41 88	99.86 128	99.10 54	99.59 132	99.04 212

xiu_mvs_v2_base		99.26 66	99.25 57	99.29 146	99.53 152	98.91 166	99.02 283	99.45 185	98.80 53	99.71 50	99.26 293	98.94 34	99.98 6	99.34 30	99.23 154	98.98 219

test20.03		96.12 305	95.96 302	96.63 331	97.44 349	95.45 323	99.51 126	99.38 222	96.55 268	96.16 345	99.25 294	93.76 253	96.17 364	87.35 361	94.22 322	98.27 330

PS-MVSNAJ		99.32 56	99.32 32	99.30 143	99.57 144	98.94 162	98.97 297	99.46 173	98.92 42	99.71 50	99.24 295	99.01 19	99.98 6	99.35 26	99.66 123	98.97 220

Test_1112_low_res		98.89 118	98.66 134	99.57 92	99.69 100	98.95 159	99.03 280	99.47 163	96.98 236	99.15 193	99.23 296	96.77 145	99.89 117	98.83 93	98.78 190	99.86 13

cl22		97.85 226	97.64 229	98.48 246	99.09 267	97.87 239	98.60 335	99.33 248	97.11 226	98.87 242	99.22 297	92.38 287	99.17 297	98.21 167	95.99 285	98.42 321

EG-PatchMatch MVS		95.97 307	95.69 307	96.81 329	97.78 346	92.79 356	99.16 251	98.93 311	96.16 299	94.08 354	99.22 297	82.72 359	99.47 240	95.67 306	97.50 244	98.17 335

TR-MVS		97.76 241	97.41 258	98.82 214	99.06 272	97.87 239	98.87 311	98.56 341	96.63 262	98.68 269	99.22 297	92.49 281	99.65 220	95.40 311	97.79 228	98.95 225

ET-MVSNet_ETH3D		96.49 297	95.64 308	99.05 170	99.53 152	98.82 177	98.84 313	97.51 358	97.63 172	84.77 362	99.21 300	92.09 290	98.91 333	98.98 64	92.21 343	99.41 180

WR-MVS_H		98.13 186	97.87 205	98.90 195	99.02 279	98.84 173	99.70 38	99.59 43	97.27 209	98.40 295	99.19 301	95.53 186	99.23 285	98.34 159	93.78 328	98.61 300

miper_enhance_ethall		98.16 182	98.08 179	98.41 257	98.96 288	97.72 247	98.45 342	99.32 258	96.95 240	98.97 227	99.17 302	97.06 135	99.22 288	97.86 197	95.99 285	98.29 329

baseline2		97.87 223	97.55 235	98.82 214	99.18 247	98.02 229	99.41 176	96.58 365	96.97 237	96.51 341	99.17 302	93.43 255	99.57 233	97.71 213	99.03 172	98.86 227

MIMVSNet1		95.51 310	95.04 314	96.92 327	97.38 350	95.60 316	99.52 122	99.50 124	93.65 338	96.97 339	99.17 302	85.28 355	96.56 363	88.36 358	95.55 298	98.60 303

gm-plane-assit					98.54 334	92.96 355			94.65 329		99.15 305		99.64 223	97.56 228

MIMVSNet		97.73 249	97.45 248	98.57 236	99.45 183	97.50 252	99.02 283	98.98 306	96.11 304	99.41 131	99.14 306	90.28 317	98.74 337	95.74 303	98.93 179	99.47 171

LCM-MVSNet-Re		97.83 231	98.15 170	96.87 328	99.30 217	92.25 358	99.59 83	98.26 345	97.43 195	96.20 344	99.13 307	96.27 161	98.73 338	98.17 173	98.99 176	99.64 126

UniMVSNet (Re)		98.29 171	98.00 188	99.13 164	99.00 281	99.36 106	99.49 142	99.51 104	97.95 137	98.97 227	99.13 307	96.30 160	99.38 257	98.36 158	93.34 332	98.66 277

N_pmnet		94.95 317	95.83 305	92.31 342	98.47 336	79.33 369	99.12 259	92.81 376	93.87 335	97.68 323	99.13 307	93.87 249	99.01 319	91.38 348	96.19 280	98.59 304

PAPR		98.63 151	98.34 160	99.51 110	99.40 194	99.03 145	98.80 317	99.36 232	96.33 283	99.00 223	99.12 310	98.46 82	99.84 139	95.23 315	99.37 148	99.66 115

tpm cat1		97.39 280	97.36 263	97.50 314	99.17 253	93.73 348	99.43 167	99.31 261	91.27 350	98.71 261	99.08 311	94.31 236	99.77 176	96.41 292	98.50 202	99.00 216

FMVSNet5		96.43 299	96.19 297	97.15 319	99.11 262	95.89 312	99.32 211	99.52 91	94.47 332	98.34 300	99.07 312	87.54 349	97.07 359	92.61 345	95.72 294	98.47 314

PMMVS		98.80 137	98.62 141	99.34 133	99.27 226	98.70 185	98.76 321	99.31 261	97.34 202	99.21 181	99.07 312	97.20 130	99.82 157	98.56 136	98.87 184	99.52 155

Anonymous20231206		96.22 301	96.03 300	96.79 330	97.31 353	94.14 344	99.63 64	99.08 297	96.17 297	97.04 337	99.06 314	93.94 247	97.76 354	86.96 362	95.06 308	98.47 314

DeepMVS_CX				93.34 340	99.29 221	82.27 366		99.22 279	85.15 359	96.33 343	99.05 315	90.97 312	99.73 191	93.57 334	97.77 229	98.01 342

YYNet1		95.36 313	94.51 319	97.92 294	97.89 344	97.10 265	99.10 267	99.23 278	93.26 343	80.77 366	99.04 316	92.81 267	98.02 347	94.30 325	94.18 323	98.64 281

Anonymous20240521		96.20 303	95.89 304	97.13 321	97.72 347	94.96 334	99.79 24	99.29 270	93.01 344	97.20 333	99.03 317	89.69 327	98.36 342	91.16 349	96.13 281	98.07 338

MDA-MVSNet-bldmvs		94.96 316	93.98 322	97.92 294	98.24 341	97.27 258	99.15 255	99.33 248	93.80 336	80.09 368	99.03 317	88.31 341	97.86 352	93.49 335	94.36 320	98.62 291

test_method		91.10 327	91.36 330	90.31 346	95.85 360	73.72 374	94.89 363	99.25 275	68.39 367	95.82 348	99.02 319	80.50 363	98.95 331	93.64 333	94.89 313	98.25 332

BH-w/o		98.00 208	97.89 204	98.32 266	99.35 203	96.20 307	99.01 288	98.90 318	96.42 280	98.38 296	99.00 320	95.26 197	99.72 195	96.06 296	98.61 193	99.03 213

Effi-MVS+-dtu		98.78 138	98.89 105	98.47 250	99.33 208	96.91 284	99.57 97	99.30 265	98.47 73	99.41 131	98.99 321	96.78 143	99.74 184	98.73 106	99.38 142	98.74 245

MVS_0304		96.79 292	96.52 292	97.59 310	99.22 238	94.92 335	99.04 279	99.59 43	96.49 271	98.43 293	98.99 321	80.48 364	99.39 255	97.15 259	99.27 152	98.47 314

UnsupCasMVSNet_eth		96.44 298	96.12 298	97.40 316	98.65 324	95.65 315	99.36 200	99.51 104	97.13 221	96.04 347	98.99 321	88.40 340	98.17 344	96.71 282	90.27 347	98.40 324

test0.0.03 1		97.71 255	97.42 257	98.56 238	98.41 338	97.82 242	98.78 319	98.63 339	97.34 202	98.05 314	98.98 324	94.45 231	98.98 322	95.04 318	97.15 263	98.89 226

MDA-MVSNet_test_wron		95.45 311	94.60 317	98.01 287	98.16 342	97.21 263	99.11 265	99.24 277	93.49 340	80.73 367	98.98 324	93.02 261	98.18 343	94.22 328	94.45 318	98.64 281

FPMVS		84.93 331	85.65 332	82.75 351	86.77 372	63.39 376	98.35 345	98.92 313	74.11 364	83.39 364	98.98 324	50.85 371	92.40 368	84.54 365	94.97 310	92.46 362

alignmvs		98.81 134	98.56 149	99.58 90	99.43 185	99.42 101	99.51 126	98.96 309	98.61 65	99.35 149	98.92 327	94.78 213	99.77 176	99.35 26	98.11 222	99.54 149

test-LLR		98.06 193	97.90 200	98.55 240	98.79 306	97.10 265	98.67 328	97.75 354	97.34 202	98.61 281	98.85 328	94.45 231	99.45 243	97.25 249	99.38 142	99.10 199

test-mter		97.49 277	97.13 282	98.55 240	98.79 306	97.10 265	98.67 328	97.75 354	96.65 259	98.61 281	98.85 328	88.23 342	99.45 243	97.25 249	99.38 142	99.10 199

canonicalmvs		99.02 107	98.86 111	99.51 110	99.42 186	99.32 108	99.80 19	99.48 145	98.63 63	99.31 155	98.81 330	97.09 133	99.75 183	99.27 38	97.90 226	99.47 171

DWT-MVSNet_test		97.53 270	97.40 259	97.93 293	99.03 278	94.86 336	99.57 97	98.63 339	96.59 267	98.36 298	98.79 331	89.32 330	99.74 184	98.14 176	98.16 220	99.20 195

new_pmnet		96.38 300	96.03 300	97.41 315	98.13 343	95.16 331	99.05 274	99.20 283	93.94 334	97.39 328	98.79 331	91.61 304	99.04 313	90.43 351	95.77 291	98.05 340

cascas		97.69 257	97.43 256	98.48 246	98.60 330	97.30 256	98.18 354	99.39 216	92.96 345	98.41 294	98.78 333	93.77 252	99.27 281	98.16 174	98.61 193	98.86 227

PVSNet_0	94.43 19	96.09 306	95.47 309	97.94 292	99.31 216	94.34 343	97.81 358	99.70 15	97.12 223	97.46 326	98.75 334	89.71 326	99.79 169	97.69 216	81.69 360	99.68 109

patchmatchnet-post											98.70 335	94.79 212	99.74 184

Patchmatch-RL test		95.84 308	95.81 306	95.95 336	95.61 361	90.57 361	98.24 351	98.39 344	95.10 321	95.20 350	98.67 336	94.78 213	97.77 353	96.28 294	90.02 348	99.51 161

thres100view900		97.76 241	97.45 248	98.69 227	99.72 84	97.86 241	99.59 83	98.74 330	97.93 139	99.26 170	98.62 337	91.75 296	99.83 150	93.22 337	98.18 216	98.37 327

thres600view7		97.86 225	97.51 241	98.92 189	99.72 84	97.95 236	99.59 83	98.74 330	97.94 138	99.27 165	98.62 337	91.75 296	99.86 128	93.73 332	98.19 215	98.96 222

DSMNet-mixed		97.25 284	97.35 265	96.95 326	97.84 345	93.61 352	99.57 97	96.63 364	96.13 303	98.87 242	98.61 339	94.59 225	97.70 355	95.08 317	98.86 185	99.55 147

IB-MVS	95.67 18	96.22 301	95.44 311	98.57 236	99.21 240	96.70 290	98.65 331	97.74 356	96.71 254	97.27 330	98.54 340	86.03 352	99.92 83	98.47 147	86.30 354	99.10 199
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
GG-mvs-BLEND				98.45 252	98.55 333	98.16 223	99.43 167	93.68 373		97.23 331	98.46 341	89.30 331	99.22 288	95.43 310	98.22 212	97.98 346

tfpn200view9		97.72 251	97.38 261	98.72 225	99.69 100	97.96 234	99.50 132	98.73 335	97.83 148	99.17 191	98.45 342	91.67 300	99.83 150	93.22 337	98.18 216	98.37 327

thres400		97.77 240	97.38 261	98.92 189	99.69 100	97.96 234	99.50 132	98.73 335	97.83 148	99.17 191	98.45 342	91.67 300	99.83 150	93.22 337	98.18 216	98.96 222

KD-MVS_2432*1600		94.62 318	93.72 324	97.31 317	97.19 356	95.82 313	98.34 346	99.20 283	95.00 322	97.57 324	98.35 344	87.95 346	98.10 345	92.87 342	77.00 364	98.01 342

miper_refine_blended		94.62 318	93.72 324	97.31 317	97.19 356	95.82 313	98.34 346	99.20 283	95.00 322	97.57 324	98.35 344	87.95 346	98.10 345	92.87 342	77.00 364	98.01 342

thres200		97.61 266	97.28 274	98.62 230	99.64 121	98.03 228	99.26 235	98.74 330	97.68 166	99.09 207	98.32 346	91.66 302	99.81 161	92.88 341	98.22 212	98.03 341

OpenMVS_ROB	92.34 20	94.38 322	93.70 326	96.41 334	97.38 350	93.17 354	99.06 272	98.75 327	86.58 358	94.84 353	98.26 347	81.53 362	99.32 273	89.01 355	97.87 227	96.76 357

CL-MVSNet_self_test		94.49 320	93.97 323	96.08 335	96.16 359	93.67 351	98.33 348	99.38 222	95.13 317	97.33 329	98.15 348	92.69 275	96.57 362	88.67 356	79.87 362	97.99 345

pmmvs3		94.09 324	93.25 327	96.60 332	94.76 365	94.49 339	98.92 305	98.18 349	89.66 354	96.48 342	98.06 349	86.28 351	97.33 357	89.68 354	87.20 353	97.97 347

PM-MVS		92.96 326	92.23 329	95.14 338	95.61 361	89.98 363	99.37 196	98.21 347	94.80 326	95.04 352	97.69 350	65.06 367	97.90 351	94.30 325	89.98 349	97.54 356

pmmvs-eth3d		95.34 314	94.73 316	97.15 319	95.53 363	95.94 311	99.35 206	99.10 294	95.13 317	93.55 355	97.54 351	88.15 344	97.91 350	94.58 322	89.69 350	97.61 353

ambc				93.06 341	92.68 366	82.36 365	98.47 341	98.73 335		95.09 351	97.41 352	55.55 370	99.10 309	96.42 291	91.32 345	97.71 352

RPMNet		96.72 293	95.90 303	99.19 158	99.18 247	98.49 207	99.22 245	99.52 91	88.72 357	99.56 98	97.38 353	94.08 244	99.95 46	86.87 363	98.58 196	99.14 196

new-patchmatchnet		94.48 321	94.08 321	95.67 337	95.08 364	92.41 357	99.18 249	99.28 272	94.55 331	93.49 356	97.37 354	87.86 348	97.01 360	91.57 347	88.36 351	97.61 353

KD-MVS_self_test		95.00 315	94.34 320	96.96 325	97.07 358	95.39 325	99.56 104	99.44 194	95.11 319	97.13 335	97.32 355	91.86 294	97.27 358	90.35 352	81.23 361	98.23 334

PatchT		97.03 289	96.44 293	98.79 219	98.99 282	98.34 216	99.16 251	99.07 299	92.13 347	99.52 108	97.31 356	94.54 229	98.98 322	88.54 357	98.73 192	99.03 213

UnsupCasMVSNet_bld		93.53 325	92.51 328	96.58 333	97.38 350	93.82 346	98.24 351	99.48 145	91.10 352	93.10 357	96.66 357	74.89 365	98.37 341	94.03 330	87.71 352	97.56 355

LCM-MVSNet		86.80 330	85.22 334	91.53 344	87.81 371	80.96 367	98.23 353	98.99 305	71.05 365	90.13 361	96.51 358	48.45 373	96.88 361	90.51 350	85.30 355	96.76 357

PMMVS2		86.87 329	85.37 333	91.35 345	90.21 369	83.80 364	98.89 308	97.45 359	83.13 362	91.67 360	95.03 359	48.49 372	94.70 366	85.86 364	77.62 363	95.54 360

Gipuma		90.99 328	90.15 331	93.51 339	98.73 315	90.12 362	93.98 364	99.45 185	79.32 363	92.28 358	94.91 360	69.61 366	97.98 349	87.42 360	95.67 295	92.45 363
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
JIA-IIPM		97.50 274	97.02 285	98.93 187	98.73 315	97.80 243	99.30 215	98.97 307	91.73 349	98.91 235	94.86 361	95.10 201	99.71 201	97.58 223	97.98 224	99.28 191

PMVS	70.75 22	75.98 337	74.97 338	79.01 353	70.98 376	55.18 377	93.37 365	98.21 347	65.08 371	61.78 372	93.83 362	21.74 378	92.53 367	78.59 366	91.12 346	89.34 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS-HIRNet		95.75 309	95.16 313	97.51 313	99.30 217	93.69 350	98.88 309	95.78 366	85.09 360	98.78 255	92.65 363	91.29 308	99.37 260	94.85 320	99.85 58	99.46 173

E-PMN		80.61 333	79.88 336	82.81 350	90.75 368	76.38 372	97.69 359	95.76 367	66.44 369	83.52 363	92.25 364	62.54 369	87.16 370	68.53 369	61.40 367	84.89 368

EMVS		80.02 334	79.22 337	82.43 352	91.19 367	76.40 371	97.55 361	92.49 377	66.36 370	83.01 365	91.27 365	64.63 368	85.79 371	65.82 370	60.65 368	85.08 367

gg-mvs-nofinetune		96.17 304	95.32 312	98.73 223	98.79 306	98.14 225	99.38 193	94.09 372	91.07 353	98.07 313	91.04 366	89.62 329	99.35 268	96.75 279	99.09 167	98.68 262

ANet_high		77.30 335	74.86 339	84.62 349	75.88 375	77.61 370	97.63 360	93.15 375	88.81 356	64.27 371	89.29 367	36.51 374	83.93 372	75.89 367	52.31 369	92.33 364

MVE	76.82 21	76.91 336	74.31 340	84.70 348	85.38 374	76.05 373	96.88 362	93.17 374	67.39 368	71.28 370	89.01 368	21.66 379	87.69 369	71.74 368	72.29 366	90.35 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		39.17 339	43.78 341	25.37 356	36.04 379	16.84 380	98.36 344	26.56 378	20.06 373	38.51 374	67.32 369	29.64 376	15.30 375	37.59 372	39.90 371	43.98 370

test123		39.01 340	42.50 342	28.53 355	39.17 378	20.91 379	98.75 322	19.17 380	19.83 374	38.57 373	66.67 370	33.16 375	15.42 374	37.50 373	29.66 372	49.26 369

test_post											65.99 371	94.65 224	99.73 191

test_post1							99.23 240				65.14 372	94.18 241	99.71 201	97.58 223

X-MVStestdata		96.55 295	95.45 310	99.87 12	99.85 26	99.83 17	99.69 40	99.68 19	98.98 31	99.37 143	64.01 373	98.81 48	99.94 57	98.79 99	99.86 51	99.84 20

wuyk23d		40.18 338	41.29 343	36.84 354	86.18 373	49.12 378	79.73 367	22.81 379	27.64 372	25.46 375	28.45 374	21.98 377	48.89 373	55.80 371	23.56 373	12.51 371

test_blank		0.13 344	0.17 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	1.57 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uanet_test		0.02 345	0.03 348	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.27 376	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

pcd_1.5k_mvsjas		8.27 343	11.03 346	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.27 376	99.01 19	0.00 376	0.00 374	0.00 374	0.00 372

sosnet-low-res		0.02 345	0.03 348	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.27 376	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

sosnet		0.02 345	0.03 348	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.27 376	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uncertanet		0.02 345	0.03 348	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.27 376	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

Regformer		0.02 345	0.03 348	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.27 376	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uanet		0.02 345	0.03 348	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.27 376	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

FOURS1					99.91 1	99.93 1	99.87 5	99.56 57	99.10 10	99.81 24

MSC_two_6792asdad				99.87 12	99.51 156	99.76 41		99.33 248					99.96 19	98.87 80	99.84 65	99.89 2

No_MVS				99.87 12	99.51 156	99.76 41		99.33 248					99.96 19	98.87 80	99.84 65	99.89 2

eth-test2					0.00 380

eth-test					0.00 380

IU-MVS					99.84 33	99.88 8		99.32 258	98.30 94	99.84 14				98.86 85	99.85 58	99.89 2

save fliter					99.76 54	99.59 73	99.14 257	99.40 212	99.00 25

test_0728_SECOND				99.91 2	99.84 33	99.89 4	99.57 97	99.51 104					99.96 19	98.93 70	99.86 51	99.88 7

GSMVS																99.52 155

test_part2					99.81 41	99.83 17				99.77 36

sam_mvs1												94.86 209				99.52 155

sam_mvs												94.72 220

MTGPA								99.47 163

MTMP							99.54 116	98.88 320

test9_res														97.49 234	99.72 109	99.75 75

agg_prior2														97.21 251	99.73 108	99.75 75

agg_prior					99.67 105	99.62 66		99.40 212		98.87 242			99.91 94

test_prior4						99.56 78	98.99 290

test_prior				99.68 68	99.67 105	99.48 93		99.56 57					99.83 150			99.74 80

旧先验2							98.96 298		96.70 255	99.47 116			99.94 57	98.19 169

新几何2							99.01 288

无先验							98.99 290	99.51 104	96.89 244				99.93 72	97.53 231		99.72 93

原ACMM2							98.95 302

testdata2													99.95 46	96.67 285

segment_acmp												98.96 28

testdata1							98.85 312		98.32 93

test12				99.75 54	99.64 121	99.61 68		99.29 270		99.21 181		98.38 89	99.89 117		99.74 105	99.74 80

plane_prior7					99.29 221	97.03 275

plane_prior6					99.27 226	96.98 279						92.71 273

plane_prior5								99.47 163					99.69 211	97.78 204	97.63 231	98.67 269

plane_prior3						97.00 277			98.69 61	99.11 199

plane_prior2							99.39 188		98.97 34

plane_prior1					99.26 228

plane_prior						96.97 280	99.21 247		98.45 76						97.60 234

n2								0.00 381

nn								0.00 381

door-mid								98.05 350

test11								99.35 237

door								97.92 352

HQP5-MVS						96.83 285

HQP-NCC					99.19 244		98.98 294		98.24 99	98.66 270

ACMP_Plane					99.19 244		98.98 294		98.24 99	98.66 270

BP-MVS														97.19 255

HQP4-MVS										98.66 270			99.64 223			98.64 281

HQP3-MVS								99.39 216							97.58 236

HQP2-MVS												92.47 282

MDTV_nov1_ep13_2view						95.18 330	99.35 206		96.84 247	99.58 95		95.19 200		97.82 201		99.46 173

ACMMP++_ref															97.19 260

ACMMP++															97.43 252

Test By Simon												98.75 59

High-resolution multi-view benchmark