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 41	99.29 49	99.80 43	99.62 134	99.55 80	99.50 134	99.70 15	98.79 56	99.77 36	99.96 1	97.45 122	99.96 20	98.92 74	99.90 24	99.89 2

DeepC-MVS	98.35 2	99.30 59	99.19 65	99.64 80	99.82 38	99.23 121	99.62 70	99.55 67	98.94 40	99.63 83	99.95 2	95.82 178	99.94 58	99.37 27	99.97 4	99.73 89
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		97.88 222	97.77 215	98.19 276	98.71 321	96.53 296	99.88 2	99.00 306	97.79 156	98.78 257	99.94 3	91.68 300	99.35 270	97.21 253	96.99 268	98.69 259

test2506		96.81 293	96.65 291	97.29 321	99.74 75	92.21 361	99.60 77	85.06 380	99.13 8	99.77 36	99.93 4	87.82 350	99.85 135	99.38 26	99.38 143	99.80 55

test1111		98.04 200	98.11 175	97.83 301	99.74 75	93.82 348	99.58 92	95.40 370	99.12 10	99.65 78	99.93 4	90.73 315	99.84 141	99.43 24	99.38 143	99.82 39

ECVR-MVS		98.04 200	98.05 184	98.00 290	99.74 75	94.37 343	99.59 84	94.98 371	99.13 8	99.66 72	99.93 4	90.67 316	99.84 141	99.40 25	99.38 143	99.80 55

SixPastTwentyTwo		97.50 275	97.33 271	98.03 285	98.65 326	96.23 306	99.77 28	98.68 340	97.14 222	97.90 319	99.93 4	90.45 317	99.18 299	97.00 267	96.43 278	98.67 271

SD-MVS		99.41 46	99.52 7	99.05 170	99.74 75	99.68 54	99.46 158	99.52 92	99.11 11	99.88 5	99.91 8	99.43 1	97.70 358	98.72 110	99.93 11	99.77 71
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	98.10 190	97.99 190	98.44 255	99.41 191	96.96 282	99.60 77	99.56 58	98.09 122	98.15 310	99.91 8	90.87 314	99.70 209	98.88 78	97.45 252	98.67 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
patch_mono-2		99.26 66	99.62 1	98.16 278	99.81 41	94.59 340	99.52 123	99.64 33	99.33 2	99.73 49	99.90 10	99.00 25	99.99 1	99.69 1	99.98 2	99.89 2

VDDNet		97.55 269	97.02 286	99.16 161	99.49 172	98.12 227	99.38 195	99.30 266	95.35 318	99.68 61	99.90 10	82.62 363	99.93 73	99.31 35	98.13 224	99.42 180

QAPM		98.67 148	98.30 165	99.80 43	99.20 244	99.67 57	99.77 28	99.72 11	94.74 329	98.73 261	99.90 10	95.78 179	99.98 7	96.96 271	99.88 37	99.76 76

3Dnovator	97.25 9	99.24 70	99.05 78	99.81 41	99.12 262	99.66 59	99.84 10	99.74 10	99.09 15	98.92 236	99.90 10	95.94 172	99.98 7	98.95 69	99.92 12	99.79 61

Anonymous20240529		98.09 191	97.68 225	99.34 133	99.66 118	98.44 211	99.40 186	99.43 203	93.67 339	99.22 180	99.89 14	90.23 322	99.93 73	99.26 41	98.33 208	99.66 117

CHOSEN 1792x2688		99.19 73	99.10 73	99.45 122	99.89 9	98.52 203	99.39 190	99.94 1	98.73 60	99.11 201	99.89 14	95.50 188	99.94 58	99.50 12	99.97 4	99.89 2

RPSCF		98.22 175	98.62 142	96.99 326	99.82 38	91.58 363	99.72 36	99.44 195	96.61 265	99.66 72	99.89 14	95.92 173	99.82 159	97.46 240	99.10 168	99.57 147

3Dnovator+	97.12 13	99.18 75	98.97 95	99.82 38	99.17 255	99.68 54	99.81 16	99.51 105	99.20 5	98.72 262	99.89 14	95.68 183	99.97 12	98.86 87	99.86 52	99.81 45

COLMAP_ROB	97.56 6	98.86 123	98.75 125	99.17 160	99.88 12	98.53 199	99.34 211	99.59 44	97.55 181	98.70 269	99.89 14	95.83 177	99.90 110	98.10 179	99.90 24	99.08 207
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_djsdf		98.67 148	98.57 149	98.98 179	98.70 322	98.91 166	99.88 2	99.46 174	97.55 181	99.22 180	99.88 19	95.73 181	99.28 281	99.03 61	97.62 236	98.75 243

DP-MVS		99.16 79	98.95 99	99.78 48	99.77 52	99.53 85	99.41 178	99.50 125	97.03 236	99.04 217	99.88 19	97.39 123	99.92 84	98.66 120	99.90 24	99.87 13

TDRefinement		95.42 314	94.57 320	97.97 292	89.83 373	96.11 309	99.48 150	98.75 329	96.74 254	96.68 342	99.88 19	88.65 338	99.71 203	98.37 158	82.74 362	98.09 339

EPP-MVSNet		99.13 83	98.99 91	99.53 102	99.65 123	99.06 143	99.81 16	99.33 249	97.43 197	99.60 93	99.88 19	97.14 132	99.84 141	99.13 53	98.94 180	99.69 107

OpenMVS	96.50 16	98.47 156	98.12 174	99.52 108	99.04 278	99.53 85	99.82 14	99.72 11	94.56 332	98.08 312	99.88 19	94.73 220	99.98 7	97.47 239	99.76 103	99.06 213

lessismore_v0				97.79 305	98.69 323	95.44 325		94.75 372		95.71 351	99.87 24	88.69 337	99.32 276	95.89 301	94.93 315	98.62 293

Vis-MVSNet		99.12 89	98.97 95	99.56 94	99.78 47	99.10 138	99.68 46	99.66 27	98.49 76	99.86 12	99.87 24	94.77 217	99.84 141	99.19 46	99.41 142	99.74 82
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH+	97.24 10	97.92 219	97.78 213	98.32 266	99.46 181	96.68 292	99.56 105	99.54 75	98.41 84	97.79 324	99.87 24	90.18 323	99.66 218	98.05 188	97.18 264	98.62 293

ACMMP_NAP		99.47 24	99.34 31	99.88 6	99.87 16	99.86 13	99.47 155	99.48 146	98.05 132	99.76 43	99.86 27	98.82 48	99.93 73	98.82 99	99.91 17	99.84 21

RRT_MVS		98.60 153	98.44 154	99.05 170	98.88 296	99.14 133	99.49 144	99.38 223	97.76 159	99.29 162	99.86 27	95.38 191	99.36 266	98.81 100	97.16 265	98.64 283

casdiffmvs		99.13 83	98.98 94	99.56 94	99.65 123	99.16 128	99.56 105	99.50 125	98.33 95	99.41 133	99.86 27	95.92 173	99.83 152	99.45 21	99.16 160	99.70 104

PVSNet_Blended_VisFu		99.36 53	99.28 53	99.61 85	99.86 22	99.07 142	99.47 155	99.93 2	97.66 172	99.71 54	99.86 27	97.73 117	99.96 20	99.47 19	99.82 81	99.79 61

IS-MVSNet		99.05 104	98.87 108	99.57 92	99.73 83	99.32 109	99.75 32	99.20 285	98.02 136	99.56 101	99.86 27	96.54 153	99.67 215	98.09 180	99.13 164	99.73 89

USDC		97.34 282	97.20 280	97.75 306	99.07 272	95.20 329	98.51 343	99.04 304	97.99 137	98.31 303	99.86 27	89.02 333	99.55 238	95.67 308	97.36 259	98.49 313

TSAR-MVS + MP.		99.58 5	99.50 9	99.81 41	99.91 1	99.66 59	99.63 64	99.39 217	98.91 45	99.78 34	99.85 33	99.36 2	99.94 58	98.84 92	99.88 37	99.82 39
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tmp_tt		82.80 334	81.52 337	86.66 350	66.61 380	68.44 378	92.79 369	97.92 354	68.96 368	80.04 371	99.85 33	85.77 355	96.15 368	97.86 199	43.89 373	95.39 364

AllTest		98.87 120	98.72 126	99.31 139	99.86 22	98.48 209	99.56 105	99.61 36	97.85 147	99.36 148	99.85 33	95.95 170	99.85 135	96.66 288	99.83 75	99.59 142

TestCases				99.31 139	99.86 22	98.48 209		99.61 36	97.85 147	99.36 148	99.85 33	95.95 170	99.85 135	96.66 288	99.83 75	99.59 142

VDD-MVS		97.73 250	97.35 266	98.88 201	99.47 180	97.12 264	99.34 211	98.85 324	98.19 108	99.67 67	99.85 33	82.98 361	99.92 84	99.49 16	98.32 212	99.60 138

APDe-MVS		99.66 1	99.57 2	99.92 1	99.77 52	99.89 4	99.75 32	99.56 58	99.02 20	99.88 5	99.85 33	99.18 10	99.96 20	99.22 43	99.92 12	99.90 1

DeepPCF-MVS	98.18 3	98.81 135	99.37 24	97.12 325	99.60 142	91.75 362	98.61 336	99.44 195	99.35 1	99.83 19	99.85 33	98.70 67	99.81 163	99.02 63	99.91 17	99.81 45

ACMM	97.58 5	98.37 166	98.34 161	98.48 246	99.41 191	97.10 265	99.56 105	99.45 186	98.53 73	99.04 217	99.85 33	93.00 263	99.71 203	98.74 106	97.45 252	98.64 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 64	99.12 71	99.74 59	99.18 249	99.75 43	99.56 105	99.57 52	98.45 80	99.49 116	99.85 33	97.77 116	99.94 58	98.33 162	99.84 66	99.52 157

DPE-MVS		99.46 26	99.32 35	99.91 2	99.78 47	99.88 8	99.36 202	99.51 105	98.73 60	99.88 5	99.84 42	98.72 65	99.96 20	98.16 176	99.87 41	99.88 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVG-OURS		98.73 143	98.68 131	98.88 201	99.70 101	97.73 246	98.92 308	99.55 67	98.52 74	99.45 121	99.84 42	95.27 196	99.91 95	98.08 184	98.84 188	99.00 218

baseline		99.15 80	99.02 86	99.53 102	99.66 118	99.14 133	99.72 36	99.48 146	98.35 91	99.42 129	99.84 42	96.07 166	99.79 171	99.51 11	99.14 163	99.67 114

ACMMP		99.45 28	99.32 35	99.82 38	99.89 9	99.67 57	99.62 70	99.69 18	98.12 117	99.63 83	99.84 42	98.73 64	99.96 20	98.55 141	99.83 75	99.81 45
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 2	99.64 80	99.78 47	99.14 133	99.60 77	99.45 186	99.01 23	99.90 3	99.83 46	98.98 27	99.93 73	99.59 5	99.95 7	99.86 14

EI-MVSNet		98.67 148	98.67 132	98.68 228	99.35 205	97.97 232	99.50 134	99.38 223	96.93 245	99.20 186	99.83 46	97.87 112	99.36 266	98.38 156	97.56 241	98.71 251

CVMVSNet		98.57 154	98.67 132	98.30 268	99.35 205	95.59 318	99.50 134	99.55 67	98.60 68	99.39 140	99.83 46	94.48 231	99.45 245	98.75 105	98.56 201	99.85 17

LPG-MVS_test		98.22 175	98.13 173	98.49 244	99.33 210	97.05 271	99.58 92	99.55 67	97.46 190	99.24 175	99.83 46	92.58 279	99.72 197	98.09 180	97.51 245	98.68 264

LGP-MVS_train				98.49 244	99.33 210	97.05 271		99.55 67	97.46 190	99.24 175	99.83 46	92.58 279	99.72 197	98.09 180	97.51 245	98.68 264

SteuartSystems-ACMMP		99.54 10	99.42 16	99.87 12	99.82 38	99.81 27	99.59 84	99.51 105	98.62 66	99.79 29	99.83 46	99.28 4	99.97 12	98.48 146	99.90 24	99.84 21
Skip Steuart: Steuart Systems R&D Blog.
XXY-MVS		98.38 165	98.09 179	99.24 154	99.26 230	99.32 109	99.56 105	99.55 67	97.45 193	98.71 263	99.83 46	93.23 259	99.63 230	98.88 78	96.32 281	98.76 241

SR-MVS-dyc-post		99.45 28	99.31 42	99.85 28	99.76 56	99.82 23	99.63 64	99.52 92	98.38 86	99.76 43	99.82 53	98.53 77	99.95 47	98.61 127	99.81 84	99.77 71

RE-MVS-def			99.34 31		99.76 56	99.82 23	99.63 64	99.52 92	98.38 86	99.76 43	99.82 53	98.75 60		98.61 127	99.81 84	99.77 71

test0726					99.85 26	99.89 4	99.62 70	99.50 125	99.10 12	99.86 12	99.82 53	98.94 35

SMA-MVS		99.44 32	99.30 45	99.85 28	99.73 83	99.83 17	99.56 105	99.47 164	97.45 193	99.78 34	99.82 53	99.18 10	99.91 95	98.79 101	99.89 34	99.81 45
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		98.64 151	98.42 156	99.28 149	99.05 277	99.69 52	99.81 16	99.46 174	98.04 133	99.01 220	99.82 53	96.69 149	99.38 259	99.34 32	94.59 319	98.78 235

FC-MVSNet-test		98.75 142	98.62 142	99.15 163	99.08 271	99.45 97	99.86 9	99.60 41	98.23 104	98.70 269	99.82 53	96.80 143	99.22 291	99.07 59	96.38 279	98.79 234

EI-MVSNet-Vis-set		99.58 5	99.56 4	99.64 80	99.78 47	99.15 132	99.61 76	99.45 186	99.01 23	99.89 4	99.82 53	99.01 19	99.92 84	99.56 8	99.95 7	99.85 17

APD-MVS_3200maxsize		99.48 21	99.35 29	99.85 28	99.76 56	99.83 17	99.63 64	99.54 75	98.36 90	99.79 29	99.82 53	98.86 44	99.95 47	98.62 124	99.81 84	99.78 69

EU-MVSNet		97.98 211	98.03 186	97.81 304	98.72 319	96.65 293	99.66 52	99.66 27	98.09 122	98.35 301	99.82 53	95.25 199	98.01 351	97.41 245	95.30 306	98.78 235

APD-MVS		99.27 64	99.08 76	99.84 35	99.75 67	99.79 33	99.50 134	99.50 125	97.16 221	99.77 36	99.82 53	98.78 52	99.94 58	97.56 230	99.86 52	99.80 55
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.12 89	99.08 76	99.24 154	99.46 181	98.55 197	99.51 128	99.46 174	98.09 122	99.45 121	99.82 53	98.34 94	99.51 240	98.70 112	98.93 181	99.67 114

DeepC-MVS_fast	98.69 1	99.49 17	99.39 20	99.77 50	99.63 128	99.59 73	99.36 202	99.46 174	99.07 18	99.79 29	99.82 53	98.85 45	99.92 84	98.68 117	99.87 41	99.82 39
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 83	99.02 86	99.45 122	99.57 148	98.63 191	99.07 272	99.34 242	98.99 30	99.61 89	99.82 53	97.98 111	99.87 126	97.00 267	99.80 88	99.85 17

DVP-MVS++		99.59 3	99.50 9	99.88 6	99.51 160	99.88 8	99.87 6	99.51 105	98.99 30	99.88 5	99.81 66	99.27 5	99.96 20	98.85 89	99.80 88	99.81 45

test_one_0601					99.81 41	99.88 8		99.49 133	98.97 36	99.65 78	99.81 66	99.09 14

SED-MVS		99.61 2	99.52 7	99.88 6	99.84 33	99.90 2	99.60 77	99.48 146	99.08 16	99.91 1	99.81 66	99.20 7	99.96 20	98.91 75	99.85 59	99.79 61

test_241102_TWO								99.48 146	99.08 16	99.88 5	99.81 66	98.94 35	99.96 20	98.91 75	99.84 66	99.88 8

OPM-MVS		98.19 179	98.10 176	98.45 252	98.88 296	97.07 269	99.28 224	99.38 223	98.57 69	99.22 180	99.81 66	92.12 290	99.66 218	98.08 184	97.54 243	98.61 302
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
zzz-MVS		99.49 17	99.36 26	99.89 4	99.90 4	99.86 13	99.36 202	99.47 164	98.79 56	99.68 61	99.81 66	98.43 86	99.97 12	98.88 78	99.90 24	99.83 32

MTAPA		99.52 14	99.39 20	99.89 4	99.90 4	99.86 13	99.66 52	99.47 164	98.79 56	99.68 61	99.81 66	98.43 86	99.97 12	98.88 78	99.90 24	99.83 32

FIs		98.78 139	98.63 137	99.23 156	99.18 249	99.54 82	99.83 13	99.59 44	98.28 98	98.79 256	99.81 66	96.75 147	99.37 262	99.08 58	96.38 279	98.78 235

mvs_tets		98.40 164	98.23 168	98.91 193	98.67 325	98.51 205	99.66 52	99.53 86	98.19 108	98.65 278	99.81 66	92.75 269	99.44 250	99.31 35	97.48 251	98.77 239

mvs_anonymous		99.03 107	98.99 91	99.16 161	99.38 200	98.52 203	99.51 128	99.38 223	97.79 156	99.38 143	99.81 66	97.30 128	99.45 245	99.35 28	98.99 178	99.51 163

TSAR-MVS + GP.		99.36 53	99.36 26	99.36 132	99.67 109	98.61 194	99.07 272	99.33 249	99.00 27	99.82 22	99.81 66	99.06 16	99.84 141	99.09 57	99.42 141	99.65 121

abl_6		99.44 32	99.31 42	99.83 36	99.85 26	99.75 43	99.66 52	99.59 44	98.13 115	99.82 22	99.81 66	98.60 74	99.96 20	98.46 150	99.88 37	99.79 61

RRT_test8_iter05		97.72 252	97.60 233	98.08 282	99.23 236	96.08 310	99.63 64	99.49 133	97.54 184	98.94 233	99.81 66	87.99 346	99.35 270	99.21 45	96.51 276	98.81 232

EPNet		98.86 123	98.71 128	99.30 143	97.20 358	98.18 222	99.62 70	98.91 318	99.28 3	98.63 280	99.81 66	95.96 169	99.99 1	99.24 42	99.72 111	99.73 89
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ab-mvs		98.86 123	98.63 137	99.54 96	99.64 125	99.19 123	99.44 163	99.54 75	97.77 158	99.30 159	99.81 66	94.20 239	99.93 73	99.17 49	98.82 189	99.49 167

OMC-MVS		99.08 100	99.04 81	99.20 157	99.67 109	98.22 221	99.28 224	99.52 92	98.07 127	99.66 72	99.81 66	97.79 115	99.78 175	97.79 205	99.81 84	99.60 138

xxxxxxxxxxxxxcwj		99.43 36	99.32 35	99.75 54	99.76 56	99.59 73	99.14 260	99.53 86	99.00 27	99.71 54	99.80 82	98.95 32	99.93 73	98.19 171	99.84 66	99.74 82

SF-MVS		99.38 51	99.24 60	99.79 46	99.79 45	99.68 54	99.57 98	99.54 75	97.82 155	99.71 54	99.80 82	98.95 32	99.93 73	98.19 171	99.84 66	99.74 82

DVP-MVS		99.57 8	99.47 12	99.88 6	99.85 26	99.89 4	99.57 98	99.37 232	99.10 12	99.81 24	99.80 82	98.94 35	99.96 20	98.93 72	99.86 52	99.81 45
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 30	99.81 24	99.80 82	99.09 14	99.96 20	98.85 89	99.90 24	99.88 8

jajsoiax		98.43 159	98.28 166	98.88 201	98.60 332	98.43 212	99.82 14	99.53 86	98.19 108	98.63 280	99.80 82	93.22 261	99.44 250	99.22 43	97.50 247	98.77 239

Regformer-3		99.57 8	99.53 6	99.68 68	99.76 56	99.29 114	99.58 92	99.44 195	99.01 23	99.87 11	99.80 82	98.97 28	99.91 95	99.44 23	99.92 12	99.83 32

Regformer-4		99.59 3	99.54 5	99.73 61	99.76 56	99.41 101	99.58 92	99.49 133	99.02 20	99.88 5	99.80 82	99.00 25	99.94 58	99.45 21	99.92 12	99.84 21

PGM-MVS		99.45 28	99.31 42	99.86 21	99.87 16	99.78 40	99.58 92	99.65 32	97.84 149	99.71 54	99.80 82	99.12 13	99.97 12	98.33 162	99.87 41	99.83 32

TransMVSNet (Re)		97.15 287	96.58 292	98.86 208	99.12 262	98.85 172	99.49 144	98.91 318	95.48 316	97.16 336	99.80 82	93.38 257	99.11 310	94.16 331	91.73 347	98.62 293

K. test v3		97.10 289	96.79 290	98.01 288	98.72 319	96.33 303	99.87 6	97.05 362	97.59 176	96.16 347	99.80 82	88.71 336	99.04 316	96.69 286	96.55 274	98.65 281

DELS-MVS		99.48 21	99.42 16	99.65 75	99.72 88	99.40 103	99.05 277	99.66 27	99.14 7	99.57 100	99.80 82	98.46 84	99.94 58	99.57 7	99.84 66	99.60 138
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 57	99.32 35	99.32 138	99.85 26	98.29 217	99.71 38	99.66 27	98.11 119	99.41 133	99.80 82	98.37 93	99.96 20	98.99 65	99.96 6	99.72 95

test1172		99.43 36	99.29 49	99.85 28	99.75 67	99.82 23	99.60 77	99.56 58	98.28 98	99.74 47	99.79 94	98.53 77	99.95 47	98.55 141	99.78 95	99.79 61

SR-MVS		99.43 36	99.29 49	99.86 21	99.75 67	99.83 17	99.59 84	99.62 34	98.21 107	99.73 49	99.79 94	98.68 68	99.96 20	98.44 152	99.77 100	99.79 61

MP-MVS-pluss		99.37 52	99.20 64	99.88 6	99.90 4	99.87 12	99.30 218	99.52 92	97.18 219	99.60 93	99.79 94	98.79 51	99.95 47	98.83 95	99.91 17	99.83 32
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pm-mvs1		97.68 260	97.28 275	98.88 201	99.06 274	98.62 192	99.50 134	99.45 186	96.32 286	97.87 320	99.79 94	92.47 283	99.35 270	97.54 232	93.54 334	98.67 271

LFMVS		97.90 221	97.35 266	99.54 96	99.52 158	99.01 148	99.39 190	98.24 348	97.10 229	99.65 78	99.79 94	84.79 358	99.91 95	99.28 38	98.38 207	99.69 107

TinyColmap		97.12 288	96.89 288	97.83 301	99.07 272	95.52 322	98.57 339	98.74 332	97.58 178	97.81 323	99.79 94	88.16 344	99.56 236	95.10 318	97.21 262	98.39 327

ACMP	97.20 11	98.06 194	97.94 198	98.45 252	99.37 202	97.01 276	99.44 163	99.49 133	97.54 184	98.45 293	99.79 94	91.95 293	99.72 197	97.91 195	97.49 250	98.62 293
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GeoE		98.85 131	98.62 142	99.53 102	99.61 138	99.08 140	99.80 20	99.51 105	97.10 229	99.31 157	99.78 101	95.23 200	99.77 177	98.21 169	99.03 174	99.75 77

9.14			99.10 73		99.72 88		99.40 186	99.51 105	97.53 186	99.64 82	99.78 101	98.84 46	99.91 95	97.63 221	99.82 81

pmmvs6		96.53 298	96.09 301	97.82 303	98.69 323	95.47 323	99.37 198	99.47 164	93.46 343	97.41 329	99.78 101	87.06 352	99.33 274	96.92 276	92.70 344	98.65 281

MSLP-MVS++		99.46 26	99.47 12	99.44 126	99.60 142	99.16 128	99.41 178	99.71 13	98.98 33	99.45 121	99.78 101	99.19 9	99.54 239	99.28 38	99.84 66	99.63 132

VNet		99.11 94	98.90 104	99.73 61	99.52 158	99.56 78	99.41 178	99.39 217	99.01 23	99.74 47	99.78 101	95.56 186	99.92 84	99.52 10	98.18 219	99.72 95

114514_t		98.93 117	98.67 132	99.72 64	99.85 26	99.53 85	99.62 70	99.59 44	92.65 348	99.71 54	99.78 101	98.06 109	99.90 110	98.84 92	99.91 17	99.74 82

Vis-MVSNet (Re-imp)		98.87 120	98.72 126	99.31 139	99.71 94	98.88 168	99.80 20	99.44 195	97.91 143	99.36 148	99.78 101	95.49 189	99.43 254	97.91 195	99.11 165	99.62 134

UniMVSNet_ETH3D		97.32 283	96.81 289	98.87 205	99.40 196	97.46 253	99.51 128	99.53 86	95.86 313	98.54 288	99.77 108	82.44 364	99.66 218	98.68 117	97.52 244	99.50 166

anonymousdsp		98.44 158	98.28 166	98.94 185	98.50 337	98.96 157	99.77 28	99.50 125	97.07 231	98.87 244	99.77 108	94.76 218	99.28 281	98.66 120	97.60 237	98.57 308

CDS-MVSNet		99.09 98	99.03 83	99.25 152	99.42 188	98.73 183	99.45 159	99.46 174	98.11 119	99.46 120	99.77 108	98.01 110	99.37 262	98.70 112	98.92 183	99.66 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.98 113	98.80 119	99.53 102	99.76 56	99.19 123	98.75 325	99.55 67	97.25 213	99.47 118	99.77 108	97.82 114	99.87 126	96.93 274	99.90 24	99.54 151

CHOSEN 280x420		99.12 89	99.13 70	99.08 166	99.66 118	97.89 238	98.43 346	99.71 13	98.88 46	99.62 87	99.76 112	96.63 150	99.70 209	99.46 20	99.99 1	99.66 117

PS-MVSNAJss		98.92 118	98.92 101	98.90 195	98.78 311	98.53 199	99.78 26	99.54 75	98.07 127	99.00 225	99.76 112	99.01 19	99.37 262	99.13 53	97.23 261	98.81 232

Regformer-1		99.53 12	99.47 12	99.72 64	99.71 94	99.44 98	99.49 144	99.46 174	98.95 39	99.83 19	99.76 112	99.01 19	99.93 73	99.17 49	99.87 41	99.80 55

Regformer-2		99.54 10	99.47 12	99.75 54	99.71 94	99.52 88	99.49 144	99.49 133	98.94 40	99.83 19	99.76 112	99.01 19	99.94 58	99.15 52	99.87 41	99.80 55

MVS_Test		99.10 97	98.97 95	99.48 116	99.49 172	99.14 133	99.67 48	99.34 242	97.31 207	99.58 98	99.76 112	97.65 119	99.82 159	98.87 82	99.07 171	99.46 175

ETH3D-3000-0.1		99.21 71	99.02 86	99.77 50	99.73 83	99.69 52	99.38 195	99.51 105	97.45 193	99.61 89	99.75 117	98.51 80	99.91 95	97.45 242	99.83 75	99.71 102

CANet_DTU		98.97 115	98.87 108	99.25 152	99.33 210	98.42 214	99.08 271	99.30 266	99.16 6	99.43 126	99.75 117	95.27 196	99.97 12	98.56 138	99.95 7	99.36 187

mPP-MVS		99.44 32	99.30 45	99.86 21	99.88 12	99.79 33	99.69 41	99.48 146	98.12 117	99.50 113	99.75 117	98.78 52	99.97 12	98.57 135	99.89 34	99.83 32

HPM-MVS_fast		99.51 15	99.40 19	99.85 28	99.91 1	99.79 33	99.76 31	99.56 58	97.72 164	99.76 43	99.75 117	99.13 12	99.92 84	99.07 59	99.92 12	99.85 17

HyFIR lowres test		99.11 94	98.92 101	99.65 75	99.90 4	99.37 104	99.02 286	99.91 3	97.67 171	99.59 96	99.75 117	95.90 175	99.73 193	99.53 9	99.02 176	99.86 14

ITE_SJBPF				98.08 282	99.29 223	96.37 301		98.92 315	98.34 92	98.83 250	99.75 117	91.09 311	99.62 231	95.82 302	97.40 257	98.25 334

test_241102_ONE					99.84 33	99.90 2		99.48 146	99.07 18	99.91 1	99.74 123	99.20 7	99.76 183

testtj		99.12 89	98.87 108	99.86 21	99.72 88	99.79 33	99.44 163	99.51 105	97.29 209	99.59 96	99.74 123	98.15 106	99.96 20	96.74 282	99.69 117	99.81 45

Anonymous202405211		98.30 171	97.98 191	99.26 151	99.57 148	98.16 223	99.41 178	98.55 344	96.03 311	99.19 189	99.74 123	91.87 294	99.92 84	99.16 51	98.29 213	99.70 104

tttt0517		98.42 160	98.14 172	99.28 149	99.66 118	98.38 215	99.74 35	96.85 363	97.68 168	99.79 29	99.74 123	91.39 307	99.89 118	98.83 95	99.56 134	99.57 147

XVS		99.53 12	99.42 16	99.87 12	99.85 26	99.83 17	99.69 41	99.68 19	98.98 33	99.37 145	99.74 123	98.81 49	99.94 58	98.79 101	99.86 52	99.84 21

MP-MVS		99.33 56	99.15 68	99.87 12	99.88 12	99.82 23	99.66 52	99.46 174	98.09 122	99.48 117	99.74 123	98.29 97	99.96 20	97.93 194	99.87 41	99.82 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_111021_LR		99.41 46	99.33 33	99.65 75	99.77 52	99.51 90	98.94 307	99.85 6	98.82 51	99.65 78	99.74 123	98.51 80	99.80 168	98.83 95	99.89 34	99.64 128

VPNet		97.84 230	97.44 254	99.01 175	99.21 242	98.94 162	99.48 150	99.57 52	98.38 86	99.28 164	99.73 130	88.89 335	99.39 257	99.19 46	93.27 337	98.71 251

MVSTER		98.49 155	98.32 163	99.00 177	99.35 205	99.02 146	99.54 117	99.38 223	97.41 200	99.20 186	99.73 130	93.86 251	99.36 266	98.87 82	97.56 241	98.62 293

MVS_111021_HR		99.41 46	99.32 35	99.66 71	99.72 88	99.47 95	98.95 305	99.85 6	98.82 51	99.54 106	99.73 130	98.51 80	99.74 186	98.91 75	99.88 37	99.77 71

PHI-MVS		99.30 59	99.17 67	99.70 67	99.56 152	99.52 88	99.58 92	99.80 8	97.12 225	99.62 87	99.73 130	98.58 75	99.90 110	98.61 127	99.91 17	99.68 111

IterMVS-SCA-FT		97.82 235	97.75 219	98.06 284	99.57 148	96.36 302	99.02 286	99.49 133	97.18 219	98.71 263	99.72 134	92.72 272	99.14 302	97.44 243	95.86 293	98.67 271

diffmvs		99.14 81	99.02 86	99.51 112	99.61 138	98.96 157	99.28 224	99.49 133	98.46 79	99.72 53	99.71 135	96.50 154	99.88 123	99.31 35	99.11 165	99.67 114

XVG-OURS-SEG-HR		98.69 146	98.62 142	98.89 198	99.71 94	97.74 245	99.12 262	99.54 75	98.44 83	99.42 129	99.71 135	94.20 239	99.92 84	98.54 143	98.90 185	99.00 218

EPNet_dtu		98.03 202	97.96 194	98.23 274	98.27 341	95.54 321	99.23 243	98.75 329	99.02 20	97.82 322	99.71 135	96.11 165	99.48 241	93.04 342	99.65 127	99.69 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		99.42 41	99.30 45	99.78 48	99.62 134	99.71 49	99.26 238	99.52 92	98.82 51	99.39 140	99.71 135	98.96 29	99.85 135	98.59 132	99.80 88	99.77 71

PC_three_1452									98.18 111	99.84 14	99.70 139	99.31 3	98.52 343	98.30 166	99.80 88	99.81 45

OPU-MVS				99.64 80	99.56 152	99.72 47	99.60 77				99.70 139	99.27 5	99.42 255	98.24 168	99.80 88	99.79 61

CS-MVS		99.50 16	99.49 11	99.52 108	99.76 56	99.35 106	99.90 1	99.55 67	98.56 70	99.77 36	99.70 139	98.75 60	99.77 177	99.64 2	99.78 95	99.42 180

tfpnnormal		97.84 230	97.47 246	98.98 179	99.20 244	99.22 122	99.64 62	99.61 36	96.32 286	98.27 306	99.70 139	93.35 258	99.44 250	95.69 306	95.40 304	98.27 332

v7n		97.87 224	97.52 240	98.92 189	98.76 315	98.58 195	99.84 10	99.46 174	96.20 296	98.91 237	99.70 139	94.89 209	99.44 250	96.03 299	93.89 330	98.75 243

testdata				99.54 96	99.75 67	98.95 159		99.51 105	97.07 231	99.43 126	99.70 139	98.87 43	99.94 58	97.76 208	99.64 128	99.72 95

IterMVS		97.83 232	97.77 215	98.02 287	99.58 146	96.27 305	99.02 286	99.48 146	97.22 217	98.71 263	99.70 139	92.75 269	99.13 305	97.46 240	96.00 287	98.67 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS	97.08 14	97.66 264	97.06 285	99.47 119	99.61 138	99.09 139	98.04 359	99.25 277	91.24 353	98.51 289	99.70 139	94.55 229	99.91 95	92.76 346	99.85 59	99.42 180
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LTVRE_ROB	97.16 12	98.02 204	97.90 201	98.40 259	99.23 236	96.80 288	99.70 39	99.60 41	97.12 225	98.18 309	99.70 139	91.73 299	99.72 197	98.39 154	97.45 252	98.68 264
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		97.75 246	97.24 279	99.29 146	99.59 144	99.63 65	99.65 59	99.49 133	96.17 299	98.44 294	99.69 148	89.80 326	99.47 242	98.68 117	93.66 332	98.78 235

HFP-MVS		99.49 17	99.37 24	99.86 21	99.87 16	99.80 29	99.66 52	99.67 22	98.15 113	99.68 61	99.69 148	99.06 16	99.96 20	98.69 115	99.87 41	99.84 21

#test#		99.43 36	99.29 49	99.86 21	99.87 16	99.80 29	99.55 114	99.67 22	97.83 150	99.68 61	99.69 148	99.06 16	99.96 20	98.39 154	99.87 41	99.84 21

旧先验1					99.74 75	99.59 73		99.54 75			99.69 148	98.47 83			99.68 122	99.73 89

ACMMPR		99.49 17	99.36 26	99.86 21	99.87 16	99.79 33	99.66 52	99.67 22	98.15 113	99.67 67	99.69 148	98.95 32	99.96 20	98.69 115	99.87 41	99.84 21

CPTT-MVS		99.11 94	98.90 104	99.74 59	99.80 44	99.46 96	99.59 84	99.49 133	97.03 236	99.63 83	99.69 148	97.27 130	99.96 20	97.82 203	99.84 66	99.81 45

DROMVSNet		99.44 32	99.39 20	99.58 90	99.56 152	99.49 91	99.88 2	99.58 50	98.38 86	99.73 49	99.69 148	98.20 101	99.70 209	99.64 2	99.82 81	99.54 151

GST-MVS		99.40 49	99.24 60	99.85 28	99.86 22	99.79 33	99.60 77	99.67 22	97.97 138	99.63 83	99.68 155	98.52 79	99.95 47	98.38 156	99.86 52	99.81 45

Anonymous20231211		97.88 222	97.54 239	98.90 195	99.71 94	98.53 199	99.48 150	99.57 52	94.16 335	98.81 252	99.68 155	93.23 259	99.42 255	98.84 92	94.42 322	98.76 241

region2R		99.48 21	99.35 29	99.87 12	99.88 12	99.80 29	99.65 59	99.66 27	98.13 115	99.66 72	99.68 155	98.96 29	99.96 20	98.62 124	99.87 41	99.84 21

PS-CasMVS		97.93 216	97.59 235	98.95 184	98.99 284	99.06 143	99.68 46	99.52 92	97.13 223	98.31 303	99.68 155	92.44 287	99.05 315	98.51 144	94.08 328	98.75 243

HY-MVS	97.30 7	98.85 131	98.64 136	99.47 119	99.42 188	99.08 140	99.62 70	99.36 233	97.39 202	99.28 164	99.68 155	96.44 157	99.92 84	98.37 158	98.22 214	99.40 185

DP-MVS Recon		99.12 89	98.95 99	99.65 75	99.74 75	99.70 51	99.27 229	99.57 52	96.40 284	99.42 129	99.68 155	98.75 60	99.80 168	97.98 190	99.72 111	99.44 178

ETH3D cwj APD-0.16		99.06 102	98.84 114	99.72 64	99.51 160	99.60 70	99.23 243	99.44 195	97.04 234	99.39 140	99.67 161	98.30 96	99.92 84	97.27 249	99.69 117	99.64 128

ADS-MVSNet2		98.02 204	98.07 183	97.87 298	99.33 210	95.19 330	99.23 243	99.08 299	96.24 293	99.10 204	99.67 161	94.11 243	98.93 335	96.81 279	99.05 172	99.48 168

ADS-MVSNet		98.20 178	98.08 180	98.56 238	99.33 210	96.48 298	99.23 243	99.15 291	96.24 293	99.10 204	99.67 161	94.11 243	99.71 203	96.81 279	99.05 172	99.48 168

DTE-MVSNet		97.51 274	97.19 281	98.46 251	98.63 328	98.13 226	99.84 10	99.48 146	96.68 258	97.97 318	99.67 161	92.92 265	98.56 342	96.88 278	92.60 345	98.70 255

Baseline_NR-MVSNet		97.76 242	97.45 249	98.68 228	99.09 269	98.29 217	99.41 178	98.85 324	95.65 315	98.63 280	99.67 161	94.82 211	99.10 312	98.07 187	92.89 341	98.64 283

CMPMVS	69.68 23	94.13 325	94.90 317	91.84 346	97.24 357	80.01 371	98.52 342	99.48 146	89.01 357	91.99 361	99.67 161	85.67 356	99.13 305	95.44 311	97.03 267	96.39 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
原ACMM1				99.65 75	99.73 83	99.33 108		99.47 164	97.46 190	99.12 199	99.66 167	98.67 71	99.91 95	97.70 217	99.69 117	99.71 102

thisisatest0530		98.35 167	98.03 186	99.31 139	99.63 128	98.56 196	99.54 117	96.75 365	97.53 186	99.73 49	99.65 168	91.25 310	99.89 118	98.62 124	99.56 134	99.48 168

test222					99.75 67	99.49 91	98.91 310	99.49 133	96.42 282	99.34 154	99.65 168	98.28 98			99.69 117	99.72 95

1121		99.09 98	98.87 108	99.75 54	99.74 75	99.60 70	99.27 229	99.48 146	96.82 252	99.25 174	99.65 168	98.38 91	99.93 73	97.53 233	99.67 124	99.73 89

MVSFormer		99.17 77	99.12 71	99.29 146	99.51 160	98.94 162	99.88 2	99.46 174	97.55 181	99.80 27	99.65 168	97.39 123	99.28 281	99.03 61	99.85 59	99.65 121

jason		99.13 83	99.03 83	99.45 122	99.46 181	98.87 169	99.12 262	99.26 275	98.03 135	99.79 29	99.65 168	97.02 137	99.85 135	99.02 63	99.90 24	99.65 121
jason: jason.
BH-RMVSNet		98.41 162	98.08 180	99.40 128	99.41 191	98.83 176	99.30 218	98.77 328	97.70 166	98.94 233	99.65 168	92.91 267	99.74 186	96.52 290	99.55 136	99.64 128

sss		99.17 77	99.05 78	99.53 102	99.62 134	98.97 153	99.36 202	99.62 34	97.83 150	99.67 67	99.65 168	97.37 127	99.95 47	99.19 46	99.19 159	99.68 111

h-mvs33		97.70 257	97.28 275	98.97 181	99.70 101	97.27 258	99.36 202	99.45 186	98.94 40	99.66 72	99.64 175	94.93 205	99.99 1	99.48 17	84.36 359	99.65 121

ZNCC-MVS		99.47 24	99.33 33	99.87 12	99.87 16	99.81 27	99.64 62	99.67 22	98.08 126	99.55 105	99.64 175	98.91 40	99.96 20	98.72 110	99.90 24	99.82 39

新几何1				99.75 54	99.75 67	99.59 73		99.54 75	96.76 253	99.29 162	99.64 175	98.43 86	99.94 58	96.92 276	99.66 125	99.72 95

PEN-MVS		97.76 242	97.44 254	98.72 225	98.77 314	98.54 198	99.78 26	99.51 105	97.06 233	98.29 305	99.64 175	92.63 278	98.89 338	98.09 180	93.16 338	98.72 249

CP-MVSNet		98.09 191	97.78 213	99.01 175	98.97 289	99.24 120	99.67 48	99.46 174	97.25 213	98.48 292	99.64 175	93.79 252	99.06 314	98.63 123	94.10 327	98.74 247

LF4IMVS		97.52 272	97.46 248	97.70 309	98.98 287	95.55 319	99.29 222	98.82 327	98.07 127	98.66 272	99.64 175	89.97 324	99.61 232	97.01 266	96.68 269	97.94 350

bset_n11_16_dypcd		98.16 183	97.97 192	98.73 223	98.26 342	98.28 219	97.99 360	98.01 353	97.68 168	99.10 204	99.63 181	95.68 183	99.15 301	98.78 104	96.55 274	98.75 243

HPM-MVS		99.42 41	99.28 53	99.83 36	99.90 4	99.72 47	99.81 16	99.54 75	97.59 176	99.68 61	99.63 181	98.91 40	99.94 58	98.58 133	99.91 17	99.84 21
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NCCC		99.34 55	99.19 65	99.79 46	99.61 138	99.65 62	99.30 218	99.48 146	98.86 47	99.21 183	99.63 181	98.72 65	99.90 110	98.25 167	99.63 130	99.80 55

CP-MVS		99.45 28	99.32 35	99.85 28	99.83 37	99.75 43	99.69 41	99.52 92	98.07 127	99.53 108	99.63 181	98.93 39	99.97 12	98.74 106	99.91 17	99.83 32

AdaColmap		99.01 111	98.80 119	99.66 71	99.56 152	99.54 82	99.18 252	99.70 15	98.18 111	99.35 151	99.63 181	96.32 160	99.90 110	97.48 237	99.77 100	99.55 149

TAPA-MVS	97.07 15	97.74 249	97.34 269	98.94 185	99.70 101	97.53 251	99.25 240	99.51 105	91.90 350	99.30 159	99.63 181	98.78 52	99.64 225	88.09 362	99.87 41	99.65 121
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ppachtmachnet_test		97.49 278	97.45 249	97.61 311	98.62 329	95.24 328	98.80 320	99.46 174	96.11 306	98.22 307	99.62 187	96.45 156	98.97 332	93.77 333	95.97 291	98.61 302

MCST-MVS		99.43 36	99.30 45	99.82 38	99.79 45	99.74 46	99.29 222	99.40 213	98.79 56	99.52 110	99.62 187	98.91 40	99.90 110	98.64 122	99.75 104	99.82 39

WTY-MVS		99.06 102	98.88 107	99.61 85	99.62 134	99.16 128	99.37 198	99.56 58	98.04 133	99.53 108	99.62 187	96.84 142	99.94 58	98.85 89	98.49 205	99.72 95

MDTV_nov1_ep13			98.32 163		99.11 264	94.44 342	99.27 229	98.74 332	97.51 188	99.40 138	99.62 187	94.78 214	99.76 183	97.59 224	98.81 191

CANet		99.25 69	99.14 69	99.59 87	99.41 191	99.16 128	99.35 208	99.57 52	98.82 51	99.51 112	99.61 191	96.46 155	99.95 47	99.59 5	99.98 2	99.65 121

HQP_MVS		98.27 174	98.22 169	98.44 255	99.29 223	96.97 280	99.39 190	99.47 164	98.97 36	99.11 201	99.61 191	92.71 274	99.69 213	97.78 206	97.63 234	98.67 271

plane_prior4											99.61 191

ETH3 D test6400		98.70 144	98.35 160	99.73 61	99.69 104	99.60 70	99.16 254	99.45 186	95.42 317	99.27 167	99.60 194	97.39 123	99.91 95	95.36 315	99.83 75	99.70 104

baseline1		98.31 169	97.95 196	99.38 131	99.50 170	98.74 182	99.59 84	98.93 313	98.41 84	99.14 196	99.60 194	94.59 226	99.79 171	98.48 146	93.29 336	99.61 136

TranMVSNet+NR-MVSNet		97.93 216	97.66 227	98.76 222	98.78 311	98.62 192	99.65 59	99.49 133	97.76 159	98.49 291	99.60 194	94.23 238	98.97 332	98.00 189	92.90 340	98.70 255

tpmrst		98.33 168	98.48 153	97.90 297	99.16 257	94.78 338	99.31 216	99.11 295	97.27 211	99.45 121	99.59 197	95.33 194	99.84 141	98.48 146	98.61 195	99.09 206

IterMVS-LS		98.46 157	98.42 156	98.58 235	99.59 144	98.00 230	99.37 198	99.43 203	96.94 244	99.07 211	99.59 197	97.87 112	99.03 318	98.32 164	95.62 299	98.71 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		99.19 73	99.04 81	99.64 80	99.78 47	99.27 117	99.42 176	99.54 75	97.29 209	99.41 133	99.59 197	98.42 89	99.93 73	98.19 171	99.69 117	99.73 89

pmmvs4		98.13 187	97.90 201	98.81 216	98.61 331	98.87 169	98.99 293	99.21 284	96.44 280	99.06 215	99.58 200	95.90 175	99.11 310	97.18 259	96.11 285	98.46 320

1112_ss		98.98 113	98.77 122	99.59 87	99.68 108	99.02 146	99.25 240	99.48 146	97.23 216	99.13 197	99.58 200	96.93 141	99.90 110	98.87 82	98.78 192	99.84 21

ab-mvs-re		8.30 345	11.06 348	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	99.58 200	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

PatchmatchNet		98.31 169	98.36 158	98.19 276	99.16 257	95.32 327	99.27 229	98.92 315	97.37 203	99.37 145	99.58 200	94.90 208	99.70 209	97.43 244	99.21 157	99.54 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		98.19 179	98.16 170	98.27 273	99.30 219	95.55 319	99.07 272	98.97 309	97.57 179	99.43 126	99.57 204	92.72 272	99.74 186	97.58 225	99.20 158	99.52 157

Patchmatch-test		97.93 216	97.65 228	98.77 221	99.18 249	97.07 269	99.03 283	99.14 293	96.16 301	98.74 260	99.57 204	94.56 228	99.72 197	93.36 338	99.11 165	99.52 157

PVSNet	96.02 17	98.85 131	98.84 114	98.89 198	99.73 83	97.28 257	98.32 352	99.60 41	97.86 145	99.50 113	99.57 204	96.75 147	99.86 129	98.56 138	99.70 116	99.54 151

cdsmvs_eth3d_5k		24.64 344	32.85 347	0.00 360	0.00 383	0.00 384	0.00 371	99.51 105	0.00 378	0.00 379	99.56 207	96.58 151	0.00 379	0.00 377	0.00 377	0.00 375

1314		98.68 147	98.54 151	99.11 165	98.89 295	98.65 189	99.27 229	99.49 133	96.89 246	97.99 317	99.56 207	97.72 118	99.83 152	97.74 211	99.27 154	98.84 231

lupinMVS		99.13 83	99.01 90	99.46 121	99.51 160	98.94 162	99.05 277	99.16 290	97.86 145	99.80 27	99.56 207	97.39 123	99.86 129	98.94 70	99.85 59	99.58 146

miper_lstm_enhance		98.00 209	97.91 200	98.28 272	99.34 209	97.43 254	98.88 312	99.36 233	96.48 277	98.80 254	99.55 210	95.98 168	98.91 336	97.27 249	95.50 303	98.51 312

DPM-MVS		98.95 116	98.71 128	99.66 71	99.63 128	99.55 80	98.64 335	99.10 296	97.93 141	99.42 129	99.55 210	98.67 71	99.80 168	95.80 304	99.68 122	99.61 136

CDPH-MVS		99.13 83	98.91 103	99.80 43	99.75 67	99.71 49	99.15 258	99.41 207	96.60 267	99.60 93	99.55 210	98.83 47	99.90 110	97.48 237	99.83 75	99.78 69

dp		97.75 246	97.80 209	97.59 312	99.10 267	93.71 351	99.32 214	98.88 322	96.48 277	99.08 210	99.55 210	92.67 277	99.82 159	96.52 290	98.58 198	99.24 195

CLD-MVS		98.16 183	98.10 176	98.33 264	99.29 223	96.82 287	98.75 325	99.44 195	97.83 150	99.13 197	99.55 210	92.92 265	99.67 215	98.32 164	97.69 233	98.48 314
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 94	99.79 33		99.61 36	96.84 249	99.56 101	99.54 215	98.58 75	99.96 20	96.93 274	99.75 104

cl____		98.01 207	97.84 208	98.55 240	99.25 234	97.97 232	98.71 329	99.34 242	96.47 279	98.59 286	99.54 215	95.65 185	99.21 296	97.21 253	95.77 294	98.46 320

DIV-MVS_self_test		98.01 207	97.85 207	98.48 246	99.24 235	97.95 236	98.71 329	99.35 238	96.50 272	98.60 285	99.54 215	95.72 182	99.03 318	97.21 253	95.77 294	98.46 320

MVS		97.28 284	96.55 293	99.48 116	98.78 311	98.95 159	99.27 229	99.39 217	83.53 363	98.08 312	99.54 215	96.97 139	99.87 126	94.23 329	99.16 160	99.63 132

pmmvs5		97.52 272	97.30 274	98.16 278	98.57 334	96.73 289	99.27 229	98.90 320	96.14 304	98.37 299	99.53 219	91.54 306	99.14 302	97.51 235	95.87 292	98.63 291

HPM-MVS++		99.39 50	99.23 62	99.87 12	99.75 67	99.84 16	99.43 169	99.51 105	98.68 64	99.27 167	99.53 219	98.64 73	99.96 20	98.44 152	99.80 88	99.79 61

PatchMatch-RL		98.84 134	98.62 142	99.52 108	99.71 94	99.28 115	99.06 275	99.77 9	97.74 163	99.50 113	99.53 219	95.41 190	99.84 141	97.17 260	99.64 128	99.44 178

eth_miper_zixun_eth		98.05 199	97.96 194	98.33 264	99.26 230	97.38 255	98.56 341	99.31 262	96.65 261	98.88 242	99.52 222	96.58 151	99.12 309	97.39 246	95.53 302	98.47 316

test_prior3		99.21 71	99.05 78	99.68 68	99.67 109	99.48 93	98.96 301	99.56 58	98.34 92	99.01 220	99.52 222	98.68 68	99.83 152	97.96 191	99.74 107	99.74 82

test_prior2							98.96 301		98.34 92	99.01 220	99.52 222	98.68 68		97.96 191	99.74 107

test_0402		96.64 296	96.24 298	97.85 299	98.85 304	96.43 300	99.44 163	99.26 275	93.52 341	96.98 340	99.52 222	88.52 340	99.20 298	92.58 348	97.50 247	97.93 351

test_yl		98.86 123	98.63 137	99.54 96	99.49 172	99.18 125	99.50 134	99.07 301	98.22 105	99.61 89	99.51 226	95.37 192	99.84 141	98.60 130	98.33 208	99.59 142

DCV-MVSNet		98.86 123	98.63 137	99.54 96	99.49 172	99.18 125	99.50 134	99.07 301	98.22 105	99.61 89	99.51 226	95.37 192	99.84 141	98.60 130	98.33 208	99.59 142

v148		97.79 240	97.55 236	98.50 243	98.74 316	97.72 247	99.54 117	99.33 249	96.26 291	98.90 239	99.51 226	94.68 222	99.14 302	97.83 202	93.15 339	98.63 291

DU-MVS		98.08 193	97.79 210	98.96 182	98.87 300	98.98 150	99.41 178	99.45 186	97.87 144	98.71 263	99.50 229	94.82 211	99.22 291	98.57 135	92.87 342	98.68 264

NR-MVSNet		97.97 214	97.61 232	99.02 174	98.87 300	99.26 118	99.47 155	99.42 205	97.63 174	97.08 338	99.50 229	95.07 203	99.13 305	97.86 199	93.59 333	98.68 264

XVG-ACMP-BASELINE		97.83 232	97.71 223	98.20 275	99.11 264	96.33 303	99.41 178	99.52 92	98.06 131	99.05 216	99.50 229	89.64 329	99.73 193	97.73 212	97.38 258	98.53 310

MSP-MVS		99.42 41	99.27 55	99.88 6	99.89 9	99.80 29	99.67 48	99.50 125	98.70 62	99.77 36	99.49 232	98.21 100	99.95 47	98.46 150	99.77 100	99.88 8
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					99.67 109	99.65 62	99.05 277	99.41 207	96.22 295	98.95 231	99.49 232	98.77 55	99.91 95

train_agg		99.02 108	98.77 122	99.77 50	99.67 109	99.65 62	99.05 277	99.41 207	96.28 288	98.95 231	99.49 232	98.76 57	99.91 95	97.63 221	99.72 111	99.75 77

agg_prior1		99.01 111	98.76 124	99.76 53	99.67 109	99.62 66	98.99 293	99.40 213	96.26 291	98.87 244	99.49 232	98.77 55	99.91 95	97.69 218	99.72 111	99.75 77

PVSNet_Blended		99.08 100	98.97 95	99.42 127	99.76 56	98.79 180	98.78 322	99.91 3	96.74 254	99.67 67	99.49 232	97.53 120	99.88 123	98.98 66	99.85 59	99.60 138

CNLPA		99.14 81	98.99 91	99.59 87	99.58 146	99.41 101	99.16 254	99.44 195	98.45 80	99.19 189	99.49 232	98.08 108	99.89 118	97.73 212	99.75 104	99.48 168

test_8					99.67 109	99.61 68	99.03 283	99.41 207	96.28 288	98.93 235	99.48 238	98.76 57	99.91 95

EPMVS		97.82 235	97.65 228	98.35 263	98.88 296	95.98 311	99.49 144	94.71 373	97.57 179	99.26 172	99.48 238	92.46 286	99.71 203	97.87 198	99.08 170	99.35 188

PLC	97.94 4	99.02 108	98.85 113	99.53 102	99.66 118	99.01 148	99.24 242	99.52 92	96.85 248	99.27 167	99.48 238	98.25 99	99.91 95	97.76 208	99.62 131	99.65 121
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 55	99.34 133	99.63 128	98.97 153	99.12 262	99.51 105	98.86 47	99.84 14	99.47 241	98.18 102	99.99 1	99.50 12	99.31 151	99.08 207

xiu_mvs_v1_base		99.29 61	99.27 55	99.34 133	99.63 128	98.97 153	99.12 262	99.51 105	98.86 47	99.84 14	99.47 241	98.18 102	99.99 1	99.50 12	99.31 151	99.08 207

xiu_mvs_v1_base_debi		99.29 61	99.27 55	99.34 133	99.63 128	98.97 153	99.12 262	99.51 105	98.86 47	99.84 14	99.47 241	98.18 102	99.99 1	99.50 12	99.31 151	99.08 207

v1921920		97.80 239	97.45 249	98.84 212	98.80 307	98.53 199	99.52 123	99.34 242	96.15 303	99.24 175	99.47 241	93.98 247	99.29 280	95.40 313	95.13 310	98.69 259

UniMVSNet_NR-MVSNet		98.22 175	97.97 192	98.96 182	98.92 293	98.98 150	99.48 150	99.53 86	97.76 159	98.71 263	99.46 245	96.43 158	99.22 291	98.57 135	92.87 342	98.69 259

testgi		97.65 265	97.50 243	98.13 281	99.36 204	96.45 299	99.42 176	99.48 146	97.76 159	97.87 320	99.45 246	91.09 311	98.81 339	94.53 325	98.52 203	99.13 201

EIA-MVS		99.18 75	99.09 75	99.45 122	99.49 172	99.18 125	99.67 48	99.53 86	97.66 172	99.40 138	99.44 247	98.10 107	99.81 163	98.94 70	99.62 131	99.35 188

CS-MVS-test		99.42 41	99.39 20	99.52 108	99.77 52	99.35 106	99.80 20	99.57 52	98.56 70	99.77 36	99.44 247	98.16 105	99.77 177	99.64 2	99.78 95	99.42 180

tpm2		97.44 280	97.34 269	97.74 307	99.15 260	94.36 344	99.45 159	98.94 312	93.45 344	98.90 239	99.44 247	91.35 308	99.59 234	97.31 247	98.07 226	99.29 193

thisisatest0515		98.14 186	97.79 210	99.19 158	99.50 170	98.50 206	98.61 336	96.82 364	96.95 242	99.54 106	99.43 250	91.66 303	99.86 129	98.08 184	99.51 138	99.22 196

mvs-test1		98.86 123	98.84 114	98.89 198	99.33 210	97.77 244	99.44 163	99.30 266	98.47 77	99.10 204	99.43 250	96.78 144	99.95 47	98.73 108	99.02 176	98.96 224

WR-MVS		98.06 194	97.73 221	99.06 168	98.86 303	99.25 119	99.19 251	99.35 238	97.30 208	98.66 272	99.43 250	93.94 248	99.21 296	98.58 133	94.28 324	98.71 251

hse-mvs2		97.50 275	97.14 282	98.59 232	99.49 172	97.05 271	99.28 224	99.22 281	98.94 40	99.66 72	99.42 253	94.93 205	99.65 222	99.48 17	83.80 361	99.08 207

v8		97.95 215	97.63 231	98.93 187	98.95 291	98.81 179	99.80 20	99.41 207	96.03 311	99.10 204	99.42 253	94.92 207	99.30 279	96.94 273	94.08 328	98.66 279

tpmvs		97.98 211	98.02 188	97.84 300	99.04 278	94.73 339	99.31 216	99.20 285	96.10 310	98.76 259	99.42 253	94.94 204	99.81 163	96.97 270	98.45 206	98.97 222

UGNet		98.87 120	98.69 130	99.40 128	99.22 240	98.72 184	99.44 163	99.68 19	99.24 4	99.18 192	99.42 253	92.74 271	99.96 20	99.34 32	99.94 10	99.53 156
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		96.88 291	96.31 297	98.59 232	99.48 179	97.04 274	99.27 229	99.22 281	97.44 196	98.51 289	99.41 257	91.97 292	99.66 218	97.71 215	83.83 360	99.07 212

Effi-MVS+		98.81 135	98.59 148	99.48 116	99.46 181	99.12 137	98.08 358	99.50 125	97.50 189	99.38 143	99.41 257	96.37 159	99.81 163	99.11 55	98.54 202	99.51 163

v10		97.85 227	97.52 240	98.86 208	98.99 284	98.67 187	99.75 32	99.41 207	95.70 314	98.98 227	99.41 257	94.75 219	99.23 288	96.01 300	94.63 318	98.67 271

v144192		97.92 219	97.60 233	98.87 205	98.83 306	98.65 189	99.55 114	99.34 242	96.20 296	99.32 156	99.40 260	94.36 234	99.26 285	96.37 295	95.03 312	98.70 255

NP-MVS					99.23 236	96.92 283					99.40 260

HQP-MVS		98.02 204	97.90 201	98.37 262	99.19 246	96.83 285	98.98 297	99.39 217	98.24 101	98.66 272	99.40 260	92.47 283	99.64 225	97.19 257	97.58 239	98.64 283

MAR-MVS		98.86 123	98.63 137	99.54 96	99.37 202	99.66 59	99.45 159	99.54 75	96.61 265	99.01 220	99.40 260	97.09 134	99.86 129	97.68 220	99.53 137	99.10 202
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		99.04 105	99.03 83	99.06 168	99.40 196	99.31 112	99.55 114	99.56 58	98.54 72	99.33 155	99.39 264	98.76 57	99.78 175	96.98 269	99.78 95	98.07 340

CR-MVSNet		98.17 182	97.93 199	98.87 205	99.18 249	98.49 207	99.22 248	99.33 249	96.96 240	99.56 101	99.38 265	94.33 235	99.00 323	94.83 323	98.58 198	99.14 199

Patchmtry		97.75 246	97.40 260	98.81 216	99.10 267	98.87 169	99.11 268	99.33 249	94.83 327	98.81 252	99.38 265	94.33 235	99.02 320	96.10 297	95.57 300	98.53 310

BH-untuned		98.42 160	98.36 158	98.59 232	99.49 172	96.70 290	99.27 229	99.13 294	97.24 215	98.80 254	99.38 265	95.75 180	99.74 186	97.07 265	99.16 160	99.33 191

V42		98.06 194	97.79 210	98.86 208	98.98 287	98.84 173	99.69 41	99.34 242	96.53 271	99.30 159	99.37 268	94.67 223	99.32 276	97.57 229	94.66 317	98.42 323

VPA-MVSNet		98.29 172	97.95 196	99.30 143	99.16 257	99.54 82	99.50 134	99.58 50	98.27 100	99.35 151	99.37 268	92.53 281	99.65 222	99.35 28	94.46 320	98.72 249

PVSNet_BlendedMVS		98.86 123	98.80 119	99.03 173	99.76 56	98.79 180	99.28 224	99.91 3	97.42 199	99.67 67	99.37 268	97.53 120	99.88 123	98.98 66	97.29 260	98.42 323

D2MVS		98.41 162	98.50 152	98.15 280	99.26 230	96.62 294	99.40 186	99.61 36	97.71 165	98.98 227	99.36 271	96.04 167	99.67 215	98.70 112	97.41 256	98.15 338

MVP-Stereo		97.81 237	97.75 219	97.99 291	97.53 351	96.60 295	98.96 301	98.85 324	97.22 217	97.23 333	99.36 271	95.28 195	99.46 244	95.51 310	99.78 95	97.92 352
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1240		97.69 258	97.32 272	98.79 219	98.85 304	98.43 212	99.48 150	99.36 233	96.11 306	99.27 167	99.36 271	93.76 254	99.24 287	94.46 326	95.23 307	98.70 255

v1144		97.98 211	97.69 224	98.85 211	98.87 300	98.66 188	99.54 117	99.35 238	96.27 290	99.23 179	99.35 274	94.67 223	99.23 288	96.73 283	95.16 309	98.68 264

v2v482		98.06 194	97.77 215	98.92 189	98.90 294	98.82 177	99.57 98	99.36 233	96.65 261	99.19 189	99.35 274	94.20 239	99.25 286	97.72 214	94.97 313	98.69 259

CostFormer		97.72 252	97.73 221	97.71 308	99.15 260	94.02 347	99.54 117	99.02 305	94.67 330	99.04 217	99.35 274	92.35 289	99.77 177	98.50 145	97.94 228	99.34 190

our_test_3		97.65 265	97.68 225	97.55 314	98.62 329	94.97 334	98.84 316	99.30 266	96.83 251	98.19 308	99.34 277	97.01 138	99.02 320	95.00 321	96.01 286	98.64 283

c3_l		98.12 189	98.04 185	98.38 261	99.30 219	97.69 250	98.81 319	99.33 249	96.67 259	98.83 250	99.34 277	97.11 133	98.99 324	97.58 225	95.34 305	98.48 314

Fast-Effi-MVS+-dtu		98.77 141	98.83 118	98.60 231	99.41 191	96.99 278	99.52 123	99.49 133	98.11 119	99.24 175	99.34 277	96.96 140	99.79 171	97.95 193	99.45 139	99.02 217

Fast-Effi-MVS+		98.70 144	98.43 155	99.51 112	99.51 160	99.28 115	99.52 123	99.47 164	96.11 306	99.01 220	99.34 277	96.20 164	99.84 141	97.88 197	98.82 189	99.39 186

v1192		97.81 237	97.44 254	98.91 193	98.88 296	98.68 186	99.51 128	99.34 242	96.18 298	99.20 186	99.34 277	94.03 246	99.36 266	95.32 316	95.18 308	98.69 259

tpm		97.67 263	97.55 236	98.03 285	99.02 281	95.01 333	99.43 169	98.54 345	96.44 280	99.12 199	99.34 277	91.83 296	99.60 233	97.75 210	96.46 277	99.48 168

PAPM		97.59 268	97.09 284	99.07 167	99.06 274	98.26 220	98.30 353	99.10 296	94.88 326	98.08 312	99.34 277	96.27 162	99.64 225	89.87 355	98.92 183	99.31 192

GBi-Net		97.68 260	97.48 244	98.29 269	99.51 160	97.26 260	99.43 169	99.48 146	96.49 273	99.07 211	99.32 284	90.26 319	98.98 325	97.10 262	96.65 270	98.62 293

test1		97.68 260	97.48 244	98.29 269	99.51 160	97.26 260	99.43 169	99.48 146	96.49 273	99.07 211	99.32 284	90.26 319	98.98 325	97.10 262	96.65 270	98.62 293

FMVSNet1		96.84 292	96.36 296	98.29 269	99.32 217	97.26 260	99.43 169	99.48 146	95.11 321	98.55 287	99.32 284	83.95 360	98.98 325	95.81 303	96.26 282	98.62 293

MS-PatchMatch		97.24 286	97.32 272	96.99 326	98.45 339	93.51 355	98.82 318	99.32 259	97.41 200	98.13 311	99.30 287	88.99 334	99.56 236	95.68 307	99.80 88	97.90 353

GA-MVS		97.85 227	97.47 246	99.00 177	99.38 200	97.99 231	98.57 339	99.15 291	97.04 234	98.90 239	99.30 287	89.83 325	99.38 259	96.70 285	98.33 208	99.62 134

miper_ehance_all_eth		98.18 181	98.10 176	98.41 257	99.23 236	97.72 247	98.72 328	99.31 262	96.60 267	98.88 242	99.29 289	97.29 129	99.13 305	97.60 223	95.99 288	98.38 328

FMVSNet2		97.72 252	97.36 264	98.80 218	99.51 160	98.84 173	99.45 159	99.42 205	96.49 273	98.86 249	99.29 289	90.26 319	98.98 325	96.44 292	96.56 273	98.58 307

TESTMET0.1,1		97.55 269	97.27 278	98.40 259	98.93 292	96.53 296	98.67 331	97.61 359	96.96 240	98.64 279	99.28 291	88.63 339	99.45 245	97.30 248	99.38 143	99.21 197

FMVSNet3		98.03 202	97.76 218	98.84 212	99.39 199	98.98 150	99.40 186	99.38 223	96.67 259	99.07 211	99.28 291	92.93 264	98.98 325	97.10 262	96.65 270	98.56 309

PAPM_NR		99.04 105	98.84 114	99.66 71	99.74 75	99.44 98	99.39 190	99.38 223	97.70 166	99.28 164	99.28 291	98.34 94	99.85 135	96.96 271	99.45 139	99.69 107

EGC-MVSNET		82.80 334	77.86 340	97.62 310	97.91 346	96.12 308	99.33 213	99.28 273	8.40 377	25.05 378	99.27 294	84.11 359	99.33 274	89.20 357	98.22 214	97.42 359

ETV-MVS		99.26 66	99.21 63	99.40 128	99.46 181	99.30 113	99.56 105	99.52 92	98.52 74	99.44 125	99.27 294	98.41 90	99.86 129	99.10 56	99.59 133	99.04 214

xiu_mvs_v2_base		99.26 66	99.25 59	99.29 146	99.53 156	98.91 166	99.02 286	99.45 186	98.80 55	99.71 54	99.26 296	98.94 35	99.98 7	99.34 32	99.23 156	98.98 221

test20.03		96.12 307	95.96 304	96.63 334	97.44 352	95.45 324	99.51 128	99.38 223	96.55 270	96.16 347	99.25 297	93.76 254	96.17 367	87.35 364	94.22 325	98.27 332

PS-MVSNAJ		99.32 57	99.32 35	99.30 143	99.57 148	98.94 162	98.97 300	99.46 174	98.92 44	99.71 54	99.24 298	99.01 19	99.98 7	99.35 28	99.66 125	98.97 222

Test_1112_low_res		98.89 119	98.66 135	99.57 92	99.69 104	98.95 159	99.03 283	99.47 164	96.98 238	99.15 195	99.23 299	96.77 146	99.89 118	98.83 95	98.78 192	99.86 14

cl22		97.85 227	97.64 230	98.48 246	99.09 269	97.87 239	98.60 338	99.33 249	97.11 228	98.87 244	99.22 300	92.38 288	99.17 300	98.21 169	95.99 288	98.42 323

EG-PatchMatch MVS		95.97 309	95.69 309	96.81 332	97.78 349	92.79 358	99.16 254	98.93 313	96.16 301	94.08 356	99.22 300	82.72 362	99.47 242	95.67 308	97.50 247	98.17 337

TR-MVS		97.76 242	97.41 259	98.82 214	99.06 274	97.87 239	98.87 314	98.56 343	96.63 264	98.68 271	99.22 300	92.49 282	99.65 222	95.40 313	97.79 231	98.95 227

ET-MVSNet_ETH3D		96.49 299	95.64 310	99.05 170	99.53 156	98.82 177	98.84 316	97.51 360	97.63 174	84.77 364	99.21 303	92.09 291	98.91 336	98.98 66	92.21 346	99.41 184

WR-MVS_H		98.13 187	97.87 206	98.90 195	99.02 281	98.84 173	99.70 39	99.59 44	97.27 211	98.40 297	99.19 304	95.53 187	99.23 288	98.34 161	93.78 331	98.61 302

miper_enhance_ethall		98.16 183	98.08 180	98.41 257	98.96 290	97.72 247	98.45 345	99.32 259	96.95 242	98.97 229	99.17 305	97.06 136	99.22 291	97.86 199	95.99 288	98.29 331

baseline2		97.87 224	97.55 236	98.82 214	99.18 249	98.02 229	99.41 178	96.58 367	96.97 239	96.51 343	99.17 305	93.43 256	99.57 235	97.71 215	99.03 174	98.86 229

MIMVSNet1		95.51 312	95.04 316	96.92 330	97.38 353	95.60 317	99.52 123	99.50 125	93.65 340	96.97 341	99.17 305	85.28 357	96.56 366	88.36 361	95.55 301	98.60 305

gm-plane-assit					98.54 336	92.96 357			94.65 331		99.15 308		99.64 225	97.56 230

MIMVSNet		97.73 250	97.45 249	98.57 236	99.45 186	97.50 252	99.02 286	98.98 308	96.11 306	99.41 133	99.14 309	90.28 318	98.74 340	95.74 305	98.93 181	99.47 173

LCM-MVSNet-Re		97.83 232	98.15 171	96.87 331	99.30 219	92.25 360	99.59 84	98.26 347	97.43 197	96.20 346	99.13 310	96.27 162	98.73 341	98.17 175	98.99 178	99.64 128

UniMVSNet (Re)		98.29 172	98.00 189	99.13 164	99.00 283	99.36 105	99.49 144	99.51 105	97.95 139	98.97 229	99.13 310	96.30 161	99.38 259	98.36 160	93.34 335	98.66 279

N_pmnet		94.95 319	95.83 307	92.31 345	98.47 338	79.33 372	99.12 262	92.81 378	93.87 337	97.68 325	99.13 310	93.87 250	99.01 322	91.38 350	96.19 283	98.59 306

PAPR		98.63 152	98.34 161	99.51 112	99.40 196	99.03 145	98.80 320	99.36 233	96.33 285	99.00 225	99.12 313	98.46 84	99.84 141	95.23 317	99.37 150	99.66 117

tpm cat1		97.39 281	97.36 264	97.50 316	99.17 255	93.73 350	99.43 169	99.31 262	91.27 352	98.71 263	99.08 314	94.31 237	99.77 177	96.41 294	98.50 204	99.00 218

FMVSNet5		96.43 301	96.19 299	97.15 322	99.11 264	95.89 313	99.32 214	99.52 92	94.47 334	98.34 302	99.07 315	87.54 351	97.07 362	92.61 347	95.72 297	98.47 316

PMMVS		98.80 138	98.62 142	99.34 133	99.27 228	98.70 185	98.76 324	99.31 262	97.34 204	99.21 183	99.07 315	97.20 131	99.82 159	98.56 138	98.87 186	99.52 157

Anonymous20231206		96.22 303	96.03 302	96.79 333	97.31 356	94.14 346	99.63 64	99.08 299	96.17 299	97.04 339	99.06 317	93.94 248	97.76 357	86.96 365	95.06 311	98.47 316

DeepMVS_CX				93.34 343	99.29 223	82.27 369		99.22 281	85.15 361	96.33 345	99.05 318	90.97 313	99.73 193	93.57 336	97.77 232	98.01 344

YYNet1		95.36 315	94.51 321	97.92 295	97.89 347	97.10 265	99.10 270	99.23 280	93.26 345	80.77 368	99.04 319	92.81 268	98.02 350	94.30 327	94.18 326	98.64 283

Anonymous20240521		96.20 305	95.89 306	97.13 324	97.72 350	94.96 335	99.79 25	99.29 271	93.01 346	97.20 335	99.03 320	89.69 328	98.36 345	91.16 351	96.13 284	98.07 340

MDA-MVSNet-bldmvs		94.96 318	93.98 324	97.92 295	98.24 343	97.27 258	99.15 258	99.33 249	93.80 338	80.09 370	99.03 320	88.31 342	97.86 355	93.49 337	94.36 323	98.62 293

test_method		91.10 329	91.36 332	90.31 349	95.85 363	73.72 377	94.89 366	99.25 277	68.39 369	95.82 350	99.02 322	80.50 366	98.95 334	93.64 335	94.89 316	98.25 334

BH-w/o		98.00 209	97.89 205	98.32 266	99.35 205	96.20 307	99.01 291	98.90 320	96.42 282	98.38 298	99.00 323	95.26 198	99.72 197	96.06 298	98.61 195	99.03 215

Effi-MVS+-dtu		98.78 139	98.89 106	98.47 250	99.33 210	96.91 284	99.57 98	99.30 266	98.47 77	99.41 133	98.99 324	96.78 144	99.74 186	98.73 108	99.38 143	98.74 247

MVS_0304		96.79 294	96.52 294	97.59 312	99.22 240	94.92 336	99.04 282	99.59 44	96.49 273	98.43 295	98.99 324	80.48 367	99.39 257	97.15 261	99.27 154	98.47 316

UnsupCasMVSNet_eth		96.44 300	96.12 300	97.40 318	98.65 326	95.65 316	99.36 202	99.51 105	97.13 223	96.04 349	98.99 324	88.40 341	98.17 347	96.71 284	90.27 350	98.40 326

test0.0.03 1		97.71 256	97.42 258	98.56 238	98.41 340	97.82 242	98.78 322	98.63 341	97.34 204	98.05 316	98.98 327	94.45 232	98.98 325	95.04 320	97.15 266	98.89 228

MDA-MVSNet_test_wron		95.45 313	94.60 319	98.01 288	98.16 344	97.21 263	99.11 268	99.24 279	93.49 342	80.73 369	98.98 327	93.02 262	98.18 346	94.22 330	94.45 321	98.64 283

FPMVS		84.93 333	85.65 334	82.75 354	86.77 375	63.39 379	98.35 348	98.92 315	74.11 366	83.39 366	98.98 327	50.85 374	92.40 371	84.54 368	94.97 313	92.46 365

alignmvs		98.81 135	98.56 150	99.58 90	99.43 187	99.42 100	99.51 128	98.96 311	98.61 67	99.35 151	98.92 330	94.78 214	99.77 177	99.35 28	98.11 225	99.54 151

test-LLR		98.06 194	97.90 201	98.55 240	98.79 308	97.10 265	98.67 331	97.75 356	97.34 204	98.61 283	98.85 331	94.45 232	99.45 245	97.25 251	99.38 143	99.10 202

test-mter		97.49 278	97.13 283	98.55 240	98.79 308	97.10 265	98.67 331	97.75 356	96.65 261	98.61 283	98.85 331	88.23 343	99.45 245	97.25 251	99.38 143	99.10 202

canonicalmvs		99.02 108	98.86 112	99.51 112	99.42 188	99.32 109	99.80 20	99.48 146	98.63 65	99.31 157	98.81 333	97.09 134	99.75 185	99.27 40	97.90 229	99.47 173

DWT-MVSNet_test		97.53 271	97.40 260	97.93 294	99.03 280	94.86 337	99.57 98	98.63 341	96.59 269	98.36 300	98.79 334	89.32 331	99.74 186	98.14 178	98.16 223	99.20 198

new_pmnet		96.38 302	96.03 302	97.41 317	98.13 345	95.16 332	99.05 277	99.20 285	93.94 336	97.39 330	98.79 334	91.61 305	99.04 316	90.43 353	95.77 294	98.05 342

cascas		97.69 258	97.43 257	98.48 246	98.60 332	97.30 256	98.18 357	99.39 217	92.96 347	98.41 296	98.78 336	93.77 253	99.27 284	98.16 176	98.61 195	98.86 229

PVSNet_0	94.43 19	96.09 308	95.47 311	97.94 293	99.31 218	94.34 345	97.81 361	99.70 15	97.12 225	97.46 328	98.75 337	89.71 327	99.79 171	97.69 218	81.69 363	99.68 111

patchmatchnet-post											98.70 338	94.79 213	99.74 186

Patchmatch-RL test		95.84 310	95.81 308	95.95 339	95.61 364	90.57 364	98.24 354	98.39 346	95.10 323	95.20 352	98.67 339	94.78 214	97.77 356	96.28 296	90.02 351	99.51 163

thres100view900		97.76 242	97.45 249	98.69 227	99.72 88	97.86 241	99.59 84	98.74 332	97.93 141	99.26 172	98.62 340	91.75 297	99.83 152	93.22 339	98.18 219	98.37 329

thres600view7		97.86 226	97.51 242	98.92 189	99.72 88	97.95 236	99.59 84	98.74 332	97.94 140	99.27 167	98.62 340	91.75 297	99.86 129	93.73 334	98.19 218	98.96 224

DSMNet-mixed		97.25 285	97.35 266	96.95 329	97.84 348	93.61 354	99.57 98	96.63 366	96.13 305	98.87 244	98.61 342	94.59 226	97.70 358	95.08 319	98.86 187	99.55 149

IB-MVS	95.67 18	96.22 303	95.44 313	98.57 236	99.21 242	96.70 290	98.65 334	97.74 358	96.71 256	97.27 332	98.54 343	86.03 354	99.92 84	98.47 149	86.30 357	99.10 202
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	98.55 335	98.16 223	99.43 169	93.68 375		97.23 333	98.46 344	89.30 332	99.22 291	95.43 312	98.22 214	97.98 348

tfpn200view9		97.72 252	97.38 262	98.72 225	99.69 104	97.96 234	99.50 134	98.73 337	97.83 150	99.17 193	98.45 345	91.67 301	99.83 152	93.22 339	98.18 219	98.37 329

thres400		97.77 241	97.38 262	98.92 189	99.69 104	97.96 234	99.50 134	98.73 337	97.83 150	99.17 193	98.45 345	91.67 301	99.83 152	93.22 339	98.18 219	98.96 224

KD-MVS_2432*1600		94.62 320	93.72 326	97.31 319	97.19 359	95.82 314	98.34 349	99.20 285	95.00 324	97.57 326	98.35 347	87.95 347	98.10 348	92.87 344	77.00 367	98.01 344

miper_refine_blended		94.62 320	93.72 326	97.31 319	97.19 359	95.82 314	98.34 349	99.20 285	95.00 324	97.57 326	98.35 347	87.95 347	98.10 348	92.87 344	77.00 367	98.01 344

thres200		97.61 267	97.28 275	98.62 230	99.64 125	98.03 228	99.26 238	98.74 332	97.68 168	99.09 209	98.32 349	91.66 303	99.81 163	92.88 343	98.22 214	98.03 343

OpenMVS_ROB	92.34 20	94.38 324	93.70 328	96.41 337	97.38 353	93.17 356	99.06 275	98.75 329	86.58 360	94.84 355	98.26 350	81.53 365	99.32 276	89.01 358	97.87 230	96.76 360

CL-MVSNet_self_test		94.49 322	93.97 325	96.08 338	96.16 362	93.67 353	98.33 351	99.38 223	95.13 319	97.33 331	98.15 351	92.69 276	96.57 365	88.67 359	79.87 365	97.99 347

pmmvs3		94.09 326	93.25 329	96.60 335	94.76 368	94.49 341	98.92 308	98.18 351	89.66 356	96.48 344	98.06 352	86.28 353	97.33 360	89.68 356	87.20 356	97.97 349

PM-MVS		92.96 328	92.23 331	95.14 341	95.61 364	89.98 366	99.37 198	98.21 349	94.80 328	95.04 354	97.69 353	65.06 370	97.90 354	94.30 327	89.98 352	97.54 358

pmmvs-eth3d		95.34 316	94.73 318	97.15 322	95.53 366	95.94 312	99.35 208	99.10 296	95.13 319	93.55 357	97.54 354	88.15 345	97.91 353	94.58 324	89.69 353	97.61 355

ambc				93.06 344	92.68 369	82.36 368	98.47 344	98.73 337		95.09 353	97.41 355	55.55 373	99.10 312	96.42 293	91.32 348	97.71 354

RPMNet		96.72 295	95.90 305	99.19 158	99.18 249	98.49 207	99.22 248	99.52 92	88.72 359	99.56 101	97.38 356	94.08 245	99.95 47	86.87 366	98.58 198	99.14 199

new-patchmatchnet		94.48 323	94.08 323	95.67 340	95.08 367	92.41 359	99.18 252	99.28 273	94.55 333	93.49 358	97.37 357	87.86 349	97.01 363	91.57 349	88.36 354	97.61 355

KD-MVS_self_test		95.00 317	94.34 322	96.96 328	97.07 361	95.39 326	99.56 105	99.44 195	95.11 321	97.13 337	97.32 358	91.86 295	97.27 361	90.35 354	81.23 364	98.23 336

PatchT		97.03 290	96.44 295	98.79 219	98.99 284	98.34 216	99.16 254	99.07 301	92.13 349	99.52 110	97.31 359	94.54 230	98.98 325	88.54 360	98.73 194	99.03 215

UnsupCasMVSNet_bld		93.53 327	92.51 330	96.58 336	97.38 353	93.82 348	98.24 354	99.48 146	91.10 354	93.10 359	96.66 360	74.89 368	98.37 344	94.03 332	87.71 355	97.56 357

LCM-MVSNet		86.80 332	85.22 336	91.53 347	87.81 374	80.96 370	98.23 356	98.99 307	71.05 367	90.13 363	96.51 361	48.45 376	96.88 364	90.51 352	85.30 358	96.76 360

PMMVS2		86.87 331	85.37 335	91.35 348	90.21 372	83.80 367	98.89 311	97.45 361	83.13 364	91.67 362	95.03 362	48.49 375	94.70 369	85.86 367	77.62 366	95.54 363

Gipuma		90.99 330	90.15 333	93.51 342	98.73 317	90.12 365	93.98 367	99.45 186	79.32 365	92.28 360	94.91 363	69.61 369	97.98 352	87.42 363	95.67 298	92.45 366
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
JIA-IIPM		97.50 275	97.02 286	98.93 187	98.73 317	97.80 243	99.30 218	98.97 309	91.73 351	98.91 237	94.86 364	95.10 202	99.71 203	97.58 225	97.98 227	99.28 194

PMVS	70.75 22	75.98 340	74.97 341	79.01 356	70.98 379	55.18 380	93.37 368	98.21 349	65.08 373	61.78 374	93.83 365	21.74 381	92.53 370	78.59 369	91.12 349	89.34 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS-HIRNet		95.75 311	95.16 315	97.51 315	99.30 219	93.69 352	98.88 312	95.78 368	85.09 362	98.78 257	92.65 366	91.29 309	99.37 262	94.85 322	99.85 59	99.46 175

E-PMN		80.61 336	79.88 338	82.81 353	90.75 371	76.38 375	97.69 362	95.76 369	66.44 371	83.52 365	92.25 367	62.54 372	87.16 373	68.53 372	61.40 370	84.89 371

EMVS		80.02 337	79.22 339	82.43 355	91.19 370	76.40 374	97.55 364	92.49 379	66.36 372	83.01 367	91.27 368	64.63 371	85.79 374	65.82 373	60.65 371	85.08 370

gg-mvs-nofinetune		96.17 306	95.32 314	98.73 223	98.79 308	98.14 225	99.38 195	94.09 374	91.07 355	98.07 315	91.04 369	89.62 330	99.35 270	96.75 281	99.09 169	98.68 264

ANet_high		77.30 338	74.86 342	84.62 352	75.88 378	77.61 373	97.63 363	93.15 377	88.81 358	64.27 373	89.29 370	36.51 377	83.93 375	75.89 370	52.31 372	92.33 367

MVE	76.82 21	76.91 339	74.31 343	84.70 351	85.38 377	76.05 376	96.88 365	93.17 376	67.39 370	71.28 372	89.01 371	21.66 382	87.69 372	71.74 371	72.29 369	90.35 368
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		39.17 342	43.78 344	25.37 359	36.04 382	16.84 383	98.36 347	26.56 381	20.06 375	38.51 376	67.32 372	29.64 379	15.30 378	37.59 375	39.90 374	43.98 373

test123		39.01 343	42.50 345	28.53 358	39.17 381	20.91 382	98.75 325	19.17 383	19.83 376	38.57 375	66.67 373	33.16 378	15.42 377	37.50 376	29.66 375	49.26 372

test_post											65.99 374	94.65 225	99.73 193

test_post1							99.23 243				65.14 375	94.18 242	99.71 203	97.58 225

X-MVStestdata		96.55 297	95.45 312	99.87 12	99.85 26	99.83 17	99.69 41	99.68 19	98.98 33	99.37 145	64.01 376	98.81 49	99.94 58	98.79 101	99.86 52	99.84 21

wuyk23d		40.18 341	41.29 346	36.84 357	86.18 376	49.12 381	79.73 370	22.81 382	27.64 374	25.46 377	28.45 377	21.98 380	48.89 376	55.80 374	23.56 376	12.51 374

test_blank		0.13 347	0.17 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	1.57 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.02 348	0.03 351	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.27 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		8.27 346	11.03 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.27 379	99.01 19	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.02 348	0.03 351	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.27 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.02 348	0.03 351	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.27 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.02 348	0.03 351	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.27 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.02 348	0.03 351	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.27 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.02 348	0.03 351	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.27 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

FOURS1					99.91 1	99.93 1	99.87 6	99.56 58	99.10 12	99.81 24

MSC_two_6792asdad				99.87 12	99.51 160	99.76 41		99.33 249					99.96 20	98.87 82	99.84 66	99.89 2

No_MVS				99.87 12	99.51 160	99.76 41		99.33 249					99.96 20	98.87 82	99.84 66	99.89 2

eth-test2					0.00 383

eth-test					0.00 383

IU-MVS					99.84 33	99.88 8		99.32 259	98.30 97	99.84 14				98.86 87	99.85 59	99.89 2

save fliter					99.76 56	99.59 73	99.14 260	99.40 213	99.00 27

test_0728_SECOND				99.91 2	99.84 33	99.89 4	99.57 98	99.51 105					99.96 20	98.93 72	99.86 52	99.88 8

GSMVS																99.52 157

test_part2					99.81 41	99.83 17				99.77 36

sam_mvs1												94.86 210				99.52 157

sam_mvs												94.72 221

MTGPA								99.47 164

MTMP							99.54 117	98.88 322

test9_res														97.49 236	99.72 111	99.75 77

agg_prior2														97.21 253	99.73 110	99.75 77

agg_prior					99.67 109	99.62 66		99.40 213		98.87 244			99.91 95

test_prior4						99.56 78	98.99 293

test_prior				99.68 68	99.67 109	99.48 93		99.56 58					99.83 152			99.74 82

旧先验2							98.96 301		96.70 257	99.47 118			99.94 58	98.19 171

新几何2							99.01 291

无先验							98.99 293	99.51 105	96.89 246				99.93 73	97.53 233		99.72 95

原ACMM2							98.95 305

testdata2													99.95 47	96.67 287

segment_acmp												98.96 29

testdata1							98.85 315		98.32 96

test12				99.75 54	99.64 125	99.61 68		99.29 271		99.21 183		98.38 91	99.89 118		99.74 107	99.74 82

plane_prior7					99.29 223	97.03 275

plane_prior6					99.27 228	96.98 279						92.71 274

plane_prior5								99.47 164					99.69 213	97.78 206	97.63 234	98.67 271

plane_prior3						97.00 277			98.69 63	99.11 201

plane_prior2							99.39 190		98.97 36

plane_prior1					99.26 230

plane_prior						96.97 280	99.21 250		98.45 80						97.60 237

n2								0.00 384

nn								0.00 384

door-mid								98.05 352

test11								99.35 238

door								97.92 354

HQP5-MVS						96.83 285

HQP-NCC					99.19 246		98.98 297		98.24 101	98.66 272

ACMP_Plane					99.19 246		98.98 297		98.24 101	98.66 272

BP-MVS														97.19 257

HQP4-MVS										98.66 272			99.64 225			98.64 283

HQP3-MVS								99.39 217							97.58 239

HQP2-MVS												92.47 283

MDTV_nov1_ep13_2view						95.18 331	99.35 208		96.84 249	99.58 98		95.19 201		97.82 203		99.46 175

ACMMP++_ref															97.19 263

ACMMP++															97.43 255

Test By Simon												98.75 60

High-resolution multi-view benchmark