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.

MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 19	99.11 8	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 61	99.07 11	100.00 1	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	100.00 1

CHOSEN 280x420		99.85 3	99.87 1	99.80 118	99.99 49	99.97 21	99.97 279	99.98 16	98.96 34	100.00 1	100.00 1	99.96 4	99.42 310	100.00 1	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 56	99.19 5	100.00 1	100.00 1	99.31 71	100.00 1	100.00 1	100.00 1	100.00 1

CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 48	99.07 11	100.00 1	100.00 1	99.39 64	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 119	99.06 13	100.00 1	100.00 1	99.56 27	99.99 102	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	99.03 21	100.00 1	100.00 1	99.50 41	100.00 1	100.00 1	100.00 1	100.00 1

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	99.04 16	100.00 1	100.00 1	99.53 33	100.00 1	100.00 1	100.00 1	100.00 1
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
TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 128	99.05 15	100.00 1	100.00 1	99.45 50	99.99 102	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 39	98.88 55	99.96 145	100.00 1	99.21 84	100.00 1	100.00 1	100.00 1	99.99 118

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	98.79 74	100.00 1	100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 69	100.00 1	99.86 84	100.00 1	99.42 147	98.87 58	100.00 1	100.00 1	99.65 19	99.96 160	100.00 1	100.00 1	100.00 1
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
SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 147	99.01 26	100.00 1	100.00 1	99.33 66	100.00 1	100.00 1	100.00 1	100.00 1
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
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 147	98.91 50	100.00 1	100.00 1	99.22 83	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 82	100.00 1	100.00 1	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 163	99.95 32	100.00 1	99.42 147	98.69 80	100.00 1	100.00 1	99.52 36	99.99 102	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PAPM		99.78 16	99.76 12	99.85 99	99.01 339	99.95 32	100.00 1	99.75 52	99.37 3	99.99 124	100.00 1	99.76 12	99.60 266	100.00 1	100.00 1	100.00 1

reproduce_model		99.76 18	99.69 22	99.98 23	99.96 98	99.93 47	100.00 1	99.42 147	98.81 70	100.00 1	100.00 1	98.98 107	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 18	99.69 22	99.98 23	99.96 98	99.94 41	100.00 1	99.42 147	98.82 66	100.00 1	100.00 1	98.99 104	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 18	99.69 22	99.98 23	99.96 98	99.94 41	100.00 1	99.42 147	98.82 66	100.00 1	100.00 1	98.99 104	100.00 1	100.00 1	100.00 1	100.00 1

DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 72	97.99 129	99.99 124	100.00 1	99.72 14	100.00 1	99.96 101	100.00 1	100.00 1

PAPR		99.76 18	99.68 28	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	98.16 115	100.00 1	100.00 1	99.51 37	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 23	99.67 30	99.99 12	99.99 49	99.96 24	99.73 357	99.52 72	99.06 13	100.00 1	100.00 1	98.80 129	100.00 1	99.95 107	100.00 1	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.75 23	99.68 28	99.97 35	100.00 1	99.91 58	99.98 272	99.47 79	99.09 10	100.00 1	100.00 1	98.59 139	100.00 1	99.95 107	100.00 1	100.00 1

HFP-MVS		99.74 25	99.67 30	99.96 47	100.00 1	99.89 72	100.00 1	99.76 49	97.95 137	100.00 1	100.00 1	99.31 71	100.00 1	99.99 72	100.00 1	100.00 1

ACMMPR		99.74 25	99.67 30	99.96 47	100.00 1	99.89 72	100.00 1	99.76 49	97.95 137	100.00 1	100.00 1	99.29 77	100.00 1	99.99 72	100.00 1	100.00 1

PAPM_NR		99.74 25	99.66 33	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 142	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 28	99.64 37	99.99 12	100.00 1	99.97 21	100.00 1	99.42 147	98.02 127	100.00 1	100.00 1	99.32 69	99.99 102	100.00 1	100.00 1	100.00 1

MGCNet		99.72 29	99.65 34	99.93 73	99.99 49	99.79 96	100.00 1	99.91 35	99.17 6	100.00 1	100.00 1	97.84 167	100.00 1	100.00 1	99.95 122	100.00 1

region2R		99.72 29	99.64 37	99.97 35	100.00 1	99.90 65	100.00 1	99.74 55	97.86 143	100.00 1	100.00 1	99.19 86	100.00 1	99.99 72	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 122	99.97 92	99.37 166	99.96 286	99.94 22	98.48 92	100.00 1	100.00 1	98.92 118	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 29	99.65 34	99.91 78	99.97 92	99.72 109	100.00 1	99.47 79	98.43 95	99.88 195	100.00 1	99.14 91	100.00 1	99.97 99	100.00 1	100.00 1

ZNCC-MVS		99.71 33	99.62 44	99.97 35	99.99 49	99.90 65	100.00 1	99.79 45	97.97 133	99.97 138	100.00 1	98.97 109	100.00 1	99.94 109	100.00 1	100.00 1

train_agg		99.71 33	99.63 41	99.97 35	100.00 1	99.95 32	100.00 1	99.42 147	97.70 156	100.00 1	100.00 1	99.51 37	99.97 143	100.00 1	100.00 1	100.00 1

MVS_111021_HR		99.71 33	99.63 41	99.93 73	99.95 102	99.83 91	100.00 1	100.00 1	98.89 54	100.00 1	100.00 1	97.85 165	99.95 173	100.00 1	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 92	100.00 1	99.64 121	99.98 272	99.44 119	98.35 105	99.99 124	100.00 1	99.04 101	99.96 160	99.98 87	100.00 1	100.00 1

MVS_111021_LR		99.70 36	99.65 34	99.88 90	99.96 98	99.70 114	100.00 1	99.97 17	98.96 34	100.00 1	100.00 1	97.93 159	99.95 173	99.99 72	100.00 1	100.00 1

PLC	98.56 2	99.70 36	99.74 16	99.58 167	100.00 1	98.79 223	100.00 1	99.54 71	98.58 87	99.96 145	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 145
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SMA-MVS		99.69 39	99.59 47	99.98 23	99.99 49	99.93 47	100.00 1	99.43 128	97.50 187	100.00 1	100.00 1	99.43 55	100.00 1	100.00 1	100.00 1	100.00 1
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
EI-MVSNet-UG-set		99.69 39	99.63 41	99.87 92	99.99 49	99.64 121	99.95 294	99.44 119	98.35 105	100.00 1	100.00 1	98.98 107	99.97 143	99.98 87	100.00 1	100.00 1

PGM-MVS		99.69 39	99.61 45	99.95 56	99.99 49	99.85 87	100.00 1	99.58 67	97.69 158	100.00 1	100.00 1	99.44 51	100.00 1	99.79 137	100.00 1	100.00 1

mPP-MVS		99.69 39	99.60 46	99.97 35	100.00 1	99.91 58	100.00 1	99.42 147	97.91 139	100.00 1	100.00 1	99.04 101	100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS		99.68 43	99.58 49	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 175	100.00 1	100.00 1	98.99 104	99.99 102	100.00 1	100.00 1	100.00 1

MTAPA		99.68 43	99.59 47	99.97 35	99.99 49	99.91 58	100.00 1	99.42 147	98.32 107	99.94 178	100.00 1	98.65 135	100.00 1	99.96 101	100.00 1	100.00 1

APD-MVS		99.68 43	99.58 49	99.97 35	99.99 49	99.96 24	100.00 1	99.42 147	97.53 182	100.00 1	100.00 1	99.27 80	99.97 143	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		99.67 46	99.57 52	99.97 35	99.98 88	99.92 54	100.00 1	99.42 147	97.83 144	100.00 1	100.00 1	98.89 121	100.00 1	99.98 87	100.00 1	100.00 1

CP-MVS		99.67 46	99.58 49	99.95 56	100.00 1	99.84 89	100.00 1	99.42 147	97.77 151	100.00 1	100.00 1	99.07 95	100.00 1	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 48	99.57 52	99.95 56	99.99 49	99.85 87	100.00 1	99.42 147	97.67 159	100.00 1	100.00 1	99.05 98	99.99 102	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 49	99.55 59	99.97 35	99.99 49	99.91 58	100.00 1	99.48 78	97.54 179	100.00 1	100.00 1	98.97 109	99.99 102	99.98 87	100.00 1	100.00 1

ACMMP		99.65 49	99.57 52	99.89 85	99.99 49	99.66 119	99.75 351	99.73 56	98.16 115	99.75 222	100.00 1	98.90 120	100.00 1	99.96 101	99.88 145	100.00 1
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
lecture		99.64 51	99.53 62	99.98 23	99.99 49	99.93 47	100.00 1	99.47 79	98.53 88	100.00 1	100.00 1	97.88 163	100.00 1	99.98 87	99.92 134	100.00 1

GST-MVS		99.64 51	99.53 62	99.95 56	100.00 1	99.86 84	100.00 1	99.79 45	97.72 154	99.95 175	100.00 1	98.39 148	100.00 1	99.96 101	99.99 103	100.00 1

PS-MVSNAJ		99.64 51	99.57 52	99.85 99	99.78 160	99.81 93	99.95 294	99.42 147	98.38 99	100.00 1	100.00 1	98.75 131	100.00 1	99.88 119	99.99 103	99.74 275

F-COLMAP		99.64 51	99.64 37	99.67 148	99.99 49	99.07 197	100.00 1	99.44 119	98.30 108	99.90 190	100.00 1	99.18 87	99.99 102	99.91 114	100.00 1	99.94 145

fmvsm_l_conf0.5_n_a		99.63 55	99.55 59	99.86 95	99.83 128	99.58 129	100.00 1	99.36 226	98.98 30	100.00 1	100.00 1	97.85 165	99.99 102	100.00 1	99.94 127	100.00 1

fmvsm_l_conf0.5_n		99.63 55	99.56 57	99.86 95	99.81 137	99.59 127	100.00 1	99.36 226	98.98 30	100.00 1	100.00 1	97.92 160	99.99 102	100.00 1	99.95 122	100.00 1

MM		99.63 55	99.52 65	99.94 69	99.99 49	99.82 92	100.00 1	99.97 17	99.11 8	100.00 1	100.00 1	96.65 217	100.00 1	100.00 1	99.97 116	100.00 1

SR-MVS-dyc-post		99.63 55	99.52 65	99.97 35	99.99 49	99.91 58	100.00 1	99.42 147	97.62 167	100.00 1	100.00 1	98.65 135	99.99 102	99.99 72	100.00 1	100.00 1

DPM-MVS		99.63 55	99.51 67	100.00 1	99.90 114	100.00 1	100.00 1	99.43 128	99.00 27	100.00 1	100.00 1	99.58 26	100.00 1	97.64 320	100.00 1	100.00 1

EPNet		99.62 60	99.69 22	99.42 192	99.99 49	98.37 259	100.00 1	99.89 37	98.83 64	100.00 1	100.00 1	98.97 109	100.00 1	99.90 115	99.61 179	99.89 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 60	99.56 57	99.82 107	99.92 110	99.45 155	100.00 1	99.78 47	98.92 47	99.73 226	100.00 1	97.70 174	100.00 1	99.93 111	100.00 1	100.00 1
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
MP-MVS-pluss		99.61 62	99.50 68	99.97 35	99.98 88	99.92 54	100.00 1	99.42 147	97.53 182	99.77 219	100.00 1	98.77 130	100.00 1	99.99 72	100.00 1	99.99 118
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		99.61 62	99.49 70	99.98 23	99.99 49	99.94 41	100.00 1	99.42 147	97.82 146	99.99 124	100.00 1	98.20 151	100.00 1	99.99 72	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + GP.		99.61 62	99.69 22	99.35 206	99.99 49	98.06 289	100.00 1	99.36 226	99.83 2	100.00 1	100.00 1	98.95 113	99.99 102	100.00 1	99.11 192	100.00 1

HPM-MVS_fast		99.60 65	99.49 70	99.91 78	99.99 49	99.78 97	100.00 1	99.42 147	97.09 223	100.00 1	100.00 1	98.95 113	99.96 160	99.98 87	100.00 1	100.00 1

HPM-MVS		99.59 66	99.50 68	99.89 85	100.00 1	99.70 114	100.00 1	99.42 147	97.46 191	100.00 1	100.00 1	98.60 138	99.96 160	99.99 72	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mvsany_test1		99.57 67	99.48 73	99.85 99	99.86 121	99.54 136	100.00 1	99.36 226	98.94 40	100.00 1	100.00 1	97.97 157	100.00 1	99.88 119	99.28 187	100.00 1

BP-MVS1		99.56 68	99.48 73	99.79 122	99.48 274	99.61 124	100.00 1	99.32 250	97.34 203	99.94 178	100.00 1	99.74 13	99.89 202	99.75 151	99.72 167	99.87 205

test_fmvsmconf_n		99.56 68	99.46 76	99.86 95	99.68 180	99.58 129	100.00 1	99.31 259	98.92 47	99.88 195	100.00 1	97.35 193	99.99 102	99.98 87	99.99 103	100.00 1

test_fmvsm_n_1920		99.55 70	99.49 70	99.73 137	99.85 123	99.19 188	100.00 1	99.41 196	98.87 58	100.00 1	100.00 1	97.34 194	100.00 1	99.98 87	99.90 141	100.00 1

WTY-MVS		99.54 71	99.40 78	99.95 56	99.81 137	99.93 47	100.00 1	100.00 1	97.98 131	99.84 199	100.00 1	98.94 115	99.98 134	99.86 123	98.21 247	99.94 145

test_yl		99.51 72	99.37 83	99.95 56	99.82 131	99.90 65	100.00 1	99.47 79	97.48 189	100.00 1	100.00 1	99.80 6	100.00 1	99.98 87	97.75 284	99.94 145

DCV-MVSNet		99.51 72	99.37 83	99.95 56	99.82 131	99.90 65	100.00 1	99.47 79	97.48 189	100.00 1	100.00 1	99.80 6	100.00 1	99.98 87	97.75 284	99.94 145

xiu_mvs_v2_base		99.51 72	99.41 77	99.82 107	99.70 172	99.73 107	99.92 310	99.40 200	98.15 117	100.00 1	100.00 1	98.50 143	100.00 1	99.85 125	99.13 191	99.74 275

HY-MVS	96.53 9	99.50 75	99.35 88	99.96 47	99.81 137	99.93 47	99.64 370	100.00 1	97.97 133	99.84 199	99.85 294	98.94 115	99.99 102	99.86 123	98.23 246	99.95 140

PHI-MVS		99.50 75	99.39 79	99.82 107	100.00 1	99.45 155	100.00 1	99.94 22	96.38 303	100.00 1	100.00 1	98.18 152	100.00 1	100.00 1	100.00 1	100.00 1

CPTT-MVS		99.49 77	99.38 80	99.85 99	100.00 1	99.54 136	100.00 1	99.42 147	97.58 176	99.98 132	100.00 1	97.43 191	100.00 1	99.99 72	100.00 1	100.00 1

MAR-MVS		99.49 77	99.36 86	99.89 85	99.97 92	99.66 119	99.74 352	99.95 19	97.89 140	100.00 1	100.00 1	96.71 216	100.00 1	100.00 1	100.00 1	100.00 1
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
test2506		99.48 79	99.38 80	99.75 133	99.89 116	99.51 143	99.45 391	100.00 1	98.38 99	99.83 202	100.00 1	98.86 122	99.81 233	99.25 235	98.78 201	99.94 145

PVSNet_Blended		99.48 79	99.36 86	99.83 105	99.98 88	99.60 125	100.00 1	100.00 1	97.79 149	100.00 1	100.00 1	96.57 219	99.99 102	100.00 1	99.88 145	99.90 173

NormalMVS		99.47 81	99.48 73	99.43 189	99.99 49	98.55 240	99.94 302	99.28 279	98.39 97	100.00 1	100.00 1	98.44 145	99.98 134	99.36 224	99.92 134	99.75 268

test_fmvsmvis_n_1920		99.46 82	99.37 83	99.73 137	98.88 356	99.18 190	100.00 1	99.26 300	98.85 60	99.79 216	100.00 1	97.70 174	100.00 1	99.98 87	99.86 151	100.00 1

testing3-2		99.45 83	99.31 91	99.86 95	99.70 172	99.73 107	100.00 1	99.47 79	97.46 191	99.97 138	99.97 242	99.48 47	100.00 1	99.78 143	97.99 263	99.85 210

sss		99.45 83	99.34 90	99.80 118	99.76 163	99.50 145	100.00 1	99.91 35	97.72 154	99.98 132	99.94 272	98.45 144	100.00 1	99.53 211	98.75 204	99.89 181

AdaColmap		99.44 85	99.26 99	99.95 56	100.00 1	99.86 84	99.70 363	99.99 13	98.53 88	99.90 190	100.00 1	95.34 239	100.00 1	99.92 112	100.00 1	100.00 1

balanced_conf03		99.43 86	99.28 93	99.85 99	99.68 180	99.68 117	99.97 279	99.28 279	97.03 229	99.96 145	99.97 242	97.90 161	99.93 190	99.77 145	100.00 1	99.94 145

thisisatest0515		99.42 87	99.31 91	99.74 134	99.59 223	99.55 133	100.00 1	99.46 97	96.65 277	99.92 185	100.00 1	99.44 51	99.85 221	99.09 249	99.63 178	99.81 233

myMVS_eth3d28		99.41 88	99.28 93	99.80 118	99.69 175	99.53 138	100.00 1	99.43 128	97.12 222	99.98 132	99.97 242	99.41 61	100.00 1	99.81 136	98.07 260	99.88 194

CANet		99.40 89	99.24 105	99.89 85	99.99 49	99.76 101	100.00 1	99.73 56	98.40 96	99.78 218	100.00 1	95.28 240	99.96 160	100.00 1	99.99 103	99.96 134

GDP-MVS		99.39 90	99.26 99	99.77 130	99.53 244	99.55 133	100.00 1	99.11 381	97.14 218	99.96 145	100.00 1	99.83 5	99.89 202	98.47 283	99.26 188	99.87 205

MVSMamba_PlusPlus		99.39 90	99.25 101	99.80 118	99.68 180	99.59 127	99.99 240	99.30 265	96.66 276	99.96 145	99.97 242	97.89 162	99.92 194	99.76 147	100.00 1	99.90 173

114514_t		99.39 90	99.25 101	99.81 112	99.97 92	99.48 153	100.00 1	99.42 147	95.53 337	100.00 1	100.00 1	98.37 149	99.95 173	99.97 99	100.00 1	100.00 1

fmvsm_l_conf0.5_n_3		99.38 93	99.20 114	99.92 77	99.80 150	99.78 97	100.00 1	99.35 237	98.94 40	100.00 1	100.00 1	94.77 257	99.99 102	99.99 72	99.92 134	100.00 1

alignmvs		99.38 93	99.21 110	99.91 78	99.73 168	99.92 54	100.00 1	99.51 76	97.61 171	100.00 1	100.00 1	99.06 96	99.93 190	99.83 129	97.12 296	99.90 173

1314		99.38 93	99.19 115	99.96 47	98.88 356	99.89 72	99.24 412	99.93 30	98.88 55	98.79 307	100.00 1	97.02 200	100.00 1	100.00 1	100.00 1	100.00 1

thisisatest0530		99.37 96	99.27 95	99.69 144	99.59 223	99.41 161	100.00 1	99.46 97	96.46 294	99.90 190	100.00 1	99.44 51	99.85 221	98.97 254	99.58 180	99.80 254

UBG		99.36 97	99.27 95	99.63 155	99.63 207	99.01 206	100.00 1	99.43 128	96.99 232	100.00 1	99.92 278	99.69 17	99.99 102	99.74 152	98.06 261	99.88 194

fmvsm_l_conf0.5_n_9		99.35 98	99.15 120	99.95 56	99.83 128	99.84 89	100.00 1	99.30 265	98.92 47	100.00 1	100.00 1	94.32 273	100.00 1	100.00 1	99.93 131	100.00 1

xiu_mvs_v1_base_debu		99.35 98	99.21 110	99.79 122	99.67 189	99.71 110	99.78 342	99.36 226	98.13 119	100.00 1	100.00 1	97.00 204	100.00 1	99.83 129	99.07 193	99.66 289

xiu_mvs_v1_base		99.35 98	99.21 110	99.79 122	99.67 189	99.71 110	99.78 342	99.36 226	98.13 119	100.00 1	100.00 1	97.00 204	100.00 1	99.83 129	99.07 193	99.66 289

xiu_mvs_v1_base_debi		99.35 98	99.21 110	99.79 122	99.67 189	99.71 110	99.78 342	99.36 226	98.13 119	100.00 1	100.00 1	97.00 204	100.00 1	99.83 129	99.07 193	99.66 289

fmvsm_s_conf0.5_n_8		99.34 102	99.14 122	99.91 78	99.83 128	99.74 105	100.00 1	99.38 216	98.94 40	100.00 1	100.00 1	94.25 275	99.99 102	100.00 1	99.91 139	100.00 1

ETV-MVS		99.34 102	99.24 105	99.64 154	99.58 228	99.33 169	100.00 1	99.25 304	97.57 177	99.96 145	100.00 1	97.44 190	99.79 238	99.70 166	99.65 175	99.81 233

tttt0517		99.34 102	99.23 108	99.67 148	99.57 232	99.38 163	100.00 1	99.46 97	96.33 308	99.89 193	100.00 1	99.44 51	99.84 225	98.93 256	99.46 184	99.78 264

CS-MVS		99.33 105	99.27 95	99.50 175	99.99 49	99.00 209	100.00 1	99.13 373	97.26 211	99.96 145	100.00 1	97.79 170	99.64 264	99.64 184	99.67 172	99.87 205

PVSNet_Blended_VisFu		99.33 105	99.18 118	99.78 127	99.82 131	99.49 149	100.00 1	99.95 19	97.36 200	99.63 234	100.00 1	96.45 223	99.95 173	99.79 137	99.65 175	99.89 181

fmvsm_s_conf0.5_n_a		99.32 107	99.15 120	99.81 112	99.80 150	99.47 154	100.00 1	99.35 237	98.22 110	100.00 1	100.00 1	95.21 245	99.99 102	99.96 101	99.86 151	99.98 121

HyFIR lowres test		99.32 107	99.24 105	99.58 167	99.95 102	99.26 178	100.00 1	99.99 13	96.72 265	99.29 264	99.91 281	99.49 43	99.47 300	99.74 152	98.08 259	100.00 1

SPE-MVS-test		99.31 109	99.27 95	99.43 189	99.99 49	98.77 224	100.00 1	99.19 338	97.24 212	99.96 145	100.00 1	97.56 182	99.70 261	99.68 174	99.81 161	99.82 222

LS3D		99.31 109	99.13 123	99.87 92	99.99 49	99.71 110	99.55 381	99.46 97	97.32 206	99.82 210	100.00 1	96.85 211	99.97 143	99.14 243	100.00 1	99.92 158

SymmetryMVS		99.30 111	99.25 101	99.45 184	99.79 155	98.55 240	99.94 302	99.47 79	98.39 97	100.00 1	100.00 1	98.44 145	99.98 134	99.36 224	97.83 277	99.83 216

fmvsm_s_conf0.5_n_6		99.30 111	99.12 125	99.84 104	99.24 320	99.56 131	100.00 1	99.31 259	98.90 53	100.00 1	100.00 1	94.75 259	99.97 143	99.98 87	99.88 145	100.00 1

PVSNet	94.91 18	99.30 111	99.25 101	99.44 186	100.00 1	98.32 267	100.00 1	99.86 38	98.04 126	100.00 1	100.00 1	96.10 227	100.00 1	99.55 205	99.73 166	100.00 1

UWE-MVS-28		99.29 114	99.23 108	99.48 180	99.73 168	98.86 218	100.00 1	99.43 128	96.97 235	99.99 124	99.83 297	99.43 55	99.77 244	99.35 227	98.31 237	99.80 254

lupinMVS		99.29 114	99.16 119	99.69 144	99.45 289	99.49 149	100.00 1	99.15 360	97.45 193	99.97 138	100.00 1	96.76 212	99.76 248	99.67 177	100.00 1	99.81 233

CSCG		99.28 116	99.35 88	99.05 243	99.99 49	97.15 335	100.00 1	99.47 79	97.44 195	99.42 250	100.00 1	97.83 169	100.00 1	99.99 72	100.00 1	100.00 1

thres200		99.27 117	99.04 133	99.96 47	99.81 137	99.90 65	100.00 1	99.94 22	97.31 208	99.83 202	99.96 259	97.04 197	100.00 1	99.62 191	97.88 272	99.98 121

OMC-MVS		99.27 117	99.38 80	98.96 251	99.95 102	97.06 339	100.00 1	99.40 200	98.83 64	99.88 195	100.00 1	97.01 201	99.86 214	99.47 217	99.84 156	99.97 128

testing11		99.26 119	99.19 115	99.46 182	99.64 205	98.61 236	100.00 1	99.43 128	96.94 237	99.92 185	99.94 272	99.43 55	99.97 143	99.67 177	97.79 282	99.82 222

EIA-MVS		99.26 119	99.19 115	99.45 184	99.63 207	98.75 225	100.00 1	99.27 293	96.93 238	99.95 175	100.00 1	97.47 187	99.79 238	99.74 152	99.72 167	99.82 222

tfpn200view9		99.26 119	99.03 134	99.96 47	99.81 137	99.89 72	100.00 1	99.94 22	97.23 214	99.83 202	99.96 259	97.04 197	100.00 1	99.59 198	97.85 274	99.98 121

thres400		99.26 119	99.03 134	99.95 56	99.81 137	99.89 72	100.00 1	99.94 22	97.23 214	99.83 202	99.96 259	97.04 197	100.00 1	99.59 198	97.85 274	99.97 128

test_fmvsmconf0.1_n		99.25 123	99.05 132	99.82 107	98.92 352	99.55 133	100.00 1	99.23 314	98.91 50	99.75 222	99.97 242	94.79 256	99.94 186	99.94 109	99.99 103	99.97 128

thres100view900		99.25 123	99.01 136	99.95 56	99.81 137	99.87 81	100.00 1	99.94 22	97.13 220	99.83 202	99.96 259	97.01 201	100.00 1	99.59 198	97.85 274	99.98 121

EPMVS		99.25 123	99.13 123	99.60 161	99.60 219	99.20 187	99.60 376	100.00 1	96.93 238	99.92 185	99.36 374	99.05 98	99.71 260	98.77 265	98.94 198	99.90 173

thres600view7		99.24 126	99.00 139	99.95 56	99.81 137	99.87 81	100.00 1	99.94 22	97.13 220	99.83 202	99.96 259	97.01 201	100.00 1	99.54 208	97.77 283	99.97 128

MVS		99.22 127	98.96 145	99.98 23	99.00 343	99.95 32	99.24 412	99.94 22	98.14 118	98.88 297	100.00 1	95.63 236	100.00 1	99.85 125	100.00 1	100.00 1

guyue		99.21 128	99.07 130	99.62 157	99.55 238	99.29 173	100.00 1	99.32 250	97.66 160	99.96 145	100.00 1	95.84 231	99.84 225	99.63 189	99.67 172	99.75 268

fmvsm_s_conf0.5_n		99.21 128	99.01 136	99.83 105	99.84 124	99.53 138	100.00 1	99.38 216	98.29 109	100.00 1	100.00 1	93.62 283	99.99 102	99.99 72	99.93 131	99.98 121

EC-MVSNet		99.19 130	99.09 129	99.48 180	99.42 293	99.07 197	100.00 1	99.21 331	96.95 236	99.96 145	100.00 1	96.88 210	99.48 298	99.64 184	99.79 165	99.88 194

testing91		99.18 131	99.10 127	99.41 193	99.60 219	98.43 251	100.00 1	99.43 128	96.76 255	99.82 210	99.92 278	99.05 98	99.98 134	99.62 191	97.67 288	99.81 233

testing99		99.18 131	99.10 127	99.41 193	99.60 219	98.43 251	100.00 1	99.43 128	96.76 255	99.84 199	99.92 278	99.06 96	99.98 134	99.62 191	97.67 288	99.81 233

UWE-MVS		99.18 131	99.06 131	99.51 172	99.67 189	98.80 222	100.00 1	99.43 128	96.80 251	99.93 184	99.86 289	99.79 8	99.94 186	97.78 316	98.33 234	99.80 254

ETVMVS		99.16 134	98.98 142	99.69 144	99.67 189	99.56 131	100.00 1	99.45 105	96.36 305	99.98 132	99.95 266	98.65 135	99.64 264	99.11 247	97.63 291	99.88 194

FE-MVS		99.16 134	98.99 141	99.66 151	99.65 199	99.18 190	99.58 378	99.43 128	95.24 349	99.91 188	99.59 348	99.37 65	99.97 143	98.31 290	99.81 161	99.83 216

testing222		99.14 136	98.94 150	99.73 137	99.67 189	99.51 143	100.00 1	99.43 128	96.90 243	99.99 124	99.90 283	98.55 141	99.86 214	98.85 260	97.18 295	99.81 233

PMMVS		99.12 137	98.97 144	99.58 167	99.57 232	98.98 211	100.00 1	99.30 265	97.14 218	99.96 145	100.00 1	96.53 222	99.82 230	99.70 166	98.49 214	99.94 145

jason		99.11 138	98.96 145	99.59 163	99.17 323	99.31 172	100.00 1	99.13 373	97.38 199	99.83 202	100.00 1	95.54 237	99.72 258	99.57 204	99.97 116	99.74 275
jason: jason.
EPP-MVSNet		99.10 139	99.00 139	99.40 197	99.51 262	98.68 231	99.92 310	99.43 128	95.47 343	99.65 233	100.00 1	99.51 37	99.76 248	99.53 211	98.00 262	99.75 268

fmvsm_s_conf0.5_n_10		99.08 140	98.78 167	99.97 35	99.84 124	99.92 54	100.00 1	99.28 279	98.93 44	100.00 1	100.00 1	91.07 324	99.99 102	100.00 1	99.95 122	100.00 1

TESTMET0.1,1		99.08 140	98.96 145	99.44 186	99.63 207	99.38 163	100.00 1	99.45 105	95.53 337	99.48 244	100.00 1	99.71 15	99.02 331	96.84 348	99.99 103	99.91 162

IS-MVSNet		99.08 140	98.91 155	99.59 163	99.65 199	99.38 163	99.78 342	99.24 310	96.70 271	99.51 242	100.00 1	98.44 145	99.52 291	98.47 283	98.39 222	99.88 194

LuminaMVS		99.07 143	98.92 154	99.50 175	98.87 359	99.12 195	99.92 310	99.22 319	97.45 193	99.82 210	99.98 230	96.29 225	99.85 221	99.71 162	99.05 196	99.52 297

UA-Net		99.06 144	98.83 162	99.74 134	99.52 257	99.40 162	99.08 439	99.45 105	97.64 164	99.83 202	100.00 1	95.80 232	99.94 186	98.35 288	99.80 164	99.88 194

3Dnovator	95.63 14	99.06 144	98.76 171	99.96 47	98.86 361	99.90 65	99.98 272	99.93 30	98.95 37	98.49 327	100.00 1	92.91 297	100.00 1	99.71 162	100.00 1	100.00 1

mvsmamba		99.05 146	98.98 142	99.27 230	99.57 232	98.10 285	100.00 1	99.28 279	95.92 323	99.96 145	99.97 242	96.73 215	99.89 202	99.72 158	99.65 175	99.81 233

fmvsm_s_conf0.5_n_9		99.04 147	98.78 167	99.81 112	99.86 121	99.44 158	100.00 1	99.32 250	98.94 40	100.00 1	100.00 1	91.00 327	99.99 102	100.00 1	99.94 127	100.00 1

patch_mono-2		99.04 147	99.79 6	96.81 380	99.92 110	90.47 432	100.00 1	99.41 196	98.95 37	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 140

VNet		99.04 147	98.75 172	99.90 82	99.81 137	99.75 102	99.50 387	99.47 79	98.36 103	100.00 1	99.99 222	94.66 262	100.00 1	99.90 115	97.09 297	99.96 134

AstraMVS		99.03 150	99.01 136	99.09 240	99.46 285	97.66 313	100.00 1	99.23 314	97.83 144	99.95 175	100.00 1	95.52 238	99.86 214	99.74 152	99.39 186	99.74 275

sasdasda		99.03 150	98.73 175	99.94 69	99.75 165	99.95 32	100.00 1	99.30 265	97.64 164	100.00 1	100.00 1	95.22 243	99.97 143	99.76 147	96.90 302	99.91 162

canonicalmvs		99.03 150	98.73 175	99.94 69	99.75 165	99.95 32	100.00 1	99.30 265	97.64 164	100.00 1	100.00 1	95.22 243	99.97 143	99.76 147	96.90 302	99.91 162

test-LLR		99.03 150	98.91 155	99.40 197	99.40 300	99.28 175	100.00 1	99.45 105	96.70 271	99.42 250	99.12 386	99.31 71	99.01 332	96.82 349	99.99 103	99.91 162

PatchmatchNet		99.03 150	98.96 145	99.26 231	99.49 271	98.33 265	99.38 399	99.45 105	96.64 278	99.96 145	99.58 350	99.49 43	99.50 296	97.63 321	99.00 197	99.93 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
3Dnovator+	95.58 15	99.03 150	98.71 181	99.96 47	98.99 346	99.89 72	100.00 1	99.51 76	98.96 34	98.32 338	100.00 1	92.78 299	100.00 1	99.87 122	100.00 1	100.00 1

CANet_DTU		99.02 156	98.90 158	99.41 193	99.88 118	98.71 229	100.00 1	99.29 273	98.84 62	100.00 1	100.00 1	94.02 278	100.00 1	98.08 299	99.96 120	99.52 297

PatchMatch-RL		99.02 156	98.78 167	99.74 134	99.99 49	99.29 173	100.00 1	100.00 1	98.38 99	99.89 193	99.81 304	93.14 294	99.99 102	97.85 310	99.98 113	99.95 140

MGCFI-Net		99.01 158	98.70 183	99.93 73	99.74 167	99.94 41	100.00 1	99.29 273	97.60 174	100.00 1	100.00 1	95.10 249	99.96 160	99.74 152	96.85 304	99.91 162

fmvsm_s_conf0.5_n_5		99.00 159	98.70 183	99.88 90	99.81 137	99.64 121	100.00 1	99.26 300	98.78 77	99.97 138	100.00 1	90.65 334	99.99 102	100.00 1	99.89 142	99.99 118

FA-MVS(test-final)		99.00 159	98.75 172	99.73 137	99.63 207	99.43 159	99.83 331	99.43 128	95.84 329	99.52 241	99.37 373	97.84 167	99.96 160	97.63 321	99.68 170	99.79 260

CHOSEN 1792x2688		99.00 159	98.91 155	99.25 232	99.90 114	97.79 309	100.00 1	99.99 13	98.79 74	98.28 341	100.00 1	93.63 282	99.95 173	99.66 182	99.95 122	100.00 1

DeepC-MVS	97.84 5	99.00 159	98.80 166	99.60 161	99.93 107	99.03 202	100.00 1	99.40 200	98.61 86	99.33 261	100.00 1	92.23 310	99.95 173	99.74 152	99.96 120	99.83 216
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_3		98.99 163	98.69 185	99.89 85	99.70 172	99.69 116	100.00 1	99.39 213	98.93 44	100.00 1	100.00 1	90.20 342	99.99 102	100.00 1	99.95 122	100.00 1

baseline2		98.99 163	98.93 152	99.18 236	99.26 319	99.15 193	100.00 1	99.46 97	96.71 270	96.79 397	100.00 1	99.42 59	99.25 321	98.75 267	99.94 127	99.15 308

QAPM		98.99 163	98.66 188	99.96 47	99.01 339	99.87 81	99.88 323	99.93 30	97.99 129	98.68 312	100.00 1	93.17 292	100.00 1	99.32 231	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 163	98.89 159	99.29 224	99.64 205	98.89 217	99.98 272	99.31 259	96.74 260	99.48 244	100.00 1	98.11 154	99.10 327	98.39 286	98.34 231	99.89 181

fmvsm_s_conf0.5_n_7		98.98 167	98.85 161	99.37 204	99.67 189	98.34 264	100.00 1	99.31 259	98.97 32	100.00 1	100.00 1	91.70 315	99.97 143	99.99 72	99.97 116	99.80 254

fmvsm_s_conf0.5_n_4		98.98 167	98.74 174	99.68 147	99.81 137	99.50 145	100.00 1	99.26 300	98.91 50	100.00 1	100.00 1	90.87 331	99.97 143	99.99 72	99.81 161	99.57 294

tpmrst		98.98 167	98.93 152	99.14 239	99.61 216	97.74 310	99.52 385	99.36 226	96.05 320	99.98 132	99.64 336	99.04 101	99.86 214	98.94 255	98.19 250	99.82 222

test-mter		98.96 170	98.82 163	99.40 197	99.40 300	99.28 175	100.00 1	99.45 105	95.44 348	99.42 250	99.12 386	99.70 16	99.01 332	96.82 349	99.99 103	99.91 162

diffmvs		98.96 170	98.73 175	99.63 155	99.54 241	99.16 192	100.00 1	99.18 345	97.33 205	99.96 145	100.00 1	94.60 264	99.91 196	99.66 182	98.33 234	99.82 222
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CDS-MVSNet		98.96 170	98.95 149	99.01 247	99.48 274	98.36 262	99.93 308	99.37 220	96.79 252	99.31 263	99.83 297	99.77 11	98.91 344	98.07 301	97.98 264	99.77 265
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n_2		98.95 173	98.69 185	99.73 137	99.61 216	99.74 105	100.00 1	99.23 314	98.95 37	99.97 138	100.00 1	90.92 330	99.97 143	100.00 1	99.58 180	99.47 300

mamv4		98.95 173	99.11 126	98.46 279	99.68 180	95.67 363	99.14 432	99.27 293	96.43 295	99.94 178	99.97 242	97.79 170	99.88 210	99.77 145	100.00 1	99.84 212

MVSFormer		98.94 175	98.82 163	99.28 227	99.45 289	99.49 149	100.00 1	99.13 373	95.46 344	99.97 138	100.00 1	96.76 212	98.59 375	98.63 275	100.00 1	99.74 275

MVS_Test		98.93 176	98.65 189	99.77 130	99.62 214	99.50 145	99.99 240	99.19 338	95.52 339	99.96 145	99.86 289	96.54 221	99.98 134	98.65 272	98.48 215	99.82 222

diffmvs_AUTHOR		98.92 177	98.73 175	99.49 179	99.48 274	98.81 221	99.94 302	99.14 367	97.24 212	99.96 145	100.00 1	94.85 254	99.87 213	99.67 177	98.31 237	99.79 260

baseline1		98.91 178	98.61 194	99.81 112	99.71 170	99.77 100	99.78 342	99.44 119	97.51 186	98.81 305	99.99 222	98.25 150	99.76 248	98.60 278	95.41 319	99.89 181

1112_ss		98.91 178	98.71 181	99.51 172	99.69 175	98.75 225	99.99 240	99.15 360	96.82 249	98.84 302	100.00 1	97.45 188	99.89 202	98.66 270	97.75 284	99.89 181

viewcassd2359sk11		98.90 180	98.73 175	99.40 197	99.57 232	98.47 249	99.99 240	99.22 319	96.79 252	99.82 210	100.00 1	95.24 242	99.91 196	99.54 208	98.38 224	99.82 222

fmvsm_s_conf0.5_n_2		98.90 180	98.57 200	99.90 82	99.79 155	99.78 97	100.00 1	99.25 304	98.97 32	100.00 1	100.00 1	89.22 361	99.99 102	100.00 1	99.88 145	99.92 158

MSDG		98.90 180	98.63 192	99.70 143	99.92 110	99.25 180	100.00 1	99.37 220	95.71 331	99.40 256	100.00 1	96.58 218	99.95 173	96.80 351	99.94 127	99.91 162

dcpmvs_2		98.87 183	99.53 62	96.90 374	99.87 120	90.88 430	99.94 302	99.07 395	98.20 113	100.00 1	100.00 1	98.69 134	99.86 214	100.00 1	100.00 1	99.95 140

viewmanbaseed2359cas		98.86 184	98.68 187	99.40 197	99.51 262	98.51 248	99.98 272	99.22 319	97.05 228	99.72 227	100.00 1	94.77 257	99.89 202	99.58 201	98.31 237	99.81 233

DP-MVS		98.86 184	98.54 203	99.81 112	99.97 92	99.45 155	99.52 385	99.40 200	94.35 373	98.36 333	100.00 1	96.13 226	99.97 143	99.12 246	100.00 1	100.00 1

CostFormer		98.84 186	98.77 170	99.04 245	99.41 295	97.58 316	99.67 368	99.35 237	94.66 362	99.96 145	99.36 374	99.28 79	99.74 253	99.41 222	97.81 279	99.81 233

Test_1112_low_res		98.83 187	98.60 196	99.51 172	99.69 175	98.75 225	99.99 240	99.14 367	96.81 250	98.84 302	99.06 390	97.45 188	99.89 202	98.66 270	97.75 284	99.89 181

BH-w/o		98.82 188	98.81 165	98.88 256	99.62 214	96.71 346	100.00 1	99.28 279	97.09 223	98.81 305	100.00 1	94.91 253	99.96 160	99.54 208	100.00 1	99.96 134

mvs_anonymous		98.80 189	98.60 196	99.38 203	99.57 232	99.24 182	100.00 1	99.21 331	95.87 324	98.92 294	99.82 301	96.39 224	99.03 330	99.13 245	98.50 213	99.88 194

viewdifsd2359ckpt09		98.78 190	98.60 196	99.31 222	99.53 244	98.37 259	100.00 1	99.20 336	96.85 246	99.32 262	100.00 1	94.68 261	99.74 253	99.46 218	98.36 227	99.81 233

fmvsm_s_conf0.1_n		98.77 191	98.42 218	99.82 107	99.47 279	99.52 142	100.00 1	99.27 293	97.53 182	100.00 1	100.00 1	89.73 353	99.96 160	99.84 128	99.93 131	99.97 128

SSM_0404		98.76 192	98.56 201	99.35 206	99.53 244	98.65 234	99.80 340	99.15 360	96.53 286	99.47 247	100.00 1	94.38 270	99.76 248	99.64 184	98.59 209	99.64 293

TAMVS		98.76 192	98.73 175	98.86 257	99.44 291	97.69 311	99.57 379	99.34 244	96.57 283	99.12 276	99.81 304	98.83 126	99.16 325	97.97 307	97.91 270	99.73 284

OpenMVS	95.20 17	98.76 192	98.41 220	99.78 127	98.89 355	99.81 93	99.99 240	99.76 49	98.02 127	98.02 356	100.00 1	91.44 317	100.00 1	99.63 189	99.97 116	99.55 295

RRT-MVS		98.75 195	98.52 206	99.44 186	99.65 199	98.57 239	99.90 316	99.08 390	96.51 290	99.96 145	99.95 266	92.59 305	99.96 160	99.60 196	99.45 185	99.81 233

viewdifsd2359ckpt07		98.72 196	98.52 206	99.34 208	99.47 279	98.28 271	99.99 240	99.20 336	96.98 233	99.60 236	100.00 1	93.45 287	99.93 190	99.58 201	98.36 227	99.82 222

viewdifsd2359ckpt13		98.72 196	98.52 206	99.34 208	99.55 238	98.46 250	99.99 240	99.22 319	96.50 292	99.05 285	100.00 1	94.54 265	99.73 256	99.46 218	98.35 229	99.81 233

SSM_0407		98.72 196	98.52 206	99.33 216	99.53 244	98.52 245	99.88 323	99.15 360	96.53 286	98.95 290	100.00 1	94.38 270	99.72 258	99.64 184	98.62 206	99.75 268

dp		98.72 196	98.61 194	99.03 246	99.53 244	97.39 322	99.45 391	99.39 213	95.62 334	99.94 178	99.52 360	98.83 126	99.82 230	96.77 354	98.42 219	99.89 181

fmvsm_s_conf0.1_n_a		98.71 200	98.36 230	99.78 127	99.09 329	99.42 160	100.00 1	99.26 300	97.42 197	100.00 1	100.00 1	89.78 351	99.96 160	99.82 134	99.85 154	99.97 128

PVSNet_BlendedMVS		98.71 200	98.62 193	98.98 250	99.98 88	99.60 125	100.00 1	100.00 1	97.23 214	100.00 1	99.03 396	96.57 219	99.99 102	100.00 1	94.75 344	97.35 417

ADS-MVSNet		98.70 202	98.51 211	99.28 227	99.51 262	98.39 256	99.24 412	99.44 119	95.52 339	99.96 145	99.70 320	97.57 180	99.58 272	97.11 339	98.54 211	99.88 194

baseline		98.69 203	98.45 215	99.41 193	99.52 257	98.67 232	100.00 1	99.17 354	97.03 229	99.13 275	100.00 1	93.17 292	99.74 253	99.70 166	98.34 231	99.81 233

PCF-MVS	98.23 3	98.69 203	98.37 228	99.62 157	99.78 160	99.02 204	99.23 419	99.06 403	96.43 295	98.08 350	100.00 1	94.72 260	99.95 173	98.16 297	99.91 139	99.90 173
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		98.65 205	98.38 226	99.46 182	99.52 257	98.74 228	100.00 1	99.15 360	96.91 241	99.05 285	100.00 1	92.75 300	99.83 227	99.70 166	98.38 224	99.81 233
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs_mvg		98.64 206	98.39 224	99.40 197	99.50 267	98.60 237	100.00 1	99.22 319	96.85 246	99.10 278	100.00 1	92.75 300	99.78 243	99.71 162	98.35 229	99.81 233
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpm2		98.64 206	98.58 199	98.81 261	99.42 293	97.12 336	99.69 365	99.37 220	93.63 390	99.94 178	99.67 328	98.96 112	99.47 300	98.62 277	97.95 268	99.83 216

BH-untuned		98.64 206	98.65 189	98.60 271	99.59 223	96.17 353	100.00 1	99.28 279	96.67 275	98.41 330	100.00 1	94.52 266	99.83 227	99.41 222	100.00 1	99.81 233

mamba_0408		98.63 209	98.40 221	99.34 208	99.53 244	98.52 245	99.24 412	99.16 355	96.43 295	98.95 290	99.98 230	94.47 267	99.76 248	99.21 241	98.62 206	99.75 268

test_cas_vis1_n_1920		98.63 209	98.25 237	99.77 130	99.69 175	99.32 170	100.00 1	99.31 259	98.84 62	99.96 145	100.00 1	87.42 384	99.99 102	99.14 243	99.86 151	100.00 1

KinetiMVS		98.61 211	98.26 236	99.65 153	99.46 285	99.24 182	99.96 286	99.44 119	97.54 179	99.99 124	99.99 222	90.83 332	99.95 173	97.18 337	99.92 134	99.75 268

reproduce_monomvs		98.61 211	98.54 203	98.82 258	99.97 92	99.28 175	100.00 1	99.33 247	98.51 91	97.87 364	99.24 380	99.98 3	99.45 306	99.02 252	92.93 361	97.74 354

test_fmvsmconf0.01_n		98.60 213	98.24 240	99.67 148	96.90 433	99.21 186	99.99 240	99.04 408	98.80 71	99.57 239	99.96 259	90.12 345	99.91 196	99.89 117	99.89 142	99.90 173

SSM_04072		98.59 214	98.40 221	99.15 237	99.53 244	98.52 245	99.24 412	99.16 355	96.43 295	98.95 290	99.98 230	94.47 267	99.19 324	99.21 241	98.62 206	99.75 268

tpmvs		98.59 214	98.38 226	99.23 233	99.69 175	97.90 301	99.31 407	99.47 79	94.52 367	99.68 231	99.28 378	97.64 177	99.89 202	97.71 318	98.17 252	99.89 181

Effi-MVS+		98.58 216	98.24 240	99.61 159	99.60 219	99.26 178	97.85 458	99.10 384	96.22 315	99.97 138	99.89 284	93.75 280	99.77 244	99.43 220	98.34 231	99.81 233

MVSTER		98.58 216	98.52 206	98.77 263	99.65 199	99.68 117	100.00 1	99.29 273	95.63 333	98.65 313	99.80 307	99.78 9	98.88 350	98.59 279	95.31 323	97.73 361

viewmacassd2359aftdt		98.57 218	98.31 233	99.33 216	99.49 271	98.31 269	99.89 320	99.21 331	96.87 245	99.10 278	100.00 1	92.48 308	99.88 210	99.50 213	98.28 241	99.81 233

viewmambaseed2359dif		98.57 218	98.34 232	99.28 227	99.46 285	98.23 275	100.00 1	99.16 355	96.26 311	99.11 277	100.00 1	93.12 295	99.79 238	99.61 194	98.33 234	99.80 254

CVMVSNet		98.56 220	98.47 214	98.82 258	99.11 326	97.67 312	99.74 352	99.47 79	97.57 177	99.06 284	100.00 1	95.72 234	98.97 338	98.21 296	97.33 294	99.83 216

kuosan		98.55 221	98.53 205	98.62 269	99.66 197	96.16 354	100.00 1	99.44 119	93.93 383	99.81 215	99.98 230	97.58 178	99.81 233	98.08 299	98.28 241	99.89 181

MonoMVSNet		98.55 221	98.64 191	98.26 297	98.21 392	95.76 361	99.94 302	99.16 355	96.23 312	99.47 247	99.24 380	96.75 214	99.22 322	99.61 194	99.17 189	99.81 233

AllTest		98.55 221	98.40 221	98.99 248	99.93 107	97.35 325	100.00 1	99.40 200	97.08 225	99.09 280	99.98 230	93.37 288	99.95 173	96.94 343	99.84 156	99.68 287

DeepPCF-MVS	98.03 4	98.54 224	99.72 19	94.98 410	99.99 49	84.94 450	100.00 1	99.42 147	99.98 1	100.00 1	100.00 1	98.11 154	100.00 1	100.00 1	100.00 1	100.00 1

EPNet_dtu		98.53 225	98.23 243	99.43 189	99.92 110	99.01 206	99.96 286	99.47 79	98.80 71	99.96 145	99.96 259	98.56 140	99.30 318	87.78 441	99.68 170	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
myMVS_eth3d		98.52 226	98.51 211	98.53 275	99.50 267	97.98 294	100.00 1	99.57 68	96.23 312	98.07 351	100.00 1	99.09 94	97.81 426	96.17 362	97.96 266	99.82 222

Vis-MVSNet		98.52 226	98.25 237	99.34 208	99.68 180	98.55 240	99.68 367	99.41 196	97.34 203	99.94 178	100.00 1	90.38 341	99.70 261	99.03 251	98.84 199	99.76 267
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+-dtu		98.51 228	98.86 160	97.47 348	99.77 162	94.21 400	100.00 1	98.94 420	97.61 171	99.91 188	98.75 414	95.89 229	99.51 293	99.36 224	99.48 183	98.68 314

SDMVSNet		98.49 229	98.08 252	99.73 137	99.82 131	99.53 138	99.99 240	99.45 105	97.62 167	99.38 258	99.86 289	90.06 348	99.88 210	99.92 112	96.61 309	99.79 260

BH-RMVSNet		98.46 230	98.08 252	99.59 163	99.61 216	99.19 188	100.00 1	99.28 279	97.06 227	98.95 290	100.00 1	88.99 364	99.82 230	98.83 263	100.00 1	99.77 265

testing3		98.44 231	98.37 228	98.65 267	99.51 262	98.32 267	100.00 1	99.62 66	96.43 295	97.93 360	99.99 222	99.11 92	97.81 426	94.88 384	97.80 280	99.82 222

ECVR-MVS		98.43 232	98.14 246	99.32 220	99.89 116	98.21 278	99.46 389	100.00 1	98.38 99	99.47 247	100.00 1	87.91 377	99.80 237	99.35 227	98.78 201	99.94 145

cascas		98.43 232	98.07 254	99.50 175	99.65 199	99.02 204	100.00 1	99.22 319	94.21 376	99.72 227	99.98 230	92.03 313	99.93 190	99.68 174	98.12 257	99.54 296

test1111		98.42 234	98.12 247	99.29 224	99.88 118	98.15 280	99.46 389	100.00 1	98.36 103	99.42 250	100.00 1	87.91 377	99.79 238	99.31 232	98.78 201	99.94 145

ab-mvs		98.42 234	98.02 258	99.61 159	99.71 170	99.00 209	99.10 436	99.64 64	96.70 271	99.04 287	99.81 304	90.64 335	99.98 134	99.64 184	97.93 269	99.84 212

UGNet		98.41 236	98.11 248	99.31 222	99.54 241	98.55 240	99.18 422	100.00 1	98.64 85	99.79 216	99.04 393	87.61 382	100.00 1	99.30 233	99.89 142	99.40 303
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
Fast-Effi-MVS+		98.40 237	98.02 258	99.55 171	99.63 207	99.06 199	100.00 1	99.15 360	95.07 351	99.42 250	99.95 266	93.26 291	99.73 256	97.44 328	98.24 245	99.87 205

Fast-Effi-MVS+-dtu		98.38 238	98.56 201	97.82 338	99.58 228	94.44 397	100.00 1	99.16 355	96.75 257	99.51 242	99.63 340	95.03 251	99.60 266	97.71 318	99.67 172	99.42 302

IMVS_0403		98.37 239	98.39 224	98.29 292	99.38 304	95.36 366	99.97 279	99.18 345	96.72 265	99.68 231	100.00 1	94.61 263	99.77 244	97.84 311	98.15 253	99.74 275

test_fmvs1		98.37 239	98.04 256	99.34 208	99.84 124	98.07 287	100.00 1	99.00 415	98.85 60	100.00 1	100.00 1	85.11 405	99.96 160	99.69 173	99.88 145	100.00 1

IMVS_0407		98.36 241	98.42 218	98.19 304	99.38 304	95.36 366	99.73 357	99.18 345	96.72 265	99.58 237	100.00 1	95.17 247	99.47 300	97.84 311	98.15 253	99.74 275

miper_enhance_ethall		98.33 242	98.27 235	98.51 276	99.66 197	99.04 201	100.00 1	99.22 319	97.53 182	98.51 325	99.38 372	99.49 43	98.75 360	98.02 303	92.61 364	97.76 321

icg_test_0407_2		98.30 243	98.45 215	97.85 337	99.38 304	95.36 366	99.99 240	99.18 345	96.72 265	99.58 237	100.00 1	95.17 247	98.45 388	97.84 311	98.15 253	99.74 275

SCA		98.30 243	97.98 260	99.23 233	99.41 295	98.25 274	99.99 240	99.45 105	96.91 241	99.76 221	99.58 350	89.65 355	99.54 285	98.31 290	98.79 200	99.91 162

XVG-OURS		98.30 243	98.36 230	98.13 312	99.58 228	95.91 357	100.00 1	99.36 226	98.69 80	99.23 268	100.00 1	91.20 321	99.92 194	99.34 229	97.82 278	98.56 317

dongtai		98.29 246	98.25 237	98.42 283	99.58 228	95.86 359	100.00 1	99.44 119	93.46 396	99.69 230	99.97 242	97.53 183	99.51 293	96.28 361	98.27 244	99.89 181

COLMAP_ROB	97.10 7	98.29 246	98.17 245	98.65 267	99.94 105	97.39 322	99.30 408	99.40 200	95.64 332	97.75 370	100.00 1	92.69 304	99.95 173	98.89 258	99.92 134	98.62 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		98.28 248	98.51 211	97.62 344	99.51 262	95.03 375	99.24 412	99.41 196	95.52 339	99.96 145	99.70 320	97.57 180	97.94 423	97.11 339	98.54 211	99.88 194

XVG-OURS-SEG-HR		98.27 249	98.31 233	98.14 309	99.59 223	95.92 356	100.00 1	99.36 226	98.48 92	99.21 269	100.00 1	89.27 360	99.94 186	99.76 147	99.17 189	98.56 317

tpm		98.24 250	98.22 244	98.32 291	99.13 325	95.79 360	99.53 384	99.12 379	95.20 350	99.96 145	99.36 374	97.58 178	99.28 320	97.41 330	96.67 307	99.88 194

VortexMVS		98.23 251	98.11 248	98.59 272	99.56 237	99.37 166	99.95 294	99.03 411	96.47 293	98.69 310	99.55 356	95.91 228	98.66 365	99.01 253	94.80 343	97.73 361

cl22		98.23 251	98.11 248	98.58 274	99.82 131	99.01 206	100.00 1	99.28 279	96.92 240	98.33 337	99.21 383	98.09 156	98.97 338	98.72 268	92.61 364	97.76 321

WBMVS		98.19 253	98.10 251	98.47 278	99.63 207	99.03 202	100.00 1	99.32 250	95.46 344	98.39 332	99.40 371	99.69 17	98.61 370	98.64 273	92.39 369	97.76 321

TR-MVS		98.14 254	97.74 270	99.33 216	99.59 223	98.28 271	99.27 409	99.21 331	96.42 300	99.15 274	99.94 272	88.87 367	99.79 238	98.88 259	98.29 240	99.93 156

Elysia		98.12 255	97.72 273	99.34 208	99.30 314	98.96 214	99.95 294	99.28 279	96.64 278	99.75 222	99.99 222	88.71 369	99.81 233	95.99 364	99.84 156	99.26 304

StellarMVS		98.12 255	97.72 273	99.34 208	99.30 314	98.96 214	99.95 294	99.28 279	96.64 278	99.75 222	99.99 222	88.71 369	99.81 233	95.99 364	99.84 156	99.26 304

test0.0.03 1		98.12 255	98.03 257	98.39 285	99.11 326	98.07 287	100.00 1	99.93 30	96.70 271	96.91 393	99.95 266	99.31 71	98.19 402	91.93 414	98.44 217	98.91 312

GeoE		98.06 258	97.65 277	99.29 224	99.47 279	98.41 253	100.00 1	99.19 338	94.85 356	98.88 297	100.00 1	91.21 320	99.59 268	97.02 341	98.19 250	99.88 194

tpm cat1		98.05 259	97.76 269	98.92 253	99.50 267	97.10 338	99.77 347	99.30 265	90.20 431	99.72 227	98.71 415	97.71 173	99.86 214	96.75 355	98.20 249	99.81 233

PS-MVSNAJss		98.03 260	98.06 255	97.94 331	97.63 414	97.33 328	99.89 320	99.23 314	96.27 310	98.03 354	99.59 348	98.75 131	98.78 355	98.52 281	94.61 347	97.70 377

CR-MVSNet		98.02 261	97.71 275	98.93 252	99.31 311	98.86 218	99.13 433	99.00 415	96.53 286	99.96 145	98.98 400	96.94 207	98.10 412	91.18 419	98.40 220	99.84 212

viewdifsd2359ckpt11		97.98 262	97.89 262	98.26 297	99.47 279	94.98 377	99.99 240	99.22 319	96.74 260	99.24 266	100.00 1	90.14 343	99.90 200	99.49 215	96.73 305	99.90 173

viewmsd2359difaftdt		97.98 262	97.89 262	98.27 294	99.47 279	94.99 376	99.99 240	99.22 319	96.74 260	99.24 266	100.00 1	90.14 343	99.90 200	99.49 215	96.73 305	99.90 173

EI-MVSNet		97.98 262	97.93 261	98.16 308	99.11 326	97.84 306	99.74 352	99.29 273	94.39 372	98.65 313	100.00 1	97.21 195	98.88 350	97.62 324	95.31 323	97.75 332

FIs		97.95 265	97.73 272	98.62 269	98.53 375	99.24 182	100.00 1	99.43 128	96.74 260	97.87 364	99.82 301	95.27 241	98.89 347	98.78 264	93.07 358	97.74 354

SD_0403		97.92 266	98.43 217	96.39 388	99.68 180	89.74 438	99.92 310	99.34 244	96.75 257	99.39 257	99.93 277	93.54 286	99.51 293	99.11 247	98.21 247	99.92 158

IMVS_0404		97.87 267	97.89 262	97.81 339	99.38 304	95.36 366	99.84 329	99.18 345	96.72 265	98.41 330	100.00 1	91.43 318	98.32 395	97.84 311	98.15 253	99.74 275

Anonymous202405211		97.87 267	97.53 279	98.90 254	99.81 137	96.70 347	99.35 402	99.46 97	92.98 407	98.83 304	99.99 222	90.63 336	100.00 1	99.70 166	97.03 298	100.00 1

FC-MVSNet-test		97.84 269	97.63 278	98.45 281	98.30 385	99.05 200	100.00 1	99.43 128	96.63 282	97.61 376	99.82 301	95.19 246	98.57 378	98.64 273	93.05 359	97.73 361

Patchmatch-test		97.83 270	97.42 282	99.06 241	99.08 330	97.66 313	98.66 450	99.21 331	93.65 389	98.25 345	99.58 350	99.47 48	99.57 273	90.25 429	98.59 209	99.95 140

sd_testset		97.81 271	97.48 280	98.79 262	99.82 131	96.80 344	99.32 404	99.45 105	97.62 167	99.38 258	99.86 289	85.56 403	99.77 244	99.72 158	96.61 309	99.79 260

miper_ehance_all_eth		97.81 271	97.66 276	98.23 300	99.49 271	98.37 259	99.99 240	99.11 381	94.78 357	98.25 345	99.21 383	98.18 152	98.57 378	97.35 334	92.61 364	97.76 321

test_vis1_n_1920		97.77 273	97.24 294	99.34 208	99.79 155	98.04 291	100.00 1	99.25 304	98.88 55	100.00 1	100.00 1	77.52 437	100.00 1	99.88 119	99.85 154	100.00 1

HQP-MVS		97.73 274	97.85 266	97.39 350	99.07 331	94.82 381	100.00 1	99.40 200	99.04 16	99.17 270	99.97 242	88.61 372	99.57 273	99.79 137	95.58 313	97.77 319

GA-MVS		97.72 275	97.27 292	99.06 241	99.24 320	97.93 300	100.00 1	99.24 310	95.80 330	98.99 289	99.64 336	89.77 352	99.36 313	95.12 381	97.62 292	99.89 181

HQP_MVS		97.71 276	97.82 268	97.37 351	99.00 343	94.80 384	100.00 1	99.40 200	99.00 27	99.08 282	99.97 242	88.58 374	99.55 282	99.79 137	95.57 317	97.76 321

nrg030		97.64 277	97.27 292	98.75 264	98.34 380	99.53 138	100.00 1	99.22 319	96.21 316	98.27 343	99.95 266	94.40 269	98.98 336	99.23 238	89.78 403	97.75 332

TAPA-MVS	96.40 10	97.64 277	97.37 286	98.45 281	99.94 105	95.70 362	100.00 1	99.40 200	97.65 162	99.53 240	100.00 1	99.31 71	99.66 263	80.48 456	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		97.64 277	97.74 270	97.36 352	99.01 339	94.76 389	100.00 1	99.34 244	99.30 4	99.00 288	99.97 242	87.49 383	99.57 273	99.96 101	95.58 313	97.75 332
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
D2MVS		97.63 280	97.83 267	97.05 365	98.83 364	94.60 393	100.00 1	99.82 40	96.89 244	98.28 341	99.03 396	94.05 276	99.47 300	98.58 280	94.97 341	97.09 423

c3_l		97.58 281	97.42 282	98.06 319	99.48 274	98.16 279	99.96 286	99.10 384	94.54 366	98.13 349	99.20 385	97.87 164	98.25 400	97.28 335	91.20 391	97.75 332

IterMVS-LS		97.56 282	97.44 281	97.92 334	99.38 304	97.90 301	99.89 320	99.10 384	94.41 371	98.32 338	99.54 359	97.21 195	98.11 409	97.50 326	91.62 383	97.75 332
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		97.55 283	97.38 285	98.07 315	97.50 422	97.99 293	100.00 1	99.13 373	95.46 344	98.47 328	99.85 294	92.01 314	98.59 375	98.63 275	95.36 321	97.62 400

dmvs_re		97.54 284	97.88 265	96.54 385	99.55 238	90.35 433	99.86 326	99.46 97	97.00 231	99.41 255	100.00 1	90.78 333	99.30 318	99.60 196	95.24 328	99.96 134

cl____		97.54 284	97.32 288	98.18 305	99.47 279	98.14 282	100.00 1	99.10 384	94.16 379	97.60 377	99.63 340	97.52 184	98.65 367	96.47 356	91.97 377	97.76 321

IB-MVS	96.24 12	97.54 284	96.95 299	99.33 216	99.67 189	98.10 285	100.00 1	99.47 79	97.42 197	99.26 265	99.69 323	98.83 126	99.89 202	99.43 220	78.77 454	100.00 1
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
DIV-MVS_self_test		97.52 287	97.35 287	98.05 323	99.46 285	98.11 283	100.00 1	99.10 384	94.21 376	97.62 375	99.63 340	97.65 176	98.29 397	96.47 356	91.98 376	97.76 321

eth_miper_zixun_eth		97.47 288	97.28 290	98.06 319	99.41 295	97.94 299	99.62 374	99.08 390	94.46 370	98.19 348	99.56 355	96.91 209	98.50 383	96.78 352	91.49 386	97.74 354

test_fmvs1_n		97.43 289	96.86 302	99.15 237	99.68 180	97.48 319	99.99 240	98.98 418	98.82 66	100.00 1	100.00 1	74.85 445	99.96 160	99.67 177	99.70 169	100.00 1

LFMVS		97.42 290	96.62 311	99.81 112	99.80 150	99.50 145	99.16 428	99.56 70	94.48 369	100.00 1	100.00 1	79.35 431	100.00 1	99.89 117	97.37 293	99.94 145

miper_lstm_enhance		97.40 291	97.28 290	97.75 341	99.48 274	97.52 317	100.00 1	99.07 395	94.08 380	98.01 357	99.61 346	97.38 192	97.98 421	96.44 359	91.47 388	97.76 321

RPSCF		97.37 292	98.24 240	94.76 413	99.80 150	84.57 451	99.99 240	99.05 405	94.95 354	99.82 210	100.00 1	94.03 277	100.00 1	98.15 298	98.38 224	99.70 285

ACMM	97.17 6	97.37 292	97.40 284	97.29 357	99.01 339	94.64 392	100.00 1	99.25 304	98.07 125	98.44 329	99.98 230	87.38 385	99.55 282	99.25 235	95.19 331	97.69 382
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		97.31 294	97.32 288	97.28 358	98.85 362	94.60 393	100.00 1	99.37 220	97.35 201	98.85 300	99.98 230	86.66 391	99.56 277	99.55 205	95.26 325	97.70 377

FMVSNet3		97.30 295	96.95 299	98.37 287	99.65 199	99.25 180	99.71 361	99.28 279	94.23 374	98.53 322	98.91 407	93.30 290	98.11 409	95.31 377	93.60 352	97.73 361

UniMVSNet (Re)		97.29 296	96.85 303	98.59 272	98.49 376	99.13 194	100.00 1	99.42 147	96.52 289	98.24 347	98.90 408	94.93 252	98.89 347	97.54 325	87.61 422	97.75 332

OPM-MVS		97.21 297	97.18 297	97.32 355	98.08 398	94.66 390	100.00 1	99.28 279	98.65 84	98.92 294	99.98 230	86.03 399	99.56 277	98.28 294	95.41 319	97.72 368
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	97.00 8	97.19 298	97.16 298	97.27 360	98.97 348	94.58 396	100.00 1	99.32 250	97.97 133	97.45 381	99.98 230	85.79 401	99.56 277	99.70 166	95.24 328	97.67 388
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs4		97.17 299	96.80 304	98.27 294	97.68 413	98.64 235	100.00 1	99.18 345	94.22 375	98.55 320	99.71 317	93.67 281	98.47 386	95.66 371	92.57 367	97.71 376

anonymousdsp		97.16 300	96.88 301	98.00 327	97.08 432	98.06 289	99.81 335	99.15 360	94.58 364	97.84 366	99.62 344	90.49 338	98.60 373	97.98 304	95.32 322	97.33 418

UniMVSNet_NR-MVSNet		97.16 300	96.80 304	98.22 301	98.38 379	98.41 253	100.00 1	99.45 105	96.14 318	97.76 367	99.64 336	95.05 250	98.50 383	97.98 304	86.84 428	97.75 332

XXY-MVS		97.14 302	96.63 310	98.67 266	98.65 369	98.92 216	99.54 383	99.29 273	95.57 336	97.63 373	99.83 297	87.79 381	99.35 315	98.39 286	92.95 360	97.75 332

WR-MVS		97.09 303	96.64 309	98.46 279	98.43 377	99.09 196	99.97 279	99.33 247	95.62 334	97.76 367	99.67 328	91.17 322	98.56 380	98.49 282	89.28 409	97.74 354

JIA-IIPM		97.09 303	96.34 325	99.36 205	98.88 356	98.59 238	99.81 335	99.43 128	84.81 449	99.96 145	90.34 462	98.55 141	99.52 291	97.00 342	98.28 241	99.98 121

jajsoiax		97.07 305	96.79 306	97.89 335	97.28 430	97.12 336	99.95 294	99.19 338	96.55 284	97.31 384	99.69 323	87.35 387	98.91 344	98.70 269	95.12 336	97.66 389

MIMVSNet		97.06 306	96.73 307	98.05 323	99.38 304	96.64 349	98.47 454	99.35 237	93.41 397	99.48 244	98.53 422	89.66 354	97.70 432	94.16 394	98.11 258	99.80 254

X-MVStestdata		97.04 307	96.06 336	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 82	100.00 1	66.97 473	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

h-mvs33		97.03 308	96.53 314	98.51 276	99.79 155	95.90 358	99.45 391	99.45 105	98.21 111	100.00 1	99.78 311	97.49 185	99.99 102	99.72 158	74.92 456	99.65 292

VPA-MVSNet		97.03 308	96.43 320	98.82 258	98.64 370	99.32 170	99.38 399	99.47 79	96.73 264	98.91 296	98.94 405	87.00 389	99.40 311	99.23 238	89.59 404	97.76 321

WB-MVSnew		97.02 310	97.24 294	96.37 390	99.44 291	97.36 324	100.00 1	99.43 128	96.12 319	99.35 260	99.89 284	93.60 284	98.42 390	88.91 440	98.39 222	93.33 456

mvs_tets		97.00 311	96.69 308	97.94 331	97.41 429	97.27 330	99.60 376	99.18 345	96.51 290	97.35 383	99.69 323	86.53 393	98.91 344	98.84 261	95.09 337	97.65 394

gg-mvs-nofinetune		96.95 312	96.10 334	99.50 175	99.41 295	99.36 168	99.07 441	99.52 72	83.69 451	99.96 145	83.60 470	100.00 1	99.20 323	99.68 174	99.99 103	99.96 134

Anonymous20240529		96.93 313	96.22 330	99.05 243	99.79 155	97.30 329	99.16 428	99.47 79	88.51 437	98.69 310	100.00 1	83.50 416	100.00 1	99.83 129	97.02 299	99.83 216

DU-MVS		96.93 313	96.49 317	98.22 301	98.31 383	98.41 253	100.00 1	99.37 220	96.41 301	97.76 367	99.65 332	92.14 311	98.50 383	97.98 304	86.84 428	97.75 332

Patchmtry		96.81 315	96.37 323	98.14 309	99.31 311	98.55 240	98.91 444	99.00 415	90.45 427	97.92 361	98.98 400	96.94 207	98.12 407	94.27 391	91.53 385	97.75 332

hse-mvs2		96.79 316	96.38 322	98.04 325	99.68 180	95.54 365	99.81 335	99.42 147	98.21 111	100.00 1	99.80 307	97.49 185	99.46 305	99.72 158	73.27 459	99.12 309

ACMH	96.25 11	96.77 317	96.62 311	97.21 361	98.96 349	94.43 398	99.64 370	99.33 247	97.43 196	96.55 402	99.97 242	83.52 415	99.54 285	99.07 250	95.13 335	97.66 389
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		96.76 318	96.46 319	97.63 342	99.41 295	96.89 341	99.99 240	99.13 373	94.74 360	97.59 378	99.66 330	89.63 357	98.28 398	95.71 369	92.31 371	97.72 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		96.73 319	96.25 328	98.18 305	98.21 392	98.67 232	99.77 347	99.32 250	95.06 352	97.20 387	99.65 332	90.10 346	98.19 402	98.06 302	88.90 413	97.66 389

WR-MVS_H		96.73 319	96.32 327	97.95 330	98.26 389	97.88 303	99.72 360	99.43 128	95.06 352	96.99 390	98.68 417	93.02 296	98.53 381	97.43 329	88.33 418	97.43 413

IterMVS-SCA-FT		96.72 321	96.42 321	97.62 344	99.40 300	96.83 343	99.99 240	99.14 367	94.65 363	97.55 379	99.72 315	89.65 355	98.31 396	95.62 373	92.05 374	97.73 361

v2v482		96.70 322	96.18 331	98.27 294	98.04 399	98.39 256	100.00 1	99.13 373	94.19 378	98.58 318	99.08 389	90.48 339	98.67 364	95.69 370	90.44 399	97.75 332

test_vis1_n		96.69 323	95.81 347	99.32 220	99.14 324	97.98 294	99.97 279	98.98 418	98.45 94	100.00 1	100.00 1	66.44 459	99.99 102	99.78 143	99.57 182	100.00 1

V42		96.65 324	96.16 333	98.11 314	98.17 396	98.23 275	99.99 240	99.09 389	93.97 381	98.74 309	99.05 392	91.09 323	98.82 353	95.46 375	89.90 401	97.27 419

EU-MVSNet		96.63 325	96.53 314	96.94 372	97.59 418	96.87 342	99.76 349	99.47 79	96.35 306	96.85 395	99.78 311	92.57 306	96.27 446	95.33 376	91.08 392	97.68 384

NR-MVSNet		96.63 325	96.04 337	98.38 286	98.31 383	98.98 211	99.22 421	99.35 237	95.87 324	94.43 428	99.65 332	92.73 302	98.40 391	96.78 352	88.05 419	97.75 332

XVG-ACMP-BASELINE		96.60 327	96.52 316	96.84 378	98.41 378	93.29 410	99.99 240	99.32 250	97.76 153	98.51 325	99.29 377	81.95 422	99.54 285	98.40 285	95.03 338	97.68 384

VDD-MVS		96.58 328	95.99 339	98.34 289	99.52 257	95.33 370	99.18 422	99.38 216	96.64 278	99.77 219	100.00 1	72.51 450	100.00 1	100.00 1	96.94 301	99.70 285

tt0805		96.52 329	96.23 329	97.40 349	99.30 314	93.55 405	99.32 404	99.45 105	96.75 257	97.88 363	99.99 222	79.99 429	99.59 268	97.39 332	95.98 312	99.06 311

LCM-MVSNet-Re		96.52 329	97.21 296	94.44 414	99.27 317	85.80 448	99.85 328	96.61 465	95.98 321	92.75 437	98.48 424	93.97 279	97.55 433	99.58 201	98.43 218	99.98 121

our_test_3		96.51 331	96.35 324	96.98 370	97.61 416	95.05 374	99.98 272	99.01 414	94.68 361	96.77 399	99.06 390	95.87 230	98.14 405	91.81 415	92.37 370	97.75 332

MVP-Stereo		96.51 331	96.48 318	96.60 384	95.65 444	94.25 399	98.84 446	98.16 440	95.85 328	95.23 418	99.04 393	92.54 307	99.13 326	92.98 407	99.98 113	96.43 436
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		96.51 331	95.97 341	98.13 312	97.98 403	98.04 291	99.99 240	99.08 390	93.51 394	98.62 316	98.98 400	90.98 329	98.62 369	93.79 398	90.79 395	97.74 354

ACMH+	96.20 13	96.49 334	96.33 326	97.00 368	99.06 335	93.80 403	99.81 335	99.31 259	97.32 206	95.89 415	99.97 242	82.62 420	99.54 285	98.34 289	94.63 346	97.65 394

TranMVSNet+NR-MVSNet		96.45 335	96.01 338	97.79 340	98.00 402	97.62 315	100.00 1	99.35 237	95.98 321	97.31 384	99.64 336	90.09 347	98.00 419	96.89 347	86.80 431	97.75 332

ET-MVSNet_ETH3D		96.41 336	95.48 367	99.20 235	99.81 137	99.75 102	100.00 1	99.02 412	97.30 210	78.33 462	100.00 1	97.73 172	97.94 423	99.70 166	87.41 424	99.92 158

VPNet		96.41 336	95.76 352	98.33 290	98.61 371	98.30 270	99.48 388	99.45 105	96.98 233	98.87 299	99.88 286	81.57 423	98.93 342	99.22 240	87.82 421	97.76 321

PVSNet_0	93.57 19	96.41 336	95.74 353	98.41 284	99.84 124	95.22 372	100.00 1	100.00 1	98.08 124	97.55 379	99.78 311	84.40 408	100.00 1	100.00 1	81.99 445	100.00 1

v144192		96.40 339	95.81 347	98.17 307	97.89 406	98.11 283	99.99 240	99.06 403	93.39 398	98.75 308	99.09 388	90.43 340	98.66 365	93.10 406	90.55 398	97.75 332

VDDNet		96.39 340	95.55 362	98.90 254	99.27 317	97.45 320	99.15 430	99.92 34	91.28 420	99.98 132	100.00 1	73.55 446	100.00 1	99.85 125	96.98 300	99.24 306

tfpnnormal		96.36 341	95.69 358	98.37 287	98.55 373	98.71 229	99.69 365	99.45 105	93.16 405	96.69 401	99.71 317	88.44 376	98.99 335	94.17 392	91.38 389	97.41 414

v8		96.35 342	95.73 354	98.21 303	98.11 397	98.23 275	99.94 302	99.07 395	92.66 413	98.29 340	99.00 399	91.46 316	98.77 358	94.17 392	88.83 415	97.62 400

PS-CasMVS		96.34 343	95.78 351	98.03 326	98.18 395	98.27 273	99.71 361	99.32 250	94.75 358	96.82 396	99.65 332	86.98 390	98.15 404	97.74 317	88.85 414	97.66 389

LTVRE_ROB	95.29 16	96.32 344	96.10 334	96.99 369	98.55 373	93.88 402	99.45 391	99.28 279	94.50 368	96.46 403	99.52 360	84.86 406	99.48 298	97.26 336	95.03 338	97.59 404
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
Anonymous20231211		96.29 345	95.70 355	98.07 315	99.80 150	97.49 318	99.15 430	99.40 200	89.11 434	97.75 370	99.45 367	88.93 366	98.98 336	98.26 295	89.47 406	97.73 361

v148		96.29 345	95.84 346	97.63 342	97.74 411	96.53 351	100.00 1	99.07 395	93.52 393	98.01 357	99.42 369	91.22 319	98.60 373	96.37 360	87.22 427	97.75 332

AUN-MVS		96.26 347	95.67 359	98.06 319	99.68 180	95.60 364	99.82 334	99.42 147	96.78 254	99.88 195	99.80 307	94.84 255	99.47 300	97.48 327	73.29 458	99.12 309

ttmdpeth		96.24 348	95.88 344	97.32 355	97.80 408	96.61 350	99.95 294	98.77 431	97.80 148	93.42 433	99.28 378	86.42 394	99.01 332	97.63 321	91.84 379	96.33 438

FMVSNet2		96.22 349	95.60 361	98.06 319	99.53 244	98.33 265	99.45 391	99.27 293	93.71 385	98.03 354	98.84 410	84.23 410	98.10 412	93.97 396	93.40 355	97.73 361

LF4IMVS		96.19 350	96.18 331	96.23 394	98.26 389	92.09 421	100.00 1	97.89 451	97.82 146	97.94 359	99.87 287	82.71 419	99.38 312	97.41 330	93.71 351	97.20 420

v1192		96.18 351	95.49 365	98.26 297	98.01 401	98.15 280	99.99 240	99.08 390	93.36 399	98.54 321	98.97 403	89.47 358	98.89 347	91.15 420	90.82 394	97.75 332

testgi		96.18 351	95.93 342	96.93 373	98.98 347	94.20 401	100.00 1	99.07 395	97.16 217	96.06 412	99.86 289	84.08 413	97.79 429	90.38 428	97.80 280	98.81 313

Syy-MVS		96.17 353	96.57 313	95.00 408	99.50 267	87.37 446	100.00 1	99.57 68	96.23 312	98.07 351	100.00 1	92.41 309	97.81 426	85.34 446	97.96 266	99.82 222

ppachtmachnet_test		96.17 353	95.89 343	97.02 367	97.61 416	95.24 371	99.99 240	99.24 310	93.31 401	96.71 400	99.62 344	94.34 272	98.07 414	89.87 430	92.30 372	97.75 332

v1921920		96.16 355	95.50 363	98.14 309	97.88 407	97.96 297	99.99 240	99.07 395	93.33 400	98.60 317	99.24 380	89.37 359	98.71 362	91.28 418	90.74 396	97.75 332

Baseline_NR-MVSNet		96.16 355	95.70 355	97.56 347	98.28 388	96.79 345	100.00 1	97.86 452	91.93 417	97.63 373	99.47 366	92.14 311	98.35 394	97.13 338	86.83 430	97.54 407

v10		96.14 357	95.50 363	98.07 315	98.19 394	97.96 297	99.83 331	99.07 395	92.10 416	98.07 351	98.94 405	91.07 324	98.61 370	92.41 413	89.82 402	97.63 398

OurMVSNet-221017-0		96.14 357	95.98 340	96.62 383	97.49 424	93.44 407	99.92 310	98.16 440	95.86 326	97.65 372	99.95 266	85.71 402	98.78 355	94.93 383	94.18 350	97.64 397

GBi-Net		96.07 359	95.80 349	96.89 375	99.53 244	94.87 378	99.18 422	99.27 293	93.71 385	98.53 322	98.81 411	84.23 410	98.07 414	95.31 377	93.60 352	97.72 368

test1		96.07 359	95.80 349	96.89 375	99.53 244	94.87 378	99.18 422	99.27 293	93.71 385	98.53 322	98.81 411	84.23 410	98.07 414	95.31 377	93.60 352	97.72 368

v7n		96.06 361	95.42 371	97.99 329	97.58 419	97.35 325	99.86 326	99.11 381	92.81 412	97.91 362	99.49 364	90.99 328	98.92 343	92.51 410	88.49 417	97.70 377

PEN-MVS		96.01 362	95.48 367	97.58 346	97.74 411	97.26 331	99.90 316	99.29 273	94.55 365	96.79 397	99.55 356	87.38 385	97.84 425	96.92 346	87.24 426	97.65 394

v1240		95.96 363	95.25 372	98.07 315	97.91 405	97.87 305	99.96 286	99.07 395	93.24 403	98.64 315	98.96 404	88.98 365	98.61 370	89.58 434	90.92 393	97.75 332

pmmvs5		95.94 364	95.61 360	96.95 371	97.42 427	94.66 390	100.00 1	98.08 444	93.60 391	97.05 389	99.43 368	87.02 388	98.46 387	95.76 367	92.12 373	97.72 368

PatchT		95.90 365	94.95 380	98.75 264	99.03 337	98.39 256	99.08 439	99.32 250	85.52 447	99.96 145	94.99 454	97.94 158	98.05 418	80.20 457	98.47 216	99.81 233

USDC		95.90 365	95.70 355	96.50 386	98.60 372	92.56 419	100.00 1	98.30 438	97.77 151	96.92 391	99.94 272	81.25 426	99.45 306	93.54 401	94.96 342	97.49 410

pm-mvs1		95.76 367	95.01 377	98.00 327	98.23 391	97.45 320	99.24 412	99.04 408	93.13 406	95.93 414	99.72 315	86.28 395	98.84 352	95.62 373	87.92 420	97.72 368

SixPastTwentyTwo		95.71 368	95.49 365	96.38 389	97.42 427	93.01 411	99.84 329	98.23 439	94.75 358	95.98 413	99.97 242	85.35 404	98.43 389	94.71 385	93.17 357	97.69 382

MS-PatchMatch		95.66 369	95.87 345	95.05 406	97.80 408	89.25 440	98.88 445	99.30 265	96.35 306	96.86 394	99.01 398	81.35 425	99.43 308	93.30 403	99.98 113	96.46 435

DTE-MVSNet		95.52 370	94.99 378	97.08 364	97.49 424	96.45 352	100.00 1	99.25 304	93.82 384	96.17 408	99.57 354	87.81 380	97.18 434	94.57 387	86.26 434	97.62 400

TinyColmap		95.50 371	95.12 376	96.64 382	98.69 368	93.00 412	99.40 397	97.75 454	96.40 302	96.14 409	99.87 287	79.47 430	99.50 296	93.62 400	94.72 345	97.40 415

K. test v3		95.46 372	95.14 375	96.40 387	97.53 421	93.40 408	99.99 240	99.23 314	95.49 342	92.70 438	99.73 314	84.26 409	98.12 407	93.94 397	93.38 356	97.68 384

SSC-MVS3.2		95.32 373	94.97 379	96.37 390	98.29 387	92.75 415	100.00 1	99.30 265	95.46 344	98.36 333	99.42 369	78.92 433	98.63 368	93.28 405	91.72 382	97.72 368

FMVSNet5		95.32 373	95.43 370	94.99 409	99.39 303	92.99 413	99.25 411	99.24 310	90.45 427	97.44 382	98.45 425	95.78 233	94.39 455	87.02 442	91.88 378	97.59 404

UniMVSNet_ETH3D		95.28 375	94.41 381	97.89 335	98.91 353	95.14 373	99.13 433	99.35 237	92.11 415	97.17 388	99.66 330	70.28 454	99.36 313	97.88 309	95.18 332	99.16 307

RPMNet		95.26 376	93.82 385	99.56 170	99.31 311	98.86 218	99.13 433	99.42 147	79.82 456	99.96 145	95.13 452	95.69 235	99.98 134	77.54 462	98.40 220	99.84 212

DSMNet-mixed		95.18 377	95.21 374	95.08 405	96.03 439	90.21 435	99.65 369	93.64 471	92.91 408	98.34 336	97.40 441	90.05 349	95.51 452	91.02 421	97.86 273	99.51 299

test_fmvs2		95.17 378	95.23 373	95.01 407	98.95 351	88.99 442	99.99 240	97.77 453	97.79 149	98.58 318	99.70 320	73.36 447	99.34 316	95.88 366	95.03 338	96.70 431

TransMVSNet (Re)		94.78 379	93.72 386	97.93 333	98.34 380	97.88 303	99.23 419	97.98 449	91.60 418	94.55 425	99.71 317	87.89 379	98.36 393	89.30 436	84.92 436	97.56 406

mmtdpeth		94.58 380	94.18 382	95.81 400	98.82 366	91.09 429	99.99 240	98.61 435	96.38 303	100.00 1	97.23 442	76.52 441	99.85 221	99.82 134	80.22 450	96.48 434

FMVSNet1		94.45 381	93.63 388	96.89 375	98.87 359	94.87 378	99.18 422	99.27 293	90.95 424	97.31 384	98.81 411	72.89 449	98.07 414	92.61 408	92.81 362	97.72 368

test_0402		94.35 382	93.70 387	96.32 392	97.92 404	93.60 404	99.61 375	98.85 427	88.19 441	94.68 423	99.48 365	80.01 428	98.58 377	89.39 435	95.15 334	96.77 429

MVStest1		94.27 383	93.30 392	97.19 362	98.83 364	97.18 334	99.93 308	98.79 430	86.80 444	84.88 459	99.04 393	94.32 273	98.25 400	90.55 425	86.57 432	96.12 441

UnsupCasMVSNet_eth		94.25 384	93.89 384	95.34 403	97.63 414	92.13 420	99.73 357	99.36 226	94.88 355	92.78 435	98.63 419	82.72 418	96.53 442	94.57 387	84.73 437	97.36 416

KD-MVS_2432*1600		94.15 385	93.08 394	97.35 353	99.53 244	97.83 307	99.63 372	99.19 338	92.88 409	96.29 405	97.68 438	98.84 124	96.70 438	89.73 431	63.92 463	97.53 408

miper_refine_blended		94.15 385	93.08 394	97.35 353	99.53 244	97.83 307	99.63 372	99.19 338	92.88 409	96.29 405	97.68 438	98.84 124	96.70 438	89.73 431	63.92 463	97.53 408

MVS-HIRNet		94.12 387	92.73 401	98.29 292	99.33 310	95.95 355	99.38 399	99.19 338	74.54 462	98.26 344	86.34 466	86.07 397	99.06 329	91.60 417	99.87 150	99.85 210

new_pmnet		94.11 388	93.47 390	96.04 398	96.60 436	92.82 414	99.97 279	98.91 423	90.21 430	95.26 417	98.05 436	85.89 400	98.14 405	84.28 448	92.01 375	97.16 421

mvs5depth		93.81 389	93.00 396	96.23 394	94.25 452	93.33 409	97.43 460	98.07 445	93.47 395	94.15 430	99.58 350	77.52 437	98.97 338	93.64 399	88.92 412	96.39 437

pmmvs6		93.64 390	92.87 398	95.94 399	97.47 426	91.41 426	98.92 443	99.02 412	87.84 442	95.01 420	99.61 346	77.24 439	98.77 358	94.33 390	86.41 433	97.63 398

Patchmatch-RL test		93.49 391	93.63 388	93.05 425	91.78 456	83.41 452	98.21 456	96.95 461	91.58 419	91.05 440	97.64 440	99.40 63	95.83 450	94.11 395	81.95 446	99.91 162

Anonymous20231206		93.45 392	93.17 393	94.30 417	95.00 449	89.69 439	99.98 272	98.43 437	93.30 402	94.50 427	98.59 420	90.52 337	95.73 451	77.46 463	90.73 397	97.48 412

Anonymous20240521		93.29 393	92.76 400	94.90 412	95.64 445	91.27 427	99.97 279	98.82 428	87.04 443	94.71 422	98.19 431	83.86 414	96.80 437	84.04 449	92.56 368	96.64 432

dmvs_testset		93.27 394	95.48 367	86.65 438	98.74 367	68.42 467	99.92 310	98.91 423	96.19 317	93.28 434	100.00 1	91.06 326	91.67 463	89.64 433	91.54 384	99.86 209

test20.03		93.11 395	92.85 399	93.88 422	95.19 448	91.83 422	100.00 1	98.87 426	93.68 388	92.76 436	98.88 409	89.20 362	92.71 461	77.88 461	89.19 410	97.09 423

test_vis1_rt		93.10 396	92.93 397	93.58 423	99.63 207	85.07 449	99.99 240	93.71 470	97.49 188	90.96 441	97.10 443	60.40 461	99.95 173	99.24 237	97.90 271	95.72 445

APD_test1		93.07 397	94.14 383	89.85 432	99.18 322	72.49 460	99.76 349	98.90 425	92.86 411	96.35 404	99.94 272	75.56 443	99.91 196	86.73 443	97.98 264	97.15 422

EG-PatchMatch MVS		92.94 398	92.49 402	94.29 418	95.87 441	87.07 447	99.07 441	98.11 443	93.19 404	88.98 447	98.66 418	70.89 452	99.08 328	92.43 412	95.21 330	96.72 430

MDA-MVSNet_test_wron		92.61 399	91.09 409	97.19 362	96.71 435	97.26 331	100.00 1	99.14 367	88.61 436	67.90 468	98.32 430	89.03 363	96.57 441	90.47 427	89.59 404	97.74 354

sc_t1		92.52 400	91.34 404	96.09 396	97.80 408	89.86 437	98.61 451	99.12 379	77.73 457	96.09 410	99.79 310	68.64 456	98.94 341	96.94 343	87.31 425	99.46 301

YYNet1		92.44 401	90.92 410	97.03 366	96.20 437	97.06 339	99.99 240	99.14 367	88.21 440	67.93 467	98.43 427	88.63 371	96.28 445	90.64 422	89.08 411	97.74 354

tt0320		92.36 402	91.28 405	95.58 402	98.30 385	90.65 431	98.69 449	99.14 367	76.73 458	96.07 411	99.50 363	72.28 451	98.39 392	93.29 404	87.56 423	97.70 377

MIMVSNet1		91.96 403	91.20 406	94.23 419	94.94 450	91.69 424	99.34 403	99.22 319	88.23 438	94.18 429	98.45 425	75.52 444	93.41 459	79.37 458	91.49 386	97.60 403

TDRefinement		91.93 404	90.48 413	96.27 393	81.60 470	92.65 418	99.10 436	97.61 457	93.96 382	93.77 431	99.85 294	80.03 427	99.53 290	97.82 315	70.59 460	96.63 433

OpenMVS_ROB	88.34 20	91.89 405	91.12 407	94.19 420	95.55 446	87.63 445	99.26 410	98.03 446	86.61 446	90.65 445	96.82 445	70.14 455	98.78 355	86.54 444	96.50 311	96.15 439

N_pmnet		91.88 406	93.37 391	87.40 437	97.24 431	66.33 470	99.90 316	91.05 473	89.77 433	95.65 416	98.58 421	90.05 349	98.11 409	85.39 445	92.72 363	97.75 332

pmmvs-eth3d		91.73 407	90.67 411	94.92 411	91.63 458	92.71 417	99.90 316	98.54 436	91.19 421	88.08 450	95.50 449	79.31 432	96.13 447	90.55 425	81.32 449	95.91 444

tt0320-xc		91.69 408	90.50 412	95.26 404	98.04 399	90.12 436	98.60 452	98.70 433	76.63 460	94.66 424	99.52 360	68.57 457	97.99 420	94.61 386	85.18 435	97.66 389

MDA-MVSNet-bldmvs		91.65 409	89.94 417	96.79 381	96.72 434	96.70 347	99.42 396	98.94 420	88.89 435	66.97 470	98.37 428	81.43 424	95.91 449	89.24 437	89.46 407	97.75 332

KD-MVS_self_test		91.16 410	90.09 415	94.35 416	94.44 451	91.27 427	99.74 352	99.08 390	90.82 425	94.53 426	94.91 455	86.11 396	94.78 454	82.67 451	68.52 461	96.99 425

CL-MVSNet_self_test		91.07 411	90.35 414	93.24 424	93.27 453	89.16 441	99.55 381	99.25 304	92.34 414	95.23 418	97.05 444	88.86 368	93.59 458	80.67 455	66.95 462	96.96 426

test_method		91.04 412	91.10 408	90.85 429	98.34 380	77.63 456	100.00 1	98.93 422	76.69 459	96.25 407	98.52 423	70.44 453	97.98 421	89.02 439	91.74 380	96.92 427

CMPMVS	66.12 22	90.65 413	92.04 403	86.46 439	96.18 438	66.87 469	98.03 457	99.38 216	83.38 452	85.49 456	99.55 356	77.59 436	98.80 354	94.44 389	94.31 349	93.72 454
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		90.62 414	89.36 420	94.40 415	90.53 463	91.49 425	100.00 1	96.73 463	84.21 450	93.65 432	96.65 446	82.56 421	94.83 453	82.28 452	77.62 455	96.89 428

new-patchmatchnet		90.30 415	89.46 419	92.84 427	90.77 461	88.55 444	99.83 331	98.80 429	90.07 432	87.86 451	95.00 453	78.77 434	94.30 456	84.86 447	79.15 452	95.68 447

FE-MVSNET		89.50 416	88.33 422	93.00 426	88.89 464	90.24 434	99.96 286	96.86 462	88.23 438	88.46 448	95.47 450	77.03 440	93.37 460	78.54 460	81.56 448	95.39 449

UnsupCasMVSNet_bld		89.50 416	88.00 423	93.99 421	95.30 447	88.86 443	98.52 453	99.28 279	85.50 448	87.80 452	94.11 456	61.63 460	96.96 436	90.63 423	79.26 451	96.15 439

mvsany_test3		89.36 418	88.96 421	90.56 430	91.95 455	78.97 455	99.74 352	96.59 466	96.84 248	89.25 446	96.07 447	52.59 463	97.11 435	95.17 380	82.44 444	95.58 448

PM-MVS		88.39 419	87.41 424	91.31 428	91.73 457	82.02 454	99.79 341	96.62 464	91.06 423	90.71 444	95.73 448	48.60 465	95.96 448	90.56 424	81.91 447	95.97 443

WB-MVS		88.24 420	90.09 415	82.68 445	91.56 459	69.51 465	100.00 1	98.73 432	90.72 426	87.29 453	98.12 432	92.87 298	85.01 467	62.19 468	89.34 408	93.54 455

SSC-MVS		87.61 421	89.47 418	82.04 446	90.63 462	68.77 466	99.99 240	98.66 434	90.34 429	86.70 454	98.08 433	92.72 303	84.12 468	59.41 471	88.71 416	93.22 459

test_fmvs3		87.19 422	87.02 425	87.71 436	92.69 454	76.64 457	99.96 286	97.27 458	93.55 392	90.82 443	94.03 457	38.00 471	92.19 462	93.49 402	83.35 443	94.32 451

test_f		86.87 423	86.06 426	89.28 433	91.45 460	76.37 458	99.87 325	97.11 459	91.10 422	88.46 448	93.05 459	38.31 470	96.66 440	91.77 416	83.46 442	94.82 450

Gipuma		84.73 424	83.50 429	88.40 435	97.50 422	82.21 453	88.87 464	99.05 405	65.81 464	85.71 455	90.49 461	53.70 462	96.31 444	78.64 459	91.74 380	86.67 463
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		84.40 425	84.79 427	83.23 443	95.71 442	58.71 476	98.79 447	97.75 454	81.58 453	84.94 457	98.07 434	45.33 467	97.73 430	77.09 464	83.85 439	93.24 457

APD_test2		84.40 425	84.79 427	83.23 443	95.71 442	58.71 476	98.79 447	97.75 454	81.58 453	84.94 457	98.07 434	45.33 467	97.73 430	77.09 464	83.85 439	93.24 457

testmvs		80.17 427	81.95 430	74.80 450	58.54 477	59.58 475	100.00 1	87.14 476	76.09 461	99.61 235	100.00 1	67.06 458	74.19 473	98.84 261	50.30 467	90.64 462

test_vis3_rt		79.61 428	78.19 433	83.86 442	88.68 465	69.56 464	99.81 335	82.19 478	86.78 445	68.57 466	84.51 469	25.06 475	98.26 399	89.18 438	78.94 453	83.75 466

EGC-MVSNET		79.46 429	74.04 437	95.72 401	96.00 440	92.73 416	99.09 438	99.04 408	5.08 474	16.72 474	98.71 415	73.03 448	98.74 361	82.05 453	96.64 308	95.69 446

test123		79.44 430	79.23 432	80.05 448	80.03 471	71.72 461	100.00 1	77.93 479	62.52 465	94.81 421	99.69 323	78.21 435	74.53 472	92.57 409	27.33 472	93.90 452

PMMVS2		79.15 431	77.28 434	84.76 441	82.34 469	72.66 459	99.70 363	95.11 469	71.68 463	84.78 460	90.87 460	32.05 473	89.99 464	75.53 466	63.45 465	91.64 460

LCM-MVSNet		79.01 432	76.93 435	85.27 440	78.28 472	68.01 468	96.57 461	98.03 446	55.10 468	82.03 461	93.27 458	31.99 474	93.95 457	82.72 450	74.37 457	93.84 453

FPMVS		77.92 433	79.45 431	73.34 452	76.87 473	46.81 479	98.24 455	99.05 405	59.89 467	73.55 463	98.34 429	36.81 472	86.55 465	80.96 454	91.35 390	86.65 464

tmp_tt		75.80 434	74.26 436	80.43 447	52.91 479	53.67 478	87.42 466	97.98 449	61.80 466	67.04 469	100.00 1	76.43 442	96.40 443	96.47 356	28.26 471	91.23 461

E-PMN		70.72 435	70.06 438	72.69 453	83.92 468	65.48 472	99.95 294	92.72 472	49.88 470	72.30 464	86.26 467	47.17 466	77.43 470	53.83 472	44.49 468	75.17 470

EMVS		69.88 436	69.09 439	72.24 454	84.70 467	65.82 471	99.96 286	87.08 477	49.82 471	71.51 465	84.74 468	49.30 464	75.32 471	50.97 473	43.71 469	75.59 469

MVE	68.59 21	67.22 437	64.68 441	74.84 449	74.67 475	62.32 474	95.84 462	90.87 474	50.98 469	58.72 471	81.05 471	12.20 479	78.95 469	61.06 470	56.75 466	83.24 467
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 438	63.44 442	73.88 451	61.14 476	63.45 473	95.68 463	87.18 475	79.93 455	47.35 472	80.68 472	22.35 476	72.33 474	61.24 469	35.42 470	85.88 465

PMVS	60.66 23	65.98 439	65.05 440	68.75 455	55.06 478	38.40 480	88.19 465	96.98 460	48.30 472	44.82 473	88.52 464	12.22 478	86.49 466	67.58 467	83.79 441	81.35 468
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		28.28 440	29.73 444	23.92 456	75.89 474	32.61 481	66.50 467	12.88 480	16.09 473	14.59 475	16.59 474	12.35 477	32.36 475	39.36 474	13.36 473	6.79 471

cdsmvs_eth3d_5k		24.41 441	32.55 443	0.00 457	0.00 480	0.00 482	0.00 468	99.39 213	0.00 475	0.00 476	100.00 1	93.55 285	0.00 476	0.00 475	0.00 474	0.00 472

ab-mvs-re		8.33 442	11.11 445	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	100.00 1	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

pcd_1.5k_mvsjas		8.24 443	10.99 446	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	98.75 131	0.00 476	0.00 475	0.00 474	0.00 472

test_blank		0.07 444	0.09 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.79 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

mmdepth		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

monomultidepth		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet_test		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

DCPMVS		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

sosnet-low-res		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

sosnet		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uncertanet		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

Regformer		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet		0.01 445	0.02 448	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.14 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

WAC-MVS						97.98 294								95.74 368

FOURS1					100.00 1	99.97 21	100.00 1	99.42 147	98.52 90	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 71	100.00 1	100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 147	98.72 79	100.00 1	100.00 1	99.60 21

eth-test2					0.00 480

eth-test					0.00 480

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 208	100.00 1	100.00 1	99.32 69	99.99 102	100.00 1	100.00 1

RE-MVS-def			99.55 59		99.99 49	99.91 58	100.00 1	99.42 147	97.62 167	100.00 1	100.00 1	98.94 115		99.99 72	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 147	99.12 7	100.00 1				100.00 1	100.00 1	100.00 1

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 147	99.03 21	100.00 1	100.00 1	99.56 27	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 147	99.03 21	100.00 1	100.00 1	99.50 41	100.00 1

9.14			99.57 52		99.99 49		100.00 1	99.42 147	97.54 179	100.00 1	100.00 1	99.15 90	99.99 102	100.00 1	100.00 1

save fliter					99.99 49	99.93 47	100.00 1	99.42 147	98.93 44

test_0728_THIRD									98.79 74	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 5	100.00 1	99.42 147	99.04 16	100.00 1	100.00 1	99.53 33

GSMVS																99.91 162

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 77				99.91 162

sam_mvs												99.33 66

ambc				88.45 434	86.84 466	70.76 463	97.79 459	98.02 448		90.91 442	95.14 451	38.69 469	98.51 382	94.97 382	84.23 438	96.09 442

MTGPA								99.42 147

test_post1							99.32 404				88.24 465	99.33 66	99.59 268	98.31 290

test_post											89.05 463	99.49 43	99.59 268

patchmatchnet-post											97.79 437	99.41 61	99.54 285

GG-mvs-BLEND				99.59 163	99.54 241	99.49 149	99.17 427	99.52 72		99.96 145	99.68 327	100.00 1	99.33 317	99.71 162	99.99 103	99.96 134

MTMP							100.00 1	99.18 345

gm-plane-assit					99.52 257	97.26 331			95.86 326		100.00 1		99.43 308	98.76 266

test9_res														100.00 1	100.00 1	100.00 1

TEST9					100.00 1	99.95 32	100.00 1	99.42 147	97.65 162	100.00 1	100.00 1	99.53 33	99.97 143

test_8					100.00 1	99.91 58	100.00 1	99.42 147	97.70 156	100.00 1	100.00 1	99.51 37	99.98 134

agg_prior2														100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 79		99.42 147		100.00 1			99.97 143

TestCases				98.99 248	99.93 107	97.35 325		99.40 200	97.08 225	99.09 280	99.98 230	93.37 288	99.95 173	96.94 343	99.84 156	99.68 287

test_prior4						99.93 47	100.00 1

test_prior2							100.00 1		98.82 66	100.00 1	100.00 1	99.47 48		100.00 1	100.00 1

test_prior				99.90 82	100.00 1	99.75 102		99.73 56					99.97 143			100.00 1

旧先验2							100.00 1		98.11 123	100.00 1			100.00 1	99.67 177

新几何2							100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 140	100.00 1	100.00 1	99.20 85	100.00 1	97.91 308	100.00 1	100.00 1

旧先验1					99.99 49	99.88 79		99.82 40			100.00 1	99.27 80			100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 131				100.00 1	99.33 230		100.00 1

原ACMM2							100.00 1

原ACMM1				99.93 73	100.00 1	99.80 95		99.66 63	98.18 114	100.00 1	100.00 1	99.43 55	100.00 1	99.50 213	100.00 1	100.00 1

test222					99.99 49	99.90 65	100.00 1	99.69 62	97.66 160	100.00 1	100.00 1	99.30 76			100.00 1	100.00 1

testdata2													100.00 1	97.36 333

segment_acmp												99.55 29

testdata				99.66 151	99.99 49	98.97 213		99.73 56	97.96 136	100.00 1	100.00 1	99.42 59	100.00 1	99.28 234	100.00 1	100.00 1

testdata1							100.00 1		98.77 78

test12				99.95 56	99.99 49	99.89 72		99.42 147		100.00 1		99.24 82	99.97 143		100.00 1	100.00 1

plane_prior7					99.00 343	94.78 388

plane_prior6					99.06 335	94.80 384						88.58 374

plane_prior5								99.40 200					99.55 282	99.79 137	95.57 317	97.76 321

plane_prior4											99.97 242

plane_prior3						94.79 387			99.03 21	99.08 282

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 338

plane_prior						94.80 384	100.00 1		99.03 21						95.58 313

n2								0.00 481

nn								0.00 481

door-mid								96.32 467

lessismore_v0				96.05 397	97.55 420	91.80 423		99.22 319		91.87 439	99.91 281	83.50 416	98.68 363	92.48 411	90.42 400	97.68 384

LGP-MVS_train				97.28 358	98.85 362	94.60 393		99.37 220	97.35 201	98.85 300	99.98 230	86.66 391	99.56 277	99.55 205	95.26 325	97.70 377

test11								99.42 147

door								96.13 468

HQP5-MVS						94.82 381

HQP-NCC					99.07 331		100.00 1		99.04 16	99.17 270

ACMP_Plane					99.07 331		100.00 1		99.04 16	99.17 270

BP-MVS														99.79 137

HQP4-MVS										99.17 270			99.57 273			97.77 319

HQP3-MVS								99.40 200							95.58 313

HQP2-MVS												88.61 372

NP-MVS					99.07 331	94.81 383					99.97 242

MDTV_nov1_ep13_2view						99.24 182	99.56 380		96.31 309	99.96 145		98.86 122		98.92 257		99.89 181

MDTV_nov1_ep13			98.94 150		99.53 244	98.36 262	99.39 398	99.46 97	96.54 285	99.99 124	99.63 340	98.92 118	99.86 214	98.30 293	98.71 205

ACMMP++_ref															94.58 348

ACMMP++															95.17 333

Test By Simon												99.10 93

ITE_SJBPF				96.84 378	98.96 349	93.49 406		98.12 442	98.12 122	98.35 335	99.97 242	84.45 407	99.56 277	95.63 372	95.25 327	97.49 410

DeepMVS_CX				89.98 431	98.90 354	71.46 462		99.18 345	97.61 171	96.92 391	99.83 297	86.07 397	99.83 227	96.02 363	97.65 290	98.65 315

High-resolution multi-view benchmark