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	99.80 117	99.99 49	99.97 21	99.97 276	99.98 16	98.96 34	100.00 1	100.00 1	99.96 4	99.42 307	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 54	99.96 144	100.00 1	99.21 84	100.00 1	100.00 1	100.00 1	99.99 117

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 73	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 68	100.00 1	99.86 83	100.00 1	99.42 147	98.87 57	100.00 1	100.00 1	99.65 19	99.96 159	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 49	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 81	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	99.76 162	99.95 32	100.00 1	99.42 147	98.69 79	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 98	99.01 336	99.95 32	100.00 1	99.75 52	99.37 3	99.99 123	100.00 1	99.76 12	99.60 263	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 69	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 65	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 65	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	97.99 128	99.99 123	100.00 1	99.72 14	100.00 1	99.96 100	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	98.16 114	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	99.73 354	99.52 72	99.06 13	100.00 1	100.00 1	98.80 129	100.00 1	99.95 106	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 57	99.98 269	99.47 79	99.09 10	100.00 1	100.00 1	98.59 139	100.00 1	99.95 106	100.00 1	100.00 1

HFP-MVS		99.74 25	99.67 30	99.96 46	100.00 1	99.89 71	100.00 1	99.76 49	97.95 136	100.00 1	100.00 1	99.31 71	100.00 1	99.99 71	100.00 1	100.00 1

ACMMPR		99.74 25	99.67 30	99.96 46	100.00 1	99.89 71	100.00 1	99.76 49	97.95 136	100.00 1	100.00 1	99.29 77	100.00 1	99.99 71	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	97.87 141	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	98.02 126	100.00 1	100.00 1	99.32 69	99.99 102	100.00 1	100.00 1	100.00 1

MVS_0304		99.72 29	99.65 34	99.93 72	99.99 49	99.79 95	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 64	100.00 1	99.74 55	97.86 142	100.00 1	100.00 1	99.19 86	100.00 1	99.99 71	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 121	99.97 92	99.37 165	99.96 283	99.94 22	98.48 91	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 77	99.97 92	99.72 108	100.00 1	99.47 79	98.43 94	99.88 194	100.00 1	99.14 91	100.00 1	99.97 98	100.00 1	100.00 1

ZNCC-MVS		99.71 33	99.62 44	99.97 35	99.99 49	99.90 64	100.00 1	99.79 45	97.97 132	99.97 137	100.00 1	98.97 109	100.00 1	99.94 108	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	97.70 155	100.00 1	100.00 1	99.51 37	99.97 142	100.00 1	100.00 1	100.00 1

MVS_111021_HR		99.71 33	99.63 41	99.93 72	99.95 102	99.83 90	100.00 1	100.00 1	98.89 53	100.00 1	100.00 1	97.85 165	99.95 172	100.00 1	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 91	100.00 1	99.64 120	99.98 269	99.44 119	98.35 104	99.99 123	100.00 1	99.04 101	99.96 159	99.98 86	100.00 1	100.00 1

MVS_111021_LR		99.70 36	99.65 34	99.88 89	99.96 98	99.70 113	100.00 1	99.97 17	98.96 34	100.00 1	100.00 1	97.93 159	99.95 172	99.99 71	100.00 1	100.00 1

PLC	98.56 2	99.70 36	99.74 16	99.58 166	100.00 1	98.79 222	100.00 1	99.54 71	98.58 86	99.96 144	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 144
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	97.50 186	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 91	99.99 49	99.64 120	99.95 291	99.44 119	98.35 104	100.00 1	100.00 1	98.98 107	99.97 142	99.98 86	100.00 1	100.00 1

PGM-MVS		99.69 39	99.61 45	99.95 55	99.99 49	99.85 86	100.00 1	99.58 67	97.69 157	100.00 1	100.00 1	99.44 51	100.00 1	99.79 136	100.00 1	100.00 1

mPP-MVS		99.69 39	99.60 46	99.97 35	100.00 1	99.91 57	100.00 1	99.42 147	97.91 138	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	97.59 174	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 57	100.00 1	99.42 147	98.32 106	99.94 177	100.00 1	98.65 135	100.00 1	99.96 100	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	97.53 181	100.00 1	100.00 1	99.27 80	99.97 142	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	97.83 143	100.00 1	100.00 1	98.89 121	100.00 1	99.98 86	100.00 1	100.00 1

CP-MVS		99.67 46	99.58 49	99.95 55	100.00 1	99.84 88	100.00 1	99.42 147	97.77 150	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 55	99.99 49	99.85 86	100.00 1	99.42 147	97.67 158	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 57	100.00 1	99.48 78	97.54 178	100.00 1	100.00 1	98.97 109	99.99 102	99.98 86	100.00 1	100.00 1

ACMMP		99.65 49	99.57 52	99.89 84	99.99 49	99.66 118	99.75 348	99.73 56	98.16 114	99.75 221	100.00 1	98.90 120	100.00 1	99.96 100	99.88 144	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 87	100.00 1	100.00 1	97.88 163	100.00 1	99.98 86	99.92 133	100.00 1

GST-MVS		99.64 51	99.53 62	99.95 55	100.00 1	99.86 83	100.00 1	99.79 45	97.72 153	99.95 174	100.00 1	98.39 148	100.00 1	99.96 100	99.99 103	100.00 1

PS-MVSNAJ		99.64 51	99.57 52	99.85 98	99.78 159	99.81 92	99.95 291	99.42 147	98.38 98	100.00 1	100.00 1	98.75 131	100.00 1	99.88 118	99.99 103	99.74 272

F-COLMAP		99.64 51	99.64 37	99.67 147	99.99 49	99.07 196	100.00 1	99.44 119	98.30 107	99.90 189	100.00 1	99.18 87	99.99 102	99.91 113	100.00 1	99.94 144

fmvsm_l_conf0.5_n_a		99.63 55	99.55 59	99.86 94	99.83 127	99.58 128	100.00 1	99.36 226	98.98 30	100.00 1	100.00 1	97.85 165	99.99 102	100.00 1	99.94 126	100.00 1

fmvsm_l_conf0.5_n		99.63 55	99.56 57	99.86 94	99.81 136	99.59 126	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 68	99.99 49	99.82 91	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 57	100.00 1	99.42 147	97.62 166	100.00 1	100.00 1	98.65 135	99.99 102	99.99 71	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	97.64 317	100.00 1	100.00 1

EPNet		99.62 60	99.69 22	99.42 191	99.99 49	98.37 258	100.00 1	99.89 37	98.83 63	100.00 1	100.00 1	98.97 109	100.00 1	99.90 114	99.61 178	99.89 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 60	99.56 57	99.82 106	99.92 110	99.45 154	100.00 1	99.78 47	98.92 46	99.73 225	100.00 1	97.70 174	100.00 1	99.93 110	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	97.53 181	99.77 218	100.00 1	98.77 130	100.00 1	99.99 71	100.00 1	99.99 117
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	97.82 145	99.99 123	100.00 1	98.20 151	100.00 1	99.99 71	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	99.35 205	99.99 49	98.06 286	100.00 1	99.36 226	99.83 2	100.00 1	100.00 1	98.95 113	99.99 102	100.00 1	99.11 191	100.00 1

HPM-MVS_fast		99.60 65	99.49 70	99.91 77	99.99 49	99.78 96	100.00 1	99.42 147	97.09 222	100.00 1	100.00 1	98.95 113	99.96 159	99.98 86	100.00 1	100.00 1

HPM-MVS		99.59 66	99.50 68	99.89 84	100.00 1	99.70 113	100.00 1	99.42 147	97.46 190	100.00 1	100.00 1	98.60 138	99.96 159	99.99 71	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 98	99.86 121	99.54 135	100.00 1	99.36 226	98.94 40	100.00 1	100.00 1	97.97 157	100.00 1	99.88 118	99.28 186	100.00 1

BP-MVS1		99.56 68	99.48 73	99.79 121	99.48 272	99.61 123	100.00 1	99.32 250	97.34 202	99.94 177	100.00 1	99.74 13	99.89 200	99.75 150	99.72 166	99.87 204

test_fmvsmconf_n		99.56 68	99.46 76	99.86 94	99.68 179	99.58 128	100.00 1	99.31 259	98.92 46	99.88 194	100.00 1	97.35 193	99.99 102	99.98 86	99.99 103	100.00 1

test_fmvsm_n_1920		99.55 70	99.49 70	99.73 136	99.85 123	99.19 187	100.00 1	99.41 196	98.87 57	100.00 1	100.00 1	97.34 194	100.00 1	99.98 86	99.90 140	100.00 1

WTY-MVS		99.54 71	99.40 78	99.95 55	99.81 136	99.93 47	100.00 1	100.00 1	97.98 130	99.84 198	100.00 1	98.94 115	99.98 133	99.86 122	98.21 244	99.94 144

test_yl		99.51 72	99.37 83	99.95 55	99.82 130	99.90 64	100.00 1	99.47 79	97.48 188	100.00 1	100.00 1	99.80 6	100.00 1	99.98 86	97.75 281	99.94 144

DCV-MVSNet		99.51 72	99.37 83	99.95 55	99.82 130	99.90 64	100.00 1	99.47 79	97.48 188	100.00 1	100.00 1	99.80 6	100.00 1	99.98 86	97.75 281	99.94 144

xiu_mvs_v2_base		99.51 72	99.41 77	99.82 106	99.70 171	99.73 106	99.92 307	99.40 200	98.15 116	100.00 1	100.00 1	98.50 143	100.00 1	99.85 124	99.13 190	99.74 272

HY-MVS	96.53 9	99.50 75	99.35 88	99.96 46	99.81 136	99.93 47	99.64 367	100.00 1	97.97 132	99.84 198	99.85 291	98.94 115	99.99 102	99.86 122	98.23 243	99.95 139

PHI-MVS		99.50 75	99.39 79	99.82 106	100.00 1	99.45 154	100.00 1	99.94 22	96.38 300	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 98	100.00 1	99.54 135	100.00 1	99.42 147	97.58 175	99.98 131	100.00 1	97.43 191	100.00 1	99.99 71	100.00 1	100.00 1

MAR-MVS		99.49 77	99.36 86	99.89 84	99.97 92	99.66 118	99.74 349	99.95 19	97.89 139	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	99.75 132	99.89 116	99.51 142	99.45 388	100.00 1	98.38 98	99.83 201	100.00 1	98.86 122	99.81 231	99.25 232	98.78 200	99.94 144

PVSNet_Blended		99.48 79	99.36 86	99.83 104	99.98 88	99.60 124	100.00 1	100.00 1	97.79 148	100.00 1	100.00 1	96.57 219	99.99 102	100.00 1	99.88 144	99.90 172

NormalMVS		99.47 81	99.48 73	99.43 188	99.99 49	98.55 239	99.94 299	99.28 279	98.39 96	100.00 1	100.00 1	98.44 145	99.98 133	99.36 221	99.92 133	99.75 265

test_fmvsmvis_n_1920		99.46 82	99.37 83	99.73 136	98.88 353	99.18 189	100.00 1	99.26 299	98.85 59	99.79 215	100.00 1	97.70 174	100.00 1	99.98 86	99.86 150	100.00 1

testing3-2		99.45 83	99.31 91	99.86 94	99.70 171	99.73 106	100.00 1	99.47 79	97.46 190	99.97 137	99.97 239	99.48 47	100.00 1	99.78 142	97.99 260	99.85 209

sss		99.45 83	99.34 90	99.80 117	99.76 162	99.50 144	100.00 1	99.91 35	97.72 153	99.98 131	99.94 269	98.45 144	100.00 1	99.53 209	98.75 203	99.89 180

AdaColmap		99.44 85	99.26 99	99.95 55	100.00 1	99.86 83	99.70 360	99.99 13	98.53 87	99.90 189	100.00 1	95.34 239	100.00 1	99.92 111	100.00 1	100.00 1

balanced_conf03		99.43 86	99.28 93	99.85 98	99.68 179	99.68 116	99.97 276	99.28 279	97.03 228	99.96 144	99.97 239	97.90 161	99.93 189	99.77 144	100.00 1	99.94 144

thisisatest0515		99.42 87	99.31 91	99.74 133	99.59 222	99.55 132	100.00 1	99.46 97	96.65 274	99.92 184	100.00 1	99.44 51	99.85 219	99.09 246	99.63 177	99.81 231

myMVS_eth3d28		99.41 88	99.28 93	99.80 117	99.69 174	99.53 137	100.00 1	99.43 128	97.12 221	99.98 131	99.97 239	99.41 61	100.00 1	99.81 135	98.07 257	99.88 193

CANet		99.40 89	99.24 105	99.89 84	99.99 49	99.76 100	100.00 1	99.73 56	98.40 95	99.78 217	100.00 1	95.28 240	99.96 159	100.00 1	99.99 103	99.96 133

GDP-MVS		99.39 90	99.26 99	99.77 129	99.53 243	99.55 132	100.00 1	99.11 378	97.14 217	99.96 144	100.00 1	99.83 5	99.89 200	98.47 280	99.26 187	99.87 204

MVSMamba_PlusPlus		99.39 90	99.25 101	99.80 117	99.68 179	99.59 126	99.99 238	99.30 265	96.66 273	99.96 144	99.97 239	97.89 162	99.92 192	99.76 146	100.00 1	99.90 172

114514_t		99.39 90	99.25 101	99.81 111	99.97 92	99.48 152	100.00 1	99.42 147	95.53 334	100.00 1	100.00 1	98.37 149	99.95 172	99.97 98	100.00 1	100.00 1

fmvsm_l_conf0.5_n_3		99.38 93	99.20 114	99.92 76	99.80 149	99.78 96	100.00 1	99.35 237	98.94 40	100.00 1	100.00 1	94.77 257	99.99 102	99.99 71	99.92 133	100.00 1

alignmvs		99.38 93	99.21 110	99.91 77	99.73 167	99.92 54	100.00 1	99.51 76	97.61 170	100.00 1	100.00 1	99.06 96	99.93 189	99.83 128	97.12 293	99.90 172

1314		99.38 93	99.19 115	99.96 46	98.88 353	99.89 71	99.24 409	99.93 30	98.88 54	98.79 304	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	99.69 143	99.59 222	99.41 160	100.00 1	99.46 97	96.46 291	99.90 189	100.00 1	99.44 51	99.85 219	98.97 251	99.58 179	99.80 251

UBG		99.36 97	99.27 95	99.63 154	99.63 206	99.01 205	100.00 1	99.43 128	96.99 231	100.00 1	99.92 275	99.69 17	99.99 102	99.74 151	98.06 258	99.88 193

fmvsm_l_conf0.5_n_9		99.35 98	99.15 120	99.95 55	99.83 127	99.84 88	100.00 1	99.30 265	98.92 46	100.00 1	100.00 1	94.32 272	100.00 1	100.00 1	99.93 130	100.00 1

xiu_mvs_v1_base_debu		99.35 98	99.21 110	99.79 121	99.67 188	99.71 109	99.78 339	99.36 226	98.13 118	100.00 1	100.00 1	97.00 204	100.00 1	99.83 128	99.07 192	99.66 286

xiu_mvs_v1_base		99.35 98	99.21 110	99.79 121	99.67 188	99.71 109	99.78 339	99.36 226	98.13 118	100.00 1	100.00 1	97.00 204	100.00 1	99.83 128	99.07 192	99.66 286

xiu_mvs_v1_base_debi		99.35 98	99.21 110	99.79 121	99.67 188	99.71 109	99.78 339	99.36 226	98.13 118	100.00 1	100.00 1	97.00 204	100.00 1	99.83 128	99.07 192	99.66 286

fmvsm_s_conf0.5_n_8		99.34 102	99.14 122	99.91 77	99.83 127	99.74 104	100.00 1	99.38 216	98.94 40	100.00 1	100.00 1	94.25 274	99.99 102	100.00 1	99.91 138	100.00 1

ETV-MVS		99.34 102	99.24 105	99.64 153	99.58 227	99.33 168	100.00 1	99.25 303	97.57 176	99.96 144	100.00 1	97.44 190	99.79 236	99.70 165	99.65 174	99.81 231

tttt0517		99.34 102	99.23 108	99.67 147	99.57 231	99.38 162	100.00 1	99.46 97	96.33 305	99.89 192	100.00 1	99.44 51	99.84 223	98.93 253	99.46 183	99.78 261

CS-MVS		99.33 105	99.27 95	99.50 174	99.99 49	99.00 208	100.00 1	99.13 370	97.26 210	99.96 144	100.00 1	97.79 170	99.64 261	99.64 183	99.67 171	99.87 204

PVSNet_Blended_VisFu		99.33 105	99.18 118	99.78 126	99.82 130	99.49 148	100.00 1	99.95 19	97.36 199	99.63 233	100.00 1	96.45 223	99.95 172	99.79 136	99.65 174	99.89 180

fmvsm_s_conf0.5_n_a		99.32 107	99.15 120	99.81 111	99.80 149	99.47 153	100.00 1	99.35 237	98.22 109	100.00 1	100.00 1	95.21 245	99.99 102	99.96 100	99.86 150	99.98 120

HyFIR lowres test		99.32 107	99.24 105	99.58 166	99.95 102	99.26 177	100.00 1	99.99 13	96.72 262	99.29 261	99.91 278	99.49 43	99.47 297	99.74 151	98.08 256	100.00 1

SPE-MVS-test		99.31 109	99.27 95	99.43 188	99.99 49	98.77 223	100.00 1	99.19 335	97.24 211	99.96 144	100.00 1	97.56 182	99.70 258	99.68 173	99.81 160	99.82 221

LS3D		99.31 109	99.13 123	99.87 91	99.99 49	99.71 109	99.55 378	99.46 97	97.32 205	99.82 209	100.00 1	96.85 211	99.97 142	99.14 240	100.00 1	99.92 157

SymmetryMVS		99.30 111	99.25 101	99.45 183	99.79 154	98.55 239	99.94 299	99.47 79	98.39 96	100.00 1	100.00 1	98.44 145	99.98 133	99.36 221	97.83 274	99.83 215

fmvsm_s_conf0.5_n_6		99.30 111	99.12 125	99.84 103	99.24 317	99.56 130	100.00 1	99.31 259	98.90 52	100.00 1	100.00 1	94.75 259	99.97 142	99.98 86	99.88 144	100.00 1

PVSNet	94.91 18	99.30 111	99.25 101	99.44 185	100.00 1	98.32 265	100.00 1	99.86 38	98.04 125	100.00 1	100.00 1	96.10 227	100.00 1	99.55 203	99.73 165	100.00 1

UWE-MVS-28		99.29 114	99.23 108	99.48 179	99.73 167	98.86 217	100.00 1	99.43 128	96.97 233	99.99 123	99.83 294	99.43 55	99.77 242	99.35 224	98.31 234	99.80 251

lupinMVS		99.29 114	99.16 119	99.69 143	99.45 286	99.49 148	100.00 1	99.15 357	97.45 192	99.97 137	100.00 1	96.76 212	99.76 246	99.67 176	100.00 1	99.81 231

CSCG		99.28 116	99.35 88	99.05 240	99.99 49	97.15 332	100.00 1	99.47 79	97.44 194	99.42 248	100.00 1	97.83 169	100.00 1	99.99 71	100.00 1	100.00 1

thres200		99.27 117	99.04 133	99.96 46	99.81 136	99.90 64	100.00 1	99.94 22	97.31 207	99.83 201	99.96 256	97.04 197	100.00 1	99.62 190	97.88 269	99.98 120

OMC-MVS		99.27 117	99.38 80	98.96 248	99.95 102	97.06 336	100.00 1	99.40 200	98.83 63	99.88 194	100.00 1	97.01 201	99.86 212	99.47 215	99.84 155	99.97 127

testing11		99.26 119	99.19 115	99.46 181	99.64 204	98.61 235	100.00 1	99.43 128	96.94 235	99.92 184	99.94 269	99.43 55	99.97 142	99.67 176	97.79 279	99.82 221

EIA-MVS		99.26 119	99.19 115	99.45 183	99.63 206	98.75 224	100.00 1	99.27 292	96.93 236	99.95 174	100.00 1	97.47 187	99.79 236	99.74 151	99.72 166	99.82 221

tfpn200view9		99.26 119	99.03 134	99.96 46	99.81 136	99.89 71	100.00 1	99.94 22	97.23 213	99.83 201	99.96 256	97.04 197	100.00 1	99.59 197	97.85 271	99.98 120

thres400		99.26 119	99.03 134	99.95 55	99.81 136	99.89 71	100.00 1	99.94 22	97.23 213	99.83 201	99.96 256	97.04 197	100.00 1	99.59 197	97.85 271	99.97 127

test_fmvsmconf0.1_n		99.25 123	99.05 132	99.82 106	98.92 349	99.55 132	100.00 1	99.23 313	98.91 49	99.75 221	99.97 239	94.79 256	99.94 185	99.94 108	99.99 103	99.97 127

thres100view900		99.25 123	99.01 136	99.95 55	99.81 136	99.87 80	100.00 1	99.94 22	97.13 219	99.83 201	99.96 256	97.01 201	100.00 1	99.59 197	97.85 271	99.98 120

EPMVS		99.25 123	99.13 123	99.60 160	99.60 218	99.20 186	99.60 373	100.00 1	96.93 236	99.92 184	99.36 371	99.05 98	99.71 257	98.77 262	98.94 197	99.90 172

thres600view7		99.24 126	99.00 139	99.95 55	99.81 136	99.87 80	100.00 1	99.94 22	97.13 219	99.83 201	99.96 256	97.01 201	100.00 1	99.54 206	97.77 280	99.97 127

MVS		99.22 127	98.96 145	99.98 23	99.00 340	99.95 32	99.24 409	99.94 22	98.14 117	98.88 294	100.00 1	95.63 236	100.00 1	99.85 124	100.00 1	100.00 1

guyue		99.21 128	99.07 130	99.62 156	99.55 237	99.29 172	100.00 1	99.32 250	97.66 159	99.96 144	100.00 1	95.84 231	99.84 223	99.63 188	99.67 171	99.75 265

fmvsm_s_conf0.5_n		99.21 128	99.01 136	99.83 104	99.84 124	99.53 137	100.00 1	99.38 216	98.29 108	100.00 1	100.00 1	93.62 282	99.99 102	99.99 71	99.93 130	99.98 120

EC-MVSNet		99.19 130	99.09 129	99.48 179	99.42 290	99.07 196	100.00 1	99.21 330	96.95 234	99.96 144	100.00 1	96.88 210	99.48 295	99.64 183	99.79 164	99.88 193

testing91		99.18 131	99.10 127	99.41 192	99.60 218	98.43 250	100.00 1	99.43 128	96.76 252	99.82 209	99.92 275	99.05 98	99.98 133	99.62 190	97.67 285	99.81 231

testing99		99.18 131	99.10 127	99.41 192	99.60 218	98.43 250	100.00 1	99.43 128	96.76 252	99.84 198	99.92 275	99.06 96	99.98 133	99.62 190	97.67 285	99.81 231

UWE-MVS		99.18 131	99.06 131	99.51 171	99.67 188	98.80 221	100.00 1	99.43 128	96.80 248	99.93 183	99.86 286	99.79 8	99.94 185	97.78 313	98.33 231	99.80 251

ETVMVS		99.16 134	98.98 142	99.69 143	99.67 188	99.56 130	100.00 1	99.45 105	96.36 302	99.98 131	99.95 263	98.65 135	99.64 261	99.11 244	97.63 288	99.88 193

FE-MVS		99.16 134	98.99 141	99.66 150	99.65 198	99.18 189	99.58 375	99.43 128	95.24 346	99.91 187	99.59 345	99.37 65	99.97 142	98.31 287	99.81 160	99.83 215

testing222		99.14 136	98.94 150	99.73 136	99.67 188	99.51 142	100.00 1	99.43 128	96.90 241	99.99 123	99.90 280	98.55 141	99.86 212	98.85 257	97.18 292	99.81 231

PMMVS		99.12 137	98.97 144	99.58 166	99.57 231	98.98 210	100.00 1	99.30 265	97.14 217	99.96 144	100.00 1	96.53 222	99.82 228	99.70 165	98.49 213	99.94 144

jason		99.11 138	98.96 145	99.59 162	99.17 320	99.31 171	100.00 1	99.13 370	97.38 198	99.83 201	100.00 1	95.54 237	99.72 255	99.57 202	99.97 116	99.74 272
jason: jason.
EPP-MVSNet		99.10 139	99.00 139	99.40 196	99.51 260	98.68 230	99.92 307	99.43 128	95.47 340	99.65 232	100.00 1	99.51 37	99.76 246	99.53 209	98.00 259	99.75 265

TESTMET0.1,1		99.08 140	98.96 145	99.44 185	99.63 206	99.38 162	100.00 1	99.45 105	95.53 334	99.48 242	100.00 1	99.71 15	99.02 328	96.84 345	99.99 103	99.91 161

IS-MVSNet		99.08 140	98.91 155	99.59 162	99.65 198	99.38 162	99.78 339	99.24 309	96.70 268	99.51 240	100.00 1	98.44 145	99.52 288	98.47 280	98.39 221	99.88 193

LuminaMVS		99.07 142	98.92 154	99.50 174	98.87 356	99.12 194	99.92 307	99.22 318	97.45 192	99.82 209	99.98 227	96.29 225	99.85 219	99.71 161	99.05 195	99.52 294

UA-Net		99.06 143	98.83 162	99.74 133	99.52 255	99.40 161	99.08 436	99.45 105	97.64 163	99.83 201	100.00 1	95.80 232	99.94 185	98.35 285	99.80 163	99.88 193

3Dnovator	95.63 14	99.06 143	98.76 170	99.96 46	98.86 358	99.90 64	99.98 269	99.93 30	98.95 37	98.49 324	100.00 1	92.91 295	100.00 1	99.71 161	100.00 1	100.00 1

mvsmamba		99.05 145	98.98 142	99.27 227	99.57 231	98.10 282	100.00 1	99.28 279	95.92 320	99.96 144	99.97 239	96.73 215	99.89 200	99.72 157	99.65 174	99.81 231

fmvsm_s_conf0.5_n_9		99.04 146	98.78 167	99.81 111	99.86 121	99.44 157	100.00 1	99.32 250	98.94 40	100.00 1	100.00 1	91.00 324	99.99 102	100.00 1	99.94 126	100.00 1

patch_mono-2		99.04 146	99.79 6	96.81 377	99.92 110	90.47 429	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	99.95 139

VNet		99.04 146	98.75 171	99.90 81	99.81 136	99.75 101	99.50 384	99.47 79	98.36 102	100.00 1	99.99 219	94.66 261	100.00 1	99.90 114	97.09 294	99.96 133

AstraMVS		99.03 149	99.01 136	99.09 237	99.46 282	97.66 310	100.00 1	99.23 313	97.83 143	99.95 174	100.00 1	95.52 238	99.86 212	99.74 151	99.39 185	99.74 272

sasdasda		99.03 149	98.73 174	99.94 68	99.75 164	99.95 32	100.00 1	99.30 265	97.64 163	100.00 1	100.00 1	95.22 243	99.97 142	99.76 146	96.90 299	99.91 161

canonicalmvs		99.03 149	98.73 174	99.94 68	99.75 164	99.95 32	100.00 1	99.30 265	97.64 163	100.00 1	100.00 1	95.22 243	99.97 142	99.76 146	96.90 299	99.91 161

test-LLR		99.03 149	98.91 155	99.40 196	99.40 297	99.28 174	100.00 1	99.45 105	96.70 268	99.42 248	99.12 383	99.31 71	99.01 329	96.82 346	99.99 103	99.91 161

PatchmatchNet		99.03 149	98.96 145	99.26 228	99.49 269	98.33 263	99.38 396	99.45 105	96.64 275	99.96 144	99.58 347	99.49 43	99.50 293	97.63 318	99.00 196	99.93 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
3Dnovator+	95.58 15	99.03 149	98.71 180	99.96 46	98.99 343	99.89 71	100.00 1	99.51 76	98.96 34	98.32 335	100.00 1	92.78 297	100.00 1	99.87 121	100.00 1	100.00 1

CANet_DTU		99.02 155	98.90 158	99.41 192	99.88 118	98.71 228	100.00 1	99.29 273	98.84 61	100.00 1	100.00 1	94.02 277	100.00 1	98.08 296	99.96 120	99.52 294

PatchMatch-RL		99.02 155	98.78 167	99.74 133	99.99 49	99.29 172	100.00 1	100.00 1	98.38 98	99.89 192	99.81 301	93.14 292	99.99 102	97.85 307	99.98 113	99.95 139

MGCFI-Net		99.01 157	98.70 182	99.93 72	99.74 166	99.94 41	100.00 1	99.29 273	97.60 173	100.00 1	100.00 1	95.10 249	99.96 159	99.74 151	96.85 301	99.91 161

fmvsm_s_conf0.5_n_5		99.00 158	98.70 182	99.88 89	99.81 136	99.64 120	100.00 1	99.26 299	98.78 76	99.97 137	100.00 1	90.65 331	99.99 102	100.00 1	99.89 141	99.99 117

FA-MVS(test-final)		99.00 158	98.75 171	99.73 136	99.63 206	99.43 158	99.83 328	99.43 128	95.84 326	99.52 239	99.37 370	97.84 167	99.96 159	97.63 318	99.68 169	99.79 257

CHOSEN 1792x2688		99.00 158	98.91 155	99.25 229	99.90 114	97.79 306	100.00 1	99.99 13	98.79 73	98.28 338	100.00 1	93.63 281	99.95 172	99.66 181	99.95 122	100.00 1

DeepC-MVS	97.84 5	99.00 158	98.80 166	99.60 160	99.93 107	99.03 201	100.00 1	99.40 200	98.61 85	99.33 259	100.00 1	92.23 308	99.95 172	99.74 151	99.96 120	99.83 215
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		98.99 162	98.69 184	99.89 84	99.70 171	99.69 115	100.00 1	99.39 213	98.93 44	100.00 1	100.00 1	90.20 339	99.99 102	100.00 1	99.95 122	100.00 1

baseline2		98.99 162	98.93 152	99.18 233	99.26 316	99.15 192	100.00 1	99.46 97	96.71 267	96.79 394	100.00 1	99.42 59	99.25 318	98.75 264	99.94 126	99.15 305

QAPM		98.99 162	98.66 187	99.96 46	99.01 336	99.87 80	99.88 320	99.93 30	97.99 128	98.68 309	100.00 1	93.17 290	100.00 1	99.32 228	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 162	98.89 159	99.29 221	99.64 204	98.89 216	99.98 269	99.31 259	96.74 257	99.48 242	100.00 1	98.11 154	99.10 324	98.39 283	98.34 228	99.89 180

fmvsm_s_conf0.5_n_7		98.98 166	98.85 161	99.37 203	99.67 188	98.34 262	100.00 1	99.31 259	98.97 32	100.00 1	100.00 1	91.70 313	99.97 142	99.99 71	99.97 116	99.80 251

fmvsm_s_conf0.5_n_4		98.98 166	98.74 173	99.68 146	99.81 136	99.50 144	100.00 1	99.26 299	98.91 49	100.00 1	100.00 1	90.87 328	99.97 142	99.99 71	99.81 160	99.57 291

tpmrst		98.98 166	98.93 152	99.14 236	99.61 215	97.74 307	99.52 382	99.36 226	96.05 317	99.98 131	99.64 333	99.04 101	99.86 212	98.94 252	98.19 247	99.82 221

test-mter		98.96 169	98.82 163	99.40 196	99.40 297	99.28 174	100.00 1	99.45 105	95.44 345	99.42 248	99.12 383	99.70 16	99.01 329	96.82 346	99.99 103	99.91 161

diffmvs		98.96 169	98.73 174	99.63 154	99.54 240	99.16 191	100.00 1	99.18 342	97.33 204	99.96 144	100.00 1	94.60 263	99.91 194	99.66 181	98.33 231	99.82 221
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		98.96 169	98.95 149	99.01 244	99.48 272	98.36 260	99.93 305	99.37 220	96.79 249	99.31 260	99.83 294	99.77 11	98.91 341	98.07 298	97.98 261	99.77 262
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n_2		98.95 172	98.69 184	99.73 136	99.61 215	99.74 104	100.00 1	99.23 313	98.95 37	99.97 137	100.00 1	90.92 327	99.97 142	100.00 1	99.58 179	99.47 297

mamv4		98.95 172	99.11 126	98.46 276	99.68 179	95.67 360	99.14 429	99.27 292	96.43 292	99.94 177	99.97 239	97.79 170	99.88 208	99.77 144	100.00 1	99.84 211

MVSFormer		98.94 174	98.82 163	99.28 224	99.45 286	99.49 148	100.00 1	99.13 370	95.46 341	99.97 137	100.00 1	96.76 212	98.59 372	98.63 272	100.00 1	99.74 272

MVS_Test		98.93 175	98.65 188	99.77 129	99.62 213	99.50 144	99.99 238	99.19 335	95.52 336	99.96 144	99.86 286	96.54 221	99.98 133	98.65 269	98.48 214	99.82 221

diffmvs_AUTHOR		98.92 176	98.73 174	99.49 178	99.48 272	98.81 220	99.94 299	99.14 364	97.24 211	99.96 144	100.00 1	94.85 254	99.87 211	99.67 176	98.31 234	99.79 257

baseline1		98.91 177	98.61 193	99.81 111	99.71 169	99.77 99	99.78 339	99.44 119	97.51 185	98.81 302	99.99 219	98.25 150	99.76 246	98.60 275	95.41 316	99.89 180

1112_ss		98.91 177	98.71 180	99.51 171	99.69 174	98.75 224	99.99 238	99.15 357	96.82 246	98.84 299	100.00 1	97.45 188	99.89 200	98.66 267	97.75 281	99.89 180

viewcassd2359sk11		98.90 179	98.73 174	99.40 196	99.57 231	98.47 248	99.99 238	99.22 318	96.79 249	99.82 209	100.00 1	95.24 242	99.91 194	99.54 206	98.38 223	99.82 221

fmvsm_s_conf0.5_n_2		98.90 179	98.57 198	99.90 81	99.79 154	99.78 96	100.00 1	99.25 303	98.97 32	100.00 1	100.00 1	89.22 358	99.99 102	100.00 1	99.88 144	99.92 157

MSDG		98.90 179	98.63 191	99.70 142	99.92 110	99.25 179	100.00 1	99.37 220	95.71 328	99.40 254	100.00 1	96.58 218	99.95 172	96.80 348	99.94 126	99.91 161

dcpmvs_2		98.87 182	99.53 62	96.90 371	99.87 120	90.88 427	99.94 299	99.07 392	98.20 112	100.00 1	100.00 1	98.69 134	99.86 212	100.00 1	100.00 1	99.95 139

viewmanbaseed2359cas		98.86 183	98.68 186	99.40 196	99.51 260	98.51 247	99.98 269	99.22 318	97.05 227	99.72 226	100.00 1	94.77 257	99.89 200	99.58 200	98.31 234	99.81 231

DP-MVS		98.86 183	98.54 201	99.81 111	99.97 92	99.45 154	99.52 382	99.40 200	94.35 370	98.36 330	100.00 1	96.13 226	99.97 142	99.12 243	100.00 1	100.00 1

CostFormer		98.84 185	98.77 169	99.04 242	99.41 292	97.58 313	99.67 365	99.35 237	94.66 359	99.96 144	99.36 371	99.28 79	99.74 251	99.41 219	97.81 276	99.81 231

Test_1112_low_res		98.83 186	98.60 195	99.51 171	99.69 174	98.75 224	99.99 238	99.14 364	96.81 247	98.84 299	99.06 387	97.45 188	99.89 200	98.66 267	97.75 281	99.89 180

BH-w/o		98.82 187	98.81 165	98.88 253	99.62 213	96.71 343	100.00 1	99.28 279	97.09 222	98.81 302	100.00 1	94.91 253	99.96 159	99.54 206	100.00 1	99.96 133

mvs_anonymous		98.80 188	98.60 195	99.38 202	99.57 231	99.24 181	100.00 1	99.21 330	95.87 321	98.92 291	99.82 298	96.39 224	99.03 327	99.13 242	98.50 212	99.88 193

fmvsm_s_conf0.1_n		98.77 189	98.42 215	99.82 106	99.47 277	99.52 141	100.00 1	99.27 292	97.53 181	100.00 1	100.00 1	89.73 350	99.96 159	99.84 127	99.93 130	99.97 127

SSM_0404		98.76 190	98.56 199	99.35 205	99.53 243	98.65 233	99.80 337	99.15 357	96.53 283	99.47 245	100.00 1	94.38 269	99.76 246	99.64 183	98.59 208	99.64 290

TAMVS		98.76 190	98.73 174	98.86 254	99.44 288	97.69 308	99.57 376	99.34 244	96.57 280	99.12 273	99.81 301	98.83 126	99.16 322	97.97 304	97.91 267	99.73 281

OpenMVS	95.20 17	98.76 190	98.41 217	99.78 126	98.89 352	99.81 92	99.99 238	99.76 49	98.02 126	98.02 353	100.00 1	91.44 315	100.00 1	99.63 188	99.97 116	99.55 292

RRT-MVS		98.75 193	98.52 204	99.44 185	99.65 198	98.57 238	99.90 313	99.08 387	96.51 287	99.96 144	99.95 263	92.59 303	99.96 159	99.60 195	99.45 184	99.81 231

viewdifsd2359ckpt13		98.72 194	98.52 204	99.34 207	99.55 237	98.46 249	99.99 238	99.22 318	96.50 289	99.05 282	100.00 1	94.54 264	99.73 253	99.46 216	98.35 226	99.81 231

SSM_0407		98.72 194	98.52 204	99.33 214	99.53 243	98.52 244	99.88 320	99.15 357	96.53 283	98.95 287	100.00 1	94.38 269	99.72 255	99.64 183	98.62 205	99.75 265

dp		98.72 194	98.61 193	99.03 243	99.53 243	97.39 319	99.45 388	99.39 213	95.62 331	99.94 177	99.52 357	98.83 126	99.82 228	96.77 351	98.42 218	99.89 180

fmvsm_s_conf0.1_n_a		98.71 197	98.36 227	99.78 126	99.09 326	99.42 159	100.00 1	99.26 299	97.42 196	100.00 1	100.00 1	89.78 348	99.96 159	99.82 133	99.85 153	99.97 127

PVSNet_BlendedMVS		98.71 197	98.62 192	98.98 247	99.98 88	99.60 124	100.00 1	100.00 1	97.23 213	100.00 1	99.03 393	96.57 219	99.99 102	100.00 1	94.75 341	97.35 414

ADS-MVSNet		98.70 199	98.51 208	99.28 224	99.51 260	98.39 255	99.24 409	99.44 119	95.52 336	99.96 144	99.70 317	97.57 180	99.58 269	97.11 336	98.54 210	99.88 193

baseline		98.69 200	98.45 212	99.41 192	99.52 255	98.67 231	100.00 1	99.17 351	97.03 228	99.13 272	100.00 1	93.17 290	99.74 251	99.70 165	98.34 228	99.81 231

PCF-MVS	98.23 3	98.69 200	98.37 225	99.62 156	99.78 159	99.02 203	99.23 416	99.06 400	96.43 292	98.08 347	100.00 1	94.72 260	99.95 172	98.16 294	99.91 138	99.90 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		98.65 202	98.38 223	99.46 181	99.52 255	98.74 227	100.00 1	99.15 357	96.91 239	99.05 282	100.00 1	92.75 298	99.83 225	99.70 165	98.38 223	99.81 231
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		98.64 203	98.39 221	99.40 196	99.50 265	98.60 236	100.00 1	99.22 318	96.85 244	99.10 275	100.00 1	92.75 298	99.78 241	99.71 161	98.35 226	99.81 231
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		98.64 203	98.58 197	98.81 258	99.42 290	97.12 333	99.69 362	99.37 220	93.63 387	99.94 177	99.67 325	98.96 112	99.47 297	98.62 274	97.95 265	99.83 215

BH-untuned		98.64 203	98.65 188	98.60 268	99.59 222	96.17 350	100.00 1	99.28 279	96.67 272	98.41 327	100.00 1	94.52 265	99.83 225	99.41 219	100.00 1	99.81 231

mamba_0408		98.63 206	98.40 218	99.34 207	99.53 243	98.52 244	99.24 409	99.16 352	96.43 292	98.95 287	99.98 227	94.47 266	99.76 246	99.21 238	98.62 205	99.75 265

test_cas_vis1_n_1920		98.63 206	98.25 234	99.77 129	99.69 174	99.32 169	100.00 1	99.31 259	98.84 61	99.96 144	100.00 1	87.42 381	99.99 102	99.14 240	99.86 150	100.00 1

KinetiMVS		98.61 208	98.26 233	99.65 152	99.46 282	99.24 181	99.96 283	99.44 119	97.54 178	99.99 123	99.99 219	90.83 329	99.95 172	97.18 334	99.92 133	99.75 265

reproduce_monomvs		98.61 208	98.54 201	98.82 255	99.97 92	99.28 174	100.00 1	99.33 247	98.51 90	97.87 361	99.24 377	99.98 3	99.45 303	99.02 249	92.93 358	97.74 351

test_fmvsmconf0.01_n		98.60 210	98.24 237	99.67 147	96.90 430	99.21 185	99.99 238	99.04 405	98.80 70	99.57 237	99.96 256	90.12 342	99.91 194	99.89 116	99.89 141	99.90 172

SSM_04072		98.59 211	98.40 218	99.15 234	99.53 243	98.52 244	99.24 409	99.16 352	96.43 292	98.95 287	99.98 227	94.47 266	99.19 321	99.21 238	98.62 205	99.75 265

tpmvs		98.59 211	98.38 223	99.23 230	99.69 174	97.90 298	99.31 404	99.47 79	94.52 364	99.68 230	99.28 375	97.64 177	99.89 200	97.71 315	98.17 249	99.89 180

Effi-MVS+		98.58 213	98.24 237	99.61 158	99.60 218	99.26 177	97.85 455	99.10 381	96.22 312	99.97 137	99.89 281	93.75 279	99.77 242	99.43 217	98.34 228	99.81 231

MVSTER		98.58 213	98.52 204	98.77 260	99.65 198	99.68 116	100.00 1	99.29 273	95.63 330	98.65 310	99.80 304	99.78 9	98.88 347	98.59 276	95.31 320	97.73 358

viewmacassd2359aftdt		98.57 215	98.31 230	99.33 214	99.49 269	98.31 267	99.89 317	99.21 330	96.87 243	99.10 275	100.00 1	92.48 306	99.88 208	99.50 211	98.28 238	99.81 231

viewmambaseed2359dif		98.57 215	98.34 229	99.28 224	99.46 282	98.23 272	100.00 1	99.16 352	96.26 308	99.11 274	100.00 1	93.12 293	99.79 236	99.61 193	98.33 231	99.80 251

CVMVSNet		98.56 217	98.47 211	98.82 255	99.11 323	97.67 309	99.74 349	99.47 79	97.57 176	99.06 281	100.00 1	95.72 234	98.97 335	98.21 293	97.33 291	99.83 215

kuosan		98.55 218	98.53 203	98.62 266	99.66 196	96.16 351	100.00 1	99.44 119	93.93 380	99.81 214	99.98 227	97.58 178	99.81 231	98.08 296	98.28 238	99.89 180

MonoMVSNet		98.55 218	98.64 190	98.26 294	98.21 389	95.76 358	99.94 299	99.16 352	96.23 309	99.47 245	99.24 377	96.75 214	99.22 319	99.61 193	99.17 188	99.81 231

AllTest		98.55 218	98.40 218	98.99 245	99.93 107	97.35 322	100.00 1	99.40 200	97.08 224	99.09 277	99.98 227	93.37 286	99.95 172	96.94 340	99.84 155	99.68 284

DeepPCF-MVS	98.03 4	98.54 221	99.72 19	94.98 407	99.99 49	84.94 447	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		98.53 222	98.23 240	99.43 188	99.92 110	99.01 205	99.96 283	99.47 79	98.80 70	99.96 144	99.96 256	98.56 140	99.30 315	87.78 438	99.68 169	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
myMVS_eth3d		98.52 223	98.51 208	98.53 272	99.50 265	97.98 291	100.00 1	99.57 68	96.23 309	98.07 348	100.00 1	99.09 94	97.81 423	96.17 359	97.96 263	99.82 221

Vis-MVSNet		98.52 223	98.25 234	99.34 207	99.68 179	98.55 239	99.68 364	99.41 196	97.34 202	99.94 177	100.00 1	90.38 338	99.70 258	99.03 248	98.84 198	99.76 264
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+-dtu		98.51 225	98.86 160	97.47 345	99.77 161	94.21 397	100.00 1	98.94 417	97.61 170	99.91 187	98.75 411	95.89 229	99.51 290	99.36 221	99.48 182	98.68 311

SDMVSNet		98.49 226	98.08 249	99.73 136	99.82 130	99.53 137	99.99 238	99.45 105	97.62 166	99.38 256	99.86 286	90.06 345	99.88 208	99.92 111	96.61 306	99.79 257

BH-RMVSNet		98.46 227	98.08 249	99.59 162	99.61 215	99.19 187	100.00 1	99.28 279	97.06 226	98.95 287	100.00 1	88.99 361	99.82 228	98.83 260	100.00 1	99.77 262

testing3		98.44 228	98.37 225	98.65 264	99.51 260	98.32 265	100.00 1	99.62 66	96.43 292	97.93 357	99.99 219	99.11 92	97.81 423	94.88 381	97.80 277	99.82 221

ECVR-MVS		98.43 229	98.14 243	99.32 218	99.89 116	98.21 275	99.46 386	100.00 1	98.38 98	99.47 245	100.00 1	87.91 374	99.80 235	99.35 224	98.78 200	99.94 144

cascas		98.43 229	98.07 251	99.50 174	99.65 198	99.02 203	100.00 1	99.22 318	94.21 373	99.72 226	99.98 227	92.03 311	99.93 189	99.68 173	98.12 254	99.54 293

test1111		98.42 231	98.12 244	99.29 221	99.88 118	98.15 277	99.46 386	100.00 1	98.36 102	99.42 248	100.00 1	87.91 374	99.79 236	99.31 229	98.78 200	99.94 144

ab-mvs		98.42 231	98.02 255	99.61 158	99.71 169	99.00 208	99.10 433	99.64 64	96.70 268	99.04 284	99.81 301	90.64 332	99.98 133	99.64 183	97.93 266	99.84 211

UGNet		98.41 233	98.11 245	99.31 220	99.54 240	98.55 239	99.18 419	100.00 1	98.64 84	99.79 215	99.04 390	87.61 379	100.00 1	99.30 230	99.89 141	99.40 300
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+		98.40 234	98.02 255	99.55 170	99.63 206	99.06 198	100.00 1	99.15 357	95.07 348	99.42 248	99.95 263	93.26 289	99.73 253	97.44 325	98.24 242	99.87 204

Fast-Effi-MVS+-dtu		98.38 235	98.56 199	97.82 335	99.58 227	94.44 394	100.00 1	99.16 352	96.75 254	99.51 240	99.63 337	95.03 251	99.60 263	97.71 315	99.67 171	99.42 299

IMVS_0403		98.37 236	98.39 221	98.29 289	99.38 301	95.36 363	99.97 276	99.18 342	96.72 262	99.68 230	100.00 1	94.61 262	99.77 242	97.84 308	98.15 250	99.74 272

test_fmvs1		98.37 236	98.04 253	99.34 207	99.84 124	98.07 284	100.00 1	99.00 412	98.85 59	100.00 1	100.00 1	85.11 402	99.96 159	99.69 172	99.88 144	100.00 1

IMVS_0407		98.36 238	98.42 215	98.19 301	99.38 301	95.36 363	99.73 354	99.18 342	96.72 262	99.58 235	100.00 1	95.17 247	99.47 297	97.84 308	98.15 250	99.74 272

miper_enhance_ethall		98.33 239	98.27 232	98.51 273	99.66 196	99.04 200	100.00 1	99.22 318	97.53 181	98.51 322	99.38 369	99.49 43	98.75 357	98.02 300	92.61 361	97.76 318

icg_test_0407_2		98.30 240	98.45 212	97.85 334	99.38 301	95.36 363	99.99 238	99.18 342	96.72 262	99.58 235	100.00 1	95.17 247	98.45 385	97.84 308	98.15 250	99.74 272

SCA		98.30 240	97.98 257	99.23 230	99.41 292	98.25 271	99.99 238	99.45 105	96.91 239	99.76 220	99.58 347	89.65 352	99.54 282	98.31 287	98.79 199	99.91 161

XVG-OURS		98.30 240	98.36 227	98.13 309	99.58 227	95.91 354	100.00 1	99.36 226	98.69 79	99.23 265	100.00 1	91.20 319	99.92 192	99.34 226	97.82 275	98.56 314

dongtai		98.29 243	98.25 234	98.42 280	99.58 227	95.86 356	100.00 1	99.44 119	93.46 393	99.69 229	99.97 239	97.53 183	99.51 290	96.28 358	98.27 241	99.89 180

COLMAP_ROB	97.10 7	98.29 243	98.17 242	98.65 264	99.94 105	97.39 319	99.30 405	99.40 200	95.64 329	97.75 367	100.00 1	92.69 302	99.95 172	98.89 255	99.92 133	98.62 313
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		98.28 245	98.51 208	97.62 341	99.51 260	95.03 372	99.24 409	99.41 196	95.52 336	99.96 144	99.70 317	97.57 180	97.94 420	97.11 336	98.54 210	99.88 193

XVG-OURS-SEG-HR		98.27 246	98.31 230	98.14 306	99.59 222	95.92 353	100.00 1	99.36 226	98.48 91	99.21 266	100.00 1	89.27 357	99.94 185	99.76 146	99.17 188	98.56 314

tpm		98.24 247	98.22 241	98.32 288	99.13 322	95.79 357	99.53 381	99.12 376	95.20 347	99.96 144	99.36 371	97.58 178	99.28 317	97.41 327	96.67 304	99.88 193

VortexMVS		98.23 248	98.11 245	98.59 269	99.56 236	99.37 165	99.95 291	99.03 408	96.47 290	98.69 307	99.55 353	95.91 228	98.66 362	99.01 250	94.80 340	97.73 358

cl22		98.23 248	98.11 245	98.58 271	99.82 130	99.01 205	100.00 1	99.28 279	96.92 238	98.33 334	99.21 380	98.09 156	98.97 335	98.72 265	92.61 361	97.76 318

WBMVS		98.19 250	98.10 248	98.47 275	99.63 206	99.03 201	100.00 1	99.32 250	95.46 341	98.39 329	99.40 368	99.69 17	98.61 367	98.64 270	92.39 366	97.76 318

TR-MVS		98.14 251	97.74 267	99.33 214	99.59 222	98.28 269	99.27 406	99.21 330	96.42 297	99.15 271	99.94 269	88.87 364	99.79 236	98.88 256	98.29 237	99.93 155

Elysia		98.12 252	97.72 270	99.34 207	99.30 311	98.96 213	99.95 291	99.28 279	96.64 275	99.75 221	99.99 219	88.71 366	99.81 231	95.99 361	99.84 155	99.26 301

StellarMVS		98.12 252	97.72 270	99.34 207	99.30 311	98.96 213	99.95 291	99.28 279	96.64 275	99.75 221	99.99 219	88.71 366	99.81 231	95.99 361	99.84 155	99.26 301

test0.0.03 1		98.12 252	98.03 254	98.39 282	99.11 323	98.07 284	100.00 1	99.93 30	96.70 268	96.91 390	99.95 263	99.31 71	98.19 399	91.93 411	98.44 216	98.91 309

GeoE		98.06 255	97.65 274	99.29 221	99.47 277	98.41 252	100.00 1	99.19 335	94.85 353	98.88 294	100.00 1	91.21 318	99.59 265	97.02 338	98.19 247	99.88 193

tpm cat1		98.05 256	97.76 266	98.92 250	99.50 265	97.10 335	99.77 344	99.30 265	90.20 428	99.72 226	98.71 412	97.71 173	99.86 212	96.75 352	98.20 246	99.81 231

PS-MVSNAJss		98.03 257	98.06 252	97.94 328	97.63 411	97.33 325	99.89 317	99.23 313	96.27 307	98.03 351	99.59 345	98.75 131	98.78 352	98.52 278	94.61 344	97.70 374

CR-MVSNet		98.02 258	97.71 272	98.93 249	99.31 308	98.86 217	99.13 430	99.00 412	96.53 283	99.96 144	98.98 397	96.94 207	98.10 409	91.18 416	98.40 219	99.84 211

viewdifsd2359ckpt11		97.98 259	97.89 259	98.26 294	99.47 277	94.98 374	99.99 238	99.22 318	96.74 257	99.24 263	100.00 1	90.14 340	99.90 198	99.49 213	96.73 302	99.90 172

viewmsd2359difaftdt		97.98 259	97.89 259	98.27 291	99.47 277	94.99 373	99.99 238	99.22 318	96.74 257	99.24 263	100.00 1	90.14 340	99.90 198	99.49 213	96.73 302	99.90 172

EI-MVSNet		97.98 259	97.93 258	98.16 305	99.11 323	97.84 303	99.74 349	99.29 273	94.39 369	98.65 310	100.00 1	97.21 195	98.88 347	97.62 321	95.31 320	97.75 329

FIs		97.95 262	97.73 269	98.62 266	98.53 372	99.24 181	100.00 1	99.43 128	96.74 257	97.87 361	99.82 298	95.27 241	98.89 344	98.78 261	93.07 355	97.74 351

SD_0403		97.92 263	98.43 214	96.39 385	99.68 179	89.74 435	99.92 307	99.34 244	96.75 254	99.39 255	99.93 274	93.54 285	99.51 290	99.11 244	98.21 244	99.92 157

IMVS_0404		97.87 264	97.89 259	97.81 336	99.38 301	95.36 363	99.84 326	99.18 342	96.72 262	98.41 327	100.00 1	91.43 316	98.32 392	97.84 308	98.15 250	99.74 272

Anonymous202405211		97.87 264	97.53 276	98.90 251	99.81 136	96.70 344	99.35 399	99.46 97	92.98 404	98.83 301	99.99 219	90.63 333	100.00 1	99.70 165	97.03 295	100.00 1

FC-MVSNet-test		97.84 266	97.63 275	98.45 278	98.30 382	99.05 199	100.00 1	99.43 128	96.63 279	97.61 373	99.82 298	95.19 246	98.57 375	98.64 270	93.05 356	97.73 358

Patchmatch-test		97.83 267	97.42 279	99.06 238	99.08 327	97.66 310	98.66 447	99.21 330	93.65 386	98.25 342	99.58 347	99.47 48	99.57 270	90.25 426	98.59 208	99.95 139

sd_testset		97.81 268	97.48 277	98.79 259	99.82 130	96.80 341	99.32 401	99.45 105	97.62 166	99.38 256	99.86 286	85.56 400	99.77 242	99.72 157	96.61 306	99.79 257

miper_ehance_all_eth		97.81 268	97.66 273	98.23 297	99.49 269	98.37 258	99.99 238	99.11 378	94.78 354	98.25 342	99.21 380	98.18 152	98.57 375	97.35 331	92.61 361	97.76 318

test_vis1_n_1920		97.77 270	97.24 291	99.34 207	99.79 154	98.04 288	100.00 1	99.25 303	98.88 54	100.00 1	100.00 1	77.52 434	100.00 1	99.88 118	99.85 153	100.00 1

HQP-MVS		97.73 271	97.85 263	97.39 347	99.07 328	94.82 378	100.00 1	99.40 200	99.04 16	99.17 267	99.97 239	88.61 369	99.57 270	99.79 136	95.58 310	97.77 316

GA-MVS		97.72 272	97.27 289	99.06 238	99.24 317	97.93 297	100.00 1	99.24 309	95.80 327	98.99 286	99.64 333	89.77 349	99.36 310	95.12 378	97.62 289	99.89 180

HQP_MVS		97.71 273	97.82 265	97.37 348	99.00 340	94.80 381	100.00 1	99.40 200	99.00 27	99.08 279	99.97 239	88.58 371	99.55 279	99.79 136	95.57 314	97.76 318

nrg030		97.64 274	97.27 289	98.75 261	98.34 377	99.53 137	100.00 1	99.22 318	96.21 313	98.27 340	99.95 263	94.40 268	98.98 333	99.23 235	89.78 400	97.75 329

TAPA-MVS	96.40 10	97.64 274	97.37 283	98.45 278	99.94 105	95.70 359	100.00 1	99.40 200	97.65 161	99.53 238	100.00 1	99.31 71	99.66 260	80.48 453	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		97.64 274	97.74 267	97.36 349	99.01 336	94.76 386	100.00 1	99.34 244	99.30 4	99.00 285	99.97 239	87.49 380	99.57 270	99.96 100	95.58 310	97.75 329
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
D2MVS		97.63 277	97.83 264	97.05 362	98.83 361	94.60 390	100.00 1	99.82 40	96.89 242	98.28 338	99.03 393	94.05 275	99.47 297	98.58 277	94.97 338	97.09 420

c3_l		97.58 278	97.42 279	98.06 316	99.48 272	98.16 276	99.96 283	99.10 381	94.54 363	98.13 346	99.20 382	97.87 164	98.25 397	97.28 332	91.20 388	97.75 329

IterMVS-LS		97.56 279	97.44 278	97.92 331	99.38 301	97.90 298	99.89 317	99.10 381	94.41 368	98.32 335	99.54 356	97.21 195	98.11 406	97.50 323	91.62 380	97.75 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		97.55 280	97.38 282	98.07 312	97.50 419	97.99 290	100.00 1	99.13 370	95.46 341	98.47 325	99.85 291	92.01 312	98.59 372	98.63 272	95.36 318	97.62 397

dmvs_re		97.54 281	97.88 262	96.54 382	99.55 237	90.35 430	99.86 323	99.46 97	97.00 230	99.41 253	100.00 1	90.78 330	99.30 315	99.60 195	95.24 325	99.96 133

cl____		97.54 281	97.32 285	98.18 302	99.47 277	98.14 279	100.00 1	99.10 381	94.16 376	97.60 374	99.63 337	97.52 184	98.65 364	96.47 353	91.97 374	97.76 318

IB-MVS	96.24 12	97.54 281	96.95 296	99.33 214	99.67 188	98.10 282	100.00 1	99.47 79	97.42 196	99.26 262	99.69 320	98.83 126	99.89 200	99.43 217	78.77 451	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		97.52 284	97.35 284	98.05 320	99.46 282	98.11 280	100.00 1	99.10 381	94.21 373	97.62 372	99.63 337	97.65 176	98.29 394	96.47 353	91.98 373	97.76 318

eth_miper_zixun_eth		97.47 285	97.28 287	98.06 316	99.41 292	97.94 296	99.62 371	99.08 387	94.46 367	98.19 345	99.56 352	96.91 209	98.50 380	96.78 349	91.49 383	97.74 351

test_fmvs1_n		97.43 286	96.86 299	99.15 234	99.68 179	97.48 316	99.99 238	98.98 415	98.82 65	100.00 1	100.00 1	74.85 442	99.96 159	99.67 176	99.70 168	100.00 1

LFMVS		97.42 287	96.62 308	99.81 111	99.80 149	99.50 144	99.16 425	99.56 70	94.48 366	100.00 1	100.00 1	79.35 428	100.00 1	99.89 116	97.37 290	99.94 144

miper_lstm_enhance		97.40 288	97.28 287	97.75 338	99.48 272	97.52 314	100.00 1	99.07 392	94.08 377	98.01 354	99.61 343	97.38 192	97.98 418	96.44 356	91.47 385	97.76 318

RPSCF		97.37 289	98.24 237	94.76 410	99.80 149	84.57 448	99.99 238	99.05 402	94.95 351	99.82 209	100.00 1	94.03 276	100.00 1	98.15 295	98.38 223	99.70 282

ACMM	97.17 6	97.37 289	97.40 281	97.29 354	99.01 336	94.64 389	100.00 1	99.25 303	98.07 124	98.44 326	99.98 227	87.38 382	99.55 279	99.25 232	95.19 328	97.69 379
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		97.31 291	97.32 285	97.28 355	98.85 359	94.60 390	100.00 1	99.37 220	97.35 200	98.85 297	99.98 227	86.66 388	99.56 274	99.55 203	95.26 322	97.70 374

FMVSNet3		97.30 292	96.95 296	98.37 284	99.65 198	99.25 179	99.71 358	99.28 279	94.23 371	98.53 319	98.91 404	93.30 288	98.11 406	95.31 374	93.60 349	97.73 358

UniMVSNet (Re)		97.29 293	96.85 300	98.59 269	98.49 373	99.13 193	100.00 1	99.42 147	96.52 286	98.24 344	98.90 405	94.93 252	98.89 344	97.54 322	87.61 419	97.75 329

OPM-MVS		97.21 294	97.18 294	97.32 352	98.08 395	94.66 387	100.00 1	99.28 279	98.65 83	98.92 291	99.98 227	86.03 396	99.56 274	98.28 291	95.41 316	97.72 365
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	97.00 8	97.19 295	97.16 295	97.27 357	98.97 345	94.58 393	100.00 1	99.32 250	97.97 132	97.45 378	99.98 227	85.79 398	99.56 274	99.70 165	95.24 325	97.67 385
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs4		97.17 296	96.80 301	98.27 291	97.68 410	98.64 234	100.00 1	99.18 342	94.22 372	98.55 317	99.71 314	93.67 280	98.47 383	95.66 368	92.57 364	97.71 373

anonymousdsp		97.16 297	96.88 298	98.00 324	97.08 429	98.06 286	99.81 332	99.15 357	94.58 361	97.84 363	99.62 341	90.49 335	98.60 370	97.98 301	95.32 319	97.33 415

UniMVSNet_NR-MVSNet		97.16 297	96.80 301	98.22 298	98.38 376	98.41 252	100.00 1	99.45 105	96.14 315	97.76 364	99.64 333	95.05 250	98.50 380	97.98 301	86.84 425	97.75 329

XXY-MVS		97.14 299	96.63 307	98.67 263	98.65 366	98.92 215	99.54 380	99.29 273	95.57 333	97.63 370	99.83 294	87.79 378	99.35 312	98.39 283	92.95 357	97.75 329

WR-MVS		97.09 300	96.64 306	98.46 276	98.43 374	99.09 195	99.97 276	99.33 247	95.62 331	97.76 364	99.67 325	91.17 320	98.56 377	98.49 279	89.28 406	97.74 351

JIA-IIPM		97.09 300	96.34 322	99.36 204	98.88 353	98.59 237	99.81 332	99.43 128	84.81 446	99.96 144	90.34 459	98.55 141	99.52 288	97.00 339	98.28 238	99.98 120

jajsoiax		97.07 302	96.79 303	97.89 332	97.28 427	97.12 333	99.95 291	99.19 335	96.55 281	97.31 381	99.69 320	87.35 384	98.91 341	98.70 266	95.12 333	97.66 386

MIMVSNet		97.06 303	96.73 304	98.05 320	99.38 301	96.64 346	98.47 451	99.35 237	93.41 394	99.48 242	98.53 419	89.66 351	97.70 429	94.16 391	98.11 255	99.80 251

X-MVStestdata		97.04 304	96.06 333	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 81	100.00 1	66.97 470	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

h-mvs33		97.03 305	96.53 311	98.51 273	99.79 154	95.90 355	99.45 388	99.45 105	98.21 110	100.00 1	99.78 308	97.49 185	99.99 102	99.72 157	74.92 453	99.65 289

VPA-MVSNet		97.03 305	96.43 317	98.82 255	98.64 367	99.32 169	99.38 396	99.47 79	96.73 261	98.91 293	98.94 402	87.00 386	99.40 308	99.23 235	89.59 401	97.76 318

WB-MVSnew		97.02 307	97.24 291	96.37 387	99.44 288	97.36 321	100.00 1	99.43 128	96.12 316	99.35 258	99.89 281	93.60 283	98.42 387	88.91 437	98.39 221	93.33 453

mvs_tets		97.00 308	96.69 305	97.94 328	97.41 426	97.27 327	99.60 373	99.18 342	96.51 287	97.35 380	99.69 320	86.53 390	98.91 341	98.84 258	95.09 334	97.65 391

gg-mvs-nofinetune		96.95 309	96.10 331	99.50 174	99.41 292	99.36 167	99.07 438	99.52 72	83.69 448	99.96 144	83.60 467	100.00 1	99.20 320	99.68 173	99.99 103	99.96 133

Anonymous20240529		96.93 310	96.22 327	99.05 240	99.79 154	97.30 326	99.16 425	99.47 79	88.51 434	98.69 307	100.00 1	83.50 413	100.00 1	99.83 128	97.02 296	99.83 215

DU-MVS		96.93 310	96.49 314	98.22 298	98.31 380	98.41 252	100.00 1	99.37 220	96.41 298	97.76 364	99.65 329	92.14 309	98.50 380	97.98 301	86.84 425	97.75 329

Patchmtry		96.81 312	96.37 320	98.14 306	99.31 308	98.55 239	98.91 441	99.00 412	90.45 424	97.92 358	98.98 397	96.94 207	98.12 404	94.27 388	91.53 382	97.75 329

hse-mvs2		96.79 313	96.38 319	98.04 322	99.68 179	95.54 362	99.81 332	99.42 147	98.21 110	100.00 1	99.80 304	97.49 185	99.46 302	99.72 157	73.27 456	99.12 306

ACMH	96.25 11	96.77 314	96.62 308	97.21 358	98.96 346	94.43 395	99.64 367	99.33 247	97.43 195	96.55 399	99.97 239	83.52 412	99.54 282	99.07 247	95.13 332	97.66 386
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		96.76 315	96.46 316	97.63 339	99.41 292	96.89 338	99.99 238	99.13 370	94.74 357	97.59 375	99.66 327	89.63 354	98.28 395	95.71 366	92.31 368	97.72 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		96.73 316	96.25 325	98.18 302	98.21 389	98.67 231	99.77 344	99.32 250	95.06 349	97.20 384	99.65 329	90.10 343	98.19 399	98.06 299	88.90 410	97.66 386

WR-MVS_H		96.73 316	96.32 324	97.95 327	98.26 386	97.88 300	99.72 357	99.43 128	95.06 349	96.99 387	98.68 414	93.02 294	98.53 378	97.43 326	88.33 415	97.43 410

IterMVS-SCA-FT		96.72 318	96.42 318	97.62 341	99.40 297	96.83 340	99.99 238	99.14 364	94.65 360	97.55 376	99.72 312	89.65 352	98.31 393	95.62 370	92.05 371	97.73 358

v2v482		96.70 319	96.18 328	98.27 291	98.04 396	98.39 255	100.00 1	99.13 370	94.19 375	98.58 315	99.08 386	90.48 336	98.67 361	95.69 367	90.44 396	97.75 329

test_vis1_n		96.69 320	95.81 344	99.32 218	99.14 321	97.98 291	99.97 276	98.98 415	98.45 93	100.00 1	100.00 1	66.44 456	99.99 102	99.78 142	99.57 181	100.00 1

V42		96.65 321	96.16 330	98.11 311	98.17 393	98.23 272	99.99 238	99.09 386	93.97 378	98.74 306	99.05 389	91.09 321	98.82 350	95.46 372	89.90 398	97.27 416

EU-MVSNet		96.63 322	96.53 311	96.94 369	97.59 415	96.87 339	99.76 346	99.47 79	96.35 303	96.85 392	99.78 308	92.57 304	96.27 443	95.33 373	91.08 389	97.68 381

NR-MVSNet		96.63 322	96.04 334	98.38 283	98.31 380	98.98 210	99.22 418	99.35 237	95.87 321	94.43 425	99.65 329	92.73 300	98.40 388	96.78 349	88.05 416	97.75 329

XVG-ACMP-BASELINE		96.60 324	96.52 313	96.84 375	98.41 375	93.29 407	99.99 238	99.32 250	97.76 152	98.51 322	99.29 374	81.95 419	99.54 282	98.40 282	95.03 335	97.68 381

VDD-MVS		96.58 325	95.99 336	98.34 286	99.52 255	95.33 367	99.18 419	99.38 216	96.64 275	99.77 218	100.00 1	72.51 447	100.00 1	100.00 1	96.94 298	99.70 282

tt0805		96.52 326	96.23 326	97.40 346	99.30 311	93.55 402	99.32 401	99.45 105	96.75 254	97.88 360	99.99 219	79.99 426	99.59 265	97.39 329	95.98 309	99.06 308

LCM-MVSNet-Re		96.52 326	97.21 293	94.44 411	99.27 314	85.80 445	99.85 325	96.61 462	95.98 318	92.75 434	98.48 421	93.97 278	97.55 430	99.58 200	98.43 217	99.98 120

our_test_3		96.51 328	96.35 321	96.98 367	97.61 413	95.05 371	99.98 269	99.01 411	94.68 358	96.77 396	99.06 387	95.87 230	98.14 402	91.81 412	92.37 367	97.75 329

MVP-Stereo		96.51 328	96.48 315	96.60 381	95.65 441	94.25 396	98.84 443	98.16 437	95.85 325	95.23 415	99.04 390	92.54 305	99.13 323	92.98 404	99.98 113	96.43 433
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		96.51 328	95.97 338	98.13 309	97.98 400	98.04 288	99.99 238	99.08 387	93.51 391	98.62 313	98.98 397	90.98 326	98.62 366	93.79 395	90.79 392	97.74 351

ACMH+	96.20 13	96.49 331	96.33 323	97.00 365	99.06 332	93.80 400	99.81 332	99.31 259	97.32 205	95.89 412	99.97 239	82.62 417	99.54 282	98.34 286	94.63 343	97.65 391

TranMVSNet+NR-MVSNet		96.45 332	96.01 335	97.79 337	98.00 399	97.62 312	100.00 1	99.35 237	95.98 318	97.31 381	99.64 333	90.09 344	98.00 416	96.89 344	86.80 428	97.75 329

ET-MVSNet_ETH3D		96.41 333	95.48 364	99.20 232	99.81 136	99.75 101	100.00 1	99.02 409	97.30 209	78.33 459	100.00 1	97.73 172	97.94 420	99.70 165	87.41 421	99.92 157

VPNet		96.41 333	95.76 349	98.33 287	98.61 368	98.30 268	99.48 385	99.45 105	96.98 232	98.87 296	99.88 283	81.57 420	98.93 339	99.22 237	87.82 418	97.76 318

PVSNet_0	93.57 19	96.41 333	95.74 350	98.41 281	99.84 124	95.22 369	100.00 1	100.00 1	98.08 123	97.55 376	99.78 308	84.40 405	100.00 1	100.00 1	81.99 442	100.00 1

v144192		96.40 336	95.81 344	98.17 304	97.89 403	98.11 280	99.99 238	99.06 400	93.39 395	98.75 305	99.09 385	90.43 337	98.66 362	93.10 403	90.55 395	97.75 329

VDDNet		96.39 337	95.55 359	98.90 251	99.27 314	97.45 317	99.15 427	99.92 34	91.28 417	99.98 131	100.00 1	73.55 443	100.00 1	99.85 124	96.98 297	99.24 303

tfpnnormal		96.36 338	95.69 355	98.37 284	98.55 370	98.71 228	99.69 362	99.45 105	93.16 402	96.69 398	99.71 314	88.44 373	98.99 332	94.17 389	91.38 386	97.41 411

v8		96.35 339	95.73 351	98.21 300	98.11 394	98.23 272	99.94 299	99.07 392	92.66 410	98.29 337	99.00 396	91.46 314	98.77 355	94.17 389	88.83 412	97.62 397

PS-CasMVS		96.34 340	95.78 348	98.03 323	98.18 392	98.27 270	99.71 358	99.32 250	94.75 355	96.82 393	99.65 329	86.98 387	98.15 401	97.74 314	88.85 411	97.66 386

LTVRE_ROB	95.29 16	96.32 341	96.10 331	96.99 366	98.55 370	93.88 399	99.45 388	99.28 279	94.50 365	96.46 400	99.52 357	84.86 403	99.48 295	97.26 333	95.03 335	97.59 401
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		96.29 342	95.70 352	98.07 312	99.80 149	97.49 315	99.15 427	99.40 200	89.11 431	97.75 367	99.45 364	88.93 363	98.98 333	98.26 292	89.47 403	97.73 358

v148		96.29 342	95.84 343	97.63 339	97.74 408	96.53 348	100.00 1	99.07 392	93.52 390	98.01 354	99.42 366	91.22 317	98.60 370	96.37 357	87.22 424	97.75 329

AUN-MVS		96.26 344	95.67 356	98.06 316	99.68 179	95.60 361	99.82 331	99.42 147	96.78 251	99.88 194	99.80 304	94.84 255	99.47 297	97.48 324	73.29 455	99.12 306

ttmdpeth		96.24 345	95.88 341	97.32 352	97.80 405	96.61 347	99.95 291	98.77 428	97.80 147	93.42 430	99.28 375	86.42 391	99.01 329	97.63 318	91.84 376	96.33 435

FMVSNet2		96.22 346	95.60 358	98.06 316	99.53 243	98.33 263	99.45 388	99.27 292	93.71 382	98.03 351	98.84 407	84.23 407	98.10 409	93.97 393	93.40 352	97.73 358

LF4IMVS		96.19 347	96.18 328	96.23 391	98.26 386	92.09 418	100.00 1	97.89 448	97.82 145	97.94 356	99.87 284	82.71 416	99.38 309	97.41 327	93.71 348	97.20 417

v1192		96.18 348	95.49 362	98.26 294	98.01 398	98.15 277	99.99 238	99.08 387	93.36 396	98.54 318	98.97 400	89.47 355	98.89 344	91.15 417	90.82 391	97.75 329

testgi		96.18 348	95.93 339	96.93 370	98.98 344	94.20 398	100.00 1	99.07 392	97.16 216	96.06 409	99.86 286	84.08 410	97.79 426	90.38 425	97.80 277	98.81 310

Syy-MVS		96.17 350	96.57 310	95.00 405	99.50 265	87.37 443	100.00 1	99.57 68	96.23 309	98.07 348	100.00 1	92.41 307	97.81 423	85.34 443	97.96 263	99.82 221

ppachtmachnet_test		96.17 350	95.89 340	97.02 364	97.61 413	95.24 368	99.99 238	99.24 309	93.31 398	96.71 397	99.62 341	94.34 271	98.07 411	89.87 427	92.30 369	97.75 329

v1921920		96.16 352	95.50 360	98.14 306	97.88 404	97.96 294	99.99 238	99.07 392	93.33 397	98.60 314	99.24 377	89.37 356	98.71 359	91.28 415	90.74 393	97.75 329

Baseline_NR-MVSNet		96.16 352	95.70 352	97.56 344	98.28 385	96.79 342	100.00 1	97.86 449	91.93 414	97.63 370	99.47 363	92.14 309	98.35 391	97.13 335	86.83 427	97.54 404

v10		96.14 354	95.50 360	98.07 312	98.19 391	97.96 294	99.83 328	99.07 392	92.10 413	98.07 348	98.94 402	91.07 322	98.61 367	92.41 410	89.82 399	97.63 395

OurMVSNet-221017-0		96.14 354	95.98 337	96.62 380	97.49 421	93.44 404	99.92 307	98.16 437	95.86 323	97.65 369	99.95 263	85.71 399	98.78 352	94.93 380	94.18 347	97.64 394

GBi-Net		96.07 356	95.80 346	96.89 372	99.53 243	94.87 375	99.18 419	99.27 292	93.71 382	98.53 319	98.81 408	84.23 407	98.07 411	95.31 374	93.60 349	97.72 365

test1		96.07 356	95.80 346	96.89 372	99.53 243	94.87 375	99.18 419	99.27 292	93.71 382	98.53 319	98.81 408	84.23 407	98.07 411	95.31 374	93.60 349	97.72 365

v7n		96.06 358	95.42 368	97.99 326	97.58 416	97.35 322	99.86 323	99.11 378	92.81 409	97.91 359	99.49 361	90.99 325	98.92 340	92.51 407	88.49 414	97.70 374

PEN-MVS		96.01 359	95.48 364	97.58 343	97.74 408	97.26 328	99.90 313	99.29 273	94.55 362	96.79 394	99.55 353	87.38 382	97.84 422	96.92 343	87.24 423	97.65 391

v1240		95.96 360	95.25 369	98.07 312	97.91 402	97.87 302	99.96 283	99.07 392	93.24 400	98.64 312	98.96 401	88.98 362	98.61 367	89.58 431	90.92 390	97.75 329

pmmvs5		95.94 361	95.61 357	96.95 368	97.42 424	94.66 387	100.00 1	98.08 441	93.60 388	97.05 386	99.43 365	87.02 385	98.46 384	95.76 364	92.12 370	97.72 365

PatchT		95.90 362	94.95 377	98.75 261	99.03 334	98.39 255	99.08 436	99.32 250	85.52 444	99.96 144	94.99 451	97.94 158	98.05 415	80.20 454	98.47 215	99.81 231

USDC		95.90 362	95.70 352	96.50 383	98.60 369	92.56 416	100.00 1	98.30 435	97.77 150	96.92 388	99.94 269	81.25 423	99.45 303	93.54 398	94.96 339	97.49 407

pm-mvs1		95.76 364	95.01 374	98.00 324	98.23 388	97.45 317	99.24 409	99.04 405	93.13 403	95.93 411	99.72 312	86.28 392	98.84 349	95.62 370	87.92 417	97.72 365

SixPastTwentyTwo		95.71 365	95.49 362	96.38 386	97.42 424	93.01 408	99.84 326	98.23 436	94.75 355	95.98 410	99.97 239	85.35 401	98.43 386	94.71 382	93.17 354	97.69 379

MS-PatchMatch		95.66 366	95.87 342	95.05 403	97.80 405	89.25 437	98.88 442	99.30 265	96.35 303	96.86 391	99.01 395	81.35 422	99.43 305	93.30 400	99.98 113	96.46 432

DTE-MVSNet		95.52 367	94.99 375	97.08 361	97.49 421	96.45 349	100.00 1	99.25 303	93.82 381	96.17 405	99.57 351	87.81 377	97.18 431	94.57 384	86.26 431	97.62 397

TinyColmap		95.50 368	95.12 373	96.64 379	98.69 365	93.00 409	99.40 394	97.75 451	96.40 299	96.14 406	99.87 284	79.47 427	99.50 293	93.62 397	94.72 342	97.40 412

K. test v3		95.46 369	95.14 372	96.40 384	97.53 418	93.40 405	99.99 238	99.23 313	95.49 339	92.70 435	99.73 311	84.26 406	98.12 404	93.94 394	93.38 353	97.68 381

SSC-MVS3.2		95.32 370	94.97 376	96.37 387	98.29 384	92.75 412	100.00 1	99.30 265	95.46 341	98.36 330	99.42 366	78.92 430	98.63 365	93.28 402	91.72 379	97.72 365

FMVSNet5		95.32 370	95.43 367	94.99 406	99.39 300	92.99 410	99.25 408	99.24 309	90.45 424	97.44 379	98.45 422	95.78 233	94.39 452	87.02 439	91.88 375	97.59 401

UniMVSNet_ETH3D		95.28 372	94.41 378	97.89 332	98.91 350	95.14 370	99.13 430	99.35 237	92.11 412	97.17 385	99.66 327	70.28 451	99.36 310	97.88 306	95.18 329	99.16 304

RPMNet		95.26 373	93.82 382	99.56 169	99.31 308	98.86 217	99.13 430	99.42 147	79.82 453	99.96 144	95.13 449	95.69 235	99.98 133	77.54 459	98.40 219	99.84 211

DSMNet-mixed		95.18 374	95.21 371	95.08 402	96.03 436	90.21 432	99.65 366	93.64 468	92.91 405	98.34 333	97.40 438	90.05 346	95.51 449	91.02 418	97.86 270	99.51 296

test_fmvs2		95.17 375	95.23 370	95.01 404	98.95 348	88.99 439	99.99 238	97.77 450	97.79 148	98.58 315	99.70 317	73.36 444	99.34 313	95.88 363	95.03 335	96.70 428

TransMVSNet (Re)		94.78 376	93.72 383	97.93 330	98.34 377	97.88 300	99.23 416	97.98 446	91.60 415	94.55 422	99.71 314	87.89 376	98.36 390	89.30 433	84.92 433	97.56 403

mmtdpeth		94.58 377	94.18 379	95.81 397	98.82 363	91.09 426	99.99 238	98.61 432	96.38 300	100.00 1	97.23 439	76.52 438	99.85 219	99.82 133	80.22 447	96.48 431

FMVSNet1		94.45 378	93.63 385	96.89 372	98.87 356	94.87 375	99.18 419	99.27 292	90.95 421	97.31 381	98.81 408	72.89 446	98.07 411	92.61 405	92.81 359	97.72 365

test_0402		94.35 379	93.70 384	96.32 389	97.92 401	93.60 401	99.61 372	98.85 424	88.19 438	94.68 420	99.48 362	80.01 425	98.58 374	89.39 432	95.15 331	96.77 426

MVStest1		94.27 380	93.30 389	97.19 359	98.83 361	97.18 331	99.93 305	98.79 427	86.80 441	84.88 456	99.04 390	94.32 272	98.25 397	90.55 422	86.57 429	96.12 438

UnsupCasMVSNet_eth		94.25 381	93.89 381	95.34 400	97.63 411	92.13 417	99.73 354	99.36 226	94.88 352	92.78 432	98.63 416	82.72 415	96.53 439	94.57 384	84.73 434	97.36 413

KD-MVS_2432*1600		94.15 382	93.08 391	97.35 350	99.53 243	97.83 304	99.63 369	99.19 335	92.88 406	96.29 402	97.68 435	98.84 124	96.70 435	89.73 428	63.92 460	97.53 405

miper_refine_blended		94.15 382	93.08 391	97.35 350	99.53 243	97.83 304	99.63 369	99.19 335	92.88 406	96.29 402	97.68 435	98.84 124	96.70 435	89.73 428	63.92 460	97.53 405

MVS-HIRNet		94.12 384	92.73 398	98.29 289	99.33 307	95.95 352	99.38 396	99.19 335	74.54 459	98.26 341	86.34 463	86.07 394	99.06 326	91.60 414	99.87 149	99.85 209

new_pmnet		94.11 385	93.47 387	96.04 395	96.60 433	92.82 411	99.97 276	98.91 420	90.21 427	95.26 414	98.05 433	85.89 397	98.14 402	84.28 445	92.01 372	97.16 418

mvs5depth		93.81 386	93.00 393	96.23 391	94.25 449	93.33 406	97.43 457	98.07 442	93.47 392	94.15 427	99.58 347	77.52 434	98.97 335	93.64 396	88.92 409	96.39 434

pmmvs6		93.64 387	92.87 395	95.94 396	97.47 423	91.41 423	98.92 440	99.02 409	87.84 439	95.01 417	99.61 343	77.24 436	98.77 355	94.33 387	86.41 430	97.63 395

Patchmatch-RL test		93.49 388	93.63 385	93.05 422	91.78 453	83.41 449	98.21 453	96.95 458	91.58 416	91.05 437	97.64 437	99.40 63	95.83 447	94.11 392	81.95 443	99.91 161

Anonymous20231206		93.45 389	93.17 390	94.30 414	95.00 446	89.69 436	99.98 269	98.43 434	93.30 399	94.50 424	98.59 417	90.52 334	95.73 448	77.46 460	90.73 394	97.48 409

Anonymous20240521		93.29 390	92.76 397	94.90 409	95.64 442	91.27 424	99.97 276	98.82 425	87.04 440	94.71 419	98.19 428	83.86 411	96.80 434	84.04 446	92.56 365	96.64 429

dmvs_testset		93.27 391	95.48 364	86.65 435	98.74 364	68.42 464	99.92 307	98.91 420	96.19 314	93.28 431	100.00 1	91.06 323	91.67 460	89.64 430	91.54 381	99.86 208

test20.03		93.11 392	92.85 396	93.88 419	95.19 445	91.83 419	100.00 1	98.87 423	93.68 385	92.76 433	98.88 406	89.20 359	92.71 458	77.88 458	89.19 407	97.09 420

test_vis1_rt		93.10 393	92.93 394	93.58 420	99.63 206	85.07 446	99.99 238	93.71 467	97.49 187	90.96 438	97.10 440	60.40 458	99.95 172	99.24 234	97.90 268	95.72 442

APD_test1		93.07 394	94.14 380	89.85 429	99.18 319	72.49 457	99.76 346	98.90 422	92.86 408	96.35 401	99.94 269	75.56 440	99.91 194	86.73 440	97.98 261	97.15 419

EG-PatchMatch MVS		92.94 395	92.49 399	94.29 415	95.87 438	87.07 444	99.07 438	98.11 440	93.19 401	88.98 444	98.66 415	70.89 449	99.08 325	92.43 409	95.21 327	96.72 427

MDA-MVSNet_test_wron		92.61 396	91.09 406	97.19 359	96.71 432	97.26 328	100.00 1	99.14 364	88.61 433	67.90 465	98.32 427	89.03 360	96.57 438	90.47 424	89.59 401	97.74 351

sc_t1		92.52 397	91.34 401	96.09 393	97.80 405	89.86 434	98.61 448	99.12 376	77.73 454	96.09 407	99.79 307	68.64 453	98.94 338	96.94 340	87.31 422	99.46 298

YYNet1		92.44 398	90.92 407	97.03 363	96.20 434	97.06 336	99.99 238	99.14 364	88.21 437	67.93 464	98.43 424	88.63 368	96.28 442	90.64 419	89.08 408	97.74 351

tt0320		92.36 399	91.28 402	95.58 399	98.30 382	90.65 428	98.69 446	99.14 364	76.73 455	96.07 408	99.50 360	72.28 448	98.39 389	93.29 401	87.56 420	97.70 374

MIMVSNet1		91.96 400	91.20 403	94.23 416	94.94 447	91.69 421	99.34 400	99.22 318	88.23 435	94.18 426	98.45 422	75.52 441	93.41 456	79.37 455	91.49 383	97.60 400

TDRefinement		91.93 401	90.48 410	96.27 390	81.60 467	92.65 415	99.10 433	97.61 454	93.96 379	93.77 428	99.85 291	80.03 424	99.53 287	97.82 312	70.59 457	96.63 430

OpenMVS_ROB	88.34 20	91.89 402	91.12 404	94.19 417	95.55 443	87.63 442	99.26 407	98.03 443	86.61 443	90.65 442	96.82 442	70.14 452	98.78 352	86.54 441	96.50 308	96.15 436

N_pmnet		91.88 403	93.37 388	87.40 434	97.24 428	66.33 467	99.90 313	91.05 470	89.77 430	95.65 413	98.58 418	90.05 346	98.11 406	85.39 442	92.72 360	97.75 329

pmmvs-eth3d		91.73 404	90.67 408	94.92 408	91.63 455	92.71 414	99.90 313	98.54 433	91.19 418	88.08 447	95.50 446	79.31 429	96.13 444	90.55 422	81.32 446	95.91 441

tt0320-xc		91.69 405	90.50 409	95.26 401	98.04 396	90.12 433	98.60 449	98.70 430	76.63 457	94.66 421	99.52 357	68.57 454	97.99 417	94.61 383	85.18 432	97.66 386

MDA-MVSNet-bldmvs		91.65 406	89.94 414	96.79 378	96.72 431	96.70 344	99.42 393	98.94 417	88.89 432	66.97 467	98.37 425	81.43 421	95.91 446	89.24 434	89.46 404	97.75 329

KD-MVS_self_test		91.16 407	90.09 412	94.35 413	94.44 448	91.27 424	99.74 349	99.08 387	90.82 422	94.53 423	94.91 452	86.11 393	94.78 451	82.67 448	68.52 458	96.99 422

CL-MVSNet_self_test		91.07 408	90.35 411	93.24 421	93.27 450	89.16 438	99.55 378	99.25 303	92.34 411	95.23 415	97.05 441	88.86 365	93.59 455	80.67 452	66.95 459	96.96 423

test_method		91.04 409	91.10 405	90.85 426	98.34 377	77.63 453	100.00 1	98.93 419	76.69 456	96.25 404	98.52 420	70.44 450	97.98 418	89.02 436	91.74 377	96.92 424

CMPMVS	66.12 22	90.65 410	92.04 400	86.46 436	96.18 435	66.87 466	98.03 454	99.38 216	83.38 449	85.49 453	99.55 353	77.59 433	98.80 351	94.44 386	94.31 346	93.72 451
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		90.62 411	89.36 417	94.40 412	90.53 460	91.49 422	100.00 1	96.73 460	84.21 447	93.65 429	96.65 443	82.56 418	94.83 450	82.28 449	77.62 452	96.89 425

new-patchmatchnet		90.30 412	89.46 416	92.84 424	90.77 458	88.55 441	99.83 328	98.80 426	90.07 429	87.86 448	95.00 450	78.77 431	94.30 453	84.86 444	79.15 449	95.68 444

FE-MVSNET		89.50 413	88.33 419	93.00 423	88.89 461	90.24 431	99.96 283	96.86 459	88.23 435	88.46 445	95.47 447	77.03 437	93.37 457	78.54 457	81.56 445	95.39 446

UnsupCasMVSNet_bld		89.50 413	88.00 420	93.99 418	95.30 444	88.86 440	98.52 450	99.28 279	85.50 445	87.80 449	94.11 453	61.63 457	96.96 433	90.63 420	79.26 448	96.15 436

mvsany_test3		89.36 415	88.96 418	90.56 427	91.95 452	78.97 452	99.74 349	96.59 463	96.84 245	89.25 443	96.07 444	52.59 460	97.11 432	95.17 377	82.44 441	95.58 445

PM-MVS		88.39 416	87.41 421	91.31 425	91.73 454	82.02 451	99.79 338	96.62 461	91.06 420	90.71 441	95.73 445	48.60 462	95.96 445	90.56 421	81.91 444	95.97 440

WB-MVS		88.24 417	90.09 412	82.68 442	91.56 456	69.51 462	100.00 1	98.73 429	90.72 423	87.29 450	98.12 429	92.87 296	85.01 464	62.19 465	89.34 405	93.54 452

SSC-MVS		87.61 418	89.47 415	82.04 443	90.63 459	68.77 463	99.99 238	98.66 431	90.34 426	86.70 451	98.08 430	92.72 301	84.12 465	59.41 468	88.71 413	93.22 456

test_fmvs3		87.19 419	87.02 422	87.71 433	92.69 451	76.64 454	99.96 283	97.27 455	93.55 389	90.82 440	94.03 454	38.00 468	92.19 459	93.49 399	83.35 440	94.32 448

test_f		86.87 420	86.06 423	89.28 430	91.45 457	76.37 455	99.87 322	97.11 456	91.10 419	88.46 445	93.05 456	38.31 467	96.66 437	91.77 413	83.46 439	94.82 447

Gipuma		84.73 421	83.50 426	88.40 432	97.50 419	82.21 450	88.87 461	99.05 402	65.81 461	85.71 452	90.49 458	53.70 459	96.31 441	78.64 456	91.74 377	86.67 460
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		84.40 422	84.79 424	83.23 440	95.71 439	58.71 473	98.79 444	97.75 451	81.58 450	84.94 454	98.07 431	45.33 464	97.73 427	77.09 461	83.85 436	93.24 454

APD_test2		84.40 422	84.79 424	83.23 440	95.71 439	58.71 473	98.79 444	97.75 451	81.58 450	84.94 454	98.07 431	45.33 464	97.73 427	77.09 461	83.85 436	93.24 454

testmvs		80.17 424	81.95 427	74.80 447	58.54 474	59.58 472	100.00 1	87.14 473	76.09 458	99.61 234	100.00 1	67.06 455	74.19 470	98.84 258	50.30 464	90.64 459

test_vis3_rt		79.61 425	78.19 430	83.86 439	88.68 462	69.56 461	99.81 332	82.19 475	86.78 442	68.57 463	84.51 466	25.06 472	98.26 396	89.18 435	78.94 450	83.75 463

EGC-MVSNET		79.46 426	74.04 434	95.72 398	96.00 437	92.73 413	99.09 435	99.04 405	5.08 471	16.72 471	98.71 412	73.03 445	98.74 358	82.05 450	96.64 305	95.69 443

test123		79.44 427	79.23 429	80.05 445	80.03 468	71.72 458	100.00 1	77.93 476	62.52 462	94.81 418	99.69 320	78.21 432	74.53 469	92.57 406	27.33 469	93.90 449

PMMVS2		79.15 428	77.28 431	84.76 438	82.34 466	72.66 456	99.70 360	95.11 466	71.68 460	84.78 457	90.87 457	32.05 470	89.99 461	75.53 463	63.45 462	91.64 457

LCM-MVSNet		79.01 429	76.93 432	85.27 437	78.28 469	68.01 465	96.57 458	98.03 443	55.10 465	82.03 458	93.27 455	31.99 471	93.95 454	82.72 447	74.37 454	93.84 450

FPMVS		77.92 430	79.45 428	73.34 449	76.87 470	46.81 476	98.24 452	99.05 402	59.89 464	73.55 460	98.34 426	36.81 469	86.55 462	80.96 451	91.35 387	86.65 461

tmp_tt		75.80 431	74.26 433	80.43 444	52.91 476	53.67 475	87.42 463	97.98 446	61.80 463	67.04 466	100.00 1	76.43 439	96.40 440	96.47 353	28.26 468	91.23 458

E-PMN		70.72 432	70.06 435	72.69 450	83.92 465	65.48 469	99.95 291	92.72 469	49.88 467	72.30 461	86.26 464	47.17 463	77.43 467	53.83 469	44.49 465	75.17 467

EMVS		69.88 433	69.09 436	72.24 451	84.70 464	65.82 468	99.96 283	87.08 474	49.82 468	71.51 462	84.74 465	49.30 461	75.32 468	50.97 470	43.71 466	75.59 466

MVE	68.59 21	67.22 434	64.68 438	74.84 446	74.67 472	62.32 471	95.84 459	90.87 471	50.98 466	58.72 468	81.05 468	12.20 476	78.95 466	61.06 467	56.75 463	83.24 464
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 435	63.44 439	73.88 448	61.14 473	63.45 470	95.68 460	87.18 472	79.93 452	47.35 469	80.68 469	22.35 473	72.33 471	61.24 466	35.42 467	85.88 462

PMVS	60.66 23	65.98 436	65.05 437	68.75 452	55.06 475	38.40 477	88.19 462	96.98 457	48.30 469	44.82 470	88.52 461	12.22 475	86.49 463	67.58 464	83.79 438	81.35 465
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		28.28 437	29.73 441	23.92 453	75.89 471	32.61 478	66.50 464	12.88 477	16.09 470	14.59 472	16.59 471	12.35 474	32.36 472	39.36 471	13.36 470	6.79 468

cdsmvs_eth3d_5k		24.41 438	32.55 440	0.00 454	0.00 477	0.00 479	0.00 465	99.39 213	0.00 472	0.00 473	100.00 1	93.55 284	0.00 473	0.00 472	0.00 471	0.00 469

ab-mvs-re		8.33 439	11.11 442	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

pcd_1.5k_mvsjas		8.24 440	10.99 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	98.75 131	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.07 441	0.09 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.79 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

mmdepth		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet_test		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.01 442	0.02 445	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.14 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						97.98 291								95.74 365

FOURS1					100.00 1	99.97 21	100.00 1	99.42 147	98.52 89	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 70	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 78	100.00 1	100.00 1	99.60 21

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 207	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 57	100.00 1	99.42 147	97.62 166	100.00 1	100.00 1	98.94 115		99.99 71	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	97.54 178	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 73	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																99.91 161

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 77				99.91 161

sam_mvs												99.33 66

ambc				88.45 431	86.84 463	70.76 460	97.79 456	98.02 445		90.91 439	95.14 448	38.69 466	98.51 379	94.97 379	84.23 435	96.09 439

MTGPA								99.42 147

test_post1							99.32 401				88.24 462	99.33 66	99.59 265	98.31 287

test_post											89.05 460	99.49 43	99.59 265

patchmatchnet-post											97.79 434	99.41 61	99.54 282

GG-mvs-BLEND				99.59 162	99.54 240	99.49 148	99.17 424	99.52 72		99.96 144	99.68 324	100.00 1	99.33 314	99.71 161	99.99 103	99.96 133

MTMP							100.00 1	99.18 342

gm-plane-assit					99.52 255	97.26 328			95.86 323		100.00 1		99.43 305	98.76 263

test9_res														100.00 1	100.00 1	100.00 1

TEST9					100.00 1	99.95 32	100.00 1	99.42 147	97.65 161	100.00 1	100.00 1	99.53 33	99.97 142

test_8					100.00 1	99.91 57	100.00 1	99.42 147	97.70 155	100.00 1	100.00 1	99.51 37	99.98 133

agg_prior2														100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 78		99.42 147		100.00 1			99.97 142

TestCases				98.99 245	99.93 107	97.35 322		99.40 200	97.08 224	99.09 277	99.98 227	93.37 286	99.95 172	96.94 340	99.84 155	99.68 284

test_prior4						99.93 47	100.00 1

test_prior2							100.00 1		98.82 65	100.00 1	100.00 1	99.47 48		100.00 1	100.00 1

test_prior				99.90 81	100.00 1	99.75 101		99.73 56					99.97 142			100.00 1

旧先验2							100.00 1		98.11 122	100.00 1			100.00 1	99.67 176

新几何2							100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 139	100.00 1	100.00 1	99.20 85	100.00 1	97.91 305	100.00 1	100.00 1

旧先验1					99.99 49	99.88 78		99.82 40			100.00 1	99.27 80			100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 130				100.00 1	99.33 227		100.00 1

原ACMM2							100.00 1

原ACMM1				99.93 72	100.00 1	99.80 94		99.66 63	98.18 113	100.00 1	100.00 1	99.43 55	100.00 1	99.50 211	100.00 1	100.00 1

test222					99.99 49	99.90 64	100.00 1	99.69 62	97.66 159	100.00 1	100.00 1	99.30 76			100.00 1	100.00 1

testdata2													100.00 1	97.36 330

segment_acmp												99.55 29

testdata				99.66 150	99.99 49	98.97 212		99.73 56	97.96 135	100.00 1	100.00 1	99.42 59	100.00 1	99.28 231	100.00 1	100.00 1

testdata1							100.00 1		98.77 77

test12				99.95 55	99.99 49	99.89 71		99.42 147		100.00 1		99.24 82	99.97 142		100.00 1	100.00 1

plane_prior7					99.00 340	94.78 385

plane_prior6					99.06 332	94.80 381						88.58 371

plane_prior5								99.40 200					99.55 279	99.79 136	95.57 314	97.76 318

plane_prior4											99.97 239

plane_prior3						94.79 384			99.03 21	99.08 279

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 335

plane_prior						94.80 381	100.00 1		99.03 21						95.58 310

n2								0.00 478

nn								0.00 478

door-mid								96.32 464

lessismore_v0				96.05 394	97.55 417	91.80 420		99.22 318		91.87 436	99.91 278	83.50 413	98.68 360	92.48 408	90.42 397	97.68 381

LGP-MVS_train				97.28 355	98.85 359	94.60 390		99.37 220	97.35 200	98.85 297	99.98 227	86.66 388	99.56 274	99.55 203	95.26 322	97.70 374

test11								99.42 147

door								96.13 465

HQP5-MVS						94.82 378

HQP-NCC					99.07 328		100.00 1		99.04 16	99.17 267

ACMP_Plane					99.07 328		100.00 1		99.04 16	99.17 267

BP-MVS														99.79 136

HQP4-MVS										99.17 267			99.57 270			97.77 316

HQP3-MVS								99.40 200							95.58 310

HQP2-MVS												88.61 369

NP-MVS					99.07 328	94.81 380					99.97 239

MDTV_nov1_ep13_2view						99.24 181	99.56 377		96.31 306	99.96 144		98.86 122		98.92 254		99.89 180

MDTV_nov1_ep13			98.94 150		99.53 243	98.36 260	99.39 395	99.46 97	96.54 282	99.99 123	99.63 337	98.92 118	99.86 212	98.30 290	98.71 204

ACMMP++_ref															94.58 345

ACMMP++															95.17 330

Test By Simon												99.10 93

ITE_SJBPF				96.84 375	98.96 346	93.49 403		98.12 439	98.12 121	98.35 332	99.97 239	84.45 404	99.56 274	95.63 369	95.25 324	97.49 407

DeepMVS_CX				89.98 428	98.90 351	71.46 459		99.18 342	97.61 170	96.92 388	99.83 294	86.07 394	99.83 225	96.02 360	97.65 287	98.65 312

High-resolution multi-view benchmark