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	99.80 112	99.99 49	99.97 21	99.97 261	99.98 16	98.96 33	100.00 1	100.00 1	99.96 4	99.42 279	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	99.44 117	99.06 13	100.00 1	100.00 1	99.56 27	99.99 100	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	99.42 144	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	99.42 144	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	99.43 125	99.05 15	100.00 1	100.00 1	99.45 50	99.99 100	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 49	99.96 135	100.00 1	99.21 84	100.00 1	100.00 1	100.00 1	99.99 112

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 144	98.79 68	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 66	100.00 1	99.86 82	100.00 1	99.42 144	98.87 52	100.00 1	100.00 1	99.65 19	99.96 151	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	99.42 144	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	99.42 144	98.91 45	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 76	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	99.76 156	99.95 32	100.00 1	99.42 144	98.69 74	100.00 1	100.00 1	99.52 36	99.99 100	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 95	99.01 305	99.95 32	100.00 1	99.75 52	99.37 3	99.99 115	100.00 1	99.76 12	99.60 237	100.00 1	100.00 1	100.00 1

reproduce_model		99.76 18	99.69 22	99.98 23	99.96 96	99.93 47	100.00 1	99.42 144	98.81 64	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 96	99.94 41	100.00 1	99.42 144	98.82 60	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 96	99.94 41	100.00 1	99.42 144	98.82 60	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	97.99 120	99.99 115	100.00 1	99.72 14	100.00 1	99.96 94	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	98.16 106	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	99.73 324	99.52 72	99.06 13	100.00 1	100.00 1	98.80 129	100.00 1	99.95 100	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 34	100.00 1	99.91 56	99.98 255	99.47 79	99.09 10	100.00 1	100.00 1	98.59 139	100.00 1	99.95 100	100.00 1	100.00 1

HFP-MVS		99.74 25	99.67 30	99.96 45	100.00 1	99.89 70	100.00 1	99.76 49	97.95 128	100.00 1	100.00 1	99.31 71	100.00 1	99.99 68	100.00 1	100.00 1

ACMMPR		99.74 25	99.67 30	99.96 45	100.00 1	99.89 70	100.00 1	99.76 49	97.95 128	100.00 1	100.00 1	99.29 77	100.00 1	99.99 68	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	97.87 133	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	99.42 144	98.02 118	100.00 1	100.00 1	99.32 69	99.99 100	100.00 1	100.00 1	100.00 1

MVS_0304		99.72 29	99.65 34	99.93 70	99.99 49	99.79 93	100.00 1	99.91 35	99.17 6	100.00 1	100.00 1	97.84 164	100.00 1	100.00 1	99.95 121	100.00 1

region2R		99.72 29	99.64 37	99.97 34	100.00 1	99.90 63	100.00 1	99.74 55	97.86 134	100.00 1	100.00 1	99.19 86	100.00 1	99.99 68	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 116	99.97 90	99.37 160	99.96 267	99.94 22	98.48 85	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 75	99.97 90	99.72 105	100.00 1	99.47 79	98.43 88	99.88 182	100.00 1	99.14 91	100.00 1	99.97 92	100.00 1	100.00 1

ZNCC-MVS		99.71 33	99.62 44	99.97 34	99.99 49	99.90 63	100.00 1	99.79 45	97.97 124	99.97 128	100.00 1	98.97 109	100.00 1	99.94 102	100.00 1	100.00 1

train_agg		99.71 33	99.63 41	99.97 34	100.00 1	99.95 32	100.00 1	99.42 144	97.70 146	100.00 1	100.00 1	99.51 37	99.97 136	100.00 1	100.00 1	100.00 1

MVS_111021_HR		99.71 33	99.63 41	99.93 70	99.95 100	99.83 88	100.00 1	100.00 1	98.89 48	100.00 1	100.00 1	97.85 162	99.95 164	100.00 1	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 88	100.00 1	99.64 117	99.98 255	99.44 117	98.35 96	99.99 115	100.00 1	99.04 101	99.96 151	99.98 82	100.00 1	100.00 1

MVS_111021_LR		99.70 36	99.65 34	99.88 86	99.96 96	99.70 110	100.00 1	99.97 17	98.96 33	100.00 1	100.00 1	97.93 157	99.95 164	99.99 68	100.00 1	100.00 1

PLC	98.56 2	99.70 36	99.74 16	99.58 159	100.00 1	98.79 210	100.00 1	99.54 71	98.58 81	99.96 135	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 139
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	99.43 125	97.50 175	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 88	99.99 49	99.64 117	99.95 273	99.44 117	98.35 96	100.00 1	100.00 1	98.98 107	99.97 136	99.98 82	100.00 1	100.00 1

PGM-MVS		99.69 39	99.61 45	99.95 54	99.99 49	99.85 85	100.00 1	99.58 67	97.69 148	100.00 1	100.00 1	99.44 51	100.00 1	99.79 130	100.00 1	100.00 1

mPP-MVS		99.69 39	99.60 46	99.97 34	100.00 1	99.91 56	100.00 1	99.42 144	97.91 130	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	97.59 164	100.00 1	100.00 1	98.99 104	99.99 100	100.00 1	100.00 1	100.00 1

MTAPA		99.68 43	99.59 47	99.97 34	99.99 49	99.91 56	100.00 1	99.42 144	98.32 98	99.94 165	100.00 1	98.65 135	100.00 1	99.96 94	100.00 1	100.00 1

APD-MVS		99.68 43	99.58 49	99.97 34	99.99 49	99.96 24	100.00 1	99.42 144	97.53 170	100.00 1	100.00 1	99.27 80	99.97 136	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 34	99.98 86	99.92 53	100.00 1	99.42 144	97.83 135	100.00 1	100.00 1	98.89 121	100.00 1	99.98 82	100.00 1	100.00 1

CP-MVS		99.67 46	99.58 49	99.95 54	100.00 1	99.84 87	100.00 1	99.42 144	97.77 141	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 54	99.99 49	99.85 85	100.00 1	99.42 144	97.67 149	100.00 1	100.00 1	99.05 98	99.99 100	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 49	99.55 59	99.97 34	99.99 49	99.91 56	100.00 1	99.48 78	97.54 168	100.00 1	100.00 1	98.97 109	99.99 100	99.98 82	100.00 1	100.00 1

ACMMP		99.65 49	99.57 52	99.89 81	99.99 49	99.66 115	99.75 318	99.73 56	98.16 106	99.75 207	100.00 1	98.90 120	100.00 1	99.96 94	99.88 137	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
GST-MVS		99.64 51	99.53 62	99.95 54	100.00 1	99.86 82	100.00 1	99.79 45	97.72 144	99.95 163	100.00 1	98.39 146	100.00 1	99.96 94	99.99 103	100.00 1

PS-MVSNAJ		99.64 51	99.57 52	99.85 95	99.78 153	99.81 90	99.95 273	99.42 144	98.38 90	100.00 1	100.00 1	98.75 131	100.00 1	99.88 112	99.99 103	99.74 250

F-COLMAP		99.64 51	99.64 37	99.67 142	99.99 49	99.07 187	100.00 1	99.44 117	98.30 99	99.90 177	100.00 1	99.18 87	99.99 100	99.91 107	100.00 1	99.94 139

fmvsm_l_conf0.5_n_a		99.63 54	99.55 59	99.86 91	99.83 124	99.58 125	100.00 1	99.36 222	98.98 30	100.00 1	100.00 1	97.85 162	99.99 100	100.00 1	99.94 125	100.00 1

fmvsm_l_conf0.5_n		99.63 54	99.56 57	99.86 91	99.81 131	99.59 123	100.00 1	99.36 222	98.98 30	100.00 1	100.00 1	97.92 158	99.99 100	100.00 1	99.95 121	100.00 1

MM		99.63 54	99.52 64	99.94 66	99.99 49	99.82 89	100.00 1	99.97 17	99.11 8	100.00 1	100.00 1	96.65 214	100.00 1	100.00 1	99.97 116	100.00 1

SR-MVS-dyc-post		99.63 54	99.52 64	99.97 34	99.99 49	99.91 56	100.00 1	99.42 144	97.62 156	100.00 1	100.00 1	98.65 135	99.99 100	99.99 68	100.00 1	100.00 1

DPM-MVS		99.63 54	99.51 66	100.00 1	99.90 112	100.00 1	100.00 1	99.43 125	99.00 27	100.00 1	100.00 1	99.58 26	100.00 1	97.64 288	100.00 1	100.00 1

EPNet		99.62 59	99.69 22	99.42 180	99.99 49	98.37 237	100.00 1	99.89 37	98.83 58	100.00 1	100.00 1	98.97 109	100.00 1	99.90 108	99.61 167	99.89 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 59	99.56 57	99.82 102	99.92 108	99.45 150	100.00 1	99.78 47	98.92 43	99.73 209	100.00 1	97.70 171	100.00 1	99.93 104	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 61	99.50 67	99.97 34	99.98 86	99.92 53	100.00 1	99.42 144	97.53 170	99.77 204	100.00 1	98.77 130	100.00 1	99.99 68	100.00 1	99.99 112
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		99.61 61	99.49 69	99.98 23	99.99 49	99.94 41	100.00 1	99.42 144	97.82 136	99.99 115	100.00 1	98.20 149	100.00 1	99.99 68	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 61	99.69 22	99.35 191	99.99 49	98.06 262	100.00 1	99.36 222	99.83 2	100.00 1	100.00 1	98.95 113	99.99 100	100.00 1	99.11 179	100.00 1

HPM-MVS_fast		99.60 64	99.49 69	99.91 75	99.99 49	99.78 94	100.00 1	99.42 144	97.09 209	100.00 1	100.00 1	98.95 113	99.96 151	99.98 82	100.00 1	100.00 1

HPM-MVS		99.59 65	99.50 67	99.89 81	100.00 1	99.70 110	100.00 1	99.42 144	97.46 179	100.00 1	100.00 1	98.60 138	99.96 151	99.99 68	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 66	99.48 72	99.85 95	99.86 119	99.54 131	100.00 1	99.36 222	98.94 39	100.00 1	100.00 1	97.97 155	100.00 1	99.88 112	99.28 174	100.00 1

BP-MVS1		99.56 67	99.48 72	99.79 116	99.48 254	99.61 120	100.00 1	99.32 245	97.34 190	99.94 165	100.00 1	99.74 13	99.89 188	99.75 144	99.72 156	99.87 196

test_fmvsmconf_n		99.56 67	99.46 74	99.86 91	99.68 173	99.58 125	100.00 1	99.31 252	98.92 43	99.88 182	100.00 1	97.35 190	99.99 100	99.98 82	99.99 103	100.00 1

test_fmvsm_n_1920		99.55 69	99.49 69	99.73 131	99.85 120	99.19 179	100.00 1	99.41 193	98.87 52	100.00 1	100.00 1	97.34 191	100.00 1	99.98 82	99.90 133	100.00 1

WTY-MVS		99.54 70	99.40 76	99.95 54	99.81 131	99.93 47	100.00 1	100.00 1	97.98 122	99.84 186	100.00 1	98.94 115	99.98 129	99.86 116	98.21 221	99.94 139

test_yl		99.51 71	99.37 81	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 177	100.00 1	100.00 1	99.80 6	100.00 1	99.98 82	97.75 252	99.94 139

DCV-MVSNet		99.51 71	99.37 81	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 177	100.00 1	100.00 1	99.80 6	100.00 1	99.98 82	97.75 252	99.94 139

xiu_mvs_v2_base		99.51 71	99.41 75	99.82 102	99.70 165	99.73 103	99.92 283	99.40 197	98.15 108	100.00 1	100.00 1	98.50 143	100.00 1	99.85 118	99.13 178	99.74 250

HY-MVS	96.53 9	99.50 74	99.35 86	99.96 45	99.81 131	99.93 47	99.64 336	100.00 1	97.97 124	99.84 186	99.85 260	98.94 115	99.99 100	99.86 116	98.23 220	99.95 134

PHI-MVS		99.50 74	99.39 77	99.82 102	100.00 1	99.45 150	100.00 1	99.94 22	96.38 269	100.00 1	100.00 1	98.18 150	100.00 1	100.00 1	100.00 1	100.00 1

CPTT-MVS		99.49 76	99.38 78	99.85 95	100.00 1	99.54 131	100.00 1	99.42 144	97.58 165	99.98 122	100.00 1	97.43 188	100.00 1	99.99 68	100.00 1	100.00 1

MAR-MVS		99.49 76	99.36 84	99.89 81	99.97 90	99.66 115	99.74 319	99.95 19	97.89 131	100.00 1	100.00 1	96.71 213	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 78	99.38 78	99.75 127	99.89 114	99.51 138	99.45 357	100.00 1	98.38 90	99.83 189	100.00 1	98.86 122	99.81 213	99.25 211	98.78 187	99.94 139

PVSNet_Blended		99.48 78	99.36 84	99.83 100	99.98 86	99.60 121	100.00 1	100.00 1	97.79 139	100.00 1	100.00 1	96.57 216	99.99 100	100.00 1	99.88 137	99.90 166

test_fmvsmvis_n_1920		99.46 80	99.37 81	99.73 131	98.88 322	99.18 181	100.00 1	99.26 286	98.85 54	99.79 201	100.00 1	97.70 171	100.00 1	99.98 82	99.86 142	100.00 1

testing3-2		99.45 81	99.31 89	99.86 91	99.70 165	99.73 103	100.00 1	99.47 79	97.46 179	99.97 128	99.97 209	99.48 47	100.00 1	99.78 136	97.99 232	99.85 201

sss		99.45 81	99.34 88	99.80 112	99.76 156	99.50 140	100.00 1	99.91 35	97.72 144	99.98 122	99.94 239	98.45 144	100.00 1	99.53 194	98.75 190	99.89 172

AdaColmap		99.44 83	99.26 97	99.95 54	100.00 1	99.86 82	99.70 329	99.99 13	98.53 82	99.90 177	100.00 1	95.34 232	100.00 1	99.92 105	100.00 1	100.00 1

balanced_conf03		99.43 84	99.28 91	99.85 95	99.68 173	99.68 113	99.97 261	99.28 269	97.03 214	99.96 135	99.97 209	97.90 159	99.93 180	99.77 138	100.00 1	99.94 139

thisisatest0515		99.42 85	99.31 89	99.74 128	99.59 214	99.55 128	100.00 1	99.46 95	96.65 251	99.92 172	100.00 1	99.44 51	99.85 203	99.09 222	99.63 166	99.81 221

myMVS_eth3d28		99.41 86	99.28 91	99.80 112	99.69 168	99.53 133	100.00 1	99.43 125	97.12 208	99.98 122	99.97 209	99.41 61	100.00 1	99.81 129	98.07 229	99.88 185

CANet		99.40 87	99.24 102	99.89 81	99.99 49	99.76 98	100.00 1	99.73 56	98.40 89	99.78 203	100.00 1	95.28 233	99.96 151	100.00 1	99.99 103	99.96 128

GDP-MVS		99.39 88	99.26 97	99.77 124	99.53 231	99.55 128	100.00 1	99.11 345	97.14 204	99.96 135	100.00 1	99.83 5	99.89 188	98.47 255	99.26 175	99.87 196

MVSMamba_PlusPlus		99.39 88	99.25 99	99.80 112	99.68 173	99.59 123	99.99 229	99.30 256	96.66 250	99.96 135	99.97 209	97.89 160	99.92 183	99.76 140	100.00 1	99.90 166

114514_t		99.39 88	99.25 99	99.81 107	99.97 90	99.48 148	100.00 1	99.42 144	95.53 302	100.00 1	100.00 1	98.37 147	99.95 164	99.97 92	100.00 1	100.00 1

fmvsm_l_conf0.5_n_3		99.38 91	99.20 111	99.92 74	99.80 144	99.78 94	100.00 1	99.35 233	98.94 39	100.00 1	100.00 1	94.77 246	99.99 100	99.99 68	99.92 130	100.00 1

alignmvs		99.38 91	99.21 107	99.91 75	99.73 161	99.92 53	100.00 1	99.51 76	97.61 160	100.00 1	100.00 1	99.06 96	99.93 180	99.83 122	97.12 264	99.90 166

1314		99.38 91	99.19 112	99.96 45	98.88 322	99.89 70	99.24 378	99.93 30	98.88 49	98.79 274	100.00 1	97.02 197	100.00 1	100.00 1	100.00 1	100.00 1

thisisatest0530		99.37 94	99.27 93	99.69 138	99.59 214	99.41 155	100.00 1	99.46 95	96.46 262	99.90 177	100.00 1	99.44 51	99.85 203	98.97 226	99.58 168	99.80 238

UBG		99.36 95	99.27 93	99.63 148	99.63 198	99.01 196	100.00 1	99.43 125	96.99 217	100.00 1	99.92 244	99.69 17	99.99 100	99.74 145	98.06 230	99.88 185

xiu_mvs_v1_base_debu		99.35 96	99.21 107	99.79 116	99.67 181	99.71 106	99.78 309	99.36 222	98.13 110	100.00 1	100.00 1	97.00 201	100.00 1	99.83 122	99.07 180	99.66 259

xiu_mvs_v1_base		99.35 96	99.21 107	99.79 116	99.67 181	99.71 106	99.78 309	99.36 222	98.13 110	100.00 1	100.00 1	97.00 201	100.00 1	99.83 122	99.07 180	99.66 259

xiu_mvs_v1_base_debi		99.35 96	99.21 107	99.79 116	99.67 181	99.71 106	99.78 309	99.36 222	98.13 110	100.00 1	100.00 1	97.00 201	100.00 1	99.83 122	99.07 180	99.66 259

ETV-MVS		99.34 99	99.24 102	99.64 147	99.58 219	99.33 162	100.00 1	99.25 290	97.57 166	99.96 135	100.00 1	97.44 187	99.79 216	99.70 157	99.65 163	99.81 221

tttt0517		99.34 99	99.23 105	99.67 142	99.57 223	99.38 157	100.00 1	99.46 95	96.33 274	99.89 180	100.00 1	99.44 51	99.84 206	98.93 228	99.46 172	99.78 245

CS-MVS		99.33 101	99.27 93	99.50 167	99.99 49	99.00 199	100.00 1	99.13 338	97.26 198	99.96 135	100.00 1	97.79 167	99.64 235	99.64 174	99.67 161	99.87 196

PVSNet_Blended_VisFu		99.33 101	99.18 115	99.78 121	99.82 125	99.49 144	100.00 1	99.95 19	97.36 187	99.63 215	100.00 1	96.45 220	99.95 164	99.79 130	99.65 163	99.89 172

fmvsm_s_conf0.5_n_a		99.32 103	99.15 117	99.81 107	99.80 144	99.47 149	100.00 1	99.35 233	98.22 101	100.00 1	100.00 1	95.21 237	99.99 100	99.96 94	99.86 142	99.98 115

HyFIR lowres test		99.32 103	99.24 102	99.58 159	99.95 100	99.26 170	100.00 1	99.99 13	96.72 243	99.29 239	99.91 247	99.49 43	99.47 270	99.74 145	98.08 228	100.00 1

SPE-MVS-test		99.31 105	99.27 93	99.43 178	99.99 49	98.77 211	100.00 1	99.19 314	97.24 199	99.96 135	100.00 1	97.56 179	99.70 232	99.68 165	99.81 150	99.82 212

LS3D		99.31 105	99.13 118	99.87 88	99.99 49	99.71 106	99.55 347	99.46 95	97.32 193	99.82 197	100.00 1	96.85 208	99.97 136	99.14 217	100.00 1	99.92 152

PVSNet	94.91 18	99.30 107	99.25 99	99.44 175	100.00 1	98.32 243	100.00 1	99.86 38	98.04 117	100.00 1	100.00 1	96.10 223	100.00 1	99.55 189	99.73 155	100.00 1

UWE-MVS-28		99.29 108	99.23 105	99.48 170	99.73 161	98.86 206	100.00 1	99.43 125	96.97 219	99.99 115	99.83 263	99.43 55	99.77 221	99.35 203	98.31 214	99.80 238

lupinMVS		99.29 108	99.16 116	99.69 138	99.45 262	99.49 144	100.00 1	99.15 329	97.45 181	99.97 128	100.00 1	96.76 209	99.76 224	99.67 168	100.00 1	99.81 221

CSCG		99.28 110	99.35 86	99.05 216	99.99 49	97.15 307	100.00 1	99.47 79	97.44 182	99.42 227	100.00 1	97.83 166	100.00 1	99.99 68	100.00 1	100.00 1

thres200		99.27 111	99.04 126	99.96 45	99.81 131	99.90 63	100.00 1	99.94 22	97.31 195	99.83 189	99.96 226	97.04 194	100.00 1	99.62 178	97.88 241	99.98 115

OMC-MVS		99.27 111	99.38 78	98.96 224	99.95 100	97.06 311	100.00 1	99.40 197	98.83 58	99.88 182	100.00 1	97.01 198	99.86 197	99.47 197	99.84 147	99.97 122

testing11		99.26 113	99.19 112	99.46 172	99.64 196	98.61 222	100.00 1	99.43 125	96.94 221	99.92 172	99.94 239	99.43 55	99.97 136	99.67 168	97.79 250	99.82 212

EIA-MVS		99.26 113	99.19 112	99.45 174	99.63 198	98.75 212	100.00 1	99.27 279	96.93 222	99.95 163	100.00 1	97.47 184	99.79 216	99.74 145	99.72 156	99.82 212

tfpn200view9		99.26 113	99.03 127	99.96 45	99.81 131	99.89 70	100.00 1	99.94 22	97.23 200	99.83 189	99.96 226	97.04 194	100.00 1	99.59 184	97.85 243	99.98 115

thres400		99.26 113	99.03 127	99.95 54	99.81 131	99.89 70	100.00 1	99.94 22	97.23 200	99.83 189	99.96 226	97.04 194	100.00 1	99.59 184	97.85 243	99.97 122

test_fmvsmconf0.1_n		99.25 117	99.05 125	99.82 102	98.92 318	99.55 128	100.00 1	99.23 300	98.91 45	99.75 207	99.97 209	94.79 245	99.94 176	99.94 102	99.99 103	99.97 122

thres100view900		99.25 117	99.01 129	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 206	99.83 189	99.96 226	97.01 198	100.00 1	99.59 184	97.85 243	99.98 115

EPMVS		99.25 117	99.13 118	99.60 153	99.60 210	99.20 178	99.60 342	100.00 1	96.93 222	99.92 172	99.36 336	99.05 98	99.71 231	98.77 237	98.94 184	99.90 166

thres600view7		99.24 120	99.00 131	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 206	99.83 189	99.96 226	97.01 198	100.00 1	99.54 192	97.77 251	99.97 122

MVS		99.22 121	98.96 137	99.98 23	99.00 309	99.95 32	99.24 378	99.94 22	98.14 109	98.88 264	100.00 1	95.63 230	100.00 1	99.85 118	100.00 1	100.00 1

fmvsm_s_conf0.5_n		99.21 122	99.01 129	99.83 100	99.84 121	99.53 133	100.00 1	99.38 213	98.29 100	100.00 1	100.00 1	93.62 261	99.99 100	99.99 68	99.93 128	99.98 115

EC-MVSNet		99.19 123	99.09 123	99.48 170	99.42 266	99.07 187	100.00 1	99.21 310	96.95 220	99.96 135	100.00 1	96.88 207	99.48 268	99.64 174	99.79 154	99.88 185

testing91		99.18 124	99.10 121	99.41 181	99.60 210	98.43 229	100.00 1	99.43 125	96.76 236	99.82 197	99.92 244	99.05 98	99.98 129	99.62 178	97.67 256	99.81 221

testing99		99.18 124	99.10 121	99.41 181	99.60 210	98.43 229	100.00 1	99.43 125	96.76 236	99.84 186	99.92 244	99.06 96	99.98 129	99.62 178	97.67 256	99.81 221

UWE-MVS		99.18 124	99.06 124	99.51 164	99.67 181	98.80 209	100.00 1	99.43 125	96.80 233	99.93 171	99.86 255	99.79 8	99.94 176	97.78 284	98.33 212	99.80 238

ETVMVS		99.16 127	98.98 134	99.69 138	99.67 181	99.56 127	100.00 1	99.45 103	96.36 271	99.98 122	99.95 233	98.65 135	99.64 235	99.11 221	97.63 259	99.88 185

FE-MVS		99.16 127	98.99 133	99.66 145	99.65 190	99.18 181	99.58 344	99.43 125	95.24 314	99.91 175	99.59 313	99.37 65	99.97 136	98.31 262	99.81 150	99.83 207

testing222		99.14 129	98.94 142	99.73 131	99.67 181	99.51 138	100.00 1	99.43 125	96.90 227	99.99 115	99.90 249	98.55 141	99.86 197	98.85 232	97.18 263	99.81 221

PMMVS		99.12 130	98.97 136	99.58 159	99.57 223	98.98 201	100.00 1	99.30 256	97.14 204	99.96 135	100.00 1	96.53 219	99.82 210	99.70 157	98.49 196	99.94 139

jason		99.11 131	98.96 137	99.59 155	99.17 289	99.31 165	100.00 1	99.13 338	97.38 186	99.83 189	100.00 1	95.54 231	99.72 230	99.57 188	99.97 116	99.74 250
jason: jason.
EPP-MVSNet		99.10 132	99.00 131	99.40 185	99.51 244	98.68 218	99.92 283	99.43 125	95.47 308	99.65 214	100.00 1	99.51 37	99.76 224	99.53 194	98.00 231	99.75 249

TESTMET0.1,1		99.08 133	98.96 137	99.44 175	99.63 198	99.38 157	100.00 1	99.45 103	95.53 302	99.48 222	100.00 1	99.71 15	99.02 299	96.84 314	99.99 103	99.91 155

IS-MVSNet		99.08 133	98.91 146	99.59 155	99.65 190	99.38 157	99.78 309	99.24 296	96.70 245	99.51 220	100.00 1	98.44 145	99.52 262	98.47 255	98.39 204	99.88 185

UA-Net		99.06 135	98.83 152	99.74 128	99.52 239	99.40 156	99.08 403	99.45 103	97.64 153	99.83 189	100.00 1	95.80 226	99.94 176	98.35 260	99.80 153	99.88 185

3Dnovator	95.63 14	99.06 135	98.76 159	99.96 45	98.86 326	99.90 63	99.98 255	99.93 30	98.95 36	98.49 293	100.00 1	92.91 272	100.00 1	99.71 154	100.00 1	100.00 1

mvsmamba		99.05 137	98.98 134	99.27 205	99.57 223	98.10 258	100.00 1	99.28 269	95.92 288	99.96 135	99.97 209	96.73 212	99.89 188	99.72 150	99.65 163	99.81 221

patch_mono-2		99.04 138	99.79 6	96.81 346	99.92 108	90.47 397	100.00 1	99.41 193	98.95 36	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 134

VNet		99.04 138	98.75 160	99.90 78	99.81 131	99.75 99	99.50 353	99.47 79	98.36 94	100.00 1	99.99 195	94.66 248	100.00 1	99.90 108	97.09 265	99.96 128

sasdasda		99.03 140	98.73 163	99.94 66	99.75 158	99.95 32	100.00 1	99.30 256	97.64 153	100.00 1	100.00 1	95.22 235	99.97 136	99.76 140	96.90 270	99.91 155

canonicalmvs		99.03 140	98.73 163	99.94 66	99.75 158	99.95 32	100.00 1	99.30 256	97.64 153	100.00 1	100.00 1	95.22 235	99.97 136	99.76 140	96.90 270	99.91 155

test-LLR		99.03 140	98.91 146	99.40 185	99.40 273	99.28 167	100.00 1	99.45 103	96.70 245	99.42 227	99.12 348	99.31 71	99.01 300	96.82 315	99.99 103	99.91 155

PatchmatchNet		99.03 140	98.96 137	99.26 206	99.49 252	98.33 241	99.38 365	99.45 103	96.64 252	99.96 135	99.58 315	99.49 43	99.50 266	97.63 289	99.00 183	99.93 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
3Dnovator+	95.58 15	99.03 140	98.71 167	99.96 45	98.99 312	99.89 70	100.00 1	99.51 76	98.96 33	98.32 303	100.00 1	92.78 274	100.00 1	99.87 115	100.00 1	100.00 1

CANet_DTU		99.02 145	98.90 149	99.41 181	99.88 116	98.71 216	100.00 1	99.29 263	98.84 56	100.00 1	100.00 1	94.02 256	100.00 1	98.08 271	99.96 119	99.52 266

PatchMatch-RL		99.02 145	98.78 157	99.74 128	99.99 49	99.29 166	100.00 1	100.00 1	98.38 90	99.89 180	99.81 270	93.14 270	99.99 100	97.85 282	99.98 113	99.95 134

MGCFI-Net		99.01 147	98.70 169	99.93 70	99.74 160	99.94 41	100.00 1	99.29 263	97.60 163	100.00 1	100.00 1	95.10 239	99.96 151	99.74 145	96.85 272	99.91 155

fmvsm_s_conf0.5_n_5		99.00 148	98.70 169	99.88 86	99.81 131	99.64 117	100.00 1	99.26 286	98.78 71	99.97 128	100.00 1	90.65 303	99.99 100	100.00 1	99.89 134	99.99 112

FA-MVS(test-final)		99.00 148	98.75 160	99.73 131	99.63 198	99.43 153	99.83 299	99.43 125	95.84 294	99.52 219	99.37 335	97.84 164	99.96 151	97.63 289	99.68 159	99.79 242

CHOSEN 1792x2688		99.00 148	98.91 146	99.25 207	99.90 112	97.79 282	100.00 1	99.99 13	98.79 68	98.28 306	100.00 1	93.63 260	99.95 164	99.66 172	99.95 121	100.00 1

DeepC-MVS	97.84 5	99.00 148	98.80 156	99.60 153	99.93 105	99.03 192	100.00 1	99.40 197	98.61 80	99.33 237	100.00 1	92.23 284	99.95 164	99.74 145	99.96 119	99.83 207
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		98.99 152	98.69 171	99.89 81	99.70 165	99.69 112	100.00 1	99.39 210	98.93 41	100.00 1	100.00 1	90.20 311	99.99 100	100.00 1	99.95 121	100.00 1

baseline2		98.99 152	98.93 144	99.18 211	99.26 286	99.15 184	100.00 1	99.46 95	96.71 244	96.79 362	100.00 1	99.42 59	99.25 290	98.75 239	99.94 125	99.15 273

QAPM		98.99 152	98.66 173	99.96 45	99.01 305	99.87 79	99.88 293	99.93 30	97.99 120	98.68 278	100.00 1	93.17 268	100.00 1	99.32 207	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 152	98.89 150	99.29 200	99.64 196	98.89 205	99.98 255	99.31 252	96.74 240	99.48 222	100.00 1	98.11 152	99.10 295	98.39 258	98.34 209	99.89 172

fmvsm_s_conf0.5_n_4		98.98 156	98.74 162	99.68 141	99.81 131	99.50 140	100.00 1	99.26 286	98.91 45	100.00 1	100.00 1	90.87 301	99.97 136	99.99 68	99.81 150	99.57 263

tpmrst		98.98 156	98.93 144	99.14 213	99.61 207	97.74 283	99.52 351	99.36 222	96.05 285	99.98 122	99.64 301	99.04 101	99.86 197	98.94 227	98.19 223	99.82 212

test-mter		98.96 158	98.82 153	99.40 185	99.40 273	99.28 167	100.00 1	99.45 103	95.44 313	99.42 227	99.12 348	99.70 16	99.01 300	96.82 315	99.99 103	99.91 155

diffmvs		98.96 158	98.73 163	99.63 148	99.54 228	99.16 183	100.00 1	99.18 321	97.33 192	99.96 135	100.00 1	94.60 249	99.91 185	99.66 172	98.33 212	99.82 212
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		98.96 158	98.95 141	99.01 220	99.48 254	98.36 239	99.93 281	99.37 216	96.79 234	99.31 238	99.83 263	99.77 11	98.91 311	98.07 273	97.98 233	99.77 246
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n_2		98.95 161	98.69 171	99.73 131	99.61 207	99.74 102	100.00 1	99.23 300	98.95 36	99.97 128	100.00 1	90.92 300	99.97 136	100.00 1	99.58 168	99.47 268

mamv4		98.95 161	99.11 120	98.46 251	99.68 173	95.67 335	99.14 396	99.27 279	96.43 263	99.94 165	99.97 209	97.79 167	99.88 195	99.77 138	100.00 1	99.84 203

MVSFormer		98.94 163	98.82 153	99.28 203	99.45 262	99.49 144	100.00 1	99.13 338	95.46 309	99.97 128	100.00 1	96.76 209	98.59 341	98.63 247	100.00 1	99.74 250

MVS_Test		98.93 164	98.65 174	99.77 124	99.62 205	99.50 140	99.99 229	99.19 314	95.52 304	99.96 135	99.86 255	96.54 218	99.98 129	98.65 244	98.48 197	99.82 212

baseline1		98.91 165	98.61 179	99.81 107	99.71 163	99.77 97	99.78 309	99.44 117	97.51 174	98.81 272	99.99 195	98.25 148	99.76 224	98.60 250	95.41 285	99.89 172

1112_ss		98.91 165	98.71 167	99.51 164	99.69 168	98.75 212	99.99 229	99.15 329	96.82 231	98.84 269	100.00 1	97.45 185	99.89 188	98.66 242	97.75 252	99.89 172

fmvsm_s_conf0.5_n_2		98.90 167	98.57 184	99.90 78	99.79 149	99.78 94	100.00 1	99.25 290	98.97 32	100.00 1	100.00 1	89.22 328	99.99 100	100.00 1	99.88 137	99.92 152

MSDG		98.90 167	98.63 177	99.70 137	99.92 108	99.25 172	100.00 1	99.37 216	95.71 296	99.40 233	100.00 1	96.58 215	99.95 164	96.80 317	99.94 125	99.91 155

dcpmvs_2		98.87 169	99.53 62	96.90 340	99.87 118	90.88 396	99.94 278	99.07 359	98.20 104	100.00 1	100.00 1	98.69 134	99.86 197	100.00 1	100.00 1	99.95 134

DP-MVS		98.86 170	98.54 186	99.81 107	99.97 90	99.45 150	99.52 351	99.40 197	94.35 338	98.36 298	100.00 1	96.13 222	99.97 136	99.12 220	100.00 1	100.00 1

CostFormer		98.84 171	98.77 158	99.04 218	99.41 268	97.58 288	99.67 334	99.35 233	94.66 327	99.96 135	99.36 336	99.28 79	99.74 227	99.41 200	97.81 247	99.81 221

Test_1112_low_res		98.83 172	98.60 181	99.51 164	99.69 168	98.75 212	99.99 229	99.14 334	96.81 232	98.84 269	99.06 352	97.45 185	99.89 188	98.66 242	97.75 252	99.89 172

BH-w/o		98.82 173	98.81 155	98.88 229	99.62 205	96.71 318	100.00 1	99.28 269	97.09 209	98.81 272	100.00 1	94.91 243	99.96 151	99.54 192	100.00 1	99.96 128

mvs_anonymous		98.80 174	98.60 181	99.38 189	99.57 223	99.24 174	100.00 1	99.21 310	95.87 289	98.92 261	99.82 267	96.39 221	99.03 298	99.13 219	98.50 195	99.88 185

fmvsm_s_conf0.1_n		98.77 175	98.42 196	99.82 102	99.47 258	99.52 137	100.00 1	99.27 279	97.53 170	100.00 1	100.00 1	89.73 320	99.96 151	99.84 121	99.93 128	99.97 122

TAMVS		98.76 176	98.73 163	98.86 230	99.44 264	97.69 284	99.57 345	99.34 240	96.57 255	99.12 249	99.81 270	98.83 126	99.16 293	97.97 279	97.91 239	99.73 254

OpenMVS	95.20 17	98.76 176	98.41 197	99.78 121	98.89 321	99.81 90	99.99 229	99.76 49	98.02 118	98.02 321	100.00 1	91.44 290	100.00 1	99.63 177	99.97 116	99.55 264

RRT-MVS		98.75 178	98.52 189	99.44 175	99.65 190	98.57 225	99.90 287	99.08 354	96.51 260	99.96 135	99.95 233	92.59 280	99.96 151	99.60 182	99.45 173	99.81 221

dp		98.72 179	98.61 179	99.03 219	99.53 231	97.39 294	99.45 357	99.39 210	95.62 299	99.94 165	99.52 324	98.83 126	99.82 210	96.77 320	98.42 201	99.89 172

fmvsm_s_conf0.1_n_a		98.71 180	98.36 204	99.78 121	99.09 295	99.42 154	100.00 1	99.26 286	97.42 184	100.00 1	100.00 1	89.78 318	99.96 151	99.82 127	99.85 145	99.97 122

PVSNet_BlendedMVS		98.71 180	98.62 178	98.98 223	99.98 86	99.60 121	100.00 1	100.00 1	97.23 200	100.00 1	99.03 358	96.57 216	99.99 100	100.00 1	94.75 309	97.35 379

ADS-MVSNet		98.70 182	98.51 191	99.28 203	99.51 244	98.39 234	99.24 378	99.44 117	95.52 304	99.96 135	99.70 285	97.57 177	99.58 243	97.11 306	98.54 193	99.88 185

baseline		98.69 183	98.45 195	99.41 181	99.52 239	98.67 219	100.00 1	99.17 326	97.03 214	99.13 248	100.00 1	93.17 268	99.74 227	99.70 157	98.34 209	99.81 221

PCF-MVS	98.23 3	98.69 183	98.37 202	99.62 150	99.78 153	99.02 194	99.23 383	99.06 367	96.43 263	98.08 315	100.00 1	94.72 247	99.95 164	98.16 269	99.91 132	99.90 166
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		98.65 185	98.38 200	99.46 172	99.52 239	98.74 215	100.00 1	99.15 329	96.91 225	99.05 256	100.00 1	92.75 275	99.83 207	99.70 157	98.38 206	99.81 221
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs_mvg		98.64 186	98.39 199	99.40 185	99.50 248	98.60 223	100.00 1	99.22 304	96.85 229	99.10 250	100.00 1	92.75 275	99.78 220	99.71 154	98.35 208	99.81 221
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpm2		98.64 186	98.58 183	98.81 234	99.42 266	97.12 308	99.69 331	99.37 216	93.63 355	99.94 165	99.67 293	98.96 112	99.47 270	98.62 249	97.95 237	99.83 207

BH-untuned		98.64 186	98.65 174	98.60 244	99.59 214	96.17 325	100.00 1	99.28 269	96.67 249	98.41 296	100.00 1	94.52 250	99.83 207	99.41 200	100.00 1	99.81 221

test_cas_vis1_n_1920		98.63 189	98.25 208	99.77 124	99.69 168	99.32 163	100.00 1	99.31 252	98.84 56	99.96 135	100.00 1	87.42 349	99.99 100	99.14 217	99.86 142	100.00 1

reproduce_monomvs		98.61 190	98.54 186	98.82 231	99.97 90	99.28 167	100.00 1	99.33 242	98.51 84	97.87 329	99.24 342	99.98 3	99.45 275	99.02 225	92.93 326	97.74 319

test_fmvsmconf0.01_n		98.60 191	98.24 211	99.67 142	96.90 395	99.21 177	99.99 229	99.04 372	98.80 65	99.57 217	99.96 226	90.12 312	99.91 185	99.89 110	99.89 134	99.90 166

tpmvs		98.59 192	98.38 200	99.23 208	99.69 168	97.90 274	99.31 373	99.47 79	94.52 332	99.68 213	99.28 340	97.64 174	99.89 188	97.71 286	98.17 225	99.89 172

Effi-MVS+		98.58 193	98.24 211	99.61 151	99.60 210	99.26 170	97.85 419	99.10 348	96.22 280	99.97 128	99.89 250	93.75 258	99.77 221	99.43 198	98.34 209	99.81 221

MVSTER		98.58 193	98.52 189	98.77 236	99.65 190	99.68 113	100.00 1	99.29 263	95.63 298	98.65 279	99.80 273	99.78 9	98.88 317	98.59 251	95.31 289	97.73 326

CVMVSNet		98.56 195	98.47 194	98.82 231	99.11 292	97.67 285	99.74 319	99.47 79	97.57 166	99.06 255	100.00 1	95.72 228	98.97 306	98.21 268	97.33 262	99.83 207

kuosan		98.55 196	98.53 188	98.62 242	99.66 188	96.16 326	100.00 1	99.44 117	93.93 348	99.81 200	99.98 200	97.58 175	99.81 213	98.08 271	98.28 216	99.89 172

MonoMVSNet		98.55 196	98.64 176	98.26 267	98.21 356	95.76 333	99.94 278	99.16 327	96.23 277	99.47 225	99.24 342	96.75 211	99.22 291	99.61 181	99.17 176	99.81 221

AllTest		98.55 196	98.40 198	98.99 221	99.93 105	97.35 297	100.00 1	99.40 197	97.08 211	99.09 251	99.98 200	93.37 264	99.95 164	96.94 310	99.84 147	99.68 257

DeepPCF-MVS	98.03 4	98.54 199	99.72 19	94.98 372	99.99 49	84.94 411	100.00 1	99.42 144	99.98 1	100.00 1	100.00 1	98.11 152	100.00 1	100.00 1	100.00 1	100.00 1

EPNet_dtu		98.53 200	98.23 214	99.43 178	99.92 108	99.01 196	99.96 267	99.47 79	98.80 65	99.96 135	99.96 226	98.56 140	99.30 287	87.78 403	99.68 159	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
myMVS_eth3d		98.52 201	98.51 191	98.53 247	99.50 248	97.98 267	100.00 1	99.57 68	96.23 277	98.07 316	100.00 1	99.09 94	97.81 388	96.17 328	97.96 235	99.82 212

Vis-MVSNet		98.52 201	98.25 208	99.34 192	99.68 173	98.55 226	99.68 333	99.41 193	97.34 190	99.94 165	100.00 1	90.38 310	99.70 232	99.03 224	98.84 185	99.76 248
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+-dtu		98.51 203	98.86 151	97.47 314	99.77 155	94.21 366	100.00 1	98.94 383	97.61 160	99.91 175	98.75 376	95.89 224	99.51 264	99.36 202	99.48 171	98.68 279

SDMVSNet		98.49 204	98.08 222	99.73 131	99.82 125	99.53 133	99.99 229	99.45 103	97.62 156	99.38 234	99.86 255	90.06 315	99.88 195	99.92 105	96.61 275	99.79 242

BH-RMVSNet		98.46 205	98.08 222	99.59 155	99.61 207	99.19 179	100.00 1	99.28 269	97.06 213	98.95 260	100.00 1	88.99 331	99.82 210	98.83 235	100.00 1	99.77 246

testing3		98.44 206	98.37 202	98.65 240	99.51 244	98.32 243	100.00 1	99.62 66	96.43 263	97.93 325	99.99 195	99.11 92	97.81 388	94.88 348	97.80 248	99.82 212

ECVR-MVS		98.43 207	98.14 217	99.32 197	99.89 114	98.21 251	99.46 355	100.00 1	98.38 90	99.47 225	100.00 1	87.91 342	99.80 215	99.35 203	98.78 187	99.94 139

cascas		98.43 207	98.07 224	99.50 167	99.65 190	99.02 194	100.00 1	99.22 304	94.21 341	99.72 210	99.98 200	92.03 287	99.93 180	99.68 165	98.12 226	99.54 265

test1111		98.42 209	98.12 218	99.29 200	99.88 116	98.15 253	99.46 355	100.00 1	98.36 94	99.42 227	100.00 1	87.91 342	99.79 216	99.31 208	98.78 187	99.94 139

ab-mvs		98.42 209	98.02 228	99.61 151	99.71 163	99.00 199	99.10 400	99.64 64	96.70 245	99.04 257	99.81 270	90.64 304	99.98 129	99.64 174	97.93 238	99.84 203

UGNet		98.41 211	98.11 219	99.31 199	99.54 228	98.55 226	99.18 386	100.00 1	98.64 79	99.79 201	99.04 355	87.61 347	100.00 1	99.30 209	99.89 134	99.40 270
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+		98.40 212	98.02 228	99.55 163	99.63 198	99.06 189	100.00 1	99.15 329	95.07 316	99.42 227	99.95 233	93.26 267	99.73 229	97.44 296	98.24 219	99.87 196

Fast-Effi-MVS+-dtu		98.38 213	98.56 185	97.82 305	99.58 219	94.44 363	100.00 1	99.16 327	96.75 238	99.51 220	99.63 305	95.03 241	99.60 237	97.71 286	99.67 161	99.42 269

test_fmvs1		98.37 214	98.04 226	99.34 192	99.84 121	98.07 260	100.00 1	99.00 378	98.85 54	100.00 1	100.00 1	85.11 370	99.96 151	99.69 164	99.88 137	100.00 1

miper_enhance_ethall		98.33 215	98.27 207	98.51 248	99.66 188	99.04 191	100.00 1	99.22 304	97.53 170	98.51 291	99.38 334	99.49 43	98.75 327	98.02 275	92.61 329	97.76 286

SCA		98.30 216	97.98 230	99.23 208	99.41 268	98.25 248	99.99 229	99.45 103	96.91 225	99.76 206	99.58 315	89.65 322	99.54 256	98.31 262	98.79 186	99.91 155

XVG-OURS		98.30 216	98.36 204	98.13 280	99.58 219	95.91 329	100.00 1	99.36 222	98.69 74	99.23 241	100.00 1	91.20 293	99.92 183	99.34 205	97.82 246	98.56 282

dongtai		98.29 218	98.25 208	98.42 255	99.58 219	95.86 331	100.00 1	99.44 117	93.46 361	99.69 212	99.97 209	97.53 180	99.51 264	96.28 327	98.27 218	99.89 172

COLMAP_ROB	97.10 7	98.29 218	98.17 216	98.65 240	99.94 103	97.39 294	99.30 374	99.40 197	95.64 297	97.75 335	100.00 1	92.69 279	99.95 164	98.89 230	99.92 130	98.62 281
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		98.28 220	98.51 191	97.62 310	99.51 244	95.03 343	99.24 378	99.41 193	95.52 304	99.96 135	99.70 285	97.57 177	97.94 385	97.11 306	98.54 193	99.88 185

XVG-OURS-SEG-HR		98.27 221	98.31 206	98.14 277	99.59 214	95.92 328	100.00 1	99.36 222	98.48 85	99.21 242	100.00 1	89.27 327	99.94 176	99.76 140	99.17 176	98.56 282

tpm		98.24 222	98.22 215	98.32 263	99.13 291	95.79 332	99.53 350	99.12 344	95.20 315	99.96 135	99.36 336	97.58 175	99.28 289	97.41 298	96.67 273	99.88 185

cl22		98.23 223	98.11 219	98.58 246	99.82 125	99.01 196	100.00 1	99.28 269	96.92 224	98.33 302	99.21 345	98.09 154	98.97 306	98.72 240	92.61 329	97.76 286

WBMVS		98.19 224	98.10 221	98.47 250	99.63 198	99.03 192	100.00 1	99.32 245	95.46 309	98.39 297	99.40 333	99.69 17	98.61 336	98.64 245	92.39 334	97.76 286

TR-MVS		98.14 225	97.74 237	99.33 195	99.59 214	98.28 246	99.27 375	99.21 310	96.42 266	99.15 247	99.94 239	88.87 334	99.79 216	98.88 231	98.29 215	99.93 150

test0.0.03 1		98.12 226	98.03 227	98.39 257	99.11 292	98.07 260	100.00 1	99.93 30	96.70 245	96.91 358	99.95 233	99.31 71	98.19 365	91.93 376	98.44 199	98.91 277

GeoE		98.06 227	97.65 242	99.29 200	99.47 258	98.41 231	100.00 1	99.19 314	94.85 321	98.88 264	100.00 1	91.21 292	99.59 239	97.02 308	98.19 223	99.88 185

tpm cat1		98.05 228	97.76 236	98.92 226	99.50 248	97.10 310	99.77 314	99.30 256	90.20 396	99.72 210	98.71 377	97.71 170	99.86 197	96.75 321	98.20 222	99.81 221

PS-MVSNAJss		98.03 229	98.06 225	97.94 299	97.63 376	97.33 300	99.89 291	99.23 300	96.27 276	98.03 319	99.59 313	98.75 131	98.78 322	98.52 253	94.61 312	97.70 341

CR-MVSNet		98.02 230	97.71 240	98.93 225	99.31 280	98.86 206	99.13 397	99.00 378	96.53 258	99.96 135	98.98 362	96.94 204	98.10 375	91.18 381	98.40 202	99.84 203

EI-MVSNet		97.98 231	97.93 231	98.16 276	99.11 292	97.84 279	99.74 319	99.29 263	94.39 337	98.65 279	100.00 1	97.21 192	98.88 317	97.62 292	95.31 289	97.75 297

FIs		97.95 232	97.73 239	98.62 242	98.53 340	99.24 174	100.00 1	99.43 125	96.74 240	97.87 329	99.82 267	95.27 234	98.89 314	98.78 236	93.07 323	97.74 319

Anonymous202405211		97.87 233	97.53 244	98.90 227	99.81 131	96.70 319	99.35 368	99.46 95	92.98 372	98.83 271	99.99 195	90.63 305	100.00 1	99.70 157	97.03 266	100.00 1

FC-MVSNet-test		97.84 234	97.63 243	98.45 253	98.30 350	99.05 190	100.00 1	99.43 125	96.63 254	97.61 341	99.82 267	95.19 238	98.57 344	98.64 245	93.05 324	97.73 326

Patchmatch-test		97.83 235	97.42 247	99.06 214	99.08 296	97.66 286	98.66 413	99.21 310	93.65 354	98.25 310	99.58 315	99.47 48	99.57 244	90.25 391	98.59 192	99.95 134

sd_testset		97.81 236	97.48 245	98.79 235	99.82 125	96.80 316	99.32 370	99.45 103	97.62 156	99.38 234	99.86 255	85.56 368	99.77 221	99.72 150	96.61 275	99.79 242

miper_ehance_all_eth		97.81 236	97.66 241	98.23 269	99.49 252	98.37 237	99.99 229	99.11 345	94.78 322	98.25 310	99.21 345	98.18 150	98.57 344	97.35 302	92.61 329	97.76 286

test_vis1_n_1920		97.77 238	97.24 259	99.34 192	99.79 149	98.04 264	100.00 1	99.25 290	98.88 49	100.00 1	100.00 1	77.52 402	100.00 1	99.88 112	99.85 145	100.00 1

HQP-MVS		97.73 239	97.85 233	97.39 316	99.07 297	94.82 347	100.00 1	99.40 197	99.04 16	99.17 243	99.97 209	88.61 337	99.57 244	99.79 130	95.58 279	97.77 284

GA-MVS		97.72 240	97.27 257	99.06 214	99.24 287	97.93 273	100.00 1	99.24 296	95.80 295	98.99 259	99.64 301	89.77 319	99.36 282	95.12 345	97.62 260	99.89 172

HQP_MVS		97.71 241	97.82 235	97.37 317	99.00 309	94.80 350	100.00 1	99.40 197	99.00 27	99.08 253	99.97 209	88.58 339	99.55 253	99.79 130	95.57 283	97.76 286

nrg030		97.64 242	97.27 257	98.75 237	98.34 345	99.53 133	100.00 1	99.22 304	96.21 281	98.27 308	99.95 233	94.40 251	98.98 304	99.23 214	89.78 368	97.75 297

TAPA-MVS	96.40 10	97.64 242	97.37 251	98.45 253	99.94 103	95.70 334	100.00 1	99.40 197	97.65 151	99.53 218	100.00 1	99.31 71	99.66 234	80.48 418	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		97.64 242	97.74 237	97.36 318	99.01 305	94.76 355	100.00 1	99.34 240	99.30 4	99.00 258	99.97 209	87.49 348	99.57 244	99.96 94	95.58 279	97.75 297
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
D2MVS		97.63 245	97.83 234	97.05 331	98.83 329	94.60 359	100.00 1	99.82 40	96.89 228	98.28 306	99.03 358	94.05 254	99.47 270	98.58 252	94.97 307	97.09 385

c3_l		97.58 246	97.42 247	98.06 287	99.48 254	98.16 252	99.96 267	99.10 348	94.54 331	98.13 314	99.20 347	97.87 161	98.25 363	97.28 303	91.20 356	97.75 297

IterMVS-LS		97.56 247	97.44 246	97.92 302	99.38 277	97.90 274	99.89 291	99.10 348	94.41 336	98.32 303	99.54 323	97.21 192	98.11 372	97.50 294	91.62 348	97.75 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		97.55 248	97.38 250	98.07 283	97.50 384	97.99 266	100.00 1	99.13 338	95.46 309	98.47 294	99.85 260	92.01 288	98.59 341	98.63 247	95.36 287	97.62 362

dmvs_re		97.54 249	97.88 232	96.54 351	99.55 227	90.35 398	99.86 295	99.46 95	97.00 216	99.41 232	100.00 1	90.78 302	99.30 287	99.60 182	95.24 294	99.96 128

cl____		97.54 249	97.32 253	98.18 273	99.47 258	98.14 255	100.00 1	99.10 348	94.16 344	97.60 342	99.63 305	97.52 181	98.65 333	96.47 322	91.97 342	97.76 286

IB-MVS	96.24 12	97.54 249	96.95 264	99.33 195	99.67 181	98.10 258	100.00 1	99.47 79	97.42 184	99.26 240	99.69 288	98.83 126	99.89 188	99.43 198	78.77 415	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		97.52 252	97.35 252	98.05 291	99.46 261	98.11 256	100.00 1	99.10 348	94.21 341	97.62 340	99.63 305	97.65 173	98.29 360	96.47 322	91.98 341	97.76 286

eth_miper_zixun_eth		97.47 253	97.28 255	98.06 287	99.41 268	97.94 272	99.62 340	99.08 354	94.46 335	98.19 313	99.56 320	96.91 206	98.50 349	96.78 318	91.49 351	97.74 319

test_fmvs1_n		97.43 254	96.86 267	99.15 212	99.68 173	97.48 291	99.99 229	98.98 381	98.82 60	100.00 1	100.00 1	74.85 409	99.96 151	99.67 168	99.70 158	100.00 1

LFMVS		97.42 255	96.62 276	99.81 107	99.80 144	99.50 140	99.16 392	99.56 70	94.48 334	100.00 1	100.00 1	79.35 396	100.00 1	99.89 110	97.37 261	99.94 139

miper_lstm_enhance		97.40 256	97.28 255	97.75 307	99.48 254	97.52 289	100.00 1	99.07 359	94.08 345	98.01 322	99.61 311	97.38 189	97.98 383	96.44 325	91.47 353	97.76 286

RPSCF		97.37 257	98.24 211	94.76 375	99.80 144	84.57 412	99.99 229	99.05 369	94.95 319	99.82 197	100.00 1	94.03 255	100.00 1	98.15 270	98.38 206	99.70 255

ACMM	97.17 6	97.37 257	97.40 249	97.29 323	99.01 305	94.64 358	100.00 1	99.25 290	98.07 116	98.44 295	99.98 200	87.38 350	99.55 253	99.25 211	95.19 297	97.69 345
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		97.31 259	97.32 253	97.28 324	98.85 327	94.60 359	100.00 1	99.37 216	97.35 188	98.85 267	99.98 200	86.66 356	99.56 248	99.55 189	95.26 291	97.70 341

FMVSNet3		97.30 260	96.95 264	98.37 259	99.65 190	99.25 172	99.71 327	99.28 269	94.23 339	98.53 288	98.91 369	93.30 266	98.11 372	95.31 341	93.60 317	97.73 326

UniMVSNet (Re)		97.29 261	96.85 268	98.59 245	98.49 341	99.13 185	100.00 1	99.42 144	96.52 259	98.24 312	98.90 370	94.93 242	98.89 314	97.54 293	87.61 387	97.75 297

OPM-MVS		97.21 262	97.18 262	97.32 321	98.08 362	94.66 356	100.00 1	99.28 269	98.65 78	98.92 261	99.98 200	86.03 364	99.56 248	98.28 266	95.41 285	97.72 332
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	97.00 8	97.19 263	97.16 263	97.27 326	98.97 314	94.58 362	100.00 1	99.32 245	97.97 124	97.45 346	99.98 200	85.79 366	99.56 248	99.70 157	95.24 294	97.67 351
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs4		97.17 264	96.80 269	98.27 265	97.68 375	98.64 221	100.00 1	99.18 321	94.22 340	98.55 286	99.71 282	93.67 259	98.47 352	95.66 335	92.57 332	97.71 340

anonymousdsp		97.16 265	96.88 266	98.00 295	97.08 394	98.06 262	99.81 303	99.15 329	94.58 329	97.84 331	99.62 309	90.49 307	98.60 339	97.98 276	95.32 288	97.33 380

UniMVSNet_NR-MVSNet		97.16 265	96.80 269	98.22 270	98.38 344	98.41 231	100.00 1	99.45 103	96.14 283	97.76 332	99.64 301	95.05 240	98.50 349	97.98 276	86.84 391	97.75 297

XXY-MVS		97.14 267	96.63 275	98.67 239	98.65 334	98.92 204	99.54 349	99.29 263	95.57 301	97.63 338	99.83 263	87.79 346	99.35 284	98.39 258	92.95 325	97.75 297

WR-MVS		97.09 268	96.64 274	98.46 251	98.43 342	99.09 186	99.97 261	99.33 242	95.62 299	97.76 332	99.67 293	91.17 294	98.56 346	98.49 254	89.28 374	97.74 319

JIA-IIPM		97.09 268	96.34 290	99.36 190	98.88 322	98.59 224	99.81 303	99.43 125	84.81 413	99.96 135	90.34 423	98.55 141	99.52 262	97.00 309	98.28 216	99.98 115

jajsoiax		97.07 270	96.79 271	97.89 303	97.28 392	97.12 308	99.95 273	99.19 314	96.55 256	97.31 349	99.69 288	87.35 352	98.91 311	98.70 241	95.12 302	97.66 352

MIMVSNet		97.06 271	96.73 272	98.05 291	99.38 277	96.64 321	98.47 415	99.35 233	93.41 362	99.48 222	98.53 384	89.66 321	97.70 394	94.16 357	98.11 227	99.80 238

X-MVStestdata		97.04 272	96.06 301	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 76	100.00 1	66.97 434	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

h-mvs33		97.03 273	96.53 279	98.51 248	99.79 149	95.90 330	99.45 357	99.45 103	98.21 102	100.00 1	99.78 276	97.49 182	99.99 100	99.72 150	74.92 417	99.65 262

VPA-MVSNet		97.03 273	96.43 285	98.82 231	98.64 335	99.32 163	99.38 365	99.47 79	96.73 242	98.91 263	98.94 367	87.00 354	99.40 280	99.23 214	89.59 369	97.76 286

WB-MVSnew		97.02 275	97.24 259	96.37 355	99.44 264	97.36 296	100.00 1	99.43 125	96.12 284	99.35 236	99.89 250	93.60 262	98.42 355	88.91 402	98.39 204	93.33 417

mvs_tets		97.00 276	96.69 273	97.94 299	97.41 391	97.27 302	99.60 342	99.18 321	96.51 260	97.35 348	99.69 288	86.53 358	98.91 311	98.84 233	95.09 303	97.65 356

gg-mvs-nofinetune		96.95 277	96.10 299	99.50 167	99.41 268	99.36 161	99.07 405	99.52 72	83.69 415	99.96 135	83.60 431	100.00 1	99.20 292	99.68 165	99.99 103	99.96 128

Anonymous20240529		96.93 278	96.22 295	99.05 216	99.79 149	97.30 301	99.16 392	99.47 79	88.51 402	98.69 277	100.00 1	83.50 381	100.00 1	99.83 122	97.02 267	99.83 207

DU-MVS		96.93 278	96.49 282	98.22 270	98.31 348	98.41 231	100.00 1	99.37 216	96.41 267	97.76 332	99.65 297	92.14 285	98.50 349	97.98 276	86.84 391	97.75 297

Patchmtry		96.81 280	96.37 288	98.14 277	99.31 280	98.55 226	98.91 408	99.00 378	90.45 392	97.92 326	98.98 362	96.94 204	98.12 370	94.27 354	91.53 350	97.75 297

hse-mvs2		96.79 281	96.38 287	98.04 293	99.68 173	95.54 337	99.81 303	99.42 144	98.21 102	100.00 1	99.80 273	97.49 182	99.46 274	99.72 150	73.27 420	99.12 274

ACMH	96.25 11	96.77 282	96.62 276	97.21 327	98.96 315	94.43 364	99.64 336	99.33 242	97.43 183	96.55 367	99.97 209	83.52 380	99.54 256	99.07 223	95.13 301	97.66 352
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		96.76 283	96.46 284	97.63 308	99.41 268	96.89 313	99.99 229	99.13 338	94.74 325	97.59 343	99.66 295	89.63 324	98.28 361	95.71 333	92.31 336	97.72 332
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		96.73 284	96.25 293	98.18 273	98.21 356	98.67 219	99.77 314	99.32 245	95.06 317	97.20 352	99.65 297	90.10 313	98.19 365	98.06 274	88.90 378	97.66 352

WR-MVS_H		96.73 284	96.32 292	97.95 298	98.26 353	97.88 276	99.72 326	99.43 125	95.06 317	96.99 355	98.68 379	93.02 271	98.53 347	97.43 297	88.33 383	97.43 375

IterMVS-SCA-FT		96.72 286	96.42 286	97.62 310	99.40 273	96.83 315	99.99 229	99.14 334	94.65 328	97.55 344	99.72 280	89.65 322	98.31 359	95.62 337	92.05 339	97.73 326

v2v482		96.70 287	96.18 296	98.27 265	98.04 363	98.39 234	100.00 1	99.13 338	94.19 343	98.58 284	99.08 351	90.48 308	98.67 331	95.69 334	90.44 364	97.75 297

test_vis1_n		96.69 288	95.81 312	99.32 197	99.14 290	97.98 267	99.97 261	98.98 381	98.45 87	100.00 1	100.00 1	66.44 420	99.99 100	99.78 136	99.57 170	100.00 1

V42		96.65 289	96.16 298	98.11 282	98.17 360	98.23 249	99.99 229	99.09 353	93.97 346	98.74 276	99.05 354	91.09 295	98.82 320	95.46 339	89.90 366	97.27 381

EU-MVSNet		96.63 290	96.53 279	96.94 338	97.59 380	96.87 314	99.76 316	99.47 79	96.35 272	96.85 360	99.78 276	92.57 281	96.27 408	95.33 340	91.08 357	97.68 347

NR-MVSNet		96.63 290	96.04 302	98.38 258	98.31 348	98.98 201	99.22 385	99.35 233	95.87 289	94.43 390	99.65 297	92.73 277	98.40 356	96.78 318	88.05 384	97.75 297

XVG-ACMP-BASELINE		96.60 292	96.52 281	96.84 344	98.41 343	93.29 376	99.99 229	99.32 245	97.76 143	98.51 291	99.29 339	81.95 387	99.54 256	98.40 257	95.03 304	97.68 347

VDD-MVS		96.58 293	95.99 304	98.34 261	99.52 239	95.33 338	99.18 386	99.38 213	96.64 252	99.77 204	100.00 1	72.51 414	100.00 1	100.00 1	96.94 269	99.70 255

tt0805		96.52 294	96.23 294	97.40 315	99.30 283	93.55 371	99.32 370	99.45 103	96.75 238	97.88 328	99.99 195	79.99 394	99.59 239	97.39 300	95.98 278	99.06 276

LCM-MVSNet-Re		96.52 294	97.21 261	94.44 376	99.27 284	85.80 409	99.85 297	96.61 426	95.98 286	92.75 399	98.48 386	93.97 257	97.55 395	99.58 187	98.43 200	99.98 115

our_test_3		96.51 296	96.35 289	96.98 336	97.61 378	95.05 342	99.98 255	99.01 377	94.68 326	96.77 364	99.06 352	95.87 225	98.14 368	91.81 377	92.37 335	97.75 297

MVP-Stereo		96.51 296	96.48 283	96.60 350	95.65 406	94.25 365	98.84 410	98.16 402	95.85 293	95.23 381	99.04 355	92.54 282	99.13 294	92.98 369	99.98 113	96.43 398
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		96.51 296	95.97 306	98.13 280	97.98 366	98.04 264	99.99 229	99.08 354	93.51 359	98.62 282	98.98 362	90.98 299	98.62 335	93.79 361	90.79 360	97.74 319

ACMH+	96.20 13	96.49 299	96.33 291	97.00 334	99.06 301	93.80 369	99.81 303	99.31 252	97.32 193	95.89 378	99.97 209	82.62 385	99.54 256	98.34 261	94.63 311	97.65 356

TranMVSNet+NR-MVSNet		96.45 300	96.01 303	97.79 306	98.00 365	97.62 287	100.00 1	99.35 233	95.98 286	97.31 349	99.64 301	90.09 314	98.00 382	96.89 313	86.80 394	97.75 297

ET-MVSNet_ETH3D		96.41 301	95.48 332	99.20 210	99.81 131	99.75 99	100.00 1	99.02 375	97.30 197	78.33 423	100.00 1	97.73 169	97.94 385	99.70 157	87.41 388	99.92 152

VPNet		96.41 301	95.76 317	98.33 262	98.61 336	98.30 245	99.48 354	99.45 103	96.98 218	98.87 266	99.88 252	81.57 388	98.93 309	99.22 216	87.82 386	97.76 286

PVSNet_0	93.57 19	96.41 301	95.74 318	98.41 256	99.84 121	95.22 340	100.00 1	100.00 1	98.08 115	97.55 344	99.78 276	84.40 373	100.00 1	100.00 1	81.99 407	100.00 1

v144192		96.40 304	95.81 312	98.17 275	97.89 369	98.11 256	99.99 229	99.06 367	93.39 363	98.75 275	99.09 350	90.43 309	98.66 332	93.10 368	90.55 363	97.75 297

VDDNet		96.39 305	95.55 327	98.90 227	99.27 284	97.45 292	99.15 394	99.92 34	91.28 385	99.98 122	100.00 1	73.55 410	100.00 1	99.85 118	96.98 268	99.24 271

tfpnnormal		96.36 306	95.69 323	98.37 259	98.55 338	98.71 216	99.69 331	99.45 103	93.16 370	96.69 366	99.71 282	88.44 341	98.99 303	94.17 355	91.38 354	97.41 376

v8		96.35 307	95.73 319	98.21 272	98.11 361	98.23 249	99.94 278	99.07 359	92.66 378	98.29 305	99.00 361	91.46 289	98.77 325	94.17 355	88.83 380	97.62 362

PS-CasMVS		96.34 308	95.78 316	98.03 294	98.18 359	98.27 247	99.71 327	99.32 245	94.75 323	96.82 361	99.65 297	86.98 355	98.15 367	97.74 285	88.85 379	97.66 352

LTVRE_ROB	95.29 16	96.32 309	96.10 299	96.99 335	98.55 338	93.88 368	99.45 357	99.28 269	94.50 333	96.46 368	99.52 324	84.86 371	99.48 268	97.26 304	95.03 304	97.59 366
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		96.29 310	95.70 320	98.07 283	99.80 144	97.49 290	99.15 394	99.40 197	89.11 399	97.75 335	99.45 329	88.93 333	98.98 304	98.26 267	89.47 371	97.73 326

v148		96.29 310	95.84 311	97.63 308	97.74 373	96.53 323	100.00 1	99.07 359	93.52 358	98.01 322	99.42 331	91.22 291	98.60 339	96.37 326	87.22 390	97.75 297

AUN-MVS		96.26 312	95.67 324	98.06 287	99.68 173	95.60 336	99.82 302	99.42 144	96.78 235	99.88 182	99.80 273	94.84 244	99.47 270	97.48 295	73.29 419	99.12 274

ttmdpeth		96.24 313	95.88 309	97.32 321	97.80 371	96.61 322	99.95 273	98.77 394	97.80 138	93.42 395	99.28 340	86.42 359	99.01 300	97.63 289	91.84 344	96.33 400

FMVSNet2		96.22 314	95.60 326	98.06 287	99.53 231	98.33 241	99.45 357	99.27 279	93.71 350	98.03 319	98.84 372	84.23 375	98.10 375	93.97 359	93.40 320	97.73 326

LF4IMVS		96.19 315	96.18 296	96.23 359	98.26 353	92.09 387	100.00 1	97.89 413	97.82 136	97.94 324	99.87 253	82.71 384	99.38 281	97.41 298	93.71 316	97.20 382

v1192		96.18 316	95.49 330	98.26 267	98.01 364	98.15 253	99.99 229	99.08 354	93.36 364	98.54 287	98.97 365	89.47 325	98.89 314	91.15 382	90.82 359	97.75 297

testgi		96.18 316	95.93 307	96.93 339	98.98 313	94.20 367	100.00 1	99.07 359	97.16 203	96.06 375	99.86 255	84.08 378	97.79 391	90.38 390	97.80 248	98.81 278

Syy-MVS		96.17 318	96.57 278	95.00 370	99.50 248	87.37 407	100.00 1	99.57 68	96.23 277	98.07 316	100.00 1	92.41 283	97.81 388	85.34 408	97.96 235	99.82 212

ppachtmachnet_test		96.17 318	95.89 308	97.02 333	97.61 378	95.24 339	99.99 229	99.24 296	93.31 366	96.71 365	99.62 309	94.34 252	98.07 377	89.87 392	92.30 337	97.75 297

v1921920		96.16 320	95.50 328	98.14 277	97.88 370	97.96 270	99.99 229	99.07 359	93.33 365	98.60 283	99.24 342	89.37 326	98.71 329	91.28 380	90.74 361	97.75 297

Baseline_NR-MVSNet		96.16 320	95.70 320	97.56 313	98.28 352	96.79 317	100.00 1	97.86 414	91.93 382	97.63 338	99.47 328	92.14 285	98.35 358	97.13 305	86.83 393	97.54 369

v10		96.14 322	95.50 328	98.07 283	98.19 358	97.96 270	99.83 299	99.07 359	92.10 381	98.07 316	98.94 367	91.07 296	98.61 336	92.41 375	89.82 367	97.63 360

OurMVSNet-221017-0		96.14 322	95.98 305	96.62 349	97.49 386	93.44 373	99.92 283	98.16 402	95.86 291	97.65 337	99.95 233	85.71 367	98.78 322	94.93 347	94.18 315	97.64 359

GBi-Net		96.07 324	95.80 314	96.89 341	99.53 231	94.87 344	99.18 386	99.27 279	93.71 350	98.53 288	98.81 373	84.23 375	98.07 377	95.31 341	93.60 317	97.72 332

test1		96.07 324	95.80 314	96.89 341	99.53 231	94.87 344	99.18 386	99.27 279	93.71 350	98.53 288	98.81 373	84.23 375	98.07 377	95.31 341	93.60 317	97.72 332

v7n		96.06 326	95.42 336	97.99 297	97.58 381	97.35 297	99.86 295	99.11 345	92.81 377	97.91 327	99.49 326	90.99 298	98.92 310	92.51 372	88.49 382	97.70 341

PEN-MVS		96.01 327	95.48 332	97.58 312	97.74 373	97.26 303	99.90 287	99.29 263	94.55 330	96.79 362	99.55 321	87.38 350	97.84 387	96.92 312	87.24 389	97.65 356

v1240		95.96 328	95.25 337	98.07 283	97.91 368	97.87 278	99.96 267	99.07 359	93.24 368	98.64 281	98.96 366	88.98 332	98.61 336	89.58 396	90.92 358	97.75 297

pmmvs5		95.94 329	95.61 325	96.95 337	97.42 389	94.66 356	100.00 1	98.08 406	93.60 356	97.05 354	99.43 330	87.02 353	98.46 353	95.76 331	92.12 338	97.72 332

PatchT		95.90 330	94.95 345	98.75 237	99.03 303	98.39 234	99.08 403	99.32 245	85.52 411	99.96 135	94.99 415	97.94 156	98.05 381	80.20 419	98.47 198	99.81 221

USDC		95.90 330	95.70 320	96.50 352	98.60 337	92.56 385	100.00 1	98.30 400	97.77 141	96.92 356	99.94 239	81.25 391	99.45 275	93.54 364	94.96 308	97.49 372

pm-mvs1		95.76 332	95.01 342	98.00 295	98.23 355	97.45 292	99.24 378	99.04 372	93.13 371	95.93 377	99.72 280	86.28 360	98.84 319	95.62 337	87.92 385	97.72 332

SixPastTwentyTwo		95.71 333	95.49 330	96.38 354	97.42 389	93.01 377	99.84 298	98.23 401	94.75 323	95.98 376	99.97 209	85.35 369	98.43 354	94.71 349	93.17 322	97.69 345

MS-PatchMatch		95.66 334	95.87 310	95.05 368	97.80 371	89.25 401	98.88 409	99.30 256	96.35 272	96.86 359	99.01 360	81.35 390	99.43 277	93.30 366	99.98 113	96.46 397

DTE-MVSNet		95.52 335	94.99 343	97.08 330	97.49 386	96.45 324	100.00 1	99.25 290	93.82 349	96.17 373	99.57 319	87.81 345	97.18 396	94.57 350	86.26 397	97.62 362

TinyColmap		95.50 336	95.12 341	96.64 348	98.69 333	93.00 378	99.40 363	97.75 416	96.40 268	96.14 374	99.87 253	79.47 395	99.50 266	93.62 363	94.72 310	97.40 377

K. test v3		95.46 337	95.14 340	96.40 353	97.53 383	93.40 374	99.99 229	99.23 300	95.49 307	92.70 400	99.73 279	84.26 374	98.12 370	93.94 360	93.38 321	97.68 347

SSC-MVS3.2		95.32 338	94.97 344	96.37 355	98.29 351	92.75 381	100.00 1	99.30 256	95.46 309	98.36 298	99.42 331	78.92 398	98.63 334	93.28 367	91.72 347	97.72 332

FMVSNet5		95.32 338	95.43 335	94.99 371	99.39 276	92.99 379	99.25 377	99.24 296	90.45 392	97.44 347	98.45 387	95.78 227	94.39 417	87.02 404	91.88 343	97.59 366

UniMVSNet_ETH3D		95.28 340	94.41 346	97.89 303	98.91 319	95.14 341	99.13 397	99.35 233	92.11 380	97.17 353	99.66 295	70.28 417	99.36 282	97.88 281	95.18 298	99.16 272

RPMNet		95.26 341	93.82 350	99.56 162	99.31 280	98.86 206	99.13 397	99.42 144	79.82 420	99.96 135	95.13 413	95.69 229	99.98 129	77.54 423	98.40 202	99.84 203

DSMNet-mixed		95.18 342	95.21 339	95.08 367	96.03 401	90.21 399	99.65 335	93.64 432	92.91 373	98.34 301	97.40 403	90.05 316	95.51 414	91.02 383	97.86 242	99.51 267

test_fmvs2		95.17 343	95.23 338	95.01 369	98.95 317	88.99 403	99.99 229	97.77 415	97.79 139	98.58 284	99.70 285	73.36 411	99.34 285	95.88 330	95.03 304	96.70 393

TransMVSNet (Re)		94.78 344	93.72 351	97.93 301	98.34 345	97.88 276	99.23 383	97.98 411	91.60 383	94.55 387	99.71 282	87.89 344	98.36 357	89.30 398	84.92 398	97.56 368

mmtdpeth		94.58 345	94.18 347	95.81 364	98.82 331	91.09 395	99.99 229	98.61 397	96.38 269	100.00 1	97.23 404	76.52 405	99.85 203	99.82 127	80.22 411	96.48 396

FMVSNet1		94.45 346	93.63 353	96.89 341	98.87 325	94.87 344	99.18 386	99.27 279	90.95 389	97.31 349	98.81 373	72.89 413	98.07 377	92.61 370	92.81 327	97.72 332

test_0402		94.35 347	93.70 352	96.32 357	97.92 367	93.60 370	99.61 341	98.85 390	88.19 405	94.68 386	99.48 327	80.01 393	98.58 343	89.39 397	95.15 300	96.77 391

MVStest1		94.27 348	93.30 357	97.19 328	98.83 329	97.18 306	99.93 281	98.79 393	86.80 408	84.88 420	99.04 355	94.32 253	98.25 363	90.55 387	86.57 395	96.12 403

UnsupCasMVSNet_eth		94.25 349	93.89 349	95.34 366	97.63 376	92.13 386	99.73 324	99.36 222	94.88 320	92.78 397	98.63 381	82.72 383	96.53 404	94.57 350	84.73 399	97.36 378

KD-MVS_2432*1600		94.15 350	93.08 359	97.35 319	99.53 231	97.83 280	99.63 338	99.19 314	92.88 374	96.29 370	97.68 400	98.84 124	96.70 400	89.73 393	63.92 424	97.53 370

miper_refine_blended		94.15 350	93.08 359	97.35 319	99.53 231	97.83 280	99.63 338	99.19 314	92.88 374	96.29 370	97.68 400	98.84 124	96.70 400	89.73 393	63.92 424	97.53 370

MVS-HIRNet		94.12 352	92.73 366	98.29 264	99.33 279	95.95 327	99.38 365	99.19 314	74.54 423	98.26 309	86.34 427	86.07 362	99.06 297	91.60 379	99.87 141	99.85 201

new_pmnet		94.11 353	93.47 355	96.04 362	96.60 398	92.82 380	99.97 261	98.91 386	90.21 395	95.26 380	98.05 398	85.89 365	98.14 368	84.28 410	92.01 340	97.16 383

mvs5depth		93.81 354	93.00 361	96.23 359	94.25 414	93.33 375	97.43 421	98.07 407	93.47 360	94.15 392	99.58 315	77.52 402	98.97 306	93.64 362	88.92 377	96.39 399

pmmvs6		93.64 355	92.87 363	95.94 363	97.47 388	91.41 392	98.92 407	99.02 375	87.84 406	95.01 383	99.61 311	77.24 404	98.77 325	94.33 353	86.41 396	97.63 360

Patchmatch-RL test		93.49 356	93.63 353	93.05 387	91.78 418	83.41 413	98.21 417	96.95 423	91.58 384	91.05 402	97.64 402	99.40 63	95.83 412	94.11 358	81.95 408	99.91 155

Anonymous20231206		93.45 357	93.17 358	94.30 379	95.00 411	89.69 400	99.98 255	98.43 399	93.30 367	94.50 389	98.59 382	90.52 306	95.73 413	77.46 424	90.73 362	97.48 374

Anonymous20240521		93.29 358	92.76 365	94.90 374	95.64 407	91.27 393	99.97 261	98.82 391	87.04 407	94.71 385	98.19 393	83.86 379	96.80 399	84.04 411	92.56 333	96.64 394

dmvs_testset		93.27 359	95.48 332	86.65 399	98.74 332	68.42 428	99.92 283	98.91 386	96.19 282	93.28 396	100.00 1	91.06 297	91.67 424	89.64 395	91.54 349	99.86 200

test20.03		93.11 360	92.85 364	93.88 384	95.19 410	91.83 388	100.00 1	98.87 389	93.68 353	92.76 398	98.88 371	89.20 329	92.71 422	77.88 422	89.19 375	97.09 385

test_vis1_rt		93.10 361	92.93 362	93.58 385	99.63 198	85.07 410	99.99 229	93.71 431	97.49 176	90.96 403	97.10 405	60.40 422	99.95 164	99.24 213	97.90 240	95.72 407

APD_test1		93.07 362	94.14 348	89.85 393	99.18 288	72.49 421	99.76 316	98.90 388	92.86 376	96.35 369	99.94 239	75.56 407	99.91 185	86.73 405	97.98 233	97.15 384

EG-PatchMatch MVS		92.94 363	92.49 367	94.29 380	95.87 403	87.07 408	99.07 405	98.11 405	93.19 369	88.98 409	98.66 380	70.89 415	99.08 296	92.43 374	95.21 296	96.72 392

MDA-MVSNet_test_wron		92.61 364	91.09 372	97.19 328	96.71 397	97.26 303	100.00 1	99.14 334	88.61 401	67.90 429	98.32 392	89.03 330	96.57 403	90.47 389	89.59 369	97.74 319

YYNet1		92.44 365	90.92 373	97.03 332	96.20 399	97.06 311	99.99 229	99.14 334	88.21 404	67.93 428	98.43 389	88.63 336	96.28 407	90.64 384	89.08 376	97.74 319

MIMVSNet1		91.96 366	91.20 369	94.23 381	94.94 412	91.69 390	99.34 369	99.22 304	88.23 403	94.18 391	98.45 387	75.52 408	93.41 421	79.37 420	91.49 351	97.60 365

TDRefinement		91.93 367	90.48 375	96.27 358	81.60 431	92.65 384	99.10 400	97.61 419	93.96 347	93.77 393	99.85 260	80.03 392	99.53 261	97.82 283	70.59 421	96.63 395

OpenMVS_ROB	88.34 20	91.89 368	91.12 370	94.19 382	95.55 408	87.63 406	99.26 376	98.03 408	86.61 410	90.65 407	96.82 407	70.14 418	98.78 322	86.54 406	96.50 277	96.15 401

N_pmnet		91.88 369	93.37 356	87.40 398	97.24 393	66.33 431	99.90 287	91.05 434	89.77 398	95.65 379	98.58 383	90.05 316	98.11 372	85.39 407	92.72 328	97.75 297

pmmvs-eth3d		91.73 370	90.67 374	94.92 373	91.63 420	92.71 383	99.90 287	98.54 398	91.19 386	88.08 411	95.50 411	79.31 397	96.13 409	90.55 387	81.32 410	95.91 406

MDA-MVSNet-bldmvs		91.65 371	89.94 379	96.79 347	96.72 396	96.70 319	99.42 362	98.94 383	88.89 400	66.97 431	98.37 390	81.43 389	95.91 411	89.24 399	89.46 372	97.75 297

KD-MVS_self_test		91.16 372	90.09 377	94.35 378	94.44 413	91.27 393	99.74 319	99.08 354	90.82 390	94.53 388	94.91 416	86.11 361	94.78 416	82.67 413	68.52 422	96.99 387

CL-MVSNet_self_test		91.07 373	90.35 376	93.24 386	93.27 415	89.16 402	99.55 347	99.25 290	92.34 379	95.23 381	97.05 406	88.86 335	93.59 420	80.67 417	66.95 423	96.96 388

test_method		91.04 374	91.10 371	90.85 390	98.34 345	77.63 417	100.00 1	98.93 385	76.69 421	96.25 372	98.52 385	70.44 416	97.98 383	89.02 401	91.74 345	96.92 389

CMPMVS	66.12 22	90.65 375	92.04 368	86.46 400	96.18 400	66.87 430	98.03 418	99.38 213	83.38 416	85.49 417	99.55 321	77.59 401	98.80 321	94.44 352	94.31 314	93.72 415
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		90.62 376	89.36 382	94.40 377	90.53 425	91.49 391	100.00 1	96.73 424	84.21 414	93.65 394	96.65 408	82.56 386	94.83 415	82.28 414	77.62 416	96.89 390

new-patchmatchnet		90.30 377	89.46 381	92.84 388	90.77 423	88.55 405	99.83 299	98.80 392	90.07 397	87.86 412	95.00 414	78.77 399	94.30 418	84.86 409	79.15 413	95.68 409

UnsupCasMVSNet_bld		89.50 378	88.00 384	93.99 383	95.30 409	88.86 404	98.52 414	99.28 269	85.50 412	87.80 413	94.11 417	61.63 421	96.96 398	90.63 385	79.26 412	96.15 401

mvsany_test3		89.36 379	88.96 383	90.56 391	91.95 417	78.97 416	99.74 319	96.59 427	96.84 230	89.25 408	96.07 409	52.59 424	97.11 397	95.17 344	82.44 406	95.58 410

PM-MVS		88.39 380	87.41 385	91.31 389	91.73 419	82.02 415	99.79 308	96.62 425	91.06 388	90.71 406	95.73 410	48.60 426	95.96 410	90.56 386	81.91 409	95.97 405

WB-MVS		88.24 381	90.09 377	82.68 406	91.56 421	69.51 426	100.00 1	98.73 395	90.72 391	87.29 414	98.12 394	92.87 273	85.01 428	62.19 429	89.34 373	93.54 416

SSC-MVS		87.61 382	89.47 380	82.04 407	90.63 424	68.77 427	99.99 229	98.66 396	90.34 394	86.70 415	98.08 395	92.72 278	84.12 429	59.41 432	88.71 381	93.22 420

test_fmvs3		87.19 383	87.02 386	87.71 397	92.69 416	76.64 418	99.96 267	97.27 420	93.55 357	90.82 405	94.03 418	38.00 432	92.19 423	93.49 365	83.35 405	94.32 412

test_f		86.87 384	86.06 387	89.28 394	91.45 422	76.37 419	99.87 294	97.11 421	91.10 387	88.46 410	93.05 420	38.31 431	96.66 402	91.77 378	83.46 404	94.82 411

Gipuma		84.73 385	83.50 390	88.40 396	97.50 384	82.21 414	88.87 425	99.05 369	65.81 425	85.71 416	90.49 422	53.70 423	96.31 406	78.64 421	91.74 345	86.67 424
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		84.40 386	84.79 388	83.23 404	95.71 404	58.71 437	98.79 411	97.75 416	81.58 417	84.94 418	98.07 396	45.33 428	97.73 392	77.09 425	83.85 401	93.24 418

APD_test2		84.40 386	84.79 388	83.23 404	95.71 404	58.71 437	98.79 411	97.75 416	81.58 417	84.94 418	98.07 396	45.33 428	97.73 392	77.09 425	83.85 401	93.24 418

testmvs		80.17 388	81.95 391	74.80 411	58.54 438	59.58 436	100.00 1	87.14 437	76.09 422	99.61 216	100.00 1	67.06 419	74.19 434	98.84 233	50.30 428	90.64 423

test_vis3_rt		79.61 389	78.19 394	83.86 403	88.68 426	69.56 425	99.81 303	82.19 439	86.78 409	68.57 427	84.51 430	25.06 436	98.26 362	89.18 400	78.94 414	83.75 427

EGC-MVSNET		79.46 390	74.04 398	95.72 365	96.00 402	92.73 382	99.09 402	99.04 372	5.08 435	16.72 435	98.71 377	73.03 412	98.74 328	82.05 415	96.64 274	95.69 408

test123		79.44 391	79.23 393	80.05 409	80.03 432	71.72 422	100.00 1	77.93 440	62.52 426	94.81 384	99.69 288	78.21 400	74.53 433	92.57 371	27.33 433	93.90 413

PMMVS2		79.15 392	77.28 395	84.76 402	82.34 430	72.66 420	99.70 329	95.11 430	71.68 424	84.78 421	90.87 421	32.05 434	89.99 425	75.53 427	63.45 426	91.64 421

LCM-MVSNet		79.01 393	76.93 396	85.27 401	78.28 433	68.01 429	96.57 422	98.03 408	55.10 429	82.03 422	93.27 419	31.99 435	93.95 419	82.72 412	74.37 418	93.84 414

FPMVS		77.92 394	79.45 392	73.34 413	76.87 434	46.81 440	98.24 416	99.05 369	59.89 428	73.55 424	98.34 391	36.81 433	86.55 426	80.96 416	91.35 355	86.65 425

tmp_tt		75.80 395	74.26 397	80.43 408	52.91 440	53.67 439	87.42 427	97.98 411	61.80 427	67.04 430	100.00 1	76.43 406	96.40 405	96.47 322	28.26 432	91.23 422

E-PMN		70.72 396	70.06 399	72.69 414	83.92 429	65.48 433	99.95 273	92.72 433	49.88 431	72.30 425	86.26 428	47.17 427	77.43 431	53.83 433	44.49 429	75.17 431

EMVS		69.88 397	69.09 400	72.24 415	84.70 428	65.82 432	99.96 267	87.08 438	49.82 432	71.51 426	84.74 429	49.30 425	75.32 432	50.97 434	43.71 430	75.59 430

MVE	68.59 21	67.22 398	64.68 402	74.84 410	74.67 436	62.32 435	95.84 423	90.87 435	50.98 430	58.72 432	81.05 432	12.20 440	78.95 430	61.06 431	56.75 427	83.24 428
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 399	63.44 403	73.88 412	61.14 437	63.45 434	95.68 424	87.18 436	79.93 419	47.35 433	80.68 433	22.35 437	72.33 435	61.24 430	35.42 431	85.88 426

PMVS	60.66 23	65.98 400	65.05 401	68.75 416	55.06 439	38.40 441	88.19 426	96.98 422	48.30 433	44.82 434	88.52 425	12.22 439	86.49 427	67.58 428	83.79 403	81.35 429
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		28.28 401	29.73 405	23.92 417	75.89 435	32.61 442	66.50 428	12.88 441	16.09 434	14.59 436	16.59 435	12.35 438	32.36 436	39.36 435	13.36 434	6.79 432

cdsmvs_eth3d_5k		24.41 402	32.55 404	0.00 418	0.00 441	0.00 443	0.00 429	99.39 210	0.00 436	0.00 437	100.00 1	93.55 263	0.00 437	0.00 436	0.00 435	0.00 433

ab-mvs-re		8.33 403	11.11 406	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	100.00 1	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

pcd_1.5k_mvsjas		8.24 404	10.99 407	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	98.75 131	0.00 437	0.00 436	0.00 435	0.00 433

test_blank		0.07 405	0.09 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.79 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

mmdepth		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

monomultidepth		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet_test		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

DCPMVS		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

sosnet-low-res		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

sosnet		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uncertanet		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

Regformer		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet		0.01 406	0.02 409	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.14 437	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

WAC-MVS						97.98 267								95.74 332

FOURS1					100.00 1	99.97 21	100.00 1	99.42 144	98.52 83	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 144					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 65	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		99.42 144					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 144	98.72 73	100.00 1	100.00 1	99.60 21

eth-test2					0.00 441

eth-test					0.00 441

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 195	100.00 1	100.00 1	99.32 69	99.99 100	100.00 1	100.00 1

RE-MVS-def			99.55 59		99.99 49	99.91 56	100.00 1	99.42 144	97.62 156	100.00 1	100.00 1	98.94 115		99.99 68	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 144	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								99.42 144	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		99.42 144	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	99.42 144	97.54 168	100.00 1	100.00 1	99.15 90	99.99 100	100.00 1	100.00 1

save fliter					99.99 49	99.93 47	100.00 1	99.42 144	98.93 41

test_0728_THIRD									98.79 68	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	99.42 144					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 5	100.00 1	99.42 144	99.04 16	100.00 1	100.00 1	99.53 33

GSMVS																99.91 155

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 77				99.91 155

sam_mvs												99.33 66

ambc				88.45 395	86.84 427	70.76 424	97.79 420	98.02 410		90.91 404	95.14 412	38.69 430	98.51 348	94.97 346	84.23 400	96.09 404

MTGPA								99.42 144

test_post1							99.32 370				88.24 426	99.33 66	99.59 239	98.31 262

test_post											89.05 424	99.49 43	99.59 239

patchmatchnet-post											97.79 399	99.41 61	99.54 256

GG-mvs-BLEND				99.59 155	99.54 228	99.49 144	99.17 391	99.52 72		99.96 135	99.68 292	100.00 1	99.33 286	99.71 154	99.99 103	99.96 128

MTMP							100.00 1	99.18 321

gm-plane-assit					99.52 239	97.26 303			95.86 291		100.00 1		99.43 277	98.76 238

test9_res														100.00 1	100.00 1	100.00 1

TEST9					100.00 1	99.95 32	100.00 1	99.42 144	97.65 151	100.00 1	100.00 1	99.53 33	99.97 136

test_8					100.00 1	99.91 56	100.00 1	99.42 144	97.70 146	100.00 1	100.00 1	99.51 37	99.98 129

agg_prior2														100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 77		99.42 144		100.00 1			99.97 136

TestCases				98.99 221	99.93 105	97.35 297		99.40 197	97.08 211	99.09 251	99.98 200	93.37 264	99.95 164	96.94 310	99.84 147	99.68 257

test_prior4						99.93 47	100.00 1

test_prior2							100.00 1		98.82 60	100.00 1	100.00 1	99.47 48		100.00 1	100.00 1

test_prior				99.90 78	100.00 1	99.75 99		99.73 56					99.97 136			100.00 1

旧先验2							100.00 1		98.11 114	100.00 1			100.00 1	99.67 168

新几何2							100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 131	100.00 1	100.00 1	99.20 85	100.00 1	97.91 280	100.00 1	100.00 1

旧先验1					99.99 49	99.88 77		99.82 40			100.00 1	99.27 80			100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 122				100.00 1	99.33 206		100.00 1

原ACMM2							100.00 1

原ACMM1				99.93 70	100.00 1	99.80 92		99.66 63	98.18 105	100.00 1	100.00 1	99.43 55	100.00 1	99.50 196	100.00 1	100.00 1

test222					99.99 49	99.90 63	100.00 1	99.69 62	97.66 150	100.00 1	100.00 1	99.30 76			100.00 1	100.00 1

testdata2													100.00 1	97.36 301

segment_acmp												99.55 29

testdata				99.66 145	99.99 49	98.97 203		99.73 56	97.96 127	100.00 1	100.00 1	99.42 59	100.00 1	99.28 210	100.00 1	100.00 1

testdata1							100.00 1		98.77 72

test12				99.95 54	99.99 49	99.89 70		99.42 144		100.00 1		99.24 82	99.97 136		100.00 1	100.00 1

plane_prior7					99.00 309	94.78 354

plane_prior6					99.06 301	94.80 350						88.58 339

plane_prior5								99.40 197					99.55 253	99.79 130	95.57 283	97.76 286

plane_prior4											99.97 209

plane_prior3						94.79 353			99.03 21	99.08 253

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 304

plane_prior						94.80 350	100.00 1		99.03 21						95.58 279

n2								0.00 442

nn								0.00 442

door-mid								96.32 428

lessismore_v0				96.05 361	97.55 382	91.80 389		99.22 304		91.87 401	99.91 247	83.50 381	98.68 330	92.48 373	90.42 365	97.68 347

LGP-MVS_train				97.28 324	98.85 327	94.60 359		99.37 216	97.35 188	98.85 267	99.98 200	86.66 356	99.56 248	99.55 189	95.26 291	97.70 341

test11								99.42 144

door								96.13 429

HQP5-MVS						94.82 347

HQP-NCC					99.07 297		100.00 1		99.04 16	99.17 243

ACMP_Plane					99.07 297		100.00 1		99.04 16	99.17 243

BP-MVS														99.79 130

HQP4-MVS										99.17 243			99.57 244			97.77 284

HQP3-MVS								99.40 197							95.58 279

HQP2-MVS												88.61 337

NP-MVS					99.07 297	94.81 349					99.97 209

MDTV_nov1_ep13_2view						99.24 174	99.56 346		96.31 275	99.96 135		98.86 122		98.92 229		99.89 172

MDTV_nov1_ep13			98.94 142		99.53 231	98.36 239	99.39 364	99.46 95	96.54 257	99.99 115	99.63 305	98.92 118	99.86 197	98.30 265	98.71 191

ACMMP++_ref															94.58 313

ACMMP++															95.17 299

Test By Simon												99.10 93

ITE_SJBPF				96.84 344	98.96 315	93.49 372		98.12 404	98.12 113	98.35 300	99.97 209	84.45 372	99.56 248	95.63 336	95.25 293	97.49 372

DeepMVS_CX				89.98 392	98.90 320	71.46 423		99.18 321	97.61 160	96.92 356	99.83 263	86.07 362	99.83 207	96.02 329	97.65 258	98.65 280

High-resolution multi-view benchmark