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	99.80 111	99.99 49	99.97 21	99.97 259	99.98 16	98.96 33	100.00 1	100.00 1	99.96 4	99.42 277	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 48	99.96 133	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 67	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 51	100.00 1	100.00 1	99.65 19	99.96 149	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 74	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	99.76 154	99.95 32	100.00 1	99.42 144	98.69 72	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 94	99.01 303	99.95 32	100.00 1	99.75 52	99.37 3	99.99 114	100.00 1	99.76 12	99.60 235	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 63	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 59	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 59	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	97.99 118	99.99 114	100.00 1	99.72 14	100.00 1	99.96 92	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	98.16 104	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	99.73 322	99.52 72	99.06 13	100.00 1	100.00 1	98.80 129	100.00 1	99.95 98	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	99.98 253	99.47 79	99.09 10	100.00 1	100.00 1	98.59 139	100.00 1	99.95 98	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	97.95 126	100.00 1	100.00 1	99.31 71	100.00 1	99.99 67	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	97.95 126	100.00 1	100.00 1	99.29 77	100.00 1	99.99 67	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	97.87 131	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	98.02 116	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	97.86 132	100.00 1	100.00 1	99.19 86	100.00 1	99.99 67	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 115	99.97 90	99.37 158	99.96 265	99.94 22	98.48 83	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 86	99.88 180	100.00 1	99.14 91	100.00 1	99.97 90	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	97.97 122	99.97 127	100.00 1	98.97 109	100.00 1	99.94 100	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	97.70 144	100.00 1	100.00 1	99.51 37	99.97 135	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 47	100.00 1	100.00 1	97.85 162	99.95 162	100.00 1	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 87	100.00 1	99.64 117	99.98 253	99.44 117	98.35 94	99.99 114	100.00 1	99.04 101	99.96 149	99.98 80	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	99.95 162	99.99 67	100.00 1	100.00 1

PLC	98.56 2	99.70 36	99.74 16	99.58 157	100.00 1	98.79 208	100.00 1	99.54 71	98.58 79	99.96 133	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 138
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	97.50 173	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 87	99.99 49	99.64 117	99.95 271	99.44 117	98.35 94	100.00 1	100.00 1	98.98 107	99.97 135	99.98 80	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	97.69 146	100.00 1	100.00 1	99.44 51	100.00 1	99.79 128	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	97.91 128	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	97.59 162	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 96	99.94 163	100.00 1	98.65 135	100.00 1	99.96 92	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	97.53 168	100.00 1	100.00 1	99.27 80	99.97 135	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	97.83 133	100.00 1	100.00 1	98.89 121	100.00 1	99.98 80	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	97.77 139	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	97.67 147	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	97.54 166	100.00 1	100.00 1	98.97 109	99.99 100	99.98 80	100.00 1	100.00 1

ACMMP		99.65 49	99.57 52	99.89 81	99.99 49	99.66 115	99.75 316	99.73 56	98.16 104	99.75 205	100.00 1	98.90 120	100.00 1	99.96 92	99.88 136	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	97.72 142	99.95 161	100.00 1	98.39 146	100.00 1	99.96 92	99.99 103	100.00 1

PS-MVSNAJ		99.64 51	99.57 52	99.85 94	99.78 151	99.81 90	99.95 271	99.42 144	98.38 88	100.00 1	100.00 1	98.75 131	100.00 1	99.88 110	99.99 103	99.74 249

F-COLMAP		99.64 51	99.64 37	99.67 140	99.99 49	99.07 185	100.00 1	99.44 117	98.30 97	99.90 175	100.00 1	99.18 87	99.99 100	99.91 105	100.00 1	99.94 138

fmvsm_l_conf0.5_n_a		99.63 54	99.55 59	99.86 90	99.83 124	99.58 124	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 90	99.81 131	99.59 122	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	97.62 154	100.00 1	100.00 1	98.65 135	99.99 100	99.99 67	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	97.64 286	100.00 1	100.00 1

EPNet		99.62 59	99.69 22	99.42 178	99.99 49	98.37 235	100.00 1	99.89 37	98.83 57	100.00 1	100.00 1	98.97 109	100.00 1	99.90 106	99.61 165	99.89 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 59	99.56 57	99.82 101	99.92 108	99.45 148	100.00 1	99.78 47	98.92 43	99.73 207	100.00 1	97.70 171	100.00 1	99.93 102	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	97.53 168	99.77 202	100.00 1	98.77 130	100.00 1	99.99 67	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	97.82 134	99.99 114	100.00 1	98.20 149	100.00 1	99.99 67	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	99.35 189	99.99 49	98.06 260	100.00 1	99.36 222	99.83 2	100.00 1	100.00 1	98.95 113	99.99 100	100.00 1	99.11 177	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	97.09 207	100.00 1	100.00 1	98.95 113	99.96 149	99.98 80	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	97.46 177	100.00 1	100.00 1	98.60 138	99.96 149	99.99 67	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 94	99.86 119	99.54 130	100.00 1	99.36 222	98.94 39	100.00 1	100.00 1	97.97 155	100.00 1	99.88 110	99.28 172	100.00 1

BP-MVS1		99.56 67	99.48 72	99.79 115	99.48 252	99.61 119	100.00 1	99.32 245	97.34 188	99.94 163	100.00 1	99.74 13	99.89 186	99.75 142	99.72 154	99.87 195

test_fmvsmconf_n		99.56 67	99.46 74	99.86 90	99.68 171	99.58 124	100.00 1	99.31 252	98.92 43	99.88 180	100.00 1	97.35 190	99.99 100	99.98 80	99.99 103	100.00 1

test_fmvsm_n_1920		99.55 69	99.49 69	99.73 130	99.85 120	99.19 177	100.00 1	99.41 193	98.87 51	100.00 1	100.00 1	97.34 191	100.00 1	99.98 80	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	97.98 120	99.84 184	100.00 1	98.94 115	99.98 128	99.86 114	98.21 219	99.94 138

test_yl		99.51 71	99.37 81	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 175	100.00 1	100.00 1	99.80 6	100.00 1	99.98 80	97.75 250	99.94 138

DCV-MVSNet		99.51 71	99.37 81	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 175	100.00 1	100.00 1	99.80 6	100.00 1	99.98 80	97.75 250	99.94 138

xiu_mvs_v2_base		99.51 71	99.41 75	99.82 101	99.70 163	99.73 103	99.92 281	99.40 197	98.15 106	100.00 1	100.00 1	98.50 143	100.00 1	99.85 116	99.13 176	99.74 249

HY-MVS	96.53 9	99.50 74	99.35 86	99.96 45	99.81 131	99.93 47	99.64 334	100.00 1	97.97 122	99.84 184	99.85 258	98.94 115	99.99 100	99.86 114	98.23 218	99.95 133

PHI-MVS		99.50 74	99.39 77	99.82 101	100.00 1	99.45 148	100.00 1	99.94 22	96.38 267	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 94	100.00 1	99.54 130	100.00 1	99.42 144	97.58 163	99.98 121	100.00 1	97.43 188	100.00 1	99.99 67	100.00 1	100.00 1

MAR-MVS		99.49 76	99.36 84	99.89 81	99.97 90	99.66 115	99.74 317	99.95 19	97.89 129	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	99.75 126	99.89 114	99.51 137	99.45 355	100.00 1	98.38 88	99.83 187	100.00 1	98.86 122	99.81 211	99.25 209	98.78 185	99.94 138

PVSNet_Blended		99.48 78	99.36 84	99.83 99	99.98 86	99.60 120	100.00 1	100.00 1	97.79 137	100.00 1	100.00 1	96.57 216	99.99 100	100.00 1	99.88 136	99.90 165

test_fmvsmvis_n_1920		99.46 80	99.37 81	99.73 130	98.88 320	99.18 179	100.00 1	99.26 286	98.85 53	99.79 199	100.00 1	97.70 171	100.00 1	99.98 80	99.86 141	100.00 1

testing3-2		99.45 81	99.31 89	99.86 90	99.70 163	99.73 103	100.00 1	99.47 79	97.46 177	99.97 127	99.97 207	99.48 47	100.00 1	99.78 134	97.99 230	99.85 200

sss		99.45 81	99.34 88	99.80 111	99.76 154	99.50 139	100.00 1	99.91 35	97.72 142	99.98 121	99.94 237	98.45 144	100.00 1	99.53 192	98.75 188	99.89 171

AdaColmap		99.44 83	99.26 97	99.95 54	100.00 1	99.86 82	99.70 327	99.99 13	98.53 80	99.90 175	100.00 1	95.34 232	100.00 1	99.92 103	100.00 1	100.00 1

balanced_conf03		99.43 84	99.28 91	99.85 94	99.68 171	99.68 113	99.97 259	99.28 269	97.03 212	99.96 133	99.97 207	97.90 159	99.93 178	99.77 136	100.00 1	99.94 138

thisisatest0515		99.42 85	99.31 89	99.74 127	99.59 212	99.55 127	100.00 1	99.46 95	96.65 249	99.92 170	100.00 1	99.44 51	99.85 201	99.09 220	99.63 164	99.81 220

myMVS_eth3d28		99.41 86	99.28 91	99.80 111	99.69 166	99.53 132	100.00 1	99.43 125	97.12 206	99.98 121	99.97 207	99.41 61	100.00 1	99.81 127	98.07 227	99.88 184

CANet		99.40 87	99.24 102	99.89 81	99.99 49	99.76 98	100.00 1	99.73 56	98.40 87	99.78 201	100.00 1	95.28 233	99.96 149	100.00 1	99.99 103	99.96 127

GDP-MVS		99.39 88	99.26 97	99.77 123	99.53 229	99.55 127	100.00 1	99.11 343	97.14 202	99.96 133	100.00 1	99.83 5	99.89 186	98.47 253	99.26 173	99.87 195

MVSMamba_PlusPlus		99.39 88	99.25 99	99.80 111	99.68 171	99.59 122	99.99 227	99.30 256	96.66 248	99.96 133	99.97 207	97.89 160	99.92 181	99.76 138	100.00 1	99.90 165

114514_t		99.39 88	99.25 99	99.81 106	99.97 90	99.48 146	100.00 1	99.42 144	95.53 300	100.00 1	100.00 1	98.37 147	99.95 162	99.97 90	100.00 1	100.00 1

fmvsm_l_conf0.5_n_3		99.38 91	99.20 111	99.92 74	99.80 142	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 67	99.92 130	100.00 1

alignmvs		99.38 91	99.21 107	99.91 75	99.73 159	99.92 53	100.00 1	99.51 76	97.61 158	100.00 1	100.00 1	99.06 96	99.93 178	99.83 120	97.12 262	99.90 165

1314		99.38 91	99.19 112	99.96 45	98.88 320	99.89 70	99.24 376	99.93 30	98.88 48	98.79 272	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	99.69 137	99.59 212	99.41 153	100.00 1	99.46 95	96.46 260	99.90 175	100.00 1	99.44 51	99.85 201	98.97 224	99.58 166	99.80 237

UBG		99.36 95	99.27 93	99.63 146	99.63 196	99.01 194	100.00 1	99.43 125	96.99 215	100.00 1	99.92 242	99.69 17	99.99 100	99.74 143	98.06 228	99.88 184

xiu_mvs_v1_base_debu		99.35 96	99.21 107	99.79 115	99.67 179	99.71 106	99.78 307	99.36 222	98.13 108	100.00 1	100.00 1	97.00 201	100.00 1	99.83 120	99.07 178	99.66 258

xiu_mvs_v1_base		99.35 96	99.21 107	99.79 115	99.67 179	99.71 106	99.78 307	99.36 222	98.13 108	100.00 1	100.00 1	97.00 201	100.00 1	99.83 120	99.07 178	99.66 258

xiu_mvs_v1_base_debi		99.35 96	99.21 107	99.79 115	99.67 179	99.71 106	99.78 307	99.36 222	98.13 108	100.00 1	100.00 1	97.00 201	100.00 1	99.83 120	99.07 178	99.66 258

ETV-MVS		99.34 99	99.24 102	99.64 145	99.58 217	99.33 160	100.00 1	99.25 288	97.57 164	99.96 133	100.00 1	97.44 187	99.79 214	99.70 155	99.65 161	99.81 220

tttt0517		99.34 99	99.23 105	99.67 140	99.57 221	99.38 155	100.00 1	99.46 95	96.33 272	99.89 178	100.00 1	99.44 51	99.84 204	98.93 226	99.46 170	99.78 244

CS-MVS		99.33 101	99.27 93	99.50 165	99.99 49	99.00 197	100.00 1	99.13 336	97.26 196	99.96 133	100.00 1	97.79 167	99.64 233	99.64 172	99.67 159	99.87 195

PVSNet_Blended_VisFu		99.33 101	99.18 115	99.78 120	99.82 125	99.49 142	100.00 1	99.95 19	97.36 185	99.63 213	100.00 1	96.45 220	99.95 162	99.79 128	99.65 161	99.89 171

fmvsm_s_conf0.5_n_a		99.32 103	99.15 117	99.81 106	99.80 142	99.47 147	100.00 1	99.35 233	98.22 99	100.00 1	100.00 1	95.21 237	99.99 100	99.96 92	99.86 141	99.98 114

HyFIR lowres test		99.32 103	99.24 102	99.58 157	99.95 100	99.26 168	100.00 1	99.99 13	96.72 241	99.29 237	99.91 245	99.49 43	99.47 268	99.74 143	98.08 226	100.00 1

SPE-MVS-test		99.31 105	99.27 93	99.43 176	99.99 49	98.77 209	100.00 1	99.19 312	97.24 197	99.96 133	100.00 1	97.56 179	99.70 230	99.68 163	99.81 149	99.82 211

LS3D		99.31 105	99.13 118	99.87 87	99.99 49	99.71 106	99.55 345	99.46 95	97.32 191	99.82 195	100.00 1	96.85 208	99.97 135	99.14 215	100.00 1	99.92 151

PVSNet	94.91 18	99.30 107	99.25 99	99.44 173	100.00 1	98.32 241	100.00 1	99.86 38	98.04 115	100.00 1	100.00 1	96.10 223	100.00 1	99.55 187	99.73 153	100.00 1

UWE-MVS-28		99.29 108	99.23 105	99.48 168	99.73 159	98.86 204	100.00 1	99.43 125	96.97 217	99.99 114	99.83 261	99.43 55	99.77 219	99.35 201	98.31 212	99.80 237

lupinMVS		99.29 108	99.16 116	99.69 137	99.45 260	99.49 142	100.00 1	99.15 327	97.45 179	99.97 127	100.00 1	96.76 209	99.76 222	99.67 166	100.00 1	99.81 220

CSCG		99.28 110	99.35 86	99.05 214	99.99 49	97.15 305	100.00 1	99.47 79	97.44 180	99.42 225	100.00 1	97.83 166	100.00 1	99.99 67	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	97.31 193	99.83 187	99.96 224	97.04 194	100.00 1	99.62 176	97.88 239	99.98 114

OMC-MVS		99.27 111	99.38 78	98.96 222	99.95 100	97.06 309	100.00 1	99.40 197	98.83 57	99.88 180	100.00 1	97.01 198	99.86 195	99.47 195	99.84 146	99.97 121

testing11		99.26 113	99.19 112	99.46 170	99.64 194	98.61 220	100.00 1	99.43 125	96.94 219	99.92 170	99.94 237	99.43 55	99.97 135	99.67 166	97.79 248	99.82 211

EIA-MVS		99.26 113	99.19 112	99.45 172	99.63 196	98.75 210	100.00 1	99.27 279	96.93 220	99.95 161	100.00 1	97.47 184	99.79 214	99.74 143	99.72 154	99.82 211

tfpn200view9		99.26 113	99.03 127	99.96 45	99.81 131	99.89 70	100.00 1	99.94 22	97.23 198	99.83 187	99.96 224	97.04 194	100.00 1	99.59 182	97.85 241	99.98 114

thres400		99.26 113	99.03 127	99.95 54	99.81 131	99.89 70	100.00 1	99.94 22	97.23 198	99.83 187	99.96 224	97.04 194	100.00 1	99.59 182	97.85 241	99.97 121

test_fmvsmconf0.1_n		99.25 117	99.05 125	99.82 101	98.92 316	99.55 127	100.00 1	99.23 298	98.91 45	99.75 205	99.97 207	94.79 245	99.94 174	99.94 100	99.99 103	99.97 121

thres100view900		99.25 117	99.01 129	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 204	99.83 187	99.96 224	97.01 198	100.00 1	99.59 182	97.85 241	99.98 114

EPMVS		99.25 117	99.13 118	99.60 151	99.60 208	99.20 176	99.60 340	100.00 1	96.93 220	99.92 170	99.36 334	99.05 98	99.71 229	98.77 235	98.94 182	99.90 165

thres600view7		99.24 120	99.00 131	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 204	99.83 187	99.96 224	97.01 198	100.00 1	99.54 190	97.77 249	99.97 121

MVS		99.22 121	98.96 137	99.98 23	99.00 307	99.95 32	99.24 376	99.94 22	98.14 107	98.88 262	100.00 1	95.63 230	100.00 1	99.85 116	100.00 1	100.00 1

fmvsm_s_conf0.5_n		99.21 122	99.01 129	99.83 99	99.84 121	99.53 132	100.00 1	99.38 213	98.29 98	100.00 1	100.00 1	93.62 261	99.99 100	99.99 67	99.93 128	99.98 114

EC-MVSNet		99.19 123	99.09 123	99.48 168	99.42 264	99.07 185	100.00 1	99.21 308	96.95 218	99.96 133	100.00 1	96.88 207	99.48 266	99.64 172	99.79 152	99.88 184

testing91		99.18 124	99.10 121	99.41 179	99.60 208	98.43 227	100.00 1	99.43 125	96.76 234	99.82 195	99.92 242	99.05 98	99.98 128	99.62 176	97.67 254	99.81 220

testing99		99.18 124	99.10 121	99.41 179	99.60 208	98.43 227	100.00 1	99.43 125	96.76 234	99.84 184	99.92 242	99.06 96	99.98 128	99.62 176	97.67 254	99.81 220

UWE-MVS		99.18 124	99.06 124	99.51 162	99.67 179	98.80 207	100.00 1	99.43 125	96.80 231	99.93 169	99.86 253	99.79 8	99.94 174	97.78 282	98.33 210	99.80 237

ETVMVS		99.16 127	98.98 134	99.69 137	99.67 179	99.56 126	100.00 1	99.45 103	96.36 269	99.98 121	99.95 231	98.65 135	99.64 233	99.11 219	97.63 257	99.88 184

FE-MVS		99.16 127	98.99 133	99.66 143	99.65 188	99.18 179	99.58 342	99.43 125	95.24 312	99.91 173	99.59 311	99.37 65	99.97 135	98.31 260	99.81 149	99.83 206

testing222		99.14 129	98.94 142	99.73 130	99.67 179	99.51 137	100.00 1	99.43 125	96.90 225	99.99 114	99.90 247	98.55 141	99.86 195	98.85 230	97.18 261	99.81 220

PMMVS		99.12 130	98.97 136	99.58 157	99.57 221	98.98 199	100.00 1	99.30 256	97.14 202	99.96 133	100.00 1	96.53 219	99.82 208	99.70 155	98.49 194	99.94 138

jason		99.11 131	98.96 137	99.59 153	99.17 287	99.31 163	100.00 1	99.13 336	97.38 184	99.83 187	100.00 1	95.54 231	99.72 228	99.57 186	99.97 116	99.74 249
jason: jason.
EPP-MVSNet		99.10 132	99.00 131	99.40 183	99.51 242	98.68 216	99.92 281	99.43 125	95.47 306	99.65 212	100.00 1	99.51 37	99.76 222	99.53 192	98.00 229	99.75 248

TESTMET0.1,1		99.08 133	98.96 137	99.44 173	99.63 196	99.38 155	100.00 1	99.45 103	95.53 300	99.48 220	100.00 1	99.71 15	99.02 297	96.84 312	99.99 103	99.91 154

IS-MVSNet		99.08 133	98.91 146	99.59 153	99.65 188	99.38 155	99.78 307	99.24 294	96.70 243	99.51 218	100.00 1	98.44 145	99.52 260	98.47 253	98.39 202	99.88 184

UA-Net		99.06 135	98.83 152	99.74 127	99.52 237	99.40 154	99.08 401	99.45 103	97.64 151	99.83 187	100.00 1	95.80 226	99.94 174	98.35 258	99.80 151	99.88 184

3Dnovator	95.63 14	99.06 135	98.76 159	99.96 45	98.86 324	99.90 63	99.98 253	99.93 30	98.95 36	98.49 291	100.00 1	92.91 272	100.00 1	99.71 152	100.00 1	100.00 1

mvsmamba		99.05 137	98.98 134	99.27 203	99.57 221	98.10 256	100.00 1	99.28 269	95.92 286	99.96 133	99.97 207	96.73 212	99.89 186	99.72 148	99.65 161	99.81 220

patch_mono-2		99.04 138	99.79 6	96.81 344	99.92 108	90.47 395	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	99.95 133

VNet		99.04 138	98.75 160	99.90 78	99.81 131	99.75 99	99.50 351	99.47 79	98.36 92	100.00 1	99.99 193	94.66 248	100.00 1	99.90 106	97.09 263	99.96 127

sasdasda		99.03 140	98.73 162	99.94 66	99.75 156	99.95 32	100.00 1	99.30 256	97.64 151	100.00 1	100.00 1	95.22 235	99.97 135	99.76 138	96.90 268	99.91 154

canonicalmvs		99.03 140	98.73 162	99.94 66	99.75 156	99.95 32	100.00 1	99.30 256	97.64 151	100.00 1	100.00 1	95.22 235	99.97 135	99.76 138	96.90 268	99.91 154

test-LLR		99.03 140	98.91 146	99.40 183	99.40 271	99.28 165	100.00 1	99.45 103	96.70 243	99.42 225	99.12 346	99.31 71	99.01 298	96.82 313	99.99 103	99.91 154

PatchmatchNet		99.03 140	98.96 137	99.26 204	99.49 250	98.33 239	99.38 363	99.45 103	96.64 250	99.96 133	99.58 313	99.49 43	99.50 264	97.63 287	99.00 181	99.93 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
3Dnovator+	95.58 15	99.03 140	98.71 166	99.96 45	98.99 310	99.89 70	100.00 1	99.51 76	98.96 33	98.32 301	100.00 1	92.78 274	100.00 1	99.87 113	100.00 1	100.00 1

CANet_DTU		99.02 145	98.90 149	99.41 179	99.88 116	98.71 214	100.00 1	99.29 263	98.84 55	100.00 1	100.00 1	94.02 256	100.00 1	98.08 269	99.96 119	99.52 264

PatchMatch-RL		99.02 145	98.78 157	99.74 127	99.99 49	99.29 164	100.00 1	100.00 1	98.38 88	99.89 178	99.81 268	93.14 270	99.99 100	97.85 280	99.98 113	99.95 133

MGCFI-Net		99.01 147	98.70 168	99.93 70	99.74 158	99.94 41	100.00 1	99.29 263	97.60 161	100.00 1	100.00 1	95.10 239	99.96 149	99.74 143	96.85 270	99.91 154

FA-MVS(test-final)		99.00 148	98.75 160	99.73 130	99.63 196	99.43 151	99.83 297	99.43 125	95.84 292	99.52 217	99.37 333	97.84 164	99.96 149	97.63 287	99.68 157	99.79 241

CHOSEN 1792x2688		99.00 148	98.91 146	99.25 205	99.90 112	97.79 280	100.00 1	99.99 13	98.79 67	98.28 304	100.00 1	93.63 260	99.95 162	99.66 170	99.95 121	100.00 1

DeepC-MVS	97.84 5	99.00 148	98.80 156	99.60 151	99.93 105	99.03 190	100.00 1	99.40 197	98.61 78	99.33 235	100.00 1	92.23 284	99.95 162	99.74 143	99.96 119	99.83 206
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		98.99 151	98.69 169	99.89 81	99.70 163	99.69 112	100.00 1	99.39 210	98.93 41	100.00 1	100.00 1	90.20 309	99.99 100	100.00 1	99.95 121	100.00 1

baseline2		98.99 151	98.93 144	99.18 209	99.26 284	99.15 182	100.00 1	99.46 95	96.71 242	96.79 360	100.00 1	99.42 59	99.25 288	98.75 237	99.94 125	99.15 271

QAPM		98.99 151	98.66 171	99.96 45	99.01 303	99.87 79	99.88 291	99.93 30	97.99 118	98.68 276	100.00 1	93.17 268	100.00 1	99.32 205	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 151	98.89 150	99.29 198	99.64 194	98.89 203	99.98 253	99.31 252	96.74 238	99.48 220	100.00 1	98.11 152	99.10 293	98.39 256	98.34 207	99.89 171

tpmrst		98.98 155	98.93 144	99.14 211	99.61 205	97.74 281	99.52 349	99.36 222	96.05 283	99.98 121	99.64 299	99.04 101	99.86 195	98.94 225	98.19 221	99.82 211

test-mter		98.96 156	98.82 153	99.40 183	99.40 271	99.28 165	100.00 1	99.45 103	95.44 311	99.42 225	99.12 346	99.70 16	99.01 298	96.82 313	99.99 103	99.91 154

diffmvs		98.96 156	98.73 162	99.63 146	99.54 226	99.16 181	100.00 1	99.18 319	97.33 190	99.96 133	100.00 1	94.60 249	99.91 183	99.66 170	98.33 210	99.82 211
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		98.96 156	98.95 141	99.01 218	99.48 252	98.36 237	99.93 279	99.37 216	96.79 232	99.31 236	99.83 261	99.77 11	98.91 309	98.07 271	97.98 231	99.77 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n_2		98.95 159	98.69 169	99.73 130	99.61 205	99.74 102	100.00 1	99.23 298	98.95 36	99.97 127	100.00 1	90.92 300	99.97 135	100.00 1	99.58 166	99.47 266

mamv4		98.95 159	99.11 120	98.46 249	99.68 171	95.67 333	99.14 394	99.27 279	96.43 261	99.94 163	99.97 207	97.79 167	99.88 193	99.77 136	100.00 1	99.84 202

MVSFormer		98.94 161	98.82 153	99.28 201	99.45 260	99.49 142	100.00 1	99.13 336	95.46 307	99.97 127	100.00 1	96.76 209	98.59 339	98.63 245	100.00 1	99.74 249

MVS_Test		98.93 162	98.65 172	99.77 123	99.62 203	99.50 139	99.99 227	99.19 312	95.52 302	99.96 133	99.86 253	96.54 218	99.98 128	98.65 242	98.48 195	99.82 211

baseline1		98.91 163	98.61 177	99.81 106	99.71 161	99.77 97	99.78 307	99.44 117	97.51 172	98.81 270	99.99 193	98.25 148	99.76 222	98.60 248	95.41 283	99.89 171

1112_ss		98.91 163	98.71 166	99.51 162	99.69 166	98.75 210	99.99 227	99.15 327	96.82 229	98.84 267	100.00 1	97.45 185	99.89 186	98.66 240	97.75 250	99.89 171

fmvsm_s_conf0.5_n_2		98.90 165	98.57 182	99.90 78	99.79 147	99.78 94	100.00 1	99.25 288	98.97 32	100.00 1	100.00 1	89.22 326	99.99 100	100.00 1	99.88 136	99.92 151

MSDG		98.90 165	98.63 175	99.70 136	99.92 108	99.25 170	100.00 1	99.37 216	95.71 294	99.40 231	100.00 1	96.58 215	99.95 162	96.80 315	99.94 125	99.91 154

dcpmvs_2		98.87 167	99.53 62	96.90 338	99.87 118	90.88 394	99.94 276	99.07 357	98.20 102	100.00 1	100.00 1	98.69 134	99.86 195	100.00 1	100.00 1	99.95 133

DP-MVS		98.86 168	98.54 184	99.81 106	99.97 90	99.45 148	99.52 349	99.40 197	94.35 336	98.36 296	100.00 1	96.13 222	99.97 135	99.12 218	100.00 1	100.00 1

CostFormer		98.84 169	98.77 158	99.04 216	99.41 266	97.58 286	99.67 332	99.35 233	94.66 325	99.96 133	99.36 334	99.28 79	99.74 225	99.41 198	97.81 245	99.81 220

Test_1112_low_res		98.83 170	98.60 179	99.51 162	99.69 166	98.75 210	99.99 227	99.14 332	96.81 230	98.84 267	99.06 350	97.45 185	99.89 186	98.66 240	97.75 250	99.89 171

BH-w/o		98.82 171	98.81 155	98.88 227	99.62 203	96.71 316	100.00 1	99.28 269	97.09 207	98.81 270	100.00 1	94.91 243	99.96 149	99.54 190	100.00 1	99.96 127

mvs_anonymous		98.80 172	98.60 179	99.38 187	99.57 221	99.24 172	100.00 1	99.21 308	95.87 287	98.92 259	99.82 265	96.39 221	99.03 296	99.13 217	98.50 193	99.88 184

fmvsm_s_conf0.1_n		98.77 173	98.42 194	99.82 101	99.47 256	99.52 136	100.00 1	99.27 279	97.53 168	100.00 1	100.00 1	89.73 318	99.96 149	99.84 119	99.93 128	99.97 121

TAMVS		98.76 174	98.73 162	98.86 228	99.44 262	97.69 282	99.57 343	99.34 240	96.57 253	99.12 247	99.81 268	98.83 126	99.16 291	97.97 277	97.91 237	99.73 253

OpenMVS	95.20 17	98.76 174	98.41 195	99.78 120	98.89 319	99.81 90	99.99 227	99.76 49	98.02 116	98.02 319	100.00 1	91.44 290	100.00 1	99.63 175	99.97 116	99.55 262

RRT-MVS		98.75 176	98.52 187	99.44 173	99.65 188	98.57 223	99.90 285	99.08 352	96.51 258	99.96 133	99.95 231	92.59 280	99.96 149	99.60 180	99.45 171	99.81 220

dp		98.72 177	98.61 177	99.03 217	99.53 229	97.39 292	99.45 355	99.39 210	95.62 297	99.94 163	99.52 322	98.83 126	99.82 208	96.77 318	98.42 199	99.89 171

fmvsm_s_conf0.1_n_a		98.71 178	98.36 202	99.78 120	99.09 293	99.42 152	100.00 1	99.26 286	97.42 182	100.00 1	100.00 1	89.78 316	99.96 149	99.82 125	99.85 144	99.97 121

PVSNet_BlendedMVS		98.71 178	98.62 176	98.98 221	99.98 86	99.60 120	100.00 1	100.00 1	97.23 198	100.00 1	99.03 356	96.57 216	99.99 100	100.00 1	94.75 307	97.35 377

ADS-MVSNet		98.70 180	98.51 189	99.28 201	99.51 242	98.39 232	99.24 376	99.44 117	95.52 302	99.96 133	99.70 283	97.57 177	99.58 241	97.11 304	98.54 191	99.88 184

baseline		98.69 181	98.45 193	99.41 179	99.52 237	98.67 217	100.00 1	99.17 324	97.03 212	99.13 246	100.00 1	93.17 268	99.74 225	99.70 155	98.34 207	99.81 220

PCF-MVS	98.23 3	98.69 181	98.37 200	99.62 148	99.78 151	99.02 192	99.23 381	99.06 365	96.43 261	98.08 313	100.00 1	94.72 247	99.95 162	98.16 267	99.91 132	99.90 165
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		98.65 183	98.38 198	99.46 170	99.52 237	98.74 213	100.00 1	99.15 327	96.91 223	99.05 254	100.00 1	92.75 275	99.83 205	99.70 155	98.38 204	99.81 220
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		98.64 184	98.39 197	99.40 183	99.50 246	98.60 221	100.00 1	99.22 302	96.85 227	99.10 248	100.00 1	92.75 275	99.78 218	99.71 152	98.35 206	99.81 220
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		98.64 184	98.58 181	98.81 232	99.42 264	97.12 306	99.69 329	99.37 216	93.63 353	99.94 163	99.67 291	98.96 112	99.47 268	98.62 247	97.95 235	99.83 206

BH-untuned		98.64 184	98.65 172	98.60 242	99.59 212	96.17 323	100.00 1	99.28 269	96.67 247	98.41 294	100.00 1	94.52 250	99.83 205	99.41 198	100.00 1	99.81 220

test_cas_vis1_n_1920		98.63 187	98.25 206	99.77 123	99.69 166	99.32 161	100.00 1	99.31 252	98.84 55	99.96 133	100.00 1	87.42 347	99.99 100	99.14 215	99.86 141	100.00 1

reproduce_monomvs		98.61 188	98.54 184	98.82 229	99.97 90	99.28 165	100.00 1	99.33 242	98.51 82	97.87 327	99.24 340	99.98 3	99.45 273	99.02 223	92.93 324	97.74 317

test_fmvsmconf0.01_n		98.60 189	98.24 209	99.67 140	96.90 393	99.21 175	99.99 227	99.04 370	98.80 64	99.57 215	99.96 224	90.12 310	99.91 183	99.89 108	99.89 134	99.90 165

tpmvs		98.59 190	98.38 198	99.23 206	99.69 166	97.90 272	99.31 371	99.47 79	94.52 330	99.68 211	99.28 338	97.64 174	99.89 186	97.71 284	98.17 223	99.89 171

Effi-MVS+		98.58 191	98.24 209	99.61 149	99.60 208	99.26 168	97.85 417	99.10 346	96.22 278	99.97 127	99.89 248	93.75 258	99.77 219	99.43 196	98.34 207	99.81 220

MVSTER		98.58 191	98.52 187	98.77 234	99.65 188	99.68 113	100.00 1	99.29 263	95.63 296	98.65 277	99.80 271	99.78 9	98.88 315	98.59 249	95.31 287	97.73 324

CVMVSNet		98.56 193	98.47 192	98.82 229	99.11 290	97.67 283	99.74 317	99.47 79	97.57 164	99.06 253	100.00 1	95.72 228	98.97 304	98.21 266	97.33 260	99.83 206

kuosan		98.55 194	98.53 186	98.62 240	99.66 186	96.16 324	100.00 1	99.44 117	93.93 346	99.81 198	99.98 198	97.58 175	99.81 211	98.08 269	98.28 214	99.89 171

MonoMVSNet		98.55 194	98.64 174	98.26 265	98.21 354	95.76 331	99.94 276	99.16 325	96.23 275	99.47 223	99.24 340	96.75 211	99.22 289	99.61 179	99.17 174	99.81 220

AllTest		98.55 194	98.40 196	98.99 219	99.93 105	97.35 295	100.00 1	99.40 197	97.08 209	99.09 249	99.98 198	93.37 264	99.95 162	96.94 308	99.84 146	99.68 256

DeepPCF-MVS	98.03 4	98.54 197	99.72 19	94.98 370	99.99 49	84.94 409	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		98.53 198	98.23 212	99.43 176	99.92 108	99.01 194	99.96 265	99.47 79	98.80 64	99.96 133	99.96 224	98.56 140	99.30 285	87.78 401	99.68 157	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
myMVS_eth3d		98.52 199	98.51 189	98.53 245	99.50 246	97.98 265	100.00 1	99.57 68	96.23 275	98.07 314	100.00 1	99.09 94	97.81 386	96.17 326	97.96 233	99.82 211

Vis-MVSNet		98.52 199	98.25 206	99.34 190	99.68 171	98.55 224	99.68 331	99.41 193	97.34 188	99.94 163	100.00 1	90.38 308	99.70 230	99.03 222	98.84 183	99.76 247
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+-dtu		98.51 201	98.86 151	97.47 312	99.77 153	94.21 364	100.00 1	98.94 381	97.61 158	99.91 173	98.75 374	95.89 224	99.51 262	99.36 200	99.48 169	98.68 277

SDMVSNet		98.49 202	98.08 220	99.73 130	99.82 125	99.53 132	99.99 227	99.45 103	97.62 154	99.38 232	99.86 253	90.06 313	99.88 193	99.92 103	96.61 273	99.79 241

BH-RMVSNet		98.46 203	98.08 220	99.59 153	99.61 205	99.19 177	100.00 1	99.28 269	97.06 211	98.95 258	100.00 1	88.99 329	99.82 208	98.83 233	100.00 1	99.77 245

testing3		98.44 204	98.37 200	98.65 238	99.51 242	98.32 241	100.00 1	99.62 66	96.43 261	97.93 323	99.99 193	99.11 92	97.81 386	94.88 346	97.80 246	99.82 211

ECVR-MVS		98.43 205	98.14 215	99.32 195	99.89 114	98.21 249	99.46 353	100.00 1	98.38 88	99.47 223	100.00 1	87.91 340	99.80 213	99.35 201	98.78 185	99.94 138

cascas		98.43 205	98.07 222	99.50 165	99.65 188	99.02 192	100.00 1	99.22 302	94.21 339	99.72 208	99.98 198	92.03 287	99.93 178	99.68 163	98.12 224	99.54 263

test1111		98.42 207	98.12 216	99.29 198	99.88 116	98.15 251	99.46 353	100.00 1	98.36 92	99.42 225	100.00 1	87.91 340	99.79 214	99.31 206	98.78 185	99.94 138

ab-mvs		98.42 207	98.02 226	99.61 149	99.71 161	99.00 197	99.10 398	99.64 64	96.70 243	99.04 255	99.81 268	90.64 302	99.98 128	99.64 172	97.93 236	99.84 202

UGNet		98.41 209	98.11 217	99.31 197	99.54 226	98.55 224	99.18 384	100.00 1	98.64 77	99.79 199	99.04 353	87.61 345	100.00 1	99.30 207	99.89 134	99.40 268
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+		98.40 210	98.02 226	99.55 161	99.63 196	99.06 187	100.00 1	99.15 327	95.07 314	99.42 225	99.95 231	93.26 267	99.73 227	97.44 294	98.24 217	99.87 195

Fast-Effi-MVS+-dtu		98.38 211	98.56 183	97.82 303	99.58 217	94.44 361	100.00 1	99.16 325	96.75 236	99.51 218	99.63 303	95.03 241	99.60 235	97.71 284	99.67 159	99.42 267

test_fmvs1		98.37 212	98.04 224	99.34 190	99.84 121	98.07 258	100.00 1	99.00 376	98.85 53	100.00 1	100.00 1	85.11 368	99.96 149	99.69 162	99.88 136	100.00 1

miper_enhance_ethall		98.33 213	98.27 205	98.51 246	99.66 186	99.04 189	100.00 1	99.22 302	97.53 168	98.51 289	99.38 332	99.49 43	98.75 325	98.02 273	92.61 327	97.76 284

SCA		98.30 214	97.98 228	99.23 206	99.41 266	98.25 246	99.99 227	99.45 103	96.91 223	99.76 204	99.58 313	89.65 320	99.54 254	98.31 260	98.79 184	99.91 154

XVG-OURS		98.30 214	98.36 202	98.13 278	99.58 217	95.91 327	100.00 1	99.36 222	98.69 72	99.23 239	100.00 1	91.20 293	99.92 181	99.34 203	97.82 244	98.56 280

dongtai		98.29 216	98.25 206	98.42 253	99.58 217	95.86 329	100.00 1	99.44 117	93.46 359	99.69 210	99.97 207	97.53 180	99.51 262	96.28 325	98.27 216	99.89 171

COLMAP_ROB	97.10 7	98.29 216	98.17 214	98.65 238	99.94 103	97.39 292	99.30 372	99.40 197	95.64 295	97.75 333	100.00 1	92.69 279	99.95 162	98.89 228	99.92 130	98.62 279
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		98.28 218	98.51 189	97.62 308	99.51 242	95.03 341	99.24 376	99.41 193	95.52 302	99.96 133	99.70 283	97.57 177	97.94 383	97.11 304	98.54 191	99.88 184

XVG-OURS-SEG-HR		98.27 219	98.31 204	98.14 275	99.59 212	95.92 326	100.00 1	99.36 222	98.48 83	99.21 240	100.00 1	89.27 325	99.94 174	99.76 138	99.17 174	98.56 280

tpm		98.24 220	98.22 213	98.32 261	99.13 289	95.79 330	99.53 348	99.12 342	95.20 313	99.96 133	99.36 334	97.58 175	99.28 287	97.41 296	96.67 271	99.88 184

cl22		98.23 221	98.11 217	98.58 244	99.82 125	99.01 194	100.00 1	99.28 269	96.92 222	98.33 300	99.21 343	98.09 154	98.97 304	98.72 238	92.61 327	97.76 284

WBMVS		98.19 222	98.10 219	98.47 248	99.63 196	99.03 190	100.00 1	99.32 245	95.46 307	98.39 295	99.40 331	99.69 17	98.61 334	98.64 243	92.39 332	97.76 284

TR-MVS		98.14 223	97.74 235	99.33 193	99.59 212	98.28 244	99.27 373	99.21 308	96.42 264	99.15 245	99.94 237	88.87 332	99.79 214	98.88 229	98.29 213	99.93 149

test0.0.03 1		98.12 224	98.03 225	98.39 255	99.11 290	98.07 258	100.00 1	99.93 30	96.70 243	96.91 356	99.95 231	99.31 71	98.19 363	91.93 374	98.44 197	98.91 275

GeoE		98.06 225	97.65 240	99.29 198	99.47 256	98.41 229	100.00 1	99.19 312	94.85 319	98.88 262	100.00 1	91.21 292	99.59 237	97.02 306	98.19 221	99.88 184

tpm cat1		98.05 226	97.76 234	98.92 224	99.50 246	97.10 308	99.77 312	99.30 256	90.20 394	99.72 208	98.71 375	97.71 170	99.86 195	96.75 319	98.20 220	99.81 220

PS-MVSNAJss		98.03 227	98.06 223	97.94 297	97.63 374	97.33 298	99.89 289	99.23 298	96.27 274	98.03 317	99.59 311	98.75 131	98.78 320	98.52 251	94.61 310	97.70 339

CR-MVSNet		98.02 228	97.71 238	98.93 223	99.31 278	98.86 204	99.13 395	99.00 376	96.53 256	99.96 133	98.98 360	96.94 204	98.10 373	91.18 379	98.40 200	99.84 202

EI-MVSNet		97.98 229	97.93 229	98.16 274	99.11 290	97.84 277	99.74 317	99.29 263	94.39 335	98.65 277	100.00 1	97.21 192	98.88 315	97.62 290	95.31 287	97.75 295

FIs		97.95 230	97.73 237	98.62 240	98.53 338	99.24 172	100.00 1	99.43 125	96.74 238	97.87 327	99.82 265	95.27 234	98.89 312	98.78 234	93.07 321	97.74 317

Anonymous202405211		97.87 231	97.53 242	98.90 225	99.81 131	96.70 317	99.35 366	99.46 95	92.98 370	98.83 269	99.99 193	90.63 303	100.00 1	99.70 155	97.03 264	100.00 1

FC-MVSNet-test		97.84 232	97.63 241	98.45 251	98.30 348	99.05 188	100.00 1	99.43 125	96.63 252	97.61 339	99.82 265	95.19 238	98.57 342	98.64 243	93.05 322	97.73 324

Patchmatch-test		97.83 233	97.42 245	99.06 212	99.08 294	97.66 284	98.66 411	99.21 308	93.65 352	98.25 308	99.58 313	99.47 48	99.57 242	90.25 389	98.59 190	99.95 133

sd_testset		97.81 234	97.48 243	98.79 233	99.82 125	96.80 314	99.32 368	99.45 103	97.62 154	99.38 232	99.86 253	85.56 366	99.77 219	99.72 148	96.61 273	99.79 241

miper_ehance_all_eth		97.81 234	97.66 239	98.23 267	99.49 250	98.37 235	99.99 227	99.11 343	94.78 320	98.25 308	99.21 343	98.18 150	98.57 342	97.35 300	92.61 327	97.76 284

test_vis1_n_1920		97.77 236	97.24 257	99.34 190	99.79 147	98.04 262	100.00 1	99.25 288	98.88 48	100.00 1	100.00 1	77.52 400	100.00 1	99.88 110	99.85 144	100.00 1

HQP-MVS		97.73 237	97.85 231	97.39 314	99.07 295	94.82 345	100.00 1	99.40 197	99.04 16	99.17 241	99.97 207	88.61 335	99.57 242	99.79 128	95.58 277	97.77 282

GA-MVS		97.72 238	97.27 255	99.06 212	99.24 285	97.93 271	100.00 1	99.24 294	95.80 293	98.99 257	99.64 299	89.77 317	99.36 280	95.12 343	97.62 258	99.89 171

HQP_MVS		97.71 239	97.82 233	97.37 315	99.00 307	94.80 348	100.00 1	99.40 197	99.00 27	99.08 251	99.97 207	88.58 337	99.55 251	99.79 128	95.57 281	97.76 284

nrg030		97.64 240	97.27 255	98.75 235	98.34 343	99.53 132	100.00 1	99.22 302	96.21 279	98.27 306	99.95 231	94.40 251	98.98 302	99.23 212	89.78 366	97.75 295

TAPA-MVS	96.40 10	97.64 240	97.37 249	98.45 251	99.94 103	95.70 332	100.00 1	99.40 197	97.65 149	99.53 216	100.00 1	99.31 71	99.66 232	80.48 416	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		97.64 240	97.74 235	97.36 316	99.01 303	94.76 353	100.00 1	99.34 240	99.30 4	99.00 256	99.97 207	87.49 346	99.57 242	99.96 92	95.58 277	97.75 295
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
D2MVS		97.63 243	97.83 232	97.05 329	98.83 327	94.60 357	100.00 1	99.82 40	96.89 226	98.28 304	99.03 356	94.05 254	99.47 268	98.58 250	94.97 305	97.09 383

c3_l		97.58 244	97.42 245	98.06 285	99.48 252	98.16 250	99.96 265	99.10 346	94.54 329	98.13 312	99.20 345	97.87 161	98.25 361	97.28 301	91.20 354	97.75 295

IterMVS-LS		97.56 245	97.44 244	97.92 300	99.38 275	97.90 272	99.89 289	99.10 346	94.41 334	98.32 301	99.54 321	97.21 192	98.11 370	97.50 292	91.62 346	97.75 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		97.55 246	97.38 248	98.07 281	97.50 382	97.99 264	100.00 1	99.13 336	95.46 307	98.47 292	99.85 258	92.01 288	98.59 339	98.63 245	95.36 285	97.62 360

dmvs_re		97.54 247	97.88 230	96.54 349	99.55 225	90.35 396	99.86 293	99.46 95	97.00 214	99.41 230	100.00 1	90.78 301	99.30 285	99.60 180	95.24 292	99.96 127

cl____		97.54 247	97.32 251	98.18 271	99.47 256	98.14 253	100.00 1	99.10 346	94.16 342	97.60 340	99.63 303	97.52 181	98.65 331	96.47 320	91.97 340	97.76 284

IB-MVS	96.24 12	97.54 247	96.95 262	99.33 193	99.67 179	98.10 256	100.00 1	99.47 79	97.42 182	99.26 238	99.69 286	98.83 126	99.89 186	99.43 196	78.77 413	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		97.52 250	97.35 250	98.05 289	99.46 259	98.11 254	100.00 1	99.10 346	94.21 339	97.62 338	99.63 303	97.65 173	98.29 358	96.47 320	91.98 339	97.76 284

eth_miper_zixun_eth		97.47 251	97.28 253	98.06 285	99.41 266	97.94 270	99.62 338	99.08 352	94.46 333	98.19 311	99.56 318	96.91 206	98.50 347	96.78 316	91.49 349	97.74 317

test_fmvs1_n		97.43 252	96.86 265	99.15 210	99.68 171	97.48 289	99.99 227	98.98 379	98.82 59	100.00 1	100.00 1	74.85 407	99.96 149	99.67 166	99.70 156	100.00 1

LFMVS		97.42 253	96.62 274	99.81 106	99.80 142	99.50 139	99.16 390	99.56 70	94.48 332	100.00 1	100.00 1	79.35 394	100.00 1	99.89 108	97.37 259	99.94 138

miper_lstm_enhance		97.40 254	97.28 253	97.75 305	99.48 252	97.52 287	100.00 1	99.07 357	94.08 343	98.01 320	99.61 309	97.38 189	97.98 381	96.44 323	91.47 351	97.76 284

RPSCF		97.37 255	98.24 209	94.76 373	99.80 142	84.57 410	99.99 227	99.05 367	94.95 317	99.82 195	100.00 1	94.03 255	100.00 1	98.15 268	98.38 204	99.70 254

ACMM	97.17 6	97.37 255	97.40 247	97.29 321	99.01 303	94.64 356	100.00 1	99.25 288	98.07 114	98.44 293	99.98 198	87.38 348	99.55 251	99.25 209	95.19 295	97.69 343
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		97.31 257	97.32 251	97.28 322	98.85 325	94.60 357	100.00 1	99.37 216	97.35 186	98.85 265	99.98 198	86.66 354	99.56 246	99.55 187	95.26 289	97.70 339

FMVSNet3		97.30 258	96.95 262	98.37 257	99.65 188	99.25 170	99.71 325	99.28 269	94.23 337	98.53 286	98.91 367	93.30 266	98.11 370	95.31 339	93.60 315	97.73 324

UniMVSNet (Re)		97.29 259	96.85 266	98.59 243	98.49 339	99.13 183	100.00 1	99.42 144	96.52 257	98.24 310	98.90 368	94.93 242	98.89 312	97.54 291	87.61 385	97.75 295

OPM-MVS		97.21 260	97.18 260	97.32 319	98.08 360	94.66 354	100.00 1	99.28 269	98.65 76	98.92 259	99.98 198	86.03 362	99.56 246	98.28 264	95.41 283	97.72 330
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	97.00 8	97.19 261	97.16 261	97.27 324	98.97 312	94.58 360	100.00 1	99.32 245	97.97 122	97.45 344	99.98 198	85.79 364	99.56 246	99.70 155	95.24 292	97.67 349
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs4		97.17 262	96.80 267	98.27 263	97.68 373	98.64 219	100.00 1	99.18 319	94.22 338	98.55 284	99.71 280	93.67 259	98.47 350	95.66 333	92.57 330	97.71 338

anonymousdsp		97.16 263	96.88 264	98.00 293	97.08 392	98.06 260	99.81 301	99.15 327	94.58 327	97.84 329	99.62 307	90.49 305	98.60 337	97.98 274	95.32 286	97.33 378

UniMVSNet_NR-MVSNet		97.16 263	96.80 267	98.22 268	98.38 342	98.41 229	100.00 1	99.45 103	96.14 281	97.76 330	99.64 299	95.05 240	98.50 347	97.98 274	86.84 389	97.75 295

XXY-MVS		97.14 265	96.63 273	98.67 237	98.65 332	98.92 202	99.54 347	99.29 263	95.57 299	97.63 336	99.83 261	87.79 344	99.35 282	98.39 256	92.95 323	97.75 295

WR-MVS		97.09 266	96.64 272	98.46 249	98.43 340	99.09 184	99.97 259	99.33 242	95.62 297	97.76 330	99.67 291	91.17 294	98.56 344	98.49 252	89.28 372	97.74 317

JIA-IIPM		97.09 266	96.34 288	99.36 188	98.88 320	98.59 222	99.81 301	99.43 125	84.81 411	99.96 133	90.34 421	98.55 141	99.52 260	97.00 307	98.28 214	99.98 114

jajsoiax		97.07 268	96.79 269	97.89 301	97.28 390	97.12 306	99.95 271	99.19 312	96.55 254	97.31 347	99.69 286	87.35 350	98.91 309	98.70 239	95.12 300	97.66 350

MIMVSNet		97.06 269	96.73 270	98.05 289	99.38 275	96.64 319	98.47 413	99.35 233	93.41 360	99.48 220	98.53 382	89.66 319	97.70 392	94.16 355	98.11 225	99.80 237

X-MVStestdata		97.04 270	96.06 299	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 74	100.00 1	66.97 432	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

h-mvs33		97.03 271	96.53 277	98.51 246	99.79 147	95.90 328	99.45 355	99.45 103	98.21 100	100.00 1	99.78 274	97.49 182	99.99 100	99.72 148	74.92 415	99.65 261

VPA-MVSNet		97.03 271	96.43 283	98.82 229	98.64 333	99.32 161	99.38 363	99.47 79	96.73 240	98.91 261	98.94 365	87.00 352	99.40 278	99.23 212	89.59 367	97.76 284

WB-MVSnew		97.02 273	97.24 257	96.37 353	99.44 262	97.36 294	100.00 1	99.43 125	96.12 282	99.35 234	99.89 248	93.60 262	98.42 353	88.91 400	98.39 202	93.33 415

mvs_tets		97.00 274	96.69 271	97.94 297	97.41 389	97.27 300	99.60 340	99.18 319	96.51 258	97.35 346	99.69 286	86.53 356	98.91 309	98.84 231	95.09 301	97.65 354

gg-mvs-nofinetune		96.95 275	96.10 297	99.50 165	99.41 266	99.36 159	99.07 403	99.52 72	83.69 413	99.96 133	83.60 429	100.00 1	99.20 290	99.68 163	99.99 103	99.96 127

Anonymous20240529		96.93 276	96.22 293	99.05 214	99.79 147	97.30 299	99.16 390	99.47 79	88.51 400	98.69 275	100.00 1	83.50 379	100.00 1	99.83 120	97.02 265	99.83 206

DU-MVS		96.93 276	96.49 280	98.22 268	98.31 346	98.41 229	100.00 1	99.37 216	96.41 265	97.76 330	99.65 295	92.14 285	98.50 347	97.98 274	86.84 389	97.75 295

Patchmtry		96.81 278	96.37 286	98.14 275	99.31 278	98.55 224	98.91 406	99.00 376	90.45 390	97.92 324	98.98 360	96.94 204	98.12 368	94.27 352	91.53 348	97.75 295

hse-mvs2		96.79 279	96.38 285	98.04 291	99.68 171	95.54 335	99.81 301	99.42 144	98.21 100	100.00 1	99.80 271	97.49 182	99.46 272	99.72 148	73.27 418	99.12 272

ACMH	96.25 11	96.77 280	96.62 274	97.21 325	98.96 313	94.43 362	99.64 334	99.33 242	97.43 181	96.55 365	99.97 207	83.52 378	99.54 254	99.07 221	95.13 299	97.66 350
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		96.76 281	96.46 282	97.63 306	99.41 266	96.89 311	99.99 227	99.13 336	94.74 323	97.59 341	99.66 293	89.63 322	98.28 359	95.71 331	92.31 334	97.72 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		96.73 282	96.25 291	98.18 271	98.21 354	98.67 217	99.77 312	99.32 245	95.06 315	97.20 350	99.65 295	90.10 311	98.19 363	98.06 272	88.90 376	97.66 350

WR-MVS_H		96.73 282	96.32 290	97.95 296	98.26 351	97.88 274	99.72 324	99.43 125	95.06 315	96.99 353	98.68 377	93.02 271	98.53 345	97.43 295	88.33 381	97.43 373

IterMVS-SCA-FT		96.72 284	96.42 284	97.62 308	99.40 271	96.83 313	99.99 227	99.14 332	94.65 326	97.55 342	99.72 278	89.65 320	98.31 357	95.62 335	92.05 337	97.73 324

v2v482		96.70 285	96.18 294	98.27 263	98.04 361	98.39 232	100.00 1	99.13 336	94.19 341	98.58 282	99.08 349	90.48 306	98.67 329	95.69 332	90.44 362	97.75 295

test_vis1_n		96.69 286	95.81 310	99.32 195	99.14 288	97.98 265	99.97 259	98.98 379	98.45 85	100.00 1	100.00 1	66.44 418	99.99 100	99.78 134	99.57 168	100.00 1

V42		96.65 287	96.16 296	98.11 280	98.17 358	98.23 247	99.99 227	99.09 351	93.97 344	98.74 274	99.05 352	91.09 295	98.82 318	95.46 337	89.90 364	97.27 379

EU-MVSNet		96.63 288	96.53 277	96.94 336	97.59 378	96.87 312	99.76 314	99.47 79	96.35 270	96.85 358	99.78 274	92.57 281	96.27 406	95.33 338	91.08 355	97.68 345

NR-MVSNet		96.63 288	96.04 300	98.38 256	98.31 346	98.98 199	99.22 383	99.35 233	95.87 287	94.43 388	99.65 295	92.73 277	98.40 354	96.78 316	88.05 382	97.75 295

XVG-ACMP-BASELINE		96.60 290	96.52 279	96.84 342	98.41 341	93.29 374	99.99 227	99.32 245	97.76 141	98.51 289	99.29 337	81.95 385	99.54 254	98.40 255	95.03 302	97.68 345

VDD-MVS		96.58 291	95.99 302	98.34 259	99.52 237	95.33 336	99.18 384	99.38 213	96.64 250	99.77 202	100.00 1	72.51 412	100.00 1	100.00 1	96.94 267	99.70 254

tt0805		96.52 292	96.23 292	97.40 313	99.30 281	93.55 369	99.32 368	99.45 103	96.75 236	97.88 326	99.99 193	79.99 392	99.59 237	97.39 298	95.98 276	99.06 274

LCM-MVSNet-Re		96.52 292	97.21 259	94.44 374	99.27 282	85.80 407	99.85 295	96.61 424	95.98 284	92.75 397	98.48 384	93.97 257	97.55 393	99.58 185	98.43 198	99.98 114

our_test_3		96.51 294	96.35 287	96.98 334	97.61 376	95.05 340	99.98 253	99.01 375	94.68 324	96.77 362	99.06 350	95.87 225	98.14 366	91.81 375	92.37 333	97.75 295

MVP-Stereo		96.51 294	96.48 281	96.60 348	95.65 404	94.25 363	98.84 408	98.16 400	95.85 291	95.23 379	99.04 353	92.54 282	99.13 292	92.98 367	99.98 113	96.43 396
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		96.51 294	95.97 304	98.13 278	97.98 364	98.04 262	99.99 227	99.08 352	93.51 357	98.62 280	98.98 360	90.98 299	98.62 333	93.79 359	90.79 358	97.74 317

ACMH+	96.20 13	96.49 297	96.33 289	97.00 332	99.06 299	93.80 367	99.81 301	99.31 252	97.32 191	95.89 376	99.97 207	82.62 383	99.54 254	98.34 259	94.63 309	97.65 354

TranMVSNet+NR-MVSNet		96.45 298	96.01 301	97.79 304	98.00 363	97.62 285	100.00 1	99.35 233	95.98 284	97.31 347	99.64 299	90.09 312	98.00 380	96.89 311	86.80 392	97.75 295

ET-MVSNet_ETH3D		96.41 299	95.48 330	99.20 208	99.81 131	99.75 99	100.00 1	99.02 373	97.30 195	78.33 421	100.00 1	97.73 169	97.94 383	99.70 155	87.41 386	99.92 151

VPNet		96.41 299	95.76 315	98.33 260	98.61 334	98.30 243	99.48 352	99.45 103	96.98 216	98.87 264	99.88 250	81.57 386	98.93 307	99.22 214	87.82 384	97.76 284

PVSNet_0	93.57 19	96.41 299	95.74 316	98.41 254	99.84 121	95.22 338	100.00 1	100.00 1	98.08 113	97.55 342	99.78 274	84.40 371	100.00 1	100.00 1	81.99 405	100.00 1

v144192		96.40 302	95.81 310	98.17 273	97.89 367	98.11 254	99.99 227	99.06 365	93.39 361	98.75 273	99.09 348	90.43 307	98.66 330	93.10 366	90.55 361	97.75 295

VDDNet		96.39 303	95.55 325	98.90 225	99.27 282	97.45 290	99.15 392	99.92 34	91.28 383	99.98 121	100.00 1	73.55 408	100.00 1	99.85 116	96.98 266	99.24 269

tfpnnormal		96.36 304	95.69 321	98.37 257	98.55 336	98.71 214	99.69 329	99.45 103	93.16 368	96.69 364	99.71 280	88.44 339	98.99 301	94.17 353	91.38 352	97.41 374

v8		96.35 305	95.73 317	98.21 270	98.11 359	98.23 247	99.94 276	99.07 357	92.66 376	98.29 303	99.00 359	91.46 289	98.77 323	94.17 353	88.83 378	97.62 360

PS-CasMVS		96.34 306	95.78 314	98.03 292	98.18 357	98.27 245	99.71 325	99.32 245	94.75 321	96.82 359	99.65 295	86.98 353	98.15 365	97.74 283	88.85 377	97.66 350

LTVRE_ROB	95.29 16	96.32 307	96.10 297	96.99 333	98.55 336	93.88 366	99.45 355	99.28 269	94.50 331	96.46 366	99.52 322	84.86 369	99.48 266	97.26 302	95.03 302	97.59 364
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		96.29 308	95.70 318	98.07 281	99.80 142	97.49 288	99.15 392	99.40 197	89.11 397	97.75 333	99.45 327	88.93 331	98.98 302	98.26 265	89.47 369	97.73 324

v148		96.29 308	95.84 309	97.63 306	97.74 371	96.53 321	100.00 1	99.07 357	93.52 356	98.01 320	99.42 329	91.22 291	98.60 337	96.37 324	87.22 388	97.75 295

AUN-MVS		96.26 310	95.67 322	98.06 285	99.68 171	95.60 334	99.82 300	99.42 144	96.78 233	99.88 180	99.80 271	94.84 244	99.47 268	97.48 293	73.29 417	99.12 272

ttmdpeth		96.24 311	95.88 307	97.32 319	97.80 369	96.61 320	99.95 271	98.77 392	97.80 136	93.42 393	99.28 338	86.42 357	99.01 298	97.63 287	91.84 342	96.33 398

FMVSNet2		96.22 312	95.60 324	98.06 285	99.53 229	98.33 239	99.45 355	99.27 279	93.71 348	98.03 317	98.84 370	84.23 373	98.10 373	93.97 357	93.40 318	97.73 324

LF4IMVS		96.19 313	96.18 294	96.23 357	98.26 351	92.09 385	100.00 1	97.89 411	97.82 134	97.94 322	99.87 251	82.71 382	99.38 279	97.41 296	93.71 314	97.20 380

v1192		96.18 314	95.49 328	98.26 265	98.01 362	98.15 251	99.99 227	99.08 352	93.36 362	98.54 285	98.97 363	89.47 323	98.89 312	91.15 380	90.82 357	97.75 295

testgi		96.18 314	95.93 305	96.93 337	98.98 311	94.20 365	100.00 1	99.07 357	97.16 201	96.06 373	99.86 253	84.08 376	97.79 389	90.38 388	97.80 246	98.81 276

Syy-MVS		96.17 316	96.57 276	95.00 368	99.50 246	87.37 405	100.00 1	99.57 68	96.23 275	98.07 314	100.00 1	92.41 283	97.81 386	85.34 406	97.96 233	99.82 211

ppachtmachnet_test		96.17 316	95.89 306	97.02 331	97.61 376	95.24 337	99.99 227	99.24 294	93.31 364	96.71 363	99.62 307	94.34 252	98.07 375	89.87 390	92.30 335	97.75 295

v1921920		96.16 318	95.50 326	98.14 275	97.88 368	97.96 268	99.99 227	99.07 357	93.33 363	98.60 281	99.24 340	89.37 324	98.71 327	91.28 378	90.74 359	97.75 295

Baseline_NR-MVSNet		96.16 318	95.70 318	97.56 311	98.28 350	96.79 315	100.00 1	97.86 412	91.93 380	97.63 336	99.47 326	92.14 285	98.35 356	97.13 303	86.83 391	97.54 367

v10		96.14 320	95.50 326	98.07 281	98.19 356	97.96 268	99.83 297	99.07 357	92.10 379	98.07 314	98.94 365	91.07 296	98.61 334	92.41 373	89.82 365	97.63 358

OurMVSNet-221017-0		96.14 320	95.98 303	96.62 347	97.49 384	93.44 371	99.92 281	98.16 400	95.86 289	97.65 335	99.95 231	85.71 365	98.78 320	94.93 345	94.18 313	97.64 357

GBi-Net		96.07 322	95.80 312	96.89 339	99.53 229	94.87 342	99.18 384	99.27 279	93.71 348	98.53 286	98.81 371	84.23 373	98.07 375	95.31 339	93.60 315	97.72 330

test1		96.07 322	95.80 312	96.89 339	99.53 229	94.87 342	99.18 384	99.27 279	93.71 348	98.53 286	98.81 371	84.23 373	98.07 375	95.31 339	93.60 315	97.72 330

v7n		96.06 324	95.42 334	97.99 295	97.58 379	97.35 295	99.86 293	99.11 343	92.81 375	97.91 325	99.49 324	90.99 298	98.92 308	92.51 370	88.49 380	97.70 339

PEN-MVS		96.01 325	95.48 330	97.58 310	97.74 371	97.26 301	99.90 285	99.29 263	94.55 328	96.79 360	99.55 319	87.38 348	97.84 385	96.92 310	87.24 387	97.65 354

v1240		95.96 326	95.25 335	98.07 281	97.91 366	97.87 276	99.96 265	99.07 357	93.24 366	98.64 279	98.96 364	88.98 330	98.61 334	89.58 394	90.92 356	97.75 295

pmmvs5		95.94 327	95.61 323	96.95 335	97.42 387	94.66 354	100.00 1	98.08 404	93.60 354	97.05 352	99.43 328	87.02 351	98.46 351	95.76 329	92.12 336	97.72 330

PatchT		95.90 328	94.95 343	98.75 235	99.03 301	98.39 232	99.08 401	99.32 245	85.52 409	99.96 133	94.99 413	97.94 156	98.05 379	80.20 417	98.47 196	99.81 220

USDC		95.90 328	95.70 318	96.50 350	98.60 335	92.56 383	100.00 1	98.30 398	97.77 139	96.92 354	99.94 237	81.25 389	99.45 273	93.54 362	94.96 306	97.49 370

pm-mvs1		95.76 330	95.01 340	98.00 293	98.23 353	97.45 290	99.24 376	99.04 370	93.13 369	95.93 375	99.72 278	86.28 358	98.84 317	95.62 335	87.92 383	97.72 330

SixPastTwentyTwo		95.71 331	95.49 328	96.38 352	97.42 387	93.01 375	99.84 296	98.23 399	94.75 321	95.98 374	99.97 207	85.35 367	98.43 352	94.71 347	93.17 320	97.69 343

MS-PatchMatch		95.66 332	95.87 308	95.05 366	97.80 369	89.25 399	98.88 407	99.30 256	96.35 270	96.86 357	99.01 358	81.35 388	99.43 275	93.30 364	99.98 113	96.46 395

DTE-MVSNet		95.52 333	94.99 341	97.08 328	97.49 384	96.45 322	100.00 1	99.25 288	93.82 347	96.17 371	99.57 317	87.81 343	97.18 394	94.57 348	86.26 395	97.62 360

TinyColmap		95.50 334	95.12 339	96.64 346	98.69 331	93.00 376	99.40 361	97.75 414	96.40 266	96.14 372	99.87 251	79.47 393	99.50 264	93.62 361	94.72 308	97.40 375

K. test v3		95.46 335	95.14 338	96.40 351	97.53 381	93.40 372	99.99 227	99.23 298	95.49 305	92.70 398	99.73 277	84.26 372	98.12 368	93.94 358	93.38 319	97.68 345

SSC-MVS3.2		95.32 336	94.97 342	96.37 353	98.29 349	92.75 379	100.00 1	99.30 256	95.46 307	98.36 296	99.42 329	78.92 396	98.63 332	93.28 365	91.72 345	97.72 330

FMVSNet5		95.32 336	95.43 333	94.99 369	99.39 274	92.99 377	99.25 375	99.24 294	90.45 390	97.44 345	98.45 385	95.78 227	94.39 415	87.02 402	91.88 341	97.59 364

UniMVSNet_ETH3D		95.28 338	94.41 344	97.89 301	98.91 317	95.14 339	99.13 395	99.35 233	92.11 378	97.17 351	99.66 293	70.28 415	99.36 280	97.88 279	95.18 296	99.16 270

RPMNet		95.26 339	93.82 348	99.56 160	99.31 278	98.86 204	99.13 395	99.42 144	79.82 418	99.96 133	95.13 411	95.69 229	99.98 128	77.54 421	98.40 200	99.84 202

DSMNet-mixed		95.18 340	95.21 337	95.08 365	96.03 399	90.21 397	99.65 333	93.64 430	92.91 371	98.34 299	97.40 401	90.05 314	95.51 412	91.02 381	97.86 240	99.51 265

test_fmvs2		95.17 341	95.23 336	95.01 367	98.95 315	88.99 401	99.99 227	97.77 413	97.79 137	98.58 282	99.70 283	73.36 409	99.34 283	95.88 328	95.03 302	96.70 391

TransMVSNet (Re)		94.78 342	93.72 349	97.93 299	98.34 343	97.88 274	99.23 381	97.98 409	91.60 381	94.55 385	99.71 280	87.89 342	98.36 355	89.30 396	84.92 396	97.56 366

mmtdpeth		94.58 343	94.18 345	95.81 362	98.82 329	91.09 393	99.99 227	98.61 395	96.38 267	100.00 1	97.23 402	76.52 403	99.85 201	99.82 125	80.22 409	96.48 394

FMVSNet1		94.45 344	93.63 351	96.89 339	98.87 323	94.87 342	99.18 384	99.27 279	90.95 387	97.31 347	98.81 371	72.89 411	98.07 375	92.61 368	92.81 325	97.72 330

test_0402		94.35 345	93.70 350	96.32 355	97.92 365	93.60 368	99.61 339	98.85 388	88.19 403	94.68 384	99.48 325	80.01 391	98.58 341	89.39 395	95.15 298	96.77 389

MVStest1		94.27 346	93.30 355	97.19 326	98.83 327	97.18 304	99.93 279	98.79 391	86.80 406	84.88 418	99.04 353	94.32 253	98.25 361	90.55 385	86.57 393	96.12 401

UnsupCasMVSNet_eth		94.25 347	93.89 347	95.34 364	97.63 374	92.13 384	99.73 322	99.36 222	94.88 318	92.78 395	98.63 379	82.72 381	96.53 402	94.57 348	84.73 397	97.36 376

KD-MVS_2432*1600		94.15 348	93.08 357	97.35 317	99.53 229	97.83 278	99.63 336	99.19 312	92.88 372	96.29 368	97.68 398	98.84 124	96.70 398	89.73 391	63.92 422	97.53 368

miper_refine_blended		94.15 348	93.08 357	97.35 317	99.53 229	97.83 278	99.63 336	99.19 312	92.88 372	96.29 368	97.68 398	98.84 124	96.70 398	89.73 391	63.92 422	97.53 368

MVS-HIRNet		94.12 350	92.73 364	98.29 262	99.33 277	95.95 325	99.38 363	99.19 312	74.54 421	98.26 307	86.34 425	86.07 360	99.06 295	91.60 377	99.87 140	99.85 200

new_pmnet		94.11 351	93.47 353	96.04 360	96.60 396	92.82 378	99.97 259	98.91 384	90.21 393	95.26 378	98.05 396	85.89 363	98.14 366	84.28 408	92.01 338	97.16 381

mvs5depth		93.81 352	93.00 359	96.23 357	94.25 412	93.33 373	97.43 419	98.07 405	93.47 358	94.15 390	99.58 313	77.52 400	98.97 304	93.64 360	88.92 375	96.39 397

pmmvs6		93.64 353	92.87 361	95.94 361	97.47 386	91.41 390	98.92 405	99.02 373	87.84 404	95.01 381	99.61 309	77.24 402	98.77 323	94.33 351	86.41 394	97.63 358

Patchmatch-RL test		93.49 354	93.63 351	93.05 385	91.78 416	83.41 411	98.21 415	96.95 421	91.58 382	91.05 400	97.64 400	99.40 63	95.83 410	94.11 356	81.95 406	99.91 154

Anonymous20231206		93.45 355	93.17 356	94.30 377	95.00 409	89.69 398	99.98 253	98.43 397	93.30 365	94.50 387	98.59 380	90.52 304	95.73 411	77.46 422	90.73 360	97.48 372

Anonymous20240521		93.29 356	92.76 363	94.90 372	95.64 405	91.27 391	99.97 259	98.82 389	87.04 405	94.71 383	98.19 391	83.86 377	96.80 397	84.04 409	92.56 331	96.64 392

dmvs_testset		93.27 357	95.48 330	86.65 397	98.74 330	68.42 426	99.92 281	98.91 384	96.19 280	93.28 394	100.00 1	91.06 297	91.67 422	89.64 393	91.54 347	99.86 199

test20.03		93.11 358	92.85 362	93.88 382	95.19 408	91.83 386	100.00 1	98.87 387	93.68 351	92.76 396	98.88 369	89.20 327	92.71 420	77.88 420	89.19 373	97.09 383

test_vis1_rt		93.10 359	92.93 360	93.58 383	99.63 196	85.07 408	99.99 227	93.71 429	97.49 174	90.96 401	97.10 403	60.40 420	99.95 162	99.24 211	97.90 238	95.72 405

APD_test1		93.07 360	94.14 346	89.85 391	99.18 286	72.49 419	99.76 314	98.90 386	92.86 374	96.35 367	99.94 237	75.56 405	99.91 183	86.73 403	97.98 231	97.15 382

EG-PatchMatch MVS		92.94 361	92.49 365	94.29 378	95.87 401	87.07 406	99.07 403	98.11 403	93.19 367	88.98 407	98.66 378	70.89 413	99.08 294	92.43 372	95.21 294	96.72 390

MDA-MVSNet_test_wron		92.61 362	91.09 370	97.19 326	96.71 395	97.26 301	100.00 1	99.14 332	88.61 399	67.90 427	98.32 390	89.03 328	96.57 401	90.47 387	89.59 367	97.74 317

YYNet1		92.44 363	90.92 371	97.03 330	96.20 397	97.06 309	99.99 227	99.14 332	88.21 402	67.93 426	98.43 387	88.63 334	96.28 405	90.64 382	89.08 374	97.74 317

MIMVSNet1		91.96 364	91.20 367	94.23 379	94.94 410	91.69 388	99.34 367	99.22 302	88.23 401	94.18 389	98.45 385	75.52 406	93.41 419	79.37 418	91.49 349	97.60 363

TDRefinement		91.93 365	90.48 373	96.27 356	81.60 429	92.65 382	99.10 398	97.61 417	93.96 345	93.77 391	99.85 258	80.03 390	99.53 259	97.82 281	70.59 419	96.63 393

OpenMVS_ROB	88.34 20	91.89 366	91.12 368	94.19 380	95.55 406	87.63 404	99.26 374	98.03 406	86.61 408	90.65 405	96.82 405	70.14 416	98.78 320	86.54 404	96.50 275	96.15 399

N_pmnet		91.88 367	93.37 354	87.40 396	97.24 391	66.33 429	99.90 285	91.05 432	89.77 396	95.65 377	98.58 381	90.05 314	98.11 370	85.39 405	92.72 326	97.75 295

pmmvs-eth3d		91.73 368	90.67 372	94.92 371	91.63 418	92.71 381	99.90 285	98.54 396	91.19 384	88.08 409	95.50 409	79.31 395	96.13 407	90.55 385	81.32 408	95.91 404

MDA-MVSNet-bldmvs		91.65 369	89.94 377	96.79 345	96.72 394	96.70 317	99.42 360	98.94 381	88.89 398	66.97 429	98.37 388	81.43 387	95.91 409	89.24 397	89.46 370	97.75 295

KD-MVS_self_test		91.16 370	90.09 375	94.35 376	94.44 411	91.27 391	99.74 317	99.08 352	90.82 388	94.53 386	94.91 414	86.11 359	94.78 414	82.67 411	68.52 420	96.99 385

CL-MVSNet_self_test		91.07 371	90.35 374	93.24 384	93.27 413	89.16 400	99.55 345	99.25 288	92.34 377	95.23 379	97.05 404	88.86 333	93.59 418	80.67 415	66.95 421	96.96 386

test_method		91.04 372	91.10 369	90.85 388	98.34 343	77.63 415	100.00 1	98.93 383	76.69 419	96.25 370	98.52 383	70.44 414	97.98 381	89.02 399	91.74 343	96.92 387

CMPMVS	66.12 22	90.65 373	92.04 366	86.46 398	96.18 398	66.87 428	98.03 416	99.38 213	83.38 414	85.49 415	99.55 319	77.59 399	98.80 319	94.44 350	94.31 312	93.72 413
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		90.62 374	89.36 380	94.40 375	90.53 423	91.49 389	100.00 1	96.73 422	84.21 412	93.65 392	96.65 406	82.56 384	94.83 413	82.28 412	77.62 414	96.89 388

new-patchmatchnet		90.30 375	89.46 379	92.84 386	90.77 421	88.55 403	99.83 297	98.80 390	90.07 395	87.86 410	95.00 412	78.77 397	94.30 416	84.86 407	79.15 411	95.68 407

UnsupCasMVSNet_bld		89.50 376	88.00 382	93.99 381	95.30 407	88.86 402	98.52 412	99.28 269	85.50 410	87.80 411	94.11 415	61.63 419	96.96 396	90.63 383	79.26 410	96.15 399

mvsany_test3		89.36 377	88.96 381	90.56 389	91.95 415	78.97 414	99.74 317	96.59 425	96.84 228	89.25 406	96.07 407	52.59 422	97.11 395	95.17 342	82.44 404	95.58 408

PM-MVS		88.39 378	87.41 383	91.31 387	91.73 417	82.02 413	99.79 306	96.62 423	91.06 386	90.71 404	95.73 408	48.60 424	95.96 408	90.56 384	81.91 407	95.97 403

WB-MVS		88.24 379	90.09 375	82.68 404	91.56 419	69.51 424	100.00 1	98.73 393	90.72 389	87.29 412	98.12 392	92.87 273	85.01 426	62.19 427	89.34 371	93.54 414

SSC-MVS		87.61 380	89.47 378	82.04 405	90.63 422	68.77 425	99.99 227	98.66 394	90.34 392	86.70 413	98.08 393	92.72 278	84.12 427	59.41 430	88.71 379	93.22 418

test_fmvs3		87.19 381	87.02 384	87.71 395	92.69 414	76.64 416	99.96 265	97.27 418	93.55 355	90.82 403	94.03 416	38.00 430	92.19 421	93.49 363	83.35 403	94.32 410

test_f		86.87 382	86.06 385	89.28 392	91.45 420	76.37 417	99.87 292	97.11 419	91.10 385	88.46 408	93.05 418	38.31 429	96.66 400	91.77 376	83.46 402	94.82 409

Gipuma		84.73 383	83.50 388	88.40 394	97.50 382	82.21 412	88.87 423	99.05 367	65.81 423	85.71 414	90.49 420	53.70 421	96.31 404	78.64 419	91.74 343	86.67 422
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		84.40 384	84.79 386	83.23 402	95.71 402	58.71 435	98.79 409	97.75 414	81.58 415	84.94 416	98.07 394	45.33 426	97.73 390	77.09 423	83.85 399	93.24 416

APD_test2		84.40 384	84.79 386	83.23 402	95.71 402	58.71 435	98.79 409	97.75 414	81.58 415	84.94 416	98.07 394	45.33 426	97.73 390	77.09 423	83.85 399	93.24 416

testmvs		80.17 386	81.95 389	74.80 409	58.54 436	59.58 434	100.00 1	87.14 435	76.09 420	99.61 214	100.00 1	67.06 417	74.19 432	98.84 231	50.30 426	90.64 421

test_vis3_rt		79.61 387	78.19 392	83.86 401	88.68 424	69.56 423	99.81 301	82.19 437	86.78 407	68.57 425	84.51 428	25.06 434	98.26 360	89.18 398	78.94 412	83.75 425

EGC-MVSNET		79.46 388	74.04 396	95.72 363	96.00 400	92.73 380	99.09 400	99.04 370	5.08 433	16.72 433	98.71 375	73.03 410	98.74 326	82.05 413	96.64 272	95.69 406

test123		79.44 389	79.23 391	80.05 407	80.03 430	71.72 420	100.00 1	77.93 438	62.52 424	94.81 382	99.69 286	78.21 398	74.53 431	92.57 369	27.33 431	93.90 411

PMMVS2		79.15 390	77.28 393	84.76 400	82.34 428	72.66 418	99.70 327	95.11 428	71.68 422	84.78 419	90.87 419	32.05 432	89.99 423	75.53 425	63.45 424	91.64 419

LCM-MVSNet		79.01 391	76.93 394	85.27 399	78.28 431	68.01 427	96.57 420	98.03 406	55.10 427	82.03 420	93.27 417	31.99 433	93.95 417	82.72 410	74.37 416	93.84 412

FPMVS		77.92 392	79.45 390	73.34 411	76.87 432	46.81 438	98.24 414	99.05 367	59.89 426	73.55 422	98.34 389	36.81 431	86.55 424	80.96 414	91.35 353	86.65 423

tmp_tt		75.80 393	74.26 395	80.43 406	52.91 438	53.67 437	87.42 425	97.98 409	61.80 425	67.04 428	100.00 1	76.43 404	96.40 403	96.47 320	28.26 430	91.23 420

E-PMN		70.72 394	70.06 397	72.69 412	83.92 427	65.48 431	99.95 271	92.72 431	49.88 429	72.30 423	86.26 426	47.17 425	77.43 429	53.83 431	44.49 427	75.17 429

EMVS		69.88 395	69.09 398	72.24 413	84.70 426	65.82 430	99.96 265	87.08 436	49.82 430	71.51 424	84.74 427	49.30 423	75.32 430	50.97 432	43.71 428	75.59 428

MVE	68.59 21	67.22 396	64.68 400	74.84 408	74.67 434	62.32 433	95.84 421	90.87 433	50.98 428	58.72 430	81.05 430	12.20 438	78.95 428	61.06 429	56.75 425	83.24 426
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 397	63.44 401	73.88 410	61.14 435	63.45 432	95.68 422	87.18 434	79.93 417	47.35 431	80.68 431	22.35 435	72.33 433	61.24 428	35.42 429	85.88 424

PMVS	60.66 23	65.98 398	65.05 399	68.75 414	55.06 437	38.40 439	88.19 424	96.98 420	48.30 431	44.82 432	88.52 423	12.22 437	86.49 425	67.58 426	83.79 401	81.35 427
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		28.28 399	29.73 403	23.92 415	75.89 433	32.61 440	66.50 426	12.88 439	16.09 432	14.59 434	16.59 433	12.35 436	32.36 434	39.36 433	13.36 432	6.79 430

cdsmvs_eth3d_5k		24.41 400	32.55 402	0.00 416	0.00 439	0.00 441	0.00 427	99.39 210	0.00 434	0.00 435	100.00 1	93.55 263	0.00 435	0.00 434	0.00 433	0.00 431

ab-mvs-re		8.33 401	11.11 404	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	100.00 1	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

pcd_1.5k_mvsjas		8.24 402	10.99 405	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	98.75 131	0.00 435	0.00 434	0.00 433	0.00 431

test_blank		0.07 403	0.09 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.79 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

mmdepth		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

monomultidepth		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet_test		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

DCPMVS		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet-low-res		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uncertanet		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

Regformer		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet		0.01 404	0.02 407	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.14 435	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

WAC-MVS						97.98 265								95.74 330

FOURS1					100.00 1	99.97 21	100.00 1	99.42 144	98.52 81	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 64	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 71	100.00 1	100.00 1	99.60 21

eth-test2					0.00 439

eth-test					0.00 439

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 193	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	97.62 154	100.00 1	100.00 1	98.94 115		99.99 67	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	97.54 166	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 67	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																99.91 154

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 77				99.91 154

sam_mvs												99.33 66

ambc				88.45 393	86.84 425	70.76 422	97.79 418	98.02 408		90.91 402	95.14 410	38.69 428	98.51 346	94.97 344	84.23 398	96.09 402

MTGPA								99.42 144

test_post1							99.32 368				88.24 424	99.33 66	99.59 237	98.31 260

test_post											89.05 422	99.49 43	99.59 237

patchmatchnet-post											97.79 397	99.41 61	99.54 254

GG-mvs-BLEND				99.59 153	99.54 226	99.49 142	99.17 389	99.52 72		99.96 133	99.68 290	100.00 1	99.33 284	99.71 152	99.99 103	99.96 127

MTMP							100.00 1	99.18 319

gm-plane-assit					99.52 237	97.26 301			95.86 289		100.00 1		99.43 275	98.76 236

test9_res														100.00 1	100.00 1	100.00 1

TEST9					100.00 1	99.95 32	100.00 1	99.42 144	97.65 149	100.00 1	100.00 1	99.53 33	99.97 135

test_8					100.00 1	99.91 56	100.00 1	99.42 144	97.70 144	100.00 1	100.00 1	99.51 37	99.98 128

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			99.97 135

TestCases				98.99 219	99.93 105	97.35 295		99.40 197	97.08 209	99.09 249	99.98 198	93.37 264	99.95 162	96.94 308	99.84 146	99.68 256

test_prior4						99.93 47	100.00 1

test_prior2							100.00 1		98.82 59	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					99.97 135			100.00 1

旧先验2							100.00 1		98.11 112	100.00 1			100.00 1	99.67 166

新几何2							100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 129	100.00 1	100.00 1	99.20 85	100.00 1	97.91 278	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	97.98 120				100.00 1	99.33 204		100.00 1

原ACMM2							100.00 1

原ACMM1				99.93 70	100.00 1	99.80 92		99.66 63	98.18 103	100.00 1	100.00 1	99.43 55	100.00 1	99.50 194	100.00 1	100.00 1

test222					99.99 49	99.90 63	100.00 1	99.69 62	97.66 148	100.00 1	100.00 1	99.30 76			100.00 1	100.00 1

testdata2													100.00 1	97.36 299

segment_acmp												99.55 29

testdata				99.66 143	99.99 49	98.97 201		99.73 56	97.96 125	100.00 1	100.00 1	99.42 59	100.00 1	99.28 208	100.00 1	100.00 1

testdata1							100.00 1		98.77 70

test12				99.95 54	99.99 49	99.89 70		99.42 144		100.00 1		99.24 82	99.97 135		100.00 1	100.00 1

plane_prior7					99.00 307	94.78 352

plane_prior6					99.06 299	94.80 348						88.58 337

plane_prior5								99.40 197					99.55 251	99.79 128	95.57 281	97.76 284

plane_prior4											99.97 207

plane_prior3						94.79 351			99.03 21	99.08 251

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 302

plane_prior						94.80 348	100.00 1		99.03 21						95.58 277

n2								0.00 440

nn								0.00 440

door-mid								96.32 426

lessismore_v0				96.05 359	97.55 380	91.80 387		99.22 302		91.87 399	99.91 245	83.50 379	98.68 328	92.48 371	90.42 363	97.68 345

LGP-MVS_train				97.28 322	98.85 325	94.60 357		99.37 216	97.35 186	98.85 265	99.98 198	86.66 354	99.56 246	99.55 187	95.26 289	97.70 339

test11								99.42 144

door								96.13 427

HQP5-MVS						94.82 345

HQP-NCC					99.07 295		100.00 1		99.04 16	99.17 241

ACMP_Plane					99.07 295		100.00 1		99.04 16	99.17 241

BP-MVS														99.79 128

HQP4-MVS										99.17 241			99.57 242			97.77 282

HQP3-MVS								99.40 197							95.58 277

HQP2-MVS												88.61 335

NP-MVS					99.07 295	94.81 347					99.97 207

MDTV_nov1_ep13_2view						99.24 172	99.56 344		96.31 273	99.96 133		98.86 122		98.92 227		99.89 171

MDTV_nov1_ep13			98.94 142		99.53 229	98.36 237	99.39 362	99.46 95	96.54 255	99.99 114	99.63 303	98.92 118	99.86 195	98.30 263	98.71 189

ACMMP++_ref															94.58 311

ACMMP++															95.17 297

Test By Simon												99.10 93

ITE_SJBPF				96.84 342	98.96 313	93.49 370		98.12 402	98.12 111	98.35 298	99.97 207	84.45 370	99.56 246	95.63 334	95.25 291	97.49 370

DeepMVS_CX				89.98 390	98.90 318	71.46 421		99.18 319	97.61 158	96.92 354	99.83 261	86.07 360	99.83 205	96.02 327	97.65 256	98.65 278

High-resolution multi-view benchmark