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.

MM				99.94 63		99.82 84	100.00 1	99.97 17	99.11 7	100.00 1	100.00 1	96.65 190	100.00 1	100.00 1	99.97 110	100.00 1

test_fmvsmconf_n		99.56 61	99.46 67	99.86 81	99.68 156	99.58 110	100.00 1	99.31 229	98.92 37	99.88 153	100.00 1	97.35 168	99.99 94	99.98 71	99.99 97	100.00 1

test_fmvsmvis_n_1920		99.46 73	99.37 74	99.73 114	98.88 288	99.18 160	100.00 1	99.26 257	98.85 47	99.79 169	100.00 1	97.70 151	100.00 1	99.98 71	99.86 130	100.00 1

test_fmvsm_n_1920		99.55 62	99.49 63	99.73 114	99.85 115	99.19 158	100.00 1	99.41 177	98.87 45	100.00 1	100.00 1	97.34 169	100.00 1	99.98 71	99.90 123	100.00 1

test_cas_vis1_n_1920		98.63 160	98.25 176	99.77 108	99.69 152	99.32 142	100.00 1	99.31 229	98.84 49	99.96 115	100.00 1	87.42 317	99.99 94	99.14 192	99.86 130	100.00 1

test_vis1_n_1920		97.77 205	97.24 228	99.34 164	99.79 139	98.04 237	100.00 1	99.25 259	98.88 42	100.00 1	100.00 1	77.52 368	100.00 1	99.88 101	99.85 133	100.00 1

test_vis1_n		96.69 256	95.81 279	99.32 169	99.14 256	97.98 240	99.97 231	98.98 349	98.45 75	100.00 1	100.00 1	66.44 384	99.99 94	99.78 123	99.57 154	100.00 1

test_fmvs1_n		97.43 223	96.86 235	99.15 183	99.68 156	97.48 264	99.99 201	98.98 349	98.82 53	100.00 1	100.00 1	74.85 373	99.96 132	99.67 146	99.70 144	100.00 1

mvsany_test1		99.57 60	99.48 66	99.85 83	99.86 114	99.54 113	100.00 1	99.36 205	98.94 35	100.00 1	100.00 1	97.97 140	100.00 1	99.88 101	99.28 157	100.00 1

test_fmvs1		98.37 182	98.04 193	99.34 164	99.84 116	98.07 233	100.00 1	99.00 346	98.85 47	100.00 1	100.00 1	85.11 337	99.96 132	99.69 142	99.88 126	100.00 1

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 131	98.79 58	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 131					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 55	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 131					100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 51	99.52 59	99.97 31	99.99 49	99.91 51	100.00 1	99.42 131	97.62 142	100.00 1	100.00 1	98.65 122	99.99 94	99.99 59	100.00 1	100.00 1

RE-MVS-def			99.55 55		99.99 49	99.91 51	100.00 1	99.42 131	97.62 142	100.00 1	100.00 1	98.94 102		99.99 59	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 131	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 131	99.12 6	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 26	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 131	99.03 20	100.00 1	100.00 1	99.56 23	100.00 1	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 45	99.57 49	99.95 51	99.99 49	99.85 80	100.00 1	99.42 131	97.67 136	100.00 1	100.00 1	99.05 89	99.99 94	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 29	99.62 41	99.97 31	99.99 49	99.90 58	100.00 1	99.79 45	97.97 112	99.97 110	100.00 1	98.97 96	100.00 1	99.94 91	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	99.42 131	99.04 15	100.00 1	100.00 1	99.53 29	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
test_0728_THIRD									98.79 58	100.00 1	100.00 1	99.61 16	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	99.42 131					100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS		99.68 40	99.58 46	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 149	100.00 1	100.00 1	98.99 94	99.99 94	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 51	99.51 60	100.00 1	99.90 107	100.00 1	100.00 1	99.43 121	99.00 27	100.00 1	100.00 1	99.58 22	100.00 1	97.64 258	100.00 1	100.00 1

GST-MVS		99.64 48	99.53 57	99.95 51	100.00 1	99.86 77	100.00 1	99.79 45	97.72 131	99.95 138	100.00 1	98.39 131	100.00 1	99.96 83	99.99 97	100.00 1

Anonymous202405211		97.87 200	97.53 213	98.90 198	99.81 125	96.70 291	99.35 334	99.46 94	92.98 337	98.83 239	99.99 180	90.63 275	100.00 1	99.70 135	97.03 234	100.00 1

SMA-MVS		99.69 35	99.59 44	99.98 23	99.99 49	99.93 43	100.00 1	99.43 121	97.50 160	100.00 1	100.00 1	99.43 50	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
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 131	98.91 39	100.00 1	100.00 1	99.22 75	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
CHOSEN 280x420		99.85 3	99.87 1	99.80 99	99.99 49	99.97 21	99.97 231	99.98 16	98.96 30	100.00 1	100.00 1	99.96 5	99.42 245	100.00 1	100.00 1	100.00 1

MVS_0304		99.69 35	99.63 37	99.86 81	99.96 93	99.63 107	100.00 1	99.92 34	99.03 20	99.97 110	100.00 1	97.87 143	99.96 132	100.00 1	99.96 113	100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 63	100.00 1	99.86 77	100.00 1	99.42 131	98.87 45	100.00 1	100.00 1	99.65 15	99.96 132	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
TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 121	99.05 14	100.00 1	100.00 1	99.45 45	99.99 94	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
ACMMP_NAP		99.67 43	99.57 49	99.97 31	99.98 84	99.92 48	100.00 1	99.42 131	97.83 123	100.00 1	100.00 1	98.89 108	100.00 1	99.98 71	100.00 1	100.00 1

MTAPA		99.68 40	99.59 44	99.97 31	99.99 49	99.91 51	100.00 1	99.42 131	98.32 86	99.94 140	100.00 1	98.65 122	100.00 1	99.96 83	100.00 1	100.00 1

test9_res														100.00 1	100.00 1	100.00 1

train_agg		99.71 29	99.63 37	99.97 31	100.00 1	99.95 32	100.00 1	99.42 131	97.70 133	100.00 1	100.00 1	99.51 33	99.97 121	100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 49	97.95 116	100.00 1	100.00 1	99.31 63	100.00 1	99.99 59	100.00 1	100.00 1

region2R		99.72 26	99.64 33	99.97 31	100.00 1	99.90 58	100.00 1	99.74 55	97.86 122	100.00 1	100.00 1	99.19 78	100.00 1	99.99 59	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 35	99.63 37	99.87 78	99.99 49	99.64 105	99.95 242	99.44 115	98.35 84	100.00 1	100.00 1	98.98 95	99.97 121	99.98 71	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 32	99.64 33	99.87 78	100.00 1	99.64 105	99.98 225	99.44 115	98.35 84	99.99 101	100.00 1	99.04 91	99.96 132	99.98 71	100.00 1	100.00 1

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 65	100.00 1	100.00 1	99.16 80	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 241	96.06 269	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 65	100.00 1	66.97 398	99.16 80	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 71	100.00 1	99.75 91		99.73 56					99.97 121			100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 119	100.00 1	100.00 1	99.20 77	100.00 1	97.91 251	100.00 1	100.00 1

旧先验1					99.99 49	99.88 72		99.82 40			100.00 1	99.27 72			100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 110				100.00 1	99.33 178		100.00 1

原ACMM1				99.93 66	100.00 1	99.80 87		99.66 63	98.18 93	100.00 1	100.00 1	99.43 50	100.00 1	99.50 169	100.00 1	100.00 1

test222					99.99 49	99.90 58	100.00 1	99.69 62	97.66 137	100.00 1	100.00 1	99.30 68			100.00 1	100.00 1

testdata				99.66 124	99.99 49	98.97 181		99.73 56	97.96 115	100.00 1	100.00 1	99.42 52	100.00 1	99.28 183	100.00 1	100.00 1

1314		99.38 79	99.19 97	99.96 42	98.88 288	99.89 65	99.24 344	99.93 30	98.88 42	98.79 242	100.00 1	97.02 175	100.00 1	100.00 1	100.00 1	100.00 1

MVS		99.22 105	98.96 115	99.98 23	99.00 275	99.95 32	99.24 344	99.94 22	98.14 97	98.88 232	100.00 1	95.63 208	100.00 1	99.85 107	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 131	99.01 26	100.00 1	100.00 1	99.33 58	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
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 7	100.00 1	100.00 1	99.60 17	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	99.44 115	99.06 12	100.00 1	100.00 1	99.56 23	99.99 94	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
APD-MVS_3200maxsize		99.65 46	99.55 55	99.97 31	99.99 49	99.91 51	100.00 1	99.48 78	97.54 153	100.00 1	100.00 1	98.97 96	99.99 94	99.98 71	100.00 1	100.00 1

ACMMPR		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 49	97.95 116	100.00 1	100.00 1	99.29 69	100.00 1	99.99 59	100.00 1	100.00 1

MP-MVS		99.61 55	99.49 63	99.98 23	99.99 49	99.94 40	100.00 1	99.42 131	97.82 124	99.99 101	100.00 1	98.20 134	100.00 1	99.99 59	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		99.69 35	99.61 42	99.95 51	99.99 49	99.85 80	100.00 1	99.58 67	97.69 135	100.00 1	100.00 1	99.44 46	100.00 1	99.79 117	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 63	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 25	99.64 33	99.99 12	100.00 1	99.97 21	100.00 1	99.42 131	98.02 106	100.00 1	100.00 1	99.32 61	99.99 94	100.00 1	100.00 1	100.00 1

test12				99.95 51	99.99 49	99.89 65		99.42 131		100.00 1		99.24 74	99.97 121		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 55	99.69 22	99.35 163	99.99 49	98.06 235	100.00 1	99.36 205	99.83 2	100.00 1	100.00 1	98.95 100	99.99 94	100.00 1	99.11 160	100.00 1

mPP-MVS		99.69 35	99.60 43	99.97 31	100.00 1	99.91 51	100.00 1	99.42 131	97.91 118	100.00 1	100.00 1	99.04 91	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS_fast		99.60 58	99.49 63	99.91 68	99.99 49	99.78 88	100.00 1	99.42 131	97.09 190	100.00 1	100.00 1	98.95 100	99.96 132	99.98 71	100.00 1	100.00 1

HPM-MVS		99.59 59	99.50 61	99.89 73	100.00 1	99.70 100	100.00 1	99.42 131	97.46 164	100.00 1	100.00 1	98.60 124	99.96 132	99.99 59	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
EPNet_dtu		98.53 168	98.23 181	99.43 152	99.92 103	99.01 175	99.96 236	99.47 79	98.80 55	99.96 115	99.96 207	98.56 126	99.30 253	87.78 367	99.68 145	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		99.00 124	98.91 123	99.25 178	99.90 107	97.79 255	100.00 1	99.99 13	98.79 58	98.28 273	100.00 1	93.63 234	99.95 144	99.66 149	99.95 116	100.00 1

APD-MVS		99.68 40	99.58 46	99.97 31	99.99 49	99.96 24	100.00 1	99.42 131	97.53 155	100.00 1	100.00 1	99.27 72	99.97 121	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
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 10	100.00 1	100.00 1	99.39 56	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 10	100.00 1	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 78	99.25 87	99.81 94	99.97 88	99.48 127	100.00 1	99.42 131	95.53 272	100.00 1	100.00 1	98.37 132	99.95 144	99.97 81	100.00 1	100.00 1

CP-MVS		99.67 43	99.58 46	99.95 51	100.00 1	99.84 82	100.00 1	99.42 131	97.77 128	100.00 1	100.00 1	99.07 87	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 145	99.95 32	100.00 1	99.42 131	98.69 63	100.00 1	100.00 1	99.52 32	99.99 94	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		99.62 53	99.56 54	99.82 89	99.92 103	99.45 129	100.00 1	99.78 47	98.92 37	99.73 177	100.00 1	97.70 151	100.00 1	99.93 93	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
CPTT-MVS		99.49 69	99.38 71	99.85 83	100.00 1	99.54 113	100.00 1	99.42 131	97.58 150	99.98 106	100.00 1	97.43 166	100.00 1	99.99 59	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	97.99 108	99.99 101	100.00 1	99.72 12	100.00 1	99.96 83	100.00 1	100.00 1

MVS_111021_LR		99.70 32	99.65 31	99.88 77	99.96 93	99.70 100	100.00 1	99.97 17	98.96 30	100.00 1	100.00 1	97.93 142	99.95 144	99.99 59	100.00 1	100.00 1

DP-MVS		98.86 140	98.54 157	99.81 94	99.97 88	99.45 129	99.52 317	99.40 181	94.35 306	98.36 266	100.00 1	96.13 198	99.97 121	99.12 195	100.00 1	100.00 1

QAPM		98.99 127	98.66 146	99.96 42	99.01 271	99.87 74	99.88 259	99.93 30	97.99 108	98.68 247	100.00 1	93.17 242	100.00 1	99.32 179	100.00 1	100.00 1

HyFIR lowres test		99.32 89	99.24 89	99.58 137	99.95 95	99.26 148	100.00 1	99.99 13	96.72 216	99.29 204	99.91 223	99.49 39	99.47 237	99.74 126	98.08 204	100.00 1

PAPM_NR		99.74 22	99.66 30	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 121	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 18	99.68 25	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	98.16 94	100.00 1	100.00 1	99.51 33	100.00 1	100.00 1	100.00 1	100.00 1

IB-MVS	96.24 12	97.54 218	96.95 232	99.33 167	99.67 161	98.10 232	100.00 1	99.47 79	97.42 168	99.26 205	99.69 260	98.83 113	99.89 165	99.43 171	78.77 379	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
MVS_111021_HR		99.71 29	99.63 37	99.93 66	99.95 95	99.83 83	100.00 1	100.00 1	98.89 41	100.00 1	100.00 1	97.85 145	99.95 144	100.00 1	100.00 1	100.00 1

CSCG		99.28 95	99.35 79	99.05 187	99.99 49	97.15 278	100.00 1	99.47 79	97.44 166	99.42 193	100.00 1	97.83 147	100.00 1	99.99 59	100.00 1	100.00 1

API-MVS		99.72 26	99.70 21	99.79 101	99.97 88	99.37 139	99.96 236	99.94 22	98.48 73	100.00 1	100.00 1	98.92 105	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 83	99.01 271	99.95 32	100.00 1	99.75 52	99.37 3	99.99 101	100.00 1	99.76 11	99.60 205	100.00 1	100.00 1	100.00 1

ACMMP		99.65 46	99.57 49	99.89 73	99.99 49	99.66 103	99.75 284	99.73 56	98.16 94	99.75 175	100.00 1	98.90 107	100.00 1	99.96 83	99.88 126	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
CNLPA		99.72 26	99.65 31	99.91 68	99.97 88	99.72 95	100.00 1	99.47 79	98.43 76	99.88 153	100.00 1	99.14 83	100.00 1	99.97 81	100.00 1	100.00 1

PHI-MVS		99.50 67	99.39 70	99.82 89	100.00 1	99.45 129	100.00 1	99.94 22	96.38 242	100.00 1	100.00 1	98.18 135	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 93	99.25 87	99.44 150	100.00 1	98.32 216	100.00 1	99.86 38	98.04 105	100.00 1	100.00 1	96.10 199	100.00 1	99.55 162	99.73 142	100.00 1

PVSNet_0	93.57 19	96.41 269	95.74 285	98.41 224	99.84 116	95.22 307	100.00 1	100.00 1	98.08 103	97.55 311	99.78 248	84.40 340	100.00 1	100.00 1	81.99 372	100.00 1

DeepPCF-MVS	98.03 4	98.54 167	99.72 19	94.98 336	99.99 49	84.94 375	100.00 1	99.42 131	99.98 1	100.00 1	100.00 1	98.11 137	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 20	99.67 27	99.99 12	99.99 49	99.96 24	99.73 290	99.52 72	99.06 12	100.00 1	100.00 1	98.80 116	100.00 1	99.95 89	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 20	99.68 25	99.97 31	100.00 1	99.91 51	99.98 225	99.47 79	99.09 9	100.00 1	100.00 1	98.59 125	100.00 1	99.95 89	100.00 1	100.00 1

AdaColmap		99.44 75	99.26 86	99.95 51	100.00 1	99.86 77	99.70 295	99.99 13	98.53 71	99.90 148	100.00 1	95.34 210	100.00 1	99.92 94	100.00 1	100.00 1

MAR-MVS		99.49 69	99.36 77	99.89 73	99.97 88	99.66 103	99.74 285	99.95 19	97.89 119	100.00 1	100.00 1	96.71 189	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
3Dnovator+	95.58 15	99.03 118	98.71 144	99.96 42	98.99 278	99.89 65	100.00 1	99.51 76	98.96 30	98.32 270	100.00 1	92.78 248	100.00 1	99.87 104	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 114	98.76 137	99.96 42	98.86 292	99.90 58	99.98 225	99.93 30	98.95 33	98.49 262	100.00 1	92.91 246	100.00 1	99.71 132	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 211	97.37 220	98.45 222	99.94 98	95.70 302	100.00 1	99.40 181	97.65 138	99.53 185	100.00 1	99.31 63	99.66 203	80.48 382	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MP-MVS-pluss		99.61 55	99.50 61	99.97 31	99.98 84	99.92 48	100.00 1	99.42 131	97.53 155	99.77 172	100.00 1	98.77 117	100.00 1	99.99 59	100.00 1	99.99 105
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
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 42	99.96 115	100.00 1	99.21 76	100.00 1	100.00 1	100.00 1	99.99 105

fmvsm_s_conf0.5_n_a		99.32 89	99.15 101	99.81 94	99.80 135	99.47 128	100.00 1	99.35 214	98.22 89	100.00 1	100.00 1	95.21 214	99.99 94	99.96 83	99.86 130	99.98 107

fmvsm_s_conf0.5_n		99.21 106	99.01 109	99.83 87	99.84 116	99.53 115	100.00 1	99.38 196	98.29 88	100.00 1	100.00 1	93.62 235	99.99 94	99.99 59	99.93 119	99.98 107

thres100view900		99.25 101	99.01 109	99.95 51	99.81 125	99.87 74	100.00 1	99.94 22	97.13 188	99.83 159	99.96 207	97.01 176	100.00 1	99.59 157	97.85 216	99.98 107

tfpn200view9		99.26 98	99.03 107	99.96 42	99.81 125	99.89 65	100.00 1	99.94 22	97.23 183	99.83 159	99.96 207	97.04 172	100.00 1	99.59 157	97.85 216	99.98 107

thres200		99.27 96	99.04 106	99.96 42	99.81 125	99.90 58	100.00 1	99.94 22	97.31 178	99.83 159	99.96 207	97.04 172	100.00 1	99.62 155	97.88 214	99.98 107

LCM-MVSNet-Re		96.52 262	97.21 229	94.44 340	99.27 250	85.80 373	99.85 263	96.61 390	95.98 255	92.75 364	98.48 351	93.97 231	97.55 359	99.58 160	98.43 181	99.98 107

JIA-IIPM		97.09 237	96.34 258	99.36 162	98.88 288	98.59 199	99.81 269	99.43 121	84.81 377	99.96 115	90.34 387	98.55 127	99.52 230	97.00 278	98.28 194	99.98 107

fmvsm_s_conf0.1_n_a		98.71 151	98.36 172	99.78 105	99.09 261	99.42 133	100.00 1	99.26 257	97.42 168	100.00 1	100.00 1	89.78 287	99.96 132	99.82 116	99.85 133	99.97 114

fmvsm_s_conf0.1_n		98.77 145	98.42 164	99.82 89	99.47 225	99.52 118	100.00 1	99.27 251	97.53 155	100.00 1	100.00 1	89.73 289	99.96 132	99.84 110	99.93 119	99.97 114

test_fmvsmconf0.1_n		99.25 101	99.05 105	99.82 89	98.92 284	99.55 111	100.00 1	99.23 268	98.91 39	99.75 175	99.97 193	94.79 221	99.94 156	99.94 91	99.99 97	99.97 114

thres600view7		99.24 104	99.00 111	99.95 51	99.81 125	99.87 74	100.00 1	99.94 22	97.13 188	99.83 159	99.96 207	97.01 176	100.00 1	99.54 165	97.77 223	99.97 114

thres400		99.26 98	99.03 107	99.95 51	99.81 125	99.89 65	100.00 1	99.94 22	97.23 183	99.83 159	99.96 207	97.04 172	100.00 1	99.59 157	97.85 216	99.97 114

OMC-MVS		99.27 96	99.38 71	98.96 195	99.95 95	97.06 282	100.00 1	99.40 181	98.83 51	99.88 153	100.00 1	97.01 176	99.86 170	99.47 170	99.84 135	99.97 114

dmvs_re		97.54 218	97.88 199	96.54 319	99.55 196	90.35 362	99.86 261	99.46 94	97.00 196	99.41 198	100.00 1	90.78 273	99.30 253	99.60 156	95.24 260	99.96 120

CANet		99.40 77	99.24 89	99.89 73	99.99 49	99.76 90	100.00 1	99.73 56	98.40 77	99.78 171	100.00 1	95.28 211	99.96 132	100.00 1	99.99 97	99.96 120

GG-mvs-BLEND				99.59 133	99.54 197	99.49 123	99.17 357	99.52 72		99.96 115	99.68 264	100.00 1	99.33 252	99.71 132	99.99 97	99.96 120

gg-mvs-nofinetune		96.95 245	96.10 267	99.50 144	99.41 234	99.36 140	99.07 370	99.52 72	83.69 379	99.96 115	83.60 395	100.00 1	99.20 257	99.68 143	99.99 97	99.96 120

VNet		99.04 116	98.75 139	99.90 71	99.81 125	99.75 91	99.50 319	99.47 79	98.36 82	100.00 1	99.99 180	94.66 223	100.00 1	99.90 97	97.09 233	99.96 120

BH-w/o		98.82 143	98.81 132	98.88 200	99.62 179	96.71 290	100.00 1	99.28 243	97.09 190	98.81 240	100.00 1	94.91 219	99.96 132	99.54 165	100.00 1	99.96 120

patch_mono-2		99.04 116	99.79 6	96.81 314	99.92 103	90.47 361	100.00 1	99.41 177	98.95 33	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 126

dcpmvs_2		98.87 139	99.53 57	96.90 308	99.87 113	90.88 360	99.94 247	99.07 327	98.20 92	100.00 1	100.00 1	98.69 121	99.86 170	100.00 1	100.00 1	99.95 126

Patchmatch-test		97.83 202	97.42 216	99.06 185	99.08 262	97.66 259	98.66 378	99.21 280	93.65 321	98.25 277	99.58 287	99.47 43	99.57 212	90.25 356	98.59 173	99.95 126

HY-MVS	96.53 9	99.50 67	99.35 79	99.96 42	99.81 125	99.93 43	99.64 302	100.00 1	97.97 112	99.84 157	99.85 233	98.94 102	99.99 94	99.86 105	98.23 196	99.95 126

PatchMatch-RL		99.02 122	98.78 134	99.74 111	99.99 49	99.29 145	100.00 1	100.00 1	98.38 78	99.89 151	99.81 242	93.14 244	99.99 94	97.85 253	99.98 107	99.95 126

test2506		99.48 71	99.38 71	99.75 110	99.89 109	99.51 119	99.45 323	100.00 1	98.38 78	99.83 159	100.00 1	98.86 109	99.81 184	99.25 184	98.78 168	99.94 131

test1111		98.42 177	98.12 185	99.29 172	99.88 111	98.15 227	99.46 321	100.00 1	98.36 82	99.42 193	100.00 1	87.91 310	99.79 186	99.31 180	98.78 168	99.94 131

ECVR-MVS		98.43 175	98.14 184	99.32 169	99.89 109	98.21 224	99.46 321	100.00 1	98.38 78	99.47 192	100.00 1	87.91 310	99.80 185	99.35 176	98.78 168	99.94 131

test_yl		99.51 64	99.37 74	99.95 51	99.82 119	99.90 58	100.00 1	99.47 79	97.48 162	100.00 1	100.00 1	99.80 6	100.00 1	99.98 71	97.75 224	99.94 131

DCV-MVSNet		99.51 64	99.37 74	99.95 51	99.82 119	99.90 58	100.00 1	99.47 79	97.48 162	100.00 1	100.00 1	99.80 6	100.00 1	99.98 71	97.75 224	99.94 131

LFMVS		97.42 224	96.62 244	99.81 94	99.80 135	99.50 120	99.16 358	99.56 70	94.48 302	100.00 1	100.00 1	79.35 363	100.00 1	99.89 99	97.37 230	99.94 131

WTY-MVS		99.54 63	99.40 69	99.95 51	99.81 125	99.93 43	100.00 1	100.00 1	97.98 110	99.84 157	100.00 1	98.94 102	99.98 116	99.86 105	98.21 197	99.94 131

PMMVS		99.12 109	98.97 114	99.58 137	99.57 193	98.98 179	100.00 1	99.30 233	97.14 187	99.96 115	100.00 1	96.53 195	99.82 181	99.70 135	98.49 177	99.94 131

F-COLMAP		99.64 48	99.64 33	99.67 121	99.99 49	99.07 167	100.00 1	99.44 115	98.30 87	99.90 148	100.00 1	99.18 79	99.99 94	99.91 96	100.00 1	99.94 131

PLC	98.56 2	99.70 32	99.74 16	99.58 137	100.00 1	98.79 186	100.00 1	99.54 71	98.58 70	99.96 115	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 131
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		98.14 191	97.74 205	99.33 167	99.59 185	98.28 219	99.27 341	99.21 280	96.42 238	99.15 212	99.94 219	88.87 302	99.79 186	98.88 204	98.29 193	99.93 141

PatchmatchNet		99.03 118	98.96 115	99.26 177	99.49 220	98.33 214	99.38 331	99.45 102	96.64 225	99.96 115	99.58 287	99.49 39	99.50 233	97.63 259	99.00 164	99.93 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ET-MVSNet_ETH3D		96.41 269	95.48 299	99.20 181	99.81 125	99.75 91	100.00 1	99.02 343	97.30 180	78.33 387	100.00 1	97.73 149	97.94 349	99.70 135	87.41 354	99.92 143

LS3D		99.31 91	99.13 102	99.87 78	99.99 49	99.71 96	99.55 313	99.46 94	97.32 176	99.82 167	100.00 1	96.85 186	99.97 121	99.14 192	100.00 1	99.92 143

GSMVS																99.91 145

sam_mvs1												99.29 69				99.91 145

SCA		98.30 184	97.98 197	99.23 179	99.41 234	98.25 221	99.99 201	99.45 102	96.91 202	99.76 174	99.58 287	89.65 291	99.54 224	98.31 234	98.79 167	99.91 145

Patchmatch-RL test		93.49 319	93.63 318	93.05 351	91.78 381	83.41 377	98.21 382	96.95 387	91.58 349	91.05 367	97.64 367	99.40 55	95.83 376	94.11 326	81.95 373	99.91 145

canonicalmvs		99.03 118	98.73 141	99.94 63	99.75 147	99.95 32	100.00 1	99.30 233	97.64 140	100.00 1	100.00 1	95.22 213	99.97 121	99.76 124	96.90 238	99.91 145

test-LLR		99.03 118	98.91 123	99.40 157	99.40 239	99.28 146	100.00 1	99.45 102	96.70 218	99.42 193	99.12 314	99.31 63	99.01 265	96.82 284	99.99 97	99.91 145

TESTMET0.1,1		99.08 112	98.96 115	99.44 150	99.63 174	99.38 136	100.00 1	99.45 102	95.53 272	99.48 189	100.00 1	99.71 13	99.02 264	96.84 283	99.99 97	99.91 145

test-mter		98.96 131	98.82 130	99.40 157	99.40 239	99.28 146	100.00 1	99.45 102	95.44 281	99.42 193	99.12 314	99.70 14	99.01 265	96.82 284	99.99 97	99.91 145

MSDG		98.90 138	98.63 149	99.70 118	99.92 103	99.25 150	100.00 1	99.37 199	95.71 266	99.40 199	100.00 1	96.58 191	99.95 144	96.80 286	99.94 117	99.91 145

test_fmvsmconf0.01_n		98.60 161	98.24 178	99.67 121	96.90 359	99.21 156	99.99 201	99.04 340	98.80 55	99.57 184	99.96 207	90.12 281	99.91 162	99.89 99	99.89 124	99.90 154

alignmvs		99.38 79	99.21 93	99.91 68	99.73 148	99.92 48	100.00 1	99.51 76	97.61 146	100.00 1	100.00 1	99.06 88	99.93 159	99.83 111	97.12 232	99.90 154

PVSNet_Blended		99.48 71	99.36 77	99.83 87	99.98 84	99.60 108	100.00 1	100.00 1	97.79 126	100.00 1	100.00 1	96.57 192	99.99 94	100.00 1	99.88 126	99.90 154

EPMVS		99.25 101	99.13 102	99.60 131	99.60 183	99.20 157	99.60 308	100.00 1	96.93 199	99.92 144	99.36 305	99.05 89	99.71 200	98.77 210	98.94 165	99.90 154

PCF-MVS	98.23 3	98.69 154	98.37 170	99.62 128	99.78 142	99.02 173	99.23 349	99.06 335	96.43 236	98.08 282	100.00 1	94.72 222	99.95 144	98.16 241	99.91 122	99.90 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GA-MVS		97.72 208	97.27 226	99.06 185	99.24 253	97.93 246	100.00 1	99.24 264	95.80 264	98.99 225	99.64 273	89.77 288	99.36 248	95.12 313	97.62 229	99.89 159

baseline1		98.91 136	98.61 151	99.81 94	99.71 149	99.77 89	99.78 275	99.44 115	97.51 159	98.81 240	99.99 180	98.25 133	99.76 193	98.60 223	95.41 251	99.89 159

tpmvs		98.59 162	98.38 168	99.23 179	99.69 152	97.90 247	99.31 339	99.47 79	94.52 300	99.68 180	99.28 309	97.64 154	99.89 165	97.71 256	98.17 201	99.89 159

EPNet		99.62 53	99.69 22	99.42 154	99.99 49	98.37 210	100.00 1	99.89 37	98.83 51	100.00 1	100.00 1	98.97 96	100.00 1	99.90 97	99.61 152	99.89 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		99.33 87	99.18 99	99.78 105	99.82 119	99.49 123	100.00 1	99.95 19	97.36 171	99.63 182	100.00 1	96.45 196	99.95 144	99.79 117	99.65 149	99.89 159

dp		98.72 149	98.61 151	99.03 190	99.53 200	97.39 267	99.45 323	99.39 194	95.62 269	99.94 140	99.52 295	98.83 113	99.82 181	96.77 289	98.42 182	99.89 159

sss		99.45 74	99.34 81	99.80 99	99.76 145	99.50 120	100.00 1	99.91 36	97.72 131	99.98 106	99.94 219	98.45 129	100.00 1	99.53 167	98.75 171	99.89 159

Test_1112_low_res		98.83 142	98.60 153	99.51 142	99.69 152	98.75 188	99.99 201	99.14 303	96.81 208	98.84 237	99.06 318	97.45 163	99.89 165	98.66 215	97.75 224	99.89 159

1112_ss		98.91 136	98.71 144	99.51 142	99.69 152	98.75 188	99.99 201	99.15 298	96.82 207	98.84 237	100.00 1	97.45 163	99.89 165	98.66 215	97.75 224	99.89 159

MDTV_nov1_ep13_2view						99.24 152	99.56 312		96.31 246	99.96 115		98.86 109		98.92 202		99.89 159

Vis-MVSNet (Re-imp)		98.99 127	98.89 127	99.29 172	99.64 172	98.89 183	99.98 225	99.31 229	96.74 213	99.48 189	100.00 1	98.11 137	99.10 260	98.39 230	98.34 189	99.89 159

GeoE		98.06 194	97.65 210	99.29 172	99.47 225	98.41 204	100.00 1	99.19 284	94.85 289	98.88 232	100.00 1	91.21 265	99.59 207	97.02 277	98.19 199	99.88 170

UA-Net		99.06 114	98.83 129	99.74 111	99.52 207	99.40 135	99.08 368	99.45 102	97.64 140	99.83 159	100.00 1	95.80 203	99.94 156	98.35 232	99.80 140	99.88 170

ADS-MVSNet2		98.28 187	98.51 159	97.62 280	99.51 212	95.03 310	99.24 344	99.41 177	95.52 274	99.96 115	99.70 257	97.57 156	97.94 349	97.11 275	98.54 174	99.88 170

ADS-MVSNet		98.70 153	98.51 159	99.28 175	99.51 212	98.39 207	99.24 344	99.44 115	95.52 274	99.96 115	99.70 257	97.57 156	99.58 211	97.11 275	98.54 174	99.88 170

mvs_anonymous		98.80 144	98.60 153	99.38 161	99.57 193	99.24 152	100.00 1	99.21 280	95.87 258	98.92 228	99.82 239	96.39 197	99.03 263	99.13 194	98.50 176	99.88 170

tpm		98.24 189	98.22 182	98.32 231	99.13 257	95.79 301	99.53 316	99.12 313	95.20 283	99.96 115	99.36 305	97.58 155	99.28 255	97.41 267	96.67 239	99.88 170

EC-MVSNet		99.19 107	99.09 104	99.48 147	99.42 232	99.07 167	100.00 1	99.21 280	96.95 198	99.96 115	100.00 1	96.88 185	99.48 235	99.64 151	99.79 141	99.88 170

IS-MVSNet		99.08 112	98.91 123	99.59 133	99.65 166	99.38 136	99.78 275	99.24 264	96.70 218	99.51 187	100.00 1	98.44 130	99.52 230	98.47 228	98.39 185	99.88 170

CS-MVS		99.33 87	99.27 83	99.50 144	99.99 49	99.00 177	100.00 1	99.13 307	97.26 181	99.96 115	100.00 1	97.79 148	99.64 204	99.64 151	99.67 147	99.87 178

Fast-Effi-MVS+		98.40 180	98.02 195	99.55 141	99.63 174	99.06 169	100.00 1	99.15 298	95.07 284	99.42 193	99.95 215	93.26 240	99.73 198	97.44 265	98.24 195	99.87 178

dmvs_testset		93.27 322	95.48 299	86.65 363	98.74 296	68.42 392	99.92 250	98.91 354	96.19 252	93.28 361	100.00 1	91.06 270	91.67 388	89.64 360	91.54 316	99.86 180

MVS-HIRNet		94.12 316	92.73 329	98.29 233	99.33 245	95.95 297	99.38 331	99.19 284	74.54 387	98.26 276	86.34 391	86.07 329	99.06 262	91.60 345	99.87 129	99.85 181

CR-MVSNet		98.02 197	97.71 208	98.93 196	99.31 246	98.86 184	99.13 362	99.00 346	96.53 232	99.96 115	98.98 327	96.94 182	98.10 339	91.18 347	98.40 183	99.84 182

RPMNet		95.26 307	93.82 315	99.56 140	99.31 246	98.86 184	99.13 362	99.42 131	79.82 384	99.96 115	95.13 377	95.69 206	99.98 116	77.54 387	98.40 183	99.84 182

ab-mvs		98.42 177	98.02 195	99.61 129	99.71 149	99.00 177	99.10 365	99.64 64	96.70 218	99.04 222	99.81 242	90.64 274	99.98 116	99.64 151	97.93 211	99.84 182

FE-MVS		99.16 108	98.99 113	99.66 124	99.65 166	99.18 160	99.58 310	99.43 121	95.24 282	99.91 146	99.59 285	99.37 57	99.97 121	98.31 234	99.81 138	99.83 185

Anonymous20240529		96.93 246	96.22 263	99.05 187	99.79 139	97.30 273	99.16 358	99.47 79	88.51 367	98.69 246	100.00 1	83.50 348	100.00 1	99.83 111	97.02 235	99.83 185

CVMVSNet		98.56 165	98.47 162	98.82 202	99.11 258	97.67 258	99.74 285	99.47 79	97.57 151	99.06 220	100.00 1	95.72 205	98.97 270	98.21 240	97.33 231	99.83 185

tpm2		98.64 157	98.58 155	98.81 204	99.42 232	97.12 279	99.69 297	99.37 199	93.63 322	99.94 140	99.67 265	98.96 99	99.47 237	98.62 222	97.95 210	99.83 185

DeepC-MVS	97.84 5	99.00 124	98.80 133	99.60 131	99.93 100	99.03 172	100.00 1	99.40 181	98.61 69	99.33 202	100.00 1	92.23 257	99.95 144	99.74 126	99.96 113	99.83 185
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Syy-MVS		96.17 285	96.57 246	95.00 334	99.50 216	87.37 371	100.00 1	99.57 68	96.23 248	98.07 283	100.00 1	92.41 256	97.81 352	85.34 372	97.96 208	99.82 190

myMVS_eth3d		98.52 169	98.51 159	98.53 218	99.50 216	97.98 240	100.00 1	99.57 68	96.23 248	98.07 283	100.00 1	99.09 86	97.81 352	96.17 296	97.96 208	99.82 190

testing3		98.44 174	98.37 170	98.65 210	99.51 212	98.32 216	100.00 1	99.62 66	96.43 236	97.93 293	99.99 180	99.11 84	97.81 352	94.88 316	97.80 221	99.82 190

EIA-MVS		99.26 98	99.19 97	99.45 149	99.63 174	98.75 188	100.00 1	99.27 251	96.93 199	99.95 138	100.00 1	97.47 162	99.79 186	99.74 126	99.72 143	99.82 190

CS-MVS-test		99.31 91	99.27 83	99.43 152	99.99 49	98.77 187	100.00 1	99.19 284	97.24 182	99.96 115	100.00 1	97.56 158	99.70 201	99.68 143	99.81 138	99.82 190

MVS_Test		98.93 135	98.65 147	99.77 108	99.62 179	99.50 120	99.99 201	99.19 284	95.52 274	99.96 115	99.86 229	96.54 194	99.98 116	98.65 217	98.48 178	99.82 190

diffmvs		98.96 131	98.73 141	99.63 127	99.54 197	99.16 163	100.00 1	99.18 291	97.33 175	99.96 115	100.00 1	94.60 224	99.91 162	99.66 149	98.33 192	99.82 190
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmrst		98.98 130	98.93 121	99.14 184	99.61 181	97.74 256	99.52 317	99.36 205	96.05 254	99.98 106	99.64 273	99.04 91	99.86 170	98.94 200	98.19 199	99.82 190

ETV-MVS		99.34 85	99.24 89	99.64 126	99.58 190	99.33 141	100.00 1	99.25 259	97.57 151	99.96 115	100.00 1	97.44 165	99.79 186	99.70 135	99.65 149	99.81 198

thisisatest0515		99.42 76	99.31 82	99.74 111	99.59 185	99.55 111	100.00 1	99.46 94	96.65 224	99.92 144	100.00 1	99.44 46	99.85 175	99.09 196	99.63 151	99.81 198

Effi-MVS+		98.58 163	98.24 178	99.61 129	99.60 183	99.26 148	97.85 384	99.10 317	96.22 250	99.97 110	99.89 225	93.75 232	99.77 191	99.43 171	98.34 189	99.81 198

casdiffmvs		98.65 156	98.38 168	99.46 148	99.52 207	98.74 191	100.00 1	99.15 298	96.91 202	99.05 221	100.00 1	92.75 249	99.83 178	99.70 135	98.38 186	99.81 198
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
lupinMVS		99.29 94	99.16 100	99.69 119	99.45 229	99.49 123	100.00 1	99.15 298	97.45 165	99.97 110	100.00 1	96.76 187	99.76 193	99.67 146	100.00 1	99.81 198

casdiffmvs_mvg		98.64 157	98.39 167	99.40 157	99.50 216	98.60 198	100.00 1	99.22 271	96.85 205	99.10 215	100.00 1	92.75 249	99.78 190	99.71 132	98.35 188	99.81 198
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		98.69 154	98.45 163	99.41 155	99.52 207	98.67 195	100.00 1	99.17 296	97.03 195	99.13 213	100.00 1	93.17 242	99.74 196	99.70 135	98.34 189	99.81 198

tpm cat1		98.05 195	97.76 203	98.92 197	99.50 216	97.10 281	99.77 280	99.30 233	90.20 361	99.72 178	98.71 342	97.71 150	99.86 170	96.75 290	98.20 198	99.81 198

CostFormer		98.84 141	98.77 135	99.04 189	99.41 234	97.58 261	99.67 300	99.35 214	94.66 295	99.96 115	99.36 305	99.28 71	99.74 196	99.41 173	97.81 220	99.81 198

PatchT		95.90 297	94.95 311	98.75 207	99.03 269	98.39 207	99.08 368	99.32 224	85.52 375	99.96 115	94.99 379	97.94 141	98.05 345	80.20 383	98.47 179	99.81 198

BH-untuned		98.64 157	98.65 147	98.60 213	99.59 185	96.17 296	100.00 1	99.28 243	96.67 223	98.41 265	100.00 1	94.52 225	99.83 178	99.41 173	100.00 1	99.81 198

thisisatest0530		99.37 81	99.27 83	99.69 119	99.59 185	99.41 134	100.00 1	99.46 94	96.46 235	99.90 148	100.00 1	99.44 46	99.85 175	98.97 199	99.58 153	99.80 209

MIMVSNet		97.06 240	96.73 240	98.05 260	99.38 243	96.64 293	98.47 380	99.35 214	93.41 327	99.48 189	98.53 349	89.66 290	97.70 358	94.16 325	98.11 203	99.80 209

SDMVSNet		98.49 172	98.08 188	99.73 114	99.82 119	99.53 115	99.99 201	99.45 102	97.62 142	99.38 200	99.86 229	90.06 284	99.88 169	99.92 94	96.61 241	99.79 211

sd_testset		97.81 203	97.48 214	98.79 205	99.82 119	96.80 288	99.32 336	99.45 102	97.62 142	99.38 200	99.86 229	85.56 335	99.77 191	99.72 129	96.61 241	99.79 211

FA-MVS(test-final)		99.00 124	98.75 139	99.73 114	99.63 174	99.43 132	99.83 265	99.43 121	95.84 263	99.52 186	99.37 304	97.84 146	99.96 132	97.63 259	99.68 145	99.79 211

tttt0517		99.34 85	99.23 92	99.67 121	99.57 193	99.38 136	100.00 1	99.46 94	96.33 245	99.89 151	100.00 1	99.44 46	99.84 177	98.93 201	99.46 156	99.78 214

BH-RMVSNet		98.46 173	98.08 188	99.59 133	99.61 181	99.19 158	100.00 1	99.28 243	97.06 194	98.95 227	100.00 1	88.99 299	99.82 181	98.83 208	100.00 1	99.77 215

CDS-MVSNet		98.96 131	98.95 119	99.01 191	99.48 222	98.36 212	99.93 249	99.37 199	96.79 209	99.31 203	99.83 236	99.77 10	98.91 274	98.07 244	97.98 206	99.77 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		98.52 169	98.25 176	99.34 164	99.68 156	98.55 200	99.68 299	99.41 177	97.34 174	99.94 140	100.00 1	90.38 280	99.70 201	99.03 198	98.84 166	99.76 217
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		99.10 111	99.00 111	99.40 157	99.51 212	98.68 194	99.92 250	99.43 121	95.47 278	99.65 181	100.00 1	99.51 33	99.76 193	99.53 167	98.00 205	99.75 218

xiu_mvs_v2_base		99.51 64	99.41 68	99.82 89	99.70 151	99.73 94	99.92 250	99.40 181	98.15 96	100.00 1	100.00 1	98.50 128	100.00 1	99.85 107	99.13 159	99.74 219

PS-MVSNAJ		99.64 48	99.57 49	99.85 83	99.78 142	99.81 85	99.95 242	99.42 131	98.38 78	100.00 1	100.00 1	98.75 118	100.00 1	99.88 101	99.99 97	99.74 219

MVSFormer		98.94 134	98.82 130	99.28 175	99.45 229	99.49 123	100.00 1	99.13 307	95.46 279	99.97 110	100.00 1	96.76 187	98.59 306	98.63 220	100.00 1	99.74 219

jason		99.11 110	98.96 115	99.59 133	99.17 255	99.31 144	100.00 1	99.13 307	97.38 170	99.83 159	100.00 1	95.54 209	99.72 199	99.57 161	99.97 110	99.74 219
jason: jason.
TAMVS		98.76 146	98.73 141	98.86 201	99.44 231	97.69 257	99.57 311	99.34 220	96.57 229	99.12 214	99.81 242	98.83 113	99.16 258	97.97 250	97.91 212	99.73 223

VDD-MVS		96.58 261	95.99 272	98.34 229	99.52 207	95.33 305	99.18 352	99.38 196	96.64 225	99.77 172	100.00 1	72.51 378	100.00 1	100.00 1	96.94 237	99.70 224

RPSCF		97.37 226	98.24 178	94.76 339	99.80 135	84.57 376	99.99 201	99.05 337	94.95 287	99.82 167	100.00 1	94.03 229	100.00 1	98.15 242	98.38 186	99.70 224

AllTest		98.55 166	98.40 166	98.99 192	99.93 100	97.35 269	100.00 1	99.40 181	97.08 192	99.09 216	99.98 185	93.37 237	99.95 144	96.94 279	99.84 135	99.68 226

TestCases				98.99 192	99.93 100	97.35 269		99.40 181	97.08 192	99.09 216	99.98 185	93.37 237	99.95 144	96.94 279	99.84 135	99.68 226

xiu_mvs_v1_base_debu		99.35 82	99.21 93	99.79 101	99.67 161	99.71 96	99.78 275	99.36 205	98.13 98	100.00 1	100.00 1	97.00 179	100.00 1	99.83 111	99.07 161	99.66 228

xiu_mvs_v1_base		99.35 82	99.21 93	99.79 101	99.67 161	99.71 96	99.78 275	99.36 205	98.13 98	100.00 1	100.00 1	97.00 179	100.00 1	99.83 111	99.07 161	99.66 228

xiu_mvs_v1_base_debi		99.35 82	99.21 93	99.79 101	99.67 161	99.71 96	99.78 275	99.36 205	98.13 98	100.00 1	100.00 1	97.00 179	100.00 1	99.83 111	99.07 161	99.66 228

h-mvs33		97.03 242	96.53 247	98.51 219	99.79 139	95.90 300	99.45 323	99.45 102	98.21 90	100.00 1	99.78 248	97.49 160	99.99 94	99.72 129	74.92 381	99.65 231

OpenMVS	95.20 17	98.76 146	98.41 165	99.78 105	98.89 287	99.81 85	99.99 201	99.76 49	98.02 106	98.02 288	100.00 1	91.44 263	100.00 1	99.63 154	99.97 110	99.55 232

cascas		98.43 175	98.07 190	99.50 144	99.65 166	99.02 173	100.00 1	99.22 271	94.21 309	99.72 178	99.98 185	92.03 260	99.93 159	99.68 143	98.12 202	99.54 233

CANet_DTU		99.02 122	98.90 126	99.41 155	99.88 111	98.71 192	100.00 1	99.29 237	98.84 49	100.00 1	100.00 1	94.02 230	100.00 1	98.08 243	99.96 113	99.52 234

DSMNet-mixed		95.18 308	95.21 306	95.08 331	96.03 365	90.21 363	99.65 301	93.64 396	92.91 338	98.34 268	97.40 368	90.05 285	95.51 378	91.02 349	97.86 215	99.51 235

Fast-Effi-MVS+-dtu		98.38 181	98.56 156	97.82 274	99.58 190	94.44 330	100.00 1	99.16 297	96.75 211	99.51 187	99.63 277	95.03 217	99.60 205	97.71 256	99.67 147	99.42 236

UGNet		98.41 179	98.11 186	99.31 171	99.54 197	98.55 200	99.18 352	100.00 1	98.64 68	99.79 169	99.04 321	87.61 315	100.00 1	99.30 181	99.89 124	99.40 237
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
VDDNet		96.39 273	95.55 294	98.90 198	99.27 250	97.45 265	99.15 360	99.92 34	91.28 350	99.98 106	100.00 1	73.55 374	100.00 1	99.85 107	96.98 236	99.24 238

UniMVSNet_ETH3D		95.28 306	94.41 312	97.89 272	98.91 285	95.14 308	99.13 362	99.35 214	92.11 345	97.17 320	99.66 267	70.28 381	99.36 248	97.88 252	95.18 264	99.16 239

baseline2		98.99 127	98.93 121	99.18 182	99.26 252	99.15 164	100.00 1	99.46 94	96.71 217	96.79 329	100.00 1	99.42 52	99.25 256	98.75 212	99.94 117	99.15 240

hse-mvs2		96.79 249	96.38 255	98.04 262	99.68 156	95.54 304	99.81 269	99.42 131	98.21 90	100.00 1	99.80 245	97.49 160	99.46 241	99.72 129	73.27 384	99.12 241

AUN-MVS		96.26 280	95.67 291	98.06 256	99.68 156	95.60 303	99.82 268	99.42 131	96.78 210	99.88 153	99.80 245	94.84 220	99.47 237	97.48 264	73.29 383	99.12 241

tt0805		96.52 262	96.23 262	97.40 285	99.30 249	93.55 338	99.32 336	99.45 102	96.75 211	97.88 296	99.99 180	79.99 361	99.59 207	97.39 269	95.98 244	99.06 243

test0.0.03 1		98.12 193	98.03 194	98.39 225	99.11 258	98.07 233	100.00 1	99.93 30	96.70 218	96.91 325	99.95 215	99.31 63	98.19 329	91.93 342	98.44 180	98.91 244

testgi		96.18 283	95.93 275	96.93 307	98.98 279	94.20 334	100.00 1	99.07 327	97.16 186	96.06 342	99.86 229	84.08 345	97.79 355	90.38 355	97.80 221	98.81 245

Effi-MVS+-dtu		98.51 171	98.86 128	97.47 284	99.77 144	94.21 333	100.00 1	98.94 351	97.61 146	99.91 146	98.75 341	95.89 201	99.51 232	99.36 175	99.48 155	98.68 246

DeepMVS_CX				89.98 356	98.90 286	71.46 387		99.18 291	97.61 146	96.92 323	99.83 236	86.07 329	99.83 178	96.02 297	97.65 228	98.65 247

COLMAP_ROB	97.10 7	98.29 186	98.17 183	98.65 210	99.94 98	97.39 267	99.30 340	99.40 181	95.64 267	97.75 302	100.00 1	92.69 253	99.95 144	98.89 203	99.92 121	98.62 248
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-OURS-SEG-HR		98.27 188	98.31 174	98.14 246	99.59 185	95.92 298	100.00 1	99.36 205	98.48 73	99.21 207	100.00 1	89.27 296	99.94 156	99.76 124	99.17 158	98.56 249

XVG-OURS		98.30 184	98.36 172	98.13 249	99.58 190	95.91 299	100.00 1	99.36 205	98.69 63	99.23 206	100.00 1	91.20 266	99.92 161	99.34 177	97.82 219	98.56 249

HQP4-MVS										99.17 208			99.57 212			97.77 251

HQP-MVS		97.73 207	97.85 200	97.39 286	99.07 263	94.82 314	100.00 1	99.40 181	99.04 15	99.17 208	99.97 193	88.61 305	99.57 212	99.79 117	95.58 245	97.77 251

iter_conf_final		98.72 149	98.76 137	98.59 215	99.64 172	99.17 162	100.00 1	99.22 271	96.63 227	99.02 223	99.97 193	99.98 4	98.84 282	99.22 189	95.18 264	97.76 253

cl22		98.23 190	98.11 186	98.58 217	99.82 119	99.01 175	100.00 1	99.28 243	96.92 201	98.33 269	99.21 311	98.09 139	98.97 270	98.72 213	92.61 299	97.76 253

miper_ehance_all_eth		97.81 203	97.66 209	98.23 237	99.49 220	98.37 210	99.99 201	99.11 315	94.78 290	98.25 277	99.21 311	98.18 135	98.57 309	97.35 271	92.61 299	97.76 253

miper_enhance_ethall		98.33 183	98.27 175	98.51 219	99.66 165	99.04 171	100.00 1	99.22 271	97.53 155	98.51 260	99.38 303	99.49 39	98.75 292	98.02 246	92.61 299	97.76 253

cl____		97.54 218	97.32 222	98.18 241	99.47 225	98.14 229	100.00 1	99.10 317	94.16 312	97.60 309	99.63 277	97.52 159	98.65 299	96.47 291	91.97 311	97.76 253

DIV-MVS_self_test		97.52 221	97.35 221	98.05 260	99.46 228	98.11 230	100.00 1	99.10 317	94.21 309	97.62 307	99.63 277	97.65 153	98.29 325	96.47 291	91.98 310	97.76 253

miper_lstm_enhance		97.40 225	97.28 224	97.75 277	99.48 222	97.52 262	100.00 1	99.07 327	94.08 313	98.01 289	99.61 283	97.38 167	97.98 347	96.44 294	91.47 320	97.76 253

VPNet		96.41 269	95.76 284	98.33 230	98.61 300	98.30 218	99.48 320	99.45 102	96.98 197	98.87 234	99.88 226	81.57 355	98.93 272	99.22 189	87.82 352	97.76 253

VPA-MVSNet		97.03 242	96.43 253	98.82 202	98.64 299	99.32 142	99.38 331	99.47 79	96.73 215	98.91 230	98.94 332	87.00 322	99.40 246	99.23 187	89.59 336	97.76 253

HQP_MVS		97.71 209	97.82 202	97.37 287	99.00 275	94.80 317	100.00 1	99.40 181	99.00 27	99.08 218	99.97 193	88.58 307	99.55 221	99.79 117	95.57 249	97.76 253

plane_prior5								99.40 181					99.55 221	99.79 117	95.57 249	97.76 253

our_test_3		96.51 264	96.35 257	96.98 304	97.61 342	95.05 309	99.98 225	99.01 345	94.68 294	96.77 331	99.06 318	95.87 202	98.14 332	91.81 343	92.37 304	97.75 264

ppachtmachnet_test		96.17 285	95.89 276	97.02 301	97.61 342	95.24 306	99.99 201	99.24 264	93.31 331	96.71 332	99.62 281	94.34 227	98.07 341	89.87 357	92.30 306	97.75 264

c3_l		97.58 215	97.42 216	98.06 256	99.48 222	98.16 225	99.96 236	99.10 317	94.54 299	98.13 281	99.20 313	97.87 143	98.25 328	97.28 272	91.20 323	97.75 264

nrg030		97.64 211	97.27 226	98.75 207	98.34 309	99.53 115	100.00 1	99.22 271	96.21 251	98.27 275	99.95 215	94.40 226	98.98 268	99.23 187	89.78 335	97.75 264

v144192		96.40 272	95.81 279	98.17 243	97.89 332	98.11 230	99.99 201	99.06 335	93.39 328	98.75 243	99.09 316	90.43 279	98.66 298	93.10 334	90.55 330	97.75 264

v1921920		96.16 287	95.50 295	98.14 246	97.88 334	97.96 243	99.99 201	99.07 327	93.33 330	98.60 252	99.24 310	89.37 295	98.71 295	91.28 346	90.74 328	97.75 264

v1192		96.18 283	95.49 297	98.26 236	98.01 327	98.15 227	99.99 201	99.08 323	93.36 329	98.54 256	98.97 330	89.47 294	98.89 277	91.15 348	90.82 326	97.75 264

v148		96.29 278	95.84 278	97.63 278	97.74 337	96.53 294	100.00 1	99.07 327	93.52 325	98.01 289	99.42 302	91.22 264	98.60 304	96.37 295	87.22 356	97.75 264

iter_conf05		98.73 148	98.77 135	98.60 213	99.65 166	99.22 155	100.00 1	99.22 271	96.68 222	98.98 226	99.97 193	99.99 3	98.84 282	99.29 182	95.11 270	97.75 264

v1240		95.96 295	95.25 304	98.07 252	97.91 331	97.87 251	99.96 236	99.07 327	93.24 333	98.64 250	98.96 331	88.98 300	98.61 302	89.58 361	90.92 325	97.75 264

v2v482		96.70 255	96.18 264	98.27 234	98.04 326	98.39 207	100.00 1	99.13 307	94.19 311	98.58 253	99.08 317	90.48 278	98.67 297	95.69 302	90.44 331	97.75 264

EI-MVSNet		97.98 198	97.93 198	98.16 244	99.11 258	97.84 252	99.74 285	99.29 237	94.39 305	98.65 248	100.00 1	97.21 170	98.88 280	97.62 261	95.31 255	97.75 264

MDA-MVSNet-bldmvs		91.65 334	89.94 342	96.79 315	96.72 360	96.70 291	99.42 328	98.94 351	88.89 365	66.97 395	98.37 355	81.43 356	95.91 375	89.24 364	89.46 339	97.75 264

UniMVSNet_NR-MVSNet		97.16 234	96.80 237	98.22 238	98.38 308	98.41 204	100.00 1	99.45 102	96.14 253	97.76 299	99.64 273	95.05 216	98.50 315	97.98 247	86.84 357	97.75 264

DU-MVS		96.93 246	96.49 250	98.22 238	98.31 312	98.41 204	100.00 1	99.37 199	96.41 239	97.76 299	99.65 269	92.14 258	98.50 315	97.98 247	86.84 357	97.75 264

UniMVSNet (Re)		97.29 230	96.85 236	98.59 215	98.49 305	99.13 165	100.00 1	99.42 131	96.52 233	98.24 279	98.90 335	94.93 218	98.89 277	97.54 262	87.61 353	97.75 264

NR-MVSNet		96.63 258	96.04 270	98.38 226	98.31 312	98.98 179	99.22 351	99.35 214	95.87 258	94.43 357	99.65 269	92.73 251	98.40 321	96.78 287	88.05 350	97.75 264

TranMVSNet+NR-MVSNet		96.45 268	96.01 271	97.79 275	98.00 328	97.62 260	100.00 1	99.35 214	95.98 255	97.31 316	99.64 273	90.09 283	98.00 346	96.89 282	86.80 360	97.75 264

Patchmtry		96.81 248	96.37 256	98.14 246	99.31 246	98.55 200	98.91 373	99.00 346	90.45 357	97.92 294	98.98 327	96.94 182	98.12 334	94.27 322	91.53 317	97.75 264

N_pmnet		91.88 332	93.37 321	87.40 362	97.24 357	66.33 395	99.90 254	91.05 398	89.77 363	95.65 346	98.58 348	90.05 285	98.11 336	85.39 371	92.72 298	97.75 264

XXY-MVS		97.14 236	96.63 243	98.67 209	98.65 298	98.92 182	99.54 315	99.29 237	95.57 271	97.63 305	99.83 236	87.79 314	99.35 250	98.39 230	92.95 296	97.75 264

IterMVS-LS		97.56 216	97.44 215	97.92 271	99.38 243	97.90 247	99.89 257	99.10 317	94.41 304	98.32 270	99.54 294	97.21 170	98.11 336	97.50 263	91.62 315	97.75 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		97.64 211	97.74 205	97.36 288	99.01 271	94.76 322	100.00 1	99.34 220	99.30 4	99.00 224	99.97 193	87.49 316	99.57 212	99.96 83	95.58 245	97.75 264
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
eth_miper_zixun_eth		97.47 222	97.28 224	98.06 256	99.41 234	97.94 245	99.62 306	99.08 323	94.46 303	98.19 280	99.56 291	96.91 184	98.50 315	96.78 287	91.49 318	97.74 287

FIs		97.95 199	97.73 207	98.62 212	98.53 304	99.24 152	100.00 1	99.43 121	96.74 213	97.87 297	99.82 239	95.27 212	98.89 277	98.78 209	93.07 294	97.74 287

v1144		96.51 264	95.97 274	98.13 249	97.98 329	98.04 237	99.99 201	99.08 323	93.51 326	98.62 251	98.98 327	90.98 272	98.62 301	93.79 329	90.79 327	97.74 287

YYNet1		92.44 328	90.92 336	97.03 300	96.20 363	97.06 282	99.99 201	99.14 303	88.21 369	67.93 392	98.43 354	88.63 304	96.28 371	90.64 350	89.08 343	97.74 287

MDA-MVSNet_test_wron		92.61 327	91.09 335	97.19 297	96.71 361	97.26 275	100.00 1	99.14 303	88.61 366	67.90 393	98.32 357	89.03 298	96.57 367	90.47 354	89.59 336	97.74 287

WR-MVS		97.09 237	96.64 242	98.46 221	98.43 306	99.09 166	99.97 231	99.33 222	95.62 269	97.76 299	99.67 265	91.17 267	98.56 311	98.49 227	89.28 341	97.74 287

IterMVS-SCA-FT		96.72 254	96.42 254	97.62 280	99.40 239	96.83 287	99.99 201	99.14 303	94.65 296	97.55 311	99.72 252	89.65 291	98.31 324	95.62 305	92.05 308	97.73 293

Anonymous20231211		96.29 278	95.70 287	98.07 252	99.80 135	97.49 263	99.15 360	99.40 181	89.11 364	97.75 302	99.45 300	88.93 301	98.98 268	98.26 239	89.47 338	97.73 293

FC-MVSNet-test		97.84 201	97.63 211	98.45 222	98.30 314	99.05 170	100.00 1	99.43 121	96.63 227	97.61 308	99.82 239	95.19 215	98.57 309	98.64 218	93.05 295	97.73 293

mvsmamba		98.13 192	98.06 191	98.32 231	98.22 319	98.50 203	100.00 1	99.22 271	96.41 239	98.91 230	99.96 207	95.69 206	98.73 294	99.19 191	94.95 277	97.73 293

MVSTER		98.58 163	98.52 158	98.77 206	99.65 166	99.68 102	100.00 1	99.29 237	95.63 268	98.65 248	99.80 245	99.78 8	98.88 280	98.59 224	95.31 255	97.73 293

FMVSNet3		97.30 229	96.95 232	98.37 227	99.65 166	99.25 150	99.71 293	99.28 243	94.23 307	98.53 257	98.91 334	93.30 239	98.11 336	95.31 309	93.60 288	97.73 293

FMVSNet2		96.22 281	95.60 293	98.06 256	99.53 200	98.33 214	99.45 323	99.27 251	93.71 317	98.03 286	98.84 337	84.23 342	98.10 339	93.97 327	93.40 291	97.73 293

OPM-MVS		97.21 231	97.18 230	97.32 291	98.08 325	94.66 323	100.00 1	99.28 243	98.65 67	98.92 228	99.98 185	86.03 331	99.56 216	98.28 238	95.41 251	97.72 300
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs5		95.94 296	95.61 292	96.95 305	97.42 353	94.66 323	100.00 1	98.08 371	93.60 323	97.05 321	99.43 301	87.02 321	98.46 319	95.76 299	92.12 307	97.72 300

RRT_MVS		97.77 205	97.76 203	97.78 276	97.89 332	97.06 282	100.00 1	99.29 237	95.74 265	98.00 291	99.97 193	95.94 200	98.55 312	98.87 205	94.18 285	97.72 300

pm-mvs1		95.76 299	95.01 309	98.00 264	98.23 318	97.45 265	99.24 344	99.04 340	93.13 336	95.93 344	99.72 252	86.28 327	98.84 282	95.62 305	87.92 351	97.72 300

GBi-Net		96.07 291	95.80 281	96.89 309	99.53 200	94.87 311	99.18 352	99.27 251	93.71 317	98.53 257	98.81 338	84.23 342	98.07 341	95.31 309	93.60 288	97.72 300

test1		96.07 291	95.80 281	96.89 309	99.53 200	94.87 311	99.18 352	99.27 251	93.71 317	98.53 257	98.81 338	84.23 342	98.07 341	95.31 309	93.60 288	97.72 300

FMVSNet1		94.45 311	93.63 318	96.89 309	98.87 291	94.87 311	99.18 352	99.27 251	90.95 354	97.31 316	98.81 338	72.89 377	98.07 341	92.61 336	92.81 297	97.72 300

IterMVS		96.76 251	96.46 252	97.63 278	99.41 234	96.89 285	99.99 201	99.13 307	94.74 293	97.59 310	99.66 267	89.63 293	98.28 326	95.71 301	92.31 305	97.72 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		97.17 233	96.80 237	98.27 234	97.68 339	98.64 197	100.00 1	99.18 291	94.22 308	98.55 255	99.71 254	93.67 233	98.47 318	95.66 303	92.57 302	97.71 308

v7n		96.06 293	95.42 303	97.99 266	97.58 345	97.35 269	99.86 261	99.11 315	92.81 342	97.91 295	99.49 297	90.99 271	98.92 273	92.51 338	88.49 348	97.70 309

PS-MVSNAJss		98.03 196	98.06 191	97.94 268	97.63 340	97.33 272	99.89 257	99.23 268	96.27 247	98.03 286	99.59 285	98.75 118	98.78 287	98.52 226	94.61 282	97.70 309

LPG-MVS_test		97.31 228	97.32 222	97.28 293	98.85 293	94.60 326	100.00 1	99.37 199	97.35 172	98.85 235	99.98 185	86.66 324	99.56 216	99.55 162	95.26 257	97.70 309

LGP-MVS_train				97.28 293	98.85 293	94.60 326		99.37 199	97.35 172	98.85 235	99.98 185	86.66 324	99.56 216	99.55 162	95.26 257	97.70 309

SixPastTwentyTwo		95.71 300	95.49 297	96.38 322	97.42 353	93.01 343	99.84 264	98.23 366	94.75 291	95.98 343	99.97 193	85.35 336	98.43 320	94.71 317	93.17 293	97.69 313

ACMM	97.17 6	97.37 226	97.40 218	97.29 292	99.01 271	94.64 325	100.00 1	99.25 259	98.07 104	98.44 264	99.98 185	87.38 318	99.55 221	99.25 184	95.19 263	97.69 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EU-MVSNet		96.63 258	96.53 247	96.94 306	97.59 344	96.87 286	99.76 282	99.47 79	96.35 243	96.85 327	99.78 248	92.57 254	96.27 372	95.33 308	91.08 324	97.68 315

K. test v3		95.46 304	95.14 307	96.40 321	97.53 347	93.40 341	99.99 201	99.23 268	95.49 277	92.70 365	99.73 251	84.26 341	98.12 334	93.94 328	93.38 292	97.68 315

lessismore_v0				96.05 326	97.55 346	91.80 354		99.22 271		91.87 366	99.91 223	83.50 348	98.68 296	92.48 339	90.42 332	97.68 315

XVG-ACMP-BASELINE		96.60 260	96.52 249	96.84 312	98.41 307	93.29 342	99.99 201	99.32 224	97.76 130	98.51 260	99.29 308	81.95 354	99.54 224	98.40 229	95.03 272	97.68 315

ACMP	97.00 8	97.19 232	97.16 231	97.27 295	98.97 280	94.58 329	100.00 1	99.32 224	97.97 112	97.45 313	99.98 185	85.79 333	99.56 216	99.70 135	95.24 260	97.67 319
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
bld_raw_dy_0_64		97.71 209	97.56 212	98.15 245	97.83 335	98.16 225	99.95 242	99.12 313	95.95 257	98.73 245	99.97 193	93.19 241	98.63 300	98.64 218	94.69 280	97.66 320

jajsoiax		97.07 239	96.79 239	97.89 272	97.28 356	97.12 279	99.95 242	99.19 284	96.55 230	97.31 316	99.69 260	87.35 320	98.91 274	98.70 214	95.12 269	97.66 320

PS-CasMVS		96.34 276	95.78 283	98.03 263	98.18 322	98.27 220	99.71 293	99.32 224	94.75 291	96.82 328	99.65 269	86.98 323	98.15 331	97.74 255	88.85 345	97.66 320

CP-MVSNet		96.73 252	96.25 261	98.18 241	98.21 320	98.67 195	99.77 280	99.32 224	95.06 285	97.20 319	99.65 269	90.10 282	98.19 329	98.06 245	88.90 344	97.66 320

ACMH	96.25 11	96.77 250	96.62 244	97.21 296	98.96 281	94.43 331	99.64 302	99.33 222	97.43 167	96.55 334	99.97 193	83.52 347	99.54 224	99.07 197	95.13 268	97.66 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		97.00 244	96.69 241	97.94 268	97.41 355	97.27 274	99.60 308	99.18 291	96.51 234	97.35 315	99.69 260	86.53 326	98.91 274	98.84 206	95.09 271	97.65 325

PEN-MVS		96.01 294	95.48 299	97.58 282	97.74 337	97.26 275	99.90 254	99.29 237	94.55 298	96.79 329	99.55 292	87.38 318	97.84 351	96.92 281	87.24 355	97.65 325

ACMH+	96.20 13	96.49 267	96.33 259	97.00 302	99.06 267	93.80 336	99.81 269	99.31 229	97.32 176	95.89 345	99.97 193	82.62 352	99.54 224	98.34 233	94.63 281	97.65 325

OurMVSNet-221017-0		96.14 289	95.98 273	96.62 317	97.49 350	93.44 340	99.92 250	98.16 367	95.86 260	97.65 304	99.95 215	85.71 334	98.78 287	94.93 315	94.18 285	97.64 328

pmmvs6		93.64 318	92.87 326	95.94 328	97.47 352	91.41 357	98.92 372	99.02 343	87.84 371	95.01 350	99.61 283	77.24 369	98.77 290	94.33 321	86.41 361	97.63 329

v10		96.14 289	95.50 295	98.07 252	98.19 321	97.96 243	99.83 265	99.07 327	92.10 346	98.07 283	98.94 332	91.07 269	98.61 302	92.41 341	89.82 334	97.63 329

v8		96.35 275	95.73 286	98.21 240	98.11 324	98.23 222	99.94 247	99.07 327	92.66 343	98.29 272	99.00 326	91.46 262	98.77 290	94.17 323	88.83 346	97.62 331

DTE-MVSNet		95.52 302	94.99 310	97.08 298	97.49 350	96.45 295	100.00 1	99.25 259	93.82 316	96.17 340	99.57 290	87.81 313	97.18 360	94.57 318	86.26 362	97.62 331

test_djsdf		97.55 217	97.38 219	98.07 252	97.50 348	97.99 239	100.00 1	99.13 307	95.46 279	98.47 263	99.85 233	92.01 261	98.59 306	98.63 220	95.36 253	97.62 331

MIMVSNet1		91.96 329	91.20 332	94.23 345	94.94 376	91.69 355	99.34 335	99.22 271	88.23 368	94.18 358	98.45 352	75.52 372	93.41 385	79.37 384	91.49 318	97.60 334

FMVSNet5		95.32 305	95.43 302	94.99 335	99.39 242	92.99 345	99.25 343	99.24 264	90.45 357	97.44 314	98.45 352	95.78 204	94.39 381	87.02 368	91.88 312	97.59 335

LTVRE_ROB	95.29 16	96.32 277	96.10 267	96.99 303	98.55 302	93.88 335	99.45 323	99.28 243	94.50 301	96.46 335	99.52 295	84.86 338	99.48 235	97.26 273	95.03 272	97.59 335
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
TransMVSNet (Re)		94.78 310	93.72 316	97.93 270	98.34 309	97.88 249	99.23 349	97.98 375	91.60 348	94.55 354	99.71 254	87.89 312	98.36 322	89.30 363	84.92 363	97.56 337

Baseline_NR-MVSNet		96.16 287	95.70 287	97.56 283	98.28 315	96.79 289	100.00 1	97.86 378	91.93 347	97.63 305	99.47 299	92.14 258	98.35 323	97.13 274	86.83 359	97.54 338

KD-MVS_2432*1600		94.15 314	93.08 323	97.35 289	99.53 200	97.83 253	99.63 304	99.19 284	92.88 339	96.29 337	97.68 365	98.84 111	96.70 364	89.73 358	63.92 388	97.53 339

miper_refine_blended		94.15 314	93.08 323	97.35 289	99.53 200	97.83 253	99.63 304	99.19 284	92.88 339	96.29 337	97.68 365	98.84 111	96.70 364	89.73 358	63.92 388	97.53 339

USDC		95.90 297	95.70 287	96.50 320	98.60 301	92.56 350	100.00 1	98.30 365	97.77 128	96.92 323	99.94 219	81.25 358	99.45 242	93.54 331	94.96 276	97.49 341

ITE_SJBPF				96.84 312	98.96 281	93.49 339		98.12 369	98.12 101	98.35 267	99.97 193	84.45 339	99.56 216	95.63 304	95.25 259	97.49 341

Anonymous20231206		93.45 320	93.17 322	94.30 343	95.00 375	89.69 364	99.98 225	98.43 364	93.30 332	94.50 356	98.59 347	90.52 276	95.73 377	77.46 388	90.73 329	97.48 343

WR-MVS_H		96.73 252	96.32 260	97.95 267	98.26 316	97.88 249	99.72 292	99.43 121	95.06 285	96.99 322	98.68 344	93.02 245	98.53 313	97.43 266	88.33 349	97.43 344

tfpnnormal		96.36 274	95.69 290	98.37 227	98.55 302	98.71 192	99.69 297	99.45 102	93.16 335	96.69 333	99.71 254	88.44 309	98.99 267	94.17 323	91.38 321	97.41 345

TinyColmap		95.50 303	95.12 308	96.64 316	98.69 297	93.00 344	99.40 329	97.75 380	96.40 241	96.14 341	99.87 227	79.47 362	99.50 233	93.62 330	94.72 279	97.40 346

UnsupCasMVSNet_eth		94.25 313	93.89 314	95.34 330	97.63 340	92.13 351	99.73 290	99.36 205	94.88 288	92.78 362	98.63 346	82.72 350	96.53 368	94.57 318	84.73 364	97.36 347

PVSNet_BlendedMVS		98.71 151	98.62 150	98.98 194	99.98 84	99.60 108	100.00 1	100.00 1	97.23 183	100.00 1	99.03 323	96.57 192	99.99 94	100.00 1	94.75 278	97.35 348

anonymousdsp		97.16 234	96.88 234	98.00 264	97.08 358	98.06 235	99.81 269	99.15 298	94.58 297	97.84 298	99.62 281	90.49 277	98.60 304	97.98 247	95.32 254	97.33 349

V42		96.65 257	96.16 266	98.11 251	98.17 323	98.23 222	99.99 201	99.09 322	93.97 314	98.74 244	99.05 320	91.09 268	98.82 285	95.46 307	89.90 333	97.27 350

LF4IMVS		96.19 282	96.18 264	96.23 325	98.26 316	92.09 352	100.00 1	97.89 377	97.82 124	97.94 292	99.87 227	82.71 351	99.38 247	97.41 267	93.71 287	97.20 351

new_pmnet		94.11 317	93.47 320	96.04 327	96.60 362	92.82 346	99.97 231	98.91 354	90.21 360	95.26 347	98.05 363	85.89 332	98.14 332	84.28 374	92.01 309	97.16 352

APD_test1		93.07 325	94.14 313	89.85 357	99.18 254	72.49 385	99.76 282	98.90 356	92.86 341	96.35 336	99.94 219	75.56 371	99.91 162	86.73 369	97.98 206	97.15 353

D2MVS		97.63 214	97.83 201	97.05 299	98.83 295	94.60 326	100.00 1	99.82 40	96.89 204	98.28 273	99.03 323	94.05 228	99.47 237	98.58 225	94.97 275	97.09 354

test20.03		93.11 323	92.85 327	93.88 348	95.19 374	91.83 353	100.00 1	98.87 357	93.68 320	92.76 363	98.88 336	89.20 297	92.71 386	77.88 386	89.19 342	97.09 354

KD-MVS_self_test		91.16 335	90.09 340	94.35 342	94.44 377	91.27 358	99.74 285	99.08 323	90.82 355	94.53 355	94.91 380	86.11 328	94.78 380	82.67 377	68.52 386	96.99 356

CL-MVSNet_self_test		91.07 336	90.35 339	93.24 350	93.27 378	89.16 366	99.55 313	99.25 259	92.34 344	95.23 348	97.05 370	88.86 303	93.59 384	80.67 381	66.95 387	96.96 357

test_method		91.04 337	91.10 334	90.85 354	98.34 309	77.63 381	100.00 1	98.93 353	76.69 385	96.25 339	98.52 350	70.44 380	97.98 347	89.02 366	91.74 313	96.92 358

pmmvs3		90.62 339	89.36 345	94.40 341	90.53 388	91.49 356	100.00 1	96.73 388	84.21 378	93.65 360	96.65 372	82.56 353	94.83 379	82.28 378	77.62 380	96.89 359

test_0402		94.35 312	93.70 317	96.32 323	97.92 330	93.60 337	99.61 307	98.85 358	88.19 370	94.68 353	99.48 298	80.01 360	98.58 308	89.39 362	95.15 267	96.77 360

EG-PatchMatch MVS		92.94 326	92.49 330	94.29 344	95.87 367	87.07 372	99.07 370	98.11 370	93.19 334	88.98 374	98.66 345	70.89 379	99.08 261	92.43 340	95.21 262	96.72 361

test_fmvs2		95.17 309	95.23 305	95.01 333	98.95 283	88.99 367	99.99 201	97.77 379	97.79 126	98.58 253	99.70 257	73.36 375	99.34 251	95.88 298	95.03 272	96.70 362

Anonymous20240521		93.29 321	92.76 328	94.90 338	95.64 371	91.27 358	99.97 231	98.82 359	87.04 372	94.71 352	98.19 358	83.86 346	96.80 363	84.04 375	92.56 303	96.64 363

TDRefinement		91.93 330	90.48 338	96.27 324	81.60 394	92.65 349	99.10 365	97.61 383	93.96 315	93.77 359	99.85 233	80.03 359	99.53 229	97.82 254	70.59 385	96.63 364

MS-PatchMatch		95.66 301	95.87 277	95.05 332	97.80 336	89.25 365	98.88 374	99.30 233	96.35 243	96.86 326	99.01 325	81.35 357	99.43 243	93.30 333	99.98 107	96.46 365

MVP-Stereo		96.51 264	96.48 251	96.60 318	95.65 370	94.25 332	98.84 375	98.16 367	95.85 262	95.23 348	99.04 321	92.54 255	99.13 259	92.98 335	99.98 107	96.43 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UnsupCasMVSNet_bld		89.50 341	88.00 347	93.99 347	95.30 373	88.86 368	98.52 379	99.28 243	85.50 376	87.80 378	94.11 381	61.63 385	96.96 362	90.63 351	79.26 376	96.15 367

OpenMVS_ROB	88.34 20	91.89 331	91.12 333	94.19 346	95.55 372	87.63 370	99.26 342	98.03 372	86.61 374	90.65 372	96.82 371	70.14 382	98.78 287	86.54 370	96.50 243	96.15 367

ambc				88.45 359	86.84 390	70.76 388	97.79 385	98.02 374		90.91 369	95.14 376	38.69 394	98.51 314	94.97 314	84.23 365	96.09 369

PM-MVS		88.39 343	87.41 348	91.31 353	91.73 382	82.02 379	99.79 274	96.62 389	91.06 353	90.71 371	95.73 374	48.60 390	95.96 374	90.56 352	81.91 374	95.97 370

pmmvs-eth3d		91.73 333	90.67 337	94.92 337	91.63 383	92.71 348	99.90 254	98.54 363	91.19 351	88.08 376	95.50 375	79.31 364	96.13 373	90.55 353	81.32 375	95.91 371

test_vis1_rt		93.10 324	92.93 325	93.58 349	99.63 174	85.07 374	99.99 201	93.71 395	97.49 161	90.96 368	97.10 369	60.40 386	99.95 144	99.24 186	97.90 213	95.72 372

EGC-MVSNET		79.46 353	74.04 361	95.72 329	96.00 366	92.73 347	99.09 367	99.04 340	5.08 399	16.72 399	98.71 342	73.03 376	98.74 293	82.05 379	96.64 240	95.69 373

new-patchmatchnet		90.30 340	89.46 344	92.84 352	90.77 386	88.55 369	99.83 265	98.80 360	90.07 362	87.86 377	95.00 378	78.77 365	94.30 382	84.86 373	79.15 377	95.68 374

mvsany_test3		89.36 342	88.96 346	90.56 355	91.95 380	78.97 380	99.74 285	96.59 391	96.84 206	89.25 373	96.07 373	52.59 388	97.11 361	95.17 312	82.44 371	95.58 375

test_f		86.87 347	86.06 350	89.28 358	91.45 385	76.37 383	99.87 260	97.11 385	91.10 352	88.46 375	93.05 384	38.31 395	96.66 366	91.77 344	83.46 369	94.82 376

test_fmvs3		87.19 346	87.02 349	87.71 361	92.69 379	76.64 382	99.96 236	97.27 384	93.55 324	90.82 370	94.03 382	38.00 396	92.19 387	93.49 332	83.35 370	94.32 377

test123		79.44 354	79.23 356	80.05 373	80.03 395	71.72 386	100.00 1	77.93 404	62.52 390	94.81 351	99.69 260	78.21 366	74.53 397	92.57 337	27.33 397	93.90 378

LCM-MVSNet		79.01 356	76.93 359	85.27 365	78.28 396	68.01 393	96.57 386	98.03 372	55.10 393	82.03 386	93.27 383	31.99 399	93.95 383	82.72 376	74.37 382	93.84 379

CMPMVS	66.12 22	90.65 338	92.04 331	86.46 364	96.18 364	66.87 394	98.03 383	99.38 196	83.38 380	85.49 382	99.55 292	77.59 367	98.80 286	94.44 320	94.31 284	93.72 380
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WB-MVS		88.24 344	90.09 340	82.68 370	91.56 384	69.51 390	100.00 1	98.73 361	90.72 356	87.29 379	98.12 359	92.87 247	85.01 392	62.19 393	89.34 340	93.54 381

testf1		84.40 349	84.79 351	83.23 368	95.71 368	58.71 401	98.79 376	97.75 380	81.58 381	84.94 383	98.07 361	45.33 392	97.73 356	77.09 389	83.85 366	93.24 382

APD_test2		84.40 349	84.79 351	83.23 368	95.71 368	58.71 401	98.79 376	97.75 380	81.58 381	84.94 383	98.07 361	45.33 392	97.73 356	77.09 389	83.85 366	93.24 382

SSC-MVS		87.61 345	89.47 343	82.04 371	90.63 387	68.77 391	99.99 201	98.66 362	90.34 359	86.70 380	98.08 360	92.72 252	84.12 393	59.41 396	88.71 347	93.22 384

PMMVS2		79.15 355	77.28 358	84.76 366	82.34 393	72.66 384	99.70 295	95.11 394	71.68 388	84.78 385	90.87 385	32.05 398	89.99 389	75.53 391	63.45 390	91.64 385

tmp_tt		75.80 358	74.26 360	80.43 372	52.91 403	53.67 403	87.42 391	97.98 375	61.80 391	67.04 394	100.00 1	76.43 370	96.40 369	96.47 291	28.26 396	91.23 386

testmvs		80.17 351	81.95 354	74.80 375	58.54 401	59.58 400	100.00 1	87.14 401	76.09 386	99.61 183	100.00 1	67.06 383	74.19 398	98.84 206	50.30 392	90.64 387

Gipuma		84.73 348	83.50 353	88.40 360	97.50 348	82.21 378	88.87 389	99.05 337	65.81 389	85.71 381	90.49 386	53.70 387	96.31 370	78.64 385	91.74 313	86.67 388
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		77.92 357	79.45 355	73.34 377	76.87 397	46.81 404	98.24 381	99.05 337	59.89 392	73.55 388	98.34 356	36.81 397	86.55 390	80.96 380	91.35 322	86.65 389

ANet_high		66.05 362	63.44 366	73.88 376	61.14 400	63.45 398	95.68 388	87.18 400	79.93 383	47.35 397	80.68 397	22.35 401	72.33 399	61.24 394	35.42 395	85.88 390

test_vis3_rt		79.61 352	78.19 357	83.86 367	88.68 389	69.56 389	99.81 269	82.19 403	86.78 373	68.57 391	84.51 394	25.06 400	98.26 327	89.18 365	78.94 378	83.75 391

MVE	68.59 21	67.22 361	64.68 365	74.84 374	74.67 399	62.32 399	95.84 387	90.87 399	50.98 394	58.72 396	81.05 396	12.20 404	78.95 394	61.06 395	56.75 391	83.24 392
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	60.66 23	65.98 363	65.05 364	68.75 380	55.06 402	38.40 405	88.19 390	96.98 386	48.30 397	44.82 398	88.52 389	12.22 403	86.49 391	67.58 392	83.79 368	81.35 393
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		69.88 360	69.09 363	72.24 379	84.70 391	65.82 396	99.96 236	87.08 402	49.82 396	71.51 390	84.74 393	49.30 389	75.32 396	50.97 398	43.71 394	75.59 394

E-PMN		70.72 359	70.06 362	72.69 378	83.92 392	65.48 397	99.95 242	92.72 397	49.88 395	72.30 389	86.26 392	47.17 391	77.43 395	53.83 397	44.49 393	75.17 395

wuyk23d		28.28 364	29.73 368	23.92 381	75.89 398	32.61 406	66.50 392	12.88 405	16.09 398	14.59 400	16.59 399	12.35 402	32.36 400	39.36 399	13.36 398	6.79 396

test_blank		0.07 368	0.09 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.79 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uanet_test		0.01 369	0.02 372	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

DCPMVS		0.01 369	0.02 372	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

cdsmvs_eth3d_5k		24.41 365	32.55 367	0.00 382	0.00 404	0.00 407	0.00 393	99.39 194	0.00 400	0.00 401	100.00 1	93.55 236	0.00 401	0.00 400	0.00 399	0.00 397

pcd_1.5k_mvsjas		8.24 367	10.99 370	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	98.75 118	0.00 401	0.00 400	0.00 399	0.00 397

sosnet-low-res		0.01 369	0.02 372	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

sosnet		0.01 369	0.02 372	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uncertanet		0.01 369	0.02 372	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

Regformer		0.01 369	0.02 372	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

ab-mvs-re		8.33 366	11.11 369	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	100.00 1	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uanet		0.01 369	0.02 372	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.14 401	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

WAC-MVS						97.98 240								95.74 300

FOURS1					100.00 1	99.97 21	100.00 1	99.42 131	98.52 72	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 131	98.72 62	100.00 1	100.00 1	99.60 17

eth-test2					0.00 404

eth-test					0.00 404

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 178	100.00 1	100.00 1	99.32 61	99.99 94	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 131	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1

9.14			99.57 49		99.99 49		100.00 1	99.42 131	97.54 153	100.00 1	100.00 1	99.15 82	99.99 94	100.00 1	100.00 1

save fliter					99.99 49	99.93 43	100.00 1	99.42 131	98.93 36

test0726					100.00 1	99.99 5	100.00 1	99.42 131	99.04 15	100.00 1	100.00 1	99.53 29

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs												99.33 58

MTGPA								99.42 131

test_post1							99.32 336				88.24 390	99.33 58	99.59 207	98.31 234

test_post											89.05 388	99.49 39	99.59 207

patchmatchnet-post											97.79 364	99.41 54	99.54 224

MTMP							100.00 1	99.18 291

gm-plane-assit					99.52 207	97.26 275			95.86 260		100.00 1		99.43 243	98.76 211

TEST9					100.00 1	99.95 32	100.00 1	99.42 131	97.65 138	100.00 1	100.00 1	99.53 29	99.97 121

test_8					100.00 1	99.91 51	100.00 1	99.42 131	97.70 133	100.00 1	100.00 1	99.51 33	99.98 116

agg_prior					100.00 1	99.88 72		99.42 131		100.00 1			99.97 121

test_prior4						99.93 43	100.00 1

test_prior2							100.00 1		98.82 53	100.00 1	100.00 1	99.47 43		100.00 1	100.00 1

旧先验2							100.00 1		98.11 102	100.00 1			100.00 1	99.67 146

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 270

segment_acmp												99.55 25

testdata1							100.00 1		98.77 61

plane_prior7					99.00 275	94.78 321

plane_prior6					99.06 267	94.80 317						88.58 307

plane_prior4											99.97 193

plane_prior3						94.79 320			99.03 20	99.08 218

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 270

plane_prior						94.80 317	100.00 1		99.03 20						95.58 245

n2								0.00 406

nn								0.00 406

door-mid								96.32 392

test11								99.42 131

door								96.13 393

HQP5-MVS						94.82 314

HQP-NCC					99.07 263		100.00 1		99.04 15	99.17 208

ACMP_Plane					99.07 263		100.00 1		99.04 15	99.17 208

BP-MVS														99.79 117

HQP3-MVS								99.40 181							95.58 245

HQP2-MVS												88.61 305

NP-MVS					99.07 263	94.81 316					99.97 193

MDTV_nov1_ep13			98.94 120		99.53 200	98.36 212	99.39 330	99.46 94	96.54 231	99.99 101	99.63 277	98.92 105	99.86 170	98.30 237	98.71 172

ACMMP++_ref															94.58 283

ACMMP++															95.17 266

Test By Simon												99.10 85

High-resolution multi-view benchmark