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.

fmvsm_s_conf0.5_n_11		98.92 180	98.63 195	99.80 123	99.85 128	99.86 89	100.00 1	99.24 321	98.91 55	100.00 1	100.00 1	89.69 378	99.99 107	100.00 1	99.98 117	99.54 312

MED-MVS test				99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.10 10	99.99 127	100.00 1		100.00 1	100.00 1	100.00 1	100.00 1

MED-MVS		99.88 2	99.84 3	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.05 17	99.99 127	100.00 1	99.58 27	100.00 1	100.00 1	100.00 1	100.00 1

fmvsm_s_conf0.5_n_10		99.08 143	98.78 170	99.97 40	99.84 130	99.92 59	100.00 1	99.28 290	98.93 49	100.00 1	100.00 1	91.07 342	99.99 107	100.00 1	99.95 127	100.00 1

fmvsm_l_conf0.5_n_9		99.35 101	99.15 123	99.95 61	99.83 134	99.84 95	100.00 1	99.30 273	98.92 52	100.00 1	100.00 1	94.32 281	100.00 1	100.00 1	99.93 137	100.00 1

fmvsm_s_conf0.5_n_9		99.04 150	98.78 170	99.81 117	99.86 126	99.44 164	100.00 1	99.32 258	98.94 45	100.00 1	100.00 1	91.00 345	99.99 107	100.00 1	99.94 133	100.00 1

ME-MVS		99.87 3	99.83 4	99.99 13	99.99 52	99.98 18	100.00 1	99.95 19	99.05 17	100.00 1	100.00 1	99.50 44	100.00 1	100.00 1	100.00 1	100.00 1

fmvsm_s_conf0.5_n_8		99.34 105	99.14 125	99.91 83	99.83 134	99.74 111	100.00 1	99.38 224	98.94 45	100.00 1	100.00 1	94.25 283	99.99 107	100.00 1	99.91 145	100.00 1

fmvsm_s_conf0.5_n_5		99.00 162	98.70 186	99.88 95	99.81 143	99.64 127	100.00 1	99.26 311	98.78 83	99.97 144	100.00 1	90.65 352	99.99 107	100.00 1	99.89 148	99.99 124

fmvsm_s_conf0.5_n_3		98.99 166	98.69 188	99.89 90	99.70 178	99.69 122	100.00 1	99.39 221	98.93 49	100.00 1	100.00 1	90.20 363	99.99 107	100.00 1	99.95 127	100.00 1

fmvsm_s_conf0.5_n_2		98.90 184	98.57 205	99.90 87	99.79 161	99.78 103	100.00 1	99.25 315	98.97 37	100.00 1	100.00 1	89.22 386	99.99 107	100.00 1	99.88 151	99.92 167

fmvsm_s_conf0.1_n_2		98.95 176	98.69 188	99.73 143	99.61 222	99.74 111	100.00 1	99.23 326	98.95 42	99.97 144	100.00 1	90.92 348	99.97 149	100.00 1	99.58 186	99.47 317

reproduce_model		99.76 21	99.69 25	99.98 28	99.96 103	99.93 52	100.00 1	99.42 152	98.81 76	100.00 1	100.00 1	98.98 115	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 21	99.69 25	99.98 28	99.96 103	99.94 46	100.00 1	99.42 152	98.82 72	100.00 1	100.00 1	98.99 112	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 21	99.69 25	99.98 28	99.96 103	99.94 46	100.00 1	99.42 152	98.82 72	100.00 1	100.00 1	98.99 112	100.00 1	100.00 1	100.00 1	100.00 1

fmvsm_l_conf0.5_n_a		99.63 58	99.55 62	99.86 100	99.83 134	99.58 135	100.00 1	99.36 234	98.98 35	100.00 1	100.00 1	97.85 173	99.99 107	100.00 1	99.94 133	100.00 1

fmvsm_l_conf0.5_n		99.63 58	99.56 60	99.86 100	99.81 143	99.59 133	100.00 1	99.36 234	98.98 35	100.00 1	100.00 1	97.92 168	99.99 107	100.00 1	99.95 127	100.00 1

MM		99.63 58	99.52 68	99.94 74	99.99 52	99.82 98	100.00 1	99.97 17	99.11 8	100.00 1	100.00 1	96.65 224	100.00 1	100.00 1	99.97 121	100.00 1

patch_mono-2		99.04 150	99.79 9	96.81 405	99.92 115	90.47 459	100.00 1	99.41 201	98.95 42	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 149

DVP-MVS++		99.81 14	99.75 17	100.00 1	100.00 1	99.99 6	100.00 1	99.42 152	98.79 80	100.00 1	100.00 1	99.54 33	100.00 1	100.00 1	100.00 1	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 152					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 77	100.00 1	100.00 1	99.54 33	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 152					100.00 1	100.00 1	100.00 1	100.00 1

ZD-MVS					100.00 1	99.98 18		99.80 48	97.31 214	100.00 1	100.00 1	99.32 73	99.99 107	100.00 1	100.00 1

SED-MVS		99.83 10	99.77 12	100.00 1	100.00 1	99.99 6	100.00 1	99.42 152	99.03 25	100.00 1	100.00 1	99.50 44	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 6		99.42 152	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 33	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 152	99.03 25	100.00 1	100.00 1	99.56 30	100.00 1	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 51	99.57 55	99.95 61	99.99 52	99.85 93	100.00 1	99.42 152	97.67 165	100.00 1	100.00 1	99.05 106	99.99 107	100.00 1	100.00 1	100.00 1

dcpmvs_2		98.87 187	99.53 65	96.90 393	99.87 125	90.88 457	99.94 323	99.07 420	98.20 119	100.00 1	100.00 1	98.69 142	99.86 231	100.00 1	100.00 1	99.95 149

9.14			99.57 55		99.99 52		100.00 1	99.42 152	97.54 185	100.00 1	100.00 1	99.15 95	99.99 107	100.00 1	100.00 1

DVP-MVS		99.83 10	99.78 10	100.00 1	100.00 1	99.99 6	100.00 1	99.42 152	99.04 20	100.00 1	100.00 1	99.53 36	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 80	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 52	99.99 6	100.00 1	99.42 152					100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS		99.68 46	99.58 52	99.98 28	100.00 1	99.95 37	100.00 1	99.64 69	97.59 181	100.00 1	100.00 1	98.99 112	99.99 107	100.00 1	100.00 1	100.00 1

SMA-MVS		99.69 42	99.59 50	99.98 28	99.99 52	99.93 52	100.00 1	99.43 133	97.50 193	100.00 1	100.00 1	99.43 59	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 17	99.73 20	99.99 13	99.99 52	99.98 18	100.00 1	99.42 152	98.91 55	100.00 1	100.00 1	99.22 87	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 6	99.87 1	99.80 123	99.99 52	99.97 26	99.97 299	99.98 16	98.96 39	100.00 1	100.00 1	99.96 4	99.42 327	100.00 1	100.00 1	100.00 1

CANet		99.40 92	99.24 108	99.89 90	99.99 52	99.76 107	100.00 1	99.73 61	98.40 102	99.78 228	100.00 1	95.28 248	99.96 169	100.00 1	99.99 107	99.96 143

MGCNet		99.72 32	99.65 37	99.93 78	99.99 52	99.79 102	100.00 1	99.91 40	99.17 6	100.00 1	100.00 1	97.84 175	100.00 1	100.00 1	99.95 127	100.00 1

MSP-MVS		99.81 14	99.77 12	99.94 74	100.00 1	99.86 89	100.00 1	99.42 152	98.87 64	100.00 1	100.00 1	99.65 19	99.96 169	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 12	99.77 12	99.99 13	100.00 1	99.96 29	100.00 1	99.43 133	99.05 17	100.00 1	100.00 1	99.45 54	99.99 107	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
test9_res														100.00 1	100.00 1	100.00 1

train_agg		99.71 36	99.63 44	99.97 40	100.00 1	99.95 37	100.00 1	99.42 152	97.70 162	100.00 1	100.00 1	99.51 40	99.97 149	100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

HPM-MVS++		99.82 12	99.76 15	99.99 13	99.99 52	99.98 18	100.00 1	99.83 44	98.88 61	99.96 151	100.00 1	99.21 88	100.00 1	100.00 1	100.00 1	99.99 124

XVS		99.79 17	99.73 20	99.98 28	100.00 1	99.94 46	100.00 1	99.75 57	98.67 88	100.00 1	100.00 1	99.16 93	100.00 1	100.00 1	100.00 1	100.00 1

test_prior2							100.00 1		98.82 72	100.00 1	100.00 1	99.47 52		100.00 1	100.00 1

X-MVStestdata		97.04 324	96.06 353	99.98 28	100.00 1	99.94 46	100.00 1	99.75 57	98.67 88	100.00 1	66.97 501	99.16 93	100.00 1	100.00 1	100.00 1	100.00 1

1314		99.38 96	99.19 118	99.96 52	98.88 373	99.89 77	99.24 439	99.93 35	98.88 61	98.79 324	100.00 1	97.02 207	100.00 1	100.00 1	100.00 1	100.00 1

VDD-MVS		96.58 345	95.99 356	98.34 308	99.52 265	95.33 389	99.18 449	99.38 224	96.64 289	99.77 229	100.00 1	72.51 476	100.00 1	100.00 1	96.94 315	99.70 301

SD-MVS		99.81 14	99.75 17	99.99 13	99.99 52	99.96 29	100.00 1	99.42 152	99.01 31	100.00 1	100.00 1	99.33 70	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 13	100.00 1	99.96 29	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

APDe-MVS		99.84 9	99.78 10	99.99 13	100.00 1	99.98 18	100.00 1	99.44 124	99.06 15	100.00 1	100.00 1	99.56 30	99.99 107	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
MCST-MVS		99.85 6	99.80 7	100.00 1	100.00 1	99.99 6	100.00 1	99.73 61	99.19 5	100.00 1	100.00 1	99.31 75	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 31	99.64 40	99.99 13	100.00 1	99.97 26	100.00 1	99.42 152	98.02 133	100.00 1	100.00 1	99.32 73	99.99 107	100.00 1	100.00 1	100.00 1

TSAR-MVS + GP.		99.61 65	99.69 25	99.35 215	99.99 52	98.06 305	100.00 1	99.36 234	99.83 2	100.00 1	100.00 1	98.95 121	99.99 107	100.00 1	99.11 198	100.00 1

mPP-MVS		99.69 42	99.60 49	99.97 40	100.00 1	99.91 63	100.00 1	99.42 152	97.91 145	100.00 1	100.00 1	99.04 109	100.00 1	100.00 1	100.00 1	100.00 1

APD-MVS		99.68 46	99.58 52	99.97 40	99.99 52	99.96 29	100.00 1	99.42 152	97.53 188	100.00 1	100.00 1	99.27 84	99.97 149	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 6	99.80 7	100.00 1	100.00 1	99.99 6	100.00 1	99.77 53	99.07 13	100.00 1	100.00 1	99.39 68	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 4	99.82 5	100.00 1	100.00 1	99.99 6	100.00 1	99.71 66	99.07 13	100.00 1	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	100.00 1

CP-MVS		99.67 49	99.58 52	99.95 61	100.00 1	99.84 95	100.00 1	99.42 152	97.77 157	100.00 1	100.00 1	99.07 103	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 19	99.71 23	99.98 28	99.76 169	99.95 37	100.00 1	99.42 152	98.69 86	100.00 1	100.00 1	99.52 39	99.99 107	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_BlendedMVS		98.71 206	98.62 197	98.98 264	99.98 93	99.60 131	100.00 1	100.00 1	97.23 220	100.00 1	99.03 413	96.57 226	99.99 107	100.00 1	94.75 358	97.35 444

PVSNet_Blended		99.48 82	99.36 89	99.83 110	99.98 93	99.60 131	100.00 1	100.00 1	97.79 155	100.00 1	100.00 1	96.57 226	99.99 107	100.00 1	99.88 151	99.90 182

PAPM_NR		99.74 28	99.66 36	99.99 13	100.00 1	99.96 29	100.00 1	99.47 84	97.87 148	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 21	99.68 31	99.99 13	100.00 1	99.96 29	100.00 1	99.47 84	98.16 121	100.00 1	100.00 1	99.51 40	100.00 1	100.00 1	100.00 1	100.00 1

MVS_111021_HR		99.71 36	99.63 44	99.93 78	99.95 107	99.83 97	100.00 1	100.00 1	98.89 60	100.00 1	100.00 1	97.85 173	99.95 182	100.00 1	100.00 1	100.00 1

API-MVS		99.72 32	99.70 24	99.79 128	99.97 97	99.37 172	99.96 306	99.94 27	98.48 98	100.00 1	100.00 1	98.92 126	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 19	99.76 15	99.85 104	99.01 355	99.95 37	100.00 1	99.75 57	99.37 3	99.99 127	100.00 1	99.76 12	99.60 283	100.00 1	100.00 1	100.00 1

PHI-MVS		99.50 78	99.39 82	99.82 112	100.00 1	99.45 161	100.00 1	99.94 27	96.38 317	100.00 1	100.00 1	98.18 160	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet_0	93.57 19	96.41 353	95.74 370	98.41 303	99.84 130	95.22 391	100.00 1	100.00 1	98.08 130	97.55 405	99.78 328	84.40 433	100.00 1	100.00 1	81.99 465	100.00 1

DeepPCF-MVS	98.03 4	98.54 236	99.72 22	94.98 436	99.99 52	84.94 477	100.00 1	99.42 152	99.98 1	100.00 1	100.00 1	98.11 162	100.00 1	100.00 1	100.00 1	100.00 1

PLC	98.56 2	99.70 39	99.74 19	99.58 173	100.00 1	98.79 231	100.00 1	99.54 76	98.58 93	99.96 151	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 154
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		99.49 80	99.36 89	99.89 90	99.97 97	99.66 125	99.74 378	99.95 19	97.89 146	100.00 1	100.00 1	96.71 223	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
fmvsm_s_conf0.5_n_7		98.98 170	98.85 163	99.37 211	99.67 194	98.34 275	100.00 1	99.31 267	98.97 37	100.00 1	100.00 1	91.70 333	99.97 149	99.99 76	99.97 121	99.80 270

fmvsm_s_conf0.5_n_4		98.98 170	98.74 177	99.68 153	99.81 143	99.50 151	100.00 1	99.26 311	98.91 55	100.00 1	100.00 1	90.87 349	99.97 149	99.99 76	99.81 167	99.57 310

fmvsm_l_conf0.5_n_3		99.38 96	99.20 117	99.92 82	99.80 156	99.78 103	100.00 1	99.35 245	98.94 45	100.00 1	100.00 1	94.77 265	99.99 107	99.99 76	99.92 140	100.00 1

fmvsm_s_conf0.5_n		99.21 131	99.01 138	99.83 110	99.84 130	99.53 144	100.00 1	99.38 224	98.29 115	100.00 1	100.00 1	93.62 295	99.99 107	99.99 76	99.93 137	99.98 127

SR-MVS-dyc-post		99.63 58	99.52 68	99.97 40	99.99 52	99.91 63	100.00 1	99.42 152	97.62 173	100.00 1	100.00 1	98.65 143	99.99 107	99.99 76	100.00 1	100.00 1

RE-MVS-def			99.55 62		99.99 52	99.91 63	100.00 1	99.42 152	97.62 173	100.00 1	100.00 1	98.94 123		99.99 76	100.00 1	100.00 1

MP-MVS-pluss		99.61 65	99.50 71	99.97 40	99.98 93	99.92 59	100.00 1	99.42 152	97.53 188	99.77 229	100.00 1	98.77 138	100.00 1	99.99 76	100.00 1	99.99 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		99.74 28	99.67 33	99.96 52	100.00 1	99.89 77	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.31 75	100.00 1	99.99 76	100.00 1	100.00 1

region2R		99.72 32	99.64 40	99.97 40	100.00 1	99.90 70	100.00 1	99.74 60	97.86 149	100.00 1	100.00 1	99.19 90	100.00 1	99.99 76	100.00 1	100.00 1

ACMMPR		99.74 28	99.67 33	99.96 52	100.00 1	99.89 77	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.29 81	100.00 1	99.99 76	100.00 1	100.00 1

MP-MVS		99.61 65	99.49 73	99.98 28	99.99 52	99.94 46	100.00 1	99.42 152	97.82 152	99.99 127	100.00 1	98.20 159	100.00 1	99.99 76	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS		99.59 69	99.50 71	99.89 90	100.00 1	99.70 120	100.00 1	99.42 152	97.46 197	100.00 1	100.00 1	98.60 146	99.96 169	99.99 76	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
CPTT-MVS		99.49 80	99.38 83	99.85 104	100.00 1	99.54 142	100.00 1	99.42 152	97.58 182	99.98 138	100.00 1	97.43 198	100.00 1	99.99 76	100.00 1	100.00 1

MVS_111021_LR		99.70 39	99.65 37	99.88 95	99.96 103	99.70 120	100.00 1	99.97 17	98.96 39	100.00 1	100.00 1	97.93 167	99.95 182	99.99 76	100.00 1	100.00 1

CSCG		99.28 119	99.35 91	99.05 257	99.99 52	97.15 351	100.00 1	99.47 84	97.44 201	99.42 267	100.00 1	97.83 177	100.00 1	99.99 76	100.00 1	100.00 1

lecture		99.64 54	99.53 65	99.98 28	99.99 52	99.93 52	100.00 1	99.47 84	98.53 94	100.00 1	100.00 1	97.88 171	100.00 1	99.98 91	99.92 140	100.00 1

fmvsm_s_conf0.5_n_6		99.30 114	99.12 128	99.84 109	99.24 334	99.56 137	100.00 1	99.31 267	98.90 59	100.00 1	100.00 1	94.75 267	99.97 149	99.98 91	99.88 151	100.00 1

test_fmvsmconf_n		99.56 71	99.46 79	99.86 100	99.68 186	99.58 135	100.00 1	99.31 267	98.92 52	99.88 202	100.00 1	97.35 200	99.99 107	99.98 91	99.99 107	100.00 1

test_fmvsmvis_n_1920		99.46 85	99.37 86	99.73 143	98.88 373	99.18 196	100.00 1	99.26 311	98.85 66	99.79 226	100.00 1	97.70 181	100.00 1	99.98 91	99.86 157	100.00 1

test_fmvsm_n_1920		99.55 73	99.49 73	99.73 143	99.85 128	99.19 194	100.00 1	99.41 201	98.87 64	100.00 1	100.00 1	97.34 201	100.00 1	99.98 91	99.90 147	100.00 1

test_yl		99.51 75	99.37 86	99.95 61	99.82 137	99.90 70	100.00 1	99.47 84	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 91	97.75 298	99.94 154

DCV-MVSNet		99.51 75	99.37 86	99.95 61	99.82 137	99.90 70	100.00 1	99.47 84	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 91	97.75 298	99.94 154

ACMMP_NAP		99.67 49	99.57 55	99.97 40	99.98 93	99.92 59	100.00 1	99.42 152	97.83 150	100.00 1	100.00 1	98.89 129	100.00 1	99.98 91	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 42	99.63 44	99.87 97	99.99 52	99.64 127	99.95 315	99.44 124	98.35 111	100.00 1	100.00 1	98.98 115	99.97 149	99.98 91	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 40	99.87 97	100.00 1	99.64 127	99.98 290	99.44 124	98.35 111	99.99 127	100.00 1	99.04 109	99.96 169	99.98 91	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 52	99.55 62	99.97 40	99.99 52	99.91 63	100.00 1	99.48 83	97.54 185	100.00 1	100.00 1	98.97 117	99.99 107	99.98 91	100.00 1	100.00 1

HPM-MVS_fast		99.60 68	99.49 73	99.91 83	99.99 52	99.78 103	100.00 1	99.42 152	97.09 229	100.00 1	100.00 1	98.95 121	99.96 169	99.98 91	100.00 1	100.00 1

114514_t		99.39 93	99.25 104	99.81 117	99.97 97	99.48 159	100.00 1	99.42 152	95.53 351	100.00 1	100.00 1	98.37 157	99.95 182	99.97 103	100.00 1	100.00 1

CNLPA		99.72 32	99.65 37	99.91 83	99.97 97	99.72 115	100.00 1	99.47 84	98.43 101	99.88 202	100.00 1	99.14 96	100.00 1	99.97 103	100.00 1	100.00 1

fmvsm_s_conf0.5_n_a		99.32 110	99.15 123	99.81 117	99.80 156	99.47 160	100.00 1	99.35 245	98.22 116	100.00 1	100.00 1	95.21 253	99.99 107	99.96 105	99.86 157	99.98 127

GST-MVS		99.64 54	99.53 65	99.95 61	100.00 1	99.86 89	100.00 1	99.79 50	97.72 160	99.95 182	100.00 1	98.39 156	100.00 1	99.96 105	99.99 107	100.00 1

MTAPA		99.68 46	99.59 50	99.97 40	99.99 52	99.91 63	100.00 1	99.42 152	98.32 113	99.94 185	100.00 1	98.65 143	100.00 1	99.96 105	100.00 1	100.00 1

DP-MVS Recon		99.76 21	99.69 25	99.98 28	100.00 1	99.95 37	100.00 1	99.52 77	97.99 135	99.99 127	100.00 1	99.72 14	100.00 1	99.96 105	100.00 1	100.00 1

ACMMP		99.65 52	99.57 55	99.89 90	99.99 52	99.66 125	99.75 377	99.73 61	98.16 121	99.75 232	100.00 1	98.90 128	100.00 1	99.96 105	99.88 151	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
CLD-MVS		97.64 289	97.74 282	97.36 371	99.01 355	94.76 408	100.00 1	99.34 252	99.30 4	99.00 305	99.97 256	87.49 408	99.57 290	99.96 105	95.58 327	97.75 349
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DeepC-MVS_fast	98.92 1	99.75 26	99.67 33	99.99 13	99.99 52	99.96 29	99.73 383	99.52 77	99.06 15	100.00 1	100.00 1	98.80 137	100.00 1	99.95 111	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 26	99.68 31	99.97 40	100.00 1	99.91 63	99.98 290	99.47 84	99.09 12	100.00 1	100.00 1	98.59 147	100.00 1	99.95 111	100.00 1	100.00 1

test_fmvsmconf0.1_n		99.25 126	99.05 134	99.82 112	98.92 369	99.55 139	100.00 1	99.23 326	98.91 55	99.75 232	99.97 256	94.79 264	99.94 195	99.94 113	99.99 107	99.97 137

ZNCC-MVS		99.71 36	99.62 47	99.97 40	99.99 52	99.90 70	100.00 1	99.79 50	97.97 139	99.97 144	100.00 1	98.97 117	100.00 1	99.94 113	100.00 1	100.00 1

DELS-MVS		99.62 63	99.56 60	99.82 112	99.92 115	99.45 161	100.00 1	99.78 52	98.92 52	99.73 238	100.00 1	97.70 181	100.00 1	99.93 115	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
SDMVSNet		98.49 241	98.08 264	99.73 143	99.82 137	99.53 144	99.99 258	99.45 110	97.62 173	99.38 275	99.86 303	90.06 371	99.88 228	99.92 116	96.61 323	99.79 276

AdaColmap		99.44 88	99.26 102	99.95 61	100.00 1	99.86 89	99.70 389	99.99 13	98.53 94	99.90 196	100.00 1	95.34 247	100.00 1	99.92 116	100.00 1	100.00 1

F-COLMAP		99.64 54	99.64 40	99.67 154	99.99 52	99.07 203	100.00 1	99.44 124	98.30 114	99.90 196	100.00 1	99.18 91	99.99 107	99.91 118	100.00 1	99.94 154

VNet		99.04 150	98.75 175	99.90 87	99.81 143	99.75 108	99.50 414	99.47 84	98.36 109	100.00 1	99.99 236	94.66 270	100.00 1	99.90 119	97.09 311	99.96 143

EPNet		99.62 63	99.69 25	99.42 198	99.99 52	98.37 269	100.00 1	99.89 42	98.83 70	100.00 1	100.00 1	98.97 117	100.00 1	99.90 119	99.61 185	99.89 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf0.01_n		98.60 225	98.24 252	99.67 154	96.90 452	99.21 192	99.99 258	99.04 433	98.80 77	99.57 256	99.96 273	90.12 368	99.91 207	99.89 121	99.89 148	99.90 182

LFMVS		97.42 305	96.62 328	99.81 117	99.80 156	99.50 151	99.16 455	99.56 75	94.48 385	100.00 1	100.00 1	79.35 456	100.00 1	99.89 121	97.37 307	99.94 154

test_vis1_n_1920		97.77 285	97.24 306	99.34 217	99.79 161	98.04 307	100.00 1	99.25 315	98.88 61	100.00 1	100.00 1	77.52 462	100.00 1	99.88 123	99.85 160	100.00 1

mvsany_test1		99.57 70	99.48 76	99.85 104	99.86 126	99.54 142	100.00 1	99.36 234	98.94 45	100.00 1	100.00 1	97.97 165	100.00 1	99.88 123	99.28 193	100.00 1

PS-MVSNAJ		99.64 54	99.57 55	99.85 104	99.78 166	99.81 99	99.95 315	99.42 152	98.38 105	100.00 1	100.00 1	98.75 139	100.00 1	99.88 123	99.99 107	99.74 291

3Dnovator+	95.58 15	99.03 153	98.71 184	99.96 52	98.99 363	99.89 77	100.00 1	99.51 81	98.96 39	98.32 364	100.00 1	92.78 316	100.00 1	99.87 126	100.00 1	100.00 1

balanced_ft_v1		98.70 208	98.61 198	98.94 266	99.67 194	96.90 357	99.91 337	99.30 273	96.73 273	99.96 151	99.97 256	92.18 328	99.93 199	99.86 127	99.95 127	100.00 1

WTY-MVS		99.54 74	99.40 81	99.95 61	99.81 143	99.93 52	100.00 1	100.00 1	97.98 137	99.84 206	100.00 1	98.94 123	99.98 140	99.86 127	98.21 261	99.94 154

HY-MVS	96.53 9	99.50 78	99.35 91	99.96 52	99.81 143	99.93 52	99.64 396	100.00 1	97.97 139	99.84 206	99.85 308	98.94 123	99.99 107	99.86 127	98.23 260	99.95 149

xiu_mvs_v2_base		99.51 75	99.41 80	99.82 112	99.70 178	99.73 113	99.92 331	99.40 205	98.15 123	100.00 1	100.00 1	98.50 151	100.00 1	99.85 130	99.13 197	99.74 291

VDDNet		96.39 357	95.55 379	98.90 269	99.27 331	97.45 336	99.15 457	99.92 39	91.28 439	99.98 138	100.00 1	73.55 472	100.00 1	99.85 130	96.98 314	99.24 323

MVS		99.22 130	98.96 147	99.98 28	99.00 360	99.95 37	99.24 439	99.94 27	98.14 124	98.88 314	100.00 1	95.63 244	100.00 1	99.85 130	100.00 1	100.00 1

fmvsm_s_conf0.1_n		98.77 195	98.42 226	99.82 112	99.47 290	99.52 148	100.00 1	99.27 305	97.53 188	100.00 1	100.00 1	89.73 376	99.96 169	99.84 133	99.93 137	99.97 137

Anonymous20240529		96.93 330	96.22 347	99.05 257	99.79 161	97.30 345	99.16 455	99.47 84	88.51 457	98.69 327	100.00 1	83.50 441	100.00 1	99.83 134	97.02 313	99.83 224

xiu_mvs_v1_base_debu		99.35 101	99.21 113	99.79 128	99.67 194	99.71 116	99.78 368	99.36 234	98.13 125	100.00 1	100.00 1	97.00 211	100.00 1	99.83 134	99.07 199	99.66 305

xiu_mvs_v1_base		99.35 101	99.21 113	99.79 128	99.67 194	99.71 116	99.78 368	99.36 234	98.13 125	100.00 1	100.00 1	97.00 211	100.00 1	99.83 134	99.07 199	99.66 305

xiu_mvs_v1_base_debi		99.35 101	99.21 113	99.79 128	99.67 194	99.71 116	99.78 368	99.36 234	98.13 125	100.00 1	100.00 1	97.00 211	100.00 1	99.83 134	99.07 199	99.66 305

alignmvs		99.38 96	99.21 113	99.91 83	99.73 174	99.92 59	100.00 1	99.51 81	97.61 177	100.00 1	100.00 1	99.06 104	99.93 199	99.83 134	97.12 310	99.90 182

mmtdpeth		94.58 398	94.18 400	95.81 426	98.82 383	91.09 456	99.99 258	98.61 462	96.38 317	100.00 1	97.23 468	76.52 466	99.85 238	99.82 139	80.22 471	96.48 461

fmvsm_s_conf0.1_n_a		98.71 206	98.36 242	99.78 133	99.09 344	99.42 166	100.00 1	99.26 311	97.42 203	100.00 1	100.00 1	89.78 374	99.96 169	99.82 139	99.85 160	99.97 137

myMVS_eth3d28		99.41 91	99.28 96	99.80 123	99.69 181	99.53 144	100.00 1	99.43 133	97.12 228	99.98 138	99.97 256	99.41 65	100.00 1	99.81 141	98.07 274	99.88 203

PGM-MVS		99.69 42	99.61 48	99.95 61	99.99 52	99.85 93	100.00 1	99.58 72	97.69 164	100.00 1	100.00 1	99.44 55	100.00 1	99.79 142	100.00 1	100.00 1

HQP_MVS		97.71 288	97.82 280	97.37 370	99.00 360	94.80 403	100.00 1	99.40 205	99.00 32	99.08 299	99.97 256	88.58 399	99.55 299	99.79 142	95.57 331	97.76 338

plane_prior5								99.40 205					99.55 299	99.79 142	95.57 331	97.76 338

BP-MVS														99.79 142

PVSNet_Blended_VisFu		99.33 108	99.18 121	99.78 133	99.82 137	99.49 155	100.00 1	99.95 19	97.36 206	99.63 251	100.00 1	96.45 230	99.95 182	99.79 142	99.65 181	99.89 190

HQP-MVS		97.73 286	97.85 278	97.39 369	99.07 346	94.82 400	100.00 1	99.40 205	99.04 20	99.17 287	99.97 256	88.61 397	99.57 290	99.79 142	95.58 327	97.77 336

testing3-2		99.45 86	99.31 94	99.86 100	99.70 178	99.73 113	100.00 1	99.47 84	97.46 197	99.97 144	99.97 256	99.48 51	100.00 1	99.78 148	97.99 277	99.85 219

test_vis1_n		96.69 340	95.81 364	99.32 230	99.14 338	97.98 310	99.97 299	98.98 443	98.45 100	100.00 1	100.00 1	66.44 485	99.99 107	99.78 148	99.57 188	100.00 1

balanced_conf03		99.43 89	99.28 96	99.85 104	99.68 186	99.68 123	99.97 299	99.28 290	97.03 235	99.96 151	99.97 256	97.90 169	99.93 199	99.77 150	100.00 1	99.94 154

MVSMamba_PlusPlus		99.39 93	99.25 104	99.80 123	99.68 186	99.59 133	99.99 258	99.30 273	96.66 286	99.96 151	99.97 256	97.89 170	99.92 205	99.76 151	100.00 1	99.90 182

sasdasda		99.03 153	98.73 178	99.94 74	99.75 171	99.95 37	100.00 1	99.30 273	97.64 170	100.00 1	100.00 1	95.22 251	99.97 149	99.76 151	96.90 316	99.91 171

canonicalmvs		99.03 153	98.73 178	99.94 74	99.75 171	99.95 37	100.00 1	99.30 273	97.64 170	100.00 1	100.00 1	95.22 251	99.97 149	99.76 151	96.90 316	99.91 171

XVG-OURS-SEG-HR		98.27 261	98.31 245	98.14 328	99.59 229	95.92 376	100.00 1	99.36 234	98.48 98	99.21 286	100.00 1	89.27 385	99.94 195	99.76 151	99.17 195	98.56 334

BP-MVS1		99.56 71	99.48 76	99.79 128	99.48 285	99.61 130	100.00 1	99.32 258	97.34 209	99.94 185	100.00 1	99.74 13	99.89 220	99.75 155	99.72 173	99.87 214

AstraMVS		99.03 153	99.01 138	99.09 254	99.46 297	97.66 329	100.00 1	99.23 326	97.83 150	99.95 182	100.00 1	95.52 246	99.86 231	99.74 156	99.39 192	99.74 291

MGCFI-Net		99.01 161	98.70 186	99.93 78	99.74 173	99.94 46	100.00 1	99.29 282	97.60 180	100.00 1	100.00 1	95.10 257	99.96 169	99.74 156	96.85 318	99.91 171

UBG		99.36 100	99.27 98	99.63 161	99.63 213	99.01 212	100.00 1	99.43 133	96.99 238	100.00 1	99.92 292	99.69 17	99.99 107	99.74 156	98.06 275	99.88 203

EIA-MVS		99.26 122	99.19 118	99.45 190	99.63 213	98.75 233	100.00 1	99.27 305	96.93 244	99.95 182	100.00 1	97.47 194	99.79 255	99.74 156	99.72 173	99.82 230

HyFIR lowres test		99.32 110	99.24 108	99.58 173	99.95 107	99.26 184	100.00 1	99.99 13	96.72 275	99.29 281	99.91 295	99.49 47	99.47 317	99.74 156	98.08 273	100.00 1

DeepC-MVS	97.84 5	99.00 162	98.80 168	99.60 167	99.93 112	99.03 208	100.00 1	99.40 205	98.61 92	99.33 278	100.00 1	92.23 327	99.95 182	99.74 156	99.96 125	99.83 224
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
sd_testset		97.81 283	97.48 292	98.79 279	99.82 137	96.80 361	99.32 431	99.45 110	97.62 173	99.38 275	99.86 303	85.56 428	99.77 261	99.72 162	96.61 323	99.79 276

h-mvs33		97.03 325	96.53 331	98.51 294	99.79 161	95.90 378	99.45 418	99.45 110	98.21 117	100.00 1	99.78 328	97.49 192	99.99 107	99.72 162	74.92 477	99.65 308

hse-mvs2		96.79 333	96.38 339	98.04 344	99.68 186	95.54 384	99.81 357	99.42 152	98.21 117	100.00 1	99.80 324	97.49 192	99.46 322	99.72 162	73.27 480	99.12 326

mvsmamba		99.05 149	98.98 144	99.27 244	99.57 238	98.10 301	100.00 1	99.28 290	95.92 337	99.96 151	99.97 256	96.73 222	99.89 220	99.72 162	99.65 181	99.81 244

LuminaMVS		99.07 146	98.92 156	99.50 181	98.87 376	99.12 201	99.92 331	99.22 331	97.45 199	99.82 218	99.98 244	96.29 232	99.85 238	99.71 166	99.05 202	99.52 314

GG-mvs-BLEND				99.59 169	99.54 248	99.49 155	99.17 454	99.52 77		99.96 151	99.68 344	100.00 1	99.33 334	99.71 166	99.99 107	99.96 143

casdiffmvs_mvg		98.64 214	98.39 236	99.40 204	99.50 278	98.60 245	100.00 1	99.22 331	96.85 252	99.10 295	100.00 1	92.75 317	99.78 260	99.71 166	98.35 236	99.81 244
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
3Dnovator	95.63 14	99.06 147	98.76 174	99.96 52	98.86 378	99.90 70	99.98 290	99.93 35	98.95 42	98.49 351	100.00 1	92.91 314	100.00 1	99.71 166	100.00 1	100.00 1

0.3-1-1-0.015		97.60 293	97.19 309	98.83 274	99.13 339	96.55 369	100.00 1	99.40 205	94.19 395	99.83 209	99.81 318	99.18 91	99.97 149	99.70 170	83.50 459	99.98 127

0.4-1-1-0.2		97.60 293	97.18 310	98.86 272	99.05 352	96.62 367	100.00 1	99.40 205	94.24 390	99.82 218	99.81 318	99.09 99	99.97 149	99.70 170	83.50 459	99.98 127

ET-MVSNet_ETH3D		96.41 353	95.48 384	99.20 249	99.81 143	99.75 108	100.00 1	99.02 437	97.30 216	78.33 490	100.00 1	97.73 179	97.94 449	99.70 170	87.41 440	99.92 167

ETV-MVS		99.34 105	99.24 108	99.64 160	99.58 234	99.33 175	100.00 1	99.25 315	97.57 183	99.96 151	100.00 1	97.44 197	99.79 255	99.70 170	99.65 181	99.81 244

Anonymous202405211		97.87 279	97.53 291	98.90 269	99.81 143	96.70 364	99.35 429	99.46 102	92.98 426	98.83 321	99.99 236	90.63 354	100.00 1	99.70 170	97.03 312	100.00 1

casdiffmvs		98.65 213	98.38 238	99.46 188	99.52 265	98.74 236	100.00 1	99.15 385	96.91 247	99.05 302	100.00 1	92.75 317	99.83 244	99.70 170	98.38 231	99.81 244
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		98.69 210	98.45 223	99.41 199	99.52 265	98.67 240	100.00 1	99.17 379	97.03 235	99.13 292	100.00 1	93.17 307	99.74 270	99.70 170	98.34 240	99.81 244

PMMVS		99.12 140	98.97 146	99.58 173	99.57 238	98.98 217	100.00 1	99.30 273	97.14 224	99.96 151	100.00 1	96.53 229	99.82 247	99.70 170	98.49 220	99.94 154

ACMP	97.00 8	97.19 315	97.16 312	97.27 379	98.97 365	94.58 416	100.00 1	99.32 258	97.97 139	97.45 407	99.98 244	85.79 426	99.56 294	99.70 170	95.24 342	97.67 414
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_fmvs1		98.37 251	98.04 268	99.34 217	99.84 130	98.07 303	100.00 1	99.00 440	98.85 66	100.00 1	100.00 1	85.11 430	99.96 169	99.69 179	99.88 151	100.00 1

TestfortrainingZip a		99.86 4	99.81 6	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.10 10	99.99 127	100.00 1	99.58 27	100.00 1	99.68 180	100.00 1	100.00 1

SPE-MVS-test		99.31 112	99.27 98	99.43 195	99.99 52	98.77 232	100.00 1	99.19 363	97.24 218	99.96 151	100.00 1	97.56 189	99.70 278	99.68 180	99.81 167	99.82 230

gg-mvs-nofinetune		96.95 329	96.10 351	99.50 181	99.41 309	99.36 174	99.07 467	99.52 77	83.69 477	99.96 151	83.60 498	100.00 1	99.20 340	99.68 180	99.99 107	99.96 143

cascas		98.43 244	98.07 266	99.50 181	99.65 205	99.02 210	100.00 1	99.22 331	94.21 393	99.72 239	99.98 244	92.03 331	99.93 199	99.68 180	98.12 271	99.54 312

diffmvs_AUTHOR		98.92 180	98.73 178	99.49 185	99.48 285	98.81 229	99.94 323	99.14 392	97.24 218	99.96 151	100.00 1	94.85 262	99.87 230	99.67 184	98.31 246	99.79 276

testing11		99.26 122	99.19 118	99.46 188	99.64 211	98.61 244	100.00 1	99.43 133	96.94 243	99.92 191	99.94 286	99.43 59	99.97 149	99.67 184	97.79 296	99.82 230

test_fmvs1_n		97.43 304	96.86 319	99.15 251	99.68 186	97.48 335	99.99 258	98.98 443	98.82 72	100.00 1	100.00 1	74.85 471	99.96 169	99.67 184	99.70 175	100.00 1

旧先验2							100.00 1		98.11 129	100.00 1			100.00 1	99.67 184

lupinMVS		99.29 117	99.16 122	99.69 150	99.45 303	99.49 155	100.00 1	99.15 385	97.45 199	99.97 144	100.00 1	96.76 219	99.76 265	99.67 184	100.00 1	99.81 244

diffmvs		98.96 173	98.73 178	99.63 161	99.54 248	99.16 198	100.00 1	99.18 370	97.33 211	99.96 151	100.00 1	94.60 272	99.91 207	99.66 189	98.33 243	99.82 230
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CHOSEN 1792x2688		99.00 162	98.91 157	99.25 246	99.90 119	97.79 325	100.00 1	99.99 13	98.79 80	98.28 367	100.00 1	93.63 294	99.95 182	99.66 189	99.95 127	100.00 1

0.4-1-1-0.1		97.56 296	97.15 313	98.79 279	99.01 355	96.44 372	100.00 1	99.40 205	94.11 398	99.81 224	99.81 318	99.09 99	99.97 149	99.65 191	83.48 461	99.98 127

SSM_0407		98.72 202	98.52 213	99.33 225	99.53 251	98.52 253	99.88 345	99.15 385	96.53 301	98.95 307	100.00 1	94.38 278	99.72 275	99.64 192	98.62 212	99.75 284

SSM_0404		98.76 198	98.56 208	99.35 215	99.53 251	98.65 242	99.80 362	99.15 385	96.53 301	99.47 264	100.00 1	94.38 278	99.76 265	99.64 192	98.59 215	99.64 309

CS-MVS		99.33 108	99.27 98	99.50 181	99.99 52	99.00 215	100.00 1	99.13 398	97.26 217	99.96 151	100.00 1	97.79 178	99.64 281	99.64 192	99.67 178	99.87 214

EC-MVSNet		99.19 133	99.09 131	99.48 186	99.42 307	99.07 203	100.00 1	99.21 350	96.95 242	99.96 151	100.00 1	96.88 217	99.48 315	99.64 192	99.79 171	99.88 203

ab-mvs		98.42 246	98.02 270	99.61 165	99.71 176	99.00 215	99.10 462	99.64 69	96.70 281	99.04 304	99.81 318	90.64 353	99.98 140	99.64 192	97.93 283	99.84 221

guyue		99.21 131	99.07 132	99.62 163	99.55 245	99.29 179	100.00 1	99.32 258	97.66 166	99.96 151	100.00 1	95.84 238	99.84 242	99.63 197	99.67 178	99.75 284

OpenMVS	95.20 17	98.76 198	98.41 228	99.78 133	98.89 372	99.81 99	99.99 258	99.76 54	98.02 133	98.02 382	100.00 1	91.44 335	100.00 1	99.63 197	99.97 121	99.55 311

testing91		99.18 134	99.10 129	99.41 199	99.60 225	98.43 260	100.00 1	99.43 133	96.76 263	99.82 218	99.92 292	99.05 106	99.98 140	99.62 199	97.67 302	99.81 244

testing99		99.18 134	99.10 129	99.41 199	99.60 225	98.43 260	100.00 1	99.43 133	96.76 263	99.84 206	99.92 292	99.06 104	99.98 140	99.62 199	97.67 302	99.81 244

thres200		99.27 120	99.04 135	99.96 52	99.81 143	99.90 70	100.00 1	99.94 27	97.31 214	99.83 209	99.96 273	97.04 204	100.00 1	99.62 199	97.88 286	99.98 127

viewmambaseed2359dif		98.57 230	98.34 244	99.28 241	99.46 297	98.23 287	100.00 1	99.16 380	96.26 325	99.11 294	100.00 1	93.12 312	99.79 255	99.61 202	98.33 243	99.80 270

MonoMVSNet		98.55 233	98.64 194	98.26 316	98.21 409	95.76 381	99.94 323	99.16 380	96.23 326	99.47 264	99.24 397	96.75 221	99.22 339	99.61 202	99.17 195	99.81 244

dmvs_re		97.54 299	97.88 277	96.54 411	99.55 245	90.35 460	99.86 348	99.46 102	97.00 237	99.41 272	100.00 1	90.78 351	99.30 335	99.60 204	95.24 342	99.96 143

RRT-MVS		98.75 201	98.52 213	99.44 192	99.65 205	98.57 247	99.90 338	99.08 415	96.51 305	99.96 151	99.95 280	92.59 322	99.96 169	99.60 204	99.45 191	99.81 244

thres100view900		99.25 126	99.01 138	99.95 61	99.81 143	99.87 86	100.00 1	99.94 27	97.13 226	99.83 209	99.96 273	97.01 208	100.00 1	99.59 206	97.85 288	99.98 127

tfpn200view9		99.26 122	99.03 136	99.96 52	99.81 143	99.89 77	100.00 1	99.94 27	97.23 220	99.83 209	99.96 273	97.04 204	100.00 1	99.59 206	97.85 288	99.98 127

thres400		99.26 122	99.03 136	99.95 61	99.81 143	99.89 77	100.00 1	99.94 27	97.23 220	99.83 209	99.96 273	97.04 204	100.00 1	99.59 206	97.85 288	99.97 137

viewdifsd2359ckpt07		98.72 202	98.52 213	99.34 217	99.47 290	98.28 282	99.99 258	99.20 360	96.98 239	99.60 253	100.00 1	93.45 299	99.93 199	99.58 209	98.36 234	99.82 230

viewmanbaseed2359cas		98.86 188	98.68 190	99.40 204	99.51 270	98.51 256	99.98 290	99.22 331	97.05 234	99.72 239	100.00 1	94.77 265	99.89 220	99.58 209	98.31 246	99.81 244

LCM-MVSNet-Re		96.52 346	97.21 308	94.44 441	99.27 331	85.80 475	99.85 350	96.61 493	95.98 335	92.75 463	98.48 449	93.97 289	97.55 459	99.58 209	98.43 224	99.98 127

E3new		98.95 176	98.80 168	99.41 199	99.57 238	98.50 257	100.00 1	99.22 331	96.84 254	99.89 199	100.00 1	95.70 242	99.93 199	99.57 212	98.39 228	99.82 230

jason		99.11 141	98.96 147	99.59 169	99.17 337	99.31 178	100.00 1	99.13 398	97.38 205	99.83 209	100.00 1	95.54 245	99.72 275	99.57 212	99.97 121	99.74 291
jason: jason.
LPG-MVS_test		97.31 311	97.32 300	97.28 377	98.85 379	94.60 413	100.00 1	99.37 228	97.35 207	98.85 317	99.98 244	86.66 416	99.56 294	99.55 214	95.26 339	97.70 403

LGP-MVS_train				97.28 377	98.85 379	94.60 413		99.37 228	97.35 207	98.85 317	99.98 244	86.66 416	99.56 294	99.55 214	95.26 339	97.70 403

PVSNet	94.91 18	99.30 114	99.25 104	99.44 192	100.00 1	98.32 278	100.00 1	99.86 43	98.04 132	100.00 1	100.00 1	96.10 234	100.00 1	99.55 214	99.73 172	100.00 1

viewcassd2359sk11		98.90 184	98.73 178	99.40 204	99.57 238	98.47 258	99.99 258	99.22 331	96.79 259	99.82 218	100.00 1	95.24 250	99.91 207	99.54 217	98.38 231	99.82 230

thres600view7		99.24 129	99.00 141	99.95 61	99.81 143	99.87 86	100.00 1	99.94 27	97.13 226	99.83 209	99.96 273	97.01 208	100.00 1	99.54 217	97.77 297	99.97 137

BH-w/o		98.82 192	98.81 167	98.88 271	99.62 220	96.71 363	100.00 1	99.28 290	97.09 229	98.81 322	100.00 1	94.91 261	99.96 169	99.54 217	100.00 1	99.96 143

E2		98.77 195	98.57 205	99.37 211	99.53 251	98.38 268	99.98 290	99.22 331	96.77 262	99.75 232	100.00 1	94.03 286	99.91 207	99.53 220	98.35 236	99.82 230

E3		98.77 195	98.57 205	99.36 213	99.47 290	98.36 272	99.98 290	99.22 331	96.76 263	99.75 232	100.00 1	94.10 284	99.91 207	99.53 220	98.35 236	99.82 230

sss		99.45 86	99.34 93	99.80 123	99.76 169	99.50 151	100.00 1	99.91 40	97.72 160	99.98 138	99.94 286	98.45 152	100.00 1	99.53 220	98.75 210	99.89 190

EPP-MVSNet		99.10 142	99.00 141	99.40 204	99.51 270	98.68 239	99.92 331	99.43 133	95.47 357	99.65 250	100.00 1	99.51 40	99.76 265	99.53 220	98.00 276	99.75 284

viewmacassd2359aftdt		98.57 230	98.31 245	99.33 225	99.49 282	98.31 280	99.89 342	99.21 350	96.87 251	99.10 295	100.00 1	92.48 325	99.88 228	99.50 224	98.28 250	99.81 244

原ACMM1				99.93 78	100.00 1	99.80 101		99.66 68	98.18 120	100.00 1	100.00 1	99.43 59	100.00 1	99.50 224	100.00 1	100.00 1

E4		98.68 212	98.46 222	99.33 225	99.51 270	98.27 284	99.96 306	99.21 350	96.66 286	99.68 243	100.00 1	93.38 300	99.91 207	99.49 226	98.27 253	99.81 244

viewdifsd2359ckpt11		97.98 274	97.89 274	98.26 316	99.47 290	94.98 396	99.99 258	99.22 331	96.74 269	99.24 283	100.00 1	90.14 365	99.90 218	99.49 226	96.73 319	99.90 182

viewmsd2359difaftdt		97.98 274	97.89 274	98.27 313	99.47 290	94.99 395	99.99 258	99.22 331	96.74 269	99.24 283	100.00 1	90.14 365	99.90 218	99.49 226	96.73 319	99.90 182

E6new		98.64 214	98.41 228	99.30 236	99.46 297	98.19 293	99.79 363	99.21 350	96.62 294	99.68 243	100.00 1	93.24 305	99.91 207	99.47 229	98.26 255	99.81 244

E6		98.64 214	98.41 228	99.30 236	99.46 297	98.19 293	99.79 363	99.21 350	96.62 294	99.68 243	100.00 1	93.24 305	99.91 207	99.47 229	98.26 255	99.81 244

OMC-MVS		99.27 120	99.38 83	98.96 265	99.95 107	97.06 355	100.00 1	99.40 205	98.83 70	99.88 202	100.00 1	97.01 208	99.86 231	99.47 229	99.84 162	99.97 137

E5new		98.63 219	98.41 228	99.31 232	99.51 270	98.21 290	99.79 363	99.21 350	96.62 294	99.67 248	100.00 1	93.15 309	99.91 207	99.46 232	98.26 255	99.81 244

E5		98.63 219	98.41 228	99.31 232	99.51 270	98.21 290	99.79 363	99.21 350	96.62 294	99.67 248	100.00 1	93.15 309	99.91 207	99.46 232	98.26 255	99.81 244

viewdifsd2359ckpt09		98.78 194	98.60 201	99.31 232	99.53 251	98.37 269	100.00 1	99.20 360	96.85 252	99.32 279	100.00 1	94.68 269	99.74 270	99.46 232	98.36 234	99.81 244

viewdifsd2359ckpt13		98.72 202	98.52 213	99.34 217	99.55 245	98.46 259	99.99 258	99.22 331	96.50 307	99.05 302	100.00 1	94.54 273	99.73 273	99.46 232	98.35 236	99.81 244

Effi-MVS+		98.58 228	98.24 252	99.61 165	99.60 225	99.26 184	97.85 486	99.10 409	96.22 329	99.97 144	99.89 298	93.75 292	99.77 261	99.43 236	98.34 240	99.81 244

IB-MVS	96.24 12	97.54 299	96.95 316	99.33 225	99.67 194	98.10 301	100.00 1	99.47 84	97.42 203	99.26 282	99.69 340	98.83 134	99.89 220	99.43 236	78.77 475	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
CostFormer		98.84 190	98.77 173	99.04 259	99.41 309	97.58 332	99.67 394	99.35 245	94.66 378	99.96 151	99.36 391	99.28 83	99.74 270	99.41 238	97.81 293	99.81 244

BH-untuned		98.64 214	98.65 192	98.60 289	99.59 229	96.17 373	100.00 1	99.28 290	96.67 285	98.41 356	100.00 1	94.52 274	99.83 244	99.41 238	100.00 1	99.81 244

NormalMVS		99.47 84	99.48 76	99.43 195	99.99 52	98.55 248	99.94 323	99.28 290	98.39 103	100.00 1	100.00 1	98.44 153	99.98 140	99.36 240	99.92 140	99.75 284

SymmetryMVS		99.30 114	99.25 104	99.45 190	99.79 161	98.55 248	99.94 323	99.47 84	98.39 103	100.00 1	100.00 1	98.44 153	99.98 140	99.36 240	97.83 291	99.83 224

Effi-MVS+-dtu		98.51 240	98.86 162	97.47 367	99.77 168	94.21 426	100.00 1	98.94 445	97.61 177	99.91 194	98.75 433	95.89 236	99.51 310	99.36 240	99.48 189	98.68 331

UWE-MVS-28		99.29 117	99.23 111	99.48 186	99.73 174	98.86 226	100.00 1	99.43 133	96.97 241	99.99 127	99.83 311	99.43 59	99.77 261	99.35 243	98.31 246	99.80 270

ECVR-MVS		98.43 244	98.14 258	99.32 230	99.89 121	98.21 290	99.46 416	100.00 1	98.38 105	99.47 264	100.00 1	87.91 402	99.80 254	99.35 243	98.78 207	99.94 154

XVG-OURS		98.30 255	98.36 242	98.13 331	99.58 234	95.91 377	100.00 1	99.36 234	98.69 86	99.23 285	100.00 1	91.20 339	99.92 205	99.34 245	97.82 292	98.56 334

无先验							100.00 1	99.80 48	97.98 137				100.00 1	99.33 246		100.00 1

QAPM		98.99 166	98.66 191	99.96 52	99.01 355	99.87 86	99.88 345	99.93 35	97.99 135	98.68 329	100.00 1	93.17 307	100.00 1	99.32 247	100.00 1	100.00 1

test1111		98.42 246	98.12 259	99.29 238	99.88 123	98.15 296	99.46 416	100.00 1	98.36 109	99.42 267	100.00 1	87.91 402	99.79 255	99.31 248	98.78 207	99.94 154

UGNet		98.41 248	98.11 260	99.31 232	99.54 248	98.55 248	99.18 449	100.00 1	98.64 91	99.79 226	99.04 410	87.61 407	100.00 1	99.30 249	99.89 148	99.40 320
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
testdata				99.66 157	99.99 52	98.97 219		99.73 61	97.96 142	100.00 1	100.00 1	99.42 63	100.00 1	99.28 250	100.00 1	100.00 1

test2506		99.48 82	99.38 83	99.75 139	99.89 121	99.51 149	99.45 418	100.00 1	98.38 105	99.83 209	100.00 1	98.86 130	99.81 250	99.25 251	98.78 207	99.94 154

ACMM	97.17 6	97.37 307	97.40 296	97.29 376	99.01 355	94.64 411	100.00 1	99.25 315	98.07 131	98.44 355	99.98 244	87.38 410	99.55 299	99.25 251	95.19 345	97.69 408
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_vis1_rt		93.10 419	92.93 415	93.58 450	99.63 213	85.07 476	99.99 258	93.71 498	97.49 194	90.96 468	97.10 469	60.40 487	99.95 182	99.24 253	97.90 285	95.72 472

nrg030		97.64 289	97.27 304	98.75 282	98.34 397	99.53 144	100.00 1	99.22 331	96.21 330	98.27 369	99.95 280	94.40 277	98.98 353	99.23 254	89.78 419	97.75 349

VPA-MVSNet		97.03 325	96.43 337	98.82 275	98.64 387	99.32 176	99.38 426	99.47 84	96.73 273	98.91 313	98.94 422	87.00 414	99.40 328	99.23 254	89.59 420	97.76 338

VPNet		96.41 353	95.76 369	98.33 309	98.61 388	98.30 281	99.48 415	99.45 110	96.98 239	98.87 316	99.88 300	81.57 448	98.93 359	99.22 256	87.82 437	97.76 338

mamba_0408		98.63 219	98.40 233	99.34 217	99.53 251	98.52 253	99.24 439	99.16 380	96.43 310	98.95 307	99.98 244	94.47 275	99.76 265	99.21 257	98.62 212	99.75 284

SSM_04072		98.59 226	98.40 233	99.15 251	99.53 251	98.52 253	99.24 439	99.16 380	96.43 310	98.95 307	99.98 244	94.47 275	99.19 341	99.21 257	98.62 212	99.75 284

test_cas_vis1_n_1920		98.63 219	98.25 249	99.77 136	99.69 181	99.32 176	100.00 1	99.31 267	98.84 68	99.96 151	100.00 1	87.42 409	99.99 107	99.14 259	99.86 157	100.00 1

LS3D		99.31 112	99.13 126	99.87 97	99.99 52	99.71 116	99.55 408	99.46 102	97.32 212	99.82 218	100.00 1	96.85 218	99.97 149	99.14 259	100.00 1	99.92 167

mvs_anonymous		98.80 193	98.60 201	99.38 210	99.57 238	99.24 188	100.00 1	99.21 350	95.87 338	98.92 311	99.82 315	96.39 231	99.03 347	99.13 261	98.50 219	99.88 203

DP-MVS		98.86 188	98.54 210	99.81 117	99.97 97	99.45 161	99.52 412	99.40 205	94.35 389	98.36 359	100.00 1	96.13 233	99.97 149	99.12 262	100.00 1	100.00 1

SD_0403		97.92 278	98.43 225	96.39 414	99.68 186	89.74 465	99.92 331	99.34 252	96.75 266	99.39 274	99.93 291	93.54 298	99.51 310	99.11 263	98.21 261	99.92 167

ETVMVS		99.16 137	98.98 144	99.69 150	99.67 194	99.56 137	100.00 1	99.45 110	96.36 319	99.98 138	99.95 280	98.65 143	99.64 281	99.11 263	97.63 305	99.88 203

thisisatest0515		99.42 90	99.31 94	99.74 140	99.59 229	99.55 139	100.00 1	99.46 102	96.65 288	99.92 191	100.00 1	99.44 55	99.85 238	99.09 265	99.63 184	99.81 244

ACMH	96.25 11	96.77 334	96.62 328	97.21 380	98.96 366	94.43 420	99.64 396	99.33 255	97.43 202	96.55 428	99.97 256	83.52 440	99.54 302	99.07 266	95.13 349	97.66 415
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet		98.52 238	98.25 249	99.34 217	99.68 186	98.55 248	99.68 393	99.41 201	97.34 209	99.94 185	100.00 1	90.38 362	99.70 278	99.03 267	98.84 205	99.76 283
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
reproduce_monomvs		98.61 223	98.54 210	98.82 275	99.97 97	99.28 181	100.00 1	99.33 255	98.51 97	97.87 390	99.24 397	99.98 3	99.45 323	99.02 268	92.93 377	97.74 376

VortexMVS		98.23 263	98.11 260	98.59 290	99.56 244	99.37 172	99.95 315	99.03 436	96.47 308	98.69 327	99.55 373	95.91 235	98.66 382	99.01 269	94.80 357	97.73 387

thisisatest0530		99.37 99	99.27 98	99.69 150	99.59 229	99.41 167	100.00 1	99.46 102	96.46 309	99.90 196	100.00 1	99.44 55	99.85 238	98.97 270	99.58 186	99.80 270

tpmrst		98.98 170	98.93 154	99.14 253	99.61 222	97.74 326	99.52 412	99.36 234	96.05 334	99.98 138	99.64 353	99.04 109	99.86 231	98.94 271	98.19 264	99.82 230

tttt0517		99.34 105	99.23 111	99.67 154	99.57 238	99.38 169	100.00 1	99.46 102	96.33 322	99.89 199	100.00 1	99.44 55	99.84 242	98.93 272	99.46 190	99.78 280

MDTV_nov1_ep13_2view						99.24 188	99.56 406		96.31 323	99.96 151		98.86 130		98.92 273		99.89 190

COLMAP_ROB	97.10 7	98.29 258	98.17 257	98.65 285	99.94 110	97.39 338	99.30 435	99.40 205	95.64 346	97.75 396	100.00 1	92.69 321	99.95 182	98.89 274	99.92 140	98.62 333
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		98.14 266	97.74 282	99.33 225	99.59 229	98.28 282	99.27 436	99.21 350	96.42 314	99.15 291	99.94 286	88.87 392	99.79 255	98.88 275	98.29 249	99.93 165

testing222		99.14 139	98.94 152	99.73 143	99.67 194	99.51 149	100.00 1	99.43 133	96.90 249	99.99 127	99.90 297	98.55 149	99.86 231	98.85 276	97.18 309	99.81 244

mvs_tets		97.00 328	96.69 325	97.94 350	97.41 448	97.27 346	99.60 402	99.18 370	96.51 305	97.35 409	99.69 340	86.53 418	98.91 361	98.84 277	95.09 351	97.65 420

testmvs		80.17 453	81.95 456	74.80 478	58.54 505	59.58 503	100.00 1	87.14 504	76.09 488	99.61 252	100.00 1	67.06 484	74.19 501	98.84 277	50.30 495	90.64 490

BH-RMVSNet		98.46 242	98.08 264	99.59 169	99.61 222	99.19 194	100.00 1	99.28 290	97.06 233	98.95 307	100.00 1	88.99 389	99.82 247	98.83 279	100.00 1	99.77 281

FIs		97.95 277	97.73 284	98.62 287	98.53 392	99.24 188	100.00 1	99.43 133	96.74 269	97.87 390	99.82 315	95.27 249	98.89 364	98.78 280	93.07 374	97.74 376

EPMVS		99.25 126	99.13 126	99.60 167	99.60 225	99.20 193	99.60 402	100.00 1	96.93 244	99.92 191	99.36 391	99.05 106	99.71 277	98.77 281	98.94 204	99.90 182

gm-plane-assit					99.52 265	97.26 347			95.86 340		100.00 1		99.43 325	98.76 282

baseline2		98.99 166	98.93 154	99.18 250	99.26 333	99.15 199	100.00 1	99.46 102	96.71 280	96.79 423	100.00 1	99.42 63	99.25 338	98.75 283	99.94 133	99.15 325

cl22		98.23 263	98.11 260	98.58 292	99.82 137	99.01 212	100.00 1	99.28 290	96.92 246	98.33 363	99.21 400	98.09 164	98.97 355	98.72 284	92.61 380	97.76 338

jajsoiax		97.07 322	96.79 323	97.89 354	97.28 449	97.12 352	99.95 315	99.19 363	96.55 299	97.31 410	99.69 340	87.35 412	98.91 361	98.70 285	95.12 350	97.66 415

Test_1112_low_res		98.83 191	98.60 201	99.51 178	99.69 181	98.75 233	99.99 258	99.14 392	96.81 257	98.84 319	99.06 407	97.45 195	99.89 220	98.66 286	97.75 298	99.89 190

1112_ss		98.91 182	98.71 184	99.51 178	99.69 181	98.75 233	99.99 258	99.15 385	96.82 256	98.84 319	100.00 1	97.45 195	99.89 220	98.66 286	97.75 298	99.89 190

MVS_Test		98.93 179	98.65 192	99.77 136	99.62 220	99.50 151	99.99 258	99.19 363	95.52 353	99.96 151	99.86 303	96.54 228	99.98 140	98.65 288	98.48 221	99.82 230

WBMVS		98.19 265	98.10 263	98.47 296	99.63 213	99.03 208	100.00 1	99.32 258	95.46 358	98.39 358	99.40 388	99.69 17	98.61 389	98.64 289	92.39 385	97.76 338

FC-MVSNet-test		97.84 281	97.63 290	98.45 298	98.30 402	99.05 206	100.00 1	99.43 133	96.63 293	97.61 402	99.82 315	95.19 254	98.57 397	98.64 289	93.05 375	97.73 387

MVSFormer		98.94 178	98.82 165	99.28 241	99.45 303	99.49 155	100.00 1	99.13 398	95.46 358	99.97 144	100.00 1	96.76 219	98.59 394	98.63 291	100.00 1	99.74 291

test_djsdf		97.55 298	97.38 297	98.07 334	97.50 441	97.99 309	100.00 1	99.13 398	95.46 358	98.47 352	99.85 308	92.01 332	98.59 394	98.63 291	95.36 335	97.62 426

tpm2		98.64 214	98.58 204	98.81 278	99.42 307	97.12 352	99.69 391	99.37 228	93.63 409	99.94 185	99.67 345	98.96 120	99.47 317	98.62 293	97.95 282	99.83 224

baseline1		98.91 182	98.61 198	99.81 117	99.71 176	99.77 106	99.78 368	99.44 124	97.51 192	98.81 322	99.99 236	98.25 158	99.76 265	98.60 294	95.41 333	99.89 190

MVSTER		98.58 228	98.52 213	98.77 281	99.65 205	99.68 123	100.00 1	99.29 282	95.63 347	98.65 331	99.80 324	99.78 9	98.88 367	98.59 295	95.31 337	97.73 387

D2MVS		97.63 292	97.83 279	97.05 384	98.83 381	94.60 413	100.00 1	99.82 45	96.89 250	98.28 367	99.03 413	94.05 285	99.47 317	98.58 296	94.97 355	97.09 450

PS-MVSNAJss		98.03 272	98.06 267	97.94 350	97.63 433	97.33 344	99.89 342	99.23 326	96.27 324	98.03 380	99.59 365	98.75 139	98.78 372	98.52 297	94.61 361	97.70 403

WR-MVS		97.09 320	96.64 326	98.46 297	98.43 394	99.09 202	99.97 299	99.33 255	95.62 348	97.76 393	99.67 345	91.17 340	98.56 399	98.49 298	89.28 425	97.74 376

GDP-MVS		99.39 93	99.26 102	99.77 136	99.53 251	99.55 139	100.00 1	99.11 406	97.14 224	99.96 151	100.00 1	99.83 5	99.89 220	98.47 299	99.26 194	99.87 214

IS-MVSNet		99.08 143	98.91 157	99.59 169	99.65 205	99.38 169	99.78 368	99.24 321	96.70 281	99.51 259	100.00 1	98.44 153	99.52 308	98.47 299	98.39 228	99.88 203

XVG-ACMP-BASELINE		96.60 344	96.52 333	96.84 397	98.41 395	93.29 436	99.99 258	99.32 258	97.76 159	98.51 349	99.29 394	81.95 447	99.54 302	98.40 301	95.03 352	97.68 410

XXY-MVS		97.14 319	96.63 327	98.67 284	98.65 386	98.92 222	99.54 410	99.29 282	95.57 350	97.63 399	99.83 311	87.79 406	99.35 332	98.39 302	92.95 376	97.75 349

Vis-MVSNet (Re-imp)		98.99 166	98.89 161	99.29 238	99.64 211	98.89 225	99.98 290	99.31 267	96.74 269	99.48 261	100.00 1	98.11 162	99.10 344	98.39 302	98.34 240	99.89 190

UA-Net		99.06 147	98.83 164	99.74 140	99.52 265	99.40 168	99.08 465	99.45 110	97.64 170	99.83 209	100.00 1	95.80 239	99.94 195	98.35 304	99.80 170	99.88 203

ACMH+	96.20 13	96.49 351	96.33 343	97.00 387	99.06 350	93.80 429	99.81 357	99.31 267	97.32 212	95.89 441	99.97 256	82.62 445	99.54 302	98.34 305	94.63 360	97.65 420

FE-MVS		99.16 137	98.99 143	99.66 157	99.65 205	99.18 196	99.58 404	99.43 133	95.24 363	99.91 194	99.59 365	99.37 69	99.97 149	98.31 306	99.81 167	99.83 224

test_post1							99.32 431				88.24 493	99.33 70	99.59 285	98.31 306

SCA		98.30 255	97.98 272	99.23 247	99.41 309	98.25 286	99.99 258	99.45 110	96.91 247	99.76 231	99.58 367	89.65 380	99.54 302	98.31 306	98.79 206	99.91 171

MDTV_nov1_ep13			98.94 152		99.53 251	98.36 272	99.39 425	99.46 102	96.54 300	99.99 127	99.63 357	98.92 126	99.86 231	98.30 309	98.71 211

OPM-MVS		97.21 314	97.18 310	97.32 374	98.08 415	94.66 409	100.00 1	99.28 290	98.65 90	98.92 311	99.98 244	86.03 424	99.56 294	98.28 310	95.41 333	97.72 394
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Anonymous20231211		96.29 362	95.70 372	98.07 334	99.80 156	97.49 334	99.15 457	99.40 205	89.11 454	97.75 396	99.45 384	88.93 391	98.98 353	98.26 311	89.47 422	97.73 387

CVMVSNet		98.56 232	98.47 221	98.82 275	99.11 341	97.67 328	99.74 378	99.47 84	97.57 183	99.06 301	100.00 1	95.72 241	98.97 355	98.21 312	97.33 308	99.83 224

PCF-MVS	98.23 3	98.69 210	98.37 240	99.62 163	99.78 166	99.02 210	99.23 446	99.06 428	96.43 310	98.08 376	100.00 1	94.72 268	99.95 182	98.16 313	99.91 145	99.90 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RPSCF		97.37 307	98.24 252	94.76 439	99.80 156	84.57 478	99.99 258	99.05 430	94.95 368	99.82 218	100.00 1	94.03 286	100.00 1	98.15 314	98.38 231	99.70 301

kuosan		98.55 233	98.53 212	98.62 287	99.66 203	96.16 374	100.00 1	99.44 124	93.93 402	99.81 224	99.98 244	97.58 185	99.81 250	98.08 315	98.28 250	99.89 190

CANet_DTU		99.02 159	98.90 160	99.41 199	99.88 123	98.71 237	100.00 1	99.29 282	98.84 68	100.00 1	100.00 1	94.02 288	100.00 1	98.08 315	99.96 125	99.52 314

CDS-MVSNet		98.96 173	98.95 151	99.01 261	99.48 285	98.36 272	99.93 329	99.37 228	96.79 259	99.31 280	99.83 311	99.77 11	98.91 361	98.07 317	97.98 278	99.77 281
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CP-MVSNet		96.73 336	96.25 345	98.18 324	98.21 409	98.67 240	99.77 373	99.32 258	95.06 366	97.20 413	99.65 349	90.10 369	98.19 427	98.06 318	88.90 429	97.66 415

miper_enhance_ethall		98.33 254	98.27 247	98.51 294	99.66 203	99.04 207	100.00 1	99.22 331	97.53 188	98.51 349	99.38 389	99.49 47	98.75 377	98.02 319	92.61 380	97.76 338

anonymousdsp		97.16 317	96.88 318	98.00 346	97.08 451	98.06 305	99.81 357	99.15 385	94.58 380	97.84 392	99.62 361	90.49 356	98.60 392	97.98 320	95.32 336	97.33 445

UniMVSNet_NR-MVSNet		97.16 317	96.80 321	98.22 320	98.38 396	98.41 262	100.00 1	99.45 110	96.14 332	97.76 393	99.64 353	95.05 258	98.50 402	97.98 320	86.84 444	97.75 349

DU-MVS		96.93 330	96.49 334	98.22 320	98.31 400	98.41 262	100.00 1	99.37 228	96.41 315	97.76 393	99.65 349	92.14 329	98.50 402	97.98 320	86.84 444	97.75 349

TAMVS		98.76 198	98.73 178	98.86 272	99.44 305	97.69 327	99.57 405	99.34 252	96.57 298	99.12 293	99.81 318	98.83 134	99.16 342	97.97 323	97.91 284	99.73 300

新几何1				99.99 13	100.00 1	99.96 29		99.81 47	97.89 146	100.00 1	100.00 1	99.20 89	100.00 1	97.91 324	100.00 1	100.00 1

UniMVSNet_ETH3D		95.28 393	94.41 399	97.89 354	98.91 370	95.14 392	99.13 459	99.35 245	92.11 434	97.17 414	99.66 347	70.28 480	99.36 330	97.88 325	95.18 346	99.16 324

PatchMatch-RL		99.02 159	98.78 170	99.74 140	99.99 52	99.29 179	100.00 1	100.00 1	98.38 105	99.89 199	99.81 318	93.14 311	99.99 107	97.85 326	99.98 117	99.95 149

icg_test_0407_2		98.30 255	98.45 223	97.85 356	99.38 318	95.36 385	99.99 258	99.18 370	96.72 275	99.58 254	100.00 1	95.17 255	98.45 407	97.84 327	98.15 267	99.74 291

IMVS_0407		98.36 253	98.42 226	98.19 323	99.38 318	95.36 385	99.73 383	99.18 370	96.72 275	99.58 254	100.00 1	95.17 255	99.47 317	97.84 327	98.15 267	99.74 291

IMVS_0404		97.87 279	97.89 274	97.81 358	99.38 318	95.36 385	99.84 351	99.18 370	96.72 275	98.41 356	100.00 1	91.43 336	98.32 415	97.84 327	98.15 267	99.74 291

IMVS_0403		98.37 251	98.39 236	98.29 311	99.38 318	95.36 385	99.97 299	99.18 370	96.72 275	99.68 243	100.00 1	94.61 271	99.77 261	97.84 327	98.15 267	99.74 291

TDRefinement		91.93 428	90.48 437	96.27 419	81.60 498	92.65 444	99.10 462	97.61 484	93.96 401	93.77 457	99.85 308	80.03 452	99.53 307	97.82 331	70.59 481	96.63 460

UWE-MVS		99.18 134	99.06 133	99.51 178	99.67 194	98.80 230	100.00 1	99.43 133	96.80 258	99.93 190	99.86 303	99.79 8	99.94 195	97.78 332	98.33 243	99.80 270

PS-CasMVS		96.34 360	95.78 368	98.03 345	98.18 412	98.27 284	99.71 387	99.32 258	94.75 372	96.82 422	99.65 349	86.98 415	98.15 429	97.74 333	88.85 430	97.66 415

Fast-Effi-MVS+-dtu		98.38 250	98.56 208	97.82 357	99.58 234	94.44 419	100.00 1	99.16 380	96.75 266	99.51 259	99.63 357	95.03 259	99.60 283	97.71 334	99.67 178	99.42 319

tpmvs		98.59 226	98.38 238	99.23 247	99.69 181	97.90 317	99.31 434	99.47 84	94.52 383	99.68 243	99.28 395	97.64 184	99.89 220	97.71 334	98.17 266	99.89 190

DPM-MVS		99.63 58	99.51 70	100.00 1	99.90 119	100.00 1	100.00 1	99.43 133	99.00 32	100.00 1	100.00 1	99.58 27	100.00 1	97.64 336	100.00 1	100.00 1

ttmdpeth		96.24 365	95.88 361	97.32 374	97.80 427	96.61 368	99.95 315	98.77 457	97.80 154	93.42 459	99.28 395	86.42 419	99.01 349	97.63 337	91.84 395	96.33 465

FA-MVS(test-final)		99.00 162	98.75 175	99.73 143	99.63 213	99.43 165	99.83 353	99.43 133	95.84 343	99.52 258	99.37 390	97.84 175	99.96 169	97.63 337	99.68 176	99.79 276

PatchmatchNet		99.03 153	98.96 147	99.26 245	99.49 282	98.33 276	99.38 426	99.45 110	96.64 289	99.96 151	99.58 367	99.49 47	99.50 313	97.63 337	99.00 203	99.93 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EI-MVSNet		97.98 274	97.93 273	98.16 327	99.11 341	97.84 322	99.74 378	99.29 282	94.39 388	98.65 331	100.00 1	97.21 202	98.88 367	97.62 340	95.31 337	97.75 349

UniMVSNet (Re)		97.29 313	96.85 320	98.59 290	98.49 393	99.13 200	100.00 1	99.42 152	96.52 304	98.24 373	98.90 425	94.93 260	98.89 364	97.54 341	87.61 438	97.75 349

IterMVS-LS		97.56 296	97.44 293	97.92 353	99.38 318	97.90 317	99.89 342	99.10 409	94.41 387	98.32 364	99.54 376	97.21 202	98.11 435	97.50 342	91.62 399	97.75 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AUN-MVS		96.26 364	95.67 376	98.06 338	99.68 186	95.60 383	99.82 356	99.42 152	96.78 261	99.88 202	99.80 324	94.84 263	99.47 317	97.48 343	73.29 479	99.12 326

Fast-Effi-MVS+		98.40 249	98.02 270	99.55 177	99.63 213	99.06 205	100.00 1	99.15 385	95.07 365	99.42 267	99.95 280	93.26 304	99.73 273	97.44 344	98.24 259	99.87 214

WR-MVS_H		96.73 336	96.32 344	97.95 349	98.26 406	97.88 319	99.72 386	99.43 133	95.06 366	96.99 416	98.68 436	93.02 313	98.53 400	97.43 345	88.33 434	97.43 440

tpm		98.24 262	98.22 256	98.32 310	99.13 339	95.79 380	99.53 411	99.12 404	95.20 364	99.96 151	99.36 391	97.58 185	99.28 337	97.41 346	96.67 321	99.88 203

LF4IMVS		96.19 367	96.18 348	96.23 420	98.26 406	92.09 448	100.00 1	97.89 478	97.82 152	97.94 385	99.87 301	82.71 444	99.38 329	97.41 346	93.71 367	97.20 447

tt0805		96.52 346	96.23 346	97.40 368	99.30 328	93.55 431	99.32 431	99.45 110	96.75 266	97.88 389	99.99 236	79.99 454	99.59 285	97.39 348	95.98 326	99.06 328

testdata2													100.00 1	97.36 349

miper_ehance_all_eth		97.81 283	97.66 288	98.23 319	99.49 282	98.37 269	99.99 258	99.11 406	94.78 371	98.25 371	99.21 400	98.18 160	98.57 397	97.35 350	92.61 380	97.76 338

c3_l		97.58 295	97.42 294	98.06 338	99.48 285	98.16 295	99.96 306	99.10 409	94.54 382	98.13 375	99.20 402	97.87 172	98.25 422	97.28 351	91.20 407	97.75 349

LTVRE_ROB	95.29 16	96.32 361	96.10 351	96.99 388	98.55 390	93.88 428	99.45 418	99.28 290	94.50 384	96.46 429	99.52 377	84.86 431	99.48 315	97.26 352	95.03 352	97.59 430
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
KinetiMVS		98.61 223	98.26 248	99.65 159	99.46 297	99.24 188	99.96 306	99.44 124	97.54 185	99.99 127	99.99 236	90.83 350	99.95 182	97.18 353	99.92 140	99.75 284

Baseline_NR-MVSNet		96.16 372	95.70 372	97.56 366	98.28 405	96.79 362	100.00 1	97.86 479	91.93 436	97.63 399	99.47 383	92.14 329	98.35 414	97.13 354	86.83 446	97.54 433

ADS-MVSNet2		98.28 260	98.51 218	97.62 363	99.51 270	95.03 394	99.24 439	99.41 201	95.52 353	99.96 151	99.70 337	97.57 187	97.94 449	97.11 355	98.54 217	99.88 203

ADS-MVSNet		98.70 208	98.51 218	99.28 241	99.51 270	98.39 265	99.24 439	99.44 124	95.52 353	99.96 151	99.70 337	97.57 187	99.58 289	97.11 355	98.54 217	99.88 203

GeoE		98.06 270	97.65 289	99.29 238	99.47 290	98.41 262	100.00 1	99.19 363	94.85 370	98.88 314	100.00 1	91.21 338	99.59 285	97.02 357	98.19 264	99.88 203

JIA-IIPM		97.09 320	96.34 342	99.36 213	98.88 373	98.59 246	99.81 357	99.43 133	84.81 475	99.96 151	90.34 490	98.55 149	99.52 308	97.00 358	98.28 250	99.98 127

sc_t1		92.52 424	91.34 428	96.09 422	97.80 427	89.86 464	98.61 479	99.12 404	77.73 483	96.09 436	99.79 327	68.64 482	98.94 358	96.94 359	87.31 441	99.46 318

AllTest		98.55 233	98.40 233	98.99 262	99.93 112	97.35 341	100.00 1	99.40 205	97.08 231	99.09 297	99.98 244	93.37 301	99.95 182	96.94 359	99.84 162	99.68 303

TestCases				98.99 262	99.93 112	97.35 341		99.40 205	97.08 231	99.09 297	99.98 244	93.37 301	99.95 182	96.94 359	99.84 162	99.68 303

PEN-MVS		96.01 379	95.48 384	97.58 365	97.74 430	97.26 347	99.90 338	99.29 282	94.55 381	96.79 423	99.55 373	87.38 410	97.84 451	96.92 362	87.24 442	97.65 420

TranMVSNet+NR-MVSNet		96.45 352	96.01 355	97.79 359	98.00 419	97.62 331	100.00 1	99.35 245	95.98 335	97.31 410	99.64 353	90.09 370	98.00 445	96.89 363	86.80 447	97.75 349

TESTMET0.1,1		99.08 143	98.96 147	99.44 192	99.63 213	99.38 169	100.00 1	99.45 110	95.53 351	99.48 261	100.00 1	99.71 15	99.02 348	96.84 364	99.99 107	99.91 171

test-LLR		99.03 153	98.91 157	99.40 204	99.40 314	99.28 181	100.00 1	99.45 110	96.70 281	99.42 267	99.12 403	99.31 75	99.01 349	96.82 365	99.99 107	99.91 171

test-mter		98.96 173	98.82 165	99.40 204	99.40 314	99.28 181	100.00 1	99.45 110	95.44 362	99.42 267	99.12 403	99.70 16	99.01 349	96.82 365	99.99 107	99.91 171

MSDG		98.90 184	98.63 195	99.70 149	99.92 115	99.25 186	100.00 1	99.37 228	95.71 345	99.40 273	100.00 1	96.58 225	99.95 182	96.80 367	99.94 133	99.91 171

eth_miper_zixun_eth		97.47 303	97.28 302	98.06 338	99.41 309	97.94 315	99.62 400	99.08 415	94.46 386	98.19 374	99.56 372	96.91 216	98.50 402	96.78 368	91.49 402	97.74 376

NR-MVSNet		96.63 342	96.04 354	98.38 305	98.31 400	98.98 217	99.22 448	99.35 245	95.87 338	94.43 454	99.65 349	92.73 319	98.40 410	96.78 368	88.05 435	97.75 349

dp		98.72 202	98.61 198	99.03 260	99.53 251	97.39 338	99.45 418	99.39 221	95.62 348	99.94 185	99.52 377	98.83 134	99.82 247	96.77 370	98.42 225	99.89 190

tpm cat1		98.05 271	97.76 281	98.92 268	99.50 278	97.10 354	99.77 373	99.30 273	90.20 451	99.72 239	98.71 434	97.71 180	99.86 231	96.75 371	98.20 263	99.81 244

gbinet_0.2-2-1-0.02		93.73 410	92.69 422	96.84 397	94.91 476	94.62 412	100.00 1	99.28 290	87.02 469	98.53 345	98.45 450	89.72 377	98.15 429	96.65 372	69.64 487	97.74 376

cl____		97.54 299	97.32 300	98.18 324	99.47 290	98.14 298	100.00 1	99.10 409	94.16 397	97.60 403	99.63 357	97.52 191	98.65 384	96.47 373	91.97 393	97.76 338

DIV-MVS_self_test		97.52 302	97.35 299	98.05 342	99.46 297	98.11 299	100.00 1	99.10 409	94.21 393	97.62 401	99.63 357	97.65 183	98.29 419	96.47 373	91.98 392	97.76 338

tmp_tt		75.80 460	74.26 462	80.43 475	52.91 507	53.67 506	87.42 494	97.98 476	61.80 494	67.04 497	100.00 1	76.43 467	96.40 469	96.47 373	28.26 499	91.23 489

miper_lstm_enhance		97.40 306	97.28 302	97.75 360	99.48 285	97.52 333	100.00 1	99.07 420	94.08 399	98.01 383	99.61 363	97.38 199	97.98 447	96.44 376	91.47 404	97.76 338

v148		96.29 362	95.84 363	97.63 361	97.74 430	96.53 370	100.00 1	99.07 420	93.52 412	98.01 383	99.42 386	91.22 337	98.60 392	96.37 377	87.22 443	97.75 349

usedtu_blend_shiyan5		92.75 422	91.39 427	96.82 403	95.22 468	94.40 421	99.05 469	98.64 461	75.98 489	98.54 340	98.56 442	90.48 357	98.31 416	96.31 378	69.73 483	97.75 349

blend_shiyan4		95.76 384	95.40 389	96.82 403	95.50 466	94.40 421	100.00 1	99.22 331	87.12 465	98.67 330	98.59 439	99.09 99	98.31 416	96.31 378	84.14 455	97.75 349

dongtai		98.29 258	98.25 249	98.42 302	99.58 234	95.86 379	100.00 1	99.44 124	93.46 415	99.69 242	99.97 256	97.53 190	99.51 310	96.28 380	98.27 253	99.89 190

myMVS_eth3d		98.52 238	98.51 218	98.53 293	99.50 278	97.98 310	100.00 1	99.57 73	96.23 326	98.07 377	100.00 1	99.09 99	97.81 452	96.17 381	97.96 280	99.82 230

DeepMVS_CX				89.98 459	98.90 371	71.46 490		99.18 370	97.61 177	96.92 417	99.83 311	86.07 422	99.83 244	96.02 382	97.65 304	98.65 332

Elysia		98.12 267	97.72 285	99.34 217	99.30 328	98.96 220	99.95 315	99.28 290	96.64 289	99.75 232	99.99 236	88.71 394	99.81 250	95.99 383	99.84 162	99.26 321

StellarMVS		98.12 267	97.72 285	99.34 217	99.30 328	98.96 220	99.95 315	99.28 290	96.64 289	99.75 232	99.99 236	88.71 394	99.81 250	95.99 383	99.84 162	99.26 321

FE-MVSNET3		97.34 309	96.97 314	98.43 300	97.82 425	98.91 223	100.00 1	99.29 282	94.70 375	98.46 353	98.89 426	93.95 290	98.64 385	95.88 385	93.75 365	97.74 376

test_fmvs2		95.17 396	95.23 391	95.01 433	98.95 368	88.99 469	99.99 258	97.77 480	97.79 155	98.58 337	99.70 337	73.36 473	99.34 333	95.88 385	95.03 352	96.70 458

usedtu_dtu_shiyan1		97.34 309	96.97 314	98.43 300	97.82 425	98.91 223	100.00 1	99.29 282	94.70 375	98.46 353	98.89 426	93.95 290	98.64 385	95.86 387	93.75 365	97.74 376

pmmvs5		95.94 381	95.61 377	96.95 390	97.42 446	94.66 409	100.00 1	98.08 471	93.60 410	97.05 415	99.43 385	87.02 413	98.46 406	95.76 388	92.12 389	97.72 394

WAC-MVS						97.98 310								95.74 389

IterMVS		96.76 335	96.46 336	97.63 361	99.41 309	96.89 358	99.99 258	99.13 398	94.74 374	97.59 404	99.66 347	89.63 382	98.28 420	95.71 390	92.31 387	97.72 394
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v2v482		96.70 339	96.18 348	98.27 313	98.04 416	98.39 265	100.00 1	99.13 398	94.19 395	98.58 337	99.08 406	90.48 357	98.67 381	95.69 391	90.44 415	97.75 349

wanda-best-256-512		93.76 408	92.74 419	96.84 397	95.22 468	94.54 417	100.00 1	99.22 331	87.22 463	98.54 340	98.56 442	90.48 357	98.22 424	95.67 392	69.73 483	97.75 349

FE-blended-shiyan7		93.76 408	92.74 419	96.84 397	95.22 468	94.54 417	100.00 1	99.22 331	87.22 463	98.54 340	98.56 442	90.48 357	98.22 424	95.67 392	69.73 483	97.75 349

pmmvs4		97.17 316	96.80 321	98.27 313	97.68 432	98.64 243	100.00 1	99.18 370	94.22 392	98.55 339	99.71 334	93.67 293	98.47 405	95.66 394	92.57 383	97.71 402

ITE_SJBPF				96.84 397	98.96 366	93.49 432		98.12 469	98.12 128	98.35 361	99.97 256	84.45 432	99.56 294	95.63 395	95.25 341	97.49 436

blended_shiyan6		93.70 412	92.67 424	96.78 407	95.17 472	94.38 424	100.00 1	99.22 331	87.03 468	98.54 340	98.56 442	90.14 365	98.22 424	95.62 396	69.73 483	97.75 349

IterMVS-SCA-FT		96.72 338	96.42 338	97.62 363	99.40 314	96.83 360	99.99 258	99.14 392	94.65 379	97.55 405	99.72 332	89.65 380	98.31 416	95.62 396	92.05 390	97.73 387

pm-mvs1		95.76 384	95.01 395	98.00 346	98.23 408	97.45 336	99.24 439	99.04 433	93.13 425	95.93 440	99.72 332	86.28 420	98.84 369	95.62 396	87.92 436	97.72 394

blended_shiyan8		93.73 410	92.69 422	96.84 397	95.17 472	94.40 421	100.00 1	99.20 360	87.05 466	98.60 335	98.54 446	90.15 364	98.39 411	95.54 399	69.93 482	97.74 376

V42		96.65 341	96.16 350	98.11 333	98.17 413	98.23 287	99.99 258	99.09 414	93.97 400	98.74 326	99.05 409	91.09 341	98.82 370	95.46 400	89.90 417	97.27 446

EU-MVSNet		96.63 342	96.53 331	96.94 391	97.59 437	96.87 359	99.76 375	99.47 84	96.35 320	96.85 421	99.78 328	92.57 323	96.27 472	95.33 401	91.08 408	97.68 410

GBi-Net		96.07 376	95.80 366	96.89 394	99.53 251	94.87 397	99.18 449	99.27 305	93.71 404	98.53 345	98.81 430	84.23 435	98.07 440	95.31 402	93.60 368	97.72 394

test1		96.07 376	95.80 366	96.89 394	99.53 251	94.87 397	99.18 449	99.27 305	93.71 404	98.53 345	98.81 430	84.23 435	98.07 440	95.31 402	93.60 368	97.72 394

FMVSNet3		97.30 312	96.95 316	98.37 306	99.65 205	99.25 186	99.71 387	99.28 290	94.23 391	98.53 345	98.91 424	93.30 303	98.11 435	95.31 402	93.60 368	97.73 387

mvsany_test3		89.36 443	88.96 446	90.56 458	91.95 481	78.97 483	99.74 378	96.59 494	96.84 254	89.25 473	96.07 474	52.59 490	97.11 461	95.17 405	82.44 464	95.58 476

GA-MVS		97.72 287	97.27 304	99.06 255	99.24 334	97.93 316	100.00 1	99.24 321	95.80 344	98.99 306	99.64 353	89.77 375	99.36 330	95.12 406	97.62 306	99.89 190

ambc				88.45 462	86.84 494	70.76 491	97.79 487	98.02 475		90.91 469	95.14 479	38.69 496	98.51 401	94.97 407	84.23 454	96.09 469

OurMVSNet-221017-0		96.14 374	95.98 357	96.62 409	97.49 443	93.44 433	99.92 331	98.16 467	95.86 340	97.65 398	99.95 280	85.71 427	98.78 372	94.93 408	94.18 364	97.64 423

testing3		98.44 243	98.37 240	98.65 285	99.51 270	98.32 278	100.00 1	99.62 71	96.43 310	97.93 386	99.99 236	99.11 97	97.81 452	94.88 409	97.80 294	99.82 230

SixPastTwentyTwo		95.71 386	95.49 382	96.38 415	97.42 446	93.01 437	99.84 351	98.23 466	94.75 372	95.98 439	99.97 256	85.35 429	98.43 408	94.71 410	93.17 373	97.69 408

tt0320-xc		91.69 432	90.50 436	95.26 430	98.04 416	90.12 463	98.60 480	98.70 459	76.63 486	94.66 450	99.52 377	68.57 483	97.99 446	94.61 411	85.18 451	97.66 415

DTE-MVSNet		95.52 388	94.99 396	97.08 383	97.49 443	96.45 371	100.00 1	99.25 315	93.82 403	96.17 434	99.57 371	87.81 405	97.18 460	94.57 412	86.26 450	97.62 426

UnsupCasMVSNet_eth		94.25 402	93.89 402	95.34 429	97.63 433	92.13 447	99.73 383	99.36 234	94.88 369	92.78 461	98.63 438	82.72 443	96.53 468	94.57 412	84.73 453	97.36 443

CMPMVS	66.12 22	90.65 438	92.04 426	86.46 467	96.18 457	66.87 497	98.03 485	99.38 224	83.38 478	85.49 484	99.55 373	77.59 461	98.80 371	94.44 414	94.31 363	93.72 482
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		93.64 413	92.87 416	95.94 425	97.47 445	91.41 453	98.92 470	99.02 437	87.84 462	95.01 446	99.61 363	77.24 464	98.77 375	94.33 415	86.41 449	97.63 424

Patchmtry		96.81 332	96.37 340	98.14 328	99.31 325	98.55 248	98.91 471	99.00 440	90.45 447	97.92 387	98.98 417	96.94 214	98.12 433	94.27 416	91.53 401	97.75 349

tfpnnormal		96.36 358	95.69 375	98.37 306	98.55 390	98.71 237	99.69 391	99.45 110	93.16 424	96.69 427	99.71 334	88.44 401	98.99 352	94.17 417	91.38 405	97.41 441

v8		96.35 359	95.73 371	98.21 322	98.11 414	98.23 287	99.94 323	99.07 420	92.66 432	98.29 366	99.00 416	91.46 334	98.77 375	94.17 417	88.83 431	97.62 426

MIMVSNet		97.06 323	96.73 324	98.05 342	99.38 318	96.64 366	98.47 482	99.35 245	93.41 416	99.48 261	98.53 447	89.66 379	97.70 458	94.16 419	98.11 272	99.80 270

Patchmatch-RL test		93.49 414	93.63 406	93.05 452	91.78 482	83.41 479	98.21 484	96.95 489	91.58 438	91.05 467	97.64 466	99.40 67	95.83 476	94.11 420	81.95 466	99.91 171

FMVSNet2		96.22 366	95.60 378	98.06 338	99.53 251	98.33 276	99.45 418	99.27 305	93.71 404	98.03 380	98.84 429	84.23 435	98.10 438	93.97 421	93.40 371	97.73 387

K. test v3		95.46 390	95.14 393	96.40 413	97.53 440	93.40 434	99.99 258	99.23 326	95.49 356	92.70 464	99.73 331	84.26 434	98.12 433	93.94 422	93.38 372	97.68 410

v1144		96.51 348	95.97 358	98.13 331	97.98 420	98.04 307	99.99 258	99.08 415	93.51 413	98.62 334	98.98 417	90.98 347	98.62 388	93.79 423	90.79 411	97.74 376

mvs5depth		93.81 407	93.00 414	96.23 420	94.25 478	93.33 435	97.43 488	98.07 472	93.47 414	94.15 456	99.58 367	77.52 462	98.97 355	93.64 424	88.92 428	96.39 464

TinyColmap		95.50 389	95.12 394	96.64 408	98.69 385	93.00 438	99.40 424	97.75 481	96.40 316	96.14 435	99.87 301	79.47 455	99.50 313	93.62 425	94.72 359	97.40 442

USDC		95.90 382	95.70 372	96.50 412	98.60 389	92.56 445	100.00 1	98.30 465	97.77 157	96.92 417	99.94 286	81.25 451	99.45 323	93.54 426	94.96 356	97.49 436

test_fmvs3		87.19 447	87.02 450	87.71 464	92.69 480	76.64 485	99.96 306	97.27 486	93.55 411	90.82 470	94.03 485	38.00 498	92.19 490	93.49 427	83.35 463	94.32 479

MS-PatchMatch		95.66 387	95.87 362	95.05 432	97.80 427	89.25 467	98.88 472	99.30 273	96.35 320	96.86 420	99.01 415	81.35 450	99.43 325	93.30 428	99.98 117	96.46 462

tt0320		92.36 426	91.28 429	95.58 428	98.30 402	90.65 458	98.69 477	99.14 392	76.73 484	96.07 437	99.50 380	72.28 477	98.39 411	93.29 429	87.56 439	97.70 403

SSC-MVS3.2		95.32 391	94.97 397	96.37 416	98.29 404	92.75 441	100.00 1	99.30 273	95.46 358	98.36 359	99.42 386	78.92 458	98.63 387	93.28 430	91.72 398	97.72 394

v144192		96.40 356	95.81 364	98.17 326	97.89 423	98.11 299	99.99 258	99.06 428	93.39 417	98.75 325	99.09 405	90.43 361	98.66 382	93.10 431	90.55 414	97.75 349

MVP-Stereo		96.51 348	96.48 335	96.60 410	95.65 463	94.25 425	98.84 473	98.16 467	95.85 342	95.23 444	99.04 410	92.54 324	99.13 343	92.98 432	99.98 117	96.43 463
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FMVSNet1		94.45 399	93.63 406	96.89 394	98.87 376	94.87 397	99.18 449	99.27 305	90.95 443	97.31 410	98.81 430	72.89 475	98.07 440	92.61 433	92.81 378	97.72 394

test123		79.44 456	79.23 458	80.05 476	80.03 499	71.72 489	100.00 1	77.93 507	62.52 493	94.81 447	99.69 340	78.21 460	74.53 500	92.57 434	27.33 500	93.90 480

v7n		96.06 378	95.42 388	97.99 348	97.58 438	97.35 341	99.86 348	99.11 406	92.81 431	97.91 388	99.49 381	90.99 346	98.92 360	92.51 435	88.49 433	97.70 403

lessismore_v0				96.05 423	97.55 439	91.80 450		99.22 331		91.87 465	99.91 295	83.50 441	98.68 380	92.48 436	90.42 416	97.68 410

EG-PatchMatch MVS		92.94 421	92.49 425	94.29 445	95.87 460	87.07 474	99.07 467	98.11 470	93.19 423	88.98 474	98.66 437	70.89 478	99.08 345	92.43 437	95.21 344	96.72 457

v10		96.14 374	95.50 380	98.07 334	98.19 411	97.96 313	99.83 353	99.07 420	92.10 435	98.07 377	98.94 422	91.07 342	98.61 389	92.41 438	89.82 418	97.63 424

test0.0.03 1		98.12 267	98.03 269	98.39 304	99.11 341	98.07 303	100.00 1	99.93 35	96.70 281	96.91 419	99.95 280	99.31 75	98.19 427	91.93 439	98.44 223	98.91 329

our_test_3		96.51 348	96.35 341	96.98 389	97.61 435	95.05 393	99.98 290	99.01 439	94.68 377	96.77 425	99.06 407	95.87 237	98.14 431	91.81 440	92.37 386	97.75 349

test_f		86.87 448	86.06 451	89.28 461	91.45 486	76.37 486	99.87 347	97.11 487	91.10 441	88.46 476	93.05 487	38.31 497	96.66 466	91.77 441	83.46 462	94.82 478

MVS-HIRNet		94.12 405	92.73 421	98.29 311	99.33 324	95.95 375	99.38 426	99.19 363	74.54 490	98.26 370	86.34 494	86.07 422	99.06 346	91.60 442	99.87 156	99.85 219

v1921920		96.16 372	95.50 380	98.14 328	97.88 424	97.96 313	99.99 258	99.07 420	93.33 419	98.60 335	99.24 397	89.37 384	98.71 379	91.28 443	90.74 412	97.75 349

CR-MVSNet		98.02 273	97.71 287	98.93 267	99.31 325	98.86 226	99.13 459	99.00 440	96.53 301	99.96 151	98.98 417	96.94 214	98.10 438	91.18 444	98.40 226	99.84 221

v1192		96.18 368	95.49 382	98.26 316	98.01 418	98.15 296	99.99 258	99.08 415	93.36 418	98.54 340	98.97 420	89.47 383	98.89 364	91.15 445	90.82 410	97.75 349

DSMNet-mixed		95.18 395	95.21 392	95.08 431	96.03 458	90.21 462	99.65 395	93.64 499	92.91 427	98.34 362	97.40 467	90.05 372	95.51 479	91.02 446	97.86 287	99.51 316

YYNet1		92.44 425	90.92 434	97.03 385	96.20 456	97.06 355	99.99 258	99.14 392	88.21 460	67.93 495	98.43 453	88.63 396	96.28 471	90.64 447	89.08 427	97.74 376

UnsupCasMVSNet_bld		89.50 441	88.00 448	93.99 448	95.30 467	88.86 470	98.52 481	99.28 290	85.50 474	87.80 480	94.11 484	61.63 486	96.96 462	90.63 448	79.26 472	96.15 466

PM-MVS		88.39 444	87.41 449	91.31 456	91.73 483	82.02 482	99.79 363	96.62 492	91.06 442	90.71 471	95.73 475	48.60 492	95.96 474	90.56 449	81.91 467	95.97 470

MVStest1		94.27 401	93.30 410	97.19 381	98.83 381	97.18 350	99.93 329	98.79 456	86.80 470	84.88 487	99.04 410	94.32 281	98.25 422	90.55 450	86.57 448	96.12 468

pmmvs-eth3d		91.73 431	90.67 435	94.92 437	91.63 484	92.71 443	99.90 338	98.54 463	91.19 440	88.08 478	95.50 476	79.31 457	96.13 473	90.55 450	81.32 470	95.91 471

MDA-MVSNet_test_wron		92.61 423	91.09 433	97.19 381	96.71 454	97.26 347	100.00 1	99.14 392	88.61 456	67.90 496	98.32 456	89.03 388	96.57 467	90.47 452	89.59 420	97.74 376

testgi		96.18 368	95.93 359	96.93 392	98.98 364	94.20 427	100.00 1	99.07 420	97.16 223	96.06 438	99.86 303	84.08 438	97.79 455	90.38 453	97.80 294	98.81 330

Patchmatch-test		97.83 282	97.42 294	99.06 255	99.08 345	97.66 329	98.66 478	99.21 350	93.65 408	98.25 371	99.58 367	99.47 52	99.57 290	90.25 454	98.59 215	99.95 149

ppachtmachnet_test		96.17 370	95.89 360	97.02 386	97.61 435	95.24 390	99.99 258	99.24 321	93.31 420	96.71 426	99.62 361	94.34 280	98.07 440	89.87 455	92.30 388	97.75 349

KD-MVS_2432*1600		94.15 403	93.08 412	97.35 372	99.53 251	97.83 323	99.63 398	99.19 363	92.88 428	96.29 431	97.68 464	98.84 132	96.70 464	89.73 456	63.92 491	97.53 434

miper_refine_blended		94.15 403	93.08 412	97.35 372	99.53 251	97.83 323	99.63 398	99.19 363	92.88 428	96.29 431	97.68 464	98.84 132	96.70 464	89.73 456	63.92 491	97.53 434

dmvs_testset		93.27 417	95.48 384	86.65 466	98.74 384	68.42 495	99.92 331	98.91 448	96.19 331	93.28 460	100.00 1	91.06 344	91.67 491	89.64 458	91.54 400	99.86 218

v1240		95.96 380	95.25 390	98.07 334	97.91 422	97.87 321	99.96 306	99.07 420	93.24 422	98.64 333	98.96 421	88.98 390	98.61 389	89.58 459	90.92 409	97.75 349

test_0402		94.35 400	93.70 405	96.32 418	97.92 421	93.60 430	99.61 401	98.85 453	88.19 461	94.68 449	99.48 382	80.01 453	98.58 396	89.39 460	95.15 348	96.77 456

TransMVSNet (Re)		94.78 397	93.72 404	97.93 352	98.34 397	97.88 319	99.23 446	97.98 476	91.60 437	94.55 451	99.71 334	87.89 404	98.36 413	89.30 461	84.92 452	97.56 432

MDA-MVSNet-bldmvs		91.65 433	89.94 442	96.79 406	96.72 453	96.70 364	99.42 423	98.94 445	88.89 455	66.97 498	98.37 454	81.43 449	95.91 475	89.24 462	89.46 423	97.75 349

test_vis3_rt		79.61 454	78.19 459	83.86 470	88.68 492	69.56 492	99.81 357	82.19 506	86.78 471	68.57 494	84.51 497	25.06 502	98.26 421	89.18 463	78.94 474	83.75 494

test_method		91.04 437	91.10 432	90.85 457	98.34 397	77.63 484	100.00 1	98.93 447	76.69 485	96.25 433	98.52 448	70.44 479	97.98 447	89.02 464	91.74 396	96.92 454

WB-MVSnew		97.02 327	97.24 306	96.37 416	99.44 305	97.36 340	100.00 1	99.43 133	96.12 333	99.35 277	99.89 298	93.60 296	98.42 409	88.91 465	98.39 228	93.33 484

FE-MVSNET2		91.15 435	90.00 441	94.58 440	90.74 488	92.52 446	99.56 406	98.87 451	90.82 444	88.96 475	95.40 478	76.26 468	95.56 478	87.84 466	81.59 468	95.66 475

EPNet_dtu		98.53 237	98.23 255	99.43 195	99.92 115	99.01 212	99.96 306	99.47 84	98.80 77	99.96 151	99.96 273	98.56 148	99.30 335	87.78 467	99.68 176	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		95.32 391	95.43 387	94.99 435	99.39 317	92.99 439	99.25 438	99.24 321	90.45 447	97.44 408	98.45 450	95.78 240	94.39 482	87.02 468	91.88 394	97.59 430

APD_test1		93.07 420	94.14 401	89.85 460	99.18 336	72.49 488	99.76 375	98.90 450	92.86 430	96.35 430	99.94 286	75.56 469	99.91 207	86.73 469	97.98 278	97.15 449

OpenMVS_ROB	88.34 20	91.89 429	91.12 431	94.19 447	95.55 465	87.63 472	99.26 437	98.03 473	86.61 472	90.65 472	96.82 471	70.14 481	98.78 372	86.54 470	96.50 325	96.15 466

N_pmnet		91.88 430	93.37 409	87.40 465	97.24 450	66.33 498	99.90 338	91.05 501	89.77 453	95.65 442	98.58 441	90.05 372	98.11 435	85.39 471	92.72 379	97.75 349

Syy-MVS		96.17 370	96.57 330	95.00 434	99.50 278	87.37 473	100.00 1	99.57 73	96.23 326	98.07 377	100.00 1	92.41 326	97.81 452	85.34 472	97.96 280	99.82 230

new-patchmatchnet		90.30 440	89.46 444	92.84 454	90.77 487	88.55 471	99.83 353	98.80 455	90.07 452	87.86 479	95.00 481	78.77 459	94.30 483	84.86 473	79.15 473	95.68 474

new_pmnet		94.11 406	93.47 408	96.04 424	96.60 455	92.82 440	99.97 299	98.91 448	90.21 450	95.26 443	98.05 462	85.89 425	98.14 431	84.28 474	92.01 391	97.16 448

Anonymous20240521		93.29 416	92.76 418	94.90 438	95.64 464	91.27 454	99.97 299	98.82 454	87.04 467	94.71 448	98.19 457	83.86 439	96.80 463	84.04 475	92.56 384	96.64 459

LCM-MVSNet		79.01 458	76.93 461	85.27 468	78.28 500	68.01 496	96.57 489	98.03 473	55.10 496	82.03 489	93.27 486	31.99 501	93.95 485	82.72 476	74.37 478	93.84 481

KD-MVS_self_test		91.16 434	90.09 439	94.35 443	94.44 477	91.27 454	99.74 378	99.08 415	90.82 444	94.53 452	94.91 483	86.11 421	94.78 481	82.67 477	68.52 488	96.99 452

pmmvs3		90.62 439	89.36 445	94.40 442	90.53 490	91.49 452	100.00 1	96.73 491	84.21 476	93.65 458	96.65 473	82.56 446	94.83 480	82.28 478	77.62 476	96.89 455

EGC-MVSNET		79.46 455	74.04 463	95.72 427	96.00 459	92.73 442	99.09 464	99.04 433	5.08 502	16.72 502	98.71 434	73.03 474	98.74 378	82.05 479	96.64 322	95.69 473

FPMVS		77.92 459	79.45 457	73.34 480	76.87 501	46.81 507	98.24 483	99.05 430	59.89 495	73.55 491	98.34 455	36.81 499	86.55 493	80.96 480	91.35 406	86.65 492

CL-MVSNet_self_test		91.07 436	90.35 438	93.24 451	93.27 479	89.16 468	99.55 408	99.25 315	92.34 433	95.23 444	97.05 470	88.86 393	93.59 486	80.67 481	66.95 490	96.96 453

TAPA-MVS	96.40 10	97.64 289	97.37 298	98.45 298	99.94 110	95.70 382	100.00 1	99.40 205	97.65 168	99.53 257	100.00 1	99.31 75	99.66 280	80.48 482	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchT		95.90 382	94.95 398	98.75 282	99.03 353	98.39 265	99.08 465	99.32 258	85.52 473	99.96 151	94.99 482	97.94 166	98.05 444	80.20 483	98.47 222	99.81 244

MIMVSNet1		91.96 427	91.20 430	94.23 446	94.94 475	91.69 451	99.34 430	99.22 331	88.23 458	94.18 455	98.45 450	75.52 470	93.41 487	79.37 484	91.49 402	97.60 429

Gipuma		84.73 450	83.50 454	88.40 463	97.50 441	82.21 481	88.87 492	99.05 430	65.81 492	85.71 483	90.49 489	53.70 489	96.31 470	78.64 485	91.74 396	86.67 491
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FE-MVSNET		89.50 441	88.33 447	93.00 453	88.89 491	90.24 461	99.96 306	96.86 490	88.23 458	88.46 476	95.47 477	77.03 465	93.37 488	78.54 486	81.56 469	95.39 477

test20.03		93.11 418	92.85 417	93.88 449	95.19 471	91.83 449	100.00 1	98.87 451	93.68 407	92.76 462	98.88 428	89.20 387	92.71 489	77.88 487	89.19 426	97.09 450

RPMNet		95.26 394	93.82 403	99.56 176	99.31 325	98.86 226	99.13 459	99.42 152	79.82 482	99.96 151	95.13 480	95.69 243	99.98 140	77.54 488	98.40 226	99.84 221

Anonymous20231206		93.45 415	93.17 411	94.30 444	95.00 474	89.69 466	99.98 290	98.43 464	93.30 421	94.50 453	98.59 439	90.52 355	95.73 477	77.46 489	90.73 413	97.48 439

testf1		84.40 451	84.79 452	83.23 471	95.71 461	58.71 504	98.79 474	97.75 481	81.58 479	84.94 485	98.07 460	45.33 494	97.73 456	77.09 490	83.85 456	93.24 485

APD_test2		84.40 451	84.79 452	83.23 471	95.71 461	58.71 504	98.79 474	97.75 481	81.58 479	84.94 485	98.07 460	45.33 494	97.73 456	77.09 490	83.85 456	93.24 485

PMMVS2		79.15 457	77.28 460	84.76 469	82.34 497	72.66 487	99.70 389	95.11 497	71.68 491	84.78 488	90.87 488	32.05 500	89.99 492	75.53 492	63.45 493	91.64 488

usedtu_dtu_shiyan2		85.34 449	83.22 455	91.71 455	88.10 493	83.34 480	98.75 476	97.59 485	76.21 487	91.11 466	96.80 472	58.14 488	94.30 483	75.00 493	67.24 489	97.49 436

PMVS	60.66 23	65.98 465	65.05 466	68.75 483	55.06 506	38.40 508	88.19 493	96.98 488	48.30 500	44.82 501	88.52 492	12.22 505	86.49 494	67.58 494	83.79 458	81.35 496
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		88.24 445	90.09 439	82.68 473	91.56 485	69.51 493	100.00 1	98.73 458	90.72 446	87.29 481	98.12 458	92.87 315	85.01 495	62.19 495	89.34 424	93.54 483

ANet_high		66.05 464	63.44 468	73.88 479	61.14 504	63.45 501	95.68 491	87.18 503	79.93 481	47.35 500	80.68 500	22.35 503	72.33 502	61.24 496	35.42 498	85.88 493

MVE	68.59 21	67.22 463	64.68 467	74.84 477	74.67 503	62.32 502	95.84 490	90.87 502	50.98 497	58.72 499	81.05 499	12.20 506	78.95 497	61.06 497	56.75 494	83.24 495
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS		87.61 446	89.47 443	82.04 474	90.63 489	68.77 494	99.99 258	98.66 460	90.34 449	86.70 482	98.08 459	92.72 320	84.12 496	59.41 498	88.71 432	93.22 487

E-PMN		70.72 461	70.06 464	72.69 481	83.92 496	65.48 500	99.95 315	92.72 500	49.88 498	72.30 492	86.26 495	47.17 493	77.43 498	53.83 499	44.49 496	75.17 498

EMVS		69.88 462	69.09 465	72.24 482	84.70 495	65.82 499	99.96 306	87.08 505	49.82 499	71.51 493	84.74 496	49.30 491	75.32 499	50.97 500	43.71 497	75.59 497

wuyk23d		28.28 466	29.73 470	23.92 484	75.89 502	32.61 509	66.50 495	12.88 508	16.09 501	14.59 503	16.59 502	12.35 504	32.36 503	39.36 501	13.36 501	6.79 499

mmdepth		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

monomultidepth		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

test_blank		0.07 470	0.09 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.79 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet_test		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

DCPMVS		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

cdsmvs_eth3d_5k		24.41 467	32.55 469	0.00 485	0.00 508	0.00 510	0.00 496	99.39 221	0.00 503	0.00 504	100.00 1	93.55 297	0.00 504	0.00 502	0.00 502	0.00 500

pcd_1.5k_mvsjas		8.24 469	10.99 472	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	98.75 139	0.00 504	0.00 502	0.00 502	0.00 500

sosnet-low-res		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

sosnet		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uncertanet		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

Regformer		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

ab-mvs-re		8.33 468	11.11 471	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	100.00 1	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet		0.01 471	0.02 474	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.14 504	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

TestfortrainingZip				100.00 1	99.99 52	100.00 1	100.00 1	99.95 19	99.03 25	100.00 1	100.00 1	99.59 24	100.00 1		100.00 1	100.00 1

FOURS1					100.00 1	99.97 26	100.00 1	99.42 152	98.52 96	100.00 1

test_one_0601					100.00 1	99.99 6		99.42 152	98.72 85	100.00 1	100.00 1	99.60 21

eth-test2					0.00 508

eth-test					0.00 508

test_241102_ONE					100.00 1	99.99 6		99.42 152	99.03 25	100.00 1	100.00 1	99.50 44	100.00 1

save fliter					99.99 52	99.93 52	100.00 1	99.42 152	98.93 49

test0726					100.00 1	99.99 6	100.00 1	99.42 152	99.04 20	100.00 1	100.00 1	99.53 36

GSMVS																99.91 171

test_part2					100.00 1	99.99 6				100.00 1

sam_mvs1												99.29 81				99.91 171

sam_mvs												99.33 70

MTGPA								99.42 152

test_post											89.05 491	99.49 47	99.59 285

patchmatchnet-post											97.79 463	99.41 65	99.54 302

MTMP							100.00 1	99.18 370

TEST9					100.00 1	99.95 37	100.00 1	99.42 152	97.65 168	100.00 1	100.00 1	99.53 36	99.97 149

test_8					100.00 1	99.91 63	100.00 1	99.42 152	97.70 162	100.00 1	100.00 1	99.51 40	99.98 140

agg_prior					100.00 1	99.88 84		99.42 152		100.00 1			99.97 149

test_prior4						99.93 52	100.00 1

test_prior				99.90 87	100.00 1	99.75 108		99.73 61					99.97 149			100.00 1

新几何2							100.00 1

旧先验1					99.99 52	99.88 84		99.82 45			100.00 1	99.27 84			100.00 1	100.00 1

原ACMM2							100.00 1

test222					99.99 52	99.90 70	100.00 1	99.69 67	97.66 166	100.00 1	100.00 1	99.30 80			100.00 1	100.00 1

segment_acmp												99.55 32

testdata1							100.00 1		98.77 84

test12				99.95 61	99.99 52	99.89 77		99.42 152		100.00 1		99.24 86	99.97 149		100.00 1	100.00 1

plane_prior7					99.00 360	94.78 407

plane_prior6					99.06 350	94.80 403						88.58 399

plane_prior4											99.97 256

plane_prior3						94.79 406			99.03 25	99.08 299

plane_prior2							100.00 1		99.00 32

plane_prior1					99.02 354

plane_prior						94.80 403	100.00 1		99.03 25						95.58 327

n2								0.00 509

nn								0.00 509

door-mid								96.32 495

test11								99.42 152

door								96.13 496

HQP5-MVS						94.82 400

HQP-NCC					99.07 346		100.00 1		99.04 20	99.17 287

ACMP_Plane					99.07 346		100.00 1		99.04 20	99.17 287

HQP4-MVS										99.17 287			99.57 290			97.77 336

HQP3-MVS								99.40 205							95.58 327

HQP2-MVS												88.61 397

NP-MVS					99.07 346	94.81 402					99.97 256

ACMMP++_ref															94.58 362

ACMMP++															95.17 347

Test By Simon												99.10 98

High-resolution multi-view benchmark