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.

aaatest				99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.10 12	99.99 129	100.00 1		100.00 1	100.00 1	100.00 1	100.00 1

MED-MVS		99.89 1	99.86 2	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.18 6	99.99 129	100.00 1	99.58 27	100.00 1	100.00 1	100.00 1	100.00 1

TestfortrainingZip a		99.85 5	99.81 6	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.18 6	100.00 1	100.00 1	99.45 53	99.99 107	99.68 183	99.99 107	100.00 1

TestfortrainingZip				100.00 1	99.99 53	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

fmvsm_s_conf0.5_n_10		99.08 143	98.78 170	99.97 40	99.84 131	99.92 60	100.00 1	99.28 291	98.93 49	100.00 1	100.00 1	91.07 351	99.99 107	100.00 1	99.95 128	100.00 1

fmvsm_l_conf0.5_n_9		99.35 101	99.15 123	99.95 61	99.83 135	99.84 96	100.00 1	99.30 274	98.92 52	100.00 1	100.00 1	94.32 283	100.00 1	100.00 1	99.93 138	100.00 1

fmvsm_s_conf0.5_n_9		99.04 150	98.78 170	99.81 117	99.86 127	99.44 165	100.00 1	99.32 259	98.94 45	100.00 1	100.00 1	91.00 354	99.99 107	100.00 1	99.94 134	100.00 1

aaEdge-Enhanced		99.87 3	99.83 4	99.99 13	99.99 53	99.98 18	100.00 1	99.95 19	99.05 18	100.00 1	100.00 1	99.50 43	100.00 1	100.00 1	100.00 1	100.00 1

lecture		99.64 54	99.53 65	99.98 28	99.99 53	99.93 53	100.00 1	99.47 85	98.53 94	100.00 1	100.00 1	97.88 172	100.00 1	99.98 92	99.92 141	100.00 1

fmvsm_s_conf0.5_n_8		99.34 105	99.14 125	99.91 83	99.83 135	99.74 112	100.00 1	99.38 225	98.94 45	100.00 1	100.00 1	94.25 285	99.99 107	100.00 1	99.91 147	100.00 1

fmvsm_s_conf0.5_n_6		99.30 114	99.12 128	99.84 109	99.24 346	99.56 138	100.00 1	99.31 268	98.90 59	100.00 1	100.00 1	94.75 269	99.97 150	99.98 92	99.88 153	100.00 1

fmvsm_l_conf0.5_n_3		99.38 96	99.20 117	99.92 82	99.80 157	99.78 104	100.00 1	99.35 246	98.94 45	100.00 1	100.00 1	94.77 267	99.99 107	99.99 77	99.92 141	100.00 1

fmvsm_s_conf0.5_n_3		98.99 166	98.69 189	99.89 90	99.70 179	99.69 123	100.00 1	99.39 222	98.93 49	100.00 1	100.00 1	90.20 373	99.99 107	100.00 1	99.95 128	100.00 1

reproduce_model		99.76 21	99.69 25	99.98 28	99.96 104	99.93 53	100.00 1	99.42 153	98.81 76	100.00 1	100.00 1	98.98 116	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 21	99.69 25	99.98 28	99.96 104	99.94 47	100.00 1	99.42 153	98.82 72	100.00 1	100.00 1	98.99 113	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 21	99.69 25	99.98 28	99.96 104	99.94 47	100.00 1	99.42 153	98.82 72	100.00 1	100.00 1	98.99 113	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	99.83 135	99.58 136	100.00 1	99.36 235	98.98 35	100.00 1	100.00 1	97.85 174	99.99 107	100.00 1	99.94 134	100.00 1

fmvsm_l_conf0.5_n		99.63 58	99.56 60	99.86 100	99.81 144	99.59 134	100.00 1	99.36 235	98.98 35	100.00 1	100.00 1	97.92 169	99.99 107	100.00 1	99.95 128	100.00 1

MM		99.63 58	99.52 68	99.94 74	99.99 53	99.82 99	100.00 1	99.97 17	99.11 10	100.00 1	100.00 1	96.65 225	100.00 1	100.00 1	99.97 122	100.00 1

test_fmvsmconf_n		99.56 71	99.46 79	99.86 100	99.68 187	99.58 136	100.00 1	99.31 268	98.92 52	99.88 208	100.00 1	97.35 201	99.99 107	99.98 92	99.99 107	100.00 1

test_fmvsmvis_n_1920		99.46 85	99.37 86	99.73 143	98.88 386	99.18 197	100.00 1	99.26 312	98.85 66	99.79 232	100.00 1	97.70 182	100.00 1	99.98 92	99.86 159	100.00 1

test_fmvsm_n_1920		99.55 73	99.49 73	99.73 143	99.85 129	99.19 195	100.00 1	99.41 202	98.87 64	100.00 1	100.00 1	97.34 202	100.00 1	99.98 92	99.90 149	100.00 1

test_cas_vis1_n_1920		98.63 225	98.25 256	99.77 136	99.69 182	99.32 177	100.00 1	99.31 268	98.84 68	99.96 152	100.00 1	87.42 420	99.99 107	99.14 269	99.86 159	100.00 1

test_vis1_n_1920		97.77 295	97.24 316	99.34 223	99.79 162	98.04 316	100.00 1	99.25 316	98.88 61	100.00 1	100.00 1	77.52 474	100.00 1	99.88 124	99.85 162	100.00 1

test_vis1_n		96.69 350	95.81 374	99.32 237	99.14 350	97.98 319	99.97 310	98.98 455	98.45 100	100.00 1	100.00 1	66.44 500	99.99 107	99.78 150	99.57 190	100.00 1

test_fmvs1_n		97.43 314	96.86 329	99.15 259	99.68 187	97.48 345	99.99 267	98.98 455	98.82 72	100.00 1	100.00 1	74.85 483	99.96 170	99.67 187	99.70 177	100.00 1

mvsany_test1		99.57 70	99.48 76	99.85 104	99.86 127	99.54 143	100.00 1	99.36 235	98.94 45	100.00 1	100.00 1	97.97 166	100.00 1	99.88 124	99.28 195	100.00 1

test_fmvs1		98.37 258	98.04 276	99.34 223	99.84 131	98.07 312	100.00 1	99.00 450	98.85 66	100.00 1	100.00 1	85.11 441	99.96 170	99.69 182	99.88 153	100.00 1

DVP-MVS++		99.81 14	99.75 17	100.00 1	100.00 1	99.99 6	100.00 1	99.42 153	98.79 80	100.00 1	100.00 1	99.54 32	100.00 1	100.00 1	100.00 1	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 153					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 32	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 153					100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 58	99.52 68	99.97 40	99.99 53	99.91 64	100.00 1	99.42 153	97.62 173	100.00 1	100.00 1	98.65 144	99.99 107	99.99 77	100.00 1	100.00 1

RE-MVS-def			99.55 62		99.99 53	99.91 64	100.00 1	99.42 153	97.62 173	100.00 1	100.00 1	98.94 124		99.99 77	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	99.42 153	99.03 25	100.00 1	100.00 1	99.50 43	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 6		99.42 153	99.12 9	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 32	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 153	99.03 25	100.00 1	100.00 1	99.56 29	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 53	99.85 94	100.00 1	99.42 153	97.67 165	100.00 1	100.00 1	99.05 107	99.99 107	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 36	99.62 47	99.97 40	99.99 53	99.90 71	100.00 1	99.79 50	97.97 139	99.97 145	100.00 1	98.97 118	100.00 1	99.94 114	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	99.42 153	99.04 20	100.00 1	100.00 1	99.53 35	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 53	99.99 6	100.00 1	99.42 153					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 38	100.00 1	99.64 70	97.59 181	100.00 1	100.00 1	98.99 113	99.99 107	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 58	99.51 70	100.00 1	99.90 120	100.00 1	100.00 1	99.43 134	99.00 32	100.00 1	100.00 1	99.58 27	100.00 1	97.64 346	100.00 1	100.00 1

GST-MVS		99.64 54	99.53 65	99.95 61	100.00 1	99.86 90	100.00 1	99.79 50	97.72 160	99.95 183	100.00 1	98.39 157	100.00 1	99.96 106	99.99 107	100.00 1

Anonymous202405211		97.87 288	97.53 300	98.90 278	99.81 144	96.70 374	99.35 445	99.46 103	92.98 440	98.83 331	99.99 243	90.63 363	100.00 1	99.70 173	97.03 323	100.00 1

SMA-MVS		99.69 42	99.59 50	99.98 28	99.99 53	99.93 53	100.00 1	99.43 134	97.50 193	100.00 1	100.00 1	99.43 60	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 53	99.98 18	100.00 1	99.42 153	98.91 55	100.00 1	100.00 1	99.22 88	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 5	99.87 1	99.80 123	99.99 53	99.97 27	99.97 310	99.98 16	98.96 39	100.00 1	100.00 1	99.96 4	99.42 338	100.00 1	100.00 1	100.00 1

MGCNet		99.72 32	99.65 37	99.93 78	99.99 53	99.79 103	100.00 1	99.91 40	99.17 8	100.00 1	100.00 1	97.84 176	100.00 1	100.00 1	99.95 128	100.00 1

MSP-MVS		99.81 14	99.77 12	99.94 74	100.00 1	99.86 90	100.00 1	99.42 153	98.87 64	100.00 1	100.00 1	99.65 19	99.96 170	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 30	100.00 1	99.43 134	99.05 18	100.00 1	100.00 1	99.45 53	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
ACMMP_NAP		99.67 49	99.57 55	99.97 40	99.98 94	99.92 60	100.00 1	99.42 153	97.83 150	100.00 1	100.00 1	98.89 130	100.00 1	99.98 92	100.00 1	100.00 1

MTAPA		99.68 46	99.59 50	99.97 40	99.99 53	99.91 64	100.00 1	99.42 153	98.32 113	99.94 190	100.00 1	98.65 144	100.00 1	99.96 106	100.00 1	100.00 1

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 38	100.00 1	99.42 153	97.70 162	100.00 1	100.00 1	99.51 39	99.97 150	100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 28	99.67 33	99.96 52	100.00 1	99.89 78	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.31 76	100.00 1	99.99 77	100.00 1	100.00 1

region2R		99.72 32	99.64 40	99.97 40	100.00 1	99.90 71	100.00 1	99.74 60	97.86 149	100.00 1	100.00 1	99.19 91	100.00 1	99.99 77	100.00 1	100.00 1

balanced_ft_v1		98.70 213	98.61 201	98.94 275	99.67 195	96.90 367	99.91 350	99.30 274	96.73 281	99.96 152	99.97 264	92.18 337	99.93 200	99.86 128	99.95 128	100.00 1

EI-MVSNet-UG-set		99.69 42	99.63 44	99.87 97	99.99 53	99.64 128	99.95 327	99.44 125	98.35 111	100.00 1	100.00 1	98.98 116	99.97 150	99.98 92	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 40	99.87 97	100.00 1	99.64 128	99.98 300	99.44 125	98.35 111	99.99 129	100.00 1	99.04 110	99.96 170	99.98 92	100.00 1	100.00 1

XVS		99.79 17	99.73 20	99.98 28	100.00 1	99.94 47	100.00 1	99.75 57	98.67 88	100.00 1	100.00 1	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 334	96.06 363	99.98 28	100.00 1	99.94 47	100.00 1	99.75 57	98.67 88	100.00 1	66.97 552	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 87	100.00 1	99.75 109		99.73 61					99.97 150			100.00 1

新几何1				99.99 13	100.00 1	99.96 30		99.81 47	97.89 146	100.00 1	100.00 1	99.20 90	100.00 1	97.91 334	100.00 1	100.00 1

旧先验1					99.99 53	99.88 85		99.82 45			100.00 1	99.27 85			100.00 1	100.00 1

无先验							100.00 1	99.80 48	97.98 137				100.00 1	99.33 256		100.00 1

原ACMM1				99.93 78	100.00 1	99.80 102		99.66 69	98.18 120	100.00 1	100.00 1	99.43 60	100.00 1	99.50 233	100.00 1	100.00 1

test222					99.99 53	99.90 71	100.00 1	99.69 67	97.66 166	100.00 1	100.00 1	99.30 81			100.00 1	100.00 1

testdata				99.66 157	99.99 53	98.97 221		99.73 61	97.96 142	100.00 1	100.00 1	99.42 64	100.00 1	99.28 260	100.00 1	100.00 1

1314		99.38 96	99.19 118	99.96 52	98.88 386	99.89 78	99.24 456	99.93 35	98.88 61	98.79 334	100.00 1	97.02 208	100.00 1	100.00 1	100.00 1	100.00 1

MVS		99.22 130	98.96 147	99.98 28	99.00 372	99.95 38	99.24 456	99.94 27	98.14 124	98.88 324	100.00 1	95.63 245	100.00 1	99.85 131	100.00 1	100.00 1

SD-MVS		99.81 14	99.75 17	99.99 13	99.99 53	99.96 30	100.00 1	99.42 153	99.01 31	100.00 1	100.00 1	99.33 71	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 3	99.99 13	100.00 1	99.96 30	100.00 1	99.95 19	99.11 10	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	99.44 125	99.06 16	100.00 1	100.00 1	99.56 29	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
APD-MVS_3200maxsize		99.65 52	99.55 62	99.97 40	99.99 53	99.91 64	100.00 1	99.48 84	97.54 185	100.00 1	100.00 1	98.97 118	99.99 107	99.98 92	100.00 1	100.00 1

ACMMPR		99.74 28	99.67 33	99.96 52	100.00 1	99.89 78	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.29 82	100.00 1	99.99 77	100.00 1	100.00 1

MP-MVS		99.61 65	99.49 73	99.98 28	99.99 53	99.94 47	100.00 1	99.42 153	97.82 152	99.99 129	100.00 1	98.20 160	100.00 1	99.99 77	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 42	99.61 48	99.95 61	99.99 53	99.85 94	100.00 1	99.58 73	97.69 164	100.00 1	100.00 1	99.44 56	100.00 1	99.79 144	100.00 1	100.00 1

MCST-MVS		99.85 5	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 76	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 27	100.00 1	99.42 153	98.02 133	100.00 1	100.00 1	99.32 74	99.99 107	100.00 1	100.00 1	100.00 1

test12				99.95 61	99.99 53	99.89 78		99.42 153		100.00 1		99.24 87	99.97 150		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 65	99.69 25	99.35 221	99.99 53	98.06 314	100.00 1	99.36 235	99.83 2	100.00 1	100.00 1	98.95 122	99.99 107	100.00 1	99.11 200	100.00 1

mPP-MVS		99.69 42	99.60 49	99.97 40	100.00 1	99.91 64	100.00 1	99.42 153	97.91 145	100.00 1	100.00 1	99.04 110	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS_fast		99.60 68	99.49 73	99.91 83	99.99 53	99.78 104	100.00 1	99.42 153	97.09 234	100.00 1	100.00 1	98.95 122	99.96 170	99.98 92	100.00 1	100.00 1

HPM-MVS		99.59 69	99.50 71	99.89 90	100.00 1	99.70 121	100.00 1	99.42 153	97.46 197	100.00 1	100.00 1	98.60 147	99.96 170	99.99 77	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		98.53 244	98.23 263	99.43 200	99.92 116	99.01 214	99.96 318	99.47 85	98.80 77	99.96 152	99.96 282	98.56 149	99.30 346	87.78 483	99.68 178	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		99.00 162	98.91 157	99.25 254	99.90 120	97.79 335	100.00 1	99.99 13	98.79 80	98.28 378	100.00 1	93.63 300	99.95 183	99.66 194	99.95 128	100.00 1

APD-MVS		99.68 46	99.58 52	99.97 40	99.99 53	99.96 30	100.00 1	99.42 153	97.53 188	100.00 1	100.00 1	99.27 85	99.97 150	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 5	99.80 7	100.00 1	100.00 1	99.99 6	100.00 1	99.77 53	99.07 14	100.00 1	100.00 1	99.39 69	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 14	100.00 1	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 93	99.25 104	99.81 117	99.97 98	99.48 160	100.00 1	99.42 153	95.53 364	100.00 1	100.00 1	98.37 158	99.95 183	99.97 104	100.00 1	100.00 1

CP-MVS		99.67 49	99.58 52	99.95 61	100.00 1	99.84 96	100.00 1	99.42 153	97.77 157	100.00 1	100.00 1	99.07 104	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 19	99.71 23	99.98 28	99.76 170	99.95 38	100.00 1	99.42 153	98.69 86	100.00 1	100.00 1	99.52 38	99.99 107	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		99.62 63	99.56 60	99.82 112	99.92 116	99.45 162	100.00 1	99.78 52	98.92 52	99.73 244	100.00 1	97.70 182	100.00 1	99.93 116	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 80	99.38 83	99.85 104	100.00 1	99.54 143	100.00 1	99.42 153	97.58 182	99.98 139	100.00 1	97.43 199	100.00 1	99.99 77	100.00 1	100.00 1

DP-MVS Recon		99.76 21	99.69 25	99.98 28	100.00 1	99.95 38	100.00 1	99.52 78	97.99 135	99.99 129	100.00 1	99.72 14	100.00 1	99.96 106	100.00 1	100.00 1

MVS_111021_LR		99.70 39	99.65 37	99.88 95	99.96 104	99.70 121	100.00 1	99.97 17	98.96 39	100.00 1	100.00 1	97.93 168	99.95 183	99.99 77	100.00 1	100.00 1

DP-MVS		98.86 190	98.54 214	99.81 117	99.97 98	99.45 162	99.52 427	99.40 206	94.35 403	98.36 370	100.00 1	96.13 234	99.97 150	99.12 272	100.00 1	100.00 1

QAPM		98.99 166	98.66 193	99.96 52	99.01 367	99.87 87	99.88 359	99.93 35	97.99 135	98.68 339	100.00 1	93.17 313	100.00 1	99.32 257	100.00 1	100.00 1

HyFIR lowres test		99.32 110	99.24 108	99.58 173	99.95 108	99.26 185	100.00 1	99.99 13	96.72 283	99.29 290	99.91 305	99.49 46	99.47 328	99.74 159	98.08 284	100.00 1

PAPM_NR		99.74 28	99.66 36	99.99 13	100.00 1	99.96 30	100.00 1	99.47 85	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 30	100.00 1	99.47 85	98.16 121	100.00 1	100.00 1	99.51 39	100.00 1	100.00 1	100.00 1	100.00 1

IB-MVS	96.24 12	97.54 309	96.95 326	99.33 231	99.67 195	98.10 310	100.00 1	99.47 85	97.42 203	99.26 291	99.69 351	98.83 135	99.89 221	99.43 245	78.77 506	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 36	99.63 44	99.93 78	99.95 108	99.83 98	100.00 1	100.00 1	98.89 60	100.00 1	100.00 1	97.85 174	99.95 183	100.00 1	100.00 1	100.00 1

CSCG		99.28 119	99.35 91	99.05 265	99.99 53	97.15 361	100.00 1	99.47 85	97.44 201	99.42 276	100.00 1	97.83 178	100.00 1	99.99 77	100.00 1	100.00 1

API-MVS		99.72 32	99.70 24	99.79 128	99.97 98	99.37 173	99.96 318	99.94 27	98.48 98	100.00 1	100.00 1	98.92 127	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 19	99.76 15	99.85 104	99.01 367	99.95 38	100.00 1	99.75 57	99.37 3	99.99 129	100.00 1	99.76 12	99.60 293	100.00 1	100.00 1	100.00 1

ACMMP		99.65 52	99.57 55	99.89 90	99.99 53	99.66 126	99.75 391	99.73 61	98.16 121	99.75 238	100.00 1	98.90 129	100.00 1	99.96 106	99.88 153	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 32	99.65 37	99.91 83	99.97 98	99.72 116	100.00 1	99.47 85	98.43 101	99.88 208	100.00 1	99.14 97	100.00 1	99.97 104	100.00 1	100.00 1

PHI-MVS		99.50 78	99.39 82	99.82 112	100.00 1	99.45 162	100.00 1	99.94 27	96.38 327	100.00 1	100.00 1	98.18 161	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 114	99.25 104	99.44 197	100.00 1	98.32 287	100.00 1	99.86 43	98.04 132	100.00 1	100.00 1	96.10 235	100.00 1	99.55 222	99.73 174	100.00 1

PVSNet_0	93.57 19	96.41 363	95.74 380	98.41 312	99.84 131	95.22 402	100.00 1	100.00 1	98.08 130	97.55 417	99.78 338	84.40 444	100.00 1	100.00 1	81.99 492	100.00 1

DeepPCF-MVS	98.03 4	98.54 243	99.72 22	94.98 448	99.99 53	84.94 493	100.00 1	99.42 153	99.98 1	100.00 1	100.00 1	98.11 163	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 26	99.67 33	99.99 13	99.99 53	99.96 30	99.73 397	99.52 78	99.06 16	100.00 1	100.00 1	98.80 138	100.00 1	99.95 112	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 64	99.98 300	99.47 85	99.09 13	100.00 1	100.00 1	98.59 148	100.00 1	99.95 112	100.00 1	100.00 1

AdaColmap		99.44 88	99.26 102	99.95 61	100.00 1	99.86 90	99.70 403	99.99 13	98.53 94	99.90 202	100.00 1	95.34 248	100.00 1	99.92 117	100.00 1	100.00 1

MAR-MVS		99.49 80	99.36 89	99.89 90	99.97 98	99.66 126	99.74 392	99.95 19	97.89 146	100.00 1	100.00 1	96.71 224	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	99.03 153	98.71 185	99.96 52	98.99 375	99.89 78	100.00 1	99.51 82	98.96 39	98.32 375	100.00 1	92.78 324	100.00 1	99.87 127	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 147	98.76 174	99.96 52	98.86 391	99.90 71	99.98 300	99.93 35	98.95 42	98.49 362	100.00 1	92.91 322	100.00 1	99.71 169	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 299	97.37 308	98.45 307	99.94 111	95.70 392	100.00 1	99.40 206	97.65 168	99.53 264	100.00 1	99.31 76	99.66 290	80.48 507	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_s_conf0.5_n_5		99.00 162	98.70 187	99.88 95	99.81 144	99.64 128	100.00 1	99.26 312	98.78 83	99.97 145	100.00 1	90.65 361	99.99 107	100.00 1	99.89 150	99.99 124

MP-MVS-pluss		99.61 65	99.50 71	99.97 40	99.98 94	99.92 60	100.00 1	99.42 153	97.53 188	99.77 235	100.00 1	98.77 139	100.00 1	99.99 77	100.00 1	99.99 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS++		99.82 12	99.76 15	99.99 13	99.99 53	99.98 18	100.00 1	99.83 44	98.88 61	99.96 152	100.00 1	99.21 89	100.00 1	100.00 1	100.00 1	99.99 124

0.3-1-1-0.015		97.60 303	97.19 319	98.83 283	99.13 351	96.55 379	100.00 1	99.40 206	94.19 409	99.83 215	99.81 328	99.18 92	99.97 150	99.70 173	83.50 485	99.98 127

0.4-1-1-0.1		97.56 306	97.15 323	98.79 288	99.01 367	96.44 382	100.00 1	99.40 206	94.11 412	99.81 230	99.81 328	99.09 100	99.97 150	99.65 196	83.48 487	99.98 127

0.4-1-1-0.2		97.60 303	97.18 320	98.86 281	99.05 364	96.62 377	100.00 1	99.40 206	94.24 404	99.82 224	99.81 328	99.09 100	99.97 150	99.70 173	83.50 485	99.98 127

fmvsm_s_conf0.5_n_a		99.32 110	99.15 123	99.81 117	99.80 157	99.47 161	100.00 1	99.35 246	98.22 116	100.00 1	100.00 1	95.21 254	99.99 107	99.96 106	99.86 159	99.98 127

fmvsm_s_conf0.5_n		99.21 131	99.01 138	99.83 110	99.84 131	99.53 145	100.00 1	99.38 225	98.29 115	100.00 1	100.00 1	93.62 301	99.99 107	99.99 77	99.93 138	99.98 127

thres100view900		99.25 126	99.01 138	99.95 61	99.81 144	99.87 87	100.00 1	99.94 27	97.13 231	99.83 215	99.96 282	97.01 209	100.00 1	99.59 213	97.85 299	99.98 127

tfpn200view9		99.26 122	99.03 136	99.96 52	99.81 144	99.89 78	100.00 1	99.94 27	97.23 223	99.83 215	99.96 282	97.04 205	100.00 1	99.59 213	97.85 299	99.98 127

thres200		99.27 120	99.04 135	99.96 52	99.81 144	99.90 71	100.00 1	99.94 27	97.31 216	99.83 215	99.96 282	97.04 205	100.00 1	99.62 206	97.88 297	99.98 127

LCM-MVSNet-Re		96.52 356	97.21 318	94.44 453	99.27 343	85.80 490	99.85 364	96.61 517	95.98 347	92.75 478	98.48 466	93.97 293	97.55 480	99.58 216	98.43 226	99.98 127

JIA-IIPM		97.09 330	96.34 352	99.36 219	98.88 386	98.59 253	99.81 371	99.43 134	84.81 494	99.96 152	90.34 523	98.55 150	99.52 319	97.00 368	98.28 259	99.98 127

fmvsm_s_conf0.1_n_a		98.71 210	98.36 248	99.78 133	99.09 356	99.42 167	100.00 1	99.26 312	97.42 203	100.00 1	100.00 1	89.78 384	99.96 170	99.82 140	99.85 162	99.97 137

fmvsm_s_conf0.1_n		98.77 199	98.42 231	99.82 112	99.47 296	99.52 149	100.00 1	99.27 306	97.53 188	100.00 1	100.00 1	89.73 386	99.96 170	99.84 134	99.93 138	99.97 137

test_fmvsmconf0.1_n		99.25 126	99.05 134	99.82 112	98.92 382	99.55 140	100.00 1	99.23 327	98.91 55	99.75 238	99.97 264	94.79 266	99.94 196	99.94 114	99.99 107	99.97 137

thres600view7		99.24 129	99.00 141	99.95 61	99.81 144	99.87 87	100.00 1	99.94 27	97.13 231	99.83 215	99.96 282	97.01 209	100.00 1	99.54 226	97.77 308	99.97 137

thres400		99.26 122	99.03 136	99.95 61	99.81 144	99.89 78	100.00 1	99.94 27	97.23 223	99.83 215	99.96 282	97.04 205	100.00 1	99.59 213	97.85 299	99.97 137

OMC-MVS		99.27 120	99.38 83	98.96 274	99.95 108	97.06 365	100.00 1	99.40 206	98.83 70	99.88 208	100.00 1	97.01 209	99.86 234	99.47 238	99.84 164	99.97 137

dmvs_re		97.54 309	97.88 286	96.54 421	99.55 247	90.35 474	99.86 362	99.46 103	97.00 243	99.41 281	100.00 1	90.78 360	99.30 346	99.60 211	95.24 354	99.96 143

CANet		99.40 92	99.24 108	99.89 90	99.99 53	99.76 108	100.00 1	99.73 61	98.40 102	99.78 234	100.00 1	95.28 249	99.96 170	100.00 1	99.99 107	99.96 143

GG-mvs-BLEND				99.59 169	99.54 250	99.49 156	99.17 471	99.52 78		99.96 152	99.68 355	100.00 1	99.33 345	99.71 169	99.99 107	99.96 143

gg-mvs-nofinetune		96.95 339	96.10 361	99.50 183	99.41 320	99.36 175	99.07 485	99.52 78	83.69 497	99.96 152	83.60 537	100.00 1	99.20 352	99.68 183	99.99 107	99.96 143

VNet		99.04 150	98.75 175	99.90 87	99.81 144	99.75 109	99.50 429	99.47 85	98.36 109	100.00 1	99.99 243	94.66 272	100.00 1	99.90 120	97.09 322	99.96 143

BH-w/o		98.82 195	98.81 167	98.88 280	99.62 222	96.71 373	100.00 1	99.28 291	97.09 234	98.81 332	100.00 1	94.91 263	99.96 170	99.54 226	100.00 1	99.96 143

patch_mono-2		99.04 150	99.79 9	96.81 415	99.92 116	90.47 473	100.00 1	99.41 202	98.95 42	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 149

dcpmvs_2		98.87 189	99.53 65	96.90 403	99.87 126	90.88 469	99.94 335	99.07 430	98.20 119	100.00 1	100.00 1	98.69 143	99.86 234	100.00 1	100.00 1	99.95 149

Patchmatch-test		97.83 292	97.42 304	99.06 263	99.08 357	97.66 339	98.66 499	99.21 351	93.65 422	98.25 382	99.58 379	99.47 51	99.57 301	90.25 467	98.59 217	99.95 149

HY-MVS	96.53 9	99.50 78	99.35 91	99.96 52	99.81 144	99.93 53	99.64 410	100.00 1	97.97 139	99.84 212	99.85 318	98.94 124	99.99 107	99.86 128	98.23 269	99.95 149

PatchMatch-RL		99.02 159	98.78 170	99.74 140	99.99 53	99.29 180	100.00 1	100.00 1	98.38 105	99.89 205	99.81 328	93.14 317	99.99 107	97.85 336	99.98 118	99.95 149

BridgeMVS		99.43 89	99.28 96	99.85 104	99.68 187	99.68 124	99.97 310	99.28 291	97.03 241	99.96 152	99.97 264	97.90 170	99.93 200	99.77 152	100.00 1	99.94 154

test2506		99.48 82	99.38 83	99.75 139	99.89 122	99.51 150	99.45 434	100.00 1	98.38 105	99.83 215	100.00 1	98.86 131	99.81 254	99.25 261	98.78 209	99.94 154

test1111		98.42 253	98.12 267	99.29 246	99.88 124	98.15 305	99.46 432	100.00 1	98.36 109	99.42 276	100.00 1	87.91 413	99.79 259	99.31 258	98.78 209	99.94 154

ECVR-MVS		98.43 251	98.14 266	99.32 237	99.89 122	98.21 299	99.46 432	100.00 1	98.38 105	99.47 272	100.00 1	87.91 413	99.80 258	99.35 253	98.78 209	99.94 154

test_yl		99.51 75	99.37 86	99.95 61	99.82 138	99.90 71	100.00 1	99.47 85	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 92	97.75 309	99.94 154

DCV-MVSNet		99.51 75	99.37 86	99.95 61	99.82 138	99.90 71	100.00 1	99.47 85	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 92	97.75 309	99.94 154

LFMVS		97.42 315	96.62 338	99.81 117	99.80 157	99.50 152	99.16 472	99.56 76	94.48 399	100.00 1	100.00 1	79.35 468	100.00 1	99.89 122	97.37 318	99.94 154

WTY-MVS		99.54 74	99.40 81	99.95 61	99.81 144	99.93 53	100.00 1	100.00 1	97.98 137	99.84 212	100.00 1	98.94 124	99.98 141	99.86 128	98.21 270	99.94 154

PMMVS		99.12 140	98.97 146	99.58 173	99.57 240	98.98 219	100.00 1	99.30 274	97.14 229	99.96 152	100.00 1	96.53 230	99.82 250	99.70 173	98.49 222	99.94 154

F-COLMAP		99.64 54	99.64 40	99.67 154	99.99 53	99.07 205	100.00 1	99.44 125	98.30 114	99.90 202	100.00 1	99.18 92	99.99 107	99.91 119	100.00 1	99.94 154

PLC	98.56 2	99.70 39	99.74 19	99.58 173	100.00 1	98.79 235	100.00 1	99.54 77	98.58 93	99.96 152	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
TR-MVS		98.14 275	97.74 291	99.33 231	99.59 231	98.28 291	99.27 452	99.21 351	96.42 324	99.15 300	99.94 296	88.87 403	99.79 259	98.88 285	98.29 256	99.93 165

PatchmatchNet		99.03 153	98.96 147	99.26 253	99.49 288	98.33 285	99.38 442	99.45 111	96.64 297	99.96 152	99.58 379	99.49 46	99.50 324	97.63 347	99.00 205	99.93 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SD_0403		97.92 287	98.43 230	96.39 424	99.68 187	89.74 479	99.92 343	99.34 253	96.75 274	99.39 283	99.93 301	93.54 304	99.51 321	99.11 273	98.21 270	99.92 167

fmvsm_s_conf0.5_n_2		98.90 185	98.57 209	99.90 87	99.79 162	99.78 104	100.00 1	99.25 316	98.97 37	100.00 1	100.00 1	89.22 397	99.99 107	100.00 1	99.88 153	99.92 167

ET-MVSNet_ETH3D		96.41 363	95.48 394	99.20 257	99.81 144	99.75 109	100.00 1	99.02 447	97.30 218	78.33 518	100.00 1	97.73 180	97.94 469	99.70 173	87.41 459	99.92 167

LS3D		99.31 112	99.13 126	99.87 97	99.99 53	99.71 117	99.55 423	99.46 103	97.32 214	99.82 224	100.00 1	96.85 219	99.97 150	99.14 269	100.00 1	99.92 167

MGCFI-Net		99.01 161	98.70 187	99.93 78	99.74 174	99.94 47	100.00 1	99.29 283	97.60 180	100.00 1	100.00 1	95.10 258	99.96 170	99.74 159	96.85 329	99.91 171

sasdasda		99.03 153	98.73 179	99.94 74	99.75 172	99.95 38	100.00 1	99.30 274	97.64 170	100.00 1	100.00 1	95.22 252	99.97 150	99.76 154	96.90 327	99.91 171

GSMVS																99.91 171

sam_mvs1												99.29 82				99.91 171

SCA		98.30 263	97.98 280	99.23 255	99.41 320	98.25 295	99.99 267	99.45 111	96.91 254	99.76 237	99.58 379	89.65 390	99.54 313	98.31 316	98.79 208	99.91 171

Patchmatch-RL test		93.49 427	93.63 418	93.05 467	91.78 517	83.41 496	98.21 508	96.95 512	91.58 452	91.05 482	97.64 486	99.40 68	95.83 497	94.11 431	81.95 493	99.91 171

canonicalmvs		99.03 153	98.73 179	99.94 74	99.75 172	99.95 38	100.00 1	99.30 274	97.64 170	100.00 1	100.00 1	95.22 252	99.97 150	99.76 154	96.90 327	99.91 171

test-LLR		99.03 153	98.91 157	99.40 209	99.40 325	99.28 182	100.00 1	99.45 111	96.70 289	99.42 276	99.12 418	99.31 76	99.01 363	96.82 375	99.99 107	99.91 171

TESTMET0.1,1		99.08 143	98.96 147	99.44 197	99.63 215	99.38 170	100.00 1	99.45 111	95.53 364	99.48 269	100.00 1	99.71 15	99.02 361	96.84 374	99.99 107	99.91 171

test-mter		98.96 173	98.82 165	99.40 209	99.40 325	99.28 182	100.00 1	99.45 111	95.44 375	99.42 276	99.12 418	99.70 16	99.01 363	96.82 375	99.99 107	99.91 171

MSDG		98.90 185	98.63 197	99.70 149	99.92 116	99.25 187	100.00 1	99.37 229	95.71 357	99.40 282	100.00 1	96.58 226	99.95 183	96.80 377	99.94 134	99.91 171

viewdifsd2359ckpt11		97.98 283	97.89 283	98.26 326	99.47 296	94.98 408	99.99 267	99.22 332	96.74 277	99.24 292	100.00 1	90.14 375	99.90 219	99.49 235	96.73 330	99.90 182

viewmsd2359difaftdt		97.98 283	97.89 283	98.27 323	99.47 296	94.99 407	99.99 267	99.22 332	96.74 277	99.24 292	100.00 1	90.14 375	99.90 219	99.49 235	96.73 330	99.90 182

MVSMamba_PlusPlus		99.39 93	99.25 104	99.80 123	99.68 187	99.59 134	99.99 267	99.30 274	96.66 294	99.96 152	99.97 264	97.89 171	99.92 206	99.76 154	100.00 1	99.90 182

test_fmvsmconf0.01_n		98.60 231	98.24 259	99.67 154	96.90 476	99.21 193	99.99 267	99.04 443	98.80 77	99.57 263	99.96 282	90.12 378	99.91 208	99.89 122	99.89 150	99.90 182

alignmvs		99.38 96	99.21 113	99.91 83	99.73 175	99.92 60	100.00 1	99.51 82	97.61 177	100.00 1	100.00 1	99.06 105	99.93 200	99.83 135	97.12 321	99.90 182

PVSNet_Blended		99.48 82	99.36 89	99.83 110	99.98 94	99.60 132	100.00 1	100.00 1	97.79 155	100.00 1	100.00 1	96.57 227	99.99 107	100.00 1	99.88 153	99.90 182

EPMVS		99.25 126	99.13 126	99.60 167	99.60 227	99.20 194	99.60 417	100.00 1	96.93 251	99.92 197	99.36 405	99.05 107	99.71 286	98.77 291	98.94 206	99.90 182

PCF-MVS	98.23 3	98.69 215	98.37 246	99.62 163	99.78 167	99.02 212	99.23 463	99.06 438	96.43 320	98.08 387	100.00 1	94.72 270	99.95 183	98.16 323	99.91 147	99.90 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
dongtai		98.29 266	98.25 256	98.42 311	99.58 236	95.86 389	100.00 1	99.44 125	93.46 429	99.69 248	99.97 264	97.53 191	99.51 321	96.28 390	98.27 262	99.89 190

kuosan		98.55 240	98.53 216	98.62 296	99.66 205	96.16 384	100.00 1	99.44 125	93.93 416	99.81 230	99.98 251	97.58 186	99.81 254	98.08 325	98.28 259	99.89 190

GA-MVS		97.72 297	97.27 314	99.06 263	99.24 346	97.93 325	100.00 1	99.24 322	95.80 356	98.99 315	99.64 365	89.77 385	99.36 341	95.12 416	97.62 317	99.89 190

baseline1		98.91 183	98.61 201	99.81 117	99.71 177	99.77 107	99.78 382	99.44 125	97.51 192	98.81 332	99.99 243	98.25 159	99.76 272	98.60 304	95.41 345	99.89 190

tpmvs		98.59 232	98.38 244	99.23 255	99.69 182	97.90 326	99.31 450	99.47 85	94.52 397	99.68 249	99.28 409	97.64 185	99.89 221	97.71 344	98.17 276	99.89 190

EPNet		99.62 63	99.69 25	99.42 203	99.99 53	98.37 277	100.00 1	99.89 42	98.83 70	100.00 1	100.00 1	98.97 118	100.00 1	99.90 120	99.61 187	99.89 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		99.33 108	99.18 121	99.78 133	99.82 138	99.49 156	100.00 1	99.95 19	97.36 207	99.63 258	100.00 1	96.45 231	99.95 183	99.79 144	99.65 183	99.89 190

dp		98.72 206	98.61 201	99.03 268	99.53 254	97.39 348	99.45 434	99.39 222	95.62 361	99.94 190	99.52 389	98.83 135	99.82 250	96.77 380	98.42 227	99.89 190

sss		99.45 86	99.34 93	99.80 123	99.76 170	99.50 152	100.00 1	99.91 40	97.72 160	99.98 139	99.94 296	98.45 153	100.00 1	99.53 229	98.75 212	99.89 190

Test_1112_low_res		98.83 194	98.60 204	99.51 180	99.69 182	98.75 237	99.99 267	99.14 401	96.81 264	98.84 329	99.06 422	97.45 196	99.89 221	98.66 296	97.75 309	99.89 190

1112_ss		98.91 183	98.71 185	99.51 180	99.69 182	98.75 237	99.99 267	99.15 394	96.82 263	98.84 329	100.00 1	97.45 196	99.89 221	98.66 296	97.75 309	99.89 190

MDTV_nov1_ep13_2view						99.24 189	99.56 421		96.31 335	99.96 152		98.86 131		98.92 283		99.89 190

Vis-MVSNet (Re-imp)		98.99 166	98.89 161	99.29 246	99.64 213	98.89 228	99.98 300	99.31 268	96.74 277	99.48 269	100.00 1	98.11 163	99.10 356	98.39 312	98.34 246	99.89 190

myMVS_eth3d28		99.41 91	99.28 96	99.80 123	99.69 182	99.53 145	100.00 1	99.43 134	97.12 233	99.98 139	99.97 264	99.41 66	100.00 1	99.81 142	98.07 285	99.88 203

UBG		99.36 100	99.27 98	99.63 161	99.63 215	99.01 214	100.00 1	99.43 134	96.99 244	100.00 1	99.92 302	99.69 17	99.99 107	99.74 159	98.06 286	99.88 203

ETVMVS		99.16 137	98.98 144	99.69 150	99.67 195	99.56 138	100.00 1	99.45 111	96.36 329	99.98 139	99.95 290	98.65 144	99.64 291	99.11 273	97.63 316	99.88 203

GeoE		98.06 279	97.65 298	99.29 246	99.47 296	98.41 269	100.00 1	99.19 365	94.85 383	98.88 324	100.00 1	91.21 347	99.59 295	97.02 367	98.19 273	99.88 203

UA-Net		99.06 147	98.83 164	99.74 140	99.52 268	99.40 169	99.08 483	99.45 111	97.64 170	99.83 215	100.00 1	95.80 240	99.94 196	98.35 314	99.80 172	99.88 203

ADS-MVSNet2		98.28 268	98.51 222	97.62 373	99.51 275	95.03 406	99.24 456	99.41 202	95.52 366	99.96 152	99.70 348	97.57 188	97.94 469	97.11 365	98.54 219	99.88 203

ADS-MVSNet		98.70 213	98.51 222	99.28 249	99.51 275	98.39 273	99.24 456	99.44 125	95.52 366	99.96 152	99.70 348	97.57 188	99.58 299	97.11 365	98.54 219	99.88 203

mvs_anonymous		98.80 197	98.60 204	99.38 216	99.57 240	99.24 189	100.00 1	99.21 351	95.87 350	98.92 321	99.82 325	96.39 232	99.03 360	99.13 271	98.50 221	99.88 203

tpm		98.24 271	98.22 264	98.32 320	99.13 351	95.79 390	99.53 426	99.12 414	95.20 377	99.96 152	99.36 405	97.58 186	99.28 348	97.41 356	96.67 332	99.88 203

EC-MVSNet		99.19 133	99.09 131	99.48 188	99.42 318	99.07 205	100.00 1	99.21 351	96.95 249	99.96 152	100.00 1	96.88 218	99.48 326	99.64 197	99.79 173	99.88 203

IS-MVSNet		99.08 143	98.91 157	99.59 169	99.65 207	99.38 170	99.78 382	99.24 322	96.70 289	99.51 266	100.00 1	98.44 154	99.52 319	98.47 309	98.39 231	99.88 203

GDP-MVS		99.39 93	99.26 102	99.77 136	99.53 254	99.55 140	100.00 1	99.11 416	97.14 229	99.96 152	100.00 1	99.83 5	99.89 221	98.47 309	99.26 196	99.87 214

BP-MVS1		99.56 71	99.48 76	99.79 128	99.48 291	99.61 131	100.00 1	99.32 259	97.34 211	99.94 190	100.00 1	99.74 13	99.89 221	99.75 158	99.72 175	99.87 214

CS-MVS		99.33 108	99.27 98	99.50 183	99.99 53	99.00 217	100.00 1	99.13 408	97.26 220	99.96 152	100.00 1	97.79 179	99.64 291	99.64 197	99.67 180	99.87 214

Fast-Effi-MVS+		98.40 256	98.02 278	99.55 178	99.63 215	99.06 207	100.00 1	99.15 394	95.07 378	99.42 276	99.95 290	93.26 310	99.73 282	97.44 354	98.24 268	99.87 214

dmvs_testset		93.27 430	95.48 394	86.65 489	98.74 397	68.42 524	99.92 343	98.91 461	96.19 343	93.28 475	100.00 1	91.06 353	91.67 520	89.64 473	91.54 414	99.86 218

testing3-2		99.45 86	99.31 94	99.86 100	99.70 179	99.73 114	100.00 1	99.47 85	97.46 197	99.97 145	99.97 264	99.48 50	100.00 1	99.78 150	97.99 288	99.85 219

MVS-HIRNet		94.12 417	92.73 434	98.29 321	99.33 335	95.95 385	99.38 442	99.19 365	74.54 515	98.26 381	86.34 531	86.07 433	99.06 358	91.60 454	99.87 158	99.85 219

CR-MVSNet		98.02 282	97.71 296	98.93 276	99.31 336	98.86 229	99.13 476	99.00 450	96.53 310	99.96 152	98.98 432	96.94 215	98.10 458	91.18 457	98.40 229	99.84 221

RPMNet		95.26 404	93.82 415	99.56 176	99.31 336	98.86 229	99.13 476	99.42 153	79.82 506	99.96 152	95.13 506	95.69 244	99.98 141	77.54 516	98.40 229	99.84 221

ab-mvs		98.42 253	98.02 278	99.61 165	99.71 177	99.00 217	99.10 480	99.64 70	96.70 289	99.04 313	99.81 328	90.64 362	99.98 141	99.64 197	97.93 294	99.84 221

SymmetryMVS		99.30 114	99.25 104	99.45 194	99.79 162	98.55 255	99.94 335	99.47 85	98.39 103	100.00 1	100.00 1	98.44 154	99.98 141	99.36 249	97.83 302	99.83 224

FE-MVS		99.16 137	98.99 143	99.66 157	99.65 207	99.18 197	99.58 419	99.43 134	95.24 376	99.91 200	99.59 377	99.37 70	99.97 150	98.31 316	99.81 169	99.83 224

Anonymous20240529		96.93 340	96.22 357	99.05 265	99.79 162	97.30 355	99.16 472	99.47 85	88.51 472	98.69 337	100.00 1	83.50 452	100.00 1	99.83 135	97.02 324	99.83 224

CVMVSNet		98.56 239	98.47 225	98.82 284	99.11 353	97.67 338	99.74 392	99.47 85	97.57 183	99.06 310	100.00 1	95.72 242	98.97 369	98.21 322	97.33 319	99.83 224

tpm2		98.64 219	98.58 208	98.81 287	99.42 318	97.12 362	99.69 405	99.37 229	93.63 423	99.94 190	99.67 356	98.96 121	99.47 328	98.62 303	97.95 293	99.83 224

DeepC-MVS	97.84 5	99.00 162	98.80 168	99.60 167	99.93 113	99.03 210	100.00 1	99.40 206	98.61 92	99.33 287	100.00 1	92.23 336	99.95 183	99.74 159	99.96 126	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
hybridnocas07		98.85 192	98.63 197	99.53 179	99.52 268	98.95 224	100.00 1	99.19 365	97.15 228	99.93 195	100.00 1	93.83 297	99.82 250	99.67 187	98.38 235	99.82 230

hybridcas		98.64 219	98.41 233	99.33 231	99.54 250	98.41 269	100.00 1	99.18 375	96.78 268	99.68 249	100.00 1	92.58 331	99.75 277	99.57 219	98.38 235	99.82 230

E3new		98.95 176	98.80 168	99.41 204	99.57 240	98.50 264	100.00 1	99.22 332	96.84 261	99.89 205	100.00 1	95.70 243	99.93 200	99.57 219	98.39 231	99.82 230

E2		98.77 199	98.57 209	99.37 217	99.53 254	98.38 276	99.98 300	99.22 332	96.77 270	99.75 238	100.00 1	94.03 289	99.91 208	99.53 229	98.35 242	99.82 230

E3		98.77 199	98.57 209	99.36 219	99.47 296	98.36 280	99.98 300	99.22 332	96.76 271	99.75 238	100.00 1	94.10 286	99.91 208	99.53 229	98.35 242	99.82 230

viewdifsd2359ckpt07		98.72 206	98.52 217	99.34 223	99.47 296	98.28 291	99.99 267	99.20 361	96.98 245	99.60 260	100.00 1	93.45 305	99.93 200	99.58 216	98.36 240	99.82 230

viewcassd2359sk11		98.90 185	98.73 179	99.40 209	99.57 240	98.47 265	99.99 267	99.22 332	96.79 266	99.82 224	100.00 1	95.24 251	99.91 208	99.54 226	98.38 235	99.82 230

testing11		99.26 122	99.19 118	99.46 190	99.64 213	98.61 251	100.00 1	99.43 134	96.94 250	99.92 197	99.94 296	99.43 60	99.97 150	99.67 187	97.79 307	99.82 230

Syy-MVS		96.17 380	96.57 340	95.00 446	99.50 284	87.37 487	100.00 1	99.57 74	96.23 338	98.07 388	100.00 1	92.41 335	97.81 472	85.34 491	97.96 291	99.82 230

myMVS_eth3d		98.52 245	98.51 222	98.53 302	99.50 284	97.98 319	100.00 1	99.57 74	96.23 338	98.07 388	100.00 1	99.09 100	97.81 472	96.17 391	97.96 291	99.82 230

testing3		98.44 250	98.37 246	98.65 294	99.51 275	98.32 287	100.00 1	99.62 72	96.43 320	97.93 397	99.99 243	99.11 98	97.81 472	94.88 419	97.80 305	99.82 230

EIA-MVS		99.26 122	99.19 118	99.45 194	99.63 215	98.75 237	100.00 1	99.27 306	96.93 251	99.95 183	100.00 1	97.47 195	99.79 259	99.74 159	99.72 175	99.82 230

SPE-MVS-test		99.31 112	99.27 98	99.43 200	99.99 53	98.77 236	100.00 1	99.19 365	97.24 221	99.96 152	100.00 1	97.56 190	99.70 288	99.68 183	99.81 169	99.82 230

MVS_Test		98.93 179	98.65 194	99.77 136	99.62 222	99.50 152	99.99 267	99.19 365	95.52 366	99.96 152	99.86 313	96.54 229	99.98 141	98.65 298	98.48 223	99.82 230

diffmvs		98.96 173	98.73 179	99.63 161	99.54 250	99.16 199	100.00 1	99.18 375	97.33 213	99.96 152	100.00 1	94.60 274	99.91 208	99.66 194	98.33 249	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
tpmrst		98.98 170	98.93 154	99.14 261	99.61 224	97.74 336	99.52 427	99.36 235	96.05 346	99.98 139	99.64 365	99.04 110	99.86 234	98.94 281	98.19 273	99.82 230

PRO-TEST		98.27 269	98.24 259	98.37 315	99.67 195	95.43 395	100.00 1	98.99 454	96.55 307	99.95 183	99.98 251	89.26 396	99.87 231	99.81 142	99.92 141	99.81 246

onestephybrid01		98.89 188	98.67 192	99.56 176	99.51 275	99.08 204	100.00 1	99.20 361	97.30 218	99.95 183	100.00 1	94.04 288	99.79 259	99.77 152	98.29 256	99.81 246

viewmamba		98.92 180	98.74 177	99.46 190	99.46 304	98.83 232	100.00 1	99.19 365	97.18 226	99.95 183	100.00 1	94.97 261	99.74 278	99.64 197	98.29 256	99.81 246

Casviewmamba		98.71 210	98.47 225	99.46 190	99.47 296	98.70 244	100.00 1	99.17 385	96.97 247	99.45 275	100.00 1	93.04 319	99.87 231	99.67 187	98.41 228	99.81 246

hybrid		98.81 196	98.60 204	99.45 194	99.52 268	98.74 240	100.00 1	99.19 365	97.04 240	99.95 183	100.00 1	93.89 296	99.78 265	99.64 197	98.19 273	99.81 246

casdiffseed414692147		98.31 262	97.94 281	99.40 209	99.46 304	98.67 246	99.91 350	99.17 385	96.33 333	98.66 341	99.97 264	90.47 370	99.71 286	99.36 249	98.16 277	99.81 246

E5new		98.63 225	98.41 233	99.31 239	99.51 275	98.21 299	99.79 377	99.21 351	96.62 302	99.67 255	100.00 1	93.15 315	99.91 208	99.46 241	98.26 264	99.81 246

E6new		98.64 219	98.41 233	99.30 243	99.46 304	98.19 302	99.79 377	99.21 351	96.62 302	99.68 249	100.00 1	93.24 311	99.91 208	99.47 238	98.26 264	99.81 246

E6		98.64 219	98.41 233	99.30 243	99.46 304	98.19 302	99.79 377	99.21 351	96.62 302	99.68 249	100.00 1	93.24 311	99.91 208	99.47 238	98.26 264	99.81 246

E5		98.63 225	98.41 233	99.31 239	99.51 275	98.21 299	99.79 377	99.21 351	96.62 302	99.67 255	100.00 1	93.15 315	99.91 208	99.46 241	98.26 264	99.81 246

E4		98.68 217	98.46 227	99.33 231	99.51 275	98.27 293	99.96 318	99.21 351	96.66 294	99.68 249	100.00 1	93.38 306	99.91 208	99.49 235	98.27 262	99.81 246

viewdifsd2359ckpt09		98.78 198	98.60 204	99.31 239	99.53 254	98.37 277	100.00 1	99.20 361	96.85 259	99.32 288	100.00 1	94.68 271	99.74 278	99.46 241	98.36 240	99.81 246

viewdifsd2359ckpt13		98.72 206	98.52 217	99.34 223	99.55 247	98.46 266	99.99 267	99.22 332	96.50 317	99.05 311	100.00 1	94.54 275	99.73 282	99.46 241	98.35 242	99.81 246

viewmacassd2359aftdt		98.57 236	98.31 252	99.33 231	99.49 288	98.31 289	99.89 356	99.21 351	96.87 258	99.10 304	100.00 1	92.48 334	99.88 229	99.50 233	98.28 259	99.81 246

viewmanbaseed2359cas		98.86 190	98.68 191	99.40 209	99.51 275	98.51 263	99.98 300	99.22 332	97.05 239	99.72 245	100.00 1	94.77 267	99.89 221	99.58 216	98.31 253	99.81 246

testing91		99.18 134	99.10 129	99.41 204	99.60 227	98.43 267	100.00 1	99.43 134	96.76 271	99.82 224	99.92 302	99.05 107	99.98 141	99.62 206	97.67 313	99.81 246

testing99		99.18 134	99.10 129	99.41 204	99.60 227	98.43 267	100.00 1	99.43 134	96.76 271	99.84 212	99.92 302	99.06 105	99.98 141	99.62 206	97.67 313	99.81 246

testing222		99.14 139	98.94 152	99.73 143	99.67 195	99.51 150	100.00 1	99.43 134	96.90 256	99.99 129	99.90 307	98.55 150	99.86 234	98.85 286	97.18 320	99.81 246

MonoMVSNet		98.55 240	98.64 196	98.26 326	98.21 427	95.76 391	99.94 335	99.16 388	96.23 338	99.47 272	99.24 412	96.75 222	99.22 350	99.61 209	99.17 197	99.81 246

ETV-MVS		99.34 105	99.24 108	99.64 160	99.58 236	99.33 176	100.00 1	99.25 316	97.57 183	99.96 152	100.00 1	97.44 198	99.79 259	99.70 173	99.65 183	99.81 246

thisisatest0515		99.42 90	99.31 94	99.74 140	99.59 231	99.55 140	100.00 1	99.46 103	96.65 296	99.92 197	100.00 1	99.44 56	99.85 241	99.09 275	99.63 186	99.81 246

Effi-MVS+		98.58 234	98.24 259	99.61 165	99.60 227	99.26 185	97.85 514	99.10 419	96.22 341	99.97 145	99.89 308	93.75 298	99.77 267	99.43 245	98.34 246	99.81 246

RRT-MVS		98.75 205	98.52 217	99.44 197	99.65 207	98.57 254	99.90 352	99.08 425	96.51 315	99.96 152	99.95 290	92.59 330	99.96 170	99.60 211	99.45 193	99.81 246

casdiffmvs		98.65 218	98.38 244	99.46 190	99.52 268	98.74 240	100.00 1	99.15 394	96.91 254	99.05 311	100.00 1	92.75 325	99.83 247	99.70 173	98.38 235	99.81 246
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mvsmamba		99.05 149	98.98 144	99.27 252	99.57 240	98.10 310	100.00 1	99.28 291	95.92 349	99.96 152	99.97 264	96.73 223	99.89 221	99.72 165	99.65 183	99.81 246

lupinMVS		99.29 117	99.16 122	99.69 150	99.45 312	99.49 156	100.00 1	99.15 394	97.45 199	99.97 145	100.00 1	96.76 220	99.76 272	99.67 187	100.00 1	99.81 246

casdiffmvs_mvg		98.64 219	98.39 242	99.40 209	99.50 284	98.60 252	100.00 1	99.22 332	96.85 259	99.10 304	100.00 1	92.75 325	99.78 265	99.71 169	98.35 242	99.81 246
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		98.69 215	98.45 228	99.41 204	99.52 268	98.67 246	100.00 1	99.17 385	97.03 241	99.13 301	100.00 1	93.17 313	99.74 278	99.70 173	98.34 246	99.81 246

tpm cat1		98.05 280	97.76 290	98.92 277	99.50 284	97.10 364	99.77 387	99.30 274	90.20 465	99.72 245	98.71 451	97.71 181	99.86 234	96.75 381	98.20 272	99.81 246

CostFormer		98.84 193	98.77 173	99.04 267	99.41 320	97.58 342	99.67 408	99.35 246	94.66 392	99.96 152	99.36 405	99.28 84	99.74 278	99.41 247	97.81 304	99.81 246

PatchT		95.90 392	94.95 409	98.75 291	99.03 365	98.39 273	99.08 483	99.32 259	85.52 491	99.96 152	94.99 509	97.94 167	98.05 464	80.20 508	98.47 224	99.81 246

BH-untuned		98.64 219	98.65 194	98.60 298	99.59 231	96.17 383	100.00 1	99.28 291	96.67 293	98.41 367	100.00 1	94.52 276	99.83 247	99.41 247	100.00 1	99.81 246

viewmambaseed2359dif		98.57 236	98.34 250	99.28 249	99.46 304	98.23 296	100.00 1	99.16 388	96.26 337	99.11 303	100.00 1	93.12 318	99.79 259	99.61 209	98.33 249	99.80 278

fmvsm_s_conf0.5_n_7		98.98 170	98.85 163	99.37 217	99.67 195	98.34 284	100.00 1	99.31 268	98.97 37	100.00 1	100.00 1	91.70 342	99.97 150	99.99 77	99.97 122	99.80 278

UWE-MVS-28		99.29 117	99.23 111	99.48 188	99.73 175	98.86 229	100.00 1	99.43 134	96.97 247	99.99 129	99.83 321	99.43 60	99.77 267	99.35 253	98.31 253	99.80 278

UWE-MVS		99.18 134	99.06 133	99.51 180	99.67 195	98.80 234	100.00 1	99.43 134	96.80 265	99.93 195	99.86 313	99.79 8	99.94 196	97.78 342	98.33 249	99.80 278

thisisatest0530		99.37 99	99.27 98	99.69 150	99.59 231	99.41 168	100.00 1	99.46 103	96.46 319	99.90 202	100.00 1	99.44 56	99.85 241	98.97 280	99.58 188	99.80 278

MIMVSNet		97.06 333	96.73 334	98.05 352	99.38 329	96.64 376	98.47 505	99.35 246	93.41 430	99.48 269	98.53 464	89.66 389	97.70 478	94.16 430	98.11 283	99.80 278

dtuplus		98.57 236	98.32 251	99.30 243	99.44 314	98.35 283	100.00 1	99.14 401	96.36 329	98.97 316	100.00 1	93.04 319	99.77 267	99.55 222	98.39 231	99.79 284

diffmvs_AUTHOR		98.92 180	98.73 179	99.49 187	99.48 291	98.81 233	99.94 335	99.14 401	97.24 221	99.96 152	100.00 1	94.85 264	99.87 231	99.67 187	98.31 253	99.79 284

SDMVSNet		98.49 248	98.08 272	99.73 143	99.82 138	99.53 145	99.99 267	99.45 111	97.62 173	99.38 284	99.86 313	90.06 381	99.88 229	99.92 117	96.61 334	99.79 284

sd_testset		97.81 293	97.48 301	98.79 288	99.82 138	96.80 371	99.32 447	99.45 111	97.62 173	99.38 284	99.86 313	85.56 439	99.77 267	99.72 165	96.61 334	99.79 284

FA-MVS(test-final)		99.00 162	98.75 175	99.73 143	99.63 215	99.43 166	99.83 367	99.43 134	95.84 355	99.52 265	99.37 404	97.84 176	99.96 170	97.63 347	99.68 178	99.79 284

tttt0517		99.34 105	99.23 111	99.67 154	99.57 240	99.38 170	100.00 1	99.46 103	96.33 333	99.89 205	100.00 1	99.44 56	99.84 245	98.93 282	99.46 192	99.78 289

BH-RMVSNet		98.46 249	98.08 272	99.59 169	99.61 224	99.19 195	100.00 1	99.28 291	97.06 238	98.95 317	100.00 1	88.99 400	99.82 250	98.83 289	100.00 1	99.77 290

CDS-MVSNet		98.96 173	98.95 151	99.01 270	99.48 291	98.36 280	99.93 341	99.37 229	96.79 266	99.31 289	99.83 321	99.77 11	98.91 375	98.07 327	97.98 289	99.77 290
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		98.52 245	98.25 256	99.34 223	99.68 187	98.55 255	99.68 407	99.41 202	97.34 211	99.94 190	100.00 1	90.38 372	99.70 288	99.03 277	98.84 207	99.76 292
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mamba_0408		98.63 225	98.40 239	99.34 223	99.53 254	98.52 260	99.24 456	99.16 388	96.43 320	98.95 317	99.98 251	94.47 277	99.76 272	99.21 267	98.62 214	99.75 293

SSM_04072		98.59 232	98.40 239	99.15 259	99.53 254	98.52 260	99.24 456	99.16 388	96.43 320	98.95 317	99.98 251	94.47 277	99.19 353	99.21 267	98.62 214	99.75 293

SSM_0407		98.72 206	98.52 217	99.33 231	99.53 254	98.52 260	99.88 359	99.15 394	96.53 310	98.95 317	100.00 1	94.38 280	99.72 284	99.64 197	98.62 214	99.75 293

NormalMVS		99.47 84	99.48 76	99.43 200	99.99 53	98.55 255	99.94 335	99.28 291	98.39 103	100.00 1	100.00 1	98.44 154	99.98 141	99.36 249	99.92 141	99.75 293

KinetiMVS		98.61 229	98.26 255	99.65 159	99.46 304	99.24 189	99.96 318	99.44 125	97.54 185	99.99 129	99.99 243	90.83 359	99.95 183	97.18 363	99.92 141	99.75 293

guyue		99.21 131	99.07 132	99.62 163	99.55 247	99.29 180	100.00 1	99.32 259	97.66 166	99.96 152	100.00 1	95.84 239	99.84 245	99.63 204	99.67 180	99.75 293

EPP-MVSNet		99.10 142	99.00 141	99.40 209	99.51 275	98.68 245	99.92 343	99.43 134	95.47 370	99.65 257	100.00 1	99.51 39	99.76 272	99.53 229	98.00 287	99.75 293

icg_test_0407_2		98.30 263	98.45 228	97.85 366	99.38 329	95.36 396	99.99 267	99.18 375	96.72 283	99.58 261	100.00 1	95.17 256	98.45 424	97.84 337	98.15 278	99.74 300

IMVS_0407		98.36 260	98.42 231	98.19 333	99.38 329	95.36 396	99.73 397	99.18 375	96.72 283	99.58 261	100.00 1	95.17 256	99.47 328	97.84 337	98.15 278	99.74 300

IMVS_0404		97.87 288	97.89 283	97.81 368	99.38 329	95.36 396	99.84 365	99.18 375	96.72 283	98.41 367	100.00 1	91.43 345	98.32 433	97.84 337	98.15 278	99.74 300

IMVS_0403		98.37 258	98.39 242	98.29 321	99.38 329	95.36 396	99.97 310	99.18 375	96.72 283	99.68 249	100.00 1	94.61 273	99.77 267	97.84 337	98.15 278	99.74 300

AstraMVS		99.03 153	99.01 138	99.09 262	99.46 304	97.66 339	100.00 1	99.23 327	97.83 150	99.95 183	100.00 1	95.52 247	99.86 234	99.74 159	99.39 194	99.74 300

xiu_mvs_v2_base		99.51 75	99.41 80	99.82 112	99.70 179	99.73 114	99.92 343	99.40 206	98.15 123	100.00 1	100.00 1	98.50 152	100.00 1	99.85 131	99.13 199	99.74 300

PS-MVSNAJ		99.64 54	99.57 55	99.85 104	99.78 167	99.81 100	99.95 327	99.42 153	98.38 105	100.00 1	100.00 1	98.75 140	100.00 1	99.88 124	99.99 107	99.74 300

MVSFormer		98.94 178	98.82 165	99.28 249	99.45 312	99.49 156	100.00 1	99.13 408	95.46 371	99.97 145	100.00 1	96.76 220	98.59 410	98.63 301	100.00 1	99.74 300

jason		99.11 141	98.96 147	99.59 169	99.17 349	99.31 179	100.00 1	99.13 408	97.38 206	99.83 215	100.00 1	95.54 246	99.72 284	99.57 219	99.97 122	99.74 300
jason: jason.
TAMVS		98.76 202	98.73 179	98.86 281	99.44 314	97.69 337	99.57 420	99.34 253	96.57 306	99.12 302	99.81 328	98.83 135	99.16 354	97.97 333	97.91 295	99.73 309

VDD-MVS		96.58 355	95.99 366	98.34 318	99.52 268	95.33 400	99.18 466	99.38 225	96.64 297	99.77 235	100.00 1	72.51 489	100.00 1	100.00 1	96.94 326	99.70 310

RPSCF		97.37 317	98.24 259	94.76 451	99.80 157	84.57 494	99.99 267	99.05 440	94.95 381	99.82 224	100.00 1	94.03 289	100.00 1	98.15 324	98.38 235	99.70 310

AllTest		98.55 240	98.40 239	98.99 271	99.93 113	97.35 351	100.00 1	99.40 206	97.08 236	99.09 306	99.98 251	93.37 307	99.95 183	96.94 369	99.84 164	99.68 312

TestCases				98.99 271	99.93 113	97.35 351		99.40 206	97.08 236	99.09 306	99.98 251	93.37 307	99.95 183	96.94 369	99.84 164	99.68 312

xiu_mvs_v1_base_debu		99.35 101	99.21 113	99.79 128	99.67 195	99.71 117	99.78 382	99.36 235	98.13 125	100.00 1	100.00 1	97.00 212	100.00 1	99.83 135	99.07 201	99.66 314

xiu_mvs_v1_base		99.35 101	99.21 113	99.79 128	99.67 195	99.71 117	99.78 382	99.36 235	98.13 125	100.00 1	100.00 1	97.00 212	100.00 1	99.83 135	99.07 201	99.66 314

xiu_mvs_v1_base_debi		99.35 101	99.21 113	99.79 128	99.67 195	99.71 117	99.78 382	99.36 235	98.13 125	100.00 1	100.00 1	97.00 212	100.00 1	99.83 135	99.07 201	99.66 314

h-mvs33		97.03 335	96.53 341	98.51 303	99.79 162	95.90 388	99.45 434	99.45 111	98.21 117	100.00 1	99.78 338	97.49 193	99.99 107	99.72 165	74.92 509	99.65 317

SSM_0404		98.76 202	98.56 212	99.35 221	99.53 254	98.65 249	99.80 376	99.15 394	96.53 310	99.47 272	100.00 1	94.38 280	99.76 272	99.64 197	98.59 217	99.64 318

dtuonly		97.85 290	97.46 302	99.02 269	98.44 408	97.89 328	99.99 267	97.62 503	96.53 310	99.49 268	99.96 282	94.01 292	99.58 299	92.75 444	98.32 252	99.59 319

fmvsm_s_conf0.5_n_4		98.98 170	98.74 177	99.68 153	99.81 144	99.50 152	100.00 1	99.26 312	98.91 55	100.00 1	100.00 1	90.87 358	99.97 150	99.99 77	99.81 169	99.57 320

OpenMVS	95.20 17	98.76 202	98.41 233	99.78 133	98.89 385	99.81 100	99.99 267	99.76 54	98.02 133	98.02 393	100.00 1	91.44 344	100.00 1	99.63 204	99.97 122	99.55 321

fmvsm_s_conf0.5_n_11		98.92 180	98.63 197	99.80 123	99.85 129	99.86 90	100.00 1	99.24 322	98.91 55	100.00 1	100.00 1	89.69 388	99.99 107	100.00 1	99.98 118	99.54 322

cascas		98.43 251	98.07 274	99.50 183	99.65 207	99.02 212	100.00 1	99.22 332	94.21 407	99.72 245	99.98 251	92.03 340	99.93 200	99.68 183	98.12 282	99.54 322

LuminaMVS		99.07 146	98.92 156	99.50 183	98.87 389	99.12 202	99.92 343	99.22 332	97.45 199	99.82 224	99.98 251	96.29 233	99.85 241	99.71 169	99.05 204	99.52 324

CANet_DTU		99.02 159	98.90 160	99.41 204	99.88 124	98.71 242	100.00 1	99.29 283	98.84 68	100.00 1	100.00 1	94.02 291	100.00 1	98.08 325	99.96 126	99.52 324

DSMNet-mixed		95.18 405	95.21 403	95.08 443	96.03 486	90.21 476	99.65 409	93.64 527	92.91 441	98.34 373	97.40 487	90.05 382	95.51 501	91.02 459	97.86 298	99.51 326

fmvsm_s_conf0.1_n_2		98.95 176	98.69 189	99.73 143	99.61 224	99.74 112	100.00 1	99.23 327	98.95 42	99.97 145	100.00 1	90.92 357	99.97 150	100.00 1	99.58 188	99.47 327

sc_t1		92.52 437	91.34 442	96.09 432	97.80 447	89.86 478	98.61 501	99.12 414	77.73 507	96.09 449	99.79 337	68.64 496	98.94 372	96.94 369	87.31 461	99.46 328

Fast-Effi-MVS+-dtu		98.38 257	98.56 212	97.82 367	99.58 236	94.44 431	100.00 1	99.16 388	96.75 274	99.51 266	99.63 369	95.03 260	99.60 293	97.71 344	99.67 180	99.42 329

UGNet		98.41 255	98.11 268	99.31 239	99.54 250	98.55 255	99.18 466	100.00 1	98.64 91	99.79 232	99.04 425	87.61 418	100.00 1	99.30 259	99.89 150	99.40 330
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
Elysia		98.12 276	97.72 294	99.34 223	99.30 339	98.96 222	99.95 327	99.28 291	96.64 297	99.75 238	99.99 243	88.71 405	99.81 254	95.99 393	99.84 164	99.26 331

StellarMVS		98.12 276	97.72 294	99.34 223	99.30 339	98.96 222	99.95 327	99.28 291	96.64 297	99.75 238	99.99 243	88.71 405	99.81 254	95.99 393	99.84 164	99.26 331

VDDNet		96.39 367	95.55 389	98.90 278	99.27 343	97.45 346	99.15 474	99.92 39	91.28 453	99.98 139	100.00 1	73.55 485	100.00 1	99.85 131	96.98 325	99.24 333

UniMVSNet_ETH3D		95.28 403	94.41 410	97.89 364	98.91 383	95.14 403	99.13 476	99.35 246	92.11 448	97.17 427	99.66 358	70.28 494	99.36 341	97.88 335	95.18 358	99.16 334

baseline2		98.99 166	98.93 154	99.18 258	99.26 345	99.15 200	100.00 1	99.46 103	96.71 288	96.79 436	100.00 1	99.42 64	99.25 349	98.75 293	99.94 134	99.15 335

hse-mvs2		96.79 343	96.38 349	98.04 354	99.68 187	95.54 394	99.81 371	99.42 153	98.21 117	100.00 1	99.80 334	97.49 193	99.46 333	99.72 165	73.27 512	99.12 336

AUN-MVS		96.26 374	95.67 386	98.06 348	99.68 187	95.60 393	99.82 370	99.42 153	96.78 268	99.88 208	99.80 334	94.84 265	99.47 328	97.48 353	73.29 511	99.12 336

tt0805		96.52 356	96.23 356	97.40 378	99.30 339	93.55 443	99.32 447	99.45 111	96.75 274	97.88 400	99.99 243	79.99 466	99.59 295	97.39 358	95.98 338	99.06 338

test0.0.03 1		98.12 276	98.03 277	98.39 313	99.11 353	98.07 312	100.00 1	99.93 35	96.70 289	96.91 432	99.95 290	99.31 76	98.19 446	91.93 451	98.44 225	98.91 339

testgi		96.18 378	95.93 369	96.93 402	98.98 376	94.20 439	100.00 1	99.07 430	97.16 227	96.06 451	99.86 313	84.08 449	97.79 475	90.38 466	97.80 305	98.81 340

dtuonlycased		95.07 407	95.43 397	93.98 461	98.26 422	85.63 491	99.98 300	98.92 460	94.83 384	94.13 471	99.47 395	82.60 457	97.61 479	94.66 421	96.01 337	98.70 341

Effi-MVS+-dtu		98.51 247	98.86 162	97.47 377	99.77 169	94.21 438	100.00 1	98.94 457	97.61 177	99.91 200	98.75 450	95.89 237	99.51 321	99.36 249	99.48 191	98.68 342

DeepMVS_CX				89.98 478	98.90 384	71.46 516		99.18 375	97.61 177	96.92 430	99.83 321	86.07 433	99.83 247	96.02 392	97.65 315	98.65 343

COLMAP_ROB	97.10 7	98.29 266	98.17 265	98.65 294	99.94 111	97.39 348	99.30 451	99.40 206	95.64 359	97.75 407	100.00 1	92.69 329	99.95 183	98.89 284	99.92 141	98.62 344
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		98.27 269	98.31 252	98.14 338	99.59 231	95.92 386	100.00 1	99.36 235	98.48 98	99.21 295	100.00 1	89.27 395	99.94 196	99.76 154	99.17 197	98.56 345

XVG-OURS		98.30 263	98.36 248	98.13 341	99.58 236	95.91 387	100.00 1	99.36 235	98.69 86	99.23 294	100.00 1	91.20 348	99.92 206	99.34 255	97.82 303	98.56 345

HQP4-MVS										99.17 296			99.57 301			97.77 347

HQP-MVS		97.73 296	97.85 287	97.39 379	99.07 358	94.82 412	100.00 1	99.40 206	99.04 20	99.17 296	99.97 264	88.61 408	99.57 301	99.79 144	95.58 339	97.77 347

WBMVS		98.19 274	98.10 271	98.47 305	99.63 215	99.03 210	100.00 1	99.32 259	95.46 371	98.39 369	99.40 402	99.69 17	98.61 405	98.64 299	92.39 398	97.76 349

cl22		98.23 272	98.11 268	98.58 301	99.82 138	99.01 214	100.00 1	99.28 291	96.92 253	98.33 374	99.21 415	98.09 165	98.97 369	98.72 294	92.61 393	97.76 349

miper_ehance_all_eth		97.81 293	97.66 297	98.23 329	99.49 288	98.37 277	99.99 267	99.11 416	94.78 385	98.25 382	99.21 415	98.18 161	98.57 414	97.35 360	92.61 393	97.76 349

miper_enhance_ethall		98.33 261	98.27 254	98.51 303	99.66 205	99.04 209	100.00 1	99.22 332	97.53 188	98.51 360	99.38 403	99.49 46	98.75 391	98.02 329	92.61 393	97.76 349

cl____		97.54 309	97.32 310	98.18 334	99.47 296	98.14 307	100.00 1	99.10 419	94.16 411	97.60 414	99.63 369	97.52 192	98.65 398	96.47 383	91.97 406	97.76 349

DIV-MVS_self_test		97.52 312	97.35 309	98.05 352	99.46 304	98.11 308	100.00 1	99.10 419	94.21 407	97.62 412	99.63 369	97.65 184	98.29 438	96.47 383	91.98 405	97.76 349

miper_lstm_enhance		97.40 316	97.28 312	97.75 370	99.48 291	97.52 343	100.00 1	99.07 430	94.08 413	98.01 394	99.61 375	97.38 200	97.98 467	96.44 386	91.47 418	97.76 349

VPNet		96.41 363	95.76 379	98.33 319	98.61 401	98.30 290	99.48 430	99.45 111	96.98 245	98.87 326	99.88 310	81.57 460	98.93 373	99.22 266	87.82 456	97.76 349

VPA-MVSNet		97.03 335	96.43 347	98.82 284	98.64 400	99.32 177	99.38 442	99.47 85	96.73 281	98.91 323	98.94 438	87.00 425	99.40 339	99.23 264	89.59 437	97.76 349

HQP_MVS		97.71 298	97.82 289	97.37 380	99.00 372	94.80 415	100.00 1	99.40 206	99.00 32	99.08 308	99.97 264	88.58 410	99.55 310	99.79 144	95.57 343	97.76 349

plane_prior5								99.40 206					99.55 310	99.79 144	95.57 343	97.76 349

PatchmatchNet1														86.42 488	92.76 391	97.75 360
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
wanda-best-256-512		93.76 420	92.74 432	96.84 407	95.22 498	94.54 429	100.00 1	99.22 332	87.22 480	98.54 351	98.56 459	90.48 366	98.22 443	95.67 402	69.73 520	97.75 360

FE-blended-shiyan7		93.76 420	92.74 432	96.84 407	95.22 498	94.54 429	100.00 1	99.22 332	87.22 480	98.54 351	98.56 459	90.48 366	98.22 443	95.67 402	69.73 520	97.75 360

blended_shiyan6		93.70 425	92.67 437	96.78 417	95.17 502	94.38 436	100.00 1	99.22 332	87.03 485	98.54 351	98.56 459	90.14 375	98.22 443	95.62 406	69.73 520	97.75 360

usedtu_blend_shiyan5		92.75 435	91.39 441	96.82 413	95.22 498	94.40 433	99.05 487	98.64 475	75.98 514	98.54 351	98.56 459	90.48 366	98.31 434	96.31 388	69.73 520	97.75 360

blend_shiyan4		95.76 394	95.40 400	96.82 413	95.50 496	94.40 433	100.00 1	99.22 332	87.12 482	98.67 340	98.59 456	99.09 100	98.31 434	96.31 388	84.14 480	97.75 360

our_test_3		96.51 358	96.35 351	96.98 399	97.61 457	95.05 405	99.98 300	99.01 449	94.68 391	96.77 438	99.06 422	95.87 238	98.14 450	91.81 452	92.37 399	97.75 360

ppachtmachnet_test		96.17 380	95.89 370	97.02 396	97.61 457	95.24 401	99.99 267	99.24 322	93.31 434	96.71 439	99.62 373	94.34 282	98.07 460	89.87 470	92.30 401	97.75 360

c3_l		97.58 305	97.42 304	98.06 348	99.48 291	98.16 304	99.96 318	99.10 419	94.54 396	98.13 386	99.20 417	97.87 173	98.25 441	97.28 361	91.20 421	97.75 360

nrg030		97.64 299	97.27 314	98.75 291	98.34 412	99.53 145	100.00 1	99.22 332	96.21 342	98.27 380	99.95 290	94.40 279	98.98 367	99.23 264	89.78 436	97.75 360

v144192		96.40 366	95.81 374	98.17 336	97.89 443	98.11 308	99.99 267	99.06 438	93.39 431	98.75 335	99.09 420	90.43 371	98.66 396	93.10 442	90.55 428	97.75 360

v1921920		96.16 382	95.50 390	98.14 338	97.88 444	97.96 322	99.99 267	99.07 430	93.33 433	98.60 346	99.24 412	89.37 394	98.71 393	91.28 455	90.74 426	97.75 360

v1192		96.18 378	95.49 392	98.26 326	98.01 438	98.15 305	99.99 267	99.08 425	93.36 432	98.54 351	98.97 436	89.47 393	98.89 378	91.15 458	90.82 424	97.75 360

v148		96.29 372	95.84 373	97.63 371	97.74 450	96.53 380	100.00 1	99.07 430	93.52 426	98.01 394	99.42 400	91.22 346	98.60 408	96.37 387	87.22 464	97.75 360

v1240		95.96 390	95.25 401	98.07 344	97.91 442	97.87 331	99.96 318	99.07 430	93.24 436	98.64 344	98.96 437	88.98 401	98.61 405	89.58 475	90.92 423	97.75 360

v2v482		96.70 349	96.18 358	98.27 323	98.04 435	98.39 273	100.00 1	99.13 408	94.19 409	98.58 348	99.08 421	90.48 366	98.67 395	95.69 401	90.44 430	97.75 360

EI-MVSNet		97.98 283	97.93 282	98.16 337	99.11 353	97.84 332	99.74 392	99.29 283	94.39 402	98.65 342	100.00 1	97.21 203	98.88 381	97.62 350	95.31 349	97.75 360

MDA-MVSNet-bldmvs		91.65 447	89.94 456	96.79 416	96.72 477	96.70 374	99.42 439	98.94 457	88.89 470	66.97 536	98.37 473	81.43 461	95.91 496	89.24 478	89.46 441	97.75 360

UniMVSNet_NR-MVSNet		97.16 327	96.80 331	98.22 330	98.38 411	98.41 269	100.00 1	99.45 111	96.14 344	97.76 404	99.64 365	95.05 259	98.50 419	97.98 330	86.84 466	97.75 360

DU-MVS		96.93 340	96.49 344	98.22 330	98.31 416	98.41 269	100.00 1	99.37 229	96.41 325	97.76 404	99.65 361	92.14 338	98.50 419	97.98 330	86.84 466	97.75 360

UniMVSNet (Re)		97.29 323	96.85 330	98.59 299	98.49 407	99.13 201	100.00 1	99.42 153	96.52 314	98.24 384	98.90 441	94.93 262	98.89 378	97.54 351	87.61 457	97.75 360

NR-MVSNet		96.63 352	96.04 364	98.38 314	98.31 416	98.98 219	99.22 465	99.35 246	95.87 350	94.43 468	99.65 361	92.73 327	98.40 427	96.78 378	88.05 453	97.75 360

TranMVSNet+NR-MVSNet		96.45 362	96.01 365	97.79 369	98.00 439	97.62 341	100.00 1	99.35 246	95.98 347	97.31 422	99.64 365	90.09 380	98.00 465	96.89 373	86.80 469	97.75 360

Patchmtry		96.81 342	96.37 350	98.14 338	99.31 336	98.55 255	98.91 490	99.00 450	90.45 461	97.92 398	98.98 432	96.94 215	98.12 452	94.27 427	91.53 415	97.75 360

N_pmnet		91.88 444	93.37 421	87.40 487	97.24 474	66.33 531	99.90 352	91.05 531	89.77 468	95.65 455	98.58 458	90.05 382	98.11 454	85.39 490	92.72 392	97.75 360

XXY-MVS		97.14 329	96.63 337	98.67 293	98.65 399	98.92 225	99.54 425	99.29 283	95.57 363	97.63 410	99.83 321	87.79 417	99.35 343	98.39 312	92.95 388	97.75 360

IterMVS-LS		97.56 306	97.44 303	97.92 363	99.38 329	97.90 326	99.89 356	99.10 419	94.41 401	98.32 375	99.54 388	97.21 203	98.11 454	97.50 352	91.62 413	97.75 360
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		97.64 299	97.74 291	97.36 381	99.01 367	94.76 420	100.00 1	99.34 253	99.30 4	99.00 314	99.97 264	87.49 419	99.57 301	99.96 106	95.58 339	97.75 360
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SP-NN		83.33 476	82.73 478	85.13 494	98.98 376	65.96 532	97.92 513	95.13 523	56.43 528	83.71 511	90.52 519	58.27 507	91.69 519	71.99 523	91.66 412	97.74 388

gbinet_0.2-2-1-0.02		93.73 423	92.69 435	96.84 407	94.91 506	94.62 424	100.00 1	99.28 291	87.02 486	98.53 356	98.45 468	89.72 387	98.15 448	96.65 382	69.64 524	97.74 388

usedtu_dtu_shiyan1		97.34 319	96.97 324	98.43 309	97.82 445	98.91 226	100.00 1	99.29 283	94.70 389	98.46 364	98.89 442	93.95 294	98.64 400	95.86 397	93.75 377	97.74 388

blended_shiyan8		93.73 423	92.69 435	96.84 407	95.17 502	94.40 433	100.00 1	99.20 361	87.05 483	98.60 346	98.54 463	90.15 374	98.39 428	95.54 409	69.93 519	97.74 388

FE-MVSNET3		97.34 319	96.97 324	98.43 309	97.82 445	98.91 226	100.00 1	99.29 283	94.70 389	98.46 364	98.89 442	93.95 294	98.64 400	95.88 395	93.75 377	97.74 388

reproduce_monomvs		98.61 229	98.54 214	98.82 284	99.97 98	99.28 182	100.00 1	99.33 256	98.51 97	97.87 401	99.24 412	99.98 3	99.45 334	99.02 278	92.93 389	97.74 388

eth_miper_zixun_eth		97.47 313	97.28 312	98.06 348	99.41 320	97.94 324	99.62 415	99.08 425	94.46 400	98.19 385	99.56 384	96.91 217	98.50 419	96.78 378	91.49 416	97.74 388

FIs		97.95 286	97.73 293	98.62 296	98.53 406	99.24 189	100.00 1	99.43 134	96.74 277	97.87 401	99.82 325	95.27 250	98.89 378	98.78 290	93.07 386	97.74 388

v1144		96.51 358	95.97 368	98.13 341	97.98 440	98.04 316	99.99 267	99.08 425	93.51 427	98.62 345	98.98 432	90.98 356	98.62 404	93.79 434	90.79 425	97.74 388

YYNet1		92.44 438	90.92 448	97.03 395	96.20 482	97.06 365	99.99 267	99.14 401	88.21 475	67.93 533	98.43 471	88.63 407	96.28 492	90.64 460	89.08 445	97.74 388

MDA-MVSNet_test_wron		92.61 436	91.09 447	97.19 391	96.71 478	97.26 357	100.00 1	99.14 401	88.61 471	67.90 534	98.32 475	89.03 399	96.57 488	90.47 465	89.59 437	97.74 388

WR-MVS		97.09 330	96.64 336	98.46 306	98.43 409	99.09 203	99.97 310	99.33 256	95.62 361	97.76 404	99.67 356	91.17 349	98.56 416	98.49 308	89.28 443	97.74 388

VortexMVS		98.23 272	98.11 268	98.59 299	99.56 246	99.37 173	99.95 327	99.03 446	96.47 318	98.69 337	99.55 385	95.91 236	98.66 396	99.01 279	94.80 369	97.73 400

IterMVS-SCA-FT		96.72 348	96.42 348	97.62 373	99.40 325	96.83 370	99.99 267	99.14 401	94.65 393	97.55 417	99.72 343	89.65 390	98.31 434	95.62 406	92.05 403	97.73 400

Anonymous20231211		96.29 372	95.70 382	98.07 344	99.80 157	97.49 344	99.15 474	99.40 206	89.11 469	97.75 407	99.45 398	88.93 402	98.98 367	98.26 321	89.47 440	97.73 400

FC-MVSNet-test		97.84 291	97.63 299	98.45 307	98.30 418	99.05 208	100.00 1	99.43 134	96.63 301	97.61 413	99.82 325	95.19 255	98.57 414	98.64 299	93.05 387	97.73 400

MVSTER		98.58 234	98.52 217	98.77 290	99.65 207	99.68 124	100.00 1	99.29 283	95.63 360	98.65 342	99.80 334	99.78 9	98.88 381	98.59 305	95.31 349	97.73 400

FMVSNet3		97.30 322	96.95 326	98.37 315	99.65 207	99.25 187	99.71 401	99.28 291	94.23 405	98.53 356	98.91 440	93.30 309	98.11 454	95.31 412	93.60 380	97.73 400

FMVSNet2		96.22 376	95.60 388	98.06 348	99.53 254	98.33 285	99.45 434	99.27 306	93.71 418	98.03 391	98.84 445	84.23 446	98.10 458	93.97 432	93.40 383	97.73 400

SSC-MVS3.2		95.32 401	94.97 408	96.37 426	98.29 420	92.75 453	100.00 1	99.30 274	95.46 371	98.36 370	99.42 400	78.92 470	98.63 402	93.28 441	91.72 411	97.72 407

OPM-MVS		97.21 324	97.18 320	97.32 384	98.08 434	94.66 421	100.00 1	99.28 291	98.65 90	98.92 321	99.98 251	86.03 435	99.56 305	98.28 320	95.41 345	97.72 407
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs5		95.94 391	95.61 387	96.95 400	97.42 469	94.66 421	100.00 1	98.08 488	93.60 424	97.05 428	99.43 399	87.02 424	98.46 423	95.76 398	92.12 402	97.72 407

pm-mvs1		95.76 394	95.01 406	98.00 356	98.23 426	97.45 346	99.24 456	99.04 443	93.13 439	95.93 453	99.72 343	86.28 431	98.84 383	95.62 406	87.92 454	97.72 407

GBi-Net		96.07 386	95.80 376	96.89 404	99.53 254	94.87 409	99.18 466	99.27 306	93.71 418	98.53 356	98.81 447	84.23 446	98.07 460	95.31 412	93.60 380	97.72 407

test1		96.07 386	95.80 376	96.89 404	99.53 254	94.87 409	99.18 466	99.27 306	93.71 418	98.53 356	98.81 447	84.23 446	98.07 460	95.31 412	93.60 380	97.72 407

FMVSNet1		94.45 411	93.63 418	96.89 404	98.87 389	94.87 409	99.18 466	99.27 306	90.95 457	97.31 422	98.81 447	72.89 488	98.07 460	92.61 445	92.81 390	97.72 407

IterMVS		96.76 345	96.46 346	97.63 371	99.41 320	96.89 368	99.99 267	99.13 408	94.74 388	97.59 416	99.66 358	89.63 392	98.28 439	95.71 400	92.31 400	97.72 407
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		97.17 326	96.80 331	98.27 323	97.68 454	98.64 250	100.00 1	99.18 375	94.22 406	98.55 350	99.71 345	93.67 299	98.47 422	95.66 404	92.57 396	97.71 415

tt0320		92.36 439	91.28 443	95.58 439	98.30 418	90.65 471	98.69 498	99.14 401	76.73 508	96.07 450	99.50 392	72.28 490	98.39 428	93.29 440	87.56 458	97.70 416

v7n		96.06 388	95.42 399	97.99 358	97.58 460	97.35 351	99.86 362	99.11 416	92.81 445	97.91 399	99.49 393	90.99 355	98.92 374	92.51 447	88.49 451	97.70 416

PS-MVSNAJss		98.03 281	98.06 275	97.94 360	97.63 455	97.33 354	99.89 356	99.23 327	96.27 336	98.03 391	99.59 377	98.75 140	98.78 386	98.52 307	94.61 373	97.70 416

LPG-MVS_test		97.31 321	97.32 310	97.28 387	98.85 392	94.60 425	100.00 1	99.37 229	97.35 208	98.85 327	99.98 251	86.66 427	99.56 305	99.55 222	95.26 351	97.70 416

LGP-MVS_train				97.28 387	98.85 392	94.60 425		99.37 229	97.35 208	98.85 327	99.98 251	86.66 427	99.56 305	99.55 222	95.26 351	97.70 416

SixPastTwentyTwo		95.71 396	95.49 392	96.38 425	97.42 469	93.01 449	99.84 365	98.23 481	94.75 386	95.98 452	99.97 264	85.35 440	98.43 425	94.71 420	93.17 385	97.69 421

ACMM	97.17 6	97.37 317	97.40 306	97.29 386	99.01 367	94.64 423	100.00 1	99.25 316	98.07 131	98.44 366	99.98 251	87.38 421	99.55 310	99.25 261	95.19 357	97.69 421
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EU-MVSNet		96.63 352	96.53 341	96.94 401	97.59 459	96.87 369	99.76 389	99.47 85	96.35 331	96.85 434	99.78 338	92.57 332	96.27 493	95.33 411	91.08 422	97.68 423

K. test v3		95.46 400	95.14 404	96.40 423	97.53 462	93.40 446	99.99 267	99.23 327	95.49 369	92.70 479	99.73 342	84.26 445	98.12 452	93.94 433	93.38 384	97.68 423

lessismore_v0				96.05 433	97.55 461	91.80 462		99.22 332		91.87 480	99.91 305	83.50 452	98.68 394	92.48 448	90.42 432	97.68 423

XVG-ACMP-BASELINE		96.60 354	96.52 343	96.84 407	98.41 410	93.29 448	99.99 267	99.32 259	97.76 159	98.51 360	99.29 408	81.95 459	99.54 313	98.40 311	95.03 364	97.68 423

ACMP	97.00 8	97.19 325	97.16 322	97.27 389	98.97 378	94.58 428	100.00 1	99.32 259	97.97 139	97.45 419	99.98 251	85.79 437	99.56 305	99.70 173	95.24 354	97.67 427
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tt0320-xc		91.69 446	90.50 450	95.26 442	98.04 435	90.12 477	98.60 502	98.70 473	76.63 510	94.66 464	99.52 389	68.57 497	97.99 466	94.61 422	85.18 474	97.66 428

jajsoiax		97.07 332	96.79 333	97.89 364	97.28 473	97.12 362	99.95 327	99.19 365	96.55 307	97.31 422	99.69 351	87.35 423	98.91 375	98.70 295	95.12 362	97.66 428

PS-CasMVS		96.34 370	95.78 378	98.03 355	98.18 430	98.27 293	99.71 401	99.32 259	94.75 386	96.82 435	99.65 361	86.98 426	98.15 448	97.74 343	88.85 448	97.66 428

CP-MVSNet		96.73 346	96.25 355	98.18 334	98.21 427	98.67 246	99.77 387	99.32 259	95.06 379	97.20 426	99.65 361	90.10 379	98.19 446	98.06 328	88.90 447	97.66 428

ACMH	96.25 11	96.77 344	96.62 338	97.21 390	98.96 379	94.43 432	99.64 410	99.33 256	97.43 202	96.55 441	99.97 264	83.52 451	99.54 313	99.07 276	95.13 361	97.66 428
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		97.00 338	96.69 335	97.94 360	97.41 471	97.27 356	99.60 417	99.18 375	96.51 315	97.35 421	99.69 351	86.53 429	98.91 375	98.84 287	95.09 363	97.65 433

PEN-MVS		96.01 389	95.48 394	97.58 375	97.74 450	97.26 357	99.90 352	99.29 283	94.55 395	96.79 436	99.55 385	87.38 421	97.84 471	96.92 372	87.24 463	97.65 433

ACMH+	96.20 13	96.49 361	96.33 353	97.00 397	99.06 362	93.80 441	99.81 371	99.31 268	97.32 214	95.89 454	99.97 264	82.62 456	99.54 313	98.34 315	94.63 372	97.65 433

OurMVSNet-221017-0		96.14 384	95.98 367	96.62 419	97.49 465	93.44 445	99.92 343	98.16 483	95.86 352	97.65 409	99.95 290	85.71 438	98.78 386	94.93 418	94.18 376	97.64 436

pmmvs6		93.64 426	92.87 429	95.94 435	97.47 467	91.41 465	98.92 489	99.02 447	87.84 478	95.01 460	99.61 375	77.24 476	98.77 389	94.33 426	86.41 471	97.63 437

v10		96.14 384	95.50 390	98.07 344	98.19 429	97.96 322	99.83 367	99.07 430	92.10 449	98.07 388	98.94 438	91.07 351	98.61 405	92.41 450	89.82 435	97.63 437

v8		96.35 369	95.73 381	98.21 332	98.11 433	98.23 296	99.94 335	99.07 430	92.66 446	98.29 377	99.00 431	91.46 343	98.77 389	94.17 428	88.83 449	97.62 439

DTE-MVSNet		95.52 398	94.99 407	97.08 393	97.49 465	96.45 381	100.00 1	99.25 316	93.82 417	96.17 447	99.57 383	87.81 416	97.18 481	94.57 423	86.26 472	97.62 439

test_djsdf		97.55 308	97.38 307	98.07 344	97.50 463	97.99 318	100.00 1	99.13 408	95.46 371	98.47 363	99.85 318	92.01 341	98.59 410	98.63 301	95.36 347	97.62 439

MIMVSNet1		91.96 440	91.20 444	94.23 458	94.94 505	91.69 463	99.34 446	99.22 332	88.23 473	94.18 469	98.45 468	75.52 482	93.41 513	79.37 509	91.49 416	97.60 442

FMVSNet5		95.32 401	95.43 397	94.99 447	99.39 328	92.99 451	99.25 455	99.24 322	90.45 461	97.44 420	98.45 468	95.78 241	94.39 505	87.02 485	91.88 407	97.59 443

LTVRE_ROB	95.29 16	96.32 371	96.10 361	96.99 398	98.55 404	93.88 440	99.45 434	99.28 291	94.50 398	96.46 442	99.52 389	84.86 442	99.48 326	97.26 362	95.03 364	97.59 443
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)		94.78 408	93.72 416	97.93 362	98.34 412	97.88 329	99.23 463	97.98 493	91.60 451	94.55 465	99.71 345	87.89 415	98.36 430	89.30 477	84.92 475	97.56 445

Baseline_NR-MVSNet		96.16 382	95.70 382	97.56 376	98.28 421	96.79 372	100.00 1	97.86 497	91.93 450	97.63 410	99.47 395	92.14 338	98.35 431	97.13 364	86.83 468	97.54 446

KD-MVS_2432*1600		94.15 415	93.08 425	97.35 382	99.53 254	97.83 333	99.63 412	99.19 365	92.88 442	96.29 444	97.68 484	98.84 133	96.70 485	89.73 471	63.92 532	97.53 447

miper_refine_blended		94.15 415	93.08 425	97.35 382	99.53 254	97.83 333	99.63 412	99.19 365	92.88 442	96.29 444	97.68 484	98.84 133	96.70 485	89.73 471	63.92 532	97.53 447

usedtu_dtu_shiyan2		85.34 470	83.22 477	91.71 472	88.10 534	83.34 497	98.75 496	97.59 505	76.21 512	91.11 481	96.80 492	58.14 509	94.30 506	75.00 522	67.24 530	97.49 449

USDC		95.90 392	95.70 382	96.50 422	98.60 402	92.56 457	100.00 1	98.30 480	97.77 157	96.92 430	99.94 296	81.25 463	99.45 334	93.54 437	94.96 368	97.49 449

ITE_SJBPF				96.84 407	98.96 379	93.49 444		98.12 485	98.12 128	98.35 372	99.97 264	84.45 443	99.56 305	95.63 405	95.25 353	97.49 449

Anonymous20231206		93.45 428	93.17 423	94.30 456	95.00 504	89.69 480	99.98 300	98.43 478	93.30 435	94.50 467	98.59 456	90.52 364	95.73 499	77.46 517	90.73 427	97.48 452

WR-MVS_H		96.73 346	96.32 354	97.95 359	98.26 422	97.88 329	99.72 400	99.43 134	95.06 379	96.99 429	98.68 453	93.02 321	98.53 417	97.43 355	88.33 452	97.43 453

tfpnnormal		96.36 368	95.69 385	98.37 315	98.55 404	98.71 242	99.69 405	99.45 111	93.16 438	96.69 440	99.71 345	88.44 412	98.99 366	94.17 428	91.38 419	97.41 454

TinyColmap		95.50 399	95.12 405	96.64 418	98.69 398	93.00 450	99.40 440	97.75 500	96.40 326	96.14 448	99.87 311	79.47 467	99.50 324	93.62 436	94.72 371	97.40 455

UnsupCasMVSNet_eth		94.25 414	93.89 414	95.34 441	97.63 455	92.13 459	99.73 397	99.36 235	94.88 382	92.78 476	98.63 455	82.72 454	96.53 489	94.57 423	84.73 476	97.36 456

PVSNet_BlendedMVS		98.71 210	98.62 200	98.98 273	99.98 94	99.60 132	100.00 1	100.00 1	97.23 223	100.00 1	99.03 428	96.57 227	99.99 107	100.00 1	94.75 370	97.35 457

anonymousdsp		97.16 327	96.88 328	98.00 356	97.08 475	98.06 314	99.81 371	99.15 394	94.58 394	97.84 403	99.62 373	90.49 365	98.60 408	97.98 330	95.32 348	97.33 458

V42		96.65 351	96.16 360	98.11 343	98.17 431	98.23 296	99.99 267	99.09 424	93.97 414	98.74 336	99.05 424	91.09 350	98.82 384	95.46 410	89.90 434	97.27 459

LF4IMVS		96.19 377	96.18 358	96.23 430	98.26 422	92.09 460	100.00 1	97.89 496	97.82 152	97.94 396	99.87 311	82.71 455	99.38 340	97.41 356	93.71 379	97.20 460

new_pmnet		94.11 418	93.47 420	96.04 434	96.60 481	92.82 452	99.97 310	98.91 461	90.21 464	95.26 457	98.05 482	85.89 436	98.14 450	84.28 496	92.01 404	97.16 461

APD_test1		93.07 433	94.14 412	89.85 479	99.18 348	72.49 514	99.76 389	98.90 463	92.86 444	96.35 443	99.94 296	75.56 481	99.91 208	86.73 486	97.98 289	97.15 462

D2MVS		97.63 302	97.83 288	97.05 394	98.83 394	94.60 425	100.00 1	99.82 45	96.89 257	98.28 378	99.03 428	94.05 287	99.47 328	98.58 306	94.97 367	97.09 463

test20.03		93.11 431	92.85 430	93.88 462	95.19 501	91.83 461	100.00 1	98.87 464	93.68 421	92.76 477	98.88 444	89.20 398	92.71 515	77.88 515	89.19 444	97.09 463

ArgMatch-SfM		93.74 422	93.14 424	95.54 440	98.57 403	90.54 472	99.97 310	98.86 466	97.35 208	97.60 414	99.66 358	71.88 491	99.02 361	90.18 468	84.16 479	97.07 465

ArgMatch-Sym		94.50 410	94.12 413	95.63 438	98.16 432	90.84 470	100.00 1	99.00 450	97.42 203	97.22 425	99.76 341	73.91 484	99.05 359	91.22 456	90.43 431	97.01 466

KD-MVS_self_test		91.16 448	90.09 453	94.35 455	94.44 507	91.27 466	99.74 392	99.08 425	90.82 458	94.53 466	94.91 510	86.11 432	94.78 504	82.67 500	68.52 525	96.99 467

CL-MVSNet_self_test		91.07 450	90.35 452	93.24 465	93.27 510	89.16 482	99.55 423	99.25 316	92.34 447	95.23 458	97.05 490	88.86 404	93.59 511	80.67 506	66.95 531	96.96 468

test_method		91.04 451	91.10 446	90.85 475	98.34 412	77.63 506	100.00 1	98.93 459	76.69 509	96.25 446	98.52 465	70.44 493	97.98 467	89.02 480	91.74 409	96.92 469

pmmvs3		90.62 453	89.36 460	94.40 454	90.53 526	91.49 464	100.00 1	96.73 515	84.21 496	93.65 473	96.65 494	82.56 458	94.83 502	82.28 501	77.62 507	96.89 470

test_0402		94.35 412	93.70 417	96.32 428	97.92 441	93.60 442	99.61 416	98.85 467	88.19 476	94.68 463	99.48 394	80.01 465	98.58 413	89.39 476	95.15 360	96.77 471

DenseAffine		90.43 454	89.28 461	93.87 463	97.71 453	86.21 489	99.13 476	98.10 487	87.86 477	90.15 488	98.43 471	60.76 504	98.65 398	84.48 495	86.90 465	96.74 472

LoFTR		88.61 461	87.13 467	93.06 466	96.18 483	83.87 495	99.48 430	97.21 508	86.37 490	82.32 514	96.66 493	58.07 510	98.59 410	81.76 503	86.15 473	96.72 473

EG-PatchMatch MVS		92.94 434	92.49 438	94.29 457	95.87 489	87.07 488	99.07 485	98.11 486	93.19 437	88.98 491	98.66 454	70.89 492	99.08 357	92.43 449	95.21 356	96.72 473

test_fmvs2		95.17 406	95.23 402	95.01 445	98.95 381	88.99 483	99.99 267	97.77 499	97.79 155	98.58 348	99.70 348	73.36 486	99.34 344	95.88 395	95.03 364	96.70 475

Anonymous20240521		93.29 429	92.76 431	94.90 450	95.64 494	91.27 466	99.97 310	98.82 468	87.04 484	94.71 462	98.19 477	83.86 450	96.80 484	84.04 497	92.56 397	96.64 476

TDRefinement		91.93 441	90.48 451	96.27 429	81.60 547	92.65 456	99.10 480	97.61 504	93.96 415	93.77 472	99.85 318	80.03 464	99.53 318	97.82 341	70.59 518	96.63 477

MatchFormer		86.71 469	84.75 475	92.57 471	96.14 485	82.52 500	99.27 452	97.86 497	80.17 504	78.74 517	96.16 497	54.81 516	98.63 402	75.87 520	83.75 484	96.56 478

RoMa-SfM		90.39 455	89.63 457	92.66 470	97.47 467	83.18 498	98.81 493	98.21 482	85.44 493	89.21 490	99.46 397	63.72 501	98.30 437	87.11 484	87.25 462	96.51 479

mmtdpeth		94.58 409	94.18 411	95.81 436	98.82 396	91.09 468	99.99 267	98.61 476	96.38 327	100.00 1	97.23 488	76.52 478	99.85 241	99.82 140	80.22 500	96.48 480

MS-PatchMatch		95.66 397	95.87 372	95.05 444	97.80 447	89.25 481	98.88 491	99.30 274	96.35 331	96.86 433	99.01 430	81.35 462	99.43 336	93.30 439	99.98 118	96.46 481

MVP-Stereo		96.51 358	96.48 345	96.60 420	95.65 493	94.25 437	98.84 492	98.16 483	95.85 354	95.23 458	99.04 425	92.54 333	99.13 355	92.98 443	99.98 118	96.43 482
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mvs5depth		93.81 419	93.00 427	96.23 430	94.25 508	93.33 447	97.43 520	98.07 489	93.47 428	94.15 470	99.58 379	77.52 474	98.97 369	93.64 435	88.92 446	96.39 483

ttmdpeth		96.24 375	95.88 371	97.32 384	97.80 447	96.61 378	99.95 327	98.77 471	97.80 154	93.42 474	99.28 409	86.42 430	99.01 363	97.63 347	91.84 408	96.33 484

SP-MNN		81.80 480	81.08 484	83.94 500	98.26 422	64.81 535	98.20 509	93.56 528	55.15 529	77.43 519	90.43 521	56.33 514	90.69 525	70.11 527	90.27 433	96.32 485

SP-SuperGlue		82.71 478	81.92 480	85.07 495	98.02 437	67.96 527	98.10 510	95.26 522	57.79 525	82.47 513	90.37 522	57.02 511	91.04 522	70.34 526	87.92 454	96.23 486

SP-LightGlue		82.73 477	81.92 480	85.19 493	97.73 452	68.40 525	98.05 511	94.51 525	56.95 527	82.72 512	90.14 525	58.20 508	90.97 523	71.57 524	87.38 460	96.20 487

UnsupCasMVSNet_bld		89.50 457	88.00 464	93.99 460	95.30 497	88.86 484	98.52 504	99.28 291	85.50 492	87.80 497	94.11 512	61.63 502	96.96 483	90.63 461	79.26 502	96.15 488

OpenMVS_ROB	88.34 20	91.89 443	91.12 445	94.19 459	95.55 495	87.63 486	99.26 454	98.03 490	86.61 489	90.65 487	96.82 491	70.14 495	98.78 386	86.54 487	96.50 336	96.15 488

MVStest1		94.27 413	93.30 422	97.19 391	98.83 394	97.18 360	99.93 341	98.79 470	86.80 487	84.88 508	99.04 425	94.32 283	98.25 441	90.55 463	86.57 470	96.12 490

ambc				88.45 484	86.84 537	70.76 517	97.79 516	98.02 492		90.91 484	95.14 505	38.69 531	98.51 418	94.97 417	84.23 478	96.09 491

MASt3R-SfM		91.92 442	92.47 439	90.28 477	96.64 480	75.61 510	99.63 412	98.31 479	95.70 358	95.42 456	98.84 445	67.34 498	99.22 350	89.92 469	90.47 429	96.01 492

PM-MVS		88.39 462	87.41 466	91.31 474	91.73 518	82.02 502	99.79 377	96.62 516	91.06 456	90.71 486	95.73 500	48.60 524	95.96 495	90.56 462	81.91 494	95.97 493

DKM		88.67 460	87.74 465	91.44 473	97.38 472	82.60 499	98.95 488	97.94 495	87.54 479	87.00 499	98.48 466	55.08 515	95.81 498	86.05 489	81.29 499	95.91 494

pmmvs-eth3d		91.73 445	90.67 449	94.92 449	91.63 519	92.71 455	99.90 352	98.54 477	91.19 454	88.08 495	95.50 501	79.31 469	96.13 494	90.55 463	81.32 498	95.91 494

test_vis1_rt		93.10 432	92.93 428	93.58 464	99.63 215	85.07 492	99.99 267	93.71 526	97.49 194	90.96 483	97.10 489	60.40 505	99.95 183	99.24 263	97.90 296	95.72 496

EGC-MVSNET		79.46 486	74.04 496	95.72 437	96.00 487	92.73 454	99.09 482	99.04 443	5.08 554	16.72 554	98.71 451	73.03 487	98.74 392	82.05 502	96.64 333	95.69 497

new-patchmatchnet		90.30 456	89.46 459	92.84 469	90.77 522	88.55 485	99.83 367	98.80 469	90.07 466	87.86 496	95.00 508	78.77 471	94.30 506	84.86 494	79.15 503	95.68 498

FE-MVSNET2		91.15 449	90.00 455	94.58 452	90.74 523	92.52 458	99.56 421	98.87 464	90.82 458	88.96 492	95.40 504	76.26 480	95.56 500	87.84 482	81.59 496	95.66 499

SP-DiffGlue		85.17 471	85.16 472	85.22 492	93.54 509	69.16 521	97.83 515	95.33 521	60.61 523	86.04 502	92.86 516	61.04 503	90.90 524	89.62 474	89.57 439	95.59 500

mvsany_test3		89.36 459	88.96 462	90.56 476	91.95 516	78.97 504	99.74 392	96.59 518	96.84 261	89.25 489	96.07 498	52.59 521	97.11 482	95.17 415	82.44 491	95.58 501

FE-MVSNET		89.50 457	88.33 463	93.00 468	88.89 530	90.24 475	99.96 318	96.86 513	88.23 473	88.46 493	95.47 502	77.03 477	93.37 514	78.54 512	81.56 497	95.39 502

DKM-HiRes		87.00 467	86.38 470	88.84 483	96.71 478	79.05 503	98.73 497	97.57 506	84.56 495	84.00 510	98.23 476	52.90 520	92.48 516	84.95 493	79.77 501	95.00 503

PMatch-SfM		81.57 481	79.80 485	86.88 488	92.36 513	73.86 512	97.50 519	92.66 530	80.39 503	73.10 526	96.35 495	33.54 540	91.86 518	81.28 504	71.01 517	94.92 504

RoMa-HiRes		87.37 465	86.72 469	89.32 481	95.81 490	78.25 505	98.63 500	97.01 510	82.18 500	86.32 501	99.25 411	56.48 513	94.79 503	83.17 498	81.62 495	94.91 505

test_f		86.87 468	86.06 471	89.28 482	91.45 521	76.37 508	99.87 361	97.11 509	91.10 455	88.46 493	93.05 515	38.31 532	96.66 487	91.77 453	83.46 488	94.82 506

PMatch-Up-SfM		79.27 488	77.62 491	84.22 499	90.58 525	69.08 522	96.98 522	90.47 533	76.44 511	71.47 529	96.27 496	30.15 545	88.77 527	78.74 510	67.46 527	94.81 507

ELoFTR		83.63 475	81.67 482	89.53 480	92.30 514	75.98 509	98.27 506	96.74 514	83.38 498	74.05 524	95.78 499	43.66 529	98.11 454	78.01 513	72.80 514	94.48 508

test_fmvs3		87.19 466	87.02 468	87.71 486	92.69 512	76.64 507	99.96 318	97.27 507	93.55 425	90.82 485	94.03 513	38.00 533	92.19 517	93.49 438	83.35 489	94.32 509

test123		79.44 487	79.23 488	80.05 510	80.03 549	71.72 515	100.00 1	77.93 548	62.52 520	94.81 461	99.69 351	78.21 472	74.53 546	92.57 446	27.33 548	93.90 510

LCM-MVSNet		79.01 490	76.93 493	85.27 491	78.28 550	68.01 526	96.57 524	98.03 490	55.10 530	82.03 515	93.27 514	31.99 544	93.95 508	82.72 499	74.37 510	93.84 511

CMPMVS	66.12 22	90.65 452	92.04 440	86.46 490	96.18 483	66.87 529	98.03 512	99.38 225	83.38 498	85.49 505	99.55 385	77.59 473	98.80 385	94.44 425	94.31 375	93.72 512
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WB-MVS		88.24 463	90.09 453	82.68 505	91.56 520	69.51 519	100.00 1	98.73 472	90.72 460	87.29 498	98.12 478	92.87 323	85.01 534	62.19 532	89.34 442	93.54 513

WB-MVSnew		97.02 337	97.24 316	96.37 426	99.44 314	97.36 350	100.00 1	99.43 134	96.12 345	99.35 286	99.89 308	93.60 302	98.42 426	88.91 481	98.39 231	93.33 514

testf1		84.40 473	84.79 473	83.23 503	95.71 491	58.71 540	98.79 494	97.75 500	81.58 501	84.94 506	98.07 480	45.33 527	97.73 476	77.09 518	83.85 481	93.24 515

APD_test2		84.40 473	84.79 473	83.23 503	95.71 491	58.71 540	98.79 494	97.75 500	81.58 501	84.94 506	98.07 480	45.33 527	97.73 476	77.09 518	83.85 481	93.24 515

SSC-MVS		87.61 464	89.47 458	82.04 506	90.63 524	68.77 523	99.99 267	98.66 474	90.34 463	86.70 500	98.08 479	92.72 328	84.12 535	59.41 535	88.71 450	93.22 517

ALIKED-NN		82.28 479	81.49 483	84.63 497	99.44 314	67.26 528	97.36 521	90.47 533	62.09 521	81.26 516	95.45 503	59.17 506	93.89 509	63.93 531	84.26 477	92.75 518

PMMVS2		79.15 489	77.28 492	84.76 496	82.34 544	72.66 513	99.70 403	95.11 524	71.68 517	84.78 509	90.87 518	32.05 543	89.99 526	75.53 521	63.45 534	91.64 519

ALIKED-MNN		79.54 485	78.11 490	83.80 502	99.29 342	66.55 530	97.70 517	90.37 535	57.60 526	74.96 523	92.30 517	53.12 519	93.57 512	58.80 536	78.89 505	91.27 520

tmp_tt		75.80 493	74.26 495	80.43 508	52.91 559	53.67 542	87.42 542	97.98 493	61.80 522	67.04 535	100.00 1	76.43 479	96.40 490	96.47 383	28.26 547	91.23 521

testmvs		80.17 483	81.95 479	74.80 512	58.54 557	59.58 539	100.00 1	87.14 538	76.09 513	99.61 259	100.00 1	67.06 499	74.19 547	98.84 287	50.30 536	90.64 522

ALIKED-LG		80.86 482	79.70 486	84.33 498	98.33 415	69.33 520	97.59 518	90.14 536	65.38 519	76.03 521	94.87 511	54.78 517	93.65 510	57.59 537	82.61 490	90.01 523

VLMVS		69.79 499	73.02 499	60.12 528	72.70 555	33.43 560	87.87 541	83.71 541	40.13 545	86.04 502	98.98 432	34.57 536	58.39 554	85.00 492	68.17 526	88.54 524

GLUNet-SfM		70.22 497	66.87 503	80.24 509	84.13 541	61.64 538	96.72 523	82.62 542	51.83 531	60.24 540	88.02 530	36.12 535	91.44 521	67.32 529	34.86 545	87.65 525

Gipuma		84.73 472	83.50 476	88.40 485	97.50 463	82.21 501	88.87 537	99.05 440	65.81 518	85.71 504	90.49 520	53.70 518	96.31 491	78.64 511	91.74 409	86.67 526
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		77.92 491	79.45 487	73.34 516	76.87 551	46.81 545	98.24 507	99.05 440	59.89 524	73.55 525	98.34 474	36.81 534	86.55 528	80.96 505	91.35 420	86.65 527

ANet_high		66.05 502	63.44 508	73.88 515	61.14 556	63.45 536	95.68 528	87.18 537	79.93 505	47.35 543	80.68 548	22.35 553	72.33 549	61.24 533	35.42 543	85.88 528

XFeat-NN		75.54 494	76.00 494	74.19 514	93.25 511	52.63 544	95.93 526	81.98 544	46.32 536	75.32 522	90.27 524	56.80 512	85.05 533	71.26 525	72.85 513	84.87 529

PDCNetPlus		75.87 492	73.92 497	81.72 507	89.55 529	74.48 511	98.59 503	62.34 553	72.19 516	76.04 520	95.03 507	47.66 525	86.31 530	77.97 514	45.88 538	84.35 530

XFeat-MNN		73.39 495	73.10 498	74.25 513	89.63 528	53.35 543	96.25 525	84.01 540	43.66 537	69.74 530	89.91 526	52.56 522	85.32 531	64.72 530	67.44 528	84.08 531

test_vis3_rt		79.61 484	78.19 489	83.86 501	88.68 533	69.56 518	99.81 371	82.19 543	86.78 488	68.57 532	84.51 535	25.06 550	98.26 440	89.18 479	78.94 504	83.75 532

MVE	68.59 21	67.22 501	64.68 507	74.84 511	74.67 554	62.32 537	95.84 527	90.87 532	50.98 532	58.72 541	81.05 546	12.20 558	78.95 540	61.06 534	56.75 535	83.24 533
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	60.66 23	65.98 503	65.05 505	68.75 519	55.06 558	38.40 557	88.19 540	96.98 511	48.30 535	44.82 546	88.52 528	12.22 557	86.49 529	67.58 528	83.79 483	81.35 534
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		69.88 498	69.09 501	72.24 518	84.70 540	65.82 533	99.96 318	87.08 539	49.82 534	71.51 528	84.74 534	49.30 523	75.32 545	50.97 539	43.71 540	75.59 535

E-PMN		70.72 496	70.06 500	72.69 517	83.92 542	65.48 534	99.95 327	92.72 529	49.88 533	72.30 527	86.26 532	47.17 526	77.43 543	53.83 538	44.49 539	75.17 536

SIFT-MNN		64.77 504	65.11 504	63.77 521	92.18 515	44.02 548	91.93 531	78.84 546	41.80 540	61.69 538	84.03 536	33.92 539	81.69 537	29.20 546	72.39 515	65.59 537

SIFT-NN		67.52 500	68.28 502	65.25 520	96.00 487	45.92 546	93.38 529	80.01 545	43.05 538	69.06 531	85.13 533	39.13 530	85.13 532	32.15 541	76.58 508	64.70 538

SIFT-NN-CMatch		60.63 506	60.17 509	62.02 523	86.89 536	43.32 550	90.70 534	71.03 549	41.60 542	61.16 539	83.16 539	33.45 541	78.31 541	30.28 543	43.26 541	64.44 539

SIFT-NN-NCMNet		64.49 505	64.92 506	63.20 522	88.84 531	44.41 547	92.37 530	78.67 547	41.90 539	62.62 537	83.27 538	34.31 537	81.88 536	30.88 542	71.40 516	63.31 540

SIFT-NN-PointCN		57.34 509	56.95 512	58.53 529	82.11 545	41.35 555	90.36 535	61.72 554	40.01 546	54.78 542	80.99 547	32.74 542	72.39 548	29.64 545	40.16 542	61.83 541

SIFT-NCM-Cal		59.75 507	59.15 510	61.53 524	90.12 527	43.18 551	91.26 532	70.04 551	40.34 544	38.39 549	81.51 545	27.19 546	79.90 538	26.25 551	67.30 529	61.50 542

SIFT-NN-UMatch		59.27 508	58.65 511	61.13 525	83.27 543	43.66 549	91.00 533	70.69 550	41.78 541	44.38 547	82.21 543	34.17 538	79.10 539	30.07 544	50.25 537	60.64 543

SIFT-ConvMatch		56.83 510	55.72 513	60.16 526	88.80 532	43.02 552	88.55 538	64.15 552	40.75 543	45.84 544	83.12 540	27.00 547	77.01 544	28.36 547	34.89 544	60.45 544

SIFT-UMatch		55.48 511	53.92 514	60.16 526	85.84 539	42.45 553	89.09 536	61.68 555	39.97 547	41.34 548	82.92 541	26.90 548	77.66 542	27.36 548	30.17 546	60.37 545

SIFT-CM-Cal		53.99 512	52.89 515	57.28 530	87.31 535	41.77 554	86.71 544	54.86 557	39.82 549	45.09 545	82.10 544	25.89 549	71.72 550	27.27 549	26.97 549	58.36 546

SIFT-PointCN		49.44 514	48.89 517	51.12 532	81.24 548	34.25 558	87.16 543	56.78 556	36.95 550	33.84 550	76.32 550	20.17 554	61.65 553	21.99 553	25.53 550	57.46 547

SIFT-UM-Cal		51.73 513	50.25 516	56.15 531	85.87 538	41.10 556	88.21 539	50.44 558	39.83 548	33.54 551	82.23 542	23.59 551	71.25 551	27.05 550	21.52 551	56.10 548

SIFT-PCN-Cal		47.97 515	47.56 518	49.20 533	81.85 546	33.99 559	86.00 545	49.11 559	36.44 551	32.13 552	77.60 549	22.63 552	62.04 552	23.11 552	19.17 552	51.55 549

SIFT-NCMNet		41.74 516	41.17 519	43.45 534	76.48 552	31.10 562	80.74 546	30.14 560	35.07 552	28.33 553	71.87 551	16.32 555	52.56 555	19.72 554	11.82 554	46.67 550

wuyk23d		28.28 517	29.73 521	23.92 535	75.89 553	32.61 561	66.50 547	12.88 561	16.09 553	14.59 555	16.59 553	12.35 556	32.36 556	39.36 540	13.36 553	6.79 551

mmdepth		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

monomultidepth		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

test_blank		0.07 521	0.09 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.79 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

uanet_test		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

DCPMVS		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

cdsmvs_eth3d_5k		24.41 518	32.55 520	0.00 536	0.00 560	0.00 563	0.00 548	99.39 222	0.00 555	0.00 556	100.00 1	93.55 303	0.00 557	0.00 555	0.00 555	0.00 552

pcd_1.5k_mvsjas		8.24 520	10.99 523	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	98.75 140	0.00 557	0.00 555	0.00 555	0.00 552

sosnet-low-res		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

sosnet		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

uncertanet		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

Regformer		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

ab-mvs-re		8.33 519	11.11 522	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	100.00 1	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

uanet		0.01 522	0.02 525	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.14 555	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

PatchmatchNet2					0.00 560	95.13 404	99.92 343	99.16 388	89.91 467
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													98.34 432
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					100.00 1	99.98 18		99.69 67		100.00 1		99.45 53	100.00 1	100.00 1	100.00 1

WAC-MVS						97.98 319								95.74 399

FOURS1					100.00 1	99.97 27	100.00 1	99.42 153	98.52 96	100.00 1

test_one_0601					100.00 1	99.99 6		99.42 153	98.72 85	100.00 1	100.00 1	99.60 21

eth-test2					0.00 560

eth-test					0.00 560

ZD-MVS					100.00 1	99.98 18		99.80 48	97.31 216	100.00 1	100.00 1	99.32 74	99.99 107	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 6		99.42 153	99.03 25	100.00 1	100.00 1	99.50 43	100.00 1

9.14			99.57 55		99.99 53		100.00 1	99.42 153	97.54 185	100.00 1	100.00 1	99.15 96	99.99 107	100.00 1	100.00 1

save fliter					99.99 53	99.93 53	100.00 1	99.42 153	98.93 49

test0726					100.00 1	99.99 6	100.00 1	99.42 153	99.04 20	100.00 1	100.00 1	99.53 35

test_part2					100.00 1	99.99 6				100.00 1

sam_mvs												99.33 71

MTGPA								99.42 153

test_post1							99.32 447				88.24 529	99.33 71	99.59 295	98.31 316

test_post											89.05 527	99.49 46	99.59 295

patchmatchnet-post											97.79 483	99.41 66	99.54 313

MTMP							100.00 1	99.18 375

gm-plane-assit					99.52 268	97.26 357			95.86 352		100.00 1		99.43 336	98.76 292

TEST9					100.00 1	99.95 38	100.00 1	99.42 153	97.65 168	100.00 1	100.00 1	99.53 35	99.97 150

test_8					100.00 1	99.91 64	100.00 1	99.42 153	97.70 162	100.00 1	100.00 1	99.51 39	99.98 141

agg_prior					100.00 1	99.88 85		99.42 153		100.00 1			99.97 150

test_prior4						99.93 53	100.00 1

test_prior2							100.00 1		98.82 72	100.00 1	100.00 1	99.47 51		100.00 1	100.00 1

旧先验2							100.00 1		98.11 129	100.00 1			100.00 1	99.67 187

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 359

segment_acmp												99.55 31

testdata1							100.00 1		98.77 84

plane_prior7					99.00 372	94.78 419

plane_prior6					99.06 362	94.80 415						88.58 410

plane_prior4											99.97 264

plane_prior3						94.79 418			99.03 25	99.08 308

plane_prior2							100.00 1		99.00 32

plane_prior1					99.02 366

plane_prior						94.80 415	100.00 1		99.03 25						95.58 339

n2								0.00 562

nn								0.00 562

door-mid								96.32 519

test11								99.42 153

door								96.13 520

HQP5-MVS						94.82 412

HQP-NCC					99.07 358		100.00 1		99.04 20	99.17 296

ACMP_Plane					99.07 358		100.00 1		99.04 20	99.17 296

BP-MVS														99.79 144

HQP3-MVS								99.40 206							95.58 339

HQP2-MVS												88.61 408

NP-MVS					99.07 358	94.81 414					99.97 264

MDTV_nov1_ep13			98.94 152		99.53 254	98.36 280	99.39 441	99.46 103	96.54 309	99.99 129	99.63 369	98.92 127	99.86 234	98.30 319	98.71 213

ACMMP++_ref															94.58 374

ACMMP++															95.17 359

Test By Simon												99.10 99

High-resolution multi-view benchmark