Table 3 Quantitative evaluation of the NYU depth V2 dataset with various monocular depth estimation methods.

From: LapUNet: a novel approach to monocular depth estimation using dynamic laplacian residual U-shape networks

Method

Abs Rel

Log 10

RMSE

δ < 1.25

δ < 1.252

δ < 1.253

Low is better

High is better

Eigen et al.5

0.215

0.095

0.641

0.611

0.887

0.971

Li et al.49

0.232

0.094

0.821

0.621

0.886

0.968

Liu et al.27

0.213

0.087

0.759

0.650

0.906

0.976

Laina et al.26

0.127

0.055

0.569

0.811

0.953

0.988

Xu et al.9

0.125

0.057

0.593

0.806

0.952

0.986

Fu et al.29

0.115

0.051

0.509

0.828

0.965

0.992

Lee et al.48

0.111

0.048

0.399

0.880

0.977

0.994

Song et al.30

0.110

0.047

0.393

0.885

0.979

0.995

Ours

0.089

0.035

0.406

0.923

0.987

0.995