Abstract
Full resolution depth is required in many realworld engineering
applications. However, exist depth sensorsonly offer sparse depth sample
points with limited resolutionand noise, e.g., LiDARs. We here propose a
deep learningbased full resolution depth recovery method from
monocularimages and corresponding sparse depth measurements of
targetenvironment. The novelty of our idea is that the structure
similarinformation between the RGB image and depth image is used
torefine the dense depth estimation result. This important
similarstructure information can be found using a correlation layerin
the regression neural network. We show that the proposedmethod can
achieve higher estimation accuracy compared tothe state of the art
methods. The experiments conducted on theNYU Depth V2 prove the novelty
of our idea.