  Monocular depth estimation enables 3D perception from a single 2D image, thus
attracting much research attention for years. Almost all methods treat
foreground and background regions ("things and stuff") in an image equally.
However, not all pixels are equal. Depth of foreground objects plays a crucial
role in 3D object recognition and localization. To date how to boost the depth
prediction accuracy of foreground objects is rarely discussed. In this paper,
we first analyse the data distributions and interaction of foreground and
background, then propose the foreground-background separated monocular depth
estimation (ForeSeE) method, to estimate the foreground depth and background
depth using separate optimization objectives and depth decoders. Our method
significantly improves the depth estimation performance on foreground objects.
Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new
state-of-the-art results among other monocular methods. Code will be available
at: https://github.com/WXinlong/ForeSeE.
