  Depth cues are known to be useful for visual perception. However, direct
measurement of depth is often impracticable. Fortunately, though, modern
learning-based methods offer promising depth maps by inference in the wild. In
this work, we adapt such depth inference models for object segmentation using
the objects' "pop-out" prior in 3D. The "pop-out" is a simple composition prior
that assumes objects reside on the background surface. Such compositional prior
allows us to reason about objects in the 3D space. More specifically, we adapt
the inferred depth maps such that objects can be localized using only 3D
information. Such separation, however, requires knowledge about contact surface
which we learn using the weak supervision of the segmentation mask. Our
intermediate representation of contact surface, and thereby reasoning about
objects purely in 3D, allows us to better transfer the depth knowledge into
semantics. The proposed adaptation method uses only the depth model without
needing the source data used for training, making the learning process
efficient and practical. Our experiments on eight datasets of two challenging
tasks, namely camouflaged object detection and salient object detection,
consistently demonstrate the benefit of our method in terms of both performance
and generalizability.
