  Monocular depth estimation has been a popular area of research for several
years, especially since self-supervised networks have shown increasingly good
results in bridging the gap with supervised and stereo methods. However, these
approaches focus their interest on dense 3D reconstruction and sometimes on
tiny details that are superfluous for autonomous navigation. In this paper, we
propose to address this issue by estimating the navigation map under a quadtree
representation. The objective is to create an adaptive depth map prediction
that only extract details that are essential for the obstacle avoidance. Other
3D space which leaves large room for navigation will be provided with
approximate distance. Experiment on KITTI dataset shows that our method can
significantly reduce the number of output information without major loss of
accuracy.
