  Estimating a semantically segmented bird's-eye-view (BEV) map from a single
image has become a popular technique for autonomous control and navigation.
However, they show an increase in localization error with distance from the
camera. While such an increase in error is entirely expected - localization is
harder at distance - much of the drop in performance can be attributed to the
cues used by current texture-based models, in particular, they make heavy use
of object-ground intersections (such as shadows), which become increasingly
sparse and uncertain for distant objects. In this work, we address these
shortcomings in BEV-mapping by learning the spatial relationship between
objects in a scene. We propose a graph neural network which predicts BEV
objects from a monocular image by spatially reasoning about an object within
the context of other objects. Our approach sets a new state-of-the-art in BEV
estimation from monocular images across three large-scale datasets, including a
50% relative improvement for objects on nuScenes.
