  Bird's-Eye-View (BEV) semantic maps have become an essential component of
automated driving pipelines due to the rich representation they provide for
decision-making tasks. However, existing approaches for generating these maps
still follow a fully supervised training paradigm and hence rely on large
amounts of annotated BEV data. In this work, we address this limitation by
proposing the first self-supervised approach for generating a BEV semantic map
using a single monocular image from the frontal view (FV). During training, we
overcome the need for BEV ground truth annotations by leveraging the more
easily available FV semantic annotations of video sequences. Thus, we propose
the SkyEye architecture that learns based on two modes of self-supervision,
namely, implicit supervision and explicit supervision. Implicit supervision
trains the model by enforcing spatial consistency of the scene over time based
on FV semantic sequences, while explicit supervision exploits BEV pseudolabels
generated from FV semantic annotations and self-supervised depth estimates.
Extensive evaluations on the KITTI-360 dataset demonstrate that our
self-supervised approach performs on par with the state-of-the-art fully
supervised methods and achieves competitive results using only 1% of direct
supervision in the BEV compared to fully supervised approaches. Finally, we
publicly release both our code and the BEV datasets generated from the
KITTI-360 and Waymo datasets.
