  On-board 3D object detection in autonomous vehicles often relies on geometry
information captured by LiDAR devices. Albeit image features are typically
preferred for detection, numerous approaches take only spatial data as input.
Exploiting this information in inference usually involves the use of compact
representations such as the Bird's Eye View (BEV) projection, which entails a
loss of information and thus hinders the joint inference of all the parameters
of the objects' 3D boxes. In this paper, we present a fully end-to-end 3D
object detection framework that can infer oriented 3D boxes solely from BEV
images by using a two-stage object detector and ad-hoc regression branches,
eliminating the need for a post-processing stage. The method outperforms its
predecessor (BirdNet) by a large margin and obtains state-of-the-art results on
the KITTI 3D Object Detection Benchmark for all the categories in evaluation.
