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

BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View