# BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View Source: [BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:08:20.129Z Authors: Alejandro Barrera, Carlos Guindel, Jorge Beltr\\'an, Fernando Garc\\'ia Published: 2020-03-09T15:08:40.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()