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

arXiv: https://arxiv.org/abs/2003.04188 · HTML: https://mlsanity.com/paper/2003.04188/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation](https://mlsanity.com/paper/2211.15656/) (2022)
- [From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder](https://mlsanity.com/paper/2108.03648/) (2021)
- [M-BEV: Masked BEV Perception for Robust Autonomous Driving](https://mlsanity.com/paper/2312.12144/) (2023)
- [BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2104.10780/) (2021)
- [LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation](https://mlsanity.com/paper/2111.04875/) (2021)
- [Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View](https://mlsanity.com/paper/2104.11021/) (2021)
- [RoadBEV: Road Surface Reconstruction in Bird's Eye View](https://mlsanity.com/paper/2404.06605/) (2024)
- [Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer](https://mlsanity.com/paper/2407.08460/) (2024)
- [Multi-modal Streaming 3D Object Detection](https://mlsanity.com/paper/2209.04966/) (2022)
- [Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation](https://mlsanity.com/paper/2308.12755/) (2023)
- [End-to-End 3D Object Detection using LiDAR Point Cloud](https://mlsanity.com/paper/2312.15377/) (2023)
- [3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data](https://mlsanity.com/paper/2401.06946/) (2024)
- [IndoorBEV: Joint Detection and Footprint Completion of Objects via Mask-based Prediction in Indoor Scenarios for Bird's-Eye View Perception](https://mlsanity.com/paper/2507.17445/) (2025)
- [Enabling 3D Object Detection with a Low-Resolution LiDAR](https://mlsanity.com/paper/2105.01765/) (2021)
- [ePose: Let's Make EfficientPose More Generally Applicable](https://mlsanity.com/paper/2111.15114/) (2021)
- [MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm](https://mlsanity.com/paper/2201.04341/) (2022)
- [PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point Dilation](https://mlsanity.com/paper/2502.07822/) (2025)
- [Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks](https://mlsanity.com/paper/2605.25293/) (2026)

## Abstract (arXiv)

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.