# BirdNet: a 3D Object Detection Framework from LiDAR information

arXiv: https://arxiv.org/abs/1805.01195 · HTML: https://mlsanity.com/paper/1805.01195/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 30 of 33)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021)
- [Virtual Sparse Convolution for Multimodal 3D Object Detection](https://mlsanity.com/paper/2303.02314/) (2023)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://mlsanity.com/paper/2106.12735/) (2021)
- [SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection](https://mlsanity.com/paper/2201.01976/) (2022)
- [Transformation-Equivariant 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2211.11962/) (2022)
- [Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation](https://mlsanity.com/paper/2207.02201/) (2022)
- [S4C: Self-Supervised Semantic Scene Completion with Neural Fields](https://mlsanity.com/paper/2310.07522/) (2023)
- [Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection](https://mlsanity.com/paper/2112.07241/) (2021)
- [SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection](https://mlsanity.com/paper/2101.02672/) (2021)
- [HFT: Lifting Perspective Representations via Hybrid Feature Transformation](https://mlsanity.com/paper/2204.05068/) (2022)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Local Aggressive Adversarial Attacks on 3D Point Cloud](https://mlsanity.com/paper/2105.09090/) (2021)
- [Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction](https://mlsanity.com/paper/2201.04756/) (2022)
- [Forecasting Future Instance Segmentation with Learned Optical Flow and Warping](https://mlsanity.com/paper/2211.08049/) (2022)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2210.11237/) (2022)
- [Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles](https://mlsanity.com/paper/2409.20426/) (2024)
- [APSNet: Attention Based Point Cloud Sampling](https://mlsanity.com/paper/2210.05638/) (2022)
- [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](https://mlsanity.com/paper/2311.06043/) (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)
- [TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training](https://mlsanity.com/paper/2408.13902/) (2024)
- [Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation](https://mlsanity.com/paper/2505.01615/) (2025)
- [MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm](https://mlsanity.com/paper/2201.04341/) (2022)
- [Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques](https://mlsanity.com/paper/2202.02521/) (2022)
- [Feature-based model selection for object detection from point cloud data](https://mlsanity.com/paper/2209.12419/) (2022)
- [Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision](https://mlsanity.com/paper/2210.02533/) (2022)

## Abstract (arXiv)

Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision. We present a LiDAR-based 3D object detection pipeline entailing three stages. First, laser information is projected into a novel cell encoding for bird's eye view projection. Later, both object location on the plane and its heading are estimated through a convolutional neural network originally designed for image processing. Finally, 3D oriented detections are computed in a post-processing phase. Experiments on KITTI dataset show that the proposed framework achieves state-of-the-art results among comparable methods. Further tests with different LiDAR sensors in real scenarios assess the multi-device capabilities of the approach.