# 2D Car Detection in Radar Data with PointNets

arXiv: https://arxiv.org/abs/1904.08414 · HTML: https://mlsanity.com/paper/1904.08414/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 30 of 38)

- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving](https://mlsanity.com/paper/2103.03678/) (2021)
- [Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar](https://mlsanity.com/paper/2205.02111/) (2022)
- [Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving](https://mlsanity.com/paper/2310.07602/) (2023)
- [T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals](https://mlsanity.com/paper/2303.16940/) (2023)
- [Pointillism: Accurate 3D bounding box estimation with multi-radars](https://mlsanity.com/paper/2203.04440/) (2022)
- [RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems](https://mlsanity.com/paper/2211.06108/) (2022)
- [Deep Instance Segmentation with Automotive Radar Detection Points](https://mlsanity.com/paper/2110.01775/) (2021)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection](https://mlsanity.com/paper/2310.16389/) (2023)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [Full-Velocity Radar Returns by Radar-Camera Fusion](https://mlsanity.com/paper/2108.10637/) (2021)
- [RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation](https://mlsanity.com/paper/2404.18150/) (2024)
- [A recurrent CNN for online object detection on raw radar frames](https://mlsanity.com/paper/2212.11172/) (2022)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks](https://mlsanity.com/paper/2305.15836/) (2023)
- [Radar Spectra-Language Model for Automotive Scene Parsing](https://mlsanity.com/paper/2406.02158/) (2024)
- [Fast Rule-Based Clutter Detection in Automotive Radar Data](https://mlsanity.com/paper/2108.12224/) (2021)
- [Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks](https://mlsanity.com/paper/2207.03146/) (2022)
- [ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/2309.03734/) (2023)
- [SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data](https://mlsanity.com/paper/2406.10600/) (2024)
- [Scene-aware Learning Network for Radar Object Detection](https://mlsanity.com/paper/2107.01469/) (2021)
- [Anomaly Detection in Radar Data Using PointNets](https://mlsanity.com/paper/2109.09401/) (2021)
- [The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data](https://mlsanity.com/paper/2404.01437/) (2024)
- [Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving](https://mlsanity.com/paper/2104.06772/) (2021)
- [Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion](https://mlsanity.com/paper/2307.03623/) (2023)
- [A Survey of Deep Learning Based Radar and Vision Fusion for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2406.00714/) (2024)

## Abstract (arXiv)

For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.