# RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception

arXiv: https://arxiv.org/abs/2304.06547 · HTML: https://mlsanity.com/paper/2304.06547/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications](https://mlsanity.com/paper/2104.02493/) (2021)
- [CNN based Road User Detection using the 3D Radar Cube](https://mlsanity.com/paper/2004.12165/) (2020)
- [PixSet : An Opportunity for 3D Computer Vision to Go Beyond Point Clouds With a Full-Waveform LiDAR Dataset](https://mlsanity.com/paper/2102.12010/) (2021)
- [A Multi-Stage Clustering Framework for Automotive Radar Data](https://mlsanity.com/paper/1907.03511/) (2019)

## Built on by (most cited first, 24 of 24)

- [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)
- [MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions](https://mlsanity.com/paper/2407.07462/) (2024)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2411.19860/) (2024)
- [GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling](https://mlsanity.com/paper/2409.02720/) (2024)
- [DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection](https://mlsanity.com/paper/2508.12330/) (2025)
- [SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data](https://mlsanity.com/paper/2406.10600/) (2024)
- [SIRA: Scalable Inter-frame Relation and Association for Radar Perception](https://mlsanity.com/paper/2411.02220/) (2024)
- [HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds](https://mlsanity.com/paper/2602.11554/) (2026)
- [Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection](https://mlsanity.com/paper/2402.08427/) (2024)
- [SpaRC-AD: A Baseline for Radar-Camera Fusion in End-to-End Autonomous Driving](https://mlsanity.com/paper/2508.10567/) (2025)
- [Redefining Radar Segmentation: Simultaneous Static-Moving Segmentation and Ego-Motion Estimation using Radar Point Clouds](https://mlsanity.com/paper/2511.20003/) (2025)
- [Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data](https://mlsanity.com/paper/2311.07247/) (2023)
- [RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles](https://mlsanity.com/paper/2507.00937/) (2025)
- [Graph Query Networks for Object Detection with Automotive Radar](https://mlsanity.com/paper/2511.15271/) (2025)
- [4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation](https://mlsanity.com/paper/2512.14235/) (2025)
- [On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements](https://mlsanity.com/paper/2604.01921/) (2026)
- [Horizon3D: Sparse Radar-Camera Fusion for Long-Range 3D Perception in Autonomous Driving](https://mlsanity.com/paper/2606.31096/) (2026)
- [Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2606.31609/) (2026)
- [PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter](https://mlsanity.com/paper/2607.13891/) (2026)
- [IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection](https://mlsanity.com/paper/2608.14394/) (2026)

## Abstract (arXiv)

A reliable perception has to be robust against challenging environmental conditions. Therefore, recent efforts focused on the use of radar sensors in addition to camera and lidar sensors for perception applications. However, the sparsity of radar point clouds and the poor data availability remain challenging for current perception methods. To address these challenges, a novel graph neural network is proposed that does not just use the information of the points themselves but also the relationships between the points. The model is designed to consider both point features and point-pair features, embedded in the edges of the graph. Furthermore, a general approach for achieving transformation invariance is proposed which is robust against unseen scenarios and also counteracts the limited data availability. The transformation invariance is achieved by an invariant data representation rather than an invariant model architecture, making it applicable to other methods. The proposed RadarGNN model outperforms all previous methods on the RadarScenes dataset. In addition, the effects of different invariances on the object detection and semantic segmentation quality are investigated. The code is made available as open-source software under https://github.com/TUMFTM/RadarGNN.