mlsanityData to 14 Oct 2026

Graph Query Networks for Object Detection with Automotive Radar

arXiv 2511.15271 ↗First posted 19 Nov 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

Object detection with 3D radar is essential for 360-degree automotive perception, but radar's long wavelengths produce sparse and irregular reflections that challenge traditional grid and sequence-based convolutional and transformer detectors. This paper introduces Graph Query Networks (GQN), an attention-based framework that models objects sensed by radar as graphs, to extract individualized relational and contextual features. GQN employs a novel concept of graph queries to dynamically attend over the bird's-eye view (BEV) space, constructing object-specific graphs processed by two novel modules: EdgeFocus for relational reasoning and DeepContext Pooling for contextual aggregation. On the NuScenes dataset, GQN improves relative mAP by up to +53%, including a +8.2% gain over the strongest prior radar method, while reducing peak graph construction overhead by 80% with moderate FLOPs cost.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 52 references that are in the map. First posted on arXiv 19 Nov 2025.

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30 of 52 references.

References · 30

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18 more
  1. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  2. 2018Graph R-CNN for Scene Graph Generationcited by 298
  3. 2022Vision GNN: An Image is Worth Graph of Nodescited by 186
  4. 2020CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detectioncited by 161
  5. 2023GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detectioncited by 34
  6. 2022Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radarcited by 33
  7. 2024GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detectioncited by 33
  8. 2023RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perceptioncited by 24
  9. 2024RadarPillars: Efficient Object Detection from 4D Radar Point Cloudscited by 23
  10. 2022Exploiting Temporal Relations on Radar Perception for Autonomous Drivingcited by 22
  11. 2022A recurrent CNN for online object detection on raw radar framescited by 9
  12. 2023CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radarcited by 9
  13. 2020Deep Learning on Radar Centric 3D Object Detectioncited by 7
  14. 2023Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networkscited by 6
  15. 2025FocalAD: Local Motion Planning for End-to-End Autonomous Drivingcited by 6
  16. 2023Quantification of Uncertainties in Deep Learning-based Environment Perceptioncited by 4
  17. 2024SIRA: Scalable Inter-frame Relation and Association for Radar Perceptioncited by 4
  18. 2025AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detectioncited by 3

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