IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection
Perception is a fundamental component of autonomous driving systems. While LiDAR-based methods have achieved remarkable progress in object detection, their reliability can degrade under adverse weather conditions. Radar point clouds provide a robust alternative due to their resilience to bad weather and low-illumination scenarios. However, radar point clouds are typically sparse, unordered, and less informative than LiDAR data, making it challenging to directly apply existing LiDAR-based perception methods. To address these challenges, we propose IRGNN, an Invariant Radar Graph Neural Network
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- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”
- FuzzySimilar title/name (fuzzy) · 59%sansan0/TrendRadar →
“Fuzzy title match (0.73): “IRGNN: Efficient Invariant Radar Graph Neural Network for Ra” ≈ “sansan0/TrendRadar””
- FuzzySimilar title/name (fuzzy) · 59%tirth8205/code-review-graph →
“Fuzzy title match (0.73): “IRGNN: Efficient Invariant Radar Graph Neural Network for Ra” ≈ “tirth8205/code-review-graph””
- LinkedLinked via arxiv author · 85%Xiao Guo →
“IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection”
- LinkedLinked via arxiv author · 85%Wanke Xia →
“IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection”
- LinkedLinked via arxiv author · 85%Lili Yang →
“IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection”
- LinkedLinked via arxiv author · 85%Caicong Wu →
“IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection”