Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation
Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements make dense semantic segmentation challenging. Most existing radar segmentation methods rely on grid-based encodings and pairwise interactions, which struggle to capture the higher-order relational structure formed by multiple radar returns from the same physical object. We introduce a unified higher-order structural alignment framework for multi-view radar segmentation. The proposed method refines radar feature representations using learnable hyperg
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 59%sansan0/TrendRadar →
“Fuzzy title match (0.73): “Learning Structurally Consistent Representations for Multi-V” ≈ “sansan0/TrendRadar””
- FuzzySimilar title/name (fuzzy) · 59%vllm-project/semantic-router →
“Fuzzy title match (0.73): “Learning Structurally Consistent Representations for Multi-V” ≈ “vllm-project/semantic-router””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Learning Structurally Consistent Representations for Multi-V” ≈ “aymericdamien/TopDeepLearning””
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Learning Structurally Consistent Representations for Multi-V” ≈ “microsoft/semantic-kernel””