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 hypergraphs to capture higher-order dependencies among spatially related responses. To ensure consisten...
| Authors | Ali Zia, Muhammad Umer Ramzan, Abdelwahed Khamis et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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