Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation

Radar sensors provide reliable perception under adverse weatherand lighting conditions, but their sparse, noisy, and weakly semanticmeasurements make dense semantic segmentation challenging. Most ex-isting radar segmentation methods rely on grid-based encodings andpairwise interactions, which struggle to capture the higher-order rela-tional structure formed by multiple radar returns from the same phys-ical object. We introduce a unified higher-order structural alignmentframework for multi-view radar segmentation. The proposed method re-fines radar feature representations using learnable hypergraphs to cap-ture higher-order dependencies among spatially related responses. To en-sure consistency across heterogeneous radar projections, we further alignview-specific features using Unbalanced Optimal Transport (UOT), en-abling correspondence-free alignment under varying measurement den-sities and partial observations. An adaptive attention mechanism thenfuses complementary radar views while emphasising structurally informa-tive responses under sparsity and noise. The resulting architecture learnsstructurally consistent representations across Range Angle (RA), RangeDoppler (RD), and Angle Doppler (AD) views and is trained using super-vised segmentation together with cross-view consistency regularisation.Experiments on the CARRADA and RADIal benchmarks demonstrateconsistent improvements over strong radar-specific baselines, achieving63.8% mIoU on CARRADA and 83.4% mIoU on RADIal, im-proving the previous best methods by +1.7 and +2.3 mIoU, respec-tively. These results highlight the importance of higher-order relationalmodelling for robust radar perception.