Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation
For autonomous driving perception under adverse conditions, this work provides a novel approach to improve radar semantic segmentation by modeling higher-order dependencies, though the gains are incremental over strong baselines.
The paper introduces a higher-order structural alignment framework for multi-view radar semantic segmentation, using learnable hypergraphs and Unbalanced Optimal Transport to capture and align relational structures across radar views. The method achieves 63.8% mIoU on CARRADA and 83.4% mIoU on RADIal, improving previous best methods by +1.7 and +2.3 mIoU, respectively.
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 consistency across heterogeneous radar projections, we further align view-specific features using Unbalanced Optimal Transport (UOT), enabling correspondence-free alignment under varying measurement densities and partial observations. An adaptive attention mechanism then fuses complementary radar views while emphasising structurally informative responses under sparsity and noise. The resulting architecture learns structurally consistent representations across Range Angle (RA), Range Doppler (RD), and Angle Doppler (AD) views and is trained using supervised segmentation together with cross-view consistency regularisation. Experiments on the CARRADA and RADIal benchmarks demonstrate consistent improvements over strong radar-specific baselines, achieving 63.8% mIoU on CARRADA and 83.4% mIoU on RADIal, improving the previous best methods by +1.7 and +2.3 mIoU, respectively. These results highlight the importance of higher-order relational modelling for robust radar perception.