CVGD: Cross-View Guided Disentangler for Multi-View Radar Semantic Segmentation

Neural networks continue to face challenges in effectively extracting information from the high-dimensional and sparse range-azimuth-Doppler (RAD) tensor. While multi-view architectures using three-axis projections of the RAD tensor improve efficiency and maintain accuracy, existing designs suffer from inefficient inter-view information interaction. To address this issue, this letter proposes a new multi-view information fusion viewpoint: instead of directly blending feature maps, the orthogonal relationships among the RAD coordinate axes are leveraged to guide feature decomposition across views, thereby disentangling the mixed semantics introduced during view compression. Based on this idea, a new module, termed Cross-View Guided Disentangler (CVGD), is introduced to enable inter-view information interaction in multi-view architectures. Extensive experiments on the public CARRADA dataset demonstrate that networks incorporating the proposed module achieve performance comparable to state-of-the-art methods, while utilizing 50% fewer parameters and attaining a 6-fold increase in inference speed. Additionally, promising results are also observed on RADIal, achieving near-SOTA performance with higher efficiency.