Structure-Aware Alignment for Day-Night Cross-Domain Vehicle Re-Identification

Day-night cross-domain vehicle re-identification (DN-ReID) is fundamentally challenged by drastic illumination changes that create substantial domain gaps and hinder consistent feature representation. Most existing methods focus on aligning distribution statistics but often overlook essential structural relationships, such as cross-domain clustering and geometric topology. To address this, we propose a structure-aware alignment (SAA) method that, for the first time, formulates centered kernel alignment as a trainable loss for structural alignment between domains. This approach explicitly aligns cross-domain relational structures, thereby supporting the transfer of intra-class compactness and inter-class separability from the source to the target domain for robust cross-domain matching. Extensive experiments on DN-348 and DN-Wild demonstrate that our approach consistently outperforms state-of-the-art methods, achieving a 2.7% mAP improvement on DN-348.