Learning Ego-Centric BEV Representations from a Perspective-Privileged View: Cross-View Supervision for Online HD Map Construction

Bird’s-Eye View (BEV) representations derived from multi-camera input have become a central interface for online HD map con-struction. However, most approaches rely solely on ego-centric super-vision, requiring large-scale scene structure to be inferred from incom-plete observations, occlusions, and diminishing information density atlong range, where perspective effects and spatial sparsity hinder consis-tent structural reasoning. We introduce Cross-View Supervision (CVS),a representation learning paradigm that transfers geometric and topolog-ical priors from an ego-aligned overhead perspective into camera-basedBEV encoders. Rather than adding auxiliary semantic losses, CVS alignsrepresentations in a shared BEV feature space and distills globally con-sistent structural knowledge from a perspective-privileged teacher intothe ego-centric backbone. This supervision enhances structural coher-ence without modifying the inference architecture or requiring overheadinput at test time. Experiments on nuScenes using ego-aligned aerialimagery from the AID4AD cross-view extension demonstrate consistentimprovements over StreamMapNet while maintaining identical camera-only inference. CVS yields +3.9 mAP in the standard 60 × 30 m regionand +9.9 mAP in the extended 100 × 50 m setting, corresponding toa 44% relative gain at long range. These results highlight perspective-privileged structural supervision as a promising training principle forimproving BEV representation learning in HD map construction. Theproject repository is available athttps://github.com/DriverlessMobility/CrossViewSupervision.