Cross-View Supervision transfers geometric and topological priors from ego-aligned overhead views into camera-based BEV encoders via shared feature alignment, yielding +3.9 mAP and +9.9 mAP gains on nuScenes with 44% relative improvement at long range.
In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2024)
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Learning Ego-Centric BEV Representations from a Perspective-Privileged View: Cross-View Supervision for Online HD Map Construction
Cross-View Supervision transfers geometric and topological priors from ego-aligned overhead views into camera-based BEV encoders via shared feature alignment, yielding +3.9 mAP and +9.9 mAP gains on nuScenes with 44% relative improvement at long range.