SpatialFly: Implicit 3D Prior-Guided Visual Reparameterization for Continuous UAV Vision-and-Language Navigation

UAVs play an important role in applications such as autonomous exploration, disaster response, and infrastructure inspection.

However, UAV VLN in complex 3D environments remains challenging.

A key difficulty is the structural representation mismatch between 2D visual perception and the 3D trajectory decision space, which limits spatial reasoning.

To this end, we propose SpatialFly, a geometry-guided spatial representation framework for UAV VLN.

Operating on RGB observations without explicit 3D reconstruction, SpatialFly introduces a geometry-guided 2D adaptive representation mechanism.

Specifically, the geometric prior injection module injects global structural cues into 2D semantic tokens to provide scene-level geometric guidance.

The geometry-aware reparameterization module then uses geometry-conditioned cross-modal attention and gated residual fusion to adaptively reparameterize the visual tokens.

Experimental results show that SpatialFly consistently outperforms state-of-the-art UAV VLN baselines across both seen and unseen environments, reducing NE by 4.03m and improving SR by 1.27% over the strongest baseline on the unseen Full split.

Additional trajectory-level analysis shows that SpatialFly produces trajectories with better path alignment and smoother, more stable motion.