VIREO: Learning How to Look and What to Trust for Aerial Object Navigation

Aerial object navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to search for a target specified by a textual description using visual observations. Existing methods mainly rely on spatial search signals to guide navigation, while overlooking that target recognition can vary substantially across observation viewpoints. Thus, approaching a promising candidate does not necessarily ensure reliable recognition. Even at similar distances, the same target can provide substantially different visual evidence from different viewing directions. To bridge this gap, we propose Viewpoint-Informed Recognition and Evidence Optimization (VIREO), a viewpoint-aware framework for aerial ObjectNav. To identify viewpoints that better support target recognition during navigation, VIREO models how recognition outcomes for target and non-target candidates vary across observation viewpoints. Based on these response patterns, VIREO constructs a viewpoint information field to guide the UAV toward viewpoints with higher expected information gain. To address evidence accumulation across viewpoints, VIREO introduces viewpoint-aware evidence aggregation, which adjusts each observation's contribution according to the similarity to previous viewpoints. Experiments on UAV-ON show that VIREO surpasses the best baseline.