AeroSCR: Large-Scale Scene Coordinate Regression for Aerial Visual Localization

Scene Coordinate Regression (SCR) is a learning-based visual localization method that estimates camera pose by directly regressing 3D scene coordinates corresponding to given 2D pixel locations. While effective in small-scale settings, existing methods struggle with illumination and viewpoint variations in large-scale environments. To address this, we present AeroSCR, a novel SCR framework for UAV localization. First, we design a structure-aware dual-stream encoder with an Adaptive Structure-Aware Fusion (ASAF) module, jointly learning semantic and structural representations to enhance robustness to appearance variations. Second, we introduce a region partitioning strategy that decomposes the global regression problem into multiple local subtasks, significantly improving localization performance in large and ambiguous environments. Finally, we construct UAV-SceneLoc2025, a large-scale UAV dataset covering diverse altitudes, viewpoints, lighting, and weather conditions, providing a comprehensive benchmark for SCR evaluation under challenging real-world scenarios. Extensive experiments demonstrate that AeroSCR achieves state-of-the-art performance over existing SCR methods.