Structural Radial Aggregation for Cross-View Yaw under Position Uncertainty

Ground-to-satellite localization estimates a street-level camera's position and yaw (heading) from an aerial image. Yaw is the harder part, because a small angle error moves distant scene content far. Position uncertainty turns yaw estimation into aggregating complementary evidence over all candidate poses. Prior work matches each ground-view column to aerial pixels. All positions on the ray through a matched pixel imply the same yaw, so each match votes for a yaw without knowing its distance. Each vote is precise but checks one viewing direction, which leaves many yaws nearly as plausible as the true one at each position. The scene layout is coarser but covers the whole view. Structural Radial Aggregation for Yaw (S-RAY) combines both. A structural score, learned from pose labels only, rates each yaw at each position by how well the directions where the ground view shows structure line up with the aerial structure axes, leaving a few promising yaws per position. A Fourier score of continuous match votes decides among them. Hard-bin voting, which bins every position–match pair separately, is costly and makes the score piecewise constant. Instead, each match adds a learned bump at its implied yaw. Since the implied yaw is a difference of angles, a bump of a few cosines turns scoring all positions and yaws into a few FFT correlations that form no position–match pairs; this scorer is 23× faster than prior hard-bin voting. S-RAY raises yaw recall within 1° over the prior method from 51.07% to 87.65% on KITTI Same Area, from 48.44% to 72.91% on KITTI Cross Area, and from 34.81% to 55.22% on MGL, and has the lowest mean yaw error on both VIGOR splits. As a yaw initialization, it also improves Loc² localization on KITTI and MGL.