Cross-View Yaw Estimation in Location Uncertainty with Line-Aligning Yaw Scoring

Accurate yaw estimation is a bottleneck in cross-view lo-calization between ground view and Bird’s Eye View (BEV). Existingmethods couple yaw with translation and rely on height or projectionassumptions that degrade under large yaw ambiguity. We disentangleyaw from location accuracy and introduce LAYS, a radially invariantline-consensus voting method. By exploiting the radial invariance of ourformulation, we achieve sub-degree yaw precision via 3D voting over allcandidate poses, while eliminating the need for accurate location. Ourkey observation is that a ground-image column matched to BEV pixelsinduces the same yaw across all camera positions along the radial direc-tion of the pixels. LAYS matches BEV pixels to ground columns usingfeature similarity and accumulates the induced yaw votes into discrete3D bins, where correct correspondences along the radial line concentrateinto a sharp peak for the correct yaw. Experiments on Mapillary, Ford,KITTI, and VIGOR show significant gains under unknown yaw, particu-larly for normal FoV with unknown yaw (+28∼45%p), and using LAYSas a yaw prior improves downstream 3-DoF localization.