AI RESEARCH
VIRD: View-Invariant Representation through Dual-Axis Transformation for Cross-View Pose Estimation
arXiv CS.CV
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ArXi:2603.12918v1 Announce Type: new Accurate global localization is crucial for autonomous driving and robotics, but GNSS-based approaches often degrade due to occlusion and multipath effects. As an emerging alternative, cross-view pose estimation predicts the 3-DoF camera pose corresponding to a ground-view image with respect to a geo-referenced satellite image. However, existing methods struggle to bridge the significant viewpoint gap between the ground and satellite views mainly due to limited spatial correspondences.