VIRD: View-Invariant Representation through Dual-Axis Transformation for Cross-View Pose Estimation
Accurate global localization is critical 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. We propose a novel cross-view pose estimation method that constructs view-invariant representations through dual-axis transformation (VIRD). VIRD first applies a polar transformation to the satellite view ...
| Authors | Juhye Park, Wooju Lee, Dasol Hong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 March 2026 |
| Last updated at source | 22 March 2026 |
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