VIRD: View-Invariant Representation through Dual-Axis Transformation for Cross-View Pose Estimation

• URL: http://arxiv.org/abs/2603.12918v1
• Date: Fri, 13 Mar 2026 11:48:22 GMT
• Title: VIRD: View-Invariant Representation through Dual-Axis Transformation for Cross-View Pose Estimation
• Abstract summary: Cross-view pose estimation predicts the 3-DoF camera pose corresponding to a ground-view image with respect to a geo-referenced satellite image.<n>We propose a novel cross-view pose estimation method that constructs view-invariant representations through dual-axis transformation.<n>Experiments on the KITTI and VIGOR datasets demonstrate that VIRD outperforms the state-of-the-art methods without orientation priors.
• Score: 12.845645384371876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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 to establish horizontal correspondence, then uses context-enhanced positional attention on the ground and polar-transformed satellite features to resolve vertical misalignment, explicitly mitigating the viewpoint gap. A view-reconstruction loss is introduced to strengthen the view invariance further, encouraging the derived representations to reconstruct the original and cross-view images. Experiments on the KITTI and VIGOR datasets demonstrate that VIRD outperforms the state-of-the-art methods without orientation priors, reducing median position and orientation errors by 50.7% and 76.5% on KITTI, and 18.0% and 46.8% on VIGOR, respectively.

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