Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification

• URL: http://arxiv.org/abs/2601.21405v1
• Date: Thu, 29 Jan 2026 08:41:42 GMT
• Title: Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification
• Authors: Kailash A. Hambarde, Hugo Proença,
• Abstract summary: Aerial-ground person re-identification (AG-ReID) is fundamentally challenged by extreme viewpoint and distance discrepancies.<n>Existing methods rely on geometry-aware feature learning or appearance-conditioned prompting.<n>We introduce Geometry-Induced Query-Key Transformation (GIQT), a lightweight low-rank module that rectifies the similarity space by conditioning query-key interactions on camera geometry.
• Score: 4.039576422478934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aerial-ground person re-identification (AG-ReID) is fundamentally challenged by extreme viewpoint and distance discrepancies between aerial and ground cameras, which induce severe geometric distortions and invalidate the assumption of a shared similarity space across views. Existing methods primarily rely on geometry-aware feature learning or appearance-conditioned prompting, while implicitly assuming that the geometry-invariant dot-product similarity used in attention mechanisms remains reliable under large viewpoint and scale variations. We argue that this assumption does not hold. Extreme camera geometry systematically distorts the query-key similarity space and degrades attention-based matching, even when feature representations are partially aligned. To address this issue, we introduce Geometry-Induced Query-Key Transformation (GIQT), a lightweight low-rank module that explicitly rectifies the similarity space by conditioning query-key interactions on camera geometry. Rather than modifying feature representations or the attention formulation itself, GIQT adapts the similarity computation to compensate for dominant geometry-induced anisotropic distortions. Building on this local similarity rectification, we further incorporate a geometry-conditioned prompt generation mechanism that provides global, view-adaptive representation priors derived directly from camera geometry. Experiments on four aerial-ground person re-identification benchmarks demonstrate that the proposed framework consistently improves robustness under extreme and previously unseen geometric conditions, while introducing minimal computational overhead compared to state-of-the-art methods.

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