3D-LENS: A 3D Lifting-based Elevated Novel-view Synthesis method for Single-View Aerial-Ground Re-Identification

Aerial-Ground Re-Identi(cid:28)cation (AG-ReID) is constrained by the viewpoint-domain gap, as drastic viewpoint disparities occlude or distort discriminative features, making cross-viewpoint image retrieval challenging. While existing methods rely on paired cross-view annotations, real-world deployments, such as wilderness search-and-rescue (SAR), often lack target-domain data, requiring retrieval from groundlevel references alone. To our knowledge, we are the (cid:28)rst to address this challenge by formalizing the Single-View AG-ReID (SV AGReID) setting, where models trained on a single real viewpoint must generalize to an unseen viewpoint. We propose 3D Lifting-based Elevated Novel-view Synthesis (3D-LENS), a uni(cid:28)ed framework combining geometrically-consistent novel view synthesis that leverages large-scale 3D mesh reconstruction, with a robust representation learning scheme to mitigate synthetic-to-real bias. Unlike 2D generative baselines that su(cid:27)er from geometric inconsistencies or prior 3D methods that are restricted to class-speci(cid:28)c templates, our approach ensures view-consistent synthesis across diverse categories without prede- (cid:28)ned templates that fail to capture (cid:28)ne-grained details, such as carried objects. Extensive experiments demonstrate that our method achieves state-of-the-art performance on SV AG-ReID scenarios. Code and data will be released at https://github.com/TurtleSmoke/3D-LENS. • •