LoD-Loc v4: Towards Generalized Aerial Visual Localization via Panoptic LoD 3D Map Registration
Shuaibang Peng, Chengming Zhang, Juelin Zhu, Xia Li, Xiaoya Cheng, Zeyu Cui, Shen Yan, Maojun Zhang, Yu Liu
Abstract
Aerial visual localization over Level-of-Detail (LoD) 3D Maps has recently gained significant traction due to its global scalability, storage efficiency, and privacy-preserving nature. However, existing methods rely almost exclusively on building elements, which leads to catastrophic localization failures in rural or undeveloped areas where buildings are sparse or non-existent. To address this, we construct a unified Panoptic LoD 3D Map by fusing OpenStreetMap (OSM), Digital Elevation Models (DEM), and LoD assets, reformulating the problem as a panoptic registration task. By parsing the query image into discrete building `thing' instances and continuous `stuff' regions comprising road, vegetation, and water, we establish a holistic alignment with this map to robustly optimize the camera pose. To enable reliable panoptic perception, we develop a new automated pipeline that constructs AirPan - the largest aerial panoptic segmentation dataset to date, comprising 85k images with pixel-accurate annotations. This empowers our panoptic segmentation model to exhibit remarkable zero-shot generalization capabilities. Extensive experiments demonstrate that LoD-Loc v4 consistently outperforms state-of-the-art (SOTA) baselines by a significant margin. Most notably, it successfully maintains reliable localization in building-less scenarios, completely overcoming the catastrophic failures of prior building-centric paradigms.