8 papers
PiLoT v2: Pixel-to-Orthogonal Map Alignment for Free-view UAV Geo-localization
Xinyi Liu, Xiaoya Cheng, Rouwan Wu +4
Real-time, drift-free UAV geo-localization is essential for autonomous missions in GNSS-denied environments. The pioneering system, PiLoT, achieves high precision via Neural Pixel-…
AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision
Xiaoya Cheng, Rouwan Wu, Xinyi Liu +6
Despite the rapid progress in data-driven 3D vision, aerial geometric 3D vision remains a formidable challenge due to the severe scarcity of large-scale, high-fidelity training dat…
PiLoT: Neural Pixel-to-3D Registration for UAV-based Ego and Target Geo-localization
Xiaoya Cheng, Long Wang, Yan Liu +5
We present PiLoT, a unified framework that tackles UAV-based ego and target geo-localization. Conventional approaches rely on decoupled pipelines that fuse GNSS and Visual-Inertial…
Local Precise Refinement: A Dual-Gated Mixture-of-Experts for Enhancing Foundation Model Generalization against Spectral Shifts
Xi Chen, Maojun Zhang, Yu Liu +1
Domain Generalization Semantic Segmentation (DGSS) in spectral remote sensing is severely challenged by spectral shifts across diverse acquisition conditions, which cause significa…
LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignment
Shuaibang Peng, Juelin Zhu, Xia Li +4
We present LoD-Loc v3, a novel method for generalized aerial visual localization in dense urban environments. While prior work LoD-Loc v2 achieves localization through semantic bui…
LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment
Juelin Zhu, Shuaibang Peng, Long Wang +4
We propose a novel method for aerial visual localization over low Level-of-Detail (LoD) city models. Previous wireframe-alignment-based method LoD-Loc has shown promising localizat…