6 papers
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…
Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts
Xi Chen, Shen Yan, Juelin Zhu +3
We introduce Land-MoE, a novel approach for multispectral land cover classification (MLCC). Spectral shift, which emerges from disparities in sensors and geospatial conditions, pos…
LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment
Juelin Zhu, Shen Yan, Long Wang +3
We propose a new method named LoD-Loc for visual localization in the air. Unlike existing localization algorithms, LoD-Loc does not rely on complex 3D representations and can estim…
UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization
Rouwan Wu, Xiaoya Cheng, Juelin Zhu +3
Despite significant progress in global localization of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained by the availability of datase…
Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior
Shen Yan, Xiaoya Cheng, Yuxiang Liu +4
Despite the significant progress in 6-DoF visual localization, researchers are mostly driven by ground-level benchmarks. Compared with aerial oblique photography, ground-level map…