Shuaibang Peng — accepted AI-conference papers

2026

LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignment

Shuaibang Peng, Juelin Zhu, Xia Li, Kun Yang, Yu Liu, Maojun Zhang +1

CVPR 2026

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 building silhouette alignment with low-detail city models, it suffers from two key limitations: poor cross-scene generaliza

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2025

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment

Juelin Zhu, Shuaibang Peng, Long Wang, Hanlin Tan, Yu Liu, Maojun Zhang +1

ICCV 2025poster

We propose a novel method for aerial visual localization over low Level-of-Detail (LoD) city models. Previous wireframe-alignment-based method LoD-Loc [99] has shown promising localization results leveraging LoD models. However, LoD-Loc mainly relies on high-LoD (LoD3 or LoD2) city models, but the m…

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