arXiv subjects

Shuaibang Peng

Publications and source records attributed to Shuaibang Peng.

3 recordsLinked to original sources

arXiv2026

LoD-Loc v4: Towards Generalized Aerial Visual Localization via Panoptic LoD 3D Map Registration

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.

cs.CV↗

arXiv2026

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

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 generalization and frequent failure in dense building scenes. Our method addresses these challenges through two key innovations. First, we develop a new synthetic data generation pipeline that produces InsLoD-Loc - the largest instance segmentation dataset for aerial imagery to date, comprising 100k images with precise instance building annotations. This enables trained models to exhibit remarkable zero-shot generalization capability. Second, we reformulate the localization paradigm by shifting from semantic to instance silhouette alignment, which significantly reduces pose estimation ambiguity in dense scenes. Extensive experiments demonstrate that LoD-Loc v3 outperforms existing state-of-the-art (SOTA) baselines, achieving superior performance in both cross-scene and dense urban scenarios with a large margin. The project is available at https://nudt-sawlab.github.io/LoD-Locv3/.

cs.CV↗

arXiv2025

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

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 localization results leveraging LoD models. However, LoD-Loc mainly relies on high-LoD (LoD3 or LoD2) city models, but the majority of available models and those many countries plan to construct nationwide are low-LoD (LoD1). Consequently, enabling localization on low-LoD city models could unlock drones' potential for global urban localization. To address these issues, we introduce LoD-Loc v2, which employs a coarse-to-fine strategy using explicit silhouette alignment to achieve accurate localization over low-LoD city models in the air. Specifically, given a query image, LoD-Loc v2 first applies a building segmentation network to shape building silhouettes. Then, in the coarse pose selection stage, we construct a pose cost volume by uniformly sampling pose hypotheses around a prior pose to represent the pose probability distribution. Each cost of the volume measures the degree of alignment between the projected and predicted silhouettes. We select the pose with maximum value as the coarse pose. In the fine pose estimation stage, a particle filtering method incorporating a multi-beam tracking approach is used to efficiently explore the hypothesis space and obtain the final pose estimation. To further facilitate research in this field, we release two datasets with LoD1 city models covering 10.7 km , along with real RGB queries and ground-truth pose annotations. Experimental results show that LoD-Loc v2 improves estimation accuracy with high-LoD models and enables localization with low-LoD models for the first time. Moreover, it outperforms state-of-the-art baselines by large margins, even surpassing texture-model-based methods, and broadens the convergence basin to accommodate larger prior errors.

cs.CV↗