标题： LoD-Loc v2：使用显式轮廓对齐在低细节层次城市模型上的航空视觉定位 显示英文标题

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

摘要： 我们提出了一种新的方法，用于在低细节层次（LoD）城市模型上进行空中视觉定位。基于线框对齐的先前方法LoD-Loc通过利用LoD模型展示了有希望的定位结果。然而，LoD-Loc主要依赖于高LoD（LoD3或LoD2）城市模型，但大多数可用模型以及许多国家计划建设的全国性模型是低LoD（LoD1）。因此，在低LoD城市模型上实现定位可以释放无人机在全球城市定位中的潜力。为了解决这些问题，我们引入了LoD-Loc v2，它采用一种从粗到细的策略，使用显式的轮廓对齐来实现在空中低LoD城市模型上的准确定位。具体来说，给定一个查询图像，LoD-Loc v2首先应用一个建筑物分割网络来生成建筑物轮廓。然后，在粗略姿态选择阶段，我们在先验姿态周围均匀采样姿态假设，构建姿态成本体积以表示姿态概率分布。体积中的每个成本衡量投影轮廓和预测轮廓之间的对齐程度。我们选择值最大的姿态作为粗略姿态。在精细姿态估计阶段，使用一种结合多光束跟踪方法的粒子过滤方法，以高效探索假设空间并获得最终的姿态估计。为了进一步促进该领域的研究，我们发布了两个包含LoD1城市模型的数据集，覆盖10.7公里，并附有真实的RGB查询和真实姿态标注。实验结果表明，LoD-Loc v2在高LoD模型上提高了估计精度，并首次实现了在低LoD模型上的定位。此外，它大大优于最先进的基线方法，甚至超越了基于纹理模型的方法，并扩大了收敛区域以适应更大的先验误差。 显示英文摘要

摘要： 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.