Newest papers

PlanarCityLoc: Breaking Silhouette Ambiguity in LoD Aerial Localization via 3D Planes

Oussema Dhaouadi, Shao Jie Hu-Chen, Jakob Gollreiter, Samir Jusufi, Nikolas Rieger, Caroline Wendlandt, Christian Wender, Jingfeng Mao, Guangming Wang, Zuria Bauer, Marc Pollefeys, Daniel Cremers, Olaf Wysocki · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26729 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

空中视觉定位对于城市环境中自动无人机导航至关重要,GNSS信号往往会被降级。轻质无纺织的细节级(LoD)城市模型为结构资产提供了一个地理地图替代方案。现有的基于形状的方法依赖于图像空间定位,这可能会使不同摄像机的姿势无法区分,并在分区错误扭曲建筑架构时有利于错误的姿势。我们介绍PlanarCityLoc,通过利用建筑面台和屋顶的平面地理学来超越形状重叠。经过地理学意识的初始化,我们将图像侧面板块与模型D飞机相匹配,并使用所产生的3D至2D匹配来用Prospective-n-Point(Pn)进行

Machine translation (Meta M2M100); the English original follows.

Abstract

Aerial visual localization is crucial for autonomous drone navigation in urban environments where GNSS signals are often degraded. Lightweight textureless Level-of-Detail (LoD) city models provide a geometric map alternative to textured assets. Existing silhouette-based methods rely on image-space alignment, which can leave different camera poses indistinguishable and favor incorrect poses when segmentation errors distort the building outlines. We present PlanarCityLoc, which moves beyond silhouette overlap by leveraging the planar geometry of building facades and roofs. Following geometry-aware initialization, we match image-side planar patches to LoD model planes and use the resulting 3D-to-2D correspondences to refine pose with Perspective-n-Point (PnP). The framework uses off-the-shelf geometry and segmentation models without dataset-specific training or fine-tuning. Experiments on UAVD4L-LoDv2 and Swiss-EPFLv2 achieve state-of-the-art localization recall among the evaluated off-the-shelf LoD-based methods. By grounding image-space alignment in 3D planes, PlanarCityLoc enables more accurate aerial localization with lightweight, textureless city maps.

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Topics: Silhouette, Computer vision, Artificial intelligence, Segmentation, Computer science, Drone

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