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

Japanese

GNSS信号がしばしば劣化する都市環境における自動ドローンナビゲーションにとって、空気の視覚的ローカライゼーションは極めて重要である。軽量なテクスチャーなレベル・オブ・ディテール(LoD)都市モデルは、テクスチャーされた資産に対する地理的地図の代替を提供します。既存のシルエットベースの方法は、異なるカメラのポーズを区別できないままにし、分割エラーが建物のスケジュールを歪曲するときに間違ったポーズを好みます。我々はPlanarCityLocを、建物のファサードと屋根の平面の地理学を活用することによってシルエットを超えるものに提供します。地理学に気付いたイニ

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange