SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models
SemCityLoc is a novel aerial localization method that reframes pose estimation as structured surface registration between foundation-model visual priors and standardized LoD 3D city models. It eliminates reliance on precise GNSS or radiometric 3D reconstructions by aligning semantic surfaces and monocular depth with lightweight building models. The benchmark SemCityLockeD, combining centimeter-accurate UAV poses with LoD1-LoD3 models, is introduced. Experiments show up to 36% recall improvement and mean positional error reduction from 9.89m to 2.62m in urban canyons.
[2606.27444] SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models
[Submitted on 25 Jun 2026]
Title:SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models
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Abstract:Aerial 6DoF localization typically relies on precise GNSS signals or radiometrically rich 3D reconstructions, limiting scalability and on-board deployment. We propose SemCityLoc, a semantic-geometric alignment system that reframes aerial pose estimation as structured surface registration between foundation-model-derived visual priors and standardized LoD-compliant 3D city models. Instead of matching sparse contours or dense texture, our method aligns semantic surfaces and monocular depth with lightweight semantic 3D building models, increasing pose discriminability in repetitive and occluded urban environments. To enable accurate evaluation, we introduce SemCityLockeD, the first real-world benchmark combining centimeter-accurate UAV poses with standardized LoD1--LoD3 semantic city models and challenging low-altitude imagery. Experiments demonstrate substantial improvements over existing map-based approaches, improving recall by up to 36% and reducing mean positional error from 9.89m to 2.62m in challenging urban canyons. Our results indicate that semantically structured geometry provides sufficient and scalable constraints for high-precision aerial localization without radiometric scene reconstructions. The code and data are available at this https URL.
Comments: accepted by ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.27444 [cs.CV]
(or arXiv:2606.27444v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.27444
arXiv-issued DOI via DataCite
Submission history
From: Jingfeng Mao [view email] [v1] Thu, 25 Jun 2026 18:13:39 UTC (29,847 KB)
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