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Oussema Dhaouadi

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2

ICRA Conference 2025 Conference Paper

MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models

  • Johannes Meier
  • Louis Inchingolo
  • Oussema Dhaouadi
  • Yan Xia 0003
  • Jacques Kaiser
  • Daniel Cremers

We tackle the problem of monocular 3D object detection across different sensors, environments, and camera setups. In this paper, we introduce a novel unsupervised domain adaptation approach, MonoCT, that generates highly accurate pseudo labels for self-supervision. Inspired by our observation that accurate depth estimation is critical to mitigating domain shifts, MonoCT introduces a novel Generalized Depth Enhancement (GDE) module with an ensemble concept to improve depth estimation accuracy. Moreover, we introduce a novel Pseudo Label Scoring (PLS) module by exploring inner-model consistency measurement and a Diversity Maximization (DM) strategy to further generate high-quality pseudo labels for self-training. Extensive experiments on six benchmarks show that MonoCT outperforms existing SOTA domain adaptation methods by large margins (~21% minimum for AP Mod.) and generalizes well to car, traffic camera and drone views.

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NeurIPS Conference 2025 Conference Paper

OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata

  • Oussema Dhaouadi
  • Riccardo Marin
  • Johannes Meier
  • Jacques Kaiser
  • Daniel Cremers

Accurate visual localization from aerial views is a fundamental problem with applications in mapping, large-area inspection, and search-and-rescue operations. In many scenarios, these systems require high-precision localization while operating with limited resources (e. g. , no internet connection or GNSS/GPS support), making large image databases or heavy 3D models impractical. Surprisingly, little attention has been given to leveraging orthographic geodata as an alternative paradigm, which is lightweight and increasingly available through free releases by governmental authorities (e. g. , the European Union). To fill this gap, we propose OrthoLoC, the first large-scale dataset comprising 16, 425 UAV images from Germany and the United States with multiple modalities. The dataset addresses domain shifts between UAV imagery and geospatial data. Its paired structure enables fair benchmarking of existing solutions by decoupling image retrieval from feature matching, allowing isolated evaluation of localization and calibration performance. Through comprehensive evaluation, we examine the impact of domain shifts, data resolutions, and covisibility on localization accuracy. Finally, we introduce a refinement technique called AdHoP, which can be integrated with any feature matcher, improving matching by up to 95% and reducing translation error by up to 63%. The dataset and code are available at: https: //deepscenario. github. io/OrthoLoC.

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