2 citations · 3 across the 8 of their papers we have counts for
13 papers · 1 filter
MAGneT-3D: Monocular and Domain-Generalizable Temporal 3D Detection
Mohamed Kotb, Johannes Meier, Christoph Reich +3
Monocular temporal 3D detection aims to detect objects in 3D, given a monocular video. Query-based 3D detectors unify detection and cross-view association, but their learnable quer…
OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos
Oussema Dhaouadi, Zuria Bauer, Johannes Michael Meier +3
Continuous 6-DoF pose estimation is essential for autonomous UAV operations. Yet, existing visual odometry and SLAM methods accumulate drift and yield only relative, up-to-scale tr…
SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models
Jingfeng Mao, Xuyang Chen, Qilin Zhang +6
Aerial 6DoF localization typically relies on precise GNSS signals or radiometrically rich 3D reconstructions, limiting scalability and on-board deployment. We propose SemCityLoc, a…
LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection
Johannes Meier, Jonathan Michel, Oussema Dhaouadi +7
Real-time monocular 3D object detection remains challenging due to severe depth ambiguity, viewpoint shifts, and the high computational cost of 3D reasoning. Existing approaches ei…
IDEAL-M3D: Instance Diversity-Enriched Active Learning for Monocular 3D Detection
Johannes Meier, Florian Günther, Riccardo Marin +3
Monocular 3D detection relies on just a single camera and is therefore easy to deploy. Yet, achieving reliable 3D understanding from monocular images requires substantial annotatio…
GrounDiff: Diffusion-Based Ground Surface Generation from Digital Surface Models
Oussema Dhaouadi, Johannes Meier, Jacques Kaiser +1
Digital Terrain Models (DTMs) represent the bare-earth elevation and are important in numerous geospatial applications. Such data models cannot be directly measured by sensors and…
OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata
Oussema Dhaouadi, Riccardo Marin, Johannes Meier +2
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, th…
CoProU-VO: Combining Projected Uncertainty for End-to-End Unsupervised Monocular Visual Odometry
Jingchao Xie, Oussema Dhaouadi, Weirong Chen +3
Visual Odometry (VO) is fundamental to autonomous navigation, robotics, and augmented reality, with unsupervised approaches eliminating the need for expensive ground-truth labels.…
Shape Your Ground: Refining Road Surfaces Beyond Planar Representations
Oussema Dhaouadi, Johannes Meier, Jacques Kaiser +1
Road surface reconstruction from aerial images is fundamental for autonomous driving, urban planning, and virtual simulation, where smoothness, compactness, and accuracy are critic…
Highly Accurate and Diverse Traffic Data: The DeepScenario Open 3D Dataset
Oussema Dhaouadi, Johannes Meier, Luca Wahl +7
Accurate 3D trajectory data is crucial for advancing autonomous driving. Yet, traditional datasets are usually captured by fixed sensors mounted on a car and are susceptible to occ…
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models
Johannes Meier, Louis Inchingolo, Oussema Dhaouadi +3
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…
TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes
Yan Xia, Yunxiang Lu, Rui Song +3
We tackle the problem of localizing traffic cameras within a 3D reference map and propose a novel image-to-point cloud registration (I2P) method, TrafficLoc, in a coarse-tofine mat…
CARLA Drone: Monocular 3D Object Detection from a Different Perspective
Johannes Meier, Luca Scalerandi, Oussema Dhaouadi +3
Existing techniques for monocular 3D detection have a serious restriction. They tend to perform well only on a limited set of benchmarks, faring well either on ego-centric car view…