2 citations · 4 across the 10 of their papers we have counts for
16 papers
Modular Bayesian Inference for Partially Misspecified Mixtures with Uncertain Class Labels
Maximilian Autenrieth, Konstantin Karchev, David A. van Dyk +2
Scientific inference often involves observations from multiple latent populations whose class memberships are uncertain and must be inferred from available data. While full Bayesia…
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…
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…
ECHO: Ego-Centric modeling of Human-Object interactions
Ilya A. Petrov, Vladimir Guzov, Riccardo Marin +5
Modeling human-object interactions (HOI) from an egocentric perspective is a critical yet challenging task, particularly when relying on sparse signals from wearable devices like s…
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.…
The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking
Mateo de Mayo, Daniel Cremers, Taihú Pire
Humanoid robots and mixed reality headsets benefit from the use of head-mounted sensors for tracking. While advancements in visual-inertial odometry (VIO) and simultaneous localiza…
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…
It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data
Dominik Schnaus, Nikita Araslanov, Daniel Cremers
The platonic representation hypothesis suggests that vision and language embeddings become more homogeneous as model and dataset sizes increase. In particular, pairwise distances w…
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…