10 citations · 19 across the 17 of their papers we have counts for
23 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…
ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction
Tim Engelbracht, René Zurbrügg, Mayank Mittal +4
Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these include inertia, friction, and…
Map-Det3D: Metric Feed-Forward 3D Reconstruction Prior for Multi-view 3D Object Detection from Streaming Inputs
Yung-Hsu Yang, Luigi Piccinelli, Samuel Rota Bulò +7
Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors, trading away cost, power, and integration simplicity. This mot…
DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving
Yung-Hsu Yang, Luigi Piccinelli, Siyuan Li +8
Safe autonomous navigation requires a holistic understanding of dynamic environments, necessitating the simultaneous estimation of metric depth, semantic segmentation, and instance…
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…
ArtiTwinSplat: Interactable Digital Twin Reconstruction via Gaussian Splatting from RGB-D videos
Pranjal Mishra, René Zurbrügg, Max Wilder-Smith +4
Deploying robots in unstructured real-world environments needs accurate, interactive models of the objects. Constructing these models at scale remains a critical bottleneck for rob…
PROSE: Training-Free Egocentric Scene Registration with Vision-Language Models
Zhiang Chen, Nahyuk Lee, Boyang Sun +4
Registering two captures of the same indoor space taken at different times underpins persistent spatial memory for robots and AR systems, yet the realistic version of this task is…
From Frames to Temporal Graphs: In-Context Egocentric Action Recognition with Vision-Language Models
Bessie Dominguez-Dager, Francisco Gomez-Donoso, Miguel Cazorla +3
Action reasoning in egocentric video requires capturing fine-grained transitions of hand-object interactions, a task where general-purpose Vision-Language Models (VLMs) often strug…
FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning
Zhengyu Fu, René Zurbrügg, Kaixian Qu +4
Recent work in 3D scene understanding is moving beyond purely spatial analysis toward functional scene understanding. However, existing methods often consider functional relationsh…
Articulated 3D Scene Graphs for Open-World Mobile Manipulation
Martin Büchner, Adrian Röfer, Tim Engelbracht +5
Semantic scene understanding has enabled robots to reason and perform affordance-driven object interaction. Despite this, real-world manipulation remains fundamentally limited by t…
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…
Hoi! - A Multimodal Dataset for Force-Grounded, Cross-View Articulated Manipulation
Tim Engelbracht, René Zurbrügg, Matteo Wohlrapp +5
We present a dataset for force-grounded, cross-view articulated manipulation that couples what is seen with what is done and what is felt during real human interaction. The dataset…
AIDEN: Design and Pilot Study of an AI Assistant for the Visually Impaired
Luis Marquez-Carpintero, Francisco Gomez-Donoso, Zuria Bauer +6
This paper presents AIDEN, an artificial intelligence-based assistant designed to enhance the autonomy and daily quality of life of visually impaired individuals, who often struggl…
3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection
Yung-Hsu Yang, Luigi Piccinelli, Mattia Segu +6
Monocular 3D object detection is valuable for various applications such as robotics and AR/VR. Existing methods are confined to closed-set settings, where the training and testing…
Video Perception Models for 3D Scene Synthesis
Rui Huang, Guangyao Zhai, Zuria Bauer +5
Traditionally, 3D scene synthesis requires expert knowledge and significant manual effort. Automating this process could greatly benefit fields such as architectural design, roboti…
MR.NAVI: Mixed-Reality Navigation Assistant for the Visually Impaired
Nicolas Pfitzer, Yifan Zhou, Marco Poggensee +5
Over 43 million people worldwide live with severe visual impairment, facing significant challenges in navigating unfamiliar environments. We present MR.NAVI, a mixed reality system…
Spot-On: A Mixed Reality Interface for Multi-Robot Cooperation
Tim Engelbracht, Petar Lukovic, Tjark Behrens +5
Recent progress in mixed reality (MR) and robotics is enabling increasingly sophisticated forms of human-robot collaboration. Building on these developments, we introduce a novel M…
Lost & Found: Tracking Changes from Egocentric Observations in 3D Dynamic Scene Graphs
Tjark Behrens, René Zurbrügg, Marc Pollefeys +2
Recent approaches have successfully focused on the segmentation of static reconstructions, thereby equipping downstream applications with semantic 3D understanding. However, the wo…
HoloSpot: Intuitive Object Manipulation via Mixed Reality Drag-and-Drop
Pablo Soler Garcia, Petar Lukovic, Lucie Reynaud +8
Human-robot interaction through mixed reality (MR) technologies enables novel, intuitive interfaces to control robots in remote operations. Such interfaces facilitate operations in…
MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby References
Lukas Bösiger, Mihai Dusmanu, Marc Pollefeys +1
Rendering realistic images from 3D reconstruction is an essential task of many Computer Vision and Robotics pipelines, notably for mixed-reality applications as well as training au…
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds
Oliver Lemke, Zuria Bauer, René Zurbrügg +3
In recent years, modern techniques in deep learning and large-scale datasets have led to impressive progress in 3D instance segmentation, grasp pose estimation, and robotics. This…
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation
Casimir Feldmann, Niall Siegenheim, Nikolas Hars +6
The capabilities of monocular depth estimation (MDE) models are limited by the availability of sufficient and diverse datasets. In the case of MDE models for autonomous driving, th…
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis
Zuria Bauer, Zuoyue Li, Sergio Orts-Escolano +3
Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation. In particular, we propose a novel training method split…