10 citations · 19 across the 17 of their papers we have counts for
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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…