no code implementations • 19 Mar 2024 • Juan D. Galvis, Xingxing Zuo, Simon Schaefer, Stefan Leutengger
This paper introduces a 3D shape completion approach using a 3D latent diffusion model optimized for completing shapes, represented as Truncated Signed Distance Functions (TSDFs), from partial 3D scans.
no code implementations • 17 Mar 2024 • Theresa Huber, Simon Schaefer, Stefan Leutenegger
The assumption of a static environment is common in many geometric computer vision tasks like SLAM but limits their applicability in highly dynamic scenes.
no code implementations • 19 Sep 2023 • Simon Schaefer, Dorian F. Henning, Stefan Leutenegger
An accurate and uncertainty-aware 3D human body pose estimation is key to enabling truly safe but efficient human-robot interactions.
no code implementations • 3 Sep 2023 • Dorian F. Henning, Christopher Choi, Simon Schaefer, Stefan Leutenegger
Robust, fast, and accurate human state - 6D pose and posture - estimation remains a challenging problem.
no code implementations • 20 Jun 2023 • Anna Penzkofer, Simon Schaefer, Florian Strohm, Mihai Bâce, Stefan Leutenegger, Andreas Bulling
We show that intentions of human players, i. e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma's Revenge - one of the most challenging RL tasks in the Atari2600 game suite.
no code implementations • CVPR 2022 • Simon Schaefer, Daniel Gehrig, Davide Scaramuzza
For this reason, recent works have adopted Graph Neural Networks (GNNs), which process events as ``static" spatio-temporal graphs, which are inherently "sparse".
1 code implementation • 2 Dec 2020 • Simon Schaefer, Karen Leung, Boris Ivanovic, Marco Pavone
To achieve seamless human-robot interactions, robots need to intimately reason about complex interaction dynamics and future human behaviors within their motion planning process.