Search Results for author: Simon Schaefer

Found 7 papers, 1 papers with code

SC-Diff: 3D Shape Completion with Latent Diffusion Models

no code implementations19 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.

Object

DynamicGlue: Epipolar and Time-Informed Data Association in Dynamic Environments using Graph Neural Networks

no code implementations17 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.

GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation & Tracking in the Wild

no code implementations19 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.

3D Human Pose Estimation

BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human Motion Tracking

no code implementations3 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.

Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning

no code implementations20 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.

Hierarchical Reinforcement Learning Montezuma's Revenge +2

AEGNN: Asynchronous Event-based Graph Neural Networks

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".

Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions

1 code implementation2 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.

Motion Planning Navigate +1

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