17 papers · 1 filter
TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos
Nima Rahmanian, Daniel Kienzle, Thomas Gossard +3
We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featurin…
Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation
Daniel Kienzle, Katja Ludwig, Julian Lorenz +2
Obtaining the precise 3D motion of a table tennis ball from standard monocular videos is a challenging problem, as existing methods trained on synthetic data struggle to generalize…
MMMS: Multi-Modal Multi-Surface Interactive Segmentation
Robin Schön, Julian Lorenz, Katja Ludwig +2
In this paper, we present a method to interactively create segmentation masks on the basis of user clicks. We pay particular attention to the segmentation of multiple surfaces that…
CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations
Julian Lorenz, Mrunmai Phatak, Robin Schön +4
2D scene graphs provide a structural and explainable framework for scene understanding. However, current work still struggles with the lack of accurate scene graph data. To overcom…
HOIverse: A Synthetic Scene Graph Dataset With Human Object Interactions
Mrunmai Vivek Phatak, Julian Lorenz, Nico Hörmann +2
When humans and robotic agents coexist in an environment, scene understanding becomes crucial for the agents to carry out various downstream tasks like navigation and planning. Hen…
Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer
Daniel Kienzle, Robin Schön, Rainer Lienhart +1
Analyzing a player's technique in table tennis requires knowledge of the ball's 3D trajectory and spin. While, the spin is not directly observable in standard broadcasting videos,…