TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos
We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides $140+$ hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featuring multimodal annotations like high-quality camera calibrations, precise 3D ball positions, ball spin, time segmentation, and 3D human meshes over time. This rich data provides a new foundation for virtual replay, in-depth player analysis, and robot learning. The dataset's combination of scale and precision is achieved through a novel reconstruction pipeline. Prior methods first partition a game sequence into individual shot segments based on the 2D ball track, and only then attempt reconstruction. However, 2D-ba...
| Authors | Nima Rahmanian, Daniel Kienzle, Thomas Gossard et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 May 2026 |
| Last updated at source | 9 September 2026 |
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