The Paper "Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation" by Daniel Kienzle, Julian Lorenz and Rainer Lienhart has been accepted at the "IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026".

The authors present a robust end-to-end pipeline for analyzing table tennis matches, which can, for the first time, derive the precise 3D trajectory and spin of the ball directly from common TV broadcasts.
The solution overcomes the central problem of missing 3D ground truth using a two-stage framework:
    1. Front-End (2D Perception): Segformer++ detectors accurately recognize the 2D ball positions and table keypoints in every frame. This module is trained on the new, high-resolution TTHQ dataset.
    2. Back-End (2D-to-3D Uplifting): A specialized transformer network is trained exclusively with synthetic, physically correct 3D data to lift the 2D detections into the 3D world.
Due to architectural adaptations, such as a time-proportional Positional Embedding, the pipeline is extremely robust against real-world problems like variable frame rates and faulty detections.
This work represents a practical, ready-to-use tool for detailed technique and performance analysis in table tennis sports.
More paper details are given at https://kiedani.github.io/WACV2026/index.html