This project develops a 3D reconstruction system for badminton matches, creating a digital twin from multi-view 2D videos. It accurately models shuttlecock trajectories, player movements, and racket poses using AI detection, triangulation, and physics-based correction. Advanced pose estimation ensures players are anchored on the virtual court, while geometric constraints estimate racket orientation. The result is an interactive, realistic 3D match scene for analysis and visualization.

This project fuses computer vision, machine learning and sports physics to build the first full 3D digital twin of badminton. From multi-view video it jointly reconstructs shuttle paths, player poses and racket angles, anchors them on the real court, and denoises with density clustering plus physics-based smoothing. A geometric prior estimates racket orientation, while cross-camera calibration grounds players in space. The result turns raw footage into mechanism-level tactical insight.

The 3D match reconstruction technology has strong commercial potential. In badminton, it enables tactical replay, biomechanical analysis, and injury prevention. For media and broadcasting, it enhances viewer engagement with real-time 3D visuals. It also supports VR/AR experiences and next-gen sports games by providing realistic motion data, offering immersive and interactive applications across sports tech and digital entertainment industries.