Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909640966537216 |
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| author | Ponglertnapakorn, Puntawat Suwajanakorn, Supasorn |
| author_facet | Ponglertnapakorn, Puntawat Suwajanakorn, Supasorn |
| contents | We present a method for 3D ball trajectory estimation from a 2D tracking sequence. To overcome the ambiguity in 3D from 2D estimation, we design an LSTM-based pipeline that utilizes a novel canonical 3D representation that is independent of the camera's location to handle arbitrary views and a series of intermediate representations that encourage crucial invariance and reprojection consistency. We evaluated our method on four synthetic and three real datasets and conducted extensive ablation studies on our design choices. Despite training solely on simulated data, our method achieves state-of-the-art performance and can generalize to real-world scenarios with multiple trajectories, opening up a range of applications in sport analysis and virtual replay. Please visit our page: https://where-is-the-ball.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05763 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking Ponglertnapakorn, Puntawat Suwajanakorn, Supasorn Computer Vision and Pattern Recognition We present a method for 3D ball trajectory estimation from a 2D tracking sequence. To overcome the ambiguity in 3D from 2D estimation, we design an LSTM-based pipeline that utilizes a novel canonical 3D representation that is independent of the camera's location to handle arbitrary views and a series of intermediate representations that encourage crucial invariance and reprojection consistency. We evaluated our method on four synthetic and three real datasets and conducted extensive ablation studies on our design choices. Despite training solely on simulated data, our method achieves state-of-the-art performance and can generalize to real-world scenarios with multiple trajectories, opening up a range of applications in sport analysis and virtual replay. Please visit our page: https://where-is-the-ball.github.io. |
| title | Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05763 |