从单视角2D轨迹推算3D球体运动路径，无需真实数据训练
Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking
- 用LSTM建模+新3D表征，解决视角无关的3D重建歧义
- 仅在仿真数据训练，却在实拍数据上达到顶尖精度
- 适合体育分析、虚拟回放等需要精准轨迹预测的场景
本文提出一种从2D跟踪序列估计3D球体轨迹的方法。为克服2D到3D映射的歧义性，设计了一种基于LSTM的流水线，采用与相机位置无关的新型规范3D表示，并引入一系列中间表示以增强关键不变性和重投影一致性。在四个合成数据集和三个真实数据集上进行了评估，并对设计选择进行了大量消融实验。尽管仅在模拟数据上训练，该方法仍达到当前最优性能，可泛化至包含多轨迹的真实场景，为体育分析和虚拟回放开辟了广泛应用前景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。