Abstract
This paper addresses the challenge of registering a 3D sports field from a baseball pitcher scene image. Some recent works have proposed calibrating a 2D homography matrix and using it to project keypoints onto the court field. However, this approach has limitations in 2D registration scenarios. By using 3D registration instead, these systems can provide more precise data analysis and better visual effects. Furthermore, we introduce parametric model regression to predict the 3D spatial information of the sports field. Based on parametric model regression, domain generalization is employed to improve generalizability. Experiments show that our approach significantly outperforms other 2D registration methods.