  Sports field registration in broadcast videos is typically interpreted as the
task of homography estimation, which provides a mapping between a planar field
and the corresponding visible area of the image. In contrast to previous
approaches, we consider the task as a camera calibration problem. First, we
introduce a differentiable objective function that is able to learn the camera
pose and focal length from segment correspondences (e.g., lines, point clouds),
based on pixel-level annotations for segments of a known calibration object.
The calibration module iteratively minimizes the segment reprojection error
induced by the estimated camera parameters. Second, we propose a novel approach
for 3D sports field registration from broadcast soccer images. Compared to the
typical solution, which subsequently refines an initial estimation, our
solution does it in one step. The proposed method is evaluated for sports field
registration on two datasets and achieves superior results compared to two
state-of-the-art approaches.
