Publication
PnLCalib: Sports field registration via points and lines optimization
Journal Article (2026)
Journal
Computer Vision and Image Understanding
Pages
104712
Volume
267
Doc link
https://doi.org/10.1016/j.cviu.2026.104712
File
Authors
Projects associated
Abstract
Camera calibration in broadcast sports videos presents numerous challenges for accurate sports field registration due to multiple camera angles, varying camera parameters, and frequent occlusions of the field. Traditional search-based methods depend on initial camera pose estimates, which can struggle in non-standard positions and dynamic environments. In response, we propose an optimization-based calibration pipeline that leverages a 3D soccer field model and a predefined set of keypoints to overcome these limitations. Our method also introduces a novel refinement module that improves initial calibration by using detected field lines in a non-linear optimization process. This approach outperforms existing techniques in both multi-view and single-view 3D camera calibration tasks, while maintaining competitive performance in homography estimation. Extensive experimentation on real-world soccer datasets, including SoccerNet-Calibration, WorldCup 2014, and TS-WorldCup, highlights the robustness and accuracy of our method across diverse broadcast scenarios. Our approach offers significant improvements in camera calibration precision and reliability. Our project is available at https://github.com/mguti97/PnLCalib.
Categories
computer vision, optimisation.
Author keywords
Camera Calibration, Homography Estimation, Sports Analytics, SoccerNet, World Cup
Scientific reference
M. Gutiérrez and A. Agudo. PnLCalib: Sports field registration via points and lines optimization. Computer Vision and Image Understanding, 267: 104712, 2026.
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