Publication
SoccerNet-v3D: leveraging sports broadcast replays for 3D scene understanding
Conference Article
Conference
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Workshop on Computer Vision in Sports (CVsports)
Edition
2025
Pages
5968-5977
Doc link
https://doi.org/10.1109/CVPRW67362.2025.00595
File
Authors
Projects associated
Abstract
Sports video analysis is a key domain in computer vision, enabling detailed spatial understanding through multi-view correspondences. In this work, we introduce SoccerNetv3D and ISSIA-3D, two enhanced and scalable datasets designed for 3D scene understanding in soccer broadcast analysis. These datasets extend SoccerNet-v3 and ISSIA by incorporating field-line-based camera calibration and multi-view synchronization, enabling 3D object localization through triangulation. We propose a monocular 3D ball localization task built upon the triangulation of ground-truth 2D ball annotations, along with several calibration and reprojection metrics to assess annotation quality on demand. Additionally, we present a single-image 3D ball localization method as a baseline, leveraging camera calibration and ball size priors to estimate the ball’s position from a monocular viewpoint. To further refine 2D annotations, we introduce a bounding box optimization technique that ensures alignment with the 3D scene representation. Our proposed datasets establish new benchmarks for 3D soccer scene understanding, enhancing both spatial and temporal analysis in sports analytics.
Categories
optimisation, pattern recognition.
Author keywords
Computer Vision, Sports
Scientific reference
M. Gutiérrez and A. Agudo. SoccerNet-v3D: leveraging sports broadcast replays for 3D scene understanding, 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Workshop on Computer Vision in Sports, 2025, Nashville (TN, USA), pp. 5968-5977.
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