# TVCalib: Camera Calibration for Sports Field Registration in Soccer

arXiv: https://arxiv.org/abs/2207.11709 · HTML: https://mlsanity.com/paper/2207.11709/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 24 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Self-Calibrating Neural Radiance Fields](https://mlsanity.com/paper/2108.13826/) (2021)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [Sports Camera Calibration via Synthetic Data](https://mlsanity.com/paper/1810.10658/) (2018)
- [Optimizing Through Learned Errors for Accurate Sports Field Registration](https://mlsanity.com/paper/1909.08034/) (2019)
- [Real-Time Camera Pose Estimation for Sports Fields](https://mlsanity.com/paper/2003.14109/) (2020)
- [Automated Top View Registration of Broadcast Football Videos](https://mlsanity.com/paper/1703.01437/) (2017)
- [Extraction of Positional Player Data from Broadcast Soccer Videos](https://mlsanity.com/paper/2110.11107/) (2021)

## Built on by (most cited first, 22 of 22)

- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [PnLCalib: Sports Field Registration via Points and Lines Optimization](https://mlsanity.com/paper/2404.08401/) (2024)
- [Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registration](https://mlsanity.com/paper/2304.04437/) (2023)
- [Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration](https://mlsanity.com/paper/2311.10361/) (2023)
- [Enhancing Soccer Camera Calibration Through Keypoint Exploitation](https://mlsanity.com/paper/2410.07401/) (2024)
- [OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer](https://mlsanity.com/paper/2502.02785/) (2025)
- [Tracking Skiers from the Top to the Bottom](https://mlsanity.com/paper/2312.09723/) (2023)
- [BroadTrack: Broadcast Camera Tracking for Soccer](https://mlsanity.com/paper/2412.01721/) (2024)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Can Geometry Save Central Views for Sports Field Registration?](https://mlsanity.com/paper/2504.20052/) (2025)
- [Towards long-term player tracking with graph hierarchies and domain-specific features](https://mlsanity.com/paper/2502.21242/) (2025)
- [From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction](https://mlsanity.com/paper/2504.06357/) (2025)
- [SoccerNet 2026 Challenges Results](https://mlsanity.com/paper/2607.07320/) (2026)
- [PTZ-Calib: Robust Pan-Tilt-Zoom Camera Calibration](https://mlsanity.com/paper/2502.09075/) (2025)
- [SoccerSynth Field: enhancing field detection with synthetic data from virtual soccer simulator](https://mlsanity.com/paper/2503.13969/) (2025)
- [ClearLines - Camera Calibration from Straight Lines](https://mlsanity.com/paper/2505.00452/) (2025)
- [TrajSV: A Trajectory-based Model for Sports Video Representations and Applications](https://mlsanity.com/paper/2508.11569/) (2025)
- [TacSIm: A Dataset and Benchmark for Football Tactical Style Imitation](https://mlsanity.com/paper/2603.25199/) (2026)
- [AI Driven Soccer Analysis Using Computer Vision](https://mlsanity.com/paper/2604.08722/) (2026)
- [Multi-Camera Self-Calibration in Sports Motion Capture: Leveraging Human and Stick Poses](https://mlsanity.com/paper/2604.17567/) (2026)
- [Field Converter: Geometry-Initialized Temporal Residual Refinement for World-Grounded Player Pose Estimation from Soccer Broadcasts](https://mlsanity.com/paper/2609.10498/) (2026)
- [Rule-Constrained Assignment for Cue-Ball Identification in Broadcast Snooker](https://mlsanity.com/paper/2609.23450/) (2026)

## Abstract (arXiv)

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.