# PnLCalib: Sports Field Registration via Points and Lines Optimization

arXiv: https://arxiv.org/abs/2404.08401 · HTML: https://mlsanity.com/paper/2404.08401/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 73 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 27 of 27)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model](https://mlsanity.com/paper/1709.03966/) (2017)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments](https://mlsanity.com/paper/1705.09479/) (2017)
- [A Perceptual Measure for Deep Single Image Camera Calibration](https://mlsanity.com/paper/1712.01259/) (2017)
- [TacticAI: an AI assistant for football tactics](https://mlsanity.com/paper/2310.10553/) (2023)
- [Horizon Lines in the Wild](https://mlsanity.com/paper/1604.02129/) (2016)
- [Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints](https://mlsanity.com/paper/2007.15122/) (2020)
- [Detecting Vanishing Points using Global Image Context in a Non-Manhattan World](https://mlsanity.com/paper/1608.05684/) (2016)
- [Sports Camera Calibration via Synthetic Data](https://mlsanity.com/paper/1810.10658/) (2018)
- [NeurVPS: Neural Vanishing Point Scanning via Conic Convolution](https://mlsanity.com/paper/1910.06316/) (2019)
- [TVCalib: Camera Calibration for Sports Field Registration in Soccer](https://mlsanity.com/paper/2207.11709/) (2022)
- [Optimizing Through Learned Errors for Accurate Sports Field Registration](https://mlsanity.com/paper/1909.08034/) (2019)
- [Unsupervised Homography Estimation with Coplanarity-Aware GAN](https://mlsanity.com/paper/2205.03821/) (2022)
- [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)
- [CFNet: LiDAR-Camera Registration Using Calibration Flow Network](https://mlsanity.com/paper/2104.11907/) (2021)
- [Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration](https://mlsanity.com/paper/2311.10361/) (2023)
- [KaliCalib: A Framework for Basketball Court Registration](https://mlsanity.com/paper/2209.07795/) (2022)
- [Insights into the robustness of control point configurations for homography and planar pose estimation](https://mlsanity.com/paper/1803.03025/) (2018)

## Built on by (most cited first, 14 of 14)

- [SoccerMaster: A Vision Foundation Model for Soccer Understanding](https://mlsanity.com/paper/2512.11016/) (2025)
- [SoccerNet 2024 Challenges Results](https://mlsanity.com/paper/2409.10587/) (2024)
- [SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene Understanding](https://mlsanity.com/paper/2504.10106/) (2025)
- [SoccerNet 2025 Challenges Results](https://mlsanity.com/paper/2508.19182/) (2025)
- [Can Geometry Save Central Views for Sports Field Registration?](https://mlsanity.com/paper/2504.20052/) (2025)
- [Multi-Modal Soccer Scene Analysis with Masked Pre-Training](https://mlsanity.com/paper/2512.19528/) (2025)
- [Towards Athlete Fatigue Assessment from Association Football Videos](https://mlsanity.com/paper/2604.05636/) (2026)
- [SoccerNet 2026 Challenges Results](https://mlsanity.com/paper/2607.07320/) (2026)
- [Training-Free Off-Screen Player Imputation for Broadcast-Based Spatial Football Analytics](https://mlsanity.com/paper/2607.11548/) (2026)
- [Space-Creating versus Dead Possession: An Off-Ball Possession-Quality Index for Broadcast Football](https://mlsanity.com/paper/2608.09887/) (2026)
- [A Top-Down Framework for Metric-Scale Athlete Localization from Single Broadcast Frames](https://mlsanity.com/paper/2609.02705/) (2026)
- [Field Converter: Geometry-Initialized Temporal Residual Refinement for World-Grounded Player Pose Estimation from Soccer Broadcasts](https://mlsanity.com/paper/2609.10498/) (2026)
- [How Much Velocity Does Off-Ball Space Value Need? A Broadcast-Viewport Benchmark](https://mlsanity.com/paper/2609.10801/) (2026)
- [Rule-Constrained Assignment for Cue-Ball Identification in Broadcast Snooker](https://mlsanity.com/paper/2609.23450/) (2026)

## Abstract (arXiv)

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.