# ClearLines - Camera Calibration from Straight Lines

arXiv: https://arxiv.org/abs/2505.00452 · HTML: https://mlsanity.com/paper/2505.00452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 6 of 6)

- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (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)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)
- [Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy](https://mlsanity.com/paper/2109.03462/) (2021)
- [Interaction of Autonomous and Manually Controlled Vehicles Multiscenario Vehicle Interaction Dataset](https://mlsanity.com/paper/2403.08455/) (2024)

## Abstract (arXiv)

The problem of calibration from straight lines is fundamental in geometric computer vision, with well-established theoretical foundations. However, its practical applicability remains limited, particularly in real-world outdoor scenarios. These environments pose significant challenges due to diverse and cluttered scenes, interrupted reprojections of straight 3D lines, and varying lighting conditions, making the task notoriously difficult. Furthermore, the field lacks a dedicated dataset encouraging the development of respective detection algorithms. In this study, we present a small dataset named "ClearLines", and by detailing its creation process, provide practical insights that can serve as a guide for developing and refining straight 3D line detection algorithms.