# PuzzleBoard: A New Camera Calibration Pattern with Position Encoding

arXiv: https://arxiv.org/abs/2409.20127 · HTML: https://mlsanity.com/paper/2409.20127/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 57 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (2019)
- [CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration](https://mlsanity.com/paper/2302.05097/) (2023)
- [Pyramidal Blur Aware X-Corner Chessboard Detector](https://mlsanity.com/paper/2110.13793/) (2021)
- [RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model](https://mlsanity.com/paper/2307.03505/) (2023)

## Built on by (most cited first, 2 of 2)

- [PuzzlePoles: Cylindrical Fiducial Markers Based on the PuzzleBoard Pattern](https://mlsanity.com/paper/2511.19448/) (2025)
- [Conceptual Evaluation of Deep Visual Stereo Odometry for the MARWIN Radiation Monitoring Robot in Accelerator Tunnels](https://mlsanity.com/paper/2512.00080/) (2025)

## Abstract (arXiv)

Accurate camera calibration is a well-known and widely used task in computer vision that has been researched for decades. However, the standard approach based on checkerboard calibration patterns has some drawbacks that limit its applicability. For example, the calibration pattern must be completely visible without any occlusions. Alternative solutions such as ChArUco boards allow partial occlusions, but require a higher camera resolution due to the fine details of the position encoding. We present a new calibration pattern that combines the advantages of checkerboard calibration patterns with a lightweight position coding that can be decoded at very low resolutions. The decoding algorithm includes error correction and is computationally efficient. The whole approach is backward compatible to both checkerboard calibration patterns and several checkerboard calibration algorithms. Furthermore, the method can be used not only for camera calibration but also for camera pose estimation and marker-based object localization tasks.