新标定图案可低分辨率解码，支持遮挡和旧算法兼容。
PuzzleBoard: A New Camera Calibration Pattern with Position Encoding
- 采用轻量位置编码，低分辨率下仍能准确解码。
- 支持部分遮挡，且解码算法具纠错能力。
- 兼容传统标定方法，适用于位姿估计等任务。
相机标定是计算机视觉中广泛研究的重要任务，但传统的棋盘格模式存在明显局限：必须完全可见且无遮挡。虽然ChArUco板可处理部分遮挡，但因依赖精细的位置编码，需更高分辨率摄像头。本文提出一种新型标定图案，融合棋盘格优点与轻量位置编码，可在极低分辨率下实现高效解码，且解码算法具备错误纠正能力，计算开销小。该方法与传统棋盘格标定模式及多种算法完全后向兼容，不仅可用于相机标定，还可扩展至相机位姿估计与基于标记的物体定位任务。
原文摘要 · Abstract (English)
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.
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