Rotating-star Pattern for Camera Calibration

TL;DR

The paper tackles camera calibration under aberrations where traditional star-pattern methods suffer aliasing and reduced accuracy. It introduces a rotating-series of checkerboard patterns with alternating boundaries to decompose the star into simpler patterns, boosting gradient information while reducing aliasing. A three-part pipeline—pattern design, corner initialization, and an efficient corner refinement optimized for both symmetric and asymmetric PSFs—is developed and validated in synthetic and real-world experiments. Results show improved calibration accuracy and robustness over the single star-pattern and phase-based methods across exposure levels and large pattern tilts, enabling more reliable 3D vision in challenging optical conditions.

Abstract

Camera calibration is fundamental to 3D vision, and the choice of calibration pattern greatly affects the accuracy. To address aberration issue, star-shaped pattern has been proposed as alternatives to traditional checkerboard. However, such pattern suffers from aliasing artifacts. In this paper, we present a novel solution by employing a series of checkerboard patterns rotated around a central point instead of a single star-shaped pattern. We further propose a complete feature extraction algorithm tailored for this design. Experimental results demonstrate that our approach offers improved accuracy over the conventional star-shaped pattern and achieves high stability across varying exposure levels.

Figures (6)

• Figure 1: (a) to (c): checkerboard, deltille Ha2017, and star pattern Schops2020. (d): Simulation of checkerboard corners under primary coma aberration Wyant1992Wyant2013. Red markers: aberration free center. Blue markers: OpenCV sub-pixel results. (e): Saturated phase pattern.
• Figure 2: We decompose the star-shaped pattern into a series of checkerboard patterns rotating around its local center, displayed by a screen. And we add black-and-white transition boundaries around the pattern elements for more robust detection.
• Figure 3: Model for synthetic experiment.
• Figure 4: Pairwise-distance range of refinement algorithms under primary aberration.
• Figure 5: (Top row) Display sequence and poses of two calibration groups. (Middle row) Captured checkerboard images of the right group under middle exposure level. (Bottom row) Close-up of patterns under three exposure levels at the red cross location of the middle row.