  Accurate camera calibration is crucial for various computer vision
applications. However, measuring camera parameters in the real world is
challenging and arduous, and there needs to be a dataset with ground truth to
evaluate calibration algorithms' accuracy. In this paper, we present SynthCal,
a synthetic camera calibration benchmarking pipeline that generates images of
calibration patterns to measure and enable accurate quantification of
calibration algorithm performance in camera parameter estimation. We present a
SynthCal-generated calibration dataset with four common patterns, two camera
types, and two environments with varying view, distortion, lighting, and noise
levels. The dataset evaluates single-view calibration algorithms by measuring
reprojection and root-mean-square errors for identical patterns and camera
settings. Additionally, we analyze the significance of different patterns using
Zhang's method, which estimates intrinsic and extrinsic camera parameters with
known correspondences between 3D points and their 2D projections in different
configurations and environments. The experimental results demonstrate the
effectiveness of SynthCal in evaluating various calibration algorithms and
patterns.
