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.

本文介绍了 SynthCal（一个合成相机校准基准测试管道），它生成校准图案的图像来测量相机参数，并在相机参数估计中实现准确量化，其中包括四种常见模式、两种相机类型和两种环境，评估单视图校准算法通过测量相同模式和相机设置的重投影和均方根误差，并使用 Zhang 方法分析不同模式的显着性）且实验结果证明了 SynthCal 在评估各种校准算法和模式方面的有效性。