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.

翻译：摘要：精确的相机标定对于众多计算机视觉应用至关重要。然而，在真实世界中测量相机参数极具挑战性且费时费力，同时缺乏带有真实标注的数据集来评估标定算法的精度。本文提出SynthCal——一种合成相机标定基准测试管道，通过生成标定图案图像来测量并精确量化标定算法在相机参数估计中的性能。我们生成了一个包含四种常见标定图案、两种相机类型、以及两种具有不同视角、畸变、光照和噪声水平的环境的SynthCal标定数据集。该数据集通过测量相同图案和相机设置下的重投影误差和均方根误差来评估单视角标定算法。此外，我们利用张正友方法分析了不同标定图案的重要性——该方法通过已知的三维点与不同配置及环境下二维投影之间的对应关系估计相机内参和外参。实验结果表明，SynthCal在评估各种标定算法与标定图案方面具有有效性。