Camera calibration is a basic and crucial problem in photogrammetry and computer vision. Although existing calibration techniques exhibit excellent precision and flexibility in classical cases, most of them need from 5 to 10 calibration images. Unfortunately, only a limited number of calibration images and control points can be available in many application fields such as criminal investigation, industrial robot and augmented reality. For these cases, this paper presented a two-step calibration based on soft constraint optimization, which is motivated by "no free lunch" theorem and error analysis. The key steps include (1) homography estimation with weighting function, (2) Initialization based on a simplified model, and (3) soft constraint optimization in terms of reprojection error. The proposed method provides direct access to geometric information of the object from very few images. After extensive experiments, the results demonstrate that the proposed algorithm outperforms Zhang's algorithms from the point of view of the success ratio, accuracy and precision.

相机校准是摄影测量和计算机视觉中的一个基本且至关重要的问题。尽管现有的校准技术在经典情况下显示出卓越的精度和灵活性，但大多数都需要5至10个校准图像。不幸的是，在许多应用领域，例如刑事调查，工业机器人和增强现实中，只能使用有限数量的校准图像和控制点。对于这些情况，本文提出了一种基于“软约束优化”的两步式校准，该校准是基于“免费午餐”定理和误差分析的。关键步骤包括（1）具有加权功能的单应性估计，（2）基于简化模型的初始化以及（3）就重投影误差而言的软约束优化。所提出的方法提供了从很少的图像直接访问对象的几何信息的方法。经过大量的实验，结果表明，从成功率，准确率和精确度的角度来看，该算法优于张算法。