IEEE Trans Image Process. 2017 Jun;26(6):2694-2704. doi: 10.1109/TIP.2017.2686001. Epub 2017 Mar 22.
This paper addresses the question of identifying the right camera direct or inverse distortion model, permitting a high subpixel precision to fit to real camera distortion. Five classic camera distortion models are reviewed and their precision is compared for direct or inverse distortion. By definition, the three radially symmetric models can only model a distortion radially symmetric around some distortion center. They can be extended to deal with non-radially symmetric distortions by adding tangential distortion components, but still may be too simple for very accurate modeling of real cameras. The polynomial and the rational models instead miss a physical or optical interpretation, but can cope equally with radially and non-radially symmetric distortions. Indeed, they do not require the evaluation of a distortion center. When requiring high precisions, we found that the distortion modeling must also be evaluated primarily as a numerical problem. Indeed, all models except the polynomial involve a non-linear minimization, which increases the numerical risk. The estimation of a polynomial distortion model leads instead to a linear problem, which is secure and much faster. We concluded by extensive numerical experiments that, although high degree polynomials were required to reach a high precision of 1/100 pixels, such polynomials were easily estimated and produced a precise distortion modeling without overfitting. Our conclusion is validated by three independent experimental setups: the models were compared first on the lens distortion database of the Lensfun library by their distortion simulation and inversion power; second by fitting real camera distortions estimated by a non parametric algorithm; and finally by the absolute correction measurement provided by the photographs of tightly stretched strings, warranting a high straightness.
本文探讨了正确选择相机直接或反向失真模型的问题,以便能够以高亚像素精度拟合实际相机失真。本文回顾了五种经典的相机失真模型,并比较了它们在直接或反向失真下的精度。根据定义,三个径向对称模型只能对某个失真中心周围的径向对称失真进行建模。通过添加切向失真分量,可以将它们扩展到处理非径向对称失真,但对于非常精确地建模实际相机,它们可能仍然过于简单。多项式和有理模型则没有物理或光学解释,但可以同样处理径向和非径向对称失真。实际上,它们不需要评估失真中心。当需要高精度时,我们发现失真建模也必须主要作为一个数值问题进行评估。实际上,除了多项式模型之外,所有模型都涉及非线性最小化,这增加了数值风险。多项式失真模型的估计则导致线性问题,这是安全且更快的。通过广泛的数值实验,我们得出结论,尽管需要高次多项式才能达到 1/100 像素的高精度,但这些多项式很容易估计,并产生精确的失真建模而不会过度拟合。我们的结论通过三个独立的实验设置得到验证:首先通过 Lensfun 库的镜头失真数据库比较模型的失真模拟和反转能力;其次通过非参数算法估计的实际相机失真进行拟合;最后通过紧密拉伸的字符串的照片提供的绝对校正测量进行验证,保证了很高的直线度。