This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images. Assuming that the distorted lines generated by fisheye projection should be straight after rectification, we propose a novel deep neural network to impose explicit geometry constraints onto processes of the fisheye lens calibration and the distorted image rectification. In addition, considering the nonlinearity of distortion distribution in fisheye images, the proposed network fully exploits multi-scale perception to equalize the rectification effects on the whole image. To train and evaluate the proposed model, we also create a new largescale dataset labeled with corresponding distortion parameters and well-annotated distorted lines. Compared with the state-of-the-art methods, our model achieves the best published rectification quality and the most accurate estimation of distortion parameters on a large set of synthetic and real fisheye images.
翻译:本文介绍了一种基于深层学习的新方法,以同时校准鱼眼镜头的内在参数,纠正扭曲的图像。假设鱼眼投影产生的扭曲线线在纠正后应当直线直线,我们提议建立一个新型的深神经网络,对鱼眼镜头校准过程和扭曲的图像校正过程施加明确的几何限制。此外,考虑到鱼眼图像中扭曲分布的不线性,拟议网络充分利用了多尺度的认知,以平衡对整个图像的校正效应。为了培训和评估拟议的模型,我们还创建了一个新的大型数据集,配有相应的扭曲参数和注释良好的扭曲线。与最新工艺方法相比,我们的模型实现了最佳的公布校正质量,并对大量合成和真实的鱼眼图像的扭曲参数进行了最准确的估计。