Learning to Calibrate Straight Lines for Fisheye Image Rectification

Zhucun Xue, Nan Xue, Gui-Song Xia, Weiming Shen; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1643-1651

Abstract


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 large-scale 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.

Related Material


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[bibtex]
@InProceedings{Xue_2019_CVPR,
author = {Xue, Zhucun and Xue, Nan and Xia, Gui-Song and Shen, Weiming},
title = {Learning to Calibrate Straight Lines for Fisheye Image Rectification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}