A SIMPLE YET EFFECTIVE PIPELINE FOR RADIAL DISTORTION CORRECTION

Eliminating the radial lens distortion of an image is a crucial preprocessing step for many computer vision applications. This paper explores a simple yet effective pipeline for radial distortion correction. Different from existing state-of-the-art methods that design complex network structure and concatenate multi-branch features. Our model uses a single network without any additional supervision. We design two differentiable layers to synthesize and rectify distorted images efficiently. Based on these layers, an online data synthesis strategy, a sampling grid loss, and an image reprojection loss are proposed to improve the distortion correction accuracy. Compared with the state-of-the-art methods, our model achieves the best rectification quality on both the synthetic and real distorted images with dozens of times faster inference speed. The training data and codes will be released.