  Wide field-of-view (FOV) cameras, which capture a larger scene area than
narrow FOV cameras, are used in many applications including 3D reconstruction,
autonomous driving, and video surveillance. However, wide-angle images contain
distortions that violate the assumptions underlying pinhole camera models,
resulting in object distortion, difficulties in estimating scene distance,
area, and direction, and preventing the use of off-the-shelf deep models
trained on undistorted images for downstream computer vision tasks. Image
rectification, which aims to correct these distortions, can solve these
problems. In this paper, we comprehensively survey progress in wide-angle image
rectification from transformation models to rectification methods.
Specifically, we first present a detailed description and discussion of the
camera models used in different approaches. Then, we summarize several
distortion models including radial distortion and projection distortion. Next,
we review both traditional geometry-based image rectification methods and deep
learning-based methods, where the former formulate distortion parameter
estimation as an optimization problem and the latter treat it as a regression
problem by leveraging the power of deep neural networks. We evaluate the
performance of state-of-the-art methods on public datasets and show that
although both kinds of methods can achieve good results, these methods only
work well for specific camera models and distortion types. We also provide a
strong baseline model and carry out an empirical study of different distortion
models on synthetic datasets and real-world wide-angle images. Finally, we
discuss several potential research directions that are expected to further
advance this area in the future.
