Single View Distortion Correction using Semantic Guidance
This work addresses the challenge of handling diverse and complex distortions in computer vision, which is incremental by building on existing techniques with semantic information.
The paper tackles the problem of correcting complex distortions in single-view images, such as those caused by refractive surfaces, by using semantic guidance and differentiable image sampling, achieving effective distortion correction without requiring calibration grids or multiple views.
Most distortion correction methods focus on simple forms of distortion, such as radial or linear distortions. These works undistort images either based on measurements in the presence of a calibration grid, or use multiple views to find point correspondences and predict distortion parameters. When possible distortions are more complex, e.g. in the case of a camera being placed behind a refractive surface such as glass, the standard method is to use a calibration grid. Considering a high variety of distortions, it is nonviable to conduct these measurements. In this work, we present a single view distortion correction method which is capable of undistorting images containing arbitrarily complex distortions by exploiting recent advancements in differentiable image sampling and in the usage of semantic information to augment various tasks. The results of this work show that our model is able to estimate and correct highly complex distortions, and that incorporating semantic information mitigates the process of image undistortion.