When calibrating a camera, the radial component of lens distortion is the dominant source of image distortion. To model this lens distortion, camera models incorporate a radial distortion model that conforms to a certain parametric form. In practice however, multiple parametric forms can be used to model distortion for a given lens. Ideally, one would choose the best suited parametric form using a model selection procedure. In this work, we propose the use of Gaussian Process regression to model lens distortion. With the use of a squared exponential covariance function, a Gaussian Process (GP) can describe the space of smooth distortion functions; kernel hyperparameter selection in this space then analogous to performing explicit model selection between possible parametric models. Our evaluation shows that this Gaussian Process formulation of lens distortion performs on par with parametric distortion models.
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Gaussian Process for lens distortion modeling
Semantic Scholar · Computer Science · 2012
Abstract
When calibrating a camera, the radial component of lens distortion is the dominant source of image distortion. To model this lens distortion, camera models incorporate a radial distortion model that conforms to a certain parametric form. In practice however, multiple parametric forms can be used to model distortion for a given lens. Ideally, one would choose the best suited parametric form using a model selection procedure. In this work, we propose the use of Gaussian Process regression to model lens distortion. With the use of a squared exponential covariance function, a Gaussian Process (GP) can describe the space of smooth distortion functions; kernel hyperparameter selection in this space then analogous to performing explicit model selection between possible parametric models. Our evaluation shows that this Gaussian Process formulation of lens distortion performs on par with parametric distortion models.