  Precise calibration is a must for high reliance 3D computer vision
algorithms. A challenging case is when the camera is behind a protective glass
or transparent object: due to refraction, the image is heavily distorted; the
pinhole camera model alone can not be used and a distortion correction step is
required. By directly modeling the geometry of the refractive media, we build
the image generation process by tracing individual light rays from the camera
to a target. Comparing the generated images to their distorted - observed -
counterparts, we estimate the geometry parameters of the refractive surface via
model inversion by employing an RBF neural network. We present an image
collection methodology that produces data suited for finding the distortion
parameters and test our algorithm on synthetic and real-world data. We analyze
the results of the algorithm.
