Learning features for extrinsic camera calibration of wide-angle cameras

Abstract: This thesis attempts to solve the problem of estimating the extrinsic camera parameters (pitch and roll) from a wide-angle view image. The first contributionis a data generation pipeline capable of producing wide-angle distorted images with rotation and line segment annotations. This pipeline was used to produce four datasets with distortion and rotation in the range −5◦ to 5◦. The second contribution is two neural networks aiming to estimate the roll and pitch angles, one where line segments are used, and one where ResNet and DenseNet features are used. The roll and pitch angles are predicted both directly and with vanishing points as an intermediate representation in both networks. The line segment network managed to extract line segments from distorted images, and predict the roll and pitch angles with a mean error of 3.70◦ over all datasets. The network with features from ResNet and DenseNet performed the best with a mean angle error of 1.02◦ over all datasets.