  Single image camera calibration is the task of estimating the camera
parameters from a single input image, such as the vanishing points, focal
length, and horizon line. In this work, we propose Camera calibration
TRansformer with Line-Classification (CTRL-C), an end-to-end neural
network-based approach to single image camera calibration, which directly
estimates the camera parameters from an image and a set of line segments. Our
network adopts the transformer architecture to capture the global structure of
an image with multi-modal inputs in an end-to-end manner. We also propose an
auxiliary task of line classification to train the network to extract the
global geometric information from lines effectively. Our experiments
demonstrate that CTRL-C outperforms the previous state-of-the-art methods on
the Google Street View and SUN360 benchmark datasets.
