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.
@article{arxiv.2109.02259,
title = {CTRL-C: Camera calibration TRansformer with Line-Classification},
author = {Jinwoo Lee and Hyunsung Go and Hyunjoon Lee and Sunghyun Cho and Minhyuk Sung and Junho Kim},
journal= {arXiv preprint arXiv:2109.02259},
year = {2021}
}