CTRL-C: Camera Calibration TRansformer With Line-Classification

Jinwoo Lee, Hyunsung Go, Hyunjoon Lee, Sunghyun Cho, Minhyuk Sung, Junho Kim; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 16228-16237


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. Code is available at https://github.com/jwlee-vcl/CTRL-C.

Related Material

[pdf] [supp]
@InProceedings{Lee_2021_ICCV, author = {Lee, Jinwoo and Go, Hyunsung and Lee, Hyunjoon and Cho, Sunghyun and Sung, Minhyuk and Kim, Junho}, title = {CTRL-C: Camera Calibration TRansformer With Line-Classification}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {16228-16237} }