Unsupervised Vanishing Point Detection and Camera Calibration from a Single Manhattan Image with Radial Distortion
English
Goncalves Almeida Antunes, Michel[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Barreto, Joao P.[Institute of Systems and Robotics (ISR) > University of Coimbra]
Aouada, Djamila[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2017
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Yes
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
21-07-2017 to 26-07-2017
[en] The article concerns the automatic calibration of a camera with radial distortion from a single image. It is known that, under the mild assumption of square pixels and zero skew, lines in the scene project into circles in the image, and three lines suffice to calibrate the camera up to an ambiguity between focal length and radial distortion. The calibration results highly depend on accurate circle estimation, which is hard to accomplish, because lines tend to project into short circular arcs. To overcome this problem, we show that, given a short circular arc edge, it is possible to robustly determine a line that goes through the center of the corresponding circle. These lines, henceforth called Lines of Circle Centres (LCCs), are used in a new method that detects sets of parallel lines and estimates the calibration parameters, including the center and amount of distortion, focal length, and camera orientation with respect to the Manhattan frame. Extensive experiments in both semi-synthetic and real images show that our algorithm outperforms state-
of-the-art approaches in unsupervised calibration from a single image, while providing more information.
[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
