A Global Approach for the Detection of Vanishing Points and Mutually Orthogonal Vanishing Directions
Abstract
This article presents a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions using lines detected in images of man-made environments. These two multi-model fitting problems are respectively cast as Uncapacited Facility Location (UFL) and Hierarchical Facility Location (HFL) instances that are efficiently solved using a message passing inference algorithm. We also propose new functions for measuring the consistency between an edge and a putative vanishing point, and for computing the vanishing point defined by a subset of edges. Extensive experiments in both synthetic and real images show that our algorithms outperform the state-ofthe-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations that can either share one vanishing direction (Atlanta world) or be completely independent.
Cite
Text
Antunes and Barreto. "A Global Approach for the Detection of Vanishing Points and Mutually Orthogonal Vanishing Directions." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.176Markdown
[Antunes and Barreto. "A Global Approach for the Detection of Vanishing Points and Mutually Orthogonal Vanishing Directions." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/antunes2013cvpr-global/) doi:10.1109/CVPR.2013.176BibTeX
@inproceedings{antunes2013cvpr-global,
title = {{A Global Approach for the Detection of Vanishing Points and Mutually Orthogonal Vanishing Directions}},
author = {Antunes, Michel and Barreto, Joao P.},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2013},
doi = {10.1109/CVPR.2013.176},
url = {https://mlanthology.org/cvpr/2013/antunes2013cvpr-global/}
}