Detecting Vanishing Points Using Global Image Context in a Non-Manhattan World

Menghua Zhai, Scott Workman, Nathan Jacobs; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 5657-5665

Abstract


We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidate vanishing points, then remove outliers by enforcing mutual orthogonality. Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains. A key element of our approach is the use of global image context, extracted with a deep convolutional network, to constrain the set of candidates under consideration. Our method does not make a Manhattan-world assumption and can operate effectively on scenes with only a single horizontal vanishing point. We evaluate our approach on three benchmark datasets and achieve state-of-the-art performance on each. In addition, our approach is significantly faster than the previous best method.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhai_2016_CVPR,
author = {Zhai, Menghua and Workman, Scott and Jacobs, Nathan},
title = {Detecting Vanishing Points Using Global Image Context in a Non-Manhattan World},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}