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

This repo contains the source code of the paper, Detecting Vanishing Points using Global Image Context in a Non-Manhattan World.

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.

[Poster] [Slides]

Installation:

1. Install Caffe and compile Matcaffe (see toturial at Caffe Installation).
2. Download the Caffe model, and uncompress the tarball to "assets".
3. Compile LSD (Line Segment Detector):

$ cd assets/lsd; make

Note:

We recently updated our CNN model to a state-of-the-art network, which is trained with the approach introduced by another excellent work: Horizon Lines in the Wild.

If you require the original implementation of our work, you can still check the old version:

$ git checkout v1.0

Citation

Zhai, M., Workman, S., & Jacobs, N. (2016). Detecting Vanishing Points using Global Image Context in a Non-Manhattan World. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Bibtex:

@inproceedings{zhai2016horizon,
  author = {Zhai, Menghua and Workman, Scott and Jacobs, Nathan},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  title = {Detecting Vanishing Points using Global Image Context in a Non-{Manhattan} World},
  doi = {10.1109/CVPR.2016.610},
  year = {2016}
}