%0 Generic %1 zhai2016detecting %A Zhai, Menghua %A Workman, Scott %A Jacobs, Nathan %D 2016 %K cnn deep detection estimation horizon learning line network neural point vanishing %T Detecting Vanishing Points using Global Image Context in a Non-Manhattan World %U http://arxiv.org/abs/1608.05684 %X 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.

@misc{zhai2016detecting, 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.}, added-at = {2019-02-25T17:22:24.000+0100}, author = {Zhai, Menghua and Workman, Scott and Jacobs, Nathan}, biburl = {https://www.bibsonomy.org/bibtex/27b84aab52a65586e70394ea89751a88e/kluger}, description = {[1608.05684] Detecting Vanishing Points using Global Image Context in a Non-Manhattan World}, interhash = {eafcd9dc39bd79110d8b7d9917373489}, intrahash = {7b84aab52a65586e70394ea89751a88e}, keywords = {cnn deep detection estimation horizon learning line network neural point vanishing}, note = {cite arxiv:1608.05684Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016}, timestamp = {2019-02-25T17:22:24.000+0100}, title = {Detecting Vanishing Points using Global Image Context in a Non-Manhattan World}, url = {http://arxiv.org/abs/1608.05684}, year = 2016 }