Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
This addresses the challenge of accurate vanishing point detection in non-Manhattan scenes for applications like computer vision and robotics.
The paper tackles the problem of detecting vanishing points in man-made environments without assuming a Manhattan world, achieving state-of-the-art performance on three benchmark datasets and significantly faster speed than previous methods.
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.