|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks.
Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains.
#2 best model for Horizon Line Estimation on York Urban Dataset
The horizon line is an important contextual attribute for a wide variety of image understanding tasks.
#2 best model for Horizon Line Estimation on Horizon Lines in the Wild
We present a novel approach for vanishing point detection from uncalibrated monocular images.
#3 best model for Horizon Line Estimation on York Urban Dataset
We show that, in images of man-made environments, the horizon line can usually be hypothesized based on an a contrario detection of second-order grouping events.
The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision.