Horizon Line Estimation
6 papers with code • 4 benchmarks • 2 datasets
Most implemented papers
Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses
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.
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
We present a novel approach for vanishing point detection from uncalibrated monocular images.
Horizon Lines in the Wild
The horizon line is an important contextual attribute for a wide variety of image understanding tasks.
Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains.
A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws
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.
Temporally Consistent Horizon Lines
The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision.