computer-vision-3

Horizon Line Estimation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in horizon-line-estimation-3

Trend

Dataset

Best Model

Actions

Horizon Lines in the Wild

York Urban Dataset

Libraries

i

Use these libraries to find horizon-line-estimation-3 models and implementations

Datasets

KITTI

York Urban Line Segment Database

Subtasks

No subtasks available.

Most implemented papers

Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses

Eric Brachmann, C. Rother•Thu May 09 2019

This work uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets and combines neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible.

270

Content

Eurasian Cities Dataset

KITTI Horizon

0

Paper Graph

Horizon Lines in the Wild

Scott Workman, Nathan Jacobs, Menghua Zhai•Wed Apr 06 2016

This work introduces a large, realistic evaluation dataset, Horizon Lines in the Wild (HLW), containing natural images with labeled horizon lines, and investigates the application of convolutional neural networks for directly estimating the horizon line.

82 0

Paper Graph

Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

Florian Kluger, H. Ackermann, M. Yang, B. Rosenhahn•Fri Jul 07 2017

A novel approach for vanishing point detection from uncalibrated monocular images is presented, based on a convolutional neural network which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image.

48 0

Paper Graph

A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws

Gilles Simon, Antoine Fond, M. Berger•Fri Sep 07 2018

It is shown that, in images of man-made environments, the horizon line can usually be hypothesized based on a-contrario detections of second-order grouping events, which allows constraining the extraction of the horizontal vanishing points on that line, thus reducing false detections.

31 0

Paper Graph

Temporally Consistent Horizon Lines

Florian Kluger, H. Ackermann, M. Yang, B. Rosenhahn•Mon Jul 22 2019

This paper shows how convolutional neural networks are able to utilise the temporal consistency imposed by video sequences in order to increase the accuracy and reduce the variance of horizon line estimates and proposes an adaptive loss function that ensures stable training as well as accurate results.

17 0

Paper Graph

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