4,671 research outputs found
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
We present a novel approach for vanishing point detection from uncalibrated
monocular images. In contrast to state-of-the-art, we make no a priori
assumptions about the observed scene. Our method is based on a convolutional
neural network (CNN) which does not use natural images, but a Gaussian sphere
representation arising from an inverse gnomonic projection of lines detected in
an image. This allows us to rely on synthetic data for training, eliminating
the need for labelled images. Our method achieves competitive performance on
three horizon estimation benchmark datasets. We further highlight some
additional use cases for which our vanishing point detection algorithm can be
used.Comment: Accepted for publication at German Conference on Pattern Recognition
(GCPR) 2017. This research was supported by German Research Foundation DFG
within Priority Research Programme 1894 "Volunteered Geographic Information:
Interpretation, Visualisation and Social Computing
StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments
We propose a novel visual-inertial odometry approach that adopts structural
regularity in man-made environments. Instead of using Manhattan world
assumption, we use Atlanta world model to describe such regularity. An Atlanta
world is a world that contains multiple local Manhattan worlds with different
heading directions. Each local Manhattan world is detected on-the-fly, and
their headings are gradually refined by the state estimator when new
observations are coming. With fully exploration of structural lines that
aligned with each local Manhattan worlds, our visual-inertial odometry method
become more accurate and robust, as well as much more flexible to different
kinds of complex man-made environments. Through extensive benchmark tests and
real-world tests, the results show that the proposed approach outperforms
existing visual-inertial systems in large-scale man-made environmentsComment: 15 pages,15 figure
The toulouse vanishing points dataset
International audienceIn this paper we present the Toulouse Vanishing Points Dataset, a public photographs database of Manhattan scenes taken with an iPad Air 1. The purpose of this dataset is the evaluation of vanishing points estimation algorithms. Its originality is the addition of Inertial Measurement Unit (IMU) data synchronized with the camera under the form of rotation matrices. Moreover, contrary to existing works which provide vanishing points of reference in the form of single points, we computed uncertainty regions. The Toulouse Vanishing Points Dataset is publicly available at http://ubee.enseeiht.fr/tvp
TVPD : un jeu de données pour évaluer les algorithmes d'estimation de points de fuite
International audienceNous présentons à travers cet article un nouveau jeu de données comprenant un corpus de photos de scènes dites de Manhattan prises avec un iPad Air 1. Ce jeu de données permet d'évaluer des algorithmes d'estimation de points de fuite. À la différence des jeux de données existants, des données inertielles issues des différents capteurs de l'iPad, synchronisées avec la caméra sont également présentes. Ces données inertielles peuvent être utilisées pour améliorer les performances d'un algorithme d'estimation de points de fuite. Enfin, nous présentons un nouvel algorithme d'estimation de régions d'incertitude sur les points de fuite estimés à partir des segments vérités-terrain
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal
vanishing points, and the unknown focal length of the camera, leveraging a
prior vertical direction. The direction can come from an Inertial Measurement
Unit that is a standard component of recent consumer devices, e.g.,
smartphones. We provide an exhaustive analysis of minimal line configurations
and derive two new 2-line solvers, one of which does not suffer from
singularities affecting existing solvers. Additionally, we design a new
non-minimal method, running on an arbitrary number of lines, to boost the
performance in local optimization. Combining all solvers in a hybrid robust
estimator, our method achieves increased accuracy even with a rough prior.
Experiments on synthetic and real-world datasets demonstrate the superior
accuracy of our method compared to the state of the art, while having
comparable runtimes. We further demonstrate the applicability of our solvers
for relative rotation estimation. The code is available at
https://github.com/cvg/VP-Estimation-with-Prior-Gravity.Comment: Accepted at ICCV 202
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