470 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
AUTOMATIC IMAGE TO MODEL ALIGNMENT FOR PHOTO-REALISTIC URBAN MODEL RECONSTRUCTION
We introduce a hybrid approach in which images of an urban scene are automatically alignedwith a base geometry of the scene to determine model-relative external camera parameters. Thealgorithm takes as input a model of the scene and images with approximate external cameraparameters and aligns the images to the model by extracting the facades from the images andaligning the facades with the model by minimizing over a multivariate objective function. Theresulting image-pose pairs can be used to render photo-realistic views of the model via texturemapping.Several natural extensions to the base hybrid reconstruction technique are also introduced. Theseextensions, which include vanishing point based calibration refinement and video stream basedreconstruction, increase the accuracy of the base algorithm, reduce the amount of data that mustbe provided by the user as input to the algorithm, and provide a mechanism for automaticallycalibrating a large set of images for post processing steps such as automatic model enhancementand fly-through model visualization.Traditionally, photo-realistic urban reconstruction has been approached from purely image-basedor model-based approaches. Recently, research has been conducted on hybrid approaches, whichcombine the use of images and models. Such approaches typically require user assistance forcamera calibration. Our approach is an improvement over these methods because it does notrequire user assistance for camera calibration
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
We present a robust estimator for fitting multiple parametric models of the
same form to noisy measurements. Applications include finding multiple
vanishing points in man-made scenes, fitting planes to architectural imagery,
or estimating multiple rigid motions within the same sequence. In contrast to
previous works, which resorted to hand-crafted search strategies for multiple
model detection, we learn the search strategy from data. A neural network
conditioned on previously detected models guides a RANSAC estimator to
different subsets of all measurements, thereby finding model instances one
after another. We train our method supervised as well as self-supervised. For
supervised training of the search strategy, we contribute a new dataset for
vanishing point estimation. Leveraging this dataset, the proposed algorithm is
superior with respect to other robust estimators as well as to designated
vanishing point estimation algorithms. For self-supervised learning of the
search, we evaluate the proposed algorithm on multi-homography estimation and
demonstrate an accuracy that is superior to state-of-the-art methods.Comment: CVPR 202
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
Localization in challenging, natural environments such as forests or
woodlands is an important capability for many applications from guiding a robot
navigating along a forest trail to monitoring vegetation growth with handheld
sensors. In this work we explore laser-based localization in both urban and
natural environments, which is suitable for online applications. We propose a
deep learning approach capable of learning meaningful descriptors directly from
3D point clouds by comparing triplets (anchor, positive and negative examples).
The approach learns a feature space representation for a set of segmented point
clouds that are matched between a current and previous observations. Our
learning method is tailored towards loop closure detection resulting in a small
model which can be deployed using only a CPU. The proposed learning method
would allow the full pipeline to run on robots with limited computational
payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info:
https://ori.ox.ac.uk/esm-localizatio
Temporally Consistent Horizon Lines
The horizon line is an important geometric feature for many image processing
and scene understanding tasks in computer vision. For instance, in navigation
of autonomous vehicles or driver assistance, it can be used to improve 3D
reconstruction as well as for semantic interpretation of dynamic environments.
While both algorithms and datasets exist for single images, the problem of
horizon line estimation from video sequences has not gained attention. In this
paper, we show 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. A novel CNN
architecture with an improved residual convolutional LSTM is presented for
temporally consistent horizon line estimation. We propose an adaptive loss
function that ensures stable training as well as accurate results. Furthermore,
we introduce an extension of the KITTI dataset which contains precise horizon
line labels for 43699 images across 72 video sequences. A comprehensive
evaluation shows that the proposed approach consistently achieves superior
performance compared with existing methods
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