18,765 research outputs found
Interactive Camera Network Design using a Virtual Reality Interface
Traditional literature on camera network design focuses on constructing
automated algorithms. These require problem specific input from experts in
order to produce their output. The nature of the required input is highly
unintuitive leading to an unpractical workflow for human operators. In this
work we focus on developing a virtual reality user interface allowing human
operators to manually design camera networks in an intuitive manner. From real
world practical examples we conclude that the camera networks designed using
this interface are highly competitive with, or superior to those generated by
automated algorithms, but the associated workflow is much more intuitive and
simple. The competitiveness of the human-generated camera networks is
remarkable because the structure of the optimization problem is a well known
combinatorial NP-hard problem. These results indicate that human operators can
be used in challenging geometrical combinatorial optimization problems given an
intuitive visualization of the problem.Comment: 11 pages, 8 figure
Open source environment to define constraints in route planning for GIS-T
Route planning for transportation systems is strongly related to shortest path algorithms, an optimization problem extensively studied in the literature. To find the shortest path in a network one usually assigns weights to each branch to represent the difficulty of taking such branch. The weights construct a linear preference function ordering the variety of alternatives from the most to the least attractive.Postprint (published version
Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences
Machine learning based Single Image Intrinsic Decomposition (SIID) methods
decompose a captured scene into its albedo and shading images by using the
knowledge of a large set of known and realistic ground truth decompositions.
Collecting and annotating such a dataset is an approach that cannot scale to
sufficient variety and realism. We free ourselves from this limitation by
training on unannotated images.
Our method leverages the observation that two images of the same scene but
with different lighting provide useful information on their intrinsic
properties: by definition, albedo is invariant to lighting conditions, and
cross-combining the estimated albedo of a first image with the estimated
shading of a second one should lead back to the second one's input image. We
transcribe this relationship into a siamese training scheme for a deep
convolutional neural network that decomposes a single image into albedo and
shading. The siamese setting allows us to introduce a new loss function
including such cross-combinations, and to train solely on (time-lapse) images,
discarding the need for any ground truth annotations.
As a result, our method has the good properties of i) taking advantage of the
time-varying information of image sequences in the (pre-computed) training
step, ii) not requiring ground truth data to train on, and iii) being able to
decompose single images of unseen scenes at runtime. To demonstrate and
evaluate our work, we additionally propose a new rendered dataset containing
illumination-varying scenes and a set of quantitative metrics to evaluate SIID
algorithms. Despite its unsupervised nature, our results compete with state of
the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201
Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
As many different 3D volumes could produce the same 2D x-ray image, inverting
this process is challenging. We show that recent deep learning-based
convolutional neural networks can solve this task. As the main challenge in
learning is the sheer amount of data created when extending the 2D image into a
3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which
is then fused in a second step with the input x-ray into a high-resolution
volume. To train and validate our approach we introduce a new dataset that
comprises of close to half a million computer-simulated 2D x-ray images of 3D
volumes scanned from 175 mammalian species. Applications of our approach
include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays
including changes of illumination, view pose or geometry. Our evaluation
includes comparison to previous tomography work, previous learning methods
using our data, a user study and application to a set of real x-rays
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