5,526 research outputs found
Deep Reflectance Maps
Undoing the image formation process and therefore decomposing appearance into
its intrinsic properties is a challenging task due to the under-constraint
nature of this inverse problem. While significant progress has been made on
inferring shape, materials and illumination from images only, progress in an
unconstrained setting is still limited. We propose a convolutional neural
architecture to estimate reflectance maps of specular materials in natural
lighting conditions. We achieve this in an end-to-end learning formulation that
directly predicts a reflectance map from the image itself. We show how to
improve estimates by facilitating additional supervision in an indirect scheme
that first predicts surface orientation and afterwards predicts the reflectance
map by a learning-based sparse data interpolation.
In order to analyze performance on this difficult task, we propose a new
challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg)
using both synthetic and real images. Furthermore, we show the application of
our method to a range of image-based editing tasks on real images.Comment: project page: http://homes.esat.kuleuven.be/~krematas/DRM
Theory and modeling of the magnetic field measurement in LISA PathFinder
The magnetic diagnostics subsystem of the LISA Technology Package (LTP) on
board the LISA PathFinder (LPF) spacecraft includes a set of four tri-axial
fluxgate magnetometers, intended to measure with high precision the magnetic
field at their respective positions. However, their readouts do not provide a
direct measurement of the magnetic field at the positions of the test masses,
and hence an interpolation method must be designed and implemented to obtain
the values of the magnetic field at these positions. However, such
interpolation process faces serious difficulties. Indeed, the size of the
interpolation region is excessive for a linear interpolation to be reliable
while, on the other hand, the number of magnetometer channels does not provide
sufficient data to go beyond the linear approximation. We describe an
alternative method to address this issue, by means of neural network
algorithms. The key point in this approach is the ability of neural networks to
learn from suitable training data representing the behavior of the magnetic
field. Despite the relatively large distance between the test masses and the
magnetometers, and the insufficient number of data channels, we find that our
artificial neural network algorithm is able to reduce the estimation errors of
the field and gradient down to levels below 10%, a quite satisfactory result.
Learning efficiency can be best improved by making use of data obtained in
on-ground measurements prior to mission launch in all relevant satellite
locations and in real operation conditions. Reliable information on that
appears to be essential for a meaningful assessment of magnetic noise in the
LTP.Comment: 10 pages, 8 figures, 2 tables, submitted to Physical Review
On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling
A multi-fidelity surrogate model for highly nonlinear multiscale problems is
proposed. It is based on the introduction of two different surrogate models and
an adaptive on-the-fly switching. The two concurrent surrogates are built
incrementally starting from a moderate set of evaluations of the full order
model. Therefore, a reduced order model (ROM) is generated. Using a hybrid
ROM-preconditioned FE solver, additional effective stress-strain data is
simulated while the number of samples is kept to a moderate level by using a
dedicated and physics-guided sampling technique. Machine learning (ML) is
subsequently used to build the second surrogate by means of artificial neural
networks (ANN). Different ANN architectures are explored and the features used
as inputs of the ANN are fine tuned in order to improve the overall quality of
the ML model. Additional ANN surrogates for the stress errors are generated.
Therefore, conservative design guidelines for error surrogates are presented by
adapting the loss functions of the ANN training in pure regression or pure
classification settings. The error surrogates can be used as quality indicators
in order to adaptively select the appropriate -- i.e. efficient yet accurate --
surrogate. Two strategies for the on-the-fly switching are investigated and a
practicable and robust algorithm is proposed that eliminates relevant technical
difficulties attributed to model switching. The provided algorithms and ANN
design guidelines can easily be adopted for different problem settings and,
thereby, they enable generalization of the used machine learning techniques for
a wide range of applications. The resulting hybrid surrogate is employed in
challenging multilevel FE simulations for a three-phase composite with
pseudo-plastic micro-constituents. Numerical examples highlight the performance
of the proposed approach
Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling
We present a method for simultaneously estimating 3D human pose and body
shape from a sparse set of wide-baseline camera views. We train a symmetric
convolutional autoencoder with a dual loss that enforces learning of a latent
representation that encodes skeletal joint positions, and at the same time
learns a deep representation of volumetric body shape. We harness the latter to
up-scale input volumetric data by a factor of , whilst recovering a
3D estimate of joint positions with equal or greater accuracy than the state of
the art. Inference runs in real-time (25 fps) and has the potential for passive
human behaviour monitoring where there is a requirement for high fidelity
estimation of human body shape and pose
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