73,141 research outputs found
SlicerAstro: a 3-D interactive visual analytics tool for HI data
SKA precursors are capable of detecting hundreds of galaxies in HI in a
single 12 hours pointing. In deeper surveys one will probe more easily faint HI
structures, typically located in the vicinity of galaxies, such as tails,
filaments, and extraplanar gas. The importance of interactive visualization has
proven to be fundamental for the exploration of such data as it helps users to
receive immediate feedback when manipulating the data. We have developed
SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on
traditional 2-D input/output hardware. These capabilities enhance the data
inspection, allowing faster analysis of complex sources than with traditional
tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform
open source software package for visualization and medical image processing.
We demonstrate the capabilities of the current stable binary release of
SlicerAstro, which offers the following features: i) handling of FITS files and
astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii)
interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D
modeling. In addition, SlicerAstro has been designed with a strong, stable and
modular C++ core, and its classes are also accessible via Python scripting,
allowing great flexibility for user-customized visualization and analysis
tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing.
SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr
Joint Material and Illumination Estimation from Photo Sets in the Wild
Faithful manipulation of shape, material, and illumination in 2D Internet
images would greatly benefit from a reliable factorization of appearance into
material (i.e., diffuse and specular) and illumination (i.e., environment
maps). On the one hand, current methods that produce very high fidelity
results, typically require controlled settings, expensive devices, or
significant manual effort. To the other hand, methods that are automatic and
work on 'in the wild' Internet images, often extract only low-frequency
lighting or diffuse materials. In this work, we propose to make use of a set of
photographs in order to jointly estimate the non-diffuse materials and sharp
lighting in an uncontrolled setting. Our key observation is that seeing
multiple instances of the same material under different illumination (i.e.,
environment), and different materials under the same illumination provide
valuable constraints that can be exploited to yield a high-quality solution
(i.e., specular materials and environment illumination) for all the observed
materials and environments. Similar constraints also arise when observing
multiple materials in a single environment, or a single material across
multiple environments. The core of this approach is an optimization procedure
that uses two neural networks that are trained on synthetic images to predict
good gradients in parametric space given observation of reflected light. We
evaluate our method on a range of synthetic and real examples to generate
high-quality estimates, qualitatively compare our results against
state-of-the-art alternatives via a user study, and demonstrate
photo-consistent image manipulation that is otherwise very challenging to
achieve
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