8,601 research outputs found
CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering
Intrinsic image decomposition is a challenging, long-standing computer vision
problem for which ground truth data is very difficult to acquire. We explore
the use of synthetic data for training CNN-based intrinsic image decomposition
models, then applying these learned models to real-world images. To that end,
we present \ICG, a new, large-scale dataset of physically-based rendered images
of scenes with full ground truth decompositions. The rendering process we use
is carefully designed to yield high-quality, realistic images, which we find to
be crucial for this problem domain. We also propose a new end-to-end training
method that learns better decompositions by leveraging \ICG, and optionally IIW
and SAW, two recent datasets of sparse annotations on real-world images.
Surprisingly, we find that a decomposition network trained solely on our
synthetic data outperforms the state-of-the-art on both IIW and SAW, and
performance improves even further when IIW and SAW data is added during
training. Our work demonstrates the suprising effectiveness of
carefully-rendered synthetic data for the intrinsic images task.Comment: Paper for 'CGIntrinsics: Better Intrinsic Image Decomposition through
Physically-Based Rendering' published in ECCV, 201
The Visual Centrifuge: Model-Free Layered Video Representations
True video understanding requires making sense of non-lambertian scenes where
the color of light arriving at the camera sensor encodes information about not
just the last object it collided with, but about multiple mediums -- colored
windows, dirty mirrors, smoke or rain. Layered video representations have the
potential of accurately modelling realistic scenes but have so far required
stringent assumptions on motion, lighting and shape. Here we propose a
learning-based approach for multi-layered video representation: we introduce
novel uncertainty-capturing 3D convolutional architectures and train them to
separate blended videos. We show that these models then generalize to single
videos, where they exhibit interesting abilities: color constancy, factoring
out shadows and separating reflections. We present quantitative and qualitative
results on real world videos.Comment: Appears in: 2019 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2019). This arXiv contains the CVPR Camera Ready version of
the paper (although we have included larger figures) as well as an appendix
detailing the model architectur
Dust Transport in Protostellar Disks Through Turbulence and Settling
We apply ionization balance and MHD calculations to investigate whether
magnetic activity moderated by recombination on dust can account for the mass
accretion rates and the mid-infrared spectra and variability of protostellar
disks. The MHD calculations use the stratified shearing-box approach and
include grain settling and the feedback from the changing dust abundance on the
resistivity of the gas. The two-decade spread in accretion rates among T Tauri
stars is too large to result solely from variety in the grain size and stellar
X-ray luminosity, but can be produced by varying these together with the disk
magnetic flux. The diversity in the silicate bands can come from the coupling
of grain settling to the distribution of the magneto-rotational turbulence,
through three effects: (1) Recombination on grains yields a magnetically
inactive dead zone extending above two scale heights, while turbulence in the
magnetically active disk atmosphere overshoots the dead zone boundary by only
about one scale height. (2) Grains deep in the dead zone oscillate vertically
in waves driven by the turbulent layer above, but on average settle at the
laminar rates, so the interior of the dead zone is a particle sink and the disk
atmosphere becomes dust-depleted. (3) With sufficient depletion, the dead zone
is thinner and mixing dredges grains off the midplane. The MHD results also
show that the magnetic activity intermittently lifts clouds of dust into the
atmosphere. The photosphere height changes by up to one-third over a few
orbits, while the extinction along lines of sight grazing the disk surface
varies by factors of two over times down to 0.1 orbit. We suggest that the
changing shadows cast by the dust clouds on the outer disk are a cause of the
daily to monthly mid-infrared variability in some young stars. (Abridged.)Comment: ApJ in pres
The Current Ability to Test Theories of Gravity with Black Hole Shadows
Our Galactic Center, Sagittarius A* (Sgr A*), is believed to harbour a
supermassive black hole (BH), as suggested by observations tracking individual
orbiting stars. Upcoming sub-millimetre very-long-baseline-interferometry
(VLBI) images of Sgr A* carried out by the Event-Horizon-Telescope
Collaboration (EHTC) are expected to provide critical evidence for the
existence of this supermassive BH. We assess our present ability to use EHTC
images to determine if they correspond to a Kerr BH as predicted by Einstein's
theory of general relativity (GR) or to a BH in alternative theories of
gravity. To this end, we perform general-relativistic magnetohydrodynamical
(GRMHD) simulations and use general-relativistic radiative transfer (GRRT)
calculations to generate synthetic shadow images of a magnetised accretion flow
onto a Kerr BH. In addition, and for the first time, we perform GRMHD
simulations and GRRT calculations for a dilaton BH, which we take as a
representative solution of an alternative theory of gravity. Adopting the VLBI
configuration from the 2017 EHTC campaign, we find that it could be extremely
difficult to distinguish between BHs from different theories of gravity, thus
highlighting that great caution is needed when interpreting BH images as tests
of GR.Comment: Published in Nature Astronomy on 16.04.18 (including supplementary
information); simulations at https://blackholecam.org/telling_bhs_apart
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