8,601 research outputs found

    CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering

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    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

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    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

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    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

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    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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