5,151 research outputs found
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
Variable illumination and invariant features for detecting and classifying varnish defects
This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn\u27t provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction
Common Arc Method for Diffraction Pattern Orientation
Very short pulses of x-ray free-electron lasers opened the way to obtain
diffraction signal from single particles beyond the radiation dose limit. For
3D structure reconstruction many patterns are recorded in the object's unknown
orientation. We describe a method for orientation of continuous diffraction
patterns of non-periodic objects, utilizing intensity correlations in the
curved intersections of the corresponding Ewald spheres, hence named Common Arc
orientation. Present implementation of the algorithm optionally takes into
account the Friedel law, handles missing data and is capable to determine the
point group of symmetric objects. Its performance is demonstrated on simulated
diffraction datasets and verification of the results indicates high orientation
accuracy even at low signal levels. The Common Arc method fills a gap in the
wide palette of the orientation methods.Comment: 16 pages, 10 figure
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