18,717 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
k-d Darts: Sampling by k-Dimensional Flat Searches
We formalize the notion of sampling a function using k-d darts. A k-d dart is
a set of independent, mutually orthogonal, k-dimensional subspaces called k-d
flats. Each dart has d choose k flats, aligned with the coordinate axes for
efficiency. We show that k-d darts are useful for exploring a function's
properties, such as estimating its integral, or finding an exemplar above a
threshold. We describe a recipe for converting an algorithm from point sampling
to k-d dart sampling, assuming the function can be evaluated along a k-d flat.
We demonstrate that k-d darts are more efficient than point-wise samples in
high dimensions, depending on the characteristics of the sampling domain: e.g.
the subregion of interest has small volume and evaluating the function along a
flat is not too expensive. We present three concrete applications using line
darts (1-d darts): relaxed maximal Poisson-disk sampling, high-quality
rasterization of depth-of-field blur, and estimation of the probability of
failure from a response surface for uncertainty quantification. In these
applications, line darts achieve the same fidelity output as point darts in
less time. We also demonstrate the accuracy of higher dimensional darts for a
volume estimation problem. For Poisson-disk sampling, we use significantly less
memory, enabling the generation of larger point clouds in higher dimensions.Comment: 19 pages 16 figure
The Topology ToolKit
This system paper presents the Topology ToolKit (TTK), a software platform
designed for topological data analysis in scientific visualization. TTK
provides a unified, generic, efficient, and robust implementation of key
algorithms for the topological analysis of scalar data, including: critical
points, integral lines, persistence diagrams, persistence curves, merge trees,
contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots,
Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due
to a tight integration with ParaView. It is also easily accessible to
developers through a variety of bindings (Python, VTK/C++) for fast prototyping
or through direct, dependence-free, C++, to ease integration into pre-existing
complex systems. While developing TTK, we faced several algorithmic and
software engineering challenges, which we document in this paper. In
particular, we present an algorithm for the construction of a discrete gradient
that complies to the critical points extracted in the piecewise-linear setting.
This algorithm guarantees a combinatorial consistency across the topological
abstractions supported by TTK, and importantly, a unified implementation of
topological data simplification for multi-scale exploration and analysis. We
also present a cached triangulation data structure, that supports time
efficient and generic traversals, which self-adjusts its memory usage on demand
for input simplicial meshes and which implicitly emulates a triangulation for
regular grids with no memory overhead. Finally, we describe an original
software architecture, which guarantees memory efficient and direct accesses to
TTK features, while still allowing for researchers powerful and easy bindings
and extensions. TTK is open source (BSD license) and its code, online
documentation and video tutorials are available on TTK's website
Advanced Architectures for Astrophysical Supercomputing
Astronomers have come to rely on the increasing performance of computers to
reduce, analyze, simulate and visualize their data. In this environment, faster
computation can mean more science outcomes or the opening up of new parameter
spaces for investigation. If we are to avoid major issues when implementing
codes on advanced architectures, it is important that we have a solid
understanding of our algorithms. A recent addition to the high-performance
computing scene that highlights this point is the graphics processing unit
(GPU). The hardware originally designed for speeding-up graphics rendering in
video games is now achieving speed-ups of in general-purpose
computation -- performance that cannot be ignored. We are using a generalized
approach, based on the analysis of astronomy algorithms, to identify the
optimal problem-types and techniques for taking advantage of both current GPU
hardware and future developments in computing architectures.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8
2009, Sapporo, Japan (ASP Conf. Series
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