3 research outputs found
Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization
We present an approach for hierarchical super resolution (SR) using neural
networks on an octree data representation. We train a hierarchy of neural
networks, each capable of 2x upscaling in each spatial dimension between two
levels of detail, and use these networks in tandem to facilitate large scale
factor super resolution, scaling with the number of trained networks. We
utilize these networks in a hierarchical super resolution algorithm that
upscales multiresolution data to a uniform high resolution without introducing
seam artifacts on octree node boundaries. We evaluate application of this
algorithm in a data reduction framework by dynamically downscaling input data
to an octree-based data structure to represent the multiresolution data before
compressing for additional storage reduction. We demonstrate that our approach
avoids seam artifacts common to multiresolution data formats, and show how
neural network super resolution assisted data reduction can preserve global
features better than compressors alone at the same compression ratios
Ray Tracing Structured AMR Data Using ExaBricks
Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to
adapt the domain resolution to save computation and storage, and has become one
of the dominant data representations used by scientific simulations; however,
efficiently rendering such data remains a challenge. We present an efficient
approach for volume- and iso-surface ray tracing of Structured AMR data on
GPU-equipped workstations, using a combination of two different data
structures. Together, these data structures allow a ray tracing based renderer
to quickly determine which segments along the ray need to be integrated and at
what frequency, while also providing quick access to all data values required
for a smooth sample reconstruction kernel. Our method makes use of the RTX ray
tracing hardware for surface rendering, ray marching, space skipping, and
adaptive sampling; and allows for interactive changes to the transfer function
and implicit iso-surfacing thresholds. We demonstrate that our method achieves
high performance with little memory overhead, enabling interactive high quality
rendering of complex AMR data sets on individual GPU workstations
AMM: Adaptive Multilinear Meshes
We present Adaptive Multilinear Meshes (AMM), a new framework that
significantly reduces the memory footprint compared to existing data
structures. AMM uses a hierarchy of cuboidal cells to create continuous,
piecewise multilinear representation of uniformly sampled data. Furthermore,
AMM can selectively relax or enforce constraints on conformity, continuity, and
coverage, creating a highly adaptive and flexible representation to support a
wide range of use cases. AMM supports incremental updates in both spatial
resolution and numerical precision establishing the first practical data
structure that can seamlessly explore the tradeoff between resolution and
precision. We use tensor products of linear B-spline wavelets to create an
adaptive representation and illustrate the advantages of our framework. AMM
provides a simple interface for evaluating the function defined on the adaptive
mesh, efficiently traversing the mesh, and manipulating the mesh, including
incremental, partial updates. Our framework is easy to adopt for standard
visualization and analysis tasks. As an example, we provide a VTK interface,
through efficient on-demand conversion, which can be used directly by
corresponding tools, such as VisIt, disseminating the advantages of faster
processing and a smaller memory footprint to a wider audience. We demonstrate
the advantages of our approach for simplifying scalar-valued data for commonly
used visualization and analysis tasks using incremental construction, according
to mixed resolution and precision data streams