Volumetric rendering techniques for scientific visualization
Author(s)
Advisor
Güdükkbay, UgurDate
2014Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
145
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views
32
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downloads
Abstract
Direct volume rendering is widely used in many applications where the inside of a
transparent or a partially transparent material should be visualized. We have explored
several aspects of the problem. First, we proposed a view-dependent selective refinement
scheme in order to reduce the high computational requirements without affecting
the image quality significantly. Then, we explored the parallel implementations of
direct volume rendering: both on GPU and on multi-core systems. Finally, we used direct
volume rendering approaches to create a tool, MaterialVis, to visualize amorphous
and/or crystalline materials.
Visualization of large volumetric datasets has always been an important problem.
Due to the high computational requirements of volume-rendering techniques, achieving
interactive rates is a real challenge. We present a selective refinement scheme
that dynamically refines the mesh according to the camera parameters. This scheme
automatically determines the impact of different parts of the mesh on the output image
and refines the mesh accordingly, without needing any user input. The viewdependent
refinement scheme uses a progressive mesh representation that is based
on an edge collapse-based tetrahedral mesh simplification algorithm. We tested our
view-dependent refinement framework on an existing state-of-the-art volume renderer.
Thanks to low overhead dynamic view-dependent refinement, we achieve interactive
frame rates for rendering common datasets at decent image resolutions.
Achieving interactive rates for direct volume rendering of large unstructured volumetric
grids is a challenging problem, but parallelizing direct volume rendering algorithms
can help achieve this goal. Using Compute Unified Device Architecture
(CUDA), we propose a GPU-based volume rendering algorithm that itself is based on
a cell projection-based ray-casting algorithm designed for CPU implementations. We
also propose a multi-core parallelized version of the cell-projection algorithm using OpenMP. In both algorithms, we favor image quality over rendering speed. Our algorithm
has a low memory footprint, allowing us to render large datasets. Our algorithm
support progressive rendering. We compared the GPU implementation with the serial
and multi-core implementations. We observed significant speed-ups, that, together
with progressive rendering, enabling reaching interactive rates for large datasets.
Visualization of materials is an indispensable part of their structural analysis. We
developed a visualization tool for amorphous as well as crystalline structures, called
MaterialVis. Unlike the existing tools, MaterialVis represents material structures as a
volume and a surface manifold, in addition to plain atomic coordinates. Both amorphous
and crystalline structures exhibit topological features as well as various defects.
MaterialVis provides a wide range of functionality to visualize such topological structures
and crystal defects interactively. Direct volume rendering techniques are used
to visualize the volumetric features of materials, such as crystal defects, which are
responsible for the distinct fingerprints of a specific sample. In addition, the tool provides
surface visualization to extract hidden topological features within the material.
Together with the rich set of parameters and options to control the visualization, MaterialVis
allows users to visualize various aspects of materials very efficiently as generated
by modern analytical techniques such as the Atom Probe Tomography.
Keywords
Volume visualizationdirect volume rendering
view-dependent refinement
progressive meshes
unstructured tetrahedral meshes
Graphics Processing Unit (GPU)
Compute Unified Device Architecture (CUDA)
OpenMP
material visualization, crystals
amorphous materials
crystallography
embedded nano-structure visualization
crystal visualization
crystal defects
Permalink
http://hdl.handle.net/11693/15939Collections
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