<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1244</id>
    <completedYear>2011</completedYear>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2011-03-17</completedDate>
    <publishedDate>2011-03-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Screen-Space Sampling for Volume Ray-Casting</title>
    <abstract language="eng">This work is concerned with adaptive screen-space sampling for volume ray-casting. The goal is to reduce the&#13;
number of rays being cast into the scene and, thus, the overall number of sampling points. We guarantee reliable&#13;
images through explicit error control using an error estimator that is founded in the field of finite element methods&#13;
(FEM). FEM theory further provides a well-founded theory to prove the efficiency of the presented algorithm via&#13;
convergence analysis. We, therefore, compare the convergence behavior of our method against uniform subdivisions&#13;
and a refinement scheme that was presented in the context of CPU volume ray-casting. Minimizing&#13;
the number of sampling points is of interest for rendering large datasets where each evaluation might need an expensive&#13;
decompression. Furthermore, with increasing screen resolutions high-resolution images are created more&#13;
efficiently with our method.</abstract>
    <identifier type="serial">11-04</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-12446</identifier>
    <author>Andrea Kratz</author>
    <submitter>Andrea Kratz</submitter>
    <author>Jan Reininghaus</author>
    <author>Markus Hadwiger</author>
    <author>Ingrid Hotz</author>
    <series>
      <title>ZIB-Report</title>
      <number>11-04</number>
    </series>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="kratz">Kratz, Andrea</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1244/TR-1104.pdf</file>
  </doc>
  <doc>
    <id>3676</id>
    <completedYear>2011</completedYear>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName>Poster presentation at the IEEE VisWeek 2011, Providence</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improved Visual Exploration and Hybrid Rendering of Stress Tensor Fields via Shape-Space Clustering.</title>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Andrea Kratz</author>
    <author>Markus Hadwiger</author>
    <author>Ingrid Hotz</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="compvis">Vergleichende Visualisierung</collection>
    <collection role="persons" number="kratz">Kratz, Andrea</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
