A Flexible Data Streaming Design for Interactive Visualization of Large-Scale Volume Data
dc.contributor.author | Wu, Qi | en_US |
dc.contributor.author | Doyle, Michael J. | en_US |
dc.contributor.author | Ma, Kwan-Liu | en_US |
dc.contributor.editor | Bujack, Roxana | en_US |
dc.contributor.editor | Tierny, Julien | en_US |
dc.contributor.editor | Sadlo, Filip | en_US |
dc.date.accessioned | 2022-06-02T14:36:51Z | |
dc.date.available | 2022-06-02T14:36:51Z | |
dc.date.issued | 2022 | |
dc.description.abstract | Modern simulations and experiments can produce massive amounts of high-fidelity data that are challenging to transport and visualize interactively. We have designed a data streaming system to support interactive visualization of large volume data. Our streaming system design is unique in its flexibility to support diverse data organizations and its coupling with a highly efficient CPU-based ray-tracing renderer. In this paper, we present our streaming and rendering design and demonstrate the efficacy of our system with progressive rendering of streaming tree-based AMR (TAMR) volume data and radial basis function (RBF) particle volume data. With our system, interactive visualization can be achieved using only a mid-range workstation with a single CPU and a modest quantity of RAM. | en_US |
dc.description.sectionheaders | Large Scale Visualization | |
dc.description.seriesinformation | Eurographics Symposium on Parallel Graphics and Visualization | |
dc.identifier.doi | 10.2312/pgv.20221064 | |
dc.identifier.isbn | 978-3-03868-175-5 | |
dc.identifier.issn | 1727-348X | |
dc.identifier.pages | 37-47 | |
dc.identifier.pages | 11 pages | |
dc.identifier.uri | https://doi.org/10.2312/pgv.20221064 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/pgv20221064 | |
dc.publisher | The Eurographics Association | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | A Flexible Data Streaming Design for Interactive Visualization of Large-Scale Volume Data | en_US |