Finding Efficient Spatial Distributions for Massively Instanced 3-d Models

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dc.contributor.authorZellmann, Stefanen_US
dc.contributor.authorMorrical, Nateen_US
dc.contributor.authorWald, Ingoen_US
dc.contributor.authorPascucci, Valerioen_US
dc.contributor.editorFrey, Steffen and Huang, Jian and Sadlo, Filipen_US
dc.date.accessioned2020-05-24T13:24:34Z
dc.date.available2020-05-24T13:24:34Z
dc.date.issued2020
dc.description.abstractInstancing is commonly used to reduce the memory footprint of massive 3-d models. Nevertheless, large production assets often do not fit into the memory allocated to a single rendering node or into the video memory of a single GPU. For memory intensive scenes like these, distributed rendering can be helpful. However, finding efficient data distributions for these instanced 3-d models is challenging, since a memory-efficient data distribution often results in an inefficient spatial distribution, and vice versa. Therefore, we propose a k-d tree construction algorithm that balances these two opposing goals and evaluate our scene distribution approach using publicly available instanced 3-d models like Disney's Moana Island Scene.en_US
dc.description.sectionheadersGraphics
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20201070
dc.identifier.isbn978-3-03868-107-6
dc.identifier.issn1727-348X
dc.identifier.pages1-11
dc.identifier.urihttps://doi.org/10.2312/pgv.20201070
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20201070
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectComputing methodologies
dc.subjectRay tracing
dc.subjectSelf
dc.subjectorganization
dc.titleFinding Efficient Spatial Distributions for Massively Instanced 3-d Modelsen_US
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