Massively Parallel Batch Neural Gas for Bounding Volume Hierarchy Construction

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dc.contributor.authorWeller, Renéen_US
dc.contributor.authorMainzer, Daviden_US
dc.contributor.authorSrinivas, Abhisheken_US
dc.contributor.authorTeschner, Matthiasen_US
dc.contributor.authorZachmann, Gabrielen_US
dc.contributor.editorJan Bender and Christian Duriez and Fabrice Jaillet and Gabriel Zachmannen_US
dc.date.accessioned2014-12-16T07:27:41Z
dc.date.available2014-12-16T07:27:41Z
dc.date.issued2014en_US
dc.description.abstractOrdinary bounding volume hierarchy (BVH) construction algorithms create BVHs that approximate the boundary of the objects. In this paper, we present a BVH construction that instead approximates the volume of the objects with successively finer levels. It is based on Batch Neural Gas (BNG), a clustering algorithm that is known from machine learning. Additionally, we present a novel massively parallel version of this BNG-based hierarchy construction that runs completely on the GPU. It reduces the theoretical complexity of the sequential algorithm from O(nlogn) to O(log2 n) and also our CUDA implementation outperforms the CPU version significantly in practice.en_US
dc.description.seriesinformationWorkshop on Virtual Reality Interaction and Physical Simulationen_US
dc.identifier.isbn978-3-905674-71-2en_US
dc.identifier.urihttps://doi.org/10.2312/vriphys.20141219en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.5 [Computer Graphics]en_US
dc.subjectComputational Geometry and Object Modelingen_US
dc.subjectObject hierarchiesen_US
dc.subjectI.5.3 [Pattern Recognition]en_US
dc.subjectClusteringen_US
dc.subjectAlgorithmsen_US
dc.titleMassively Parallel Batch Neural Gas for Bounding Volume Hierarchy Constructionen_US
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