A System for Fast and Scalable Point Cloud Indexing Using Task Parallelism

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dc.contributor.authorBormann, Pascalen_US
dc.contributor.authorKrämer, Michelen_US
dc.contributor.editorBiasotti, Silvia and Pintus, Ruggero and Berretti, Stefanoen_US
dc.date.accessioned2020-11-12T05:42:10Z
dc.date.available2020-11-12T05:42:10Z
dc.date.issued2020
dc.description.abstractWe introduce a system for fast, scalable indexing of arbitrarily sized point clouds based on a task-parallel computation model. Points are sorted using Morton indices in order to efficiently distribute sets of related points onto multiple concurrent indexing tasks. To achieve a high degree of parallelism, a hybrid top-down, bottom-up processing strategy is used. Our system achieves a 2.3x to 9x speedup over existing point cloud indexing systems while retaining comparable visual quality of the resulting acceleration structures. It is also fully compatible with widely used data formats in the context of web-based point cloud visualization. We demonstrate the effectiveness of our system in two experiments, evaluating scalability and general performance while processing datasets of up to 52.5 billion points.en_US
dc.description.sectionheadersSampling and Rendering
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20201250
dc.identifier.isbn978-3-03868-124-3
dc.identifier.issn2617-4855
dc.identifier.pages153-162
dc.identifier.urihttps://doi.org/10.2312/stag.20201250
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20201250
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectParallel algorithms
dc.subjectInformation systems
dc.subjectExtraction
dc.subjecttransformation and loading
dc.titleA System for Fast and Scalable Point Cloud Indexing Using Task Parallelismen_US
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Italian Chapter Conference 2020 - Smart Tools and Apps in computer Graphics