Realistic Benchmarks for Point Cloud Data Management Systems

P.J.M. van Oosterom, Oscar Martinez-Rubi, Theo Tijssen, Romulo Gonçalves

    Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

    4 Citations (Scopus)


    Lidar, photogrammetry, and various other survey technologies enable the collection of massive point clouds. Faced with hundreds of billions or trillions of points the traditional solutions for handling point clouds usually under-perform even for classical loading and retrieving operations. To obtain insight in the features affecting performance the authors carried out single-user tests with different storage models on various systems, including Oracle Spatial and Graph, PostgreSQL-PostGIS, MonetDB and LAStools (during the second half of 2014). In the summer of 2015, the tests are further extended with the latest developments of the systems, including the new version of Point Data Abstraction Library (PDAL) with efficient compression. Web services based on point cloud data are becoming popular and they have requirements that most of the available point cloud data management systems can not fulfil. This means that specific custom-made solutions are constructed. We identify the requirements of these web services and propose a realistic benchmark extension, including multi-user and level-of-detail queries. This helps in defining the future lines of work for more generic point cloud data management systems, supporting such increasingly demanded web services.
    Original languageEnglish
    Title of host publicationAdvances in 3D Geoinformation
    EditorsAlias Abdul-Rahman
    Number of pages30
    ISBN (Electronic)978-3-319-25691-7
    ISBN (Print)978-3-319-25689-4
    Publication statusPublished - 18 Oct 2016

    Publication series

    NameLecture Notes in Geoinformation and Cartography
    ISSN (Print)1863-2246

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