Project Documents Reports
  • User_Requirements _MPC.pdf: User requirements Massive Point Clouds for eSciences, The basic functionalities needed for a new Point Cloud Spatial Database Management System are identified
  • minibench_public.pdf: Mini-benchmark description, This documents describes the mini-benchmark that we have executed to make an initial evaluation of the existing point cloud management solutions
  • mediumbenchmark_public.pdf: Medium benchmark description, This document describes the medium benchmark where we further test the existing point cloud management solutions
  • mediumbenchmark.pdf: Medium benchmark results, This extended document contains the results of the medium benchmark
  • Medium benchmark logs and graphs, This ZIP file contains the loading and querying logs as well as the IO, CPU and MEM graphs
  • pcdms_usage_guide.pdf: Usage guides, Point Cloud Data Management Systems: DBMS and file-based solutions
  • eScience_PointClouds_Jan2013_v2.pdf: 2013 01 - Introduction to the NWO/SURF project: Massive point clouds for eSciences
  • OosteromIQmulus.pdf: 2014 07 - IQMULUSWORK - Extended abstract for the invited presentation at the IQmulus Workshop on Processing Large Geospatia Data, 8 July 2014, Cardiff, Wales, UK (version 23 May 2014)
  • DataScienceSymp_MPC_oct14.pdf: 2014 10 - SOFTDAYS - Point cloud data management by Peter van Oosterom, Presentation part of the Data Science Symposium within the Delft Software Days 2014 organized by Deltares and 3TU.Datacentrum
  • WORKSHOP_DUBAI_cut.pdf: 2014 11 - 3DGEOINFO - Storage and Management of Large Scale Point Cloud Data, Workshop by Orace, NLeSC and TU Delft within the 3dGeoInfo conference held in Dubai in November 2014.
  • SPAR_MPC.pdf: 2014 12 - SPAR - Databases for Massive Point Clouds, SPAR Europe 2014, Amsterdam, The Netherlands
  • JIGC_2015_keynote_pointclouds.pdf: 2015 10 - JIGC 2015 - Realistic benchmarks for point cloud data management systems
  • martinezRubi_MPC1.pdf: 2015 11 - Synthesis Project Symposium: Direct Computing with Explorative Point Clouds - Research on improved methods for point cloud data management
  • PointClouds.pdf: 2015 11 - SIG BIWA - NLeSC introduction and Point Clouds at NLeSC
  • martinezRubi_MPC.pdf: 2015 11 - Capturing Reality 2015 - Taming the beast: Free and open-source massive point cloud web visualization
  • martinezRubi_AHN2.pdf: 2015 12 - Seminar Management of massive point cloud data: wet and dry (2) - The AHN2 3D web viewer and download tool
  • Massive_pointcloud_data_management.pdf: CG2015 - Massive point cloud data management: Design, implementation and execution of a point cloud benchmark
  • mpc_sigspatial.pdf: SIGSPATIAL2015 - Benchmarking and improving point cloud data management in MonetDB, Newsletter to be included in SIGSPATIAL SPECIAL Volume 6, Number 2, July 2014
  • MonetDBMeetsPC.pdf: FOSS4GE2014 - A column-store meets the point clouds
  • GIMInt_MPC.pdf: GIMInt Sep. 2015 - Managing Massive Point Clouds, Perfomance of DBMS and File-Based solutions
  • realistic-benchmarks-point.pdf: JIGC 2015 - Realistic benchmarks for point cloud data management systems, 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, PostgreSQLPostGIS, 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.
  • taming-beast-free.pdf: CapturingReality2015 - Taming the beast: Free and open-source massive point cloud web visualization, Powered by WebGL, some renderers have recently become available for the visualization of point cloud data over the web, for example Plasio or Potree. We have extended Potree to be able to visualize massive point clouds and we have successfully used it with the second national Lidar survey of the Netherlands, AHN2, with 640 billion points. In addition to the visualization, the publicly available service at also features a multi-resolution download tool, a geographic name search bar, a measurement toolkit, a 2D orientation map with field of view depiction, a demo mode and the tuning of the visualization parameters. Potree relies on reorganizing the point cloud data into an multi-resolution octree data structure. However, this reorganization is very time consuming for massive data sets. Hence, we have used a divide and conquer approach to decrease the octree creation time. To achieve such performance improvement we divided the entire space into smaller cells, generated an octree for each of them in a distributed manner and then we merged them into a single massive octree. The merging is possible because the extent of all the nodes of the octrees is known and fixed. All the developed tools are free and open-source (FOSS) and they can be used to visualize over the web other massive point clouds.