1 / 24

Data Management of Large 3D Urban Scenes

Data Management of Large 3D Urban Scenes. Xavier Lopez Oracle, Inc. USA. 3D Data Management. <Insert Picture Here>. Technology Drivers Use Cases for 3D Data Management Overview of Spatial Databases Wrap-up. Technology & Business Drivers. Massive new sensor hardware capabilities

duane
Download Presentation

Data Management of Large 3D Urban Scenes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Management of Large 3D Urban Scenes Xavier Lopez Oracle, Inc. USA

  2. 3D Data Management <Insert Picture Here> • Technology Drivers • Use Cases for 3D Data Management • Overview of Spatial Databases • Wrap-up

  3. Technology & Business Drivers • Massive new sensor hardware capabilities • Automated Data Capture / Model Creation (sensors) • Lidar (point clouds), automated photogrammetry • 3D data coming into mainstream • Mass Market: Consumer-focused systems • Benefit from IT scalability, security, and reliability • Managing a 3D data enterprise workflow • Improve performance and scalability of existing workflows • Bridging gap between point cloud surveys, GIS, CAD, BIM • Files to Databases

  4. Challenges: Managing Point Clouds • Robust Data Management Challenges: • High Density LIDAR: Sub-meter point spacing (billions of points) • Combine with multi-spectral gridded data (terabytes of data) • Versioning, Archiving (terabytes, petabytes) • Back-up/recovery • Data Transformation • LIDAR point filtering, visualization, analysis • Surfaces and 3D vector models • Attribute Data Integration • Leverage Grid computing, clustered servers • Visualization & Analytics • Integrate 3D models into business workflows • Associate 3D objects/features to attributes • Spatial query across point cloud features

  5. What do Spatial Databases Bring? • Scalability: Large seamless 3D scenes: Terabytes • Fast Retrieval: Geospatial Indexes on 3D point clouds • Partitioning: Manage large seamless scenes • Grid computing: Massive Data processing • Interoperability: Fuse aerial imagery, close-range airborne, ground video/LIDAR, 2D vector models • Spatial analysis: traditional GIS queries on 3D scenes • Transactional Updates • Enterprise Integration: Integrate 3D models with business information • Versioning and Long Transaction Support: • Data security, access control, encryption, authent. • Open: Support by third party 3D viz and analysis tools

  6. Creating Value Added Data Products CAD/BIM server TIN server Point Cloud Repository 3D Model server Image server

  7. Spatial DBMS in a Workflow Dissemination & Exploitation Data Collection Production LIDAR Surveys Photogrammetry Aerial Photos Satellite Imagery COTS Scenes CAD Designs Model/Scene Generation Image Texture Wrapping Versioning Editing/Updates Quality Control Volumetric Analysis 3D Mapping Fly Through 3D analysis Engineering Design Predictive Analysis Navigation Systems CAD Ortho-Photos LIDAR Spatial RDBMS

  8. Use Cases

  9. GIS Analytical Modeling & Simulation Flood Plain Analysis Petroleum Exploration

  10. CAD Infrastructure Design(Super Models) • Courtesy Parsons Brinckerhoff

  11. Google Earth 3D Mash-ups

  12. Simulation, Gaming, and VR

  13. <Insert Picture Here> Overview: Spatial Databases

  14. Customer Requirements • Enterprise RDBMS to manage ALL geospatial types • 2D, 3D, Rasters, Networks, Topology, Attributes • Native type support, indexing, and analysis • Support 2D & 3D coordinate systems support • Standards based: SQL, Java, .NET • Addresses large volumes of 3D point data • Building City models (Collada, CityGML) • Laser scanning (LIDAR, sonar) • Geo-engineering (CAD) • Surfaces (TINS, DEMS) • Addresses range of 3D application domains • City Modeling, environmental analysis • Real Estate, asset management • Personal navigation • VR, gaming, simulation

  15. Spatial Database Capabilities Spatial Analysis • Spatial Indexing Spatial Data Types Spatial DBMS Fast Access to Spatial Data All Location/Spatial Data Stored in the Database Spatial Access Through SQL

  16. Spatial Databases: Managing all Geodata Types Networks (Highway network) Parcels (polygons) 3D Models (Buildings) Spatial DBMS Data Lidar (Point Clouds) Imagery (Satellite) Structured Networks/Boundaries (persistent topology)

  17. <Insert Picture Here> Oracle 11g Spatial 3D Capabilities

  18. 3D Functionality in RDBMS • SDO_GEOMETRY (3D) • SDO_TIN • SDO_POINT_CLOUD Types Building Models,.. Efficient Storage Query Analysis Surface Modeling 3D COORDINATE SYSTEMS Scene, Object Modeling

  19. 3D Spatial Data Types • Simple and composite Solids • Solids are composed of closed surfaces • It has to have one outer surface and one or more interior surfaces • Cube is an example of a simple solid • A pyramid is another example of a simple solid • Composite solids: formed by multiple solids • Always define a single contiguous volume

  20. Point Clouds: LIDAR • Large volumes of point data acquired by sensors • LIDAR (Light Detection and Ranging) • Seismic sensors • Millions of points used to model a scene • New data type introduced to efficiently manage this type of point data • TIN to create triangulation of such points

  21. TINs: Triangulated Irregular Networks • Vector-based topological data model represents terrain/surface • Contain a network of irregularly spaced triangles • 3D surface derived from irregularly spaced points • Each sample point has an x, y, z or surface value

  22. Query, Analysis of 3D Data • Given a 3D Geometry as Query, Identify data geometries that • Intersect the Query (SDO_ANYINTERACT) • Within specified distance from Query (SDO_WITHIN_DISTANCE) • Nearest to Query (SDO_NN) • Analysis Functions • Relationship: Geometry-Geometry Intersection • Length, Area, Volume Analysis • Validation of 3D Geometry: Is Solid ‘closed’ ? • Extrusion of 2-D Footprint to a 3-D Solid by specifying heights • Association of Textures with LabelStrings of Geometry Elements • Extraction of Elements using LabelString of Geometry • Conversion Functions to/from GML, to KML/Collada, from CityGML

  23. 3D Coordinate Systems • Oracle Spatial 11g supports the following EPSG types • Vertical Coordinate Systems: Essentially 1-d coordinate system (w.r.t sea-level etc.) • Geocentric: 3-d Cartesian • Geographic-2d, Projected-2d: 2-d Ellipsoidal • Geographic-3d: 3-d Ellipsoidal • Compound Coordinate System • Combines • A Vertical Coordinate System with • Either Geographic-2D or Projected-2D Coordinate System • Support transform between different 3D Coordinate Systems

  24. Summary • Support large seamless 3D scenes: Terabytes • Provide bridge to fuse 3D, 2D, CAD, raster data • Fusion of aerial imagery, close-range airborne and ground video/LIDAR, with 2D vector models • Integrated support for Web delivery • Spatial analysis: GIS queries on 3D scenes • Transactional Updates • Enterprise Integration: Integrate 3D models with business information. • Data security, access control, encryption, authent. • Open: Support by 3D viz and analysis tools

More Related