Managing Complex Augmented Reality Models Technische Universität Graz Austria 1
Introduction Summarizes research work in the STUDIERSTUBE project 2000-2006 Main focus: Making mobile AR practical Overcome the Demo Data Dillema you never know what it s really worth until you have tried it in real life 2
Comparison VR AR Models Virtual Reality Often architectural models Visual fidelity very important Geometry used for rendering No real world Often no semantic data Augmented Reality Often architectural models Visual fidelity not so important Geometry used for handling occlusions real-virtual, shadows, interaction, vision-based tracking, and also stylized rendering Registration with real world required Semantic metadata very important 3
AR Modeling Pipeline Acquisition (Tier 0) Surveying Legacy database conversion Authoring Storage (Tier 1) XML Database Delivery (Tier 2) View Generation / Context sensitive scene graph Use (Tier 3) Navigation Tracking Visualization 4
Hardware and Platform Backpack PC Ultra-Mobile PC Win.CE Smartphone 5
Muddleware: Database/Collaboration Server XML database server Persistent storage for models Communication similar to tuplespace/blackboard Non-relational, hierarchical, loosely typed Clients [1..n] Server Persistence Service Application Muddleware Client XML Queries Script Queries XML Database Read/Write Database File (XML) Query Results 6
Data Management 3-tier architecture Model schema defined as XML Schema Storage in Muddleware or Tamino XSLT transformations provide data (scene graph) for individual applications Muddleware Model Schema Transformation Processor XSLT Application 7
Case Study 1: Outdoor Navigation 8
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User Interface for Cultural Heritage Browsing Attributed features are highlighted Picking with gaze to trigger content Content displayed in HUD may consist of text, images, 3D models 10
User Interface for Navigation Network of paths Leading to address points Waypoints as cylinders and edges as arrows between them Dynamically updates according to user s position Re-compute shortest path Additional UI elements to guide in the right direction Clipping on real buildings 11
Context Sensitive Scene Graphs (1) Scene graphs (SG) are an established tool for interactive 3D applications Encapsulates behavior Once created, cannot easily modify behavior of objects hidden in SG Defeats multi-purpose SG Our solution: parameterized SG with user-defined parameters (Sub-)SGs as parameters(!) Allows on-the-fly re-purposing of SGs Where are the blue nodes? Increases render caching complexity How to affect just the blue nodes? 12
Example: Signpost SG for Cultural Heritage Render highlight Raypick Render Overlay Context of vis_type = highlight Context of vis_type = picking Context of vis_type = picking overlay Context switch on item Item A Item B Context switch on vis_type Context switch on vis_type Highlight Picking Overlay Highlight Picking Overlay 13
Signpost Modeling Pipeline Guide Book GIS Stadt Wien Mehrzweckkarte Foothpath Network Generator Muddleware Dataset from Location Query Transformation Processor XSLT Context-Sensitive Scene Graph Signpost 14
Indoor Signpost Cell/portal model Marker based tracking + inertial tracking 15
Runtime Visualization Processing Multiple ways to use visual model World in Miniature (WIM) Heads-Up Display (HUD) Generic model 16
Indoor Context Sensitive Scene Graph Navigation WIM Context of floor = green Context of floor = skip Context of wall = yellow Context of wall = z-only Context of wall = wireframe Room Floor Style = context floor Wall Geometry Style = context wall Geometry 17
Indoor Model: Generation Surveying with a mobile robot 18
Animated Tour Guide Authoring Platform APRIL (Augmented Presentation and Interaction Language) XML dialect for scripting the Studierstube system State machine concept Hardware platform independence Component concept (Indoor Signpost, Animated Agents) Used to script a guided tour through Favoritenstrasse / TU Wien 19
Indoor Signpost Modeling Pipeline Surveying + authoring Muddleware APRIL Script for Tour Guide Indoor Model data CSSG rules for Signpost XSLT XSLT Indoor Signpost scene graph APRIL rules APRILscene graph APRIL state machine APRIL I/O configuration files Mobile AR client 20
Ongoing Work Self-surveying building Manfred Klopschitz UbiTrack (autoconfigure for multi-system tracking) Eduardo Veas, Alex Bornik, Joe Newman (Cambridge) Credits: Gerhard Reitmayr (Cambridge) Joe Newman (Cambridge) Gerhard Schall Florian Ledermann (Vienna) Istvan Barakonyi Interactive reconstruction for outdoors Bernhard Reitinger, Christopher Zach (VRVis) Subsurface Visualization Erick Mendez, Gerhard Schall SEE NEXT TALK! 21