Managing Complex Augmented Reality Models

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Professor Horst Cerjak, 19.12.2005

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Dieter Schmalstieg Managing Complex Augmented Reality Models

Managing

Complex Augmented Reality Models

Dieter Schmalstieg

Technische Universität Graz Austria

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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…

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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

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AR Modeling Pipeline

Acquisition (Tier 0) Storage (Tier 1) Delivery (Tier 2) Use (Tier 3) XML Database View Generation / Context sensitive scene graph Legacy database conversion Authoring Surveying Tracking Navigation Visualization

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Hardware and Platform

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Muddleware: Database/Collaboration Server

• XML database server

• Persistent storage for models

• Communication similar to tuplespace/blackboard • Non-relational, hierarchical, loosely typed

Database File (XML)

Clients [1..n] Server

Muddleware Client

Application Persistence_Service

XML Database XML Queries

Script Queries Query Results

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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 Application Transformation Processor Model Schema XSLT

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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

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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

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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(!)

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Example: Signpost SG for Cultural Heritage

Render highlight Context of vis_type = highlight Raypick Context of vis_type = picking Context switch on item Item A Context switch on vis_type

Highlight Picking Highlight Picking Render Overlay Context of vis_type = picking Overlay Overlay Item B Context switch on vis_type overlay

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Signpost Modeling Pipeline

Muddleware

Transformation

Processor XSLT

Context-Sensitive Scene Graph Dataset from Location Query

GIS Stadt Wien Mehrzweckkarte

Foothpath Network Generator

Guide Book

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Indoor Signpost

• Cell/portal model • Marker based tracking + inertial tracking

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Runtime Visualization Processing

Generic model

World in Miniature (WIM)

Heads-Up Display (HUD)

Multiple ways to use

visual model

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Indoor Context Sensitive Scene Graph

WIM Context of floor = green Context of wall = yellow Navigation Context of floor = skip Context of wall = z-only Context of wall = wireframe Room Floor Style =

context floor Geometry

Wall

Style =

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Indoor Model: Generation

Surveying with a mobile robot

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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

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Indoor Signpost Modeling Pipeline

Muddleware XSLT XSLT Indoor Model data CSSG rules for Signpost Mobile AR APRIL Script for Tour Guide APRIL rules APRILscene graph APRIL state machine

APRIL I/O configuration files Indoor

Signpost scene graph

Surveying + authoring

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Ongoing Work

• Self-surveying building

– Manfred Klopschitz

• UbiTrack (autoconfigure for multi-system tracking)

– Eduardo Veas, Alex Bornik, Joe Newman (Cambridge)

• Interactive reconstruction for outdoors

– Bernhard Reitinger, Christopher Zach (VRVis)

• Subsurface Visualization

– Erick Mendez, Gerhard Schall – SEE NEXT TALK!

Credits:

Gerhard Reitmayr (Cambridge) Joe Newman (Cambridge)

Gerhard Schall

Florian Ledermann (Vienna) Istvan Barakonyi