automation and visualization in geographic immersive virtual environments
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Automation and Visualization in Geographic Immersive Virtual Environments. Thomas J. Pingel , Northern Illinois University Keith C. Clarke, University of California Santa Barbara AutoCarto 2012 International Research Symposium September 16-20, 2012 Columbus, Ohio. - PowerPoint PPT PresentationTRANSCRIPT
Automation and Visualization in Geographic Immersive Virtual Environments
Thomas J. Pingel, Northern Illinois UniversityKeith C. Clarke, University of California Santa Barbara
AutoCarto 2012 International Research SymposiumSeptember 16-20, 2012
Columbus, Ohio
Central Research Question:
How can we, in an automatable way, produce an immersive geographic virtual environment that
will assist in the interpretation, analysis, and understanding of specific, local events?
Outline
• Project overview• Code base• Terrain generation from LiDAR• Acquisition for of audio and video for model
overlay
Immersive Geographic Virtual Environments
• Immersive: “any virtual reality representation in which the user views her or her environment from a perspective view, and can freely move around in that environment”
• Multiple Psychologies of Space (Montello, 1993)– Figural , Vista, Environmental, Geographical
• Representing Environmental (or Geographical) spaces as Figural (or Vista) Objects while retaining some of the cognitive elements of each.
• Emphasis on representing places in a model that can both be manipulated as an object or experienced as a place.
Related Work• Google’s Earth and
Street View– Microsoft & Apple– No ability to alter the
terrain– Universality
• Virtual Tübingen– Designed for spatial
cognition testing– 200 structures, .5 x .15
km– Our study area
• 3.25 x 1.6 km• ~2000 structures
Image from Virtual Tübingen
Video Game Community
• Immense budgets and revenues– $65 billion annually
• Many perspectives– First Person Shooters– World of Warcraft – But few environment &
object perspectives• Highly structured
environments
Code Base – X3D• XML successor to VRML (and
GeoVRML)• Native Geo support• Native video texturing and
spatialized audio• Royalty free• Browsers can typically read other
3D formats (e.g., COLLADA)• Good input device integration
– Space M ouse– Microsoft Kinect– Wiimotes
X3D DevelopmentAvalon & X3DOM
• Integration of next-gen specs in Avalon– Instantreality.org
• Integration with HTML5 with X3DOM– X3dom.org
• Full rendering within browser– No-add ins required
Terrain generation
• LiDAR– Cheap– Highly accurate– Portable– But needs processing
• Assumption of little available geodata– Ground cues can be
very valuable in street network ID
Point cloud of building and surrounding area
Terrain Extraction is Important
Davidson Library sits approximately 6 meters above the ground due to a terrain layer error.
Terrain Extraction: The Simple Morphological Filter (SMRF)
• Emphasizes reducing Earth-as-Object error
• Still very good at reducing Object-as-Earth error
• Lowest total error rate of any published algorithm tested against ISPRS dataset
• tpingel.org/code
LiDAR Visualization (Bonemaps)• Image-like visualization of
Digital Surface Model• No registration errors• Slope-based intensity
mapping, w/ compensation for “cognitive slope”
• Higher contrast than hillshade
• Appropriate for mixed environments
SMRF + Bonemaps at El Pilar, Guatemala
Digital Surface Model
SMRF + Bonemaps at El Pilar, Guatemala
SMRF-derived terrain layer
Video Overlay
• Aerostat-based video capture
• Smartphone capture and relay
• Native video texturing in X3D
Acknowledgements
• IC Postdoc for funding the project.• Alan Glennon and Kitty Courier for kite
photography expertise.• William McBride for SRMF algorithm
development and aerostat design.