Automated Cell Based Generalization of Virtual 3D City Models
with Dynamic Landmark Highlighting
Tassilo Glander, Jürgen Döllner
Hasso-Plattner-InstitutDep. Computer Graphics Systems
Prof. Dr. Jürgen DöllnerUniversity of Potsdam
www.hpi.uni-potsdam.de/3dwww.3dgi.de
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Outline
1. Introduction / Generalization in 3D2. Related Work
3. Preprocessing4. Dynamic highlighting5. Demo
6. Conclusion & Outlook
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1 Introduction
Current city models reach critical size (100000+ buildings)■ Need for reduction of unnecessary details■ Usage beyond pretty photorealistic visualization?■ Adaptation of cartographic generalization principles
3D generalization has similar problems■ Present appropriate information density on limited space■ Large datasets underlying continuous updates automatic derivation
needed
…and specific problems■ Occlusion due to perspective■ Dynamic (real time) updates of scale■ Continuous scale in one image
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2 Related Work
2D – many generalization models / frameworks■ Iterative, step-by-step based (e.g. Agents)■ Global (Least Squares Adjustment, Simulated Annealing, Spring-
based)Matured frameworks for productive use
3D – first steps with single generalization operators■ Single building simplification [Kada2005, Thiemann&Sester2004, Forberg2002, Rau et
al. 2006]
■ Building aggregation & simplification [Sester2004, Anders2005]
Our previous work■ Cell-based generalization [Glander&Döllner 2007]
■ Landmark highlighting [Glander et al. 2007]
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3 Preprocessing
Cell-based generalization■ Calculate arrangement (roads cells)■ Map buildings to cells (point-in-polygon tests)■ Per cell: calculate mean height & variance■ Identify initial set of landmarks
(CAD models + outliers)
For smaller scales, remove less important streets and repeat.
Cell-based generalization■ Calculate arrangement (roads cells)■ Map buildings to cells (point-in-polygon tests)■ Per cell: calculate mean height & variance■ Identify initial set of landmarks
(CAD models + outliers)
For smaller scales, remove less important streets and repeat.
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3 Preprocessing
Create landmark hierarchy [Winter et al. 2008]
■ Use centroids of initial landmark buildings for a Delaunay triangulation■ Choose set of landmarks for next layer:
□ For each node i, vote for one node with the highest saliencyvalue within neighborhood (including i)
□ In absence of a better measure for saliency, we use the height
■ Repeat, until just one landmark is left
Integration with generalized blocks■ Alignment of landmark hierarchy with levels of abstraction (LOA)
average reduction to 1/3 in each subsequent LOA■ Cut out landmarks from block cells, place in the scene
i
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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3 Preprocessing
Creating several levels of abstraction■ Exploiting different road weights
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4 Dynamic Highlighting
Dynamic Highlighting■ Emphasize most important landmarks by enlarging them
peak of the landmark hierarchy (e.g., restricted to Top 10)■ Calculate a scaling factor
□ depending on the camera distance□ quadratic scaling function parameterized with a distance interval
[dstart,dend]
■ Use 2x maximum distance to neighbor in hierarchy for dend
□ dstart is constant for all landmarks (e.g. dstart = 2500m)
distance
Camera
startd endd
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4 Dynamic Highlighting
Demo
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Conclusion / Outlook
LOD vs LOA■ Generalization in city models is only known in
terms of level of detail so far■ Building aggregation is necessary element
Applications■ Dynamic and continuous scale■ Generalization Lenses■ Navigation scenarios
LOD4LOD3LOD2LOD1
LOD0
LOA0
LOA1LOA2
LOAn
CityGML
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Conclusion / Outlook
Further work■ Many detail improvements (better aggregation, …, CityGML export)■ Build up on existing platforms & extend towards 3D visualization?
What is gained?■ Potential visualization of 3D generalization■ Usable tourist map
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Contact
Tassilo [email protected]
Department for Computergraphic SystemsProf. Dr. Jürgen Döllnerwww.hpi.uni-potsdam.de/3d
Research Group 3D-Geoinformationwww.3dgi.de