Automated Cell Based Generalization of Virtual 3D City ......Generalization Lenses Navigation...
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Automated Cell Based Generalization of Virtual 3D City Models with Dynamic Landmark Highlighting Tassilo Glander, Jürgen Döllner Hasso-Plattner-Institut Dep. Computer Graphics Systems Prof. Dr. Jürgen Döllner University of Potsdam www.hpi.uni-potsdam.de/3d www.3dgi.de
Transcript of Automated Cell Based Generalization of Virtual 3D City ......Generalization Lenses Navigation...
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
