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 Glandertassilo.glander@hpi.uni-potsdam.de

Department for Computergraphic SystemsProf. Dr. Jürgen Döllnerwww.hpi.uni-potsdam.de/3d

Research Group 3D-Geoinformationwww.3dgi.de