3D Generalization Lenses for Interactive Focus Con

3D Generalization Lensesfor Interactive Focus + Context Visualizationof Virtual City Models: 

Matthias Trapp, Tassilo Glander, Henrik Buchholz, Jürgen Döllner Hasso-Plattner-Institut, University of Potsdam Germany 12th International Conference Information Visualization 9-11 July 2008 3D Generalization Lenses for Interactive Focus + Context Visualization of Virtual City Models

Motivation – Virtual 3D City Models: 

Motivation – Virtual 3D City Models Properties: Large number of objects High degree of visual detail Tool for communicate complex 3D geoinformation Numerous applications  Can lead to perceptional and cognitive overload

Motivation – Generalized Virtual 3D City Models: 

Motivation – Generalized Virtual 3D City Models [Glander, ACMGIS 2007, ICA WS 2008]

Motivation – Combined Generalization Levels: 

Motivation – Combined Generalization Levels Simple case: Single arbitrarily shaped, non-convex volume (lens shape) Two Levels of Abstraction (LOA) Model Complexity < 500 MB (Geometry + Textures) Context (LOA1) 3D Lens Shape

Motivation – Combined Generalization Levels: 

Motivation – Combined Generalization Levels Complex case: 3 intersecting lens shapes (multiple foci) Three Levels of Abstraction Model Complexity > 2GB Challenges: Interactive manipulation of lens shapes Combined usage (intersection, nesting) of multiple lenses Enabling arbitrary lens shapes Handling spatial data complexity

Outline: 

Outline Related Work Conceptual Overview Preprocessing & Focus + Context Mappings Real-Time Rendering Application Examples Future Work & Conclusions

Related Work : 

Related Work Interactive 3D Focus + Context Visualization with Lenses 3D Magic Lenses Viega et. al, UIST 1996 A Solution for the Focus and Context Problem in Geo-Virtual Environments Ropinski et. al, DMGIS 2005 Real-Time Volumetric Tests Using Layered Depth Images Trapp and Döllner, Eurographics 2008 Generalization 2D Building Simplification & Aggregation e.g., Mayer, ISPRS 1998 Single 3D Building Simplification e.g., Kada, ISPRS WS 2006 3D Cell-Based City Model Generalization Glander and Döllner, ACMGIS 2007

Our Approach: Conceptual Overview: 

Our Approach: Conceptual Overview Preprocessing Phase: Create levels of abstraction (LOA) and volumetric depth sprites (VDS) Rendering Phase: Create / modify focus + context mapping: FNC = map(VDS, LOA) Real-time image synthesis: render(FNC)

Automatic Generalization of Virtual 3D City Models: 

Automatic Generalization of Virtual 3D City Models Input: City Model CM Output: Levels of Abstraction LOA Process:

Preprocessing of Lens Volumes: 

Preprocessing of Lens Volumes Input: Derived or modeled solid polygonal shapes: S Output: Volumetric Depth Sprites VDS Processing:

Mapping Generalization Levels to Lens Volumes: 

Mapping Generalization Levels to Lens Volumes Mapping for n lenses and generalization levels: Mapping Properties: Prioritized, hierarchical one-to-one mapping (i = priority) Additional attributes (colors, lens positioning & scaling,…) Mapping can be changed at run-time

Real-time Rendering of Focus + Context Mapping: 

Real-time Rendering of Focus + Context Mapping Multi-pass rendering + clipping against VDS: First pass: render context Successively: one pass per LOA Start with lowest priority: i = n

Applications Examples & Usage Scenarios: 

Applications Examples & Usage Scenarios Scene Lens with intersecting foci of multiple non-convex volumes

Applications Examples & Usage Scenarios: 

Applications Examples & Usage Scenarios Camera Lens with nested foci of the same convex volume

Limitations & Future Work: 

Limitations & Future Work Current conceptual limitations: Only one-to-one mapping possible Model complexity requires out-of-core rendering Clipping limitations: Under-sampling / aliasing artifacts No capping of clipped areas Future Work: Extend mapping mechanism: one-to-many Compensate sampling artifacts Enable capping of clipped areas Use cases that exploit technical potential

Conclusions: 

Conclusions Wrap-up: Concept + technique for combing different levels of generalization Two phase process: preprocessing + rendering Hierarchical focus + content mapping Interactive multi-pass rendering Potential for future work

Thank You…: 

Thank You… Contact: Matthias Trapp matthias.trapp@hpi.uni-potsdam.de Tassilo Glander tassilo.glander@hpi.uni-potsdam.de Henrik Buchholz henrik.buchholz@hpi.uni-potsdam.de Computer Graphics Systems Group Prof. Dr. Jürgen Döllner www.hpi.uni-potsdam.de/3d Researchgroup 3D-Geoinformation www.3dgi.de

Main References: 

Main References Glander, Döllner, Cell-Based Generalization of 3D Building Groups with Outlier Management, ACMGIS, 2007 Kada: 3D Building Generalization Based on Half-Space Modeling, Proceedings of the ISPRS Workshop on Multiple Representation and Interoperability of Spatial Data, 2006 Mayer: Model-Generalization of Building Outlines on Scale-Spaces and Scale-Space Events, International Archives of Photogrammetry and Remote Sensing, Vol. 33, 1998 Trapp, Döllner, Real-Time Volumetric Tests Using Layered Depth Images, Eurographics 2008