Declarative Modelling of Virtual Environments DEM 2 ONS PROJECT 2 - - PowerPoint PPT Presentation

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Declarative Modelling of Virtual Environments DEM 2 ONS PROJECT 2 - - PowerPoint PPT Presentation

Declarative Modelling of Virtual Environments DEM 2 ONS PROJECT 2 ONS PROJECT DEM (Declarative Multimodal ModeliNg System) (Declarative Multimodal ModeliNg System) (Visual Objects: From Reality To Expression) VORTEX Team (Visual Objects: From


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Declarative Modelling of Virtual Environments

DEM DEM2

2ONS PROJECT

ONS PROJECT (Declarative Multimodal ModeliNg System) (Declarative Multimodal ModeliNg System)

VORTEX Team VORTEX Team (Visual Objects: From Reality To Expression)

(Visual Objects: From Reality To Expression)

IRIT – Toulouse III IRIT – Toulouse III

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VORTEX Research topics

Virtual Reality Complex shapes & 3D environment modelling Rendering & visualization Behavioral Simulation

Declarative modelling Declarative modelling Constraints, Optimisation Constraints, Optimisation

Meshes Points based modelling Medical imaging & Related applications Visualization on very big screens Animation of characters Distributed & cooperative simulation Collaborative virtual prototyping

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Declarative modelling: Review of our works

Application prototypes DEM²ONS Project

ORANOS (1998) (Numeric CSP) non robust

isothetic

layout

Constraints solvers prototypes

experiment platform of multimodal modeller DEMONS’93

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DEMONS_ORANOS

 Dynamical and hierarchical N-CSP [Kwaiter 98]

Isothetic

Final Scene with orientation fixed by the designer(~25 objects)

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Declarative modelling: Review of our works

Application prototypes DEM²ONS Project

ORANOS (1998) (Numeric CSP) non robust isothetic layout MANHATTAN (2002) (Geometric CSP) isothetic layout more efficient than Oranos

Constraints solvers prototypes

experiment platform of multimodal modeller DEMONS’93

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DEMONS_Manhattan: Geometric CSP

 Search algorithm: backtracking + dynamic filtering +

heuristic into a discrete space [Le Roux 02] Isothetic !

28 objects, 24 possible orientations in 3D, description by script ~10 sec. of generation + rendering time (ray-tracing)

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Declarative modelling: Review of our works

Application prototypes DEM²ONS Project

ORANOS (1998) (Numeric CSP) non robust isothetic layout MANHATTAN (2002) (Geometric CSP) isothetic layout more efficient than Oranos DEMONS-GA (99- 03) (genetic algorithm) non-isothetic layout

Constraints solvers prototypes

experiment platform of multimodal modeller DEMONS’93

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DEMONS_GA

 Genetic Algorithmes [Sanchez, Le Roux 03] Non-Isothetic

88 objects, description by script ~2 min. of generation + rendering time (ray-tracing)

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Declarative modelling: Review of our works

Application prototypes DEM²ONS Project

ORANOS (1998) (Numeric CSP) non robust

isothetic

layout MANHATTAN (2002) (Geometric CSP) isothetic layout more efficient than Oranos DEMONS-GA (99-03) (genetic algorithm)

non-isothetic layout

Constraints solvers prototypes

DEMONS- LE (2003)

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DEMONS_LE

 Metaheuristics from local search [Larive 03] Non-Isothetic

« drag and drop » from the interface towards the scene (interaction + generation in a few seconds)

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Review of our work in declarative modelling

Application prototypes DEM²ONS Project

ORANOS (1998) (Numeric CSP) non robust

isothetic layout

MANHATTAN (2002) (Geometric CSP)

isothetic layout

more efficient than Oranos ADMUNSEN (2003) (Numeric CSP)

non-isothetic

layout

Constraints solvers prototypes

DEMONS- LE (2003) experiment platform of generative processes DEMONS’03 DEMONS-GA (99- 03) (genetic algorithm)

non-isothetic

layout

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DEMONS_Admunsen: a tenacious explorer

 Numeric CSP ... and non-isothetic !

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DEMONS_Admunsen: Object position tags

 Example: the in front of

in front of constraint

Characteristic points

Quadratic location zones

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DEMONS_Admunsen: Tags

 Object position tags

The chair against against the table

On On the chair Under Under the chair In In front front of

  • f the chair
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Conclusion

 Critical steps:

– Interpretation of properties

– Efficient generation with guaranteed results (CSP) or not (Metaheuristics)

– Take into account the direct modifications, but keep the consistency with the result of the generation step

 Generalization to the complex shapes:

– Features of complex shapes ?

– Sketching

– Relevant combination of textual and gestural interactions

 Need to collaborate with qualified people:

– artists, architects, designers, etc.

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Declarative Modelling

Related applications Related applications

VORTEX Team VORTEX Team (Visual Objects: From Reality To Expression)

(Visual Objects: From Reality To Expression)

IRIT – Toulouse III IRIT – Toulouse III

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Declarative modelling of virtual tows

 Automatic generation of realistic digital mock-ups of towns:

– Geographic or social maps:

  • Population density
  • Zone maps (residential, commercial)
  • Street patterns
  • Elevation or hydrographic maps
  • etc.

[M. Larive]

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Wall grammar for automatic building generation

 Generation for any building footprint,

convex or not, even with holes

– 2.5D wall grammar based on a set of rules – Availability of various kinds of roofs independently of the footprint complexity – Used in a commercial terrain modeler

[M. Larive]

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Wall grammar for automatic building generation

 Urban area (17 362 buildings, 920 182 faces)

– Exemple of generated buildings (generation in 7mn 55sec !)

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SIAMA project

(Système d’Imagerie et d’Analyse pour le Mobilier Archéologique)

 Symbolic classification of archaeological vessel

– Qualitative e Qualitative evaluation (declarative) of the similarity

  • Allow a first sort in a huge solution space

– Quantitative e Quantitative evaluation

  • Rigid matching
  • Computation of the geometrical distance between two forms
  • Elastic matching (excessive tolerance of the archaeological

data)

[C. Maïza]

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Symbolic segmentation of the brain

 Data fusion, Symbolic

Symbolic matching of volumetric data

– Knowledge (about the brain)

  • Reference data bases (healthy patients, phantoms)
  • Expertise
  • Properties

Properties

– Applied to medical imaging and palaeontological data (bones) [F. H. Andriamanankoavy]

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Approximative shape modelling

 Sketching

– Intuitive 2D Sketching – Easy 3D adjustment (skeleton or boundary)

 To do: Constrained manipulation

[Anca Alexe]