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Métodos Interactivos para Modelado de Espacios Urbanos en 3D
Carlos Vanegas
Department of Computer SciencePurdue UniversityWest Lafayette, USA
Outline
• Introduction• Modeling of Urban Spaces• Geometric and Behavioral Urban Modeling• Conclusions, Challenges, Open Problems• Questions
Introduction
• What is urban modeling?• Why should you learn about it?• What is the main challenge?• What are the current approaches?
• Creating digital models of real or virtual cities• Cities are large collections
of complex architecturalstructures
What is urban modeling?
• Urban models are important!
Why should you learn about it?
• Urban models are important!– Entertainment
Why should you learn about it?
• Urban models are important!– Entertainment– Mapping and visualization
Why should you learn about it?
• Urban models are important!– Entertainment– Mapping and visualization– Urban planning
Why should you learn about it?
time
• Solving the content problem– As computing and display capabilities continually
improve, audience expects ever higher quality digital content
– Traditional tools are insufficient for increasing demand and few tools are available for efficient large-scale urban modeling
What is the main challenge?
• Geometric content creation:– Procedural Methods: tools to “program” the
geometry of buildings, parcels, roads, facades…
What are the current approaches?
• Geometric content creation:– Procedural Methods: tools to “program” the
geometry of buildings, parcels, roads, facades…• Non-geometric content creation:– Urban Simulation Methods: algorithms to
“simulate” urban environments (e.g., social, economic, and some geometric aspects)
What are the current approaches?
Outline
• Introduction• Modeling of Urban Spaces• Geometric and Behavioral Urban Modeling• Conclusions, Challenges, Open Problems• Questions
Modeling of Urban Spaces
• The urban modeling pipeline
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
• The geometric modeling pipeline
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
• Input– Target architectural design– Example 3D model/GIS data/Imagery– Socioeconomic data/Elevation data– Tensor field
Modeling of Urban Spaces
Modeling of Urban Spaces
• The geometric modeling pipeline
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
• Road generation– Extended L-systems– Hyperstreamlines– Directed random walks– Seed growth/traffic simulation– Shortest path (terrain adaptive)
• Müller et al., 2001• Chen et al., 2008• Aliaga et al., 2008• Vanegas et al., 2009• Weber et al., 2009• Galin et al., 2010
Modeling of Urban Spaces
• The geometric modeling pipeline
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
• Blocks and Lots– Recursive block subdivision– Voronoi-diagram based subdivision
• Müller et al., 2001• Aliaga et al., 2008
Modeling of Urban Spaces
• Buildings– Shape/split grammars– Mass/façade modeling
– Build by numbers– Image-based synthesis
• The geometric modeling pipeline
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
• Wonka et al., 2003 • Müller et al., 2006• Aliaga et al., 2007• Müller et al., 2007• Aliaga et al., 2008
Modeling of Urban Spaces
• Topics to be covered in more detail:– Procedural Modeling of Cities
(Seminal paper by Parish and Müller, 2001)
Vanegas, Aliaga, Wonka, Müller, Waddell, WatsonModeling the Appearance and Behavior of Urban Spaces
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
Procedural Modeling of Cities
• Procedural Modeling of CitiesParish and Müller
• SIGGRAPH 2001
Procedural Modeling of Cities
• Input: Various image maps– Terrain elevation– Population density
• Output: Urban Model– System of highways and streets– Blocks and lots– Building geometry
Procedural Modeling of Cities
• Approach– Road network: Extended L-
systems considering global goals and local constraints• Global: Street patterns and
population density• Local: Land/Water/Park
boundaries, elevation, crossing of streets
Procedural Modeling of Cities
• L-systems– Generation of plants
Prusinkiewicz, Lindenmayer; 1990
– Environment-sensitivePrusinkiewicz, James, Mech; 1994
– Interaction (Open L-System)Mech, Prusinkiewicz; 1996
– EcosystemsDeussen, et al.; 1998
Modeling of Urban Spaces
• Topics to be covered in more detail:– Procedural Modeling of Cities
(Seminal paper by Parish and Müller, 2001)– Modeling of buildings (3D structures)– Modeling of urban layouts (2D structures)
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
Modeling of Buildings
• Facades– Instant Architecture– Image-based Procedural Modeling of Facades
(semi-automatic rules generation)
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
Modeling of Facades
• Instant ArchitectureWonka, Wimmer, Sillion, Ribarsky
• SIGGRAPH 2003
Modeling of Facades
• Input: Target building design• Output: Textured 3D models of building
facades
Modeling of Facades
• Approach: Split grammars– Used instead of L-systems– L-systems simulate growth in open spaces (better
for plants and road networks)– Buildings have stricter spatial constraints and their
structure does not reflect a growth process
Modeling of Facades
• Take Photograph• Create abstraction
Modeling of Facades
• Facade Subdiv(“Y”,3.5,0.3,1r){ firstfloor | ledge | floors}
• Floors Repeat(“Y”,3){floor}
Modeling of Facades
• floor Repeat(“X”,tile_width){ Tile }
=
Modeling of Facades
• Tile Subdiv(“XY”, …){ Wall | Wall |…| A | Wall | … }
Modeling of Facades
• Image-based Procedural Modeling of FacadesMüller, Zeng, Wonka, Van Gool
• SIGGRAPH 2007
Modeling of Facades
• Input: Rectified photograph of a facade• Output: 3D model and shape grammar rules of
the facade
• Resulting tile subdivision
Modeling of Facades
Modeling of Buildings
• Mass– Procedural Modeling of Buildings– Interactive Visual Editing of Grammars for
Procedural Architecture (semi-automatic rules generation)
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
Modeling of Building Mass
• Procedural Modeling of BuildingsMüller, Wonka, Haegler, Ulmer, Van Gool
• SIGGRAPH 2006
• CGA shape production process:– Iteratively evolve a design by creating more and
more details– Sequential application (like Chomsky grammars)– Starting shape (axiom) is a box
Modeling of Building Mass
• Rule format: id : pred : cond successorid: an integer identifying the rulepred: text string - symbol of the shape to be replacedcond: condition on the parameters of the shapesuccessor: shapes to replace the predecessor
• Example:1: fac(h) : h > 9 floor(h/3) floor(h/3) floor(h/3)
Modeling of Building Mass
• Rule types– Transformation Rules– Split Rule– Repeat Rule– Scope Rules– Component Split– Occlusion– Snaplines
Modeling of Building Mass
• Basic shape operations– Insertion: I(objId)– Transformations: T(tx,ty,tz), S(sx,sy,sz), Rx(α)..– Branching: [ ... ]
• Simple example:1: A [ T(0,0,6) S(8,10,18) I(cube) ]
T(6,0,0) S(7,13,18) I(cube) T(0,0,16) S(8,15,8) I(cylinder)
Modeling of Building Mass
• Shape interaction problem: – The volumes are not
aware of each other– Unwanted intersections
are generated
Modeling of Building Mass
Modeling of Building Mass
• Solution: – Test spatial overlap and align
elements to important lines
• Terminal Symbols– Basic shapes: cubes,
cylinders, spheres, …– General shapes: meshes
modeled in Maya– Textures
Modeling of Building Mass
• Results
Modeling of Building Mass
• Results: Petronas Towers
Modeling of Building Mass
• Results: Procedural Pompeii
Modeling of Building Mass
• Results: Suburbia
Modeling of Building Mass
Modeling of Building Mass
• Interactive Visual Editing of Grammars for Procedural ArchitectureLipp, Wonka, Wimmer
• SIGGRAPH 2008
Modeling of Buildings
• Simultaneous Mass and Facades– Style Grammars for Interactive Visualization of
Architecture– Continuous Model Synthesis
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
Modeling of Mass with Facades
• Style Grammars for Interactive Visualization of ArchitectureAliaga, Rosen, Bekins
• TVCG 2007
Modeling of Mass with Facades
• Approach (inverse modeling):– Infer a grammar for creating architecture and
buildings– Enable rapid generation of a building in the style
of others
Modeling of Mass with Facades
• Approach: parse previously captured images to create a grammar
BuildingPhotographs
(derive)
(parse)
Modeling of Mass with Facades
• Approach: derive new buildings using the data from the grammar
Vanegas, Aliaga, Wonka, Müller, Waddell, WatsonModeling the Appearance and Behavior of Urban Spaces
BuildingPhotographs
(derive)
(parse)
Modeling of Mass with Facades
• Example result
BuildingPhotographs
(derive)
(parse)
Modeling of Mass with Facades
• Example result
Photograph Novel building Novel building plus landscaping
Modeling of Mass with Facades
• Example result
Photograph Model editing In-place viewing
Modeling of Mass with Facades
• Procedural buildings (from aerial photographs)
Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
Modeling of Mass with Facades
• Procedural buildings (from aerial photographs)
Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
Modeling of Mass with Facades
• Procedural buildings (from aerial photographs)
Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
Modeling of Mass with Facades
• Procedural buildings (from aerial photographs)
Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
Modeling of Urban Spaces
• Topics to be covered in more detail:– Procedural Modeling of Cities
(Seminal paper by Parish and Müller, 2001)– Modeling of buildings (3D structures)– Modeling of urban layouts (2D structures)
RoadsMajor Roads
Minor Roads
Blocks Lots
BuildingsMass Facades
Aerial ViewsInput
Modeling of Urban Layouts
• Example-based Urban Layout SynthesisAliaga, Vanegas, Benes
• SIGGRAPH Asia 2008
Modeling of Urban Layouts
• Input: Example urban layout– Images (aerial view)+ Structure (streets, parcels)
Modeling of Urban Layouts
• Input: Example urban layout• Output: New synthesized urban layout that
looks like the example layout
• Observation: Both image and structure information about the urban layout available
Courtesy of Google Maps
Image: aerial view Structure: street + parcels
Modeling of Urban Layouts
Image: aerial view Structure: street + parcels
Modeling of Urban Layouts
• Approach: Simultaneously synthesize structure and image
• Input: Example urban layout
Modeling of Urban Layouts
• Characterize GIS vector data
Modeling of Urban Layouts
• Compute per-parcel imagery
Modeling of Urban Layouts
• Synthesize new streets
Modeling of Urban Layouts
• Generate new blocks and parcels
Modeling of Urban Layouts
• Produce new aerial view imagery
Modeling of Urban Layouts
• Output: A new synthesized urban layout
Modeling of Urban Layouts
Modeling of Urban Layouts
• Interactive Reconfiguration of Urban LayoutsAliaga, Benes, Vanegas, Andrysco
• IEEE CG&A 2008
Modeling of Urban Layouts
• An editor providing tools to– expand, scale, replace and move
parcels and blocks of existing layouts• Exploits connectivity and zoning of parcels
Modeling of Urban Layouts
• Uses a solver to find a planar transformation for each tile that best accommodates the changes caused by the editing operations
• Two types of error:– Gap error + Deformation error
Modeling of Urban Layouts
• Procedural Modeling of StreetsChen, Esch, Wonka, Müller, Zhang
• SIGGRAPH 2008
Modeling of Urban Layouts
• Observation– Relation between
street patterns andtensor field
Tensor field patternsReal street patterns© Google Maps, 2007
© Google Maps, 2007
• Tensor fields– Second order symmetric tensor fields• Eigenvectors of tensor
values for twoorthogonal families
Modeling of Urban Layouts
• Tensor fields– Second order symmetric tensor fields• Eigenvectors of tensor
values for twoorthogonal families
– Topology Singularities– Hyperstreamlines
Modeling of Urban Layouts
Modeling of Urban Layouts
• Example result
Outline
• Introduction• Modeling of Urban Spaces• Geometric and Behavioral Urban Modeling• Conclusions, Challenges, Open Problems• Questions
Carlos VanegasAdvisor: Daniel Aliaga
Collaborators: Bedrich Benes, Paul Waddell
Department of Computer SciencePurdue University
College of Environmental Design,UC Berkeley
Geometric and BehavioralUrban Modeling
Motivation
• Urban spaces (e.g., districts, towns, cities) are a collection of man-made structures arranged into parcels, blocks, streets, and neighborhoods
Motivation
• But, the structures of an urban space are the scenarios where behavioral processes take place
• Thus, these structures are influenced by the behavioral processes
Geometric Modeling
• Geometric modeling of urban spaces has become a popular research area in Computer Graphics
• Several works have been presented to address different parts of the urban modeling pipeline
RoadsMajor Roads
Minor Roads
Blocks Lots
Buildings, LandscapesMass Facades
Aerial ViewsInput
2D 3D
Behavioral Modeling
• Behavioral modeling of urban spaces is studied in several disciplines (e.g., urban planning, earth and atmospheric sciences, civil engineering, etc.)
• Goals:– Understanding the underlying socio-economic and
environmental processes occurring within an urban space
– Assisting decision-making of urban policies in current and future urban spaces
Behavioral Modeling
• A Generalized Framework*
(*) Wegener, M. “Operational urban models state of the art”. Journal of the American Planning Association60, 17–29, 1994
Geometric + Behavioral Modeling
• Two research areas separately study two types of properties of one common space
Geometric + Behavioral Modeling
• Two research areas separately study two types of properties of one common space
Geometric Modeling
RoadsMajor Roads
Minor Roads
Blocks Lots
Buildings, LandscapesMass Facades
Aerial ViewsUser
2D 3D
Geometric + Behavioral Modeling
• Two research areas separately study two types of properties of one common space
Geometric Modeling
2D(Roads, Parcels)
3D(Buildings, Landscape)
Geometric + Behavioral Modeling
• Two research areas separately study two types of properties of one common space
Geometric Modeling
2D(Roads, Parcels)
3D(Buildings, Landscape)
Behavioral Modeling
Geometric + Behavioral Modeling
Geometric Modeling
2D(Roads, Parcels)
3D(Buildings, Landscape)
Behavioral Modeling
Socio-Econ Simulation
Weather Simulation
Traffic/Crowd Simulation
Geometric + Behavioral Modeling
Geometric Modeling
2D(Roads, Parcels)
3D(Buildings, Landscape)
Behavioral Modeling
Socio-Econ Simulation
Weather Simulation
Traffic/Crowd Simulation
User
Geometric + Behavioral Modeling
Geometric Modeling
2D(Roads, Parcels)
3D(Buildings, Landscape)
Behavioral Modeling
Socio-Econ Simulation
Weather Simulation
Traffic/Crowd Simulation
• Isolation results in:– Functional
disconnection between results
– Models that do not necessarily resemble real-world spaces
– Simulations that do not consider changes and specific layouts in the geometry
User
Geometric + Behavioral Modeling
Geometric Modeling
2D(Roads, Parcels)
3D(Buildings, Landscape)
Behavioral Modeling
Socio-Econ Simulation
Weather Simulation
Traffic/Crowd Simulation
User
• Integration brings advantages to a number of applications
Geometric + Behavioral Modeling
Behavioral Modeling
Socio-Econ Simulation
Weather Simulation
Traffic/Crowd Simulation
Geometric Modeling
2D(Roads, Parcels)
3D(Buildings, Landscape)
User
Applications
Urban Planning and Design
Content Generation
Emergency Management
• Integration brings advantages to a number of applications
Example Applications
• Urban Visualization– Infer an urban layout (images + structure) from
the values of a set of (precomputed) simulation variables at any given time step
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery”, IEEE TVCG, 2009.
Example Applications
• Urban Visualization
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery”, IEEE TVCG, 2009.
Example Applications
• Urban Visualization
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery”, IEEE TVCG, 2009.
Example Applications
• Urban Planning– Analysis of urban development scenarios
(in collaboration with Paul Waddell, from UC Berkeley)
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Example Applications
• Urban Planning
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Height of buildings in downtown increases
Number of jobs
increases
Population increases
New housing appears in accessible
areas
Example Applications
• Urban Planning
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Terrain
Town center
Parks
Population
Jobs
Parcels
Buildings
Input Output
Example Applications
• Content Generation
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph. 28 (Proceedings SIGGRAPH Asia), 2009.
Example Applications
• Content Generation
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph. 28 (Proceedings SIGGRAPH Asia), 2009.
Example Applications
• Content Generation– 225 Km2
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph. 28 (Proceedings SIGGRAPH Asia), 2009.
Geometric Modeling
• Procedural road generation (from behavioral data)– Generate set of seeds based on population/jobs
distribution
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Geometric Modeling
• Procedural road generation (from behavioral data)– Each seed is used as an intersection of the arterial
roads network and used to generate arterial segments
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Geometric Modeling
• Procedural road generation (from behavioral data)– Street seeds are generated along arterial road
segments and used to create streets
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Geometric Modeling
Tortuosity
Grid
Radial
• Procedural road generation (from behavioral data)
Geometric Modeling
• Procedural blocks and parcels generation– Voronoi-diagram based methods
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Geometric Modeling
• Procedural buildings (from behavioral data)
Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
Current and Future Work
• Validation of urban simulation model
Equilibrium
Equilibrium
Current and Future Work
• Validation of urban simulation model
Real City Synthetic City
Weather Simulation
• Can cities be designed aiming to minimize the occurrence of undesirable meteorological phenomena? (in collaboration with Purdue EAS)
• Possible approach requires:– Fast, interactive editing of a 3D urban model– Automatic generation of urban morphology data
from urban model– (desirable) Closed-loop simulation
• automatically adjusts the 3D model to reach a set of given meteorological results
Weather Simulation
How to access these technologies?
• Industry: CityEngine (www.procedural.com)– Developed by Pascal Müller and
Procedural Inc., Zürich
Outline
• Introduction• Modeling of Urban Spaces• Geometric and Behavioral Urban Modeling• Conclusions, Challenges, Open Problems• Questions
Conclusions
• Summary of topics– Modeling of Urban Spaces (pipeline and methods)– Geometric and behavioral urban modeling
Conclusions
• Generating realistic and plausible models of urban spaces is a great challenge
• Fast, accurate modeling of urban spaces is of significant interest to several applications
Challenges
• On one hand…– Urban modeling methods aim to make more
efficient the 3D design of urban spaces• On the other hand…– Urban simulation models get better at
representing the complex processes occurring in urban spaces
Challenges
• Multidisciplinary goal:– Take advantage of urban simulation models to
device more efficient and intuitive methods for generating realistic 3D urban models
– Use content generation methods to facilitate the visualization of the results generated by urban simulation
Conclusions
• Tight relation between Academia and Industry in Computer Graphics (mostly thanks to SIGGRAPH!)
• Getting to know SIGGRAPH papers is a way to see the “coming attractions” of Computer Graphics software
• We have some CG research in Colombia. There’s a need to make it more visible (have them publish at SIGGRAPH) and link it to the animation industry
Outline
• Introduction• Modeling of Urban Spaces• Geometric and Behavioral Urban Modeling• Conclusions, Challenges, Open Problems• Questions
Questions
• Thank you!
• Acknowledgements– Daniel Aliaga, Bedrich Benes, Jie Shan, Purdue University– Remco Chang, University of North Carolina, Charlotte– Guoning Chen, Gregory Esch, Oregon State University– Bernard Frisher, IATH– Simon Haegler, Pascal Müller, Basil Weber, ETH Zürich– Aaron Hertzmann, University of Toronto– Markus Lipp, TU Wien– Paul Merrell, University of North Carolina Chapel Hill– Procedural Inc. (CityEngine)– Peter Wonka, Arizona State University– Paul Waddell, UC Berkeley