20101015 bogota siggraph

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


• Urban models are important!– Entertainment– Mapping and visualization


• Urban models are important!– Entertainment– Mapping and visualization– Urban planning


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


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


Aerial ViewsInput

• Input– Target architectural design– Example 3D model/GIS data/Imagery– Socioeconomic data/Elevation data– Tensor field




RoadsMajor Roads

Minor Roads

Blocks Lots


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



RoadsMajor Roads

Minor Roads

Blocks Lots


Aerial ViewsInput

• Blocks and Lots– Recursive block subdivision– Voronoi-diagram based subdivision

• Müller et al., 2001• Aliaga et al., 2008


• Buildings– Shape/split grammars– Mass/façade modeling

– Build by numbers– Image-based synthesis


RoadsMajor Roads

Minor Roads

Blocks Lots


Aerial ViewsInput

• Wonka et al., 2003 • Müller et al., 2006• Aliaga et al., 2007• Müller et al., 2007• Aliaga et al., 2008


• 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


Aerial ViewsInput

Procedural Modeling of Cities

• Procedural Modeling of CitiesParish and Müller

• SIGGRAPH 2001


• Input: Various image maps– Terrain elevation– Population density

• Output: Urban Model– System of highways and streets– Blocks and lots– Building geometry


• 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


• L-systems– Generation of plants

Prusinkiewicz, Lindenmayer; 1990

– Environment-sensitivePrusinkiewicz, James, Mech; 1994

– Interaction (Open L-System)Mech, Prusinkiewicz; 1996

– EcosystemsDeussen, et al.; 1998



(Seminal paper by Parish and Müller, 2001)– Modeling of buildings (3D structures)– Modeling of urban layouts (2D structures)

RoadsMajor Roads

Minor Roads

Blocks Lots


Aerial ViewsInput

Modeling of Buildings

• Facades– Instant Architecture– Image-based Procedural Modeling of Facades

(semi-automatic rules generation)

RoadsMajor Roads

Minor Roads

Blocks Lots


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


• Mass– Procedural Modeling of Buildings– Interactive Visual Editing of Grammars for

Procedural Architecture (semi-automatic rules generation)

RoadsMajor Roads

Minor Roads

Blocks Lots


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


• 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)


• Rule types– Transformation Rules– Split Rule– Repeat Rule– Scope Rules– Component Split– Occlusion– Snaplines


• 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)


• Shape interaction problem: – The volumes are not

aware of each other– Unwanted intersections

are generated



• Solution: – Test spatial overlap and align

elements to important lines

• Terminal Symbols– Basic shapes: cubes,

cylinders, spheres, …– General shapes: meshes

modeled in Maya– Textures


• Results


• Results: Petronas Towers


• Results: Procedural Pompeii


• Results: Suburbia



• Interactive Visual Editing of Grammars for Procedural ArchitectureLipp, Wonka, Wimmer

• SIGGRAPH 2008


• Simultaneous Mass and Facades– Style Grammars for Interactive Visualization of

Architecture– Continuous Model Synthesis

RoadsMajor Roads

Minor Roads

Blocks Lots


Aerial ViewsInput

Modeling of Mass with Facades

• Style Grammars for Interactive Visualization of ArchitectureAliaga, Rosen, Bekins

• TVCG 2007


• Approach (inverse modeling):– Infer a grammar for creating architecture and

buildings– Enable rapid generation of a building in the style

of others


• Approach: parse previously captured images to create a grammar

BuildingPhotographs

(derive)

(parse)


• 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)


• Example result

BuildingPhotographs

(derive)

(parse)


• Example result

Photograph Novel building Novel building plus landscaping


• Example result

Photograph Model editing In-place viewing


• 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.



(Seminal paper by Parish and Müller, 2001)– Modeling of buildings (3D structures)– Modeling of urban layouts (2D structures)

RoadsMajor Roads

Minor Roads

Blocks Lots


Aerial ViewsInput

Modeling of Urban Layouts

• Example-based Urban Layout SynthesisAliaga, Vanegas, Benes

• SIGGRAPH Asia 2008


• Input: Example urban layout– Images (aerial view)+ Structure (streets, parcels)


• 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


Image: aerial view Structure: street + parcels


• Approach: Simultaneously synthesize structure and image

• Input: Example urban layout


• Characterize GIS vector data


• Compute per-parcel imagery


• Synthesize new streets


• Generate new blocks and parcels


• Produce new aerial view imagery


• Output: A new synthesized urban layout



• Interactive Reconfiguration of Urban LayoutsAliaga, Benes, Vanegas, Andrysco

• IEEE CG&A 2008


• An editor providing tools to– expand, scale, replace and move

parcels and blocks of existing layouts• Exploits connectivity and zoning of parcels


• 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


• Procedural Modeling of StreetsChen, Esch, Wonka, Müller, Zhang

• SIGGRAPH 2008


• 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


• Tensor fields– Second order symmetric tensor fields• Eigenvectors of tensor

values for twoorthogonal families

– Topology Singularities– Hyperstreamlines



• Example result

Outline


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 Modeling

RoadsMajor Roads

Minor Roads

Blocks Lots

Buildings, LandscapesMass Facades

Aerial ViewsUser

2D 3D



Geometric Modeling

2D(Roads, Parcels)

3D(Buildings, Landscape)



Geometric Modeling

2D(Roads, Parcels)


Behavioral Modeling


Geometric Modeling

2D(Roads, Parcels)


Behavioral Modeling

Socio-Econ Simulation

Weather Simulation

Traffic/Crowd Simulation


Geometric Modeling

2D(Roads, Parcels)


Behavioral Modeling


Weather Simulation


User


Geometric Modeling

2D(Roads, Parcels)


Behavioral Modeling


Weather 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 Modeling

2D(Roads, Parcels)


Behavioral Modeling


Weather Simulation


User

• Integration brings advantages to a number of applications


Behavioral Modeling


Weather Simulation


Geometric Modeling

2D(Roads, Parcels)


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.


• 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.


• 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.


• Urban Planning


Height of buildings in downtown increases

Number of jobs

increases

Population increases

New housing appears in accessible

areas


• Urban Planning


Terrain

Town center

Parks

Population

Jobs

Parcels

Buildings

Input Output


• 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.


• 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


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


Geometric Modeling

• Procedural road generation (from behavioral data)– Street seeds are generated along arterial road

segments and used to create streets


Geometric Modeling

Tortuosity

Grid

Radial

• Procedural road generation (from behavioral data)

Geometric Modeling

• Procedural blocks and parcels generation– Voronoi-diagram based methods


Geometric Modeling

• Procedural buildings (from behavioral data)


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


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


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

20101015 bogota siggraph

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