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Street Generation for City Modeling

This paper discusses the innovative approaches in city modeling, particularly focusing on the need for larger virtual environments in gaming and simulations. It highlights the challenges faced in creating realistic urban models, such as the tediousness of manual modeling and the insufficiency of automatic reconstruction methods. The proposed framework utilizes a topological phase to partition maps into streets and crossings, along with a geometric phase for optimizing graph representations of urban layouts. The contributions include methods for generating polygonal footprints from aerial photographs and existing models, enhancing the efficiency and realism in city design.

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Street Generation for City Modeling

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  1. Street Generationfor City Modeling Xavier Décoret, François Sillion iMAGIS GRAVIR/IMAG - INRIA

  2. Foreword • A Computer Graphics point of view • Graphic artists • Game developers • Researchers • A work in 2 parts • A framework • An algorithm

  3. Motivations • City Modeling is a growing field of interest • Game and Leisure • Virtual environments are widely used • Need for larger environments • Cities are natural and appealing large environments • Analysis and Simulation • Pedestrians or traffic flow • Wave transportation

  4. Motivations • Creating the virtual model is a tedious task • Realistic model • Model it by hand: long and costly • Reconstruct it automatically: not working yet • Semi-realistic model • Procedural modelling • Map is exact, geometry is approximative

  5. Motivations • Creating the virtual model is a tedious task • Realistic model • Model it by hand: long and costly • Reconstruct it automatically: not working yet • Semi-realistic model • Procedural modelling • Map is exact, geometry is approximative No existing tool

  6. Overview of the tool • Retrieve the 2D footprints of buildings • Aerial photographs • Existing 2D models • Procedurally generate buildings • Grammar, library of shapes • Style information provided by a designer (GIS) • Generate streets • Retrieve the street network • Generate geometry

  7. Overview of the tool • Retrieve the 2D footprints of buildings • Aerial photographs • Existing 2D models • Procedurally generate buildings • Grammar, library of shapes • Style information provided by a designer (GIS) • Generate streets • Retrieve the street network • Generate geometry Our contribution

  8. Input & Output Polygonal footprints Input Output +

  9. Principle • We use a median axis (skeleton) • Seems natural for roads • Goes in between 2 buildings • Goes approximately at equal distance

  10. Use of a median axis Polygonal footprints Street graph

  11. Robustness Issues (1) • Input sensitivity Ideal case Noise effect Expected result

  12. Robustness Issues (2) • Artefacts Unwanted branches requiring post-processing

  13. Our approach • A topological phase • Partition the map into • Streets • Crossings

  14. Our approach • A topological phase • Partition the map into • Streets • Crossings

  15. Our approach • A topological phase • Partition the map into • Streets • Crossings 1 2 5 4 6 7 8 3 9

  16. Our approach • A topological phase • Partition the map into • Streets • Crossings • A geometric phase • The graph is shaped to a correct position • Optimisation with constraints 1 2 5 4 6 7 8 3 9

  17. Our approach • A topological phase • Partition the map into • Streets • Crossings • A geometric phase • The graph is shaped to a correct position • Optimisation with constraints 1 2 5 4 6 7 8 3 9

  18. Topological Phase • Sample the footprints with extra vertices

  19. Topological Phase • Sample the footprints with extra vertices

  20. Topological Phase • Sample the footprints with extra vertices • Delaunay triangulate the samples

  21. Topological Phase • Sample the footprints with extra vertices • Delaunay triangulate the samples • Ignore edges joining samples of a same building

  22. Topological Phase • Sample the footprints with extra vertices • Delaunay triangulate the samples • Ignore edges joining samples of a same building

  23. Topological Phase • Sample the footprints with extra vertices • Delaunay triangulate the samples • Ignore edges joining samples of a same building • Take the dual of edges (Voronoï diagram)

  24. Topological Phase • Sample the footprints with extra vertices • Delaunay triangulate the samples • Ignore edges joining samples of a same building • Take the dual of edges (Voronoï diagram) • Construct a graph from the edges Crossings Streets

  25. Our approach • A topological phase • Partition the map into • Streets • Crossings • A geometric phase • The graph is shaped to a correct position • Optimisation with constraints 9

  26. Geometric Phase • Place sample median points

  27. Geometric Phase • Place sample median points

  28. Geometric Phase • Place sample median points

  29. Geometric Phase • Place sample median points

  30. Geometric Phase • Place sample median points

  31. Geometric Phase • Place sample median points • Compute minimum width

  32. Geometric Phase • Place sample median points • Compute minimum width • Greedily place a valid polyline in between

  33. Geometric Phase • Place sample median points • Compute minimum width • Greedily place a valid polyline in between

  34. Geometric Phase • Place sample median points • Compute minimum width • Greedily place a valid polyline in between • Split the polyline in • Segments • Curves Curve Segments

  35. Robustness • A topological phase • Partition the map into • Streets • Crossings • A geometric phase • The graph is shaped to a correct position • Optimisation with constraints - Based on distance - Robust to footprints’shape - Solves input sensitivity - Based on optimisation - Robust to footprints’shape - Solves artefacts

  36. Results

  37. Street Generation • Generate streets • Retrieve the street network • Topology • Simple primitives • Generate geometry • Match buildings boundaries • Connect correctly at crossings

  38. Workflow • Generate streets • Retrieve the street network • Topology • Simple primitives • Generate geometry • Match buildings boundaries • Connect correctly at crossings

  39. Generating geometry Use library of parametric modelsto build segments and curves Triangulate the remaining border

  40. Parametric model

  41. Results

  42. Conclusion & Future Works • We can generate geometry from a 2D map of buildings • Work in 2D1/2 • Write more parametric modules • High level features extractions • Avenues • Squares • Generate coherent trafic signs

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