A different Manhattan project: Automatic statistical model generation

C. K. Yap, H. Biermann, A. Hertzman, C. Li, J. Meyer, H. K. Pao, T. Paxia

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We address the automatic generation of large geometric models. This is important in visualization for several reasons. First, many applications need access to large but interesting data models. Second, we often need such data sets with particular characteristics (e.g., urban models, park and recreation landscape). Thus we need the ability to generate models with different parameters. We propose a new approach for generating such models. It is based on a top-down propagation of statistical parameters. We illustrate the method in the generation of a statistical model of Manhattan. But the method is generally applicable in the generation of models of large geographical regions. Our work is related to the literature on generating complex natural scenes (smoke, forests, etc) based on procedural descriptions. The difference in our approach stems from three characteristics: modeling with statistical parameters, integration of ground truth (actual map data), and a library-based approach for texture mapping.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsR.F. Erbacher, P.C. Chen, M. Grohn, J.C. Roberts, C.M. Wittenbrink
Number of pages10
StatePublished - 2002
EventVisualization and Data Analysis 2002 - San Jose, CA, United States
Duration: Jan 21 2002Jan 22 2002


OtherVisualization and Data Analysis 2002
Country/TerritoryUnited States
CitySan Jose, CA


  • Automatic model generation
  • Large geometric model
  • Procedural model generation
  • Statistical model
  • Urban model
  • Visualization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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