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Proceedings Paper

Different Manhattan project: automatic statistical model generation
Author(s): Chee Keng Yap; Henning Biermann; Aaron Hertzmann; Chen Li; Jon Meyer; Hsing-Kuo Pao; Salvatore Paxia
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Paper Abstract

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.

Paper Details

Date Published: 12 March 2002
PDF: 10 pages
Proc. SPIE 4665, Visualization and Data Analysis 2002, (12 March 2002); doi: 10.1117/12.458793
Show Author Affiliations
Chee Keng Yap, New York Univ. (United States)
Henning Biermann, New York Univ. (United States)
Aaron Hertzmann, New York Univ. (United States)
Chen Li, New York Univ. (United States)
Jon Meyer, New York Univ. (United States)
Hsing-Kuo Pao, New York Univ. (United States)
Salvatore Paxia, New York Univ. (United States)


Published in SPIE Proceedings Vol. 4665:
Visualization and Data Analysis 2002
Robert F. Erbacher; Philip C. Chen; Matti Groehn; Jonathan C. Roberts; Craig M. Wittenbrink, Editor(s)

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