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Fractal surface reconstruction for modeling natural terrain
Arakawa, K. Krotkov, E.
NTT Human Interface Labs., Tokyo;

This paper appears in: Computer Vision and Pattern Recognition, 1993. Proceedings CVPR '93., 1993 IEEE Computer Society Conference on
Publication Date: 15-17 Jun 1993
On page(s): 314-320
Meeting Date: 06/15/1993 - 06/17/1993
Location: New York, NY, USA
ISSN: 1063-6919
ISBN: 0-8186-3880-X
References Cited: 16
INSPEC Accession Number: 4834442
Digital Object Identifier: 10.1109/CVPR.1993.340963
Current Version Published: 2002-08-06

Abstract
A surface reconstruction method is developed, based on fractal geometry, for modeling natural terrain. The method estimates dense surfaces from sparse data located in any configuration while preserving roughness. A redefinition of the temperature parameter in the stochastic regularization method is presented. It plays a critical role in controlling roughness as a function of the fractal dimension. The fractalness of surfaces reconstructed with the temperature parameter is evaluated qualitatively by applying a technique for fractal dimension estimation. As a result, it is possible to reconstruct rugged natural surfaces which preserve the original roughness from sparse data sensed by, for example, scanning laser rangefinders

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