Fractal Surface Reconstruction for Modeling Natural Terrain
Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 314 - 320, June, 1993
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.
BibTeX
@conference{Arakawa-1993-13508,author = {K. Arakawa and Eric Krotkov},
title = {Fractal Surface Reconstruction for Modeling Natural Terrain},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {1993},
month = {June},
pages = {314 - 320},
}
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