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Carnegie Mellon Robotics Institute

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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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

K. Arakawa and Eric Krotkov, "Fractal Surface Reconstruction for Modeling Natural Terrain," IEEE Conference on Computer Vision and Pattern Recognition, June, 1993, pp. 314-320.

BibTeX Reference

@inproceedings{Krotkov_1993_1364,
   author = "K. Arakawa and Eric Krotkov",
   title = "Fractal Surface Reconstruction for Modeling Natural Terrain",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   pages = "314-320",
   month = "June",
   year = "1993",
}

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