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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    Title :

    Fractal surface reconstruction for modeling natural terrain


    Contributors:
    Arakawa, K. (author) / Krotkov, E. (author)


    Publication date :

    1993-01-01


    Size :

    550358 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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