Abstract
The focus of our paper is on the fitting of general curves and surfaces to 3D data. In the past researchers have used approximate distance functions rather than the Euclidean distance because of computational efficiency. We now feel that machine speeds are sufficient to ask whether it is worth considering Euclidean fitting again. Experiments with the real Euclidean distance show the limitations of suggested approximations like the Algebraic distance or Taubin’s approximation. In this paper we present our results improving the known fitting methods by an (iterative) estimation of the real Euclidean distance. The performance of our method is compared with several methods proposed in the literature and we show that the Euclidean fitting guarantees a better accuracy with an acceptable computational cost.
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Faber, P., Fisher, R. (2001). Euclidean Fitting Revisited. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_14
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DOI: https://doi.org/10.1007/3-540-45129-3_14
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