Summary
The aim of surface reconstruction is to transfer the shape of physical objects, which have been sampled by tactile or optical scanning techniques, into computer-processable descriptions. Triangulation is the most common surface model used in CAD/CAM systems. The processing time of a triangulation is decisively influenced by the number of sampling points. Hence, either the sampling points have to be reduced or efficient triangulations have to be found. Due to the fact that for interpolating triangulations the optimal distribution of the sampling points is generally difficult to find, here, self-organizing dynamic meshes are applied. The complex problem to find the best discrete approximation of a surface using a dynamic triangulation and a simple distance measure is solved by an evolution strategy. A special node-spring description always encodes valid triangulations.
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Weinert, K., Mehnen, J., Schneider, M. (2005). Evolutionary Optimization of Approximating Triangulations for Surface Reconstruction from Unstructured 3D Data. In: Wu, X., Jain, L., Graña, M., Duro, R.J., d’Anjou, A., Wang, P.P. (eds) Information Processing with Evolutionary Algorithms. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-117-2_3
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DOI: https://doi.org/10.1007/1-84628-117-2_3
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