Optimisation de la chaîne de numérisation 3D : de la surface au maillage semi-régulier

Abstract : Nowadays, 3D digitization systems generate numeric representations that are both realistic and of high geometric accuracy with respect to real surfaces. However, this geometric accuracy, obtained by oversampling surfaces, increases significantly the generated amount of data. Consequently, the resulting meshes are very dense, and not suitable to be visualized, transmitted or even stored efficiently. Nevertheless, the semi-regular representation due to its scalable and compact representation, overcomes this problem. This thesis aims at optimizing the classic 3D digitization chain, by first improving the sampling of surfaces while preserving geometric features, and secondly shortening the number of required treatments to obtain such semi-regular meshes. To achieve this goal, we integrated in a stereoscopic system the Poisson-disk sampling that realizes a good tradeoff between undersampling and oversampling, thanks to its blue noise properties. Then, we produced a semi-regular meshing technique that directly works on the stereoscopic images, and not on a meshed version of point clouds, which are usually generated by such 3D scanners. Experimental results prove that our contributions efficiently generate semi-regular representations, which are accurate with respect to real surfaces, while reducing the generated amount of data.
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Jean-Luc Peyrot. Optimisation de la chaîne de numérisation 3D : de la surface au maillage semi-régulier. Autre. Université Nice Sophia Antipolis, 2014. Français. 〈NNT : 2014NICE4126〉. 〈tel-01136457〉

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