Abstract
To represent the surface of complex objects, the samples resulting from their digitization can contain a very large number of points. Simplification techniques analyse the relevance of the data. These simplification techniques provide models with fewer points than the original ones. Whereas reconstruction of a surface, with simplified point cloud, must be close to the original. In this article, we develop a method of simplification based on the concept of entropy, which is a mathematical function that intuitively corresponds to the amount of information this allows considering only relevant points.
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Acknowledgments
The Max Planck, Atene and Tennis shoe models used in this paper are the courtesy of AIM@SHAPE shape repository.
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Mahdaoui, A., Bouazi, A., Marhraoui Hsaini, A., Sbai, E.H. (2018). Entropic Method for 3D Point Cloud Simplification. In: Ben Ahmed, M., Boudhir, A. (eds) Innovations in Smart Cities and Applications. SCAMS 2017. Lecture Notes in Networks and Systems, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-74500-8_56
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DOI: https://doi.org/10.1007/978-3-319-74500-8_56
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