Abstract
The paper discusses automated solutions for 3D object modeling at multiple resolutions in the context of virtual reality. An original solution, based on an unsupervised neural network, is proposed to guide the creation of selectively densified meshes. A neural gas network, applied over a sparse density object mesh, adapts its nodes during training to capture the embedded shape of the object. Regions of interest are then identified as areas with higher density of nodes in the adapted neural gas map. Meshes at different level of detail for an object, which preserve these regions of interest, are constructed by adapting a classical simplification algorithm, the QSlim. The simplification process will therefore only affect the regions of lower interest, ensuring that the characteristics of an object are preserved even at lower resolutions. Various interest point detectors are incorporated in selectively densified meshes in a similar manner to enable the comparison with the proposed neural gas approach. A novel solution based on learning is proposed to select the number of faces for the discrete models of an object at different resolutions. Finally, selectively densified object meshes are incorporated in a discrete level-of-detail method for presentation in virtual reality applications.
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Acknowledgments
The authors thank H. Monette-Thériault for his help with the implementation of the adapted QSlim algorithm.
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The authors acknowledge the support to this research from the Natural Sciences and Engineering Research Council of Canada, under the Discovery Grant RGPIN-2014-04953.
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Communicated by V. Loia.
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Cretu, AM., Chagnon-Forget, M. & Payeur, P. Selectively densified 3D object modeling based on regions of interest detection using neural gas networks. Soft Comput 21, 5443–5455 (2017). https://doi.org/10.1007/s00500-016-2132-z
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DOI: https://doi.org/10.1007/s00500-016-2132-z