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3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

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Abstract

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.

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Notes

  1. Kinect for XBox 360: http://www.xbox.com/kinect Microsoft.

  2. http://graphics.stanford.edu/data/3Dscanrep/.

  3. Genus: A topologically invariant property of a surface defined as the largest number of nonintersecting simple closed curves that can be drawn on the surface without separating it. Roughly speaking, it is the number of holes in a surface [26].

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Acknowledgments

This work was partially funded by the Spanish Government DPI2013-40534-R Grant.

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Authors and Affiliations

  1. Department of Computer Technology, University of Alicante, Carretera de San Vicente del Raspeig, s/n, 03690, Alicante, Spain

    Sergio Orts-Escolano, Jose Garcia-Rodriguez, Jose Antonio Serra Perez & Alberto Garcia-Garcia

  2. Department of Computer Science and Artificial Intelligence, University of Alicante, Alicante, Spain

    Vicente Morell & Miguel Cazorla

Authors
  1. Sergio Orts-Escolano
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  2. Jose Garcia-Rodriguez
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  3. Vicente Morell
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  4. Miguel Cazorla
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  5. Jose Antonio Serra Perez
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  6. Alberto Garcia-Garcia
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Correspondence to Sergio Orts-Escolano.

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Orts-Escolano, S., Garcia-Rodriguez, J., Morell, V. et al. 3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction. Neural Process Lett 43, 401–423 (2016). https://doi.org/10.1007/s11063-015-9421-x

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  • DOI: https://doi.org/10.1007/s11063-015-9421-x

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