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. 2019 Dec 6;9:18560. doi: 10.1038/s41598-019-54707-9

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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NURBS-aware convolution. In order to learn from a 3D surface, we extract the control points of the NURBS surface in the parametric space, which introduces spatial structure to the control points. We restructure the control points (from the parametric space) into three channels of an image (texture representation) to perform traditional convolution operations.