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EXPLICIT ANNOTATED 3D-CNN DEEP LEARNING OF GEOMETRIC PRIMITIVES INSTANCES

Published online by Cambridge University Press:  19 June 2023

Arthur Hilbig ,
Stefan Holtzhausen  and
Kristin Paetzold-Byhain
Arthur Hilbig*
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
Stefan Holtzhausen
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
Kristin Paetzold-Byhain
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
*
Hilbig, Arthur, Technische Universität Dresden, Germany, arthur.hilbig@tu-dresden.de

Abstract

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In reengineering technical components, the robust automation of reverse engineering (RE) could overcome the need for human supervision in the surface reconstruction process. Therefore, an enhanced computer-based geometric reasoning to derive tolerable surface deviations for reconstructing optimal surface models would promote a deeper geometric understanding of RE downstream processes. This approach integrates advanced surface information into a deep learning-based recognition framework by explicitly labeling geometric outliers and subsurface boundaries. For this purpose, a synthetic dataset is created that morphs nominal surface models to resemble the macroscopic surface pattern of physical components. For the detection of regular geometry primitives, a 3D-CNN is used to analyze the voxelized components based on signed distance field data. This explicit labeling approach enables surface fitting to derive suitable shape features that fulfill the underlying surface constraints.

Keywords

Reverse EngineeringSurface ReconstructionMachine learningComputer Aided Design (CAD)Artificial intelligence
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1775 - 1784
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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