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Engineering with Computers
Erschienen in: Engineering with Computers 6/2022

13.04.2022 | Original Article

SMA-Net: Deep learning-based identification and fitting of CAD models from point clouds

verfasst von: Sijie Hu, Arnaud Polette, Jean-Philippe Pernot

Erschienen in: Engineering with Computers | Ausgabe 6/2022

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Abstract

Identification and fitting is an important task in reverse engineering and virtual/augmented reality. Compared to the traditional approaches, carrying out such tasks with a deep learning-based method have much room to exploit. This paper presents SMA-Net (Spatial Merge Attention Network), a novel deep learning-based end-to-end bottom-up architecture, specifically focused on fast identification and fitting of CAD models from point clouds. The network is composed of three parts whose strengths are clearly highlighted: voxel-based multi-resolution feature extractor, spatial merge attention mechanism and multi-task head. It is trained with both virtually-generated point clouds and as-scanned ones created from multiple instances of CAD models, themselves obtained with randomly generated parameter values. Using this data generation pipeline, the proposed approach is validated on two different data sets that have been made publicly available: robot data set for Industry 4.0 applications, and furniture data set for virtual/augmented reality. Experiments show that this reconstruction strategy achieves compelling and accurate results in a very high speed, and that it is very robust on real data obtained for instance by laser scanner and Kinect.
Vorheriger Artikel On the virtual element method for topology optimization of non-Newtonian fluid-flow problems
Nächster Artikel Mixed reliability-oriented topology optimization for thermo-mechanical structures with multi-source uncertainties

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Metadaten
Titel
SMA-Net: Deep learning-based identification and fitting of CAD models from point clouds
verfasst von
Sijie Hu
Arnaud Polette
Jean-Philippe Pernot
Publikationsdatum
13.04.2022
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 6/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-022-01648-z

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