A cyber-physical system to design 3D models using mixed reality technologies and deep learning for additive manufacturing.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 05 10 2022
accepted: 13 07 2023
medline: 31 7 2023
pubmed: 27 7 2023
entrez: 27 7 2023
Statut: epublish

Résumé

I-nteract is a cyber-physical system that enables real-time interaction with both virtual and real artifacts to design 3D models for additive manufacturing by leveraging mixed-reality technologies. This paper presents novel advances in the development of the interaction platform to generate 3D models using both constructive solid geometry and artificial intelligence. In specific, by taking advantage of the generative capabilities of deep neural networks, the system has been automated to generate 3D models inferred from a single 2D image captured by the user. Furthermore, a novel generative neural architecture, SliceGen, has been proposed and integrated with the system to overcome the limitation of single-type genus 3D model generation imposed by differentiable-rendering-based deep neural architectures. The system also enables the user to adjust the dimensions of the 3D models with respect to their physical workspace. The effectiveness of the system is demonstrated by generating 3D models of furniture (e.g., chairs and tables) and fitting them into the physical space in a mixed reality environment. The presented developmental advances provide a novel and immersive form of interaction to facilitate the inclusion of a consumer into the design process for personal fabrication.

Identifiants

pubmed: 37498853
doi: 10.1371/journal.pone.0289207
pii: PONE-D-22-27218
pmc: PMC10374011
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0289207

Informations de copyright

Copyright: © 2023 Malik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):692-705
pubmed: 27187944
PLoS One. 2023 Jul 27;18(7):e0289207
pubmed: 37498853

Auteurs

Ammar Malik (A)

Department of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland.

Hugo Lhachemi (H)

L2S, CentraleSupélec, Gif-sur-Yvette, France.

Robert Shorten (R)

Department of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland.
Dyson School of Design Engineering, Imperial College London, London, United Kingdom.

Articles similaires

Databases, Protein Protein Domains Protein Folding Proteins Deep Learning
Humans Artificial Intelligence COVID-19 SARS-CoV-2 Pandemics
Humans Algorithms Software Artificial Intelligence Computer Simulation

Classifications MeSH