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OVERCOMING AUGMENTED REALITY ADOPTION BARRIERS IN DESIGN: A MIXED PROTOTYPING CONTENT AUTHORING TOOL SUPPORTED BY COMPUTER VISION

Published online by Cambridge University Press:  27 July 2021

Eduardo Piñones ,
Gaetano Cascini ,
Giandomenico Caruso  and
Federico Morosi
Eduardo Piñones*
Affiliation:
Politecnico di Milano
Gaetano Cascini
Affiliation:
Politecnico di Milano
Giandomenico Caruso
Affiliation:
Politecnico di Milano
Federico Morosi
Affiliation:
Politecnico di Milano
*
Piñones Zuleta, Eduardo Andrés, Politecnico di Milano, Mechanical engineering, Italy, eduardo.pinones@polimi.it

Abstract

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Enhancing the appearance of physical prototypes with digital elements, also known as mixed prototyping, has demonstrated to be a valuable approach in the product development process. However, the adoption is limited also due to the high time and competence required for authoring the digital contents. This paper presents a content authoring tool that aims to improve the user acceptance by reducing the specific competence required, which is needed for segmentation and UV mapping of the 3D model used to implement a mixed prototype. Part of the tasks related to 3D modelling software, in fact, has been transferred to simpler manual tasks applied onto the physical prototype. Moreover, the proposed tool can recognise these manual inputs thanks to a computer-vision algorithm and automatically manage the segmentation and UV mapping tasks, freeing time for the user in a task that otherwise would require complete engagement. To preliminarily evaluate effectiveness and potential of the tool, it has been used in a case study to build up the mixed prototype of a coffee machine. The result demonstrated that the tool can correctly segment the 3D model of a physical prototype in its relevant parts and generate their corresponding UV maps.

Keywords

New product developmentComputational design methodsIndustrial designMixed prototypingAugmented reality
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2359 - 2368
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), 2021. Published by Cambridge University Press

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