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User-adaptive sketch-based 3D CAD model retrieval

Liu, Yong-Jin; Luo, Xi; Joneja, Ajay; Ma, Cui-Xia; Fu, Xiao-Lan and Song, Dawei (2013). User-adaptive sketch-based 3D CAD model retrieval. IEEE Transactions on Automation Science and Engineering, 10(3) pp. 783–795.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1109/TASE.2012.2228481
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Abstract

3D CAD models are an important digital resource in the manufacturing industry. 3D CAD model retrieval has become a key technology in product lifecycle management enabling the reuse of existing design data. In this paper, we propose a new method to retrieve 3D CAD models based on 2D pen-based sketch inputs. Sketching is a common and convenient method for communicating design intent during early stages of product design, e.g., conceptual design. However, converting sketched information into precise 3D engineering models is cumbersome, and much of this effort can be avoided by reuse of existing data. To achieve this purpose, we present a user-adaptive sketch-based retrieval method in this paper. The contributions of this work are twofold. Firstly, we propose a statistical measure for CAD model retrieval: the measure is based on sketch similarity and accounts for users’ drawing habits. Secondly, for 3D CAD models in the database, we propose a sketch generation pipeline that represents each 3D CAD model by a small yet sufficient set of sketches that are perceptually similar to human drawings. User studies and experiments that demonstrate the effectiveness of the proposed method in the design process are presented.

Item Type: Journal Item
Copyright Holders: 2013 IEEE
ISSN: 1545-5955
Keywords: 3D CAD model retrieval; sketch similarity; sketch generation; conceptual design
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 35791
Depositing User: Dawei Song
Date Deposited: 18 Dec 2012 09:33
Last Modified: 02 May 2018 13:47
URI: http://oro.open.ac.uk/id/eprint/35791
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