A study of 3D model similarity based on surface bipartite graph matching
Abstract
Purpose
This study aims to compute 3D model similarity by extracting and comparing shape features from the neutral files.
Design/methodology/approach
In this work, the clear text encoding document STEP (Standard for The Exchange of Product model data) of 3D models was analysed, and the models were characterized by two-depth trees consisting of both surface and shell nodes. All surfaces in the STEP files can be subdivided into three kinds, namely, free, analytical and loop surfaces. Surface similarity is defined by the variation coefficients of distances between data points on two surfaces, and subsequently, the shell similarity and 3D model similarity are determined using an optimal algorithm for bipartite graph matching.
Findings
This approach is used to experimentally verify the effectiveness of the 3D model similarity algorithm.
Originality/value
The novelty of this study research lies in the computation of 3D model similarity by comparison of all surfaces. In addition, the study makes several key observations: surfaces reflect the most information concerning the functions and attributes of a 3D model and so the similarity between surfaces generates more comprehensive content (both external and internal); semantic-based 3D retrieval can be obtained under the premise of comparison of surface semantics; and more accurate similarity of 3D models can be obtained using the optimal algorithm of bipartite graph matching for all surfaces.
Keywords
Acknowledgements
This work was supported by the National Natural Science Foundation of China (61472233) and the Natural Science Foundation of Shandong Province (ZR2014FM018).
Citation
Wang, J. and Wang, H. (2017), "A study of 3D model similarity based on surface bipartite graph matching", Engineering Computations, Vol. 34 No. 1, pp. 174-188. https://doi.org/10.1108/EC-10-2015-0315
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited