IJAT Vol.17 No.2 pp. 176-182
doi: 10.20965/ijat.2023.p0176

Research Paper:

Automatic Parts Correspondence Determination for Transforming Assemblies via Local and Global Geometry Processing

Hayata Shibuya and Yukie Nagai ORCID Icon

Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino-shi, Tokyo 191-0065, Japan

Corresponding author

July 29, 2022
September 26, 2022
March 5, 2023
transforming assemblies, many-to-many matching, geometric descriptors, maximum weight matching, shape retrieval

Transforming assemblies are products that alter their shapes by re-assembling their parts. This idea is applied to a wide range of objects from folding gadgets as outdoor gear aimed at saving space, to robotic characters fighting in Hollywood films which drastically change their appearance. While the former type falls into a folding or packing problems, the latter requires a different viewpoint to be solved since the destination shape is not necessarily aiming at minimizing the occupation space. As a possible solution, this kind of deformation can be decomposed into segmentation of the shape to parts and parts matching. Segmentation is a general problem in shape modeling and numerous algorithms have been proposed for this. On the other hand, matching simultaneously multiple parts (many-to-many matching) has hardly been explored. This study develops a many-to-many matching algorithm for surface meshes of parts from two distinct destination shapes of a single transforming assembly. The proposed algorithm consists of a local geometry analysis and a global optimization of parts combination based on such analysis. For the local geometry analysis, the surface geometric feature is described by a local shape descriptor. Some vertices are detected as feature points by intrinsic shape signature (ISS) and the geometry at the feature points is expressed by the signature of histogram of orientation (SHOT). For all the combination of pairs from each destination shape, the number of feature points with similar descriptor values is counted. In the global optimization, the final matching is determined by the maximum weight matching on a complete bipartite graph whose nodes are the parts, and edges are weighted by the number of the feature points with similar descriptors. We present successful results for several examples to empirically show the effectiveness of the proposed algorithm.

Cite this article as:
H. Shibuya and Y. Nagai, “Automatic Parts Correspondence Determination for Transforming Assemblies via Local and Global Geometry Processing,” Int. J. Automation Technol., Vol.17 No.2, pp. 176-182, 2023.
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