Automatic Parts Correspondence Determination for Transforming Assemblies via Local and Global Geometry Processing
Automatic Parts Correspondence Determination for Transforming Assemblies via Local and Global Geometry Processing
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DOI：
10.20965/ijat.2023.p0176
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发表时间：
2023-03
期刊：
影响因子：
--
通讯作者：
Hayata Shibuya;Y. Nagai
中科院分区：
文献类型：
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作者：
Hayata Shibuya;Y. Nagai
变形组件是通过重新组装零件来改变其形状的产品。这个想法适用于各种各样的物体，从旨在节省空间的折叠小工具作为户外装备，到好莱坞电影中的机器人角色战斗，这些角色大大改变了他们的外观。前者福尔斯折叠或包装问题，后者需要不同的观点来解决，因为目标形状不一定旨在最小化占用空间。作为一种可能的解决方案，这种变形可以分解为形状到零件的分割和零件匹配。分割是形状建模中的一个普遍问题，已经提出了许多算法。另一方面，几乎没有探索同时匹配多个部件（多对多匹配）。本研究提出一种多对多的零件表面网格匹配算法，该匹配算法可以从一个变换装配体的两个不同的目标形状中进行。该算法由局部几何分析和基于这种分析的零件组合的全局优化组成。对于局部几何分析，表面几何特征由局部形状描述子描述。该方法利用内禀形状特征（ISS）检测出部分顶点作为特征点，并利用方向直方图（SHOT）特征表示特征点处的几何形状。对于来自每个目标形状的所有对的组合，对具有相似描述符值的特征点的数量进行计数。在全局优化中，最终的匹配由完全二分图上的最大权匹配决定，图的节点是部分，边由具有相似描述符的特征点的数目加权。我们提出了几个例子的成功结果，经验表明所提出的算法的有效性。
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.