Structure-aware methods can be used to create new 3D shapes by reusing existing parts of given shapes. However, it is still a challenging task to obtain the corresponding parts among different shapes using structure information. We propose a structure-aware shape correspondence network, SASCNet, which can be used to obtain the corresponding parts between a pair of over-segmented shapes and then perform 3D shape synthesis effectively. In this network, the structure features of parts, which are extracted from the structural graph of a shape by the graph attentional layer, can be further used to calculate the part correspondence matrix and obtain the corresponding parts by the correspondence module. According to our part reshuffle experiments on several pairs of shapes, reasonable new shapes are created effectively. Furthermore, the part correspondence performance of our SASCNet is verified by comparing several correspondence results with that of two reported methods. Our approach is found to achieve better performance than theirs in some experiments.

通过重用给定形状的现有零件，可以将结构感知方法用于创建新的3D形状。然而，使用结构信息来获得不同形状之间的对应零件仍然是一项艰巨的任务。我们提出了一种结构感知的形状对应网络SASCNet，该网络可用于获取一对过度分割的形状之间的对应零件，然后有效地执行3D形状合成。在该网络中，可以进一步使用通过图形关注层从形状的结构图提取的零件的结构特征来计算零件对应矩阵，并通过对应模块获得对应的零件。根据我们在几对形状上进行的零件改组实验，可以有效地创建合理的新形状。此外，通过比较几种对应结果与两种报告方法的对比结果，验证了我们SASCNet的零件对应性能。在某些实验中，我们的方法比其方法获得了更好的性能。