Template 3D shapes are useful for many tasks in graphics and vision,
including fitting observation data, analyzing shape collections, and
transferring shape attributes. Because of the variety of geometry and topology
of real-world shapes, previous methods generally use a library of hand-made
templates. In this paper, we investigate learning a general shape template from
data. To allow for widely varying geometry and topology, we choose an implicit
surface representation based on composition of local shape elements. While long
known to computer graphics, this representation has not yet been explored in
the context of machine learning for vision. We show that structured implicit
functions are suitable for learning and allow a network to smoothly and
simultaneously fit multiple classes of shapes. The learned shape template
supports applications such as shape exploration, correspondence, abstraction,
interpolation, and semantic segmentation from an RGB image.

该论文介绍了一种从数据中学习通用形状模板的方法，通过基于局部形状元素的组合来选择隐式表面表示，可以适应各种几何和拓扑变化，而结构化的隐式函数适合学习，并允许网络平滑地同时拟合多类形状，该学习形状模板支持从 RGB 图像中进行形状探索，对应、抽象、插值和语义分割等应用。