Implicit surface representations, such as signed-distance functions, combined
with deep learning have led to impressive models which can represent detailed
shapes of objects with arbitrary topology. Since a continuous function is
learned, the reconstructions can also be extracted at any arbitrary resolution.
However, large datasets such as ShapeNet are required to train such models. In
this paper, we present a new mid-level patch-based surface representation. At
the level of patches, objects across different categories share similarities,
which leads to more generalizable models. We then introduce a novel method to
learn this patch-based representation in a canonical space, such that it is as
object-agnostic as possible. We show that our representation trained on one
category of objects from ShapeNet can also well represent detailed shapes from
any other category. In addition, it can be trained using much fewer shapes,
compared to existing approaches. We show several applications of our new
representation, including shape interpolation and partial point cloud
completion. Due to explicit control over positions, orientations and scales of
patches, our representation is also more controllable compared to object-level
representations, which enables us to deform encoded shapes non-rigidly.

本文提出了一种基于 patch 的中层面向对象表示方法，将不同类别对象的相似性建模在 patch 级别，使模型更具有通用性，并且提出了一种新的在规范空间中学习 patch 表示的方法，该表示方法对物体的类别不敏感，在训练时只需要较少的数据集，在形状插值和局部点云完成等应用上也有良好的效果。