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Abstract

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.