  Point completion refers to completing the missing geometries of an object
from incomplete observations. Main-stream methods predict the missing shapes by
decoding a global feature learned from the input point cloud, which often leads
to deficient results in preserving topology consistency and surface details. In
this work, we present ME-PCN, a point completion network that leverages
`emptiness' in 3D shape space. Given a single depth scan, previous methods
often encode the occupied partial shapes while ignoring the empty regions (e.g.
holes) in depth maps. In contrast, we argue that these `emptiness' clues
indicate shape boundaries that can be used to improve topology representation
and detail granularity on surfaces. Specifically, our ME-PCN encodes both the
occupied point cloud and the neighboring `empty points'. It estimates
coarse-grained but complete and reasonable surface points in the first stage,
followed by a refinement stage to produce fine-grained surface details.
Comprehensive experiments verify that our ME-PCN presents better qualitative
and quantitative performance against the state-of-the-art. Besides, we further
prove that our `emptiness' design is lightweight and easy to embed in existing
methods, which shows consistent effectiveness in improving the CD and EMD
scores.
