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.

本文介绍了一种基于三维形状空间 “空洞” 特征的点完整性网络 ME-PCN，该网络能够在保留拓扑一致性和表面细节的前提下预测出完整的表面点云，实验表明 ME-PCN 优于现有技术并且采用了一种轻量级的 “空洞” 设计。