Point cloud completion aims to recover raw point clouds captured by scanners
from partial observations caused by occlusion and limited view angles. Many
approaches utilize a partial-complete paradigm in which missing parts are
directly predicted by a global feature learned from partial inputs. This makes
it hard to recover details because the global feature is unlikely to capture
the full details of all missing parts. In this paper, we propose a novel
approach to point cloud completion called Point-PC, which uses a memory network
to retrieve shape priors and designs an effective causal inference model to
choose missing shape information as additional geometric information to aid
point cloud completion. Specifically, we propose a memory operating mechanism
where the complete shape features and the corresponding shapes are stored in
the form of ``key-value'' pairs. To retrieve similar shapes from the partial
input, we also apply a contrastive learning-based pre-training scheme to
transfer features of incomplete shapes into the domain of complete shape
features. Moreover, we use backdoor adjustment to get rid of the confounder,
which is a part of the shape prior that has the same semantic structure as the
partial input. Experimental results on the ShapeNet-55, PCN, and KITTI datasets
demonstrate that Point-PC performs favorably against the state-of-the-art
methods.

本研究提出了一种新的点云完整性方法 Point-PC，使用记忆网络检索形状先验，并设计了有效的因果推断模型来选择缺失的形状信息以帮助点云完整性。