  Point clouds are often sparse and incomplete, which imposes difficulties for
real-world applications. Existing shape completion methods tend to generate
rough shapes without fine-grained details. Considering this, we introduce a
two-branch network for shape completion. The first branch is a cascaded shape
completion sub-network to synthesize complete objects, where we propose to use
the partial input together with the coarse output to preserve the object
details during the dense point reconstruction. The second branch is an
auto-encoder to reconstruct the original partial input. The two branches share
a same feature extractor to learn an accurate global feature for shape
completion. Furthermore, we propose two strategies to enable the training of
our network when ground truth data are not available. This is to mitigate the
dependence of existing approaches on large amounts of ground truth training
data that are often difficult to obtain in real-world applications.
Additionally, our proposed strategies are also able to improve the
reconstruction quality for fully supervised learning. We verify our approach in
self-supervised, semi-supervised and fully supervised settings with superior
performances. Quantitative and qualitative results on different datasets
demonstrate that our method achieves more realistic outputs than
state-of-the-art approaches on the point cloud completion task.
