  Point cloud shape completion is a challenging problem in 3D vision and
robotics. Existing learning-based frameworks leverage encoder-decoder
architectures to recover the complete shape from a highly encoded global
feature vector. Though the global feature can approximately represent the
overall shape of 3D objects, it would lead to the loss of shape details during
the completion process. In this work, instead of using a global feature to
recover the whole complete surface, we explore the functionality of multi-level
features and aggregate different features to represent the known part and the
missing part separately. We propose two different feature aggregation
strategies, named global \& local feature aggregation(GLFA) and residual
feature aggregation(RFA), to express the two kinds of features and reconstruct
coordinates from their combination. In addition, we also design a refinement
component to prevent the generated point cloud from non-uniform distribution
and outliers. Extensive experiments have been conducted on the ShapeNet
dataset. Qualitative and quantitative evaluations demonstrate that our proposed
network outperforms current state-of-the art methods especially on detail
preservation.
