  Point cloud completion task aims to predict the missing part of incomplete
point clouds and generate complete point clouds with details. In this paper, we
propose a novel point cloud completion network, namely CompleteDT.
Specifically, features are learned from point clouds with different
resolutions, which is sampled from the incomplete input, and are converted to a
series of \textit{spots} based on the geometrical structure. Then, the Dense
Relation Augment Module (DRA) based on the transformer is proposed to learn
features within \textit{spots} and consider the correlation among these
\textit{spots}. The DRA consists of Point Local-Attention Module (PLA) and
Point Dense Multi-Scale Attention Module (PDMA), where the PLA captures the
local information within the local \textit{spots} by adaptively measuring
weights of neighbors and the PDMA exploits the global relationship between
these \textit{spots} in a multi-scale densely connected manner. Lastly, the
complete shape is predicted from \textit{spots} by the Multi-resolution Point
Fusion Module (MPF), which gradually generates complete point clouds from
\textit{spots}, and updates \textit{spots} based on these generated point
clouds. Experimental results show that, because the DRA based on the
transformer can learn the expressive features from the incomplete input and the
MPF can fully explore these feature to predict the complete input, our method
largely outperforms the state-of-the-art methods.
