  Point clouds collected by real-world sensors are always unaligned and sparse,
which makes it hard to reconstruct the complete shape of object from a single
frame of data. In this work, we manage to provide complete point clouds from
sparse input with pose disturbance by limited translation and rotation. We also
use temporal information to enhance the completion model, refining the output
with a sequence of inputs. With the help of gated recovery units(GRU) and
attention mechanisms as temporal units, we propose a point cloud completion
framework that accepts a sequence of unaligned and sparse inputs, and outputs
consistent and aligned point clouds. Our network performs in an online manner
and presents a refined point cloud for each frame, which enables it to be
integrated into any SLAM or reconstruction pipeline. As far as we know, our
framework is the first to utilize temporal information and ensure temporal
consistency with limited transformation. Through experiments in ShapeNet and
KITTI, we prove that our framework is effective in both synthetic and
real-world datasets.
