  Currently, robotic grasping methods based on sparse partial point clouds have
attained a great grasping performance on various objects while they often
generate wrong grasping candidates due to the lack of geometric information on
the object. In this work, we propose a novel and robust shape completion model
(TransSC). This model has a transformer-based encoder to explore more
point-wise features and a manifold-based decoder to exploit more object details
using a partial point cloud as input.
  Quantitative experiments verify the effectiveness of the proposed shape
completion network and demonstrate it outperforms existing methods. Besides,
TransSC is integrated into a grasp evaluation network to generate a set of
grasp candidates. The simulation experiment shows that TransSC improves the
grasping generation result compared to the existing shape completion baselines.
Furthermore, our robotic experiment shows that with TransSC the robot is more
successful in grasping objects that are randomly placed on a support surface.
