  Real-world robotic grasping can be done robustly if a complete 3D Point Cloud
Data (PCD) of an object is available. However, in practice, PCDs are often
incomplete when objects are viewed from few and sparse viewpoints before the
grasping action, leading to the generation of wrong or inaccurate grasp poses.
We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing
geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD
completion network is a Transformer-based encoder-decoder network with an
Offset-Attention layer. Our network is inherently invariant to the object pose
and point's permutation, which generates PCDs that are geometrically consistent
and completed properly. Experiments on a wide range of partial PCD show that
3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks
and largely improves the grasping success rate in real-world scenarios. The
code and dataset will be made available upon acceptance.
