  Segmentation of three-dimensional (3D) point clouds is an important task for
autonomous systems. However, success of segmentation algorithms depends greatly
on the quality of the underlying point clouds (resolution, completeness etc.).
In particular, incomplete point clouds might reduce a downstream model's
performance. GRNet is proposed as a novel and recent deep learning solution to
complete point clouds, but it is not capable of part segmentation. On the other
hand, our proposed solution, GRJointNet, is an architecture that can perform
joint completion and segmentation on point clouds as a successor of GRNet.
Features extracted for the two tasks are also utilized by each other to
increase the overall performance. We evaluated our proposed network on the
ShapeNet-Part dataset and compared its performance to GRNet. Our results
demonstrate GRJointNet can outperform GRNet on point completion. It should also
be noted that GRNet is not capable of segmentation while GRJointNet is. This
study1, therefore, holds a promise to enhance practicality and utility of point
clouds in 3D vision for autonomous systems.
