  Unpaired 3D object completion aims to predict a complete 3D shape from an
incomplete input without knowing the correspondence between the complete and
incomplete shapes. In this paper, we propose the novel KTNet to solve this task
from the new perspective of knowledge transfer. KTNet elaborates a
teacher-assistant-student network to establish multiple knowledge transfer
processes. Specifically, the teacher network takes complete shape as input and
learns the knowledge of complete shape. The student network takes the
incomplete one as input and restores the corresponding complete shape. And the
assistant modules not only help to transfer the knowledge of complete shape
from the teacher to the student, but also judge the learning effect of the
student network. As a result, KTNet makes use of a more comprehensive
understanding to establish the geometric correspondence between complete and
incomplete shapes in a perspective of knowledge transfer, which enables more
detailed geometric inference for generating high-quality complete shapes. We
conduct comprehensive experiments on several datasets, and the results show
that our method outperforms previous methods of unpaired point cloud completion
by a large margin.
