KTNet: Knowledge Transfer for Unpaired 3D Shape Completion
This addresses the challenge of 3D shape completion without paired data, which is incremental as it builds on existing unpaired methods with a novel network design.
The paper tackles the problem of unpaired 3D shape completion, where complete shapes are predicted from incomplete inputs without known correspondences, and proposes KTNet, which uses a teacher-assistant-student network for knowledge transfer to achieve superior performance over previous methods by a large margin.
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.