We propose a self-supervised approach to deep surface deformation. Given a
pair of shapes, our algorithm directly predicts a parametric transformation
from one shape to the other respecting correspondences. Our insight is to use
cycle-consistency to define a notion of good correspondences in groups of
objects and use it as a supervisory signal to train our network. Our method
does not rely on a template, assume near isometric deformations or rely on
point-correspondence supervision. We demonstrate the efficacy of our approach
by using it to transfer segmentation across shapes. We show, on Shapenet, that
our approach is competitive with comparable state-of-the-art methods when
annotated training data is readily available, but outperforms them by a large
margin in the few-shot segmentation scenario.

该论文提出了一种自监督的深度表面变形方法，其中采用了循环一致性来定义物体组中的良好对应关系，并将其用作监督信号来训练神经网络。该方法在没有模板、假定近似等距变形或依赖于点对应监督的情况下运行，并成功地将其用于传输形状间的分割。该方法在 Shapenet 上表现优于当前同类方法，特别是在少量标注样本的情况下。