We present a new deep learning approach for matching deformable shapes by
introducing {\it Shape Deformation Networks} which jointly encode 3D shapes and
correspondences. This is achieved by factoring the surface representation into
(i) a template, that parameterizes the surface, and (ii) a learnt global
feature vector that parameterizes the transformation of the template into the
input surface. By predicting this feature for a new shape, we implicitly
predict correspondences between this shape and the template. We show that these
correspondences can be improved by an additional step which improves the shape
feature by minimizing the Chamfer distance between the input and transformed
template. We demonstrate that our simple approach improves on state-of-the-art
results on the difficult FAUST-inter challenge, with an average correspondence
error of 2.88cm. We show, on the TOSCA dataset, that our method is robust to
many types of perturbations, and generalizes to non-human shapes. This
robustness allows it to perform well on real unclean, meshes from the the SCAPE
dataset.

本论文提出用深度学习的方法，引入形状变形网络，同时编码 3D 形状和相应关系，通过将表面表示因子化为模板和学习的全局特征向量相结合，来匹配可变形的形状。