We present NeuroMorph, a new neural network architecture that takes as input
two 3D shapes and produces in one go, i.e. in a single feed forward pass, a
smooth interpolation and point-to-point correspondences between them. The
interpolation, expressed as a deformation field, changes the pose of the source
shape to resemble the target, but leaves the object identity unchanged.
NeuroMorph uses an elegant architecture combining graph convolutions with
global feature pooling to extract local features. During training, the model is
incentivized to create realistic deformations by approximating geodesics on the
underlying shape space manifold. This strong geometric prior allows to train
our model end-to-end and in a fully unsupervised manner without requiring any
manual correspondence annotations. NeuroMorph works well for a large variety of
input shapes, including non-isometric pairs from different object categories.
It obtains state-of-the-art results for both shape correspondence and
interpolation tasks, matching or surpassing the performance of recent
unsupervised and supervised methods on multiple benchmarks.

我们提出了一种新的神经网络架构 NeuroMorph，采用图卷积和全局特征池化的优美结构，实现了在单次前馈传递中对输入的两个 3D 形状进行平滑插值和点对点对应，其具有无监督学习能力，适用于不同对象类别的非等距对，并在多个基准测试中匹配或超过了最新无监督和有监督方法的表现水平。