Convolutional neural networks (CNNs) have massively impacted visual
recognition in 2D images, and are now ubiquitous in state-of-the-art
approaches. CNNs do not easily extend, however, to data that are not
represented by regular grids, such as 3D shape meshes or other graph-structured
data, to which traditional local convolution operators do not directly apply.
To address this problem, we propose a novel graph-convolution operator to
establish correspondences between filter weights and graph neighborhoods with
arbitrary connectivity. The key novelty of our approach is that these
correspondences are dynamically computed from features learned by the network,
rather than relying on predefined static coordinates over the graph as in
previous work. We obtain excellent experimental results that significantly
improve over previous state-of-the-art shape correspondence results. This shows
that our approach can learn effective shape representations from raw input
coordinates, without relying on shape descriptors.

该研究提出了一种新颖的图卷积算子，通过动态地计算网络所学习到的特征与图上邻域之间的对应关系来实现任意连接的图邻域与滤波器权重之间的对应关系，并通过实验结果验证该方法可以从原始输入坐标学习有效的形状表示，而不依赖于形状描述符。