Intrinsic graph convolution operators with differentiable kernel functions
play a crucial role in analyzing 3D shape meshes. In this paper, we present a
fast and efficient intrinsic mesh convolution operator that does not rely on
the intricate design of kernel function. We explicitly formulate the order of
aggregating neighboring vertices, instead of learning weights between nodes,
and then a fully connected layer follows to fuse local geometric structure
information with vertex features. We provide extensive evidence showing that
models based on this convolution operator are easier to train, and can
efficiently learn invariant shape features. Specifically, we evaluate our
method on three different types of tasks of dense shape correspondence, 3D
facial expression classification, and 3D shape reconstruction, and show that it
significantly outperforms state-of-the-art approaches while being significantly
faster, without relying on shape descriptors. Our source code is available on
GitHub.

本文提出了一种基于内在网格卷积运算符的快速高效算法，该算法明确形式化了聚集相邻结点的顺序而不是学习权重，并通过全连接层将局部几何结构信息与顶点特征融合在一起，从而实现了对不变形状特征的有效学习。经过在三种不同类型任务停滞面对三维面部表情分类和三维形状重建，验证结果表明，本文算法明显优于现有的方法，且速度显著更快，不需要依赖形状描述符。