Standard spatial convolutions assume input data with a regular neighborhood
structure. Existing methods typically generalize convolution to the irregular
point cloud domain by fixing a regular "view" through e.g. a fixed neighborhood
size, where the convolution kernel size remains the same for each point.
However, since point clouds are not as structured as images, the fixed neighbor
number gives an unfortunate inductive bias. We present a novel graph
convolution named Difference Graph Convolution (diffConv), which does not rely
on a regular view. diffConv operates on spatially-varying and density-dilated
neighborhoods, which are further adapted by a learned masked attention
mechanism. Experiments show that our model is very robust to the noise,
obtaining state-of-the-art performance in 3D shape classification and scene
understanding tasks, along with a faster inference speed.

本文提出了一种新型的图卷积神经网络，名为差分图卷积 (diffConv)，通过学习掩码式注意机制来适应空间变化和密度膨胀的邻域，相比现有方法在 3D 形状分类和场景理解任务上获得了更好的性能。