Statistical Convolution on Unordered Point Set

In this paper, we propose a new convolutional layer for neural networks on unordered and irregular point set. Most research advanced to date usually face multiple problem related to point cloud density and may require ad-hoc neural network architectures, which overlooks the huge treasure of architectures from computer vision or language processing. To mitigate these shortcomings, we process a point set at its distribution level by introducing statistical convolution (StatsConv). The spotlight feature of StatsConv is that it extracts various statistics to characterize the distribution of the input point set, which makes it highly scalable compared to existing point convolution operators. StatsConv is fundamentally simple, and can be used as a drop-in in any contemporary neural network architecture with negligible changes. Thorough experiments on point cloud classification and segmentation demonstrate the competence of StatsConv compared to the state of the art.