  Processing an input signal that contains arbitrary structures, e.g.,
superpixels and point clouds, remains a big challenge in computer vision.
Linear diffusion, an effective model for image processing, has been recently
integrated with deep learning algorithms. In this paper, we propose to learn
pairwise relations among data points in a global fashion to improve semantic
segmentation with arbitrarily-structured data, through spatial generalized
propagation networks (SGPN). The network propagates information on a group of
graphs, which represent the arbitrarily-structured data, through a learned,
linear diffusion process. The module is flexible to be embedded and jointly
trained with many types of networks, e.g., CNNs. We experiment with semantic
segmentation networks, where we use our propagation module to jointly train on
different data -- images, superpixels and point clouds. We show that SGPN
consistently improves the performance of both pixel and point cloud
segmentation, compared to networks that do not contain this module. Our method
suggests an effective way to model the global pairwise relations for
arbitrarily-structured data.
