  The prevalence of relation networks in computer vision is in stark contrast
to underexplored point-based methods. In this paper, we explore the
possibilities of local relation operators and survey their feasibility. We
propose a scalable and efficient module, called group relation aggregator. The
module computes a feature of a group based on the aggregation of the features
of the inner-group points weighted by geometric relations and semantic
relations. We adopt this module to design our RPNet. We further verify the
expandability of RPNet, in terms of both depth and width, on the tasks of
classification and segmentation. Surprisingly, empirical results show that
wider RPNet fits for classification, while deeper RPNet works better on
segmentation. RPNet achieves state-of-the-art for classification and
segmentation on challenging benchmarks. We also compare our local aggregator
with PointNet++, with around 30% parameters and 50% computation saving.
Finally, we conduct experiments to reveal the robustness of RPNet with regard
to rigid transformation and noises.
