  Prevalence of deeper networks driven by self-attention is in stark contrast
to underexplored point-based methods. In this paper, we propose groupwise
self-attention as the basic block to construct our network: SepNet. Our
proposed module can effectively capture both local and global dependencies.
This module computes the features of a group based on the summation of the
weighted features of any point within the group. For convenience, we generalize
groupwise operations to assemble this module. To further facilitate our
networks, we deepen and widen SepNet on the tasks of segmentation and
classification respectively, and verify its practicality. Specifically, SepNet
achieves state-of-the-art for the tasks of classification and segmentation on
most of the datasets. We show empirical evidence that SepNet can obtain extra
accuracy in classification or segmentation from increased width or depth,
respectively.
