  In point cloud generation and completion, previous methods for transforming
latent features to point clouds are generally based on fully connected layers
(FC-based) or folding operations (Folding-based). However, point clouds
generated by FC-based methods are usually troubled by outliers and rough
surfaces. For folding-based methods, their data flow is large, convergence
speed is slow, and they are also hard to handle the generation of non-smooth
surfaces. In this work, we propose AXform, an attention-based method to
transform latent features to point clouds. AXform first generates points in an
interim space, using a fully connected layer. These interim points are then
aggregated to generate the target point cloud. AXform takes both parameter
sharing and data flow into account, which makes it has fewer outliers, fewer
network parameters, and a faster convergence speed. The points generated by
AXform do not have the strong 2-manifold constraint, which improves the
generation of non-smooth surfaces. When AXform is expanded to multiple branches
for local generations, the centripetal constraint makes it has properties of
self-clustering and space consistency, which further enables unsupervised
semantic segmentation. We also adopt this scheme and design AXformNet for point
cloud completion. Considerable experiments on different datasets show that our
methods achieve state-of-the-art results.

Computer Vision and Pattern Recognition