  Point clouds are often the default choice for many applications as they
exhibit more flexibility and efficiency than volumetric data. Nevertheless,
their unorganized nature -- points are stored in an unordered way -- makes them
less suited to be processed by deep learning pipelines. In this paper, we
propose a method for 3D object completion and classification based on point
clouds. We introduce a new way of organizing the extracted features based on
their activations, which we name soft pooling. For the decoder stage, we
propose regional convolutions, a novel operator aimed at maximizing the global
activation entropy. Furthermore, inspired by the local refining procedure in
Point Completion Network (PCN), we also propose a patch-deforming operation to
simulate deconvolutional operations for point clouds. This paper proves that
our regional activation can be incorporated in many point cloud architectures
like AtlasNet and PCN, leading to better performance for geometric completion.
We evaluate our approach on different 3D tasks such as object completion and
classification, achieving state-of-the-art accuracy.
