  This paper aims at bringing some light and understanding to the field of deep
learning for dynamic point cloud processing. Specifically, we focus on the
hierarchical features learning aspect, with the ultimate goal of understanding
which features are learned at the different stages of the process and what
their meaning is. Last, we bring clarity on how hierarchical components of the
network affect the learned features and their importance for a successful
learning model. This study is conducted for point cloud prediction tasks,
useful for predicting coding applications.
