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.

翻译：本文旨在为动态点云处理的深层学习领域带来一些光和理解。 具体地说,我们侧重于分级特征学习方面,其最终目的是了解在过程的不同阶段学习哪些特征及其含义。 最后,我们澄清网络的分级部分如何影响所学特征及其对成功学习模式的重要性。本研究针对点云预测任务,对预测编码应用有用。